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Francis Diebold

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. S. Boragan Aruoba & Francis X. Diebold & Jeremy Nalewaik & Frank Schorfheide & Dongho Song, 2011. "Improving GDP Measurement: A Forecast Combination Perspective," NBER Working Papers 17421, National Bureau of Economic Research, Inc.

    Mentioned in:

    1. Better GDP estimates
      by Economic Logician in Economic Logic on 2011-10-12 19:28:00
  2. Francis X. Diebold, 2012. "Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests," PIER Working Paper Archive 12-035, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.

    Mentioned in:

    1. My "Must Read" List
      by Dave Giles in Econometrics Beat: Dave Giles' Blog on 2012-09-27 06:33:00
  3. S. Boragan Aruoba & Francis X. Diebold, 2010. "Real-Time Macroeconomic Monitoring: Real Activity, Inflation, and Interactions," PIER Working Paper Archive 10-002, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.

    Mentioned in:

    1. Nowcasting: recession ended in July 2009
      by Economic Logician in Economic Logic on 2010-02-10 21:19:00
  4. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Clara Vega, 2002. "Micro Effects of Macro Announcements: Real-Time Price Discovery in Foreign Exchange?," Center for Financial Institutions Working Papers 02-23, Wharton School Center for Financial Institutions, University of Pennsylvania.

    Mentioned in:

    1. Trading on Leaked Macroeconomic Data
      by noreply@blogger.com (Carola) in Quantitative Ease on 2015-07-08 22:14:00
    2. 'Trading on Leaked Macroeconomic Data'
      by Mark Thoma in Economist's View on 2015-07-08 15:39:09
  5. Aruoba, S. Borağan & Diebold, Francis X. & Nalewaik, Jeremy & Schorfheide, Frank & Song, Dongho, 2016. "Improving GDP measurement: A measurement-error perspective," Journal of Econometrics, Elsevier, vol. 191(2), pages 384-397.

    Mentioned in:

    1. Is the economy growing? Depends on how you measure it : GDP vs. GDI
      by ? in FRED blog on 2022-09-01 13:00:00
  6. Author Profile
    1. Where did the Phrase “Big Data” Come From?
      by Clive Jones in Business Forecasting on 2013-09-04 21:52:38
    2. The REPEC Fantasy League and My Initial Endowment of Economists
      by Matthew Kahn in Environmental and Urban Economics on 2015-07-26 23:22:00

RePEc Biblio mentions

As found on the RePEc Biblio, the curated bibliography of Economics:
  1. Christoffersen, Peter F. & Diebold, Francis X., 1997. "Optimal Prediction Under Asymmetric Loss," Econometric Theory, Cambridge University Press, vol. 13(6), pages 808-817, December.

    Mentioned in:

    1. > Econometrics > Forecasting
  2. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-263, July.

    Mentioned in:

    1. > Econometrics > Forecasting
  3. Francis X. Diebold, 2012. "A Personal Perspective on the Origin(s) and Development of “Big Data": The Phenomenon, the Term, and the Discipline, Second Version," PIER Working Paper Archive 13-003, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 26 Nov 2012.

    Mentioned in:

    1. > Econometrics > Big Data

Wikipedia or ReplicationWiki mentions

(Only mentions on Wikipedia that link back to a page on a RePEc service)
  1. Christoffersen, Peter F & Diebold, Francis X, 1996. "Further Results on Forecasting and Model Selection under Asymmetric Loss," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(5), pages 561-571, Sept.-Oct.

    Mentioned in:

    1. Further results on forecasting and model selection under asymmetric loss (Journal of Applied Econometrics 1996) in ReplicationWiki ()
  2. FrancisX. Diebold & Kamil Yilmaz, 2009. "Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets," Economic Journal, Royal Economic Society, vol. 119(534), pages 158-171, January.

    Mentioned in:

    1. Measuring financial asset return and volatility spillovers, with application to global equity markets (EJ 2009) in ReplicationWiki ()
  3. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Clara Vega, 2003. "Micro Effects of Macro Announcements: Real-Time Price Discovery in Foreign Exchange," American Economic Review, American Economic Association, vol. 93(1), pages 38-62, March.

    Mentioned in:

    1. Micro Effects of Macro Announcements: Real-Time Price Discovery in Foreign Exchange (AER 2003) in ReplicationWiki ()
  4. Diebold, Francis X & Nerlove, Marc, 1989. "The Dynamics of Exchange Rate Volatility: A Multivariate Latent Factor Arch Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 4(1), pages 1-21, Jan.-Mar..

    Mentioned in:

    1. The dynamics of exchange rate volatility: A multivariate latent factor ARCH model (Journal of Applied Econometrics 1989) in ReplicationWiki ()
  5. Diebold, Francis X, 1989. "Structural Time Series Analysis and Modelling Package: A Review," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 4(2), pages 195-204, April-Jun.

    Mentioned in:

    1. Structural time series analysis and modelling package: A review (Journal of Applied Econometrics 1989) in ReplicationWiki ()
  6. Francis X. Diebold & Lutz Kilian, 2001. "Measuring predictability: theory and macroeconomic applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(6), pages 657-669.

    Mentioned in:

    1. Measuring predictability: theory and macroeconomic applications (Journal of Applied Econometrics 2001) in ReplicationWiki ()

Working papers

  1. Francis X. Diebold & Glenn D. Rudebusch, 2023. "Climate Models Underestimate the Sensitivity of Arctic Sea Ice to Carbon Emissions," Papers 2307.03552, arXiv.org.

    Cited by:

    1. Diebold, Francis X. & Rudebusch, Glenn D. & Göbel, Maximilian & Goulet Coulombe, Philippe & Zhang, Boyuan, 2024. "Reprint of: When will Arctic sea ice disappear? Projections of area, extent, thickness, and volume," Journal of Econometrics, Elsevier, vol. 239(1).
    2. Brock, William A. & Miller, J. Isaac, 2024. "Polar amplification in a moist energy balance model: A structural econometric approach to estimation and testing," Journal of Econometrics, Elsevier, vol. 245(1).
    3. Blazsek, Szabolcs & Escribano, Álvaro & Kristof, Erzsebet, 2024. "Global, Arctic, and Antarctic sea ice volume predictions: using score-driven threshold climate models," UC3M Working papers. Economics 39546, Universidad Carlos III de Madrid. Departamento de Economía.
    4. Garcia-Jorcano, Laura & Sanchis-Marco, Lidia, 2024. "Forecasting the effect of extreme sea-level rise on financial market risk," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 1-27.
    5. Francis X. Diebold & Glenn D. Rudebusch & Maximilian Goebel & Philippe Goulet Coulombe & Boyuan Zhang, 2022. "When Will Arctic Sea Ice Disappear? Projections of Area, Extent, Thickness, and Volume," Papers 2203.04040, arXiv.org, revised May 2023.

  2. Richard T. Baillie & Francis X. Diebold & George Kapetanios & Kun Ho Kim & Aaron Mora, 2022. "On Robust Inference in Time Series Regression," Papers 2203.04080, arXiv.org, revised May 2024.

    Cited by:

    1. Richard T. Baillie & Francis X. Diebold & George Kapetanios & Kun Ho Kim, 2022. "A New Test forMarket Efficiency and Uncovered Interest Parity," PIER Working Paper Archive 22-029, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    2. Richard K. Crump & Nikolay Gospodinov & Ignacio Lopez Gaffney, 2024. "A Jackknife Variance Estimator for Panel Regressions," Staff Reports 1133, Federal Reserve Bank of New York.

  3. Richard T. Baillie & Francis X. Diebold & George Kapetanios & Kun Ho Kim, 2022. "A New Test for Market Efficiency and Uncovered Interest Parity," Papers 2211.01344, arXiv.org.

    Cited by:

    1. Constantin Bürgi & Mengdi Song, 2024. "Do Professional Forecasters Follow Uncovered Interest Rate Parity?," CESifo Working Paper Series 11338, CESifo.

  4. Francis X. Diebold & Maximilian Goebel & Philippe Goulet Coulombe, 2022. "Assessing and Comparing Fixed-Target Forecasts of Arctic Sea Ice: Glide Charts for Feature-Engineered Linear Regression and Machine Learning Models," Papers 2206.10721, arXiv.org, revised Jun 2023.

    Cited by:

    1. Brock, William A. & Miller, J. Isaac, 2024. "Polar amplification in a moist energy balance model: A structural econometric approach to estimation and testing," Journal of Econometrics, Elsevier, vol. 245(1).

  5. Francis X. Diebold & Glenn D. Rudebusch & Maximilian Goebel & Philippe Goulet Coulombe & Boyuan Zhang, 2022. "When Will Arctic Sea Ice Disappear? Projections of Area, Extent, Thickness, and Volume," Papers 2203.04040, arXiv.org, revised May 2023.

    Cited by:

    1. Francis X. Diebold & Glenn D. Rudebusch, 2023. "Climate Models Underestimate the Sensitivity of Arctic Sea Ice to Carbon Emissions," PIER Working Paper Archive 24-010, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    2. Alexandre Andorra & Maximilian Gobel, 2024. "Unveiling True Talent: The Soccer Factor Model for Skill Evaluation," Papers 2412.05911, arXiv.org.
    3. Luke P. Jackson & Katarina Juselius & Andrew B. Martinez & Felix Pretis, 2025. "Modelling the dependence between recent changes in polar ice sheets: Implications for global sea-level projections," Working Papers 2025-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    4. Brock, William A. & Miller, J. Isaac, 2024. "Polar amplification in a moist energy balance model: A structural econometric approach to estimation and testing," Journal of Econometrics, Elsevier, vol. 245(1).
    5. B. Cooper Boniece & Lajos Horv'ath & Lorenzo Trapani, 2023. "On changepoint detection in functional data using empirical energy distance," Papers 2310.04853, arXiv.org.
    6. Blazsek, Szabolcs & Escribano, Álvaro & Kristof, Erzsebet, 2024. "Global, Arctic, and Antarctic sea ice volume predictions: using score-driven threshold climate models," UC3M Working papers. Economics 39546, Universidad Carlos III de Madrid. Departamento de Economía.

  6. Francis X. Diebold & Kamil Yilmaz, 2022. "On the Past, Present, and Future of the Diebold-Yilmaz Approach to Dynamic Network Connectedness," Papers 2211.04184, arXiv.org, revised Jan 2023.

    Cited by:

    1. Chen, Yanghan & Lin, Juan, 2024. "Measuring systemic risk in Asian foreign exchange markets," Journal of International Money and Finance, Elsevier, vol. 146(C).
    2. Bastianin, Andrea & Casoli, Chiara & Galeotti, Marzio, 2023. "The connectedness of Energy Transition Metals," FEEM Working Papers 336984, Fondazione Eni Enrico Mattei (FEEM).
    3. Zhou, Bin & Shi, Huai-Long, 2024. "Quantile volatility connectedness among themes and sectors: Novel evidence from China," The Quarterly Review of Economics and Finance, Elsevier, vol. 98(C).
    4. Doğan, Buhari & Ben Jabeur, Sami & Tiwari, Aviral Kumar & Abakah, Emmanuel Joel Aikins, 2025. "Asymmetry in returns and volatility between green financial assets, sustainable investments, clean energy, and international stock markets," Research in International Business and Finance, Elsevier, vol. 73(PA).
    5. Ali, Shoaib & Naveed, Muhammad & Al-Nassar, Nassar S. & Mirza, Nawazish, 2024. "Mineral Metamorphosis: Tracing the static and dynamic nexus between minerals and global south markets," Resources Policy, Elsevier, vol. 96(C).
    6. Narayana Maharana & Ashok Kumar Panigrahi & Suman Kalyan Chaudhury, 2025. "Commodity Spillovers and Risk Hedging: The Evolving Role of Gold and Oil in the Indian Stock Market," Commodities, MDPI, vol. 4(2), pages 1-19, April.
    7. Joanna Górka & Katarzyna Kuziak, 2024. "Dynamic Connectedness Among Alternative and Conventional Energy ETFs Based on the TVP-VAR Approach," Energies, MDPI, vol. 17(23), pages 1-29, November.
    8. Lin, Zi-Luo & Ouyang, Wen-Pei & Yu, Qing-Rui, 2024. "Risk spillover effects of the Israel–Hamas War on global financial and commodity markets: A time–frequency and network analysis," Finance Research Letters, Elsevier, vol. 66(C).
    9. Zsuzsa R. Huszár & Balázs B. Kotró & Ruth S. K. Tan, 2023. "European equity markets volatility spillover: Destabilizing energy risk is the new normal," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 46(S1), pages 205-271, December.
    10. Shi, Huai-Long & Chen, Huayi, 2024. "Understanding co-movements based on heterogeneous information associations," International Review of Financial Analysis, Elsevier, vol. 94(C).
    11. Chen, Yu-Fen & Lin, Fu-Lai & Yeh, Wen-Hung, 2024. "Intra- and inter-sector spillover effects within a supply chain: Evidence from Taiwan electric motorcycle industry," Economics Letters, Elsevier, vol. 240(C).
    12. Lai, Jennifer & McNelis, Paul D., 2024. "Financial contagion among the GSIBs and regulatory interventions," Journal of Financial Stability, Elsevier, vol. 72(C).
    13. Nguyen, Anh T.H. & Le, Thanh T., 2025. "In bank runs and market stress, it matters how networks impact: Exploring the financial connectedness in Vietnam," Finance Research Letters, Elsevier, vol. 72(C).
    14. Xu, Yingying & Shao, Xuefeng & Tanasescu, Cristina, 2024. "How are artificial intelligence, carbon market, and energy sector connected? A systematic analysis of time-frequency spillovers," Energy Economics, Elsevier, vol. 132(C).
    15. Massimiliano Caporin & Oguzhan Cepni & Rangan Gupta, 2025. "Unveiling True Connectedness in US State-Level Stock Markets: The Role of Common Factors," Working Papers 202509, University of Pretoria, Department of Economics.
    16. Polat, Onur & Ertuğrul, Hasan Murat & Sakarya, Burçhan & Akgül, Ali, 2024. "TVP-VAR based time and frequency domain food & energy commodities connectedness an analysis for financial/geopolitical turmoil episodes," Applied Energy, Elsevier, vol. 357(C).
    17. Anghel, Dan Gabriel & Caraiani, Petre, 2024. "Monetary policy shocks and the high-frequency network connectedness of stock markets," International Review of Economics & Finance, Elsevier, vol. 96(PA).
    18. Hossain, Mohammad Razib & Ben Jabeur, Sami & Si Mohammed, Kamel & Shahzad, Umer, 2024. "Time-varying relatedness and structural changes among green growth, clean energy innovation, and carbon market amid exogenous shocks: A quantile VAR approach," Technological Forecasting and Social Change, Elsevier, vol. 208(C).
    19. Yang, Xin & Wang, Xuya & Cao, Jie & Zhao, Lili & Huang, Chuangxia, 2024. "Cross-regional connectedness of financial market: Measurement and determinants," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).

  7. Francis X. Diebold & Maximilian Gobel, 2021. "A Benchmark Model for Fixed-Target Arctic Sea Ice Forecasting," Papers 2101.10359, arXiv.org, revised Jan 2022.

    Cited by:

    1. Brock, William A. & Miller, J. Isaac, 2024. "Polar amplification in a moist energy balance model: A structural econometric approach to estimation and testing," Journal of Econometrics, Elsevier, vol. 245(1).
    2. Francis X. Diebold & Maximilian Gobel & Philippe Goulet Coulombe, 2022. "Assessing and Comparing Fixed-Target Forecasts of Arctic Sea Ice:Glide Charts for Feature-Engineered Linear Regression and Machine Learning Models," PIER Working Paper Archive 22-028, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.

  8. Francis X. Diebold, 2020. "Real-Time Real Economic Activity: Exiting the Great Recession and Entering the Pandemic Recession," NBER Working Papers 27482, National Bureau of Economic Research, Inc.

    Cited by:

    1. Aktham Maghyereh & Hussein Abdoh, 2022. "Global financial crisis versus COVID‐19: Evidence from sentiment analysis," International Finance, Wiley Blackwell, vol. 25(2), pages 218-248, August.
    2. Aaronson, Daniel & Brave, Scott A. & Butters, R. Andrew & Fogarty, Michael & Sacks, Daniel W. & Seo, Boyoung, 2022. "Forecasting unemployment insurance claims in realtime with Google Trends," International Journal of Forecasting, Elsevier, vol. 38(2), pages 567-581.
    3. Christiane Baumeister & Pierre Guérin, 2020. "A Comparison of Monthly Global Indicators for Forecasting Growth," CESifo Working Paper Series 8656, CESifo.
    4. Qiu, Yue & Zheng, Yuchen, 2023. "Improving box office projections through sentiment analysis: Insights from regularization-based forecast combinations," Economic Modelling, Elsevier, vol. 125(C).
    5. Marcellino, Massimiliano & Foroni, Claudia & Stevanovic, Dalibor, 2020. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," CEPR Discussion Papers 15114, C.E.P.R. Discussion Papers.
    6. Flávio Menezes & Vivian Figer & Fernanda Jardim & Pedro Medeiros, 2021. "Using electricity consumption to predict economic activity during COVID-19 in Brazil," Discussion Papers Series 641, School of Economics, University of Queensland, Australia.
    7. Bellatin, Alejandra & Galassi, Gabriela, 2022. "What COVID-19 May Leave Behind: Technology-Related Job Postings in Canada," IZA Discussion Papers 15209, Institute of Labor Economics (IZA).
    8. Marcellino, Massimiliano & Clark, Todd & Carriero, Andrea & Mertens, Elmar, 2021. "Addressing COVID-19 Outliers in BVARs with Stochastic Volatility," CEPR Discussion Papers 15964, C.E.P.R. Discussion Papers.
    9. Tesi Aliaj & Milos Ciganovic & Massimiliano Tancioni, 2023. "Nowcasting inflation with Lasso‐regularized vector autoregressions and mixed frequency data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 464-480, April.
    10. Daniel J. Lewis & Karel Mertens & James H. Stock & Mihir Trivedi, 2020. "Measuring Real Activity Using a Weekly Economic Index," Staff Reports 920, Federal Reserve Bank of New York.
    11. Christiane Baumeister & Danilo Leiva-León & Eric R. Sims, 2021. "Tracking Weekly State-Level Economic Conditions," CESifo Working Paper Series 9165, CESifo.
    12. Frank Schorfheide & Dongho Song, 2024. "Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic," International Journal of Central Banking, International Journal of Central Banking, vol. 20(4), pages 275-320, October.
    13. Menezes, Flavio & Figer, Vivian & Jardim, Fernanda & Medeiros, Pedro, 2022. "A near real-time economic activity tracker for the Brazilian economy during the COVID-19 pandemic," Economic Modelling, Elsevier, vol. 112(C).
    14. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    15. Jad Beyhum & Jonas Striaukas, 2023. "Factor-augmented sparse MIDAS regressions with an application to nowcasting," Papers 2306.13362, arXiv.org, revised Nov 2024.
    16. Afonso S. Moura & Gergely Buda & Vasco M. Carvalho & Giancarlo Corsetti & João B. Duarte & Stephen Hansen & Álvaro Ortiz & Tomasa Rodrigo & José V. Rodríguez Mora & Guilherme Alves da Silva, 2025. "The Short Lags of Monetary Policy," Working Papers w202501, Banco de Portugal, Economics and Research Department.
    17. Stavros Degiannakis, 2023. "The D-model for GDP nowcasting," Working Papers 317, Bank of Greece.
    18. Julien Chevallier, 2021. "Covid-19 Outbreak and CO2 Emissions: Macro-Financial Linkages," Working Papers 2021-004, Department of Research, Ipag Business School.

  9. Francis X. Diebold, 2020. ""Big Data" and its Origins," Papers 2008.05835, arXiv.org, revised Jan 2021.

    Cited by:

    1. Francis X. Diebold & Kamil Yilmaz, 2022. "On the Past, Present, and Future of the Diebold-Yilmaz Approach to Dynamic Network Connectedness," Koç University-TUSIAD Economic Research Forum Working Papers 2207, Koc University-TUSIAD Economic Research Forum.

  10. Francis X. Diebold & Maximilian Gobel & Philippe Goulet Coulombe & Glenn D. Rudebusch & Boyuan Zhang, 2020. "Optimal Combination of Arctic Sea Ice Extent Measures: A Dynamic Factor Modeling Approach," Papers 2003.14276, arXiv.org, revised Aug 2020.

    Cited by:

    1. Diebold, Francis X. & Rudebusch, Glenn D., 2022. "Probability assessments of an ice-free Arctic: Comparing statistical and climate model projections," Journal of Econometrics, Elsevier, vol. 231(2), pages 520-534.
    2. Ar'anzazu de Juan & Pilar Poncela & Vladimir Rodr'iguez-Caballero & Esther Ruiz, 2022. "Economic activity and climate change," Papers 2206.03187, arXiv.org, revised Jun 2022.
    3. Francis X. Diebold & Glenn D. Rudebusch, 2023. "Climate Models Underestimate the Sensitivity of Arctic Sea Ice to Carbon Emissions," PIER Working Paper Archive 24-010, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    4. Hee Soo (test record) Kim & Christian Matthes & Toan Phan, 2011. "Extreme Weather and the Macroeconomy," Working Paper 21-14, Federal Reserve Bank of Richmond.
    5. Brock, William A. & Miller, J. Isaac, 2024. "Polar amplification in a moist energy balance model: A structural econometric approach to estimation and testing," Journal of Econometrics, Elsevier, vol. 245(1).
    6. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    7. Francis X. Diebold & Maximilian Gobel & Philippe Goulet Coulombe, 2022. "Assessing and Comparing Fixed-Target Forecasts of Arctic Sea Ice:Glide Charts for Feature-Engineered Linear Regression and Machine Learning Models," PIER Working Paper Archive 22-028, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    8. Robert Amano & Marc-André Gosselin & Julien McDonald-Guimond, 2021. "Evolving Temperature Dynamics in Canada: Preliminary Evidence Based on 60 Years of Data," Staff Working Papers 21-22, Bank of Canada.
    9. Escribano, Alvaro & Peña, Daniel & Ruiz, Esther, 2021. "30 years of cointegration and dynamic factor models forecasting and its future with big data: Editorial," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1333-1337.
    10. Tarek Zaatar & Ali Cheaitou & Olivier Faury & Patrick Rigot-Muller, 2025. "Arctic sea ice thickness prediction using machine learning: a long short-term memory model," Annals of Operations Research, Springer, vol. 345(1), pages 533-568, February.
    11. Atin Aboutorabi & Ga'etan de Rassenfosse, 2024. "Nowcasting R&D Expenditures: A Machine Learning Approach," Papers 2407.11765, arXiv.org.
    12. Diego Fresoli & Pilar Poncela & Esther Ruiz, 2024. "Dealing with idiosyncratic cross-correlation when constructing confidence regions for PC factors," Papers 2407.06883, arXiv.org.
    13. Marina Friedrich & Luca Margaritella & Stephan Smeekes, 2023. "High-Dimensional Granger Causality for Climatic Attribution," Papers 2302.03996, arXiv.org, revised Jun 2024.

  11. Francis X. Diebold & Minchul Shin & Boyuan Zhang, 2020. "On the Aggregation of Probability Assessments: Regularized Mixtures of Predictive Densities for Eurozone Inflation and Real Interest Rates," Papers 2012.11649, arXiv.org, revised Jun 2022.

    Cited by:

    1. Chernis Tony, 2024. "Combining Large Numbers of Density Predictions with Bayesian Predictive Synthesis," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 28(2), pages 293-317, April.
    2. Tony Chernis & Niko Hauzenberger & Florian Huber & Gary Koop & James Mitchell, 2023. "Predictive Density Combination Using a Tree-Based Synthesis Function," Working Papers 23-30, Federal Reserve Bank of Cleveland.
    3. Anthony Garratt & Timo Henckel & Shaun P. Vahey, 2019. "Empirically-transformed linear opinion pools," CAMA Working Papers 2019-47, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    4. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023. "Machine learning advances for time series forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
    5. Todd E. Clark & Gergely Ganics & Elmar Mertens, 2022. "What is the Predictive Value of SPF Point and Density Forecasts?," Working Papers 22-37, Federal Reserve Bank of Cleveland.
    6. Francis X. Diebold & Aaron Mora & Minchul Shin, 2025. "On the Wisdom of Crowds (of Economists)," Papers 2503.09287, arXiv.org.
    7. Deniz Igan & Thomas Lambert & Prachi Mishra & Eden Zhang, 2024. "The Politics of the Paycheck Protection Program," Working Papers 133, Ashoka University, Department of Economics.
    8. Bańbura, Marta & Brenna, Federica & Paredes, Joan & Ravazzolo, Francesco, 2021. "Combining Bayesian VARs with survey density forecasts: does it pay off?," Working Paper Series 2543, European Central Bank.
    9. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    10. Bernaciak, Dawid & Griffin, Jim E., 2024. "A loss discounting framework for model averaging and selection in time series models," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1721-1733.

  12. Francis X. Diebold, 2020. "Real-Time Real Economic Activity: Entering and Exiting the Pandemic Recession of 2020," Papers 2006.15183, arXiv.org, revised Jan 2022.

    Cited by:

    1. Aktham Maghyereh & Hussein Abdoh, 2022. "Global financial crisis versus COVID‐19: Evidence from sentiment analysis," International Finance, Wiley Blackwell, vol. 25(2), pages 218-248, August.
    2. Marcellino, Massimiliano & Foroni, Claudia & Stevanovic, Dalibor, 2020. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," CEPR Discussion Papers 15114, C.E.P.R. Discussion Papers.
    3. Marcellino, Massimiliano & Clark, Todd & Carriero, Andrea & Mertens, Elmar, 2021. "Addressing COVID-19 Outliers in BVARs with Stochastic Volatility," CEPR Discussion Papers 15964, C.E.P.R. Discussion Papers.
    4. Frank Schorfheide & Dongho Song, 2024. "Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic," International Journal of Central Banking, International Journal of Central Banking, vol. 20(4), pages 275-320, October.

  13. Francis X. Diebold & Glenn D. Rudebusch, 2019. "On the Evolution of U.S. Temperature Dynamics," Papers 1907.06303, arXiv.org, revised Jan 2021.

    Cited by:

    1. María Dolores Gadea Rivas & Jesús Gonzalo, 2022. "A tale of three cities: climate heterogeneity," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 13(1), pages 475-511, May.
    2. Gadea Rivas, María Dolores & Gonzalo, Jesús, 2021. "A tale of three cities: climate heterogeneity (special issue of SERIES in homage to Juan J. Dolado)," UC3M Working papers. Economics 32200, Universidad Carlos III de Madrid. Departamento de Economía.

  14. Francis X. Diebold & Glenn D. Rudebusch, 2019. "Probability Assessments of an Ice-Free Arctic: Comparing Statistical and Climate Model Projections," Papers 1912.10774, arXiv.org, revised Jul 2021.

    Cited by:

    1. Francis X. Diebold & Maximilian Gobel & Philippe Goulet Coulombe & Glenn D. Rudebusch & Boyuan Zhang, 2020. "Optimal Combination of Arctic Sea Ice Extent Measures: A Dynamic Factor Modeling Approach," Papers 2003.14276, arXiv.org, revised Aug 2020.
    2. Francis X. Diebold & Glenn D. Rudebusch, 2023. "Climate Models Underestimate the Sensitivity of Arctic Sea Ice to Carbon Emissions," PIER Working Paper Archive 24-010, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    3. Diebold, Francis X. & Rudebusch, Glenn D. & Göbel, Maximilian & Goulet Coulombe, Philippe & Zhang, Boyuan, 2024. "Reprint of: When will Arctic sea ice disappear? Projections of area, extent, thickness, and volume," Journal of Econometrics, Elsevier, vol. 239(1).
    4. Luke P. Jackson & Katarina Juselius & Andrew B. Martinez & Felix Pretis, 2025. "Modelling the dependence between recent changes in polar ice sheets: Implications for global sea-level projections," Working Papers 2025-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    5. Brock, William A. & Miller, J. Isaac, 2024. "Polar amplification in a moist energy balance model: A structural econometric approach to estimation and testing," Journal of Econometrics, Elsevier, vol. 245(1).
    6. B. Cooper Boniece & Lajos Horv'ath & Lorenzo Trapani, 2023. "On changepoint detection in functional data using empirical energy distance," Papers 2310.04853, arXiv.org.
    7. Francis X. Diebold & Maximilian Gobel, 2022. "A Benchmark Model for Fixed-Target Arctic Sea Ice Forecasting," PIER Working Paper Archive 22-002, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    8. Francis X. Diebold & Maximilian Gobel & Philippe Goulet Coulombe, 2022. "Assessing and Comparing Fixed-Target Forecasts of Arctic Sea Ice:Glide Charts for Feature-Engineered Linear Regression and Machine Learning Models," PIER Working Paper Archive 22-028, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    9. Blazsek, Szabolcs & Escribano, Álvaro & Kristof, Erzsebet, 2024. "Global, Arctic, and Antarctic sea ice volume predictions: using score-driven threshold climate models," UC3M Working papers. Economics 39546, Universidad Carlos III de Madrid. Departamento de Economía.
    10. Marc Gronwald, 2023. "Explosive Temperatures," CESifo Working Paper Series 10680, CESifo.
    11. Francis X. Diebold & Glenn D. Rudebusch & Maximilian Goebel & Philippe Goulet Coulombe & Boyuan Zhang, 2022. "When Will Arctic Sea Ice Disappear? Projections of Area, Extent, Thickness, and Volume," Papers 2203.04040, arXiv.org, revised May 2023.
    12. Vasco J.Gabriel & Luis F. Martins & Anthoulla Phella, 2021. "Modelling Low-Frequency Covariability of Paleoclimatic Data," Working Papers 2022_17, Business School - Economics, University of Glasgow.
    13. Jennifer Castle & David Hendry, 2020. "Identifying the Causal Role of CO2 during the Ice Ages," Economics Series Working Papers 898, University of Oxford, Department of Economics.
    14. Philippe Goulet Coulombe & Maximilian Gobel, 2020. "Arctic Amplification of Anthropogenic Forcing: A Vector Autoregressive Analysis," Papers 2005.02535, arXiv.org, revised Mar 2021.

  15. Ross Askanazi & Francis X. Diebold & Frank Schorfheide & Minchul Shin, 2018. "On the Comparison of Interval Forecasts," PIER Working Paper Archive 18-013, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 02 Aug 2018.

    Cited by:

    1. Laura Liu & Hyungsik Roger Moon & Frank Schorfheide, 2019. "Forecasting with a Panel Tobit Model," CAEPR Working Papers 2019-005, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    2. James Mitchell & Martin Weale, 2021. "Censored Density Forecasts: Production and Evaluation," Working Papers 21-12R, Federal Reserve Bank of Cleveland, revised 16 Aug 2022.
    3. Makridakis, Spyros & Hyndman, Rob J. & Petropoulos, Fotios, 2020. "Forecasting in social settings: The state of the art," International Journal of Forecasting, Elsevier, vol. 36(1), pages 15-28.
    4. Hyndman, Rob J., 2020. "A brief history of forecasting competitions," International Journal of Forecasting, Elsevier, vol. 36(1), pages 7-14.
    5. Francis X. Diebold & Minchul Shin & Boyuan Zhang, 2021. "On the Aggregation of Probability Assessments: Regularized Mixtures of Predictive Densities for Eurozone Inflation and Real Interest Rates," Working Papers 21-06, Federal Reserve Bank of Philadelphia.
    6. Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    7. Francisca Corpas-Burgos & Miguel A. Martinez-Beneito, 2021. "An Autoregressive Disease Mapping Model for Spatio-Temporal Forecasting," Mathematics, MDPI, vol. 9(4), pages 1-17, February.
    8. Jonas E. Arias & Juan F. Rubio-Ramirez & Minchul Shin, 2021. "Macroeconomic Forecasting and Variable Ordering in Multivariate Stochastic Volatility Models," Working Papers 21-21, Federal Reserve Bank of Philadelphia.
    9. Hirano, Keisuke & Porter, Jack R., 2020. "Asymptotic analysis of statistical decision rules in econometrics," Handbook of Econometrics, in: Steven N. Durlauf & Lars Peter Hansen & James J. Heckman & Rosa L. Matzkin (ed.), Handbook of Econometrics, edition 1, volume 7, chapter 0, pages 283-354, Elsevier.
    10. Nico Keilman, 2020. "Evaluating Probabilistic Population Forecasts," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 520-521, pages 49-64.
    11. Spyros Makridakis & Chris Fry & Fotios Petropoulos & Evangelos Spiliotis, 2022. "The Future of Forecasting Competitions: Design Attributes and Principles," INFORMS Joural on Data Science, INFORMS, vol. 1(1), pages 96-113, April.
    12. Liu, Laura & Moon, Hyungsik Roger & Schorfheide, Frank, 2021. "Panel forecasts of country-level Covid-19 infections," Journal of Econometrics, Elsevier, vol. 220(1), pages 2-22.
    13. Sayar Karmakar & Marek Chudý & Wei Biao Wu, 2022. "Long‐term prediction intervals with many covariates," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(4), pages 587-609, July.

  16. Francis X. Diebold & Minchul Shin, 2018. "Machine Learning for Regularized Survey Forecast Combination: Partially-Egalitarian Lasso and its Derivatives," NBER Working Papers 24967, National Bureau of Economic Research, Inc.

    Cited by:

    1. Wang, Yudong & Hao, Xianfeng, 2023. "Forecasting the real prices of crude oil: What is the role of parameter instability?," Energy Economics, Elsevier, vol. 117(C).
    2. Constantin Bürgi, 2023. "How to Deal With Missing Observations in Surveys of Professional Forecasters," CESifo Working Paper Series 10203, CESifo.
    3. Granziera, Eleonora & Sekhposyan, Tatevik, 2019. "Predicting relative forecasting performance: An empirical investigation," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1636-1657.
    4. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2019. "How is Machine Learning Useful for Macroeconomic Forecasting?," CIRANO Working Papers 2019s-22, CIRANO.
    5. Diebold, Francis X. & Rudebusch, Glenn D., 2022. "Probability assessments of an ice-free Arctic: Comparing statistical and climate model projections," Journal of Econometrics, Elsevier, vol. 231(2), pages 520-534.
    6. Bertrand Candelon & Francesco Roccazzella, 2025. "Evaluating Inflation Forecasts in the Euro Area and the Role of the ECB," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(3), pages 978-1008, April.
    7. Saidjon Shiralievich Tavarov & Alexander Sidorov & Zsolt Čonka & Murodbek Safaraliev & Pavel Matrenin & Mihail Senyuk & Svetlana Beryozkina & Inga Zicmane, 2023. "Control of Operational Modes of an Urban Distribution Grid under Conditions of Uncertainty," Energies, MDPI, vol. 16(8), pages 1-18, April.
    8. Grzegorz Marcjasz & Bartosz Uniejewski & Rafał Weron, 2020. "Beating the Naïve—Combining LASSO with Naïve Intraday Electricity Price Forecasts," Energies, MDPI, vol. 13(7), pages 1-16, April.
    9. Qiu, Yue & Zheng, Yuchen, 2023. "Improving box office projections through sentiment analysis: Insights from regularization-based forecast combinations," Economic Modelling, Elsevier, vol. 125(C).
    10. Maehashi, Kohei & Shintani, Mototsugu, 2020. "Macroeconomic forecasting using factor models and machine learning: an application to Japan," Journal of the Japanese and International Economies, Elsevier, vol. 58(C).
    11. Philippe Goulet Coulombe & Karin Klieber & Christophe Barrette & Maximilian Goebel, 2024. "Maximally Forward-Looking Core Inflation," Papers 2404.05209, arXiv.org.
    12. Tae-Hwy Lee & Ekaterina Seregina, 2020. "Learning from Forecast Errors: A New Approach to Forecast Combinations," Papers 2011.02077, arXiv.org, revised May 2021.
    13. Luiz Renato Lima & Lucas Lúcio Godeiro & Mohammed Mohsin, 2021. "Time-Varying Dictionary and the Predictive Power of FED Minutes," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 149-181, January.
    14. Roccazzella, Francesco & Gambetti, Paolo & Vrins, Frédéric, 2021. "Optimal and robust combination of forecasts via constrained optimization and shrinkage," LIDAM Reprints LFIN 2021014, Université catholique de Louvain, Louvain Finance (LFIN).
    15. Li Li & Yanfei Kang & Fotios Petropoulos & Feng Li, 2022. "Feature-based intermittent demand forecast combinations: bias, accuracy and inventory implications," Papers 2204.08283, arXiv.org, revised Aug 2022.
    16. Bartosz Uniejewski & Katarzyna Maciejowska, 2022. "LASSO Principal Component Averaging -- a fully automated approach for point forecast pooling," Papers 2207.04794, arXiv.org.
    17. Qian, Yilin & Thompson, Ryan & Vasnev, Andrey L, 2022. "Global combinations of expert forecasts," Working Papers BAWP-2022-02, University of Sydney Business School, Discipline of Business Analytics.
    18. Kohei Maehashi & Mototsugu Shintani, 2020. "Macroeconomic Forecasting Using Factor Models and Machine Learning: An Application to Japan," CIRJE F-Series CIRJE-F-1146, CIRJE, Faculty of Economics, University of Tokyo.
    19. Petropoulos, Fotios & Spiliotis, Evangelos & Panagiotelis, Anastasios, 2023. "Model combinations through revised base rates," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1477-1492.
    20. Anthony Garratt & Timo Henckel & Shaun P. Vahey, 2019. "Empirically-transformed linear opinion pools," CAMA Working Papers 2019-47, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    21. Francis X. Diebold & Minchul Shin & Boyuan Zhang, 2021. "On the Aggregation of Probability Assessments: Regularized Mixtures of Predictive Densities for Eurozone Inflation and Real Interest Rates," Working Papers 21-06, Federal Reserve Bank of Philadelphia.
    22. Wen, Danyan & Liu, Li & Wang, Yudong & Zhang, Yaojie, 2022. "Forecasting crude oil market returns: Enhanced moving average technical indicators," Resources Policy, Elsevier, vol. 76(C).
    23. Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    24. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2024. "Econometrics of machine learning methods in economic forecasting," Chapters, in: Michael P. Clements & Ana Beatriz Galvão (ed.), Handbook of Research Methods and Applications in Macroeconomic Forecasting, chapter 10, pages 246-273, Edward Elgar Publishing.
    25. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023. "Machine learning advances for time series forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
    26. Zhang, Yaojie & Wahab, M.I.M. & Wang, Yudong, 2023. "Forecasting crude oil market volatility using variable selection and common factor," International Journal of Forecasting, Elsevier, vol. 39(1), pages 486-502.
    27. Ryan Thompson & Yilin Qian & Andrey L. Vasnev, 2022. "Flexible global forecast combinations," Papers 2207.07318, arXiv.org, revised Mar 2024.
    28. UÄŸur Åžener & Salvatore Joseph Terregrossa, 2024. "A Transcendental LASSO Function for Combining Machine Learning and Statistical Model Forecasts," SAGE Open, , vol. 14(3), pages 21582440241, August.
    29. Guo, Xiaozhu & Huang, Dengshi & Li, Xiafei & Liang, Chao, 2023. "Are categorical EPU indices predictable for carbon futures volatility? Evidence from the machine learning method," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 672-693.
    30. Katarzyna Maciejowska & Bartosz Uniejewski & Tomasz Serafin, 2020. "PCA Forecast Averaging—Predicting Day-Ahead and Intraday Electricity Prices," Energies, MDPI, vol. 13(14), pages 1-19, July.
    31. Daniel Borup & David E. Rapach & Erik Christian Montes Schütte, 2021. "Now- and Backcasting Initial Claims with High-Dimensional Daily Internet Search-Volume Data," CREATES Research Papers 2021-02, Department of Economics and Business Economics, Aarhus University.
    32. Qiu, Yue & Qu, Shaoguang & Shi, Zhentao & Xie, Tian, 2025. "Predicting cryptocurrency volatility: The power of model clustering," Economic Modelling, Elsevier, vol. 144(C).
    33. Francis X. Diebold & Aaron Mora & Minchul Shin, 2025. "On the Wisdom of Crowds (of Economists)," Papers 2503.09287, arXiv.org.
    34. Bin Chen & Kenwin Maung, 2020. "Time-varying Forecast Combination for High-Dimensional Data," Papers 2010.10435, arXiv.org.
    35. Chen, Bin & Maung, Kenwin, 2023. "Time-varying forecast combination for high-dimensional data," Journal of Econometrics, Elsevier, vol. 237(2).
    36. Marta Poncela-Blanco & Pilar Poncela, 2021. "Improving Wind Power Forecasts: Combination through Multivariate Dimension Reduction Techniques," Energies, MDPI, vol. 14(5), pages 1-16, March.
    37. Jeronymo Marcondes Pinto & Emerson Fernandes Marçal, 2023. "An artificial intelligence approach to forecasting when there are structural breaks: a reinforcement learning-based framework for fast switching," Empirical Economics, Springer, vol. 65(4), pages 1729-1759, October.
    38. Yoonseok Lee & Donggyu Sul, 2021. "Depth-Weighted Forecast Combination: Application to COVID-19 Cases," Center for Policy Research Working Papers 238, Center for Policy Research, Maxwell School, Syracuse University.
    39. Zhentao Shi & Liangjun Su & Tian Xie, 2020. "L2-Relaxation: With Applications to Forecast Combination and Portfolio Analysis," Papers 2010.09477, arXiv.org, revised Aug 2022.
    40. James Younker, 2022. "Calculating Effective Degrees of Freedom for Forecast Combinations and Ensemble Models," Discussion Papers 2022-19, Bank of Canada.
    41. Xi Dong & Yan Li & David E. Rapach & Guofu Zhou, 2022. "Anomalies and the Expected Market Return," Journal of Finance, American Finance Association, vol. 77(1), pages 639-681, February.
    42. Yusupova, Alisa & Pavlidis, Nicos G. & Pavlidis, Efthymios G., 2023. "Dynamic linear models with adaptive discounting," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1925-1944.
    43. Cakici, Nusret & Shahzad, Syed Jawad Hussain & Będowska-Sójka, Barbara & Zaremba, Adam, 2024. "Machine learning and the cross-section of cryptocurrency returns," International Review of Financial Analysis, Elsevier, vol. 94(C).
    44. Hu, Lei & Song, Min & Wen, Fenghua & Zhang, Yun & Zhao, Yunning, 2025. "The impact of climate attention on risk spillover effect in energy futures markets," Energy Economics, Elsevier, vol. 141(C).
    45. Rachidi Kotchoni & Maxime Leroux & Dalibor Stevanovic, 2019. "Macroeconomic Forecast Accuracy in data-rich environment," Post-Print hal-02435757, HAL.
    46. Jeronymo Marcondes Pinto & Jennifer L. Castle, 2022. "Machine Learning Dynamic Switching Approach to Forecasting in the Presence of Structural Breaks," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 18(2), pages 129-157, July.
    47. Roccazzella, Francesco & Candelon, Bertrand, 2022. "Should we care about ECB inflation expectations?," LIDAM Discussion Papers LFIN 2022004, Université catholique de Louvain, Louvain Finance (LFIN).
    48. Ryan Cumings-Menon & Minchul Shin, 2020. "Probability Forecast Combination via Entropy Regularized Wasserstein Distance," Working Papers 20-31/R, Federal Reserve Bank of Philadelphia.
    49. Wada, Tatsuma, 2022. "Out-of-sample forecasting of foreign exchange rates: The band spectral regression and LASSO," Journal of International Money and Finance, Elsevier, vol. 128(C).
    50. Anesti, Nikoleta & Kalamara, Eleni & Kapetanios, George, 2021. "Forecasting UK GDP growth with large survey panels," Bank of England working papers 923, Bank of England.
    51. Meira, Erick & Cyrino Oliveira, Fernando Luiz & Jeon, Jooyoung, 2021. "Treating and Pruning: New approaches to forecasting model selection and combination using prediction intervals," International Journal of Forecasting, Elsevier, vol. 37(2), pages 547-568.
    52. Huang, Dashan & Li, Jiangyuan & Wang, Liyao, 2021. "Are disagreements agreeable? Evidence from information aggregation," Journal of Financial Economics, Elsevier, vol. 141(1), pages 83-101.
    53. Michael T. Kiley, 2020. "Financial Conditions and Economic Activity: Insights from Machine Learning," Finance and Economics Discussion Series 2020-095, Board of Governors of the Federal Reserve System (U.S.).
    54. Pietro Giorgio Lovaglio, 2025. "Cross‐Learning With Panel Data Modeling for Stacking and Forecast Time Series Employment in Europe," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 753-780, March.
    55. Lu, Fei & Ma, Feng & Li, Pan & Huang, Dengshi, 2022. "Natural gas volatility predictability in a data-rich world," International Review of Financial Analysis, Elsevier, vol. 83(C).
    56. Jiun-Hua Su, 2021. "No-Regret Forecasting with Egalitarian Committees," Papers 2109.13801, arXiv.org.
    57. Li Liu & Xianfeng Hao & Yudong Wang, 2024. "Solving the Forecast Combination Puzzle Using Double Shrinkages," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(3), pages 714-741, June.
    58. Jesús Molina‐Muñoz & Andrés Mora‐Valencia & Javier Perote, 2024. "Predicting carbon and oil price returns using hybrid models based on machine and deep learning," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(2), June.
    59. Tim K. Tsang & Qiurui Du & Benjamin J. Cowling & Cécile Viboud, 2024. "An adaptive weight ensemble approach to forecast influenza activity in an irregular seasonality context," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    60. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    61. Godahewa, Rakshitha & Bergmeir, Christoph & Webb, Geoffrey I. & Montero-Manso, Pablo, 2023. "An accurate and fully-automated ensemble model for weekly time series forecasting," International Journal of Forecasting, Elsevier, vol. 39(2), pages 641-658.
    62. Daniel Borup & Jonas N. Eriksen & Mads M. Kjær & Martin Thyrsgaard, 2024. "Predicting Bond Return Predictability," Management Science, INFORMS, vol. 70(2), pages 931-951, February.
    63. Yumin Li & Ruiqi Yang & Xiaoman Wang & Jiaming Zhu & Nan Song, 2023. "Carbon Price Combination Forecasting Model Based on Lasso Regression and Optimal Integration," Sustainability, MDPI, vol. 15(12), pages 1-26, June.
    64. Max H. Farrell & Tengyuan Liang & Sanjog Misra, 2020. "Deep Learning for Individual Heterogeneity," Papers 2010.14694, arXiv.org, revised Apr 2025.
    65. Borup, Daniel & Rapach, David E. & Schütte, Erik Christian Montes, 2023. "Mixed-frequency machine learning: Nowcasting and backcasting weekly initial claims with daily internet search volume data," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1122-1144.

  17. Francis X. Diebold & Minchul Shin, 2017. "Beating the Simple Average: Egalitarian LASSO for Combining Economic Forecasts," PIER Working Paper Archive 17-017, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 20 Aug 2017.

    Cited by:

    1. Nikodinoska, Dragana & Käso, Mathias & Müsgens, Felix, 2022. "Solar and wind power generation forecasts using elastic net in time-varying forecast combinations," Applied Energy, Elsevier, vol. 306(PA).
    2. Camila Figueroa S. & Michael Pedersen, 2019. "A system for forecasting Chilean cash demand – the role of forecast combinations," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 22(2), pages 040-068, August.
    3. Daniele Bianchi & Kenichiro McAlinn, 2018. "Large-Scale Dynamic Predictive Regressions," Papers 1803.06738, arXiv.org.
    4. Warwick Smith & Anca M. Hanea & Mark A. Burgman, 2022. "Can Groups Improve Expert Economic and Financial Forecasts?," Forecasting, MDPI, vol. 4(3), pages 1-18, August.
    5. Nir Billfeld & Moshe Kim, 2019. "Semiparametric correction for endogenous truncation bias with Vox Populi based participation decision," Papers 1902.06286, arXiv.org.

  18. Francis X. Diebold & Laura Liu & Kamil Yilmaz, 2017. "Commodity Connectedness," NBER Working Papers 23685, National Bureau of Economic Research, Inc.

    Cited by:

    1. Juncal Cunado & David Gabauer & Rangan Gupta, 2021. "Realized Volatility Spillovers between Energy and Metal Markets: A Time-Varying Connectedness Approach," Working Papers 202180, University of Pretoria, Department of Economics.
    2. Zaheer Anwer & Ashraf Khan & Muhammad Abubakr Naeem & Aviral Kumar Tiwari, 2025. "Modelling systemic risk of energy and non-energy commodity markets during the COVID-19 pandemic," Annals of Operations Research, Springer, vol. 345(2), pages 1193-1227, February.
    3. Thiem, Christopher, 2018. "Cross-category spillovers of economic policy uncertainty," Ruhr Economic Papers 744, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    4. Najaf Iqbal & Elie Bouri & Guangrui Liu & Ashish Kumar, 2024. "Volatility spillovers during normal and high volatility states and their driving factors: A cross‐country and cross‐asset analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(1), pages 975-995, January.
    5. Bastianin, Andrea & Casoli, Chiara & Galeotti, Marzio, 2023. "The connectedness of Energy Transition Metals," FEEM Working Papers 336984, Fondazione Eni Enrico Mattei (FEEM).
    6. Lucey, Brian M. & Vigne, Samuel A. & Ballester, Laura & Barbopoulos, Leonidas & Brzeszczynski, Janusz & Carchano, Oscar & Dimic, Nebojsa & Fernandez, Viviana & Gogolin, Fabian & González-Urteaga, Ana , 2018. "Future directions in international financial integration research - A crowdsourced perspective," International Review of Financial Analysis, Elsevier, vol. 55(C), pages 35-49.
    7. Scarcioffolo, Alexandre Ribeiro & Etienne, Xiaoli L., 2019. "How connected are the U.S. regional natural gas markets in the post-deregulation era? Evidence from time-varying connectedness analysis," Journal of Commodity Markets, Elsevier, vol. 15(C), pages 1-1.
    8. Escribano, Ana & Koczar, Monika W. & Jareño, Francisco & Esparcia, Carlos, 2023. "Shock transmission between crude oil prices and stock markets," Resources Policy, Elsevier, vol. 83(C).
    9. Shah, Adil Ahmad & Dar, Arif Billah, 2021. "Exploring diversification opportunities across commodities and financial markets: Evidence from time-frequency based spillovers," Resources Policy, Elsevier, vol. 74(C).
    10. Zhou, Bin & Shi, Huai-Long, 2024. "Quantile volatility connectedness among themes and sectors: Novel evidence from China," The Quarterly Review of Economics and Finance, Elsevier, vol. 98(C).
    11. Apergis, Nicholas, 2023. "Realized higher-order moments spillovers across cryptocurrencies," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 85(C).
    12. Costola, Michele & Lorusso, Marco, 2021. "Spillovers among Energy Commodities and the Russian Stock Market," MPRA Paper 108990, University Library of Munich, Germany.
    13. Wen, Tiange & Wang, Gang-Jin, 2020. "Volatility connectedness in global foreign exchange markets," Journal of Multinational Financial Management, Elsevier, vol. 54(C).
    14. Pagnottoni, Paolo, 2023. "Superhighways and roads of multivariate time series shock transmission: Application to cryptocurrency, carbon emission and energy prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    15. Wang, Gang-Jin & Chen, Yang-Yang & Si, Hui-Bin & Xie, Chi & Chevallier, Julien, 2021. "Multilayer information spillover networks analysis of China’s financial institutions based on variance decompositions," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 325-347.
    16. Evrim Mandacı, Pınar & Cagli, Efe Çaglar & Taşkın, Dilvin, 2020. "Dynamic connectedness and portfolio strategies: Energy and metal markets," Resources Policy, Elsevier, vol. 68(C).
    17. Ma, Yan-Ran & Ji, Qiang & Wu, Fei & Pan, Jiaofeng, 2021. "Financialization, idiosyncratic information and commodity co-movements," Energy Economics, Elsevier, vol. 94(C).
    18. Zhang, Xu & Xu, Wenting & Rauf, Abdul & Ozturk, Ilhan, 2024. "Transitioning from conventional energy to clean renewable energy in G7 countries: A signed network approach," Energy, Elsevier, vol. 307(C).
    19. Muneer Shaik & Mustafa Raza Rabbani & Mohd Atif & Ahmet Faruk Aysan & Mohammad Noor Alam & Umar Nawaz Kayani, 2024. "The dynamic volatility nexus of geo-political risks, stocks, bond, bitcoin, gold and oil during COVID-19 and Russian-Ukraine war," PLOS ONE, Public Library of Science, vol. 19(2), pages 1-16, February.
    20. Choi, Insu & Kim, Woo Chang, 2024. "Practical forecasting of risk boundaries for industrial metals and critical minerals via statistical machine learning techniques," International Review of Financial Analysis, Elsevier, vol. 94(C).
    21. Mensi, Walid & Rehman, Mobeen Ur & Gök, Remzi & Gemici, Eray & Vo, Xuan Vinh, 2025. "Risk spillovers and diversification benefits between crude oil and agricultural commodity futures markets," Research in International Business and Finance, Elsevier, vol. 73(PA).
    22. Shah, Adil Ahmad & Dar, Arif Billah, 2022. "Asymmetric, time and frequency-based spillover transmission in financial and commodity markets," The Journal of Economic Asymmetries, Elsevier, vol. 25(C).
    23. Libo Yin & Hong Cao, 2024. "Financialization of commodity markets: New evidence from temporal and spatial domains," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(8), pages 1357-1382, August.
    24. Cagli, Efe Caglar, 2023. "The volatility spillover between battery metals and future mobility stocks: Evidence from the time-varying frequency connectedness approach," Resources Policy, Elsevier, vol. 86(PA).
    25. Semeyutin, Artur & Gozgor, Giray & Lau, Chi Keung Marco & Xu, Bing, 2021. "Effects of idiosyncratic jumps and co-jumps on oil, gold, and copper markets," Energy Economics, Elsevier, vol. 104(C).
    26. Gong, Xu & Xu, Jun & Liu, Tangyong & Zhou, Zicheng, 2022. "Dynamic volatility connectedness between industrial metal markets," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    27. Lukas Boeckelmann & Arthur Stalla-Bourdillon, 2021. "Structural Estimation of Time-Varying Spillovers: An Application to International Credit Risk Transmission," Working papers 798, Banque de France.
    28. Jonas Krampe & Luca Margaritella, 2024. "Decomposing Global Bank Network Connectedness: What is Common, Idiosyncratic and When?," Papers 2402.02482, arXiv.org, revised Mar 2025.
    29. Kyriazis, Nikolaos & Corbet, Shaen, 2024. "Evaluating the dynamic connectedness of financial assets and bank indices during black-swan events: A Quantile-VAR approach," Energy Economics, Elsevier, vol. 131(C).
    30. Dungey, Mardi & Islam, Raisul & Volkov, Vladimir, 2019. "Crisis transmission: visualizing vulnerability," Working Papers 2019-07, University of Tasmania, Tasmanian School of Business and Economics.
    31. Liu, Pan & Power, Gabriel J. & Vedenov, Dmitry, 2021. "Fair-weather Friends? Sector-specific volatility connectedness and transmission," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 712-736.
    32. Zheng, Tingguo & Ye, Shiqi & Hong, Yongmiao, 2023. "Fast estimation of a large TVP-VAR model with score-driven volatilities," Journal of Economic Dynamics and Control, Elsevier, vol. 157(C).
    33. Liang, Chao & Ma, Feng & Li, Ziyang & Li, Yan, 2020. "Which types of commodity price information are more useful for predicting US stock market volatility?," Economic Modelling, Elsevier, vol. 93(C), pages 642-650.
    34. Cui, Jinxin & Maghyereh, Aktham, 2023. "Higher-order moment risk connectedness and optimal investment strategies between international oil and commodity futures markets: Insights from the COVID-19 pandemic and Russia-Ukraine conflict," International Review of Financial Analysis, Elsevier, vol. 86(C).
    35. Akyildirim, Erdinc & Cepni, Oguzhan & Pham, Linh & Uddin, Gazi Salah, 2022. "How connected is the agricultural commodity market to the news-based investor sentiment?," Energy Economics, Elsevier, vol. 113(C).
    36. Muñoz Mendoza, Jorge A. & Veloso Ramos, Carmen L. & Delgado Fuentealba, Carlos L. & Cornejo Saavedra, Edinson E. & Sepúlveda Yelpo, Sandra M., 2024. "Stock, foreign exchange and commodity markets linkages: Implications for risk diversification and portfolio management," Global Finance Journal, Elsevier, vol. 63(C).
    37. Agyei, Samuel Kwaku & Umar, Zaghum & Bossman, Ahmed & Teplova, Tamara, 2023. "Dynamic connectedness between global commodity sectors, news sentiment, and sub-Saharan African equities," Emerging Markets Review, Elsevier, vol. 56(C).
    38. shah, Adil Ahmad & Bhanja, Niyati & Dar, Arif Billah, 2023. "Do gold and the US dollar diversify global sectoral risk? Evidence from connectedness and dynamic conditional correlation measures," The Journal of Economic Asymmetries, Elsevier, vol. 28(C).
    39. Abid, Ilyes & Benkraiem, Ramzi & Mzoughi, Hela & Urom, Christian, 2024. "From black gold to financial fallout: Analyzing extreme risk spillovers in oil-exporting nations," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).
    40. An, Sufang & Gao, Xiangyun & An, Haizhong & Liu, Siyao & Sun, Qingru & Jia, Nanfei, 2020. "Dynamic volatility spillovers among bulk mineral commodities: A network method," Resources Policy, Elsevier, vol. 66(C).
    41. Yi, Shuyue & Xu, Zishuang & Wang, Gang-Jin, 2018. "Volatility connectedness in the cryptocurrency market: Is Bitcoin a dominant cryptocurrency?," International Review of Financial Analysis, Elsevier, vol. 60(C), pages 98-114.
    42. Shakya, Shishir & Li, Bingxin & Etienne, Xiaoli, 2022. "Shale revolution, oil and gas prices, and drilling activities in the United States," Energy Economics, Elsevier, vol. 108(C).
    43. Morelli, Giacomo, 2023. "Stochastic ordering of systemic risk in commodity markets," Energy Economics, Elsevier, vol. 117(C).
    44. Balcilar, Mehmet & Gabauer, David & Umar, Zaghum, 2021. "Crude Oil futures contracts and commodity markets: New evidence from a TVP-VAR extended joint connectedness approach," Resources Policy, Elsevier, vol. 73(C).
    45. Wang, Xiaoyang, 2022. "Efficient markets are more connected: An entropy-based analysis of the energy, industrial metal and financial markets," Energy Economics, Elsevier, vol. 111(C).
    46. Balli, Faruk & Balli, Hatice Ozer & Dang, Tam Hoang Nhat & Gabauer, David, 2023. "Contemporaneous and lagged R2 decomposed connectedness approach: New evidence from the energy futures market," Finance Research Letters, Elsevier, vol. 57(C).
    47. Chen, Huayi & Shi, Huai-Long & Zhou, Wei-Xing, 2024. "Carbon volatility connectedness and the role of external uncertainties: Evidence from China," Journal of Commodity Markets, Elsevier, vol. 33(C).
    48. Wang, Gang-Jin & Xie, Chi & Zhao, Longfeng & Jiang, Zhi-Qiang, 2018. "Volatility connectedness in the Chinese banking system: Do state-owned commercial banks contribute more?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 57(C), pages 205-230.
    49. Cao, Yan & Cheng, Sheng & Li, Xinran, 2023. "How economic policy uncertainty affects asymmetric spillovers in food and oil prices: Evidence from wavelet analysis," Resources Policy, Elsevier, vol. 86(PB).
    50. Fan, Zaifeng & Jump, Jeff & Tse, Yiuman & Yu, Linda, 2023. "Volatility in US dairy futures markets," Journal of Commodity Markets, Elsevier, vol. 29(C).
    51. I. Abid & R. Benkraiem & H. Mzoughi & C. Urom, 2024. "From black gold to financial fallout: Analyzing extreme risk spillovers in oil-exporting nations," Post-Print hal-04681726, HAL.
    52. Pagnottoni, Paolo & Spelta, Alessandro, 2023. "The motifs of risk transmission in multivariate time series: Application to commodity prices," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    53. Christopher Thiem, 2020. "Cross-Category, Trans-Pacific Spillovers of Policy Uncertainty and Financial Market Volatility," Open Economies Review, Springer, vol. 31(2), pages 317-342, April.
    54. Ogbuabor, Jonathan E. & Anthony-Orji, Onyinye I. & Manasseh, Charles O. & Orji, Anthony, 2020. "Measuring the dynamics of COMESA output connectedness with the global economy," The Journal of Economic Asymmetries, Elsevier, vol. 21(C).

  19. Francis X. Diebold & Frank Schorfheide & Minchul Shin, 2016. "Real-Time Forecast Evaluation of DSGE Models with Stochastic Volatility," NBER Working Papers 22615, National Bureau of Economic Research, Inc.

    Cited by:

    1. Poledna, Sebastian & Miess, Michael Gregor & Hommes, Cars & Rabitsch, Katrin, 2023. "Economic forecasting with an agent-based model," European Economic Review, Elsevier, vol. 151(C).
    2. David Alaminos & M. Belén Salas & Manuel A. Fernández-Gámez, 2022. "Quantum Computing and Deep Learning Methods for GDP Growth Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 803-829, February.
    3. Wang, Yudong & Liu, Li & Wu, Chongfeng, 2020. "Forecasting commodity prices out-of-sample: Can technical indicators help?," International Journal of Forecasting, Elsevier, vol. 36(2), pages 666-683.
    4. Ivashchenko, S., 2020. "Long-term growth sources for sectors of Russian economy," Journal of the New Economic Association, New Economic Association, vol. 48(4), pages 86-112.
    5. Cars Hommes & Mario He & Sebastian Poledna & Melissa Siqueira & Yang Zhang, 2022. "CANVAS: A Canadian Behavioral Agent-Based Model," Staff Working Papers 22-51, Bank of Canada.
    6. Jan Philipp Fritsche & Mathias Klein & Malte Rieth, 2020. "Government Spending Multipliers in (Un)certain Times," Discussion Papers of DIW Berlin 1901, DIW Berlin, German Institute for Economic Research.
    7. Jonathan Benchimol & Sergey Ivashchenko, 2020. "Switching Volatility in a Nonlinear Open Economy," CFDS Discussion Paper Series 2020/8, Center for Financial Development and Stability at Henan University, Kaifeng, Henan, China.
    8. Gary Koop & Dimitris Korobilis, 2018. "Forecasting with High-Dimensional Panel VARs," Working Paper series 18-20, Rimini Centre for Economic Analysis.
    9. Sergey Ivashchenko, 2022. "Dynamic Stochastic General Equilibrium Model with Multiple Trends and Structural Breaks," Russian Journal of Money and Finance, Bank of Russia, vol. 81(1), pages 46-72, March.
    10. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    11. S. Boragan Aruoba & Luigi Bocola & Frank Schorfheide, 2013. "Assessing DSGE model nonlinearities," Working Papers 13-47, Federal Reserve Bank of Philadelphia.
    12. Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano, 2021. "Using time-varying volatility for identification in Vector Autoregressions: An application to endogenous uncertainty," Journal of Econometrics, Elsevier, vol. 225(1), pages 47-73.
    13. Michael Cai & Marco Del Negro & Edward P. Herbst & Ethan Matlin & Reca Sarfati & Frank Schorfheide, 2019. "Online Estimation of DSGE Models," Liberty Street Economics 20190821, Federal Reserve Bank of New York.
    14. Todd E. Clark & Michael W. McCracken & Elmar Mertens, 2020. "Modeling Time-Varying Uncertainty of Multiple-Horizon Forecast Errors," The Review of Economics and Statistics, MIT Press, vol. 102(1), pages 17-33, March.
    15. James Morley & Irina B Panovska, 2016. "Is Business Cycle Asymmetry Intrinsic in Industrialized Economies?," Discussion Papers 2016-12, School of Economics, The University of New South Wales.
    16. Ramis Khabibullin & Sergei Seleznev, 2022. "Fast Estimation of Bayesian State Space Models Using Amortized Simulation-Based Inference," Papers 2210.07154, arXiv.org.
    17. Jinshun Wu & Luyao Wu, 2024. "Bayesian Local Likelihood Estimation of Time-Varying DSGE Models: Allowing for Indeterminacy," Computational Economics, Springer;Society for Computational Economics, vol. 64(4), pages 2437-2476, October.
    18. Yolanda S. Stander, 2023. "The Governance and Disclosure of IFRS 9 Economic Scenarios," JRFM, MDPI, vol. 16(1), pages 1-27, January.
    19. Sergey Ivashchenko & Semih Emre Cekin & Rangan Gupta & Chien-Chiang Lee, 2022. "Real-Time Forecast of DSGE Models with Time-Varying Volatility in GARCH Form," Working Papers 202204, University of Pretoria, Department of Economics.
    20. Dmitry Kreptsev & Sergei Seleznev, 2018. "Forecasting for the Russian Economy Using Small-Scale DSGE Models," Russian Journal of Money and Finance, Bank of Russia, vol. 77(2), pages 51-67, June.
    21. Di Bartolomeo, Giovanni & Serpieri, Carolina, 2024. "Optimal monetary policy and the time-dependent price and wage Phillips curves: An international comparison," Journal of International Money and Finance, Elsevier, vol. 146(C).
    22. Siddhartha Chib & Minchul Shin & Fei Tan, 2023. "DSGE-SVt: An Econometric Toolkit for High-Dimensional DSGE Models with SV and t Errors," Computational Economics, Springer;Society for Computational Economics, vol. 61(1), pages 69-111, January.
    23. Angelini, Giovanni & Gorgi, Paolo, 2018. "DSGE Models with observation-driven time-varying volatility," Economics Letters, Elsevier, vol. 171(C), pages 169-171.
    24. Yantao Gao & Xilong Yao & Wenxi Wang & Xin Liu, 2019. "Dynamic effect of environmental tax on export trade: Based on DSGE mode," Energy & Environment, , vol. 30(7), pages 1275-1290, November.
    25. Bäurle Gregor & Kaufmann Daniel & Kaufmann Sylvia & Strachan Rodney, 2020. "Constrained interest rates and changing dynamics at the zero lower bound," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(2), pages 1-26, April.
    26. Diebold, Francis X. & Schorfheide, Frank & Shin, Minchul, 2017. "Real-time forecast evaluation of DSGE models with stochastic volatility," CFS Working Paper Series 577, Center for Financial Studies (CFS).
    27. Michael P Clements & Ana Beatriz Galvao, 2017. "Data Revisions and Real-time Probabilistic Forecasting of Macroeconomic Variables," ICMA Centre Discussion Papers in Finance icma-dp2017-01, Henley Business School, University of Reading.
    28. Siddhartha Chib & Minchul Shin & Fei Tan, 2020. "High-Dimensional DSGE Models: Pointers on Prior, Estimation, Comparison, and Prediction∗," Working Papers 20-35, Federal Reserve Bank of Philadelphia.
    29. Mertens, Elmar, 2023. "Precision-based sampling for state space models that have no measurement error," Journal of Economic Dynamics and Control, Elsevier, vol. 154(C).
    30. Farooq Akram & Andrew Binning & Junior Maih, 2016. "Joint prediction bands for macroeconomic risk management," Working Paper 2016/7, Norges Bank.
    31. Reifschneider, David & Tulip, Peter, 2019. "Gauging the uncertainty of the economic outlook using historical forecasting errors: The Federal Reserve’s approach," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1564-1582.
    32. Carriero, Andrea & Galvão, Ana Beatriz & Kapetanios, George, 2019. "A comprehensive evaluation of macroeconomic forecasting methods," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1226-1239.
    33. Musa Abdu & Adamu Jibir & Salihu Abdullahi & Aisha Adamu Hassan, 2021. "Drivers of manufacturing firms’ productivity: a micro-perspective to industrialization in Nigeria," SN Business & Economics, Springer, vol. 1(2), pages 1-17, February.
    34. David L. Reifschneider & Peter Tulip, 2017. "Gauging the Uncertainty of the Economic Outlook Using Historical Forecasting Errors : The Federal Reserve's Approach," Finance and Economics Discussion Series 2017-020, Board of Governors of the Federal Reserve System (U.S.).
    35. Li, Chenxing & Maheu, John M & Yang, Qiao, 2022. "An Infinite Hidden Markov Model with Stochastic Volatility," MPRA Paper 115456, University Library of Munich, Germany.
    36. James Morley, 2019. "The business cycle: periodic pandemic or rollercoaster ride?," International Journal of Economic Policy Studies, Springer, vol. 13(2), pages 425-431, August.
    37. Pooyan Amir-Ahmadi & Christian Matthes & Mu-Chun Wang, 2016. "Choosing Prior Hyperparameters," Working Paper 16-9, Federal Reserve Bank of Richmond.
    38. Sun Xiaojin & Tsang Kwok Ping, 2019. "What cycles? Data detrending in DSGE models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 23(3), pages 1-23, June.
    39. Sergey M. Ivashchenko, 2019. "DSGE Models: Problem of Trends," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 2, pages 81-95, April.
    40. Xiao-Li Gong & Jin-Yan Lu & Xiong Xiong & Wei Zhang, 2022. "Higher-order dynamic effects of uncertainty risk under thick-tailed stochastic volatility," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-22, December.

  20. Francis X. Diebold & Minchul Shin, 2016. "Assessing Point Forecast Accuracy by Stochastic Error Distance," NBER Working Papers 22516, National Bureau of Economic Research, Inc.

    Cited by:

    1. Borgonovo, Emanuele & Hazen, Gordon B. & Jose, Victor Richmond R. & Plischke, Elmar, 2021. "Probabilistic sensitivity measures as information value," European Journal of Operational Research, Elsevier, vol. 289(2), pages 595-610.
    2. Sainan Jin & Valentina Corradi & Norman Swanson, 2015. "Robust Forecast Comparison," Departmental Working Papers 201502, Rutgers University, Department of Economics.
    3. Francis X. Diebold & Minchul Shin & Boyuan Zhang, 2021. "On the Aggregation of Probability Assessments: Regularized Mixtures of Predictive Densities for Eurozone Inflation and Real Interest Rates," Working Papers 21-06, Federal Reserve Bank of Philadelphia.
    4. Diebold, Francis X. & Shin, Minchul, 2015. "Assessing point forecast accuracy by stochastic loss distance," Economics Letters, Elsevier, vol. 130(C), pages 37-38.
    5. Emilian Dobrescu, 2014. "Attempting to Quantify the Accuracy of Complex Macroeconomic Forecasts," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 5-21, December.
    6. Ardakani, Omid M. & Dalko, Viktoria & Shim, Hyeeun, 2025. "Information loss from perception alignment," International Review of Economics & Finance, Elsevier, vol. 97(C).
    7. Tomás Marinozzi, 2023. "Forecasting Inflation in Argentina: A Probabilistic Approach," Ensayos Económicos, Central Bank of Argentina, Economic Research Department, vol. 1(81), pages 81-110, May.
    8. Valentina Corradi & Sainan Jin & Norman R. Swanson, 2023. "Robust forecast superiority testing with an application to assessing pools of expert forecasters," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 596-622, June.
    9. Hiroyuki Kawakatsu, 2020. "Recovering Yield Curves from Dynamic Term Structure Models with Time-Varying Factors," Stats, MDPI, vol. 3(3), pages 1-46, August.

  21. Mert Demirer & Francis X. Diebold & Laura Liu & Kamil Yilmaz, 2015. "Estimating Global Bank Network Connectedness," Koç University-TUSIAD Economic Research Forum Working Papers 1512, Koc University-TUSIAD Economic Research Forum.

    Cited by:

    1. Uddin, Gazi Salah & Jayasekera, Ranadeva & Park, Donghyun & Luo, Tianqi & Tian, Shu, 2022. "Go green or stay black: Bond market dynamics in Asia," International Review of Financial Analysis, Elsevier, vol. 81(C).
    2. Aramayis Dallakyan, 2021. "Nonparanormal Structural VAR for Non-Gaussian Data," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1093-1113, April.
    3. Inekwe, John Nkwoma, 2020. "Liquidity connectedness and output synchronisation," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 66(C).
    4. Rogelio V. Mercado Jr. & Shanty Noviantie, 2019. "Financial Flows Centrality: Empirical Evidence using Bilateral Capital Flows," Working Papers wp38, South East Asian Central Banks (SEACEN) Research and Training Centre.
    5. Chen, Yu-Lun & Mo, Wan-Shin & Qin, Rong-Ling & Yang, J. Jimmy, 2023. "Return spillover across China's financial markets," Pacific-Basin Finance Journal, Elsevier, vol. 80(C).
    6. Ki-Hong Choi & Ron P. McIver & Salvatore Ferraro & Lei Xu & Sang Hoon Kang, 2021. "Dynamic volatility spillover and network connectedness across ASX sector markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 45(4), pages 677-691, October.
    7. Zhu, Zongyuan & Luo, Qingtian, 2023. "Inter-industry risk spillover, role reversal, and economic stability," Finance Research Letters, Elsevier, vol. 57(C).
    8. Abduraimova, Kumushoy, 2022. "Contagion and tail risk in complex financial networks," Journal of Banking & Finance, Elsevier, vol. 143(C).
    9. Xiang, Youtao & Borjigin, Sumuya, 2024. "Multilayer networks for measuring interconnectedness among global stock markets through the lens of trading volume-price relationship," Global Finance Journal, Elsevier, vol. 62(C).
    10. Okorie, David Iheke & Lin, Boqiang, 2022. "Givers never lack: Nigerian oil & gas asymmetric network analyses," Energy Economics, Elsevier, vol. 108(C).
    11. Boyao Wu & Difang Huang & Muzi Chen, 2024. "Estimating Contagion Mechanism in Global Equity Market with Time-Zone Effect," Papers 2404.04335, arXiv.org.
    12. Xu, Qifa & Li, Mengting & Jiang, Cuixia, 2021. "Network-augmented time-varying parametric portfolio selection: Evidence from the Chinese stock market," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    13. Muzi Chen & Yuhang Wang & Boyao Wu & Difang Huang, 2024. "Dynamic Analyses of Contagion Risk and Module Evolution on the SSE A-Shares Market Based on Minimum Information Entropy," Papers 2403.19439, arXiv.org.
    14. Hale, Galina & Lopez, Jose A., 2019. "Monitoring banking system connectedness with big data," Journal of Econometrics, Elsevier, vol. 212(1), pages 203-220.
    15. Stenfors, Alexis & Chatziantoniou, Ioannis & Gabauer, David, 2022. "Independent policy, dependent outcomes: A game of cross-country dominoes across European yield curves," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    16. Będowska-Sójka, Barbara & Górka, Joanna & Hemmings, Danial & Zaremba, Adam, 2024. "Uncertainty and cryptocurrency returns: A lesson from turbulent times," International Review of Financial Analysis, Elsevier, vol. 94(C).
    17. Nicole Branger & Patrick Konermann & Christoph Meinerding & Christian Schlag, 2021. "Equilibrium Asset Pricing in Directed Networks [Risk premia and term premia in general equilibrium]," Review of Finance, European Finance Association, vol. 25(3), pages 777-818.
    18. Paolo Bartesaghi & Fernando Diaz-Diaz & Rosanna Grassi & Pierpaolo Uberti, 2024. "Global Balance and Systemic Risk in Financial Correlation Networks," Papers 2407.14272, arXiv.org, revised Jun 2025.
    19. Aslanidis, Nektarios & Bariviera, Aurelio F. & Perez-Laborda, Alejandro, 2021. "Are cryptocurrencies becoming more interconnected?," Economics Letters, Elsevier, vol. 199(C).
    20. Ariana Paola Cortés Ángel & Mustafa Hakan Eratalay, 2022. "Deep diving into the S&P Europe 350 index network and its reaction to COVID-19," Journal of Computational Social Science, Springer, vol. 5(2), pages 1343-1408, November.
    21. Joshua C. C. Chan & Xuewen Yu, 2022. "Fast and Accurate Variational Inference for Large Bayesian VARs with Stochastic Volatility," Papers 2206.08438, arXiv.org.
    22. Zhou, Yuqin & Wu, Shan & Zhang, Zeyi, 2022. "Multidimensional risk spillovers among carbon, energy and nonferrous metals markets: Evidence from the quantile VAR network," Energy Economics, Elsevier, vol. 114(C).
    23. Magkonis, Georgios & Tsopanakis, Andreas, 2020. "The Financial Connectedness Between Eurozone Core And Periphery: A Disaggregated View," Macroeconomic Dynamics, Cambridge University Press, vol. 24(7), pages 1674-1699, October.
    24. Zhou, Xuewei & Ouyang, Zisheng & Lu, Min & Ouyang, Zhongzhe, 2024. "Multilayer network analysis of idiosyncratic volatility connectedness: Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 88(C).
    25. David Gabauer, 2020. "Volatility impulse response analysis for DCC‐GARCH models: The role of volatility transmission mechanisms," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 788-796, August.
    26. Ho, Kin-Yip & Shi, Yanlin & Zhang, Zhaoyong, 2020. "News and return volatility of Chinese bank stocks," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 1095-1105.
    27. Cheng, Tingting & Liu, Fei & Liu, Junli & Yao, Wenying, 2024. "Tail connectedness: Measuring the volatility connectedness network of equity markets during crises," Pacific-Basin Finance Journal, Elsevier, vol. 87(C).
    28. Gomez-Gonzalez, Jose Eduardo & Hirs-Garzon, Jorge & Uribe, Jorge M., 2020. "Spillovers beyond the variance: exploring the natural gas and oil higher order risk linkages with the global financial markets," Working papers 46, Red Investigadores de Economía.
    29. Aysan, Ahmet Faruk & Batten, Jonathan & Gozgor, Giray & Khalfaoui, Rabeh & Nanaeva, Zhamal, 2024. "Metaverse and financial markets: A quantile-time-frequency connectedness analysis," Research in International Business and Finance, Elsevier, vol. 72(PB).
    30. Joshua C. C. Chan, 2024. "BVARs and stochastic volatility," Chapters, in: Michael P. Clements & Ana Beatriz Galvão (ed.), Handbook of Research Methods and Applications in Macroeconomic Forecasting, chapter 3, pages 43-67, Edward Elgar Publishing.
    31. Sánchez García, Javier & Cruz Rambaud, Salvador, 2023. "Inflation and systemic risk: A network econometric model," Finance Research Letters, Elsevier, vol. 56(C).
    32. Rui Wang & Jing Kang, 2024. "Financial Interconnectedness and Bank Risk-Taking: Evidence from China," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(3), pages 11819-11847, September.
    33. Al-Nassar, Nassar S. & Assaf, Rima & Chaibi, Anis & Makram, Beljid, 2024. "The nexus between mineral, renewable commodities, and regional stock sectors during health and military crises," Resources Policy, Elsevier, vol. 96(C).
    34. Xue Cui & Lu Yang, 2024. "Systemic risk and idiosyncratic networks among global systemically important banks," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(1), pages 58-75, January.
    35. Zhou, Dong-hai & Liu, Xiao-xing, 2024. "Does systemic risk in the fund markets predict future economic downturns?," International Review of Financial Analysis, Elsevier, vol. 92(C).
    36. Mr. Adrian Alter & Jane Dokko & Dulani Seneviratne, 2018. "House Price Synchronicity, Banking Integration, and Global Financial Conditions," IMF Working Papers 2018/250, International Monetary Fund.
    37. Raisul Islam & Vladimir Volkov, 2022. "Contagion or interdependence? Comparing spillover indices," Empirical Economics, Springer, vol. 63(3), pages 1403-1455, September.
    38. Thiem, Christopher, 2018. "Cross-category spillovers of economic policy uncertainty," Ruhr Economic Papers 744, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    39. He, Wenjing & Yao, Xiaoyang & Sun, Xiaolei & Le, Wei & Yi, Ronghua, 2024. "Dynamic spillovers of green, brown, and financial industries under the low-carbon transition: Evidence from China," Energy Economics, Elsevier, vol. 139(C).
    40. Silva, Thiago Christiano & Wilhelm, Paulo Victor Berri & Amancio, Diego R., 2024. "Machine learning and economic forecasting: The role of international trade networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 649(C).
    41. International Monetary Fund, 2019. "Australia: Financial Sector Assessment Program-Technical Note-Stress Testing the Banking Sector and Systemic Risk Analysis," IMF Staff Country Reports 2019/051, International Monetary Fund.
    42. Gorkem Bostanci & Kamil Yilmaz, 2015. "How Connected is the Global Sovereign Credit Risk Network?," Koç University-TUSIAD Economic Research Forum Working Papers 1515, Koc University-TUSIAD Economic Research Forum.
    43. Verma, Ramprasad & Ahmad, Wasim & Uddin, Gazi Salah & Bekiros, Stelios, 2019. "Analysing the systemic risk of Indian banks," Economics Letters, Elsevier, vol. 176(C), pages 103-108.
    44. Beibei Zhang & Xuemei Xie & Chunmei Li, 2023. "How Connected Is China’s Systemic Financial Risk Contagion Network?—A Dynamic Network Perspective Analysis," Mathematics, MDPI, vol. 11(10), pages 1-19, May.
    45. Mr. Jorge A Chan-Lau, 2017. "Variance Decomposition Networks: Potential Pitfalls and a Simple Solution," IMF Working Papers 2017/107, International Monetary Fund.
    46. Martin Feldkircher & Florian Huber & Gary Koop & Michael Pfarrhofer, 2022. "APPROXIMATE BAYESIAN INFERENCE AND FORECASTING IN HUGE‐DIMENSIONAL MULTICOUNTRY VARs," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(4), pages 1625-1658, November.
    47. Everett Grant & Julieta Yung, 2019. "Upstream, Downstream & Common Firm Shocks," Globalization Institute Working Papers 360, Federal Reserve Bank of Dallas.
    48. Miao, Ke & Phillips, Peter C.B. & Su, Liangjun, 2023. "High-dimensional VARs with common factors," Journal of Econometrics, Elsevier, vol. 233(1), pages 155-183.
    49. Ji, Qiang & Liu, Bing-Yue & Nguyen, Duc Khuong & Fan, Ying, 2019. "Dynamic dependence and extreme risk comovement: The case of oil prices and exchange rates," MPRA Paper 101387, University Library of Munich, Germany, revised Jan 2020.
    50. Chiang, Shu-hen & Chen, Chien-Fu, 2022. "From systematic to systemic risk among G7 members: Do the stock or real estate markets matter?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).
    51. Honghai Yu & Wangyu Chu & Yu’ang Ding & Xuezhou Zhao, 2021. "Risk contagion of global stock markets under COVID‐19:A network connectedness method," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(4), pages 5745-5782, December.
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    309. Dan Yi & Sheng Lin & Jianlan Yang, 2025. "Global Climate Risk Perception and Its Dynamic Impact on the Clean Energy Market: New Evidence from Contemporaneous and Lagged R 2 Decomposition Connectivity Approaches," Sustainability, MDPI, vol. 17(8), pages 1-29, April.
    310. Mengting Li & Qifa Xu & Cuixia Jiang & Qinna Zhao, 2023. "The role of tail network topological characteristic in portfolio selection: A TNA‐PMC model," International Review of Finance, International Review of Finance Ltd., vol. 23(1), pages 37-57, March.
    311. Addi, Abdelhamid & Foglia, Matteo & Wang, Gang-Jin & Miglietta, Federica, 2025. "Crossroads of volatility spillover: Interactions between Islamic and conventional financial systems," Research in International Business and Finance, Elsevier, vol. 74(C).
    312. Rufei Zhang & Haizhen Zhang & Qingzhu Fan & Wang Gao & Xue Luo & Shixiong Yang, 2022. "Partisan Conflict, National Security Policy Uncertainty and Tourism," Sustainability, MDPI, vol. 14(17), pages 1-22, August.
    313. Bouri, Elie & Iqbal, Najaf & Klein, Tony, 2022. "Climate policy uncertainty and the price dynamics of green and brown energy stocks," Finance Research Letters, Elsevier, vol. 47(PB).
    314. Marco Valerio Geraci & Jean-Yves Gnabo, 2015. "Measuring Interconnectedness between Financial Institutions with Bayesian Time-Varying VARS," Working Papers ECARES ECARES 2015-51, ULB -- Universite Libre de Bruxelles.
    315. Bo Yu & Zhijia Chang, 2024. "Connectedness of Carbon Price and Energy Price under Shocks: A Study Based on Positive and Negative Price Volatility," Sustainability, MDPI, vol. 16(12), pages 1-26, June.
    316. Shi, Guifen & Chen, Zhizhen & Luo, Weichen & Wei, Zijun, 2024. "Cross-Border spillover of imported sovereign risk to China: Key factors identification based on XGBoost-SHAP explainable machine learning algorithm," Finance Research Letters, Elsevier, vol. 70(C).
    317. Chen, Shi & Härdle, Wolfgang Karl & López Cabrera, Brenda, 2018. "Regularization Approach for Network Modeling of German Energy Market," IRTG 1792 Discussion Papers 2018-017, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    318. Ehsan Bagheri & Seyed Babak Ebrahimi & Arman Mohammadi & Mahsa Miri & Stelios Bekiros, 2022. "The Dynamic Volatility Connectedness Structure of Energy Futures and Global Financial Markets: Evidence From a Novel Time–Frequency Domain Approach," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 1087-1111, March.
    319. Chatziantoniou, Ioannis & Elsayed, Ahmed H. & Gabauer, David & Gozgor, Giray, 2023. "Oil price shocks and exchange rate dynamics: Evidence from decomposed and partial connectedness measures for oil importing and exporting economies," Energy Economics, Elsevier, vol. 120(C).
    320. Anghel, Dan Gabriel & Caraiani, Petre, 2024. "Monetary policy shocks and the high-frequency network connectedness of stock markets," International Review of Economics & Finance, Elsevier, vol. 96(PA).
    321. Xu Zhang & Xian Yang & Jianping Li & Jun Hao, 2023. "Contemporaneous and noncontemporaneous idiosyncratic risk spillovers in commodity futures markets: A novel network topology approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(6), pages 705-733, June.
    322. Muñoz Mendoza, Jorge A. & Ferreira, Guillermo & Márquez Sanders, Vicente A., 2023. "Liquidity spillovers in the global stock markets: Lessons for risk management," Global Finance Journal, Elsevier, vol. 58(C).
    323. Paolo Giudici & Paolo Pagnottoni, 2019. "High Frequency Price Change Spillovers in Bitcoin Markets," Risks, MDPI, vol. 7(4), pages 1-18, November.
    324. Xiaoye Jin, 2024. "Salience theory value spillovers between China’s systemically important banks: evidence from quantile connectedness," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-39, December.
    325. Guðmundsson, Guðmundur Stefán & Brownlees, Christian, 2021. "Detecting groups in large vector autoregressions," Journal of Econometrics, Elsevier, vol. 225(1), pages 2-26.
    326. Emile Esmaili & Michael J. Puma & Francis Ludlow & Poul Holm & Eva Jobbova, 2024. "Warfare Ignited Price Contagion Dynamics in Early Modern Europe," Papers 2411.18978, arXiv.org, revised Dec 2024.
    327. Ogbuabor, Jonathan E. & Anthony-Orji, Onyinye I. & Manasseh, Charles O. & Orji, Anthony, 2020. "Measuring the dynamics of COMESA output connectedness with the global economy," The Journal of Economic Asymmetries, Elsevier, vol. 21(C).
    328. Miriam Kamah & Joshua Riti, 2024. "Measuring the connectedness of the Nigerian banking network and its implications for systemic risk," Modern Finance, Modern Finance Institute, vol. 2(2), pages 96-119.
    329. Marius Cristian Acatrinei, 2020. "Spillover index for European business cycle," Journal of Financial Studies, Institute of Financial Studies, vol. 9(5), pages 49-57, November.
    330. Rémi Stellian & Jenny P. Danna‐Buitrago, 2020. "Financial distress, free cash flow, and interfirm payment network: Evidence from an agent‐based model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 25(4), pages 598-616, October.
    331. Barbaglia, Luca & Croux, Christophe & Wilms, Ines, 2020. "Volatility spillovers in commodity markets: A large t-vector autoregressive approach," Energy Economics, Elsevier, vol. 85(C).

  22. S. Boragan Aruoba & Francis X. Diebold & Jeremy J. Nalewaik & Frank Schorfheide & Dongho Song, 2013. "Improving GDP measurement: a measurement-error perspective," Working Papers 13-16, Federal Reserve Bank of Philadelphia.

    Cited by:

    1. Jeremy J. Nalewaik, 2014. "Missing Variation in the Great Moderation: Lack of Signal Error and OLS Regression," Finance and Economics Discussion Series 2014-27, Board of Governors of the Federal Reserve System (U.S.).
    2. Reinelt, Timo & Meier, Matthias, 2020. "Monetary policy, markup dispersion, and aggregate TFP," Working Paper Series 2427, European Central Bank.
    3. Francis X. Diebold & Maximilian Gobel & Philippe Goulet Coulombe & Glenn D. Rudebusch & Boyuan Zhang, 2020. "Optimal Combination of Arctic Sea Ice Extent Measures: A Dynamic Factor Modeling Approach," Papers 2003.14276, arXiv.org, revised Aug 2020.
    4. Arabinda Basistha, 2023. "Estimation of short‐run predictive factor for US growth using state employment data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 34-50, January.
    5. Eduardo Rossi & Paolo Santucci de Magistris, 2014. "Indirect inference with time series observed with error," CREATES Research Papers 2014-57, Department of Economics and Business Economics, Aarhus University.
    6. John C. Williams, 2015. "Data is the new black: monetary policy by the numbers," Speech 140, Federal Reserve Bank of San Francisco.
    7. Tomaz Cajner & Leland D. Crane & Ryan A. Decker & Adrian Hamins-Puertolas & Christopher Kurz, 2019. "Improving the Accuracy of Economic Measurement with Multiple Data Sources: The Case of Payroll Employment Data," NBER Working Papers 26033, National Bureau of Economic Research, Inc.
    8. Demetrescu, Matei & Kruse-Becher, Robinson, 2025. "Is U.S. real output growth non-normal? A tale of time-varying location and scale," Journal of Economic Dynamics and Control, Elsevier, vol. 171(C).
    9. Daniel Aaronson & Scott A. Brave & Michael Fogarty & Ezra Karger & Spencer D. Krane, 2021. "Tracking U.S. Consumers in Real Time with a New Weekly Index of Retail Trade," Working Paper Series WP-2021-05, Federal Reserve Bank of Chicago, revised 18 Jun 2021.
    10. Pinkwart, Nicolas, 2018. "Short-term forecasting economic activity in Germany: A supply and demand side system of bridge equations," Discussion Papers 36/2018, Deutsche Bundesbank.
    11. Michael Cai & Marco Del Negro & Marc Giannoni & Abhi Gupta & Pearl Li & Erica Moszkowski, 2018. "DSGE forecasts of the lost recovery," Staff Reports 844, Federal Reserve Bank of New York.
    12. Anesti, Nikoleta & Galvao, Ana Beatriz & Miranda-Agrippino, Silvia, 2018. "Uncertain kingdom: nowcasting GDP and its revisions," LSE Research Online Documents on Economics 90382, London School of Economics and Political Science, LSE Library.
    13. Nalewaik, Jeremy & Pinto, Eugénio, 2015. "The response of capital goods shipments to demand over the business cycle," Journal of Macroeconomics, Elsevier, vol. 43(C), pages 62-80.
    14. Ben Zeev, Nadav & Pappa, Evi, 2015. "Multipliers of unexpected increases in defense spending: An empirical investigation," Journal of Economic Dynamics and Control, Elsevier, vol. 57(C), pages 205-226.
    15. Sekine, Toshitaka, 2022. "Looking from Gross Domestic Income: Alternative view of Japan’s economy," Japan and the World Economy, Elsevier, vol. 64(C).
    16. John C. Williams, 2015. "The recovery’s final frontier?," Speech 150, Federal Reserve Bank of San Francisco.
    17. Petrella, Ivan & Venditti, Fabrizio & Delle Monache, Davide, 2016. "Adaptive state space models with applications to the business cycle and financial stress," CEPR Discussion Papers 11599, C.E.P.R. Discussion Papers.
    18. Matteo Barigozzi & Matteo Luciani, 2017. "Common Factors, Trends, and Cycles in Large Datasets," Finance and Economics Discussion Series 2017-111, Board of Governors of the Federal Reserve System (U.S.).
    19. John C. Williams, 2015. "Looking forward: the path for monetary policy," FRBSF Economic Letter, Federal Reserve Bank of San Francisco.
    20. Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon, 2022. "Reconciled Estimates of Monthly GDP in the US," Working Papers 22-01, Federal Reserve Bank of Cleveland.
    21. Andrew C. Chang & Phillip Li, 2018. "Measurement Error In Macroeconomic Data And Economics Research: Data Revisions, Gross Domestic Product, And Gross Domestic Income," Economic Inquiry, Western Economic Association International, vol. 56(3), pages 1846-1869, July.
    22. Wankeun Oh & Jonghyun Yoo, 2020. "Long-Term Increases and Recent Slowdowns of CO 2 Emissions in Korea," Sustainability, MDPI, vol. 12(17), pages 1-13, August.
    23. Tom Stark, 2015. "First quarters in the national income and product accounts," Research Rap Special Report, Federal Reserve Bank of Philadelphia, issue May.
    24. Yannic Stucki, 2022. "Measuring Swiss employment growth: a measurement-error approach," Working Papers 2022-11, Swiss National Bank.
    25. Peter A.G. van Bergeijk, 2017. "Making Data Measurement Errors Transparent: The Case of the IMF," World Economics, World Economics, 1 Ivory Square, Plantation Wharf, London, United Kingdom, SW11 3UE, vol. 18(3), pages 133-154, July.
    26. Loria, Francesca & Matthes, Christian & Wang, Mu-Chun, 2022. "Economic theories and macroeconomic reality," Journal of Monetary Economics, Elsevier, vol. 126(C), pages 105-117.
    27. Daniel Rees & David Lancaster & Richard Finlay, 2014. "A State-space Approach to Australian GDP Measurement," RBA Research Discussion Papers rdp2014-12, Reserve Bank of Australia.
    28. Tom Stark, 2014. "Real-time performance of GDPplus and alternative model-based measures of GDP: 2005—2014," Research Rap Special Report, Federal Reserve Bank of Philadelphia, issue Nov.
    29. Nikoleta Anesti & Ana Beatriz Galvão & Silvia Miranda‐Agrippino, 2022. "Uncertain Kingdom: Nowcasting Gross Domestic Product and its revisions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 42-62, January.
    30. Almuzara, Martin & Amengual, Dante & Fiorentini, Gabriele & Sentana, Enrique, 2022. "GDP Solera: The Ideal Vintage Mix," CEPR Discussion Papers 17196, C.E.P.R. Discussion Papers.
    31. Nikolay Gospodinov & Ivana Komunjer & Serena Ng, 2014. "Minimum Distance Estimation of Dynamic Models with Errors-In-Variables," FRB Atlanta Working Paper 2014-11, Federal Reserve Bank of Atlanta.
    32. Boragan Aruoba & Francis X. Diebold & Jeremy Nalewaik & Frank Schorfheide & Dongho Song, 2011. "Improving GDP Measurement: A Forecast Combination Perspective," PIER Working Paper Archive 11-028, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    33. Jan-Benedict E. M. Steenkamp & Alberto Maydeu-Olivares, 2023. "Unrestricted factor analysis: A powerful alternative to confirmatory factor analysis," Journal of the Academy of Marketing Science, Springer, vol. 51(1), pages 86-113, January.
    34. van Bergeijk, P.A.G., 2017. "Measurement error of global production," ISS Working Papers - General Series 632, International Institute of Social Studies of Erasmus University Rotterdam (ISS), The Hague.
    35. Alessandro Giovannelli & Marco Lippi & Tommaso Proietti, 2023. "Band-Pass Filtering with High-Dimensional Time Series," CEIS Research Paper 559, Tor Vergata University, CEIS, revised 15 Jun 2023.
    36. Onno Kleen, 2024. "Scaling and measurement error sensitivity of scoring rules for distribution forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 833-849, August.
    37. Matthew Read, 2024. "Sign Restrictions and Supply-demand Decompositions of Inflation," RBA Research Discussion Papers rdp2024-05, Reserve Bank of Australia.
    38. Kurt Graden Lunsford, 2023. "The Discrepancy Between Expenditure- and Income-Side Estimates of US Output," Economic Commentary, Federal Reserve Bank of Cleveland, vol. 2023(01), pages 1-7, January.
    39. Garciga, Christian & Knotek II, Edward S., 2019. "Forecasting GDP growth with NIPA aggregates: In search of core GDP," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1814-1828.
    40. Jiang, Fuwei & Kang, Jie & Meng, Lingchao, 2024. "Certainty of uncertainty for asset pricing," Journal of Empirical Finance, Elsevier, vol. 78(C).
    41. Matei Demetrescu & Robinson Kruse-Becher, 2021. "Is U.S. real output growth really non-normal? Testing distributional assumptions in time-varying location-scale models," CREATES Research Papers 2021-07, Department of Economics and Business Economics, Aarhus University.
    42. Prydz, Espen Beer & Jolliffe, Dean & Serajuddin, Umar, 2021. "Mind the Gap," GLO Discussion Paper Series 944, Global Labor Organization (GLO).
    43. Eiji Goto & Jan P.A.M. Jacobs & Tara M. Sinclair & Simon van Norden, 2021. "Employment Reconciliation and Nowcasting," Working Papers 2021-007, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    44. J. Wang & John Fernald, 2016. "Why Has the Cyclicality of Productivity Changed? What Does It Mean?," 2016 Meeting Papers 1220, Society for Economic Dynamics.
    45. Ammi, Mehdi & Arpin, Emmanuelle & Allin, Sara, 2021. "Interpreting forty-three-year trends of expenditures on public health in Canada: Long-run trends, temporal periods, and data differences," Health Policy, Elsevier, vol. 125(12), pages 1557-1564.
    46. Carriero, Andrea & Clements, Michael P. & Galvão, Ana Beatriz, 2015. "Forecasting with Bayesian multivariate vintage-based VARs," International Journal of Forecasting, Elsevier, vol. 31(3), pages 757-768.
    47. Geng, Pei, 2022. "Estimation of functional-coefficient autoregressive models with measurement error," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    48. Gospodinov, Nikolay & Komunjer, Ivana & Ng, Serena, 2017. "Simulated minimum distance estimation of dynamic models with errors-in-variables," Journal of Econometrics, Elsevier, vol. 200(2), pages 181-193.
    49. Lee, Hangyu & Kim, Tae Bong, 2023. "The effectiveness of labor market indicators for conducting monetary policy: Evidence from the Korean economy," Economic Modelling, Elsevier, vol. 118(C).
    50. Alifatussaadah, Ardiana & Primariesty, Anindya Diva & Soleh, Agus Mohamad & Andriansyah, Andriansyah, 2019. "Nowcasting Indonesia's GDP Growth: Are Fiscal Data Useful?," MPRA Paper 105252, University Library of Munich, Germany.
    51. Hu, Yingyao & Yao, Jiaxiong, 2022. "Illuminating economic growth," Journal of Econometrics, Elsevier, vol. 228(2), pages 359-378.
    52. Jan P.A.M. Jacobs & Samad Sarferaz & Simon van Norden & Jan-Egbert Sturm, 2013. "Modeling Multivariate Data Revisions," CIRANO Working Papers 2013s-44, CIRANO.
    53. Florian Eckert & Nina Mühlebach, 2023. "Global and local components of output gaps," Empirical Economics, Springer, vol. 65(5), pages 2301-2331, November.
    54. Martín Almuzara & Dante Amengual & Enrique Sentana, 2019. "Normality tests for latent variables," Quantitative Economics, Econometric Society, vol. 10(3), pages 981-1017, July.
    55. Víctor M. Guerrero & Juan A. Mendoza, 2019. "On measuring economic growth from outer space: a single country approach," Empirical Economics, Springer, vol. 57(3), pages 971-990, September.
    56. Gyurkovics, Éva & Takács, Tibor, 2022. "Robust energy-to-peak filter design for a class of unstable polytopic systems with a macroeconomic application," Applied Mathematics and Computation, Elsevier, vol. 420(C).
    57. John C. Williams, 2015. "Looking forward, forward looking: the path for monetary policy," Speech 138, Federal Reserve Bank of San Francisco.

  23. Francis X. Diebold & Kamil Yilmaz, 2013. "Measuring the Dynamics of Global Business Cycle Connectedness," PIER Working Paper Archive 13-070, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.

    Cited by:

    1. Xiao, Binqing & Yang, Ye & Peng, Xuerong & Fang, Libing, 2019. "Measuring the connectedness of European electricity markets using the network topology of variance decompositions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    2. Magkonis, Georgios & Tsouknidis, Dimitris A., 2017. "Dynamic spillover effects across petroleum spot and futures volatilities, trading volume and open interest," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 104-118.
    3. Jozef Barunik & Tomas Krehlik, 2015. "Measuring the frequency dynamics of financial connectedness and systemic risk," Papers 1507.01729, arXiv.org, revised Dec 2017.
    4. Erhan Uluceviz & Kamil Yilmaz, 2018. "Measuring Real-Financial Connectedness in the U.S. Economy," Koç University-TUSIAD Economic Research Forum Working Papers 1812, Koc University-TUSIAD Economic Research Forum.
    5. Ilham Haouas & Naceur Kheraief & Arusha Cooray & Syed Jawad Hussain Shahzad, 2019. "Time-Varying Casual Nexuses Between Remittances and Financial Development in Some MENA Countries," Working Papers 1294, Economic Research Forum, revised 2019.
    6. Shuping Shi & Peter C. B. Phillips & Stan Hurn, 2018. "Change Detection and the Causal Impact of the Yield Curve," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(6), pages 966-987, November.
    7. Billio, Monica & Casarin, Roberto & Rossini, Luca, 2019. "Bayesian nonparametric sparse VAR models," Journal of Econometrics, Elsevier, vol. 212(1), pages 97-115.
    8. Olkhov, Victor, 2018. "The Business Cycle Model Beyond General Equilibrium," MPRA Paper 87204, University Library of Munich, Germany.
    9. Chen, Pu & Semmler, Willi, 2018. "Financial stress, regime switching and spillover effects: Evidence from a multi-regime global VAR model," Journal of Economic Dynamics and Control, Elsevier, vol. 91(C), pages 318-348.
    10. Salzmann, Leonard, 2016. "The G7 business cycle in a globalized world," VfS Annual Conference 2016 (Augsburg): Demographic Change 145633, Verein für Socialpolitik / German Economic Association.
    11. Hardik A. Marfatia & Rangan Gupta & Esin Cakan, 2017. "The International REIT's Time-Varying Response to the U.S. Monetary Policy and Macroeconomic Surprises," Working Papers 201712, University of Pretoria, Department of Economics.
    12. Mardi Dungey & Moses Kangogo & Vladimir Volkov, 2022. "Dynamic effects of network exposure on equity markets," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 12(4), pages 569-629, December.
    13. Olkhov, Victor, 2018. "Economic and Financial Transactions Govern Business Cycles," MPRA Paper 93269, University Library of Munich, Germany.
    14. Victor Olkhov, 2018. "Econophysics Beyond General Equilibrium: the Business Cycle Model," Papers 1804.04721, arXiv.org.
    15. Barunik, Jozef & Krehlik, Tomas, 2016. "Measuring the frequency dynamics of financial and macroeconomic connectedness," FinMaP-Working Papers 54, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    16. Olkhov, Victor, 2020. "Business Cycles as Collective Risk Fluctuations," MPRA Paper 104598, University Library of Munich, Germany.
    17. Sanderson Abel & Pierre Le Roux, 2016. "Determinants of Banking Sector Profitability in Zimbabwe," International Journal of Economics and Financial Issues, Econjournals, vol. 6(3), pages 845-854.
    18. Zhiping Qiu & Sichao Mai, 2022. "Topological characteristics of international business cycle synchronization: A network analysis of the BRI economies," PLOS ONE, Public Library of Science, vol. 17(6), pages 1-17, June.
    19. Olkhov, Victor, 2018. "Economic Transactions Govern Business Cycles," MPRA Paper 88531, University Library of Munich, Germany, revised 19 Aug 2018.
    20. Monica Billio & Roberto Casarin & Luca Rossini, 2016. "Bayesian nonparametric sparse seemingly unrelated regression model (SUR)," Working Papers 2016:20, Department of Economics, University of Venice "Ca' Foscari".
    21. Erdenebat Bataa & Denise R.Osborn & Marianne Sensier, 2016. "China's Increasing Global Influence: Changes in International Growth Spillovers," Centre for Growth and Business Cycle Research Discussion Paper Series 221, Economics, The University of Manchester.
    22. John Cotter & Mark Hallam & Kamil Yilmaz, 2017. "Mixed-frequency macro-financial spillovers," Working Papers 201704, Geary Institute, University College Dublin.
    23. Lai, Jennifer & Chen, Hongyi & McNelis, Paul D., 2020. "Macroeconomic adjustment with managed exchange rates and capital controls: Some lessons from China," Economic Modelling, Elsevier, vol. 91(C), pages 759-768.
    24. Lai, Jennifer & McNelis, Paul D., 2024. "Financial contagion among the GSIBs and regulatory interventions," Journal of Financial Stability, Elsevier, vol. 72(C).
    25. Křehlík, Tomáš & Baruník, Jozef, 2017. "Cyclical properties of supply-side and demand-side shocks in oil-based commodity markets," Energy Economics, Elsevier, vol. 65(C), pages 208-218.
    26. Jonathan E. Ogbuabor & Anthony Orji & Gladys C. Aneke & Oyun Erdene-Urnukh, 2016. "Measuring the Real and Financial Connectedness of Selected African Economies with the Global Economy," South African Journal of Economics, Economic Society of South Africa, vol. 84(3), pages 364-399, September.
    27. Montinari, Letizia & Stracca, Livio, 2016. "Trade, finance or policies: What drives the cross-border spill-over of business cycles?," Journal of Macroeconomics, Elsevier, vol. 49(C), pages 131-148.
    28. Tihana ŠKRINJARIĆ & Lidija DEDI & Boško ŠEGO, 2021. "Return and Volatility Spillover between Stock Prices and Exchange Rates in Croatia: A Spillover Methodology Approach," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 93-108, December.
    29. Kai Carstensen & Leonard Salzmann, 2016. "The G7 Business Cycle in a Globalized World," CESifo Working Paper Series 5980, CESifo.
    30. Zhang, Yulian & Hamori, Shigeyuki, 2022. "A connectedness analysis among BRICS’s geopolitical risks and the US macroeconomy," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 182-203.
    31. I-Chun Tsai, 2022. "The connectedness between Hong Kong and China real estate markets: spillover effect and information transmission," Empirical Economics, Springer, vol. 63(1), pages 287-311, July.
    32. James Graham, 2014. "'N Sync: how do countries' economies move together?," Reserve Bank of New Zealand Analytical Notes series AN2014/04, Reserve Bank of New Zealand.
    33. Paolo Giudici & Paolo Pagnottoni, 2019. "High Frequency Price Change Spillovers in Bitcoin Markets," Risks, MDPI, vol. 7(4), pages 1-18, November.
    34. Marius Cristian Acatrinei, 2020. "Spillover index for European business cycle," Journal of Financial Studies, Institute of Financial Studies, vol. 9(5), pages 49-57, November.

  24. Francis X. Diebold, 2012. "Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests," NBER Working Papers 18391, National Bureau of Economic Research, Inc.

    Cited by:

    1. Adam J. Check & Anna K Nolan & Tyler C. Schipper, 2019. "Forecasting GDP Growth using Disaggregated GDP Revisions," Economics Bulletin, AccessEcon, vol. 39(4), pages 2580-2588.
    2. Claudio, João C. & Heinisch, Katja & Holtemöller, Oliver, 2019. "Nowcasting East German GDP growth: A MIDAS approach," IWH Discussion Papers 24/2019, Halle Institute for Economic Research (IWH).
    3. Xuan, Chunji & Kim, Chang-Jin, 2020. "Structural breaks in the mean of dividend-price ratios: Implications of learning on stock return predictability," Japan and the World Economy, Elsevier, vol. 55(C).
    4. David I. Harvey & Stephen J. Leybourne & Yang Zu, 2024. "Tests for equal forecast accuracy under heteroskedasticity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 850-869, August.
    5. Florian Ziel & Rick Steinert & Sven Husmann, 2015. "Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets," Papers 1501.00818, arXiv.org, revised Dec 2015.
    6. Rossi, Barbara, 2013. "Exchange Rate Predictability," CEPR Discussion Papers 9575, C.E.P.R. Discussion Papers.
    7. Croonenbroeck, Carsten & Stadtmann, Georg, 2019. "Renewable generation forecast studies – Review and good practice guidance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 312-322.
    8. Isabel Figuerola‐Ferretti & Alejandro Rodríguez & Eduardo Schwartz, 2021. "Oil price analysts' forecasts," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(9), pages 1351-1374, September.
    9. Dhiman, Harsh S. & Deb, Dipankar & Guerrero, Josep M., 2019. "Hybrid machine intelligent SVR variants for wind forecasting and ramp events," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 369-379.
    10. Olkhov, Victor, 2024. "Lower bounds of uncertainty and upper limits on the accuracy of forecasts of macroeconomic variables," MPRA Paper 121628, University Library of Munich, Germany.
    11. Luisa Bisaglia & Matteo Grigoletto, 2018. "A new time-varying model for forecasting long-memory series," Papers 1812.07295, arXiv.org.
    12. Sihong Chen & Qi Li & Qiaoyu Wang & Yu Yvette Zhang, 2023. "Multivariate models of commodity futures markets: a dynamic copula approach," Empirical Economics, Springer, vol. 64(6), pages 3037-3057, June.
    13. Giovannelli, Alessandro & Massacci, Daniele & Soccorsi, Stefano, 2021. "Forecasting stock returns with large dimensional factor models," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 252-269.
    14. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    15. Leonardo Brandao Freitas Nascimento & Max Sousa Lima & Luiz H. Duczmal, 2025. "P‐min‐Stable Regression Models for Time Series With Extreme Values of Limited Range," Environmetrics, John Wiley & Sons, Ltd., vol. 36(2), March.
    16. Arroyo Marioli,Francisco & Khadan,Jeetendra & Ohnsorge,Franziska Lieselotte & Yamazaki,Takefumi, 2023. "Forecasting Industrial Commodity Prices : Literature Review and a Model Suite," Policy Research Working Paper Series 10611, The World Bank.
    17. Rocha, Jordano Vieira & Pereira, Pedro L. Valls, 2015. "Forecast comparison with nonlinear methods for Brazilian industrial production," Textos para discussão 397, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
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    203. Pavel Kapinos & Oscar A. Mitnik, 2016. "A Top-down Approach to Stress-testing Banks," Journal of Financial Services Research, Springer;Western Finance Association, vol. 49(2), pages 229-264, June.
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    205. Reinhard Ellwanger, Stephen Snudden, Lenin Arango-Castillo, 2023. "Seize the Last Day: Period-End-Point Sampling for Forecasts of Temporally Aggregated Data," LCERPA Working Papers bm0142, Laurier Centre for Economic Research and Policy Analysis.
    206. Mai Dao & Lam Nguyen, 2025. "Variable selection in macroeconomic stress test: a Bayesian quantile regression approach," Empirical Economics, Springer, vol. 68(3), pages 1113-1169, March.
    207. Simon Hirsch & Florian Ziel, 2023. "Multivariate Simulation-based Forecasting for Intraday Power Markets: Modelling Cross-Product Price Effects," Papers 2306.13419, arXiv.org.
    208. José Renato Haas Ornelas & Roberto Baltieri Mauad, 2017. "Volatility risk premia and future commodities returns," BIS Working Papers 619, Bank for International Settlements.
    209. Juan Carlos Escanciano & Ricardo Parra, 2024. "Extending the Scope of Inference About Predictive Ability to Machine Learning Methods," Papers 2402.12838, arXiv.org, revised May 2025.
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  25. Francis X. Diebold, 2012. "On the Origin(s) and Development of the Term “Big Data"," PIER Working Paper Archive 12-037, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.

    Cited by:

    1. Philip ME Garboden, 2019. "Sources and Types of Big Data for Macroeconomic Forecasting," Working Papers 2019-3, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    2. Cara Stitzlein & Simon Fielke & François Waldner & Todd Sanderson, 2021. "Reputational Risk Associated with Big Data Research and Development: An Interdisciplinary Perspective," Sustainability, MDPI, vol. 13(16), pages 1-13, August.

  26. Chen, Fei & Diebold, Francis X. & Schorfheide, Frank, 2012. "A Markov-Switching Multi-Fractal Inter-Trade Duration Model, with Application to U.S. Equities," Working Papers 12-09, University of Pennsylvania, Wharton School, Weiss Center.

    Cited by:

    1. Yogo Purwono & Irwan Adi Ekaputra & Zaäfri Ananto Husodo, 2018. "Estimation of Dynamic Mixed Hitting Time Model Using Characteristic Function Based Moments," Computational Economics, Springer;Society for Computational Economics, vol. 51(2), pages 295-321, February.
    2. Aldrich, Eric M. & Heckenbach, Indra & Laughlin, Gregory, 2016. "A compound duration model for high-frequency asset returns," Journal of Empirical Finance, Elsevier, vol. 39(PA), pages 105-128.
    3. Czellar, Veronika & Frazier, David T. & Renault, Eric, 2022. "Approximate maximum likelihood for complex structural models," Journal of Econometrics, Elsevier, vol. 231(2), pages 432-456.
    4. Herrera, Rodrigo & Rodriguez, Alejandro & Pino, Gabriel, 2017. "Modeling and forecasting extreme commodity prices: A Markov-Switching based extreme value model," Energy Economics, Elsevier, vol. 63(C), pages 129-143.
    5. Wen Cao & Clifford Hurvich & Philippe Soulier, 2012. "Drift in Transaction-Level Asset Price Models," Working Papers hal-00756372, HAL.
    6. Pérez-Rodríguez, Jorge V. & Gómez-Déniz, Emilio & Sosvilla-Rivero, Simón, 2021. "Testing unobserved market heterogeneity in financial markets: The case of Banco Popular," The Quarterly Review of Economics and Finance, Elsevier, vol. 79(C), pages 151-160.
    7. Segnon, Mawuli & Lux, Thomas, 2013. "Multifractal models in finance: Their origin, properties, and applications," Kiel Working Papers 1860, Kiel Institute for the World Economy (IfW Kiel).
    8. Kuosmanen, Petri & Nabulsi, Nasib & Vataja, Juuso, 2015. "Financial variables and economic activity in the Nordic countries," International Review of Economics & Finance, Elsevier, vol. 37(C), pages 368-379.
    9. Peter Akioyamen & Yi Zhou Tang & Hussien Hussien, 2021. "A Hybrid Learning Approach to Detecting Regime Switches in Financial Markets," Papers 2108.05801, arXiv.org.
    10. Jorge Pérez-Rodríguez & Emilio Gómez-Déniza & Simón Sosvilla-Rivero, 2019. "“Testing for private information using trade duration models with unobserved market heterogeneity: The case of Banco Popular”," IREA Working Papers 201907, University of Barcelona, Research Institute of Applied Economics, revised Apr 2019.
    11. Zhicheng Li & Haipeng Xing & Xinyun Chen, 2019. "A multifactor regime-switching model for inter-trade durations in the limit order market," Papers 1912.00764, arXiv.org.
    12. Aknouche, Abdelhakim & Francq, Christian, 2020. "Stationarity and ergodicity of Markov switching positive conditional mean models," MPRA Paper 102503, University Library of Munich, Germany.
    13. Filip Zikes & Jozef Barunik & Nikhil Shenai, 2012. "Modeling and Forecasting Persistent Financial Durations," Papers 1208.3087, arXiv.org, revised Apr 2013.
    14. Chiranjit Dutta & Kara Karpman & Sumanta Basu & Nalini Ravishanker, 2023. "Review of Statistical Approaches for Modeling High-Frequency Trading Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 1-48, May.
    15. Bjoern Schulte-Tillmann & Mawuli Segnon & Timo Wiedemann, 2023. "A comparison of high-frequency realized variance measures: Duration- vs. return-based approaches," CQE Working Papers 10523, Center for Quantitative Economics (CQE), University of Muenster.
    16. Augustyniak, Maciej & Dufays, Arnaud, 2018. "Modeling macroeconomic series with regime-switching models characterized by a high-dimensional state space," Economics Letters, Elsevier, vol. 170(C), pages 122-126.
    17. de Bruijn, L.P. & Franses, Ph.H.B.F., 2015. "Stochastic levels and duration dependence in US unemployment," Econometric Institute Research Papers EI2015-20, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    18. Marcin Wątorek & Jarosław Kwapień & Stanisław Drożdż, 2022. "Multifractal Cross-Correlations of Bitcoin and Ether Trading Characteristics in the Post-COVID-19 Time," Future Internet, MDPI, vol. 14(7), pages 1-15, July.
    19. Xin-Lan Fu & Xing-Lu Gao & Zheng Shan & Zhi-Qiang Jiang & Wei-Xing Zhou, 2018. "Multifractal characteristics and return predictability in the Chinese stock markets," Papers 1806.07604, arXiv.org.
    20. Veronika Czellar & David T. Frazier & Eric Renault, 2020. "Approximate Maximum Likelihood for Complex Structural Models," Papers 2006.10245, arXiv.org.
    21. Lux, Thomas, 2013. "Exact solutions for the transient densities of continuous-time Markov switching models: With an application to the poisson multifractal model," Kiel Working Papers 1871, Kiel Institute for the World Economy (IfW Kiel).
    22. Jeffrey R. Black & Pankaj K. Jain & Wei Sun, 2023. "Trade-time clustering," Review of Quantitative Finance and Accounting, Springer, vol. 60(3), pages 1209-1242, April.
    23. Suh, Jong Hwan, 2015. "Forecasting the daily outbreak of topic-level political risk from social media using hidden Markov model-based techniques," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 115-132.
    24. Eric M. Aldrich & Indra Heckenbach & Gregory Laughlin, 2014. "A Compound Multifractal Model for High-Frequency Asset Returns," BYU Macroeconomics and Computational Laboratory Working Paper Series 2014-05, Brigham Young University, Department of Economics, BYU Macroeconomics and Computational Laboratory.
    25. Brownlees Christian T. & Vannucci Marina, 2013. "A Bayesian approach for capturing daily heterogeneity in intra-daily durations time series," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(1), pages 21-46, February.
    26. Farzad Alavi Fard, 2014. "Optimal Bid-Ask Spread in Limit-Order Books under Regime Switching Framework," Review of Economics & Finance, Better Advances Press, Canada, vol. 4, pages 33-48, November.
    27. Zhicheng Li & Haipeng Xing, 2022. "High-Frequency Quote Volatility Measurement Using a Change-Point Intensity Model," Mathematics, MDPI, vol. 10(4), pages 1-24, February.
    28. Czellar, Veronika & Frazier, David T. & Renault, Eric, 2021. "Approximate Maximum Likelihood for Complex Structural Models," The Warwick Economics Research Paper Series (TWERPS) 1337, University of Warwick, Department of Economics.
    29. Li, Zhicheng & Chen, Xinyun & Xing, Haipeng, 2023. "A multifactor regime-switching model for inter-trade durations in the high-frequency limit order market," Economic Modelling, Elsevier, vol. 118(C).
    30. Marcin Wk{a}torek & Jaros{l}aw Kwapie'n & Stanis{l}aw Dro.zd.z, 2022. "Multifractal cross-correlations of bitcoin and ether trading characteristics in the post-COVID-19 time," Papers 2208.01445, arXiv.org.
    31. Renault, Eric & van der Heijden, Thijs & Werker, Bas J.M., 2014. "The dynamic mixed hitting-time model for multiple transaction prices and times," Journal of Econometrics, Elsevier, vol. 180(2), pages 233-250.
    32. Kang, Bo Soo & Ryu, Doojin & Ryu, Doowon, 2014. "Phase-shifting behaviour revisited: An alternative measure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 167-173.

  27. Francis X. Diebold, 2012. "A Personal Perspective on the Origin(s) and Development of “Big Data": The Phenomenon, the Term, and the Discipline, Second Version," PIER Working Paper Archive 13-003, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 26 Nov 2012.

    Cited by:

    1. Götz, Thomas B. & Knetsch, Thomas A., 2017. "Google data in bridge equation models for German GDP," Discussion Papers 18/2017, Deutsche Bundesbank.
    2. Harvey C Turner & David Atkinson, 2021. "Strategic Decision Making: The Effects of Big Data," International Journal of Operations Management, Inovatus Services Ltd., vol. 1(2), pages 38-45, January.

  28. S. Boragan Aruoba & Mr. Marco Terrones & Mr. Ayhan Kose & Mr. Francis X. Diebold, 2011. "Globalization, the Business Cycle, and Macroeconomic Monitoring," IMF Working Papers 2011/025, International Monetary Fund.

    Cited by:

    1. Mr. Maxym Kryshko, 2011. "Data-Rich DSGE and Dynamic Factor Models," IMF Working Papers 2011/216, International Monetary Fund.
    2. Roberto Golinelli & Giuseppe Parigi, 2013. "Tracking world trade and GDP in real time," Temi di discussione (Economic working papers) 920, Bank of Italy, Economic Research and International Relations Area.
    3. Peter C. B. Phillips & Zhentao Shi, 2021. "Boosting: Why You Can Use The Hp Filter," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 62(2), pages 521-570, May.
    4. Diebold, Francis X. & Yilmaz, Kamil, 2015. "Financial and Macroeconomic Connectedness: A Network Approach to Measurement and Monitoring," OUP Catalogue, Oxford University Press, number 9780199338306, Decembrie.
    5. Gitanjali Kumar, 2013. "High-Frequency Real Economic Activity Indicator for Canada," Staff Working Papers 13-42, Bank of Canada.
    6. In Choi & Dukpa Kim & Yun Jung Kim & Noh-Sun Kwark, 2016. "A Multilevel Factor Model: Identification, Asymptotic Theory and Applications," Working Papers 1609, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    7. Chiu Adrian & Wieladek Tomasz, 2013. "Is the “Great Recession” really so different from the past?," The B.E. Journal of Macroeconomics, De Gruyter, vol. 13(1), pages 1037-1084, October.
    8. Maximo Camacho & Jaime Martinez-Martin, 2015. "Monitoring the world business cycle," Working Papers 1509, Banco de España.
    9. Rodriguez, Diego & Gonzalez, Andres & Fernandez, Andres, 2015. "Sharing a Ride on the Commodities Roller Coaster: Common Factors in Business Cycles of Emerging Economies," IDB Publications (Working Papers) 7382, Inter-American Development Bank.
    10. Francis X. Diebold & Kamil Yilmaz, 2013. "Measuring the Dynamics of Global Business Cycle Connectedness," PIER Working Paper Archive 13-070, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    11. Jason Angelopoulos & Costas I. Chlomoudis, 2017. "A Generalized Dynamic Factor Model for the U.S. Port Sector," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 67(1), pages 22-37, January-M.
    12. Ron Alquist & Olivier Coibion, 2014. "Commodity Price Co-Movement and Global Economic Activity," Staff Working Papers 14-32, Bank of Canada.
    13. David Matesanz Gomez & Guillermo J. Ortega & Benno Torgler, 2011. "Measuring globalization: A hierarchical network approach," CREMA Working Paper Series 2011-11, Center for Research in Economics, Management and the Arts (CREMA).
    14. David Matesanz Gomez & Guillermo J. Ortega & Benno Torgler, 2012. "Synchronization and Diversity in Business Cycles: A Network Approach Applied to the European Union," CREMA Working Paper Series 2012-01, Center for Research in Economics, Management and the Arts (CREMA).
    15. Peter C.B. Phillips & Zhentao Shi, 2019. "Boosting the Hodrick-Prescott Filter," Cowles Foundation Discussion Papers 2192, Cowles Foundation for Research in Economics, Yale University.
    16. Sung Hoon Choi & Donggyu Kim, 2022. "Large Volatility Matrix Analysis Using Global and National Factor Models," Papers 2208.12323, arXiv.org, revised Dec 2022.
    17. Lahiri, Kajal & Zhao, Yongchen, 2019. "International propagation of shocks: A dynamic factor model using survey forecasts," International Journal of Forecasting, Elsevier, vol. 35(3), pages 929-947.
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    2103. Al-Shboul, Mohammad & Assaf, Ata & Mokni, Khaled, 2022. "When bitcoin lost its position: Cryptocurrency uncertainty and the dynamic spillover among cryptocurrencies before and during the COVID-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 83(C).
    2104. Khan, Nasir & Mejri, Sami & Hammoudeh, Shawkat, 2024. "How do global commodities react to increasing geopolitical risks? New insights into the Russia-Ukraine and Palestine-Israel conflicts," Energy Economics, Elsevier, vol. 138(C).
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  30. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2011. "Financial Risk Measurement for Financial Risk Management," CREATES Research Papers 2011-37, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Belloni, Alexandre & Chen, Mingli & Chernozhukov, Victor, 2016. "Quantile Graphical Models : Prediction and Conditional Independence with Applications to Financial Risk Management," Economic Research Papers 269321, University of Warwick - Department of Economics.
    2. Serena Ng & Jonathan H. Wright, 2013. "Facts and Challenges from the Great Recession for Forecasting and Macroeconomic Modeling," NBER Working Papers 19469, National Bureau of Economic Research, Inc.
    3. Nikolaus Hautsch & Lada M. Kyj & Peter Malec, 2015. "Do High‐Frequency Data Improve High‐Dimensional Portfolio Allocations?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(2), pages 263-290, March.
    4. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    5. Mert Demirer & Francis X. Diebold & Laura Liu & Kamil Yilmaz, 2015. "Estimating Global Bank Network Connectedness," PIER Working Paper Archive 15-025, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 25 Jul 2015.
    6. Makoto Takahashi & Toshiaki Watanabe & Yasuhiro Omori, 2014. "Volatility and Quantile Forecasts by Realized Stochastic Volatility Models with Generalized Hyperbolic Distribution," CIRJE F-Series CIRJE-F-949, CIRJE, Faculty of Economics, University of Tokyo.
    7. Mustafayeva, Konul & Wang, Weining, 2020. "Non-Parametric Estimation of Spot Covariance Matrix with High-Frequency Data," IRTG 1792 Discussion Papers 2020-025, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    8. Kyle Jurado & Sydney C. Ludvigson & Serena Ng, 2015. "Measuring Uncertainty," American Economic Review, American Economic Association, vol. 105(3), pages 1177-1216, March.
    9. Tim Bollerslev & Andrew J. Patton & Rogier Quaedvlieg, 2016. "Modeling and Forecasting (Un)Reliable Realized Covariances for More Reliable Financial Decisions," CREATES Research Papers 2016-10, Department of Economics and Business Economics, Aarhus University.
    10. Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2016. "Exploiting the errors: A simple approach for improved volatility forecasting," Journal of Econometrics, Elsevier, vol. 192(1), pages 1-18.
    11. Alexandre Belloni & Mingli Chen & Victor Chernozhukov, 2016. "Quantile Graphical Models: Prediction and Conditional Independence with Applications to Systemic Risk," Papers 1607.00286, arXiv.org, revised Oct 2019.
    12. Diebold, Francis X. & Yilmaz, Kamil, 2015. "Financial and Macroeconomic Connectedness: A Network Approach to Measurement and Monitoring," OUP Catalogue, Oxford University Press, number 9780199338306, Decembrie.
    13. Christoffersen, Peter & Lunde, Asger & Olesen, Kasper V., 2019. "Factor Structure in Commodity Futures Return and Volatility," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 54(3), pages 1083-1115, June.
    14. Fengler, Matthias R. & Okhrin, Ostap, 2016. "Managing risk with a realized copula parameter," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 131-152.
    15. Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2020. "Multivariate leverage effects and realized semicovariance GARCH models," Journal of Econometrics, Elsevier, vol. 217(2), pages 411-430.
    16. Daniele Massacci, 2017. "Tail Risk Dynamics in Stock Returns: Links to the Macroeconomy and Global Markets Connectedness," Management Science, INFORMS, vol. 63(9), pages 3072-3089, September.
    17. Hoga, Yannick, 2021. "The uncertainty in extreme risk forecasts from covariate-augmented volatility models," International Journal of Forecasting, Elsevier, vol. 37(2), pages 675-686.
    18. Tim Bollerslev & Viktor Todorov & Lai Xu, 2014. "Tail Risk Premia and Return Predictability," CREATES Research Papers 2014-49, Department of Economics and Business Economics, Aarhus University.
    19. F. Lilla, 2016. "High Frequency vs. Daily Resolution: the Economic Value of Forecasting Volatility Models," Working Papers wp1084, Dipartimento Scienze Economiche, Universita' di Bologna.
    20. Linton, Oliver & Whang, Yoon-Jae & Yen, Yu-Min, 2016. "A nonparametric test of a strong leverage hypothesis," Journal of Econometrics, Elsevier, vol. 194(1), pages 153-186.
    21. Nurulhasanah Abdul Rahman & Rafisah Mat Radzi, 2015. "Determinants of Effective Financial Risk Management in Small Business: A Theoretical Framework," Information Management and Business Review, AMH International, vol. 7(2), pages 87-92.
    22. Bent Jesper Christensen & Rasmus Tangsgaard Varneskov, 2021. "Dynamic Global Currency Hedging [Arbitrage in the Foreign Exchange Market: Turning on the Microscope]," Journal of Financial Econometrics, Oxford University Press, vol. 19(1), pages 97-127.
    23. Enzo D'Innocenzo & Andre Lucas & Bernd Schwaab & Xin Zhang, 2024. "Joint extreme Value-at-Risk and Expected Shortfall dynamics with a single integrated tail shape parameter," Tinbergen Institute Discussion Papers 24-069/III, Tinbergen Institute.
    24. David Happersberger & Harald Lohre & Ingmar Nolte, 2020. "Estimating portfolio risk for tail risk protection strategies," European Financial Management, European Financial Management Association, vol. 26(4), pages 1107-1146, September.
    25. F. Lilla, 2017. "High Frequency vs. Daily Resolution: the Economic Value of Forecasting Volatility Models - 2nd ed," Working Papers wp1099, Dipartimento Scienze Economiche, Universita' di Bologna.
    26. Massacci, Daniele, 2014. "A two-regime threshold model with conditional skewed Student t distributions for stock returns," Economic Modelling, Elsevier, vol. 43(C), pages 9-20.
    27. Fengler, Matthias R. & Herwartz, Helmut, 2015. "Measuring spot variance spillovers when (co)variances are time-varying - the case of multivariate GARCH models," MPRA Paper 72197, University Library of Munich, Germany, revised 10 Jun 2016.
    28. Alfeus, Mesias & Nikitopoulos, Christina Sklibosios, 2022. "Forecasting volatility in commodity markets with long-memory models," Journal of Commodity Markets, Elsevier, vol. 28(C).
    29. Hsuan‐Ling Chang & Yen‐Cheng Chang & Hung‐Wen Cheng & Po‐Hsiang Peng & Kevin Tseng, 2019. "Jump variance risk: Evidence from option valuation and stock returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(7), pages 890-915, July.
    30. Yu Chen & Jie Hu & Weiping Zhang, 2020. "Too Connected to Fail? Evidence from a Chinese Financial Risk Spillover Network," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 28(6), pages 78-100, November.
    31. Isao Ishida & Virmantas Kvedaras, 2015. "Modeling Autoregressive Processes with Moving-Quantiles-Implied Nonlinearity," Econometrics, MDPI, vol. 3(1), pages 1-53, January.
    32. Dias, Alexandra, 2013. "Market capitalization and Value-at-Risk," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 5248-5260.
    33. Liu, Zhenya & Lu, Shanglin & Li, Bo & Wang, Shixuan, 2023. "Time series momentum and reversal: Intraday information from realized semivariance," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 54-77.
    34. Francis X. Diebold, 2020. ""Big Data" and its Origins," Papers 2008.05835, arXiv.org, revised Jan 2021.
    35. Han-Ching Huang & Yong-Chern Su & Jen-Tien Tsui, 2015. "Asymmetric GARCH Value-at-Risk over MSCI in Financial Crisis," International Journal of Economics and Financial Issues, Econjournals, vol. 5(2), pages 390-398.
    36. Duan, Yunlong & Mu, Chang & Yang, Meng & Deng, Zhiqing & Chin, Tachia & Zhou, Li & Fang, Qifeng, 2021. "Study on early warnings of strategic risk during the process of firms’ sustainable innovation based on an optimized genetic BP neural networks model: Evidence from Chinese manufacturing firms," International Journal of Production Economics, Elsevier, vol. 242(C).
    37. Victor Olkhov, 2021. "To VaR, or Not to VaR, That is the Question," Papers 2101.08559, arXiv.org, revised Apr 2024.
    38. BALTES Nicolae & DRAGOE Alexandra-Gabriela-Maria, 2017. "Estimating The Return Of The Financial Titles Of The Companies From The Manufacturing Industry, Listed On The Bucharest Stock Exchange," Revista Economica, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 69(3), pages 19-28, August.
    39. Cipollini, Fabrizio & Gallo, Giampiero M., 2019. "Modeling Euro STOXX 50 volatility with common and market-specific components," Econometrics and Statistics, Elsevier, vol. 11(C), pages 22-42.
    40. Richard Friberg & Mark Sanctuary, 2020. "Exchange rate risk and the skill composition of labor," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 156(2), pages 287-312, May.
    41. Nolte, Ingmar & Xu, Qi, 2015. "The economic value of volatility timing with realized jumps," Journal of Empirical Finance, Elsevier, vol. 34(C), pages 45-59.
    42. Diebold, Francis X. & Yılmaz, Kamil, 2023. "Reprint of: On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 234(S), pages 70-90.
    43. Symitsi, Efthymia & Symeonidis, Lazaros & Kourtis, Apostolos & Markellos, Raphael, 2018. "Covariance forecasting in equity markets," Journal of Banking & Finance, Elsevier, vol. 96(C), pages 153-168.
    44. Conrad, Christian & Glas, Alexander, 2018. "‘Déjà vol’ revisited: Survey forecasts of macroeconomic variables predict volatility in the cross-section of industry portfolios," Working Papers 0655, University of Heidelberg, Department of Economics.
    45. Hammadi Zouari, 2022. "On the Effectiveness of Stock Index Futures for Tail Risk Protection," International Journal of Economics and Financial Issues, Econjournals, vol. 12(3), pages 38-52, May.

  31. S. Boragan Aruoba & Francis X. Diebold & Jeremy J. Nalewaik & Frank Schorfheide & Dongho Song, 2011. "Improving GDP measurement: a forecast combination perspective," Working Papers 11-41, Federal Reserve Bank of Philadelphia.

    Cited by:

    1. Jeremy J. Nalewaik, 2014. "Missing Variation in the Great Moderation: Lack of Signal Error and OLS Regression," Finance and Economics Discussion Series 2014-27, Board of Governors of the Federal Reserve System (U.S.).
    2. Tomaz Cajner & Leland D. Crane & Ryan A. Decker & Adrian Hamins-Puertolas & Christopher Kurz, 2019. "Improving the Accuracy of Economic Measurement with Multiple Data Sources: The Case of Payroll Employment Data," NBER Working Papers 26033, National Bureau of Economic Research, Inc.
    3. Diebold, Francis X. & Shin, Minchul, 2019. "Machine learning for regularized survey forecast combination: Partially-egalitarian LASSO and its derivatives," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1679-1691.
    4. Cobb, Marcus P A, 2018. "Improving Underlying Scenarios for Aggregate Forecasts: A Multi-level Combination Approach," MPRA Paper 88593, University Library of Munich, Germany.
    5. James Bishop & Troy Gill & David Lancaster, 2013. "GDP Revisions: Measurement and Implications," RBA Bulletin (Print copy discontinued), Reserve Bank of Australia, pages 11-22, March.
    6. Mary C. Daly & John G. Fernald & Òscar Jordà & Fernanda Nechio, 2013. "Shocks and Adjustments," Working Paper Series 2013-32, Federal Reserve Bank of San Francisco.
    7. S. Boragan Aruoba & Francis X. Diebold & Jeremy J. Nalewaik & Frank Schorfheide & Dongho Song, 2013. "Improving GDP measurement: a measurement-error perspective," Working Papers 13-16, Federal Reserve Bank of Philadelphia.
    8. Tom Stark, 2014. "Real-time performance of GDPplus and alternative model-based measures of GDP: 2005—2014," Research Rap Special Report, Federal Reserve Bank of Philadelphia, issue Nov.
    9. Marius Cristian Acatrinei, 2020. "Financial stability indicator for non-banking markets," Journal of Financial Studies, Institute of Financial Studies, vol. 9(5), pages 3-9, November.
    10. Francis X. Diebold & Minchul Shin, 2017. "Beating the Simple Average: Egalitarian LASSO for Combining Economic Forecasts," PIER Working Paper Archive 17-017, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 20 Aug 2017.
    11. Jan P.A.M. Jacobs & Samad Sarferaz & Simon van Norden & Jan-Egbert Sturm, 2013. "Modeling Multivariate Data Revisions," CIRANO Working Papers 2013s-44, CIRANO.

  32. Francis X. Diebold & Kamil Yilmaz, 2010. "Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers," Koç University-TUSIAD Economic Research Forum Working Papers 1001, Koc University-TUSIAD Economic Research Forum, revised Mar 2010.

    Cited by:

    1. Chang, Hao-Wen & Chang, Tsangyao & Lee, Chien-Chiang, 2023. "Return and volatility connectedness among the BRICS stock and oil markets," Resources Policy, Elsevier, vol. 86(PA).
    2. Qiao, Sen & Guo, Zi Xin & Tao, Zhang & Ren, Zheng Yu, 2023. "Analyzing the network structure of risk transmission among renewable, non-renewable energy and carbon markets," Renewable Energy, Elsevier, vol. 209(C), pages 206-217.
    3. Matkovskyy, Roman & Jalan, Akanksha & Dowling, Michael, 2020. "Effects of economic policy uncertainty shocks on the interdependence between Bitcoin and traditional financial markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 77(C), pages 150-155.
    4. Volker Seiler, 2024. "The relationship between Chinese and FOB prices of rare earth elements – Evidence in the time and frequency domain," Post-Print hal-04549980, HAL.
    5. Chinmaya Behera & Pramod Kumar Mishra, 2022. "Interconnectedness and Nonlinearity in Indian Energy Futures During the COVID-19 Pandemic," Energy RESEARCH LETTERS, Asia-Pacific Applied Economics Association, vol. 3(2), pages 1-5.
    6. Yousaf, Imran & Zeitun, Rami & Ali, Shoaib & Palma, Alessia, 2024. "Impact of tokenization on financial investments: Exploring connectedness through the case of transport and travel/tourism sectors," Finance Research Letters, Elsevier, vol. 62(PB).
    7. Shah, Adil Ahmad & Sahay, Arvind, 2024. "Is gold a preferable diversifier of cleaner equity risk across diverse scenarios? Evidence from multidimensional connectedness and spillover measures," Energy, Elsevier, vol. 305(C).
    8. Ioannis Chatziantoniou & David Gabauer, 2019. "EMU-Risk Synchronisation and Financial Fragility Through the Prism of Dynamic Connectedness," Working Papers in Economics & Finance 2019-07, University of Portsmouth, Portsmouth Business School, Economics and Finance Subject Group.
    9. Umar, Zaghum & Yousaf, Imran & Gubareva, Mariya & Vo, Xuan Vinh, 2022. "Spillover and risk transmission between the term structure of the US interest rates and Islamic equities," Pacific-Basin Finance Journal, Elsevier, vol. 72(C).
    10. Kamal, Javed Bin & Hassan, M. Kabir, 2022. "Asymmetric connectedness between cryptocurrency environment attention index and green assets," The Journal of Economic Asymmetries, Elsevier, vol. 25(C).
    11. Bouri, Elie & Cepni, Oguzhan & Gabauer, David & Gupta, Rangan, 2021. "Return connectedness across asset classes around the COVID-19 outbreak," International Review of Financial Analysis, Elsevier, vol. 73(C).
    12. Miklesh Yadav & Nandita Mishra & Shruti Ashok, 2023. "Dynamic connectedness of green bond with financial markets of European countries under OECD economies," Economic Change and Restructuring, Springer, vol. 56(1), pages 609-631, February.
    13. Chen, Jing & Han, Qian & Ryu, Doojin & Tang, Jing, 2022. "Does the world smile together? A network analysis of global index option implied volatilities," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 77(C).
    14. Zhang, Yuan-Yuan & Zhang, Yue-Jun, 2022. "The impact of institutional analyst forecast divergence on crude oil market: Evidence from the mixed frequency models," International Review of Financial Analysis, Elsevier, vol. 84(C).
    15. Inekwe, John Nkwoma, 2020. "Liquidity connectedness and output synchronisation," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 66(C).
    16. Hasan, Mudassar & Arif, Muhammad & Naeem, Muhammad Abubakr & Ngo, Quang-Thanh & Taghizadeh–Hesary, Farhad, 2021. "Time-frequency connectedness between Asian electricity sectors," Economic Analysis and Policy, Elsevier, vol. 69(C), pages 208-224.
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    2423. Wei Zhou & Jin Guo & Ning Chen & Shuai Lu, 2023. "Key market identification, mechanism transmission, and extreme shock during the risk spillover process: an empirical study of the G20 FOREX markets," Empirical Economics, Springer, vol. 65(6), pages 2549-2582, December.
    2424. Do, A. & Powell, R. & Yong, J. & Singh, A., 2020. "Time-varying asymmetric volatility spillover between global markets and China’s A, B and H-shares using EGARCH and DCC-EGARCH models," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
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    2429. Gormus, Alper & Nazlioglu, Saban & Soytas, Ugur, 2018. "High-yield bond and energy markets," Energy Economics, Elsevier, vol. 69(C), pages 101-110.
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    2431. Syed Jawad Hussain Shahzad & Rangan Gupta & Riza Demirer & Christian Pierdzioch, 2022. "Oil shocks and directional predictability of macroeconomic uncertainties of developed economies: Evidence from high‐frequency data†," Scottish Journal of Political Economy, Scottish Economic Society, vol. 69(2), pages 169-185, May.
    2432. Marinela Adriana Finta & Bart Frijns & Alireza Tourani-Rad, 2019. "Time-varying contemporaneous spillovers during the European Debt Crisis," Empirical Economics, Springer, vol. 57(2), pages 423-448, August.
    2433. Kinkyo, Takuji, 2020. "Time-frequency dynamics of exchange rates in East Asia," Research in International Business and Finance, Elsevier, vol. 52(C).
    2434. Ren, Boru & Lucey, Brian, 2022. "A clean, green haven?—Examining the relationship between clean energy, clean and dirty cryptocurrencies," Energy Economics, Elsevier, vol. 109(C).
    2435. Nishimura, Yusaku & Tsutsui, Yoshiro & Hirayama, Kenjiro, 2018. "Do international investors cause stock market spillovers? Comparing responses of cross-listed stocks between accessible and inaccessible markets," Economic Modelling, Elsevier, vol. 69(C), pages 237-248.
    2436. Ngo Thai Hung, 2021. "Financial connectedness of GCC emerging stock markets," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(4), pages 753-773, December.
    2437. Maria Elena Giuli & Alessandro Spelta, 2023. "Wasserstein barycenter regression for estimating the joint dynamics of renewable and fossil fuel energy indices," Computational Management Science, Springer, vol. 20(1), pages 1-17, December.
    2438. Ji, Qiang & Geng, Jiang-Bo & Tiwari, Aviral Kumar, 2018. "Information spillovers and connectedness networks in the oil and gas markets," Energy Economics, Elsevier, vol. 75(C), pages 71-84.
    2439. Guo, Li-Yang & Feng, Chao & Yu, Si-Qi, 2023. "Connecting the stocks of major energy firms in China to identify the systemic risk," Energy Economics, Elsevier, vol. 126(C).
    2440. Al-Shboul, Mohammad & Assaf, Ata & Mokni, Khaled, 2022. "When bitcoin lost its position: Cryptocurrency uncertainty and the dynamic spillover among cryptocurrencies before and during the COVID-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 83(C).
    2441. International Monetary Fund, 2017. "Kingdom of the Netherlands-Netherlands: Financial System Stability Assessment," IMF Staff Country Reports 2017/079, International Monetary Fund.
    2442. Samet Gunay & Gokberk Can, 2022. "The source of financial contagion and spillovers: An evaluation of the covid-19 pandemic and the global financial crisis," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-20, January.
    2443. Zhang, Dayong & Lei, Lei & Ji, Qiang & Kutan, Ali M., 2019. "Economic policy uncertainty in the US and China and their impact on the global markets," Economic Modelling, Elsevier, vol. 79(C), pages 47-56.
    2444. Yaming Ma & Ziwei Wang & Feng He, 2022. "How do economic policy uncertainties affect stock market volatility? Evidence from G7 countries," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2303-2325, April.
    2445. Li, Yanshuang & Zhuang, Xintian & Wang, Jian, 2021. "Analysis of the cross-region risk contagion effect in stock market based on volatility spillover networks: Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    2446. Ahmed Bossman & Mariya Gubareva & Samuel Kwaku Agyei & Xuan Vinh Vo, 2024. "When you need them, they are not there: hedge capacities of cryptocurrencies disappear in downtrend markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-38, December.
    2447. Chen, Xiangyu & Tongurai, Jittima, 2024. "Revisiting the interdependences across global base metal futures markets: Evidence during the main waves of the COVID-19 pandemic," Research in International Business and Finance, Elsevier, vol. 70(PB).
    2448. Chaofeng Tang & Kentaka Aruga & Yi Hu, 2023. "The Dynamic Correlation and Volatility Spillover among Green Bonds, Clean Energy Stock, and Fossil Fuel Market," Sustainability, MDPI, vol. 15(8), pages 1-23, April.
    2449. Tiwari, Aviral Kumar & Dam, Mehmet Metin & Altıntaş, Halil & Bekun, Festus Victor, 2025. "The dynamic connectedness between oil price shocks and emerging market economies stock markets: Evidence from new approaches," Energy Economics, Elsevier, vol. 141(C).
    2450. Angelini Giovanni & Costantini Mauro & Easaw Joshy, 2024. "Estimating uncertainty spillover effects across euro area using a regime dependent VAR model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 28(1), pages 39-59, February.
    2451. Dey, Shubhasis & Sampath, Aravind, 2020. "Returns, volatility and spillover – A paradigm shift in India?," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    2452. Yushi Xu & Baifan Chen & Jionghao Huang & Qingsha Hu & Shuning Kong, 2024. "Time–frequency connectedness between heterogeneous oil price shocks and inflation: a comparative analysis of developed and emerging economies," Economic Change and Restructuring, Springer, vol. 57(6), pages 1-42, December.
    2453. Ousama Ben-Salha & Mourad Zmami & Sami Sobhi Waked & Faouzi Najjar & Yazeed Mohammad Alenazi, 2025. "On the time-varying spillover between nonferrous metals prices, geopolitical risks, and global economic policy uncertainty," Economic Change and Restructuring, Springer, vol. 58(1), pages 1-26, February.
    2454. Marina Yu. Malkina, 2024. "Financial contagion in the US, European and Chinese stock markets during global shocks," Journal of New Economy, Ural State University of Economics, vol. 25(4), pages 47-67, December.
    2455. Yousaf, Imran & Jareño, Francisco & Martínez-Serna, María-Isabel, 2023. "Extreme spillovers between insurance tokens and insurance stocks: Evidence from the quantile connectedness approach," Journal of Behavioral and Experimental Finance, Elsevier, vol. 39(C).
    2456. Yang, Xin & Wang, Xuya & Cao, Jie & Zhao, Lili & Huang, Chuangxia, 2024. "Cross-regional connectedness of financial market: Measurement and determinants," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
    2457. Bratis, Theodoros & Laopodis, Nikiforos T. & Kouretas, Georgios P., 2020. "Systemic risk and financial stability dynamics during the Eurozone debt crisis," Journal of Financial Stability, Elsevier, vol. 47(C).
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  33. S. Boragan Aruoba & Francis X. Diebold, 2010. "Real-time macroeconomic monitoring: real activity, inflation, and interactions," Working Papers 10-5, Federal Reserve Bank of Philadelphia.

    Cited by:

    1. Mr. Maxym Kryshko, 2011. "Data-Rich DSGE and Dynamic Factor Models," IMF Working Papers 2011/216, International Monetary Fund.
    2. Nicholas Taylor, 2014. "Economic forecast quality: information timeliness and data vintage effects," Empirical Economics, Springer, vol. 46(1), pages 145-174, February.
    3. Jonas Krampe & Luca Margaritella, 2021. "Factor Models with Sparse VAR Idiosyncratic Components," Papers 2112.07149, arXiv.org, revised May 2022.
    4. Serena Ng & Jonathan H. Wright, 2013. "Facts and Challenges from the Great Recession for Forecasting and Macroeconomic Modeling," NBER Working Papers 19469, National Bureau of Economic Research, Inc.
    5. Jaime Martínez-Martín & Elena Rusticelli, 2020. "Keeping track of global trade in real time," Working Papers 2019, Banco de España.
    6. Helena Rodríguez, 2014. "Un indicador de la evolución del PIB uruguayo en tiempo real," Documentos de trabajo 2014009, Banco Central del Uruguay.
    7. Peter C. B. Phillips & Zhentao Shi, 2021. "Boosting: Why You Can Use The Hp Filter," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 62(2), pages 521-570, May.
    8. Constantin ANGHELACHE & Madalina-Gabriela ANGHEL & Tudor SAMSON & Radu STOICA, 2017. "Methods And Techniques For Preparing Forecasts," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 65(4), pages 26-36, April.
    9. Diebold, Francis X. & Yilmaz, Kamil, 2015. "Financial and Macroeconomic Connectedness: A Network Approach to Measurement and Monitoring," OUP Catalogue, Oxford University Press, number 9780199338306, Decembrie.
    10. Antolín-Díaz, Juan & Drechsel, Thomas & Petrella, Ivan, 2024. "Advances in nowcasting economic activity: The role of heterogeneous dynamics and fat tails," Journal of Econometrics, Elsevier, vol. 238(2).
    11. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2011. "Financial Risk Measurement for Financial Risk Management," PIER Working Paper Archive 11-037, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    12. Francis X. Diebold, 2022. "Real-Time Real Economic Activity: Entering and Exiting the Pandemic Recession of 2020," Advances in Econometrics, in: Essays in Honour of Fabio Canova, volume 44, pages 5-24, Emerald Group Publishing Limited.
    13. Foroni, Claudia & Marcellino, Massimiliano, 2014. "A comparison of mixed frequency approaches for nowcasting Euro area macroeconomic aggregates," International Journal of Forecasting, Elsevier, vol. 30(3), pages 554-568.
    14. William A. Barnett & Marcelle Chauvet & Danilo Leiva-Leon, 2014. "Real-Time Nowcasting of Nominal GDP Under Structural Breaks," Staff Working Papers 14-39, Bank of Canada.
    15. Francis X. Diebold, 2020. "Real-Time Real Economic Activity:Exiting the Great Recession and Entering the Pandemic Recession," PIER Working Paper Archive 20-023, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    16. Cremers, Martijn & Fleckenstein, Matthias & Gandhi, Priyank, 2021. "Treasury yield implied volatility and real activity," Journal of Financial Economics, Elsevier, vol. 140(2), pages 412-435.
    17. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    18. Tony Chernis & Calista Cheung & Gabriella Velasco, 2017. "A Three-Frequency Dynamic Factor Model for Nowcasting Canadian Provincial GDP Growth," Discussion Papers 17-8, Bank of Canada.
    19. Andrea Gazzani & Fabrizio Venditti & Giovanni Veronese, 2024. "Oil price shocks in real time," Temi di discussione (Economic working papers) 1448, Bank of Italy, Economic Research and International Relations Area.
    20. Coble, David & Pincheira, Pablo, 2017. "Nowcasting Building Permits with Google Trends," MPRA Paper 76514, University Library of Munich, Germany.
    21. Tuhkuri, Joonas, 2016. "Forecasting Unemployment with Google Searches," ETLA Working Papers 35, The Research Institute of the Finnish Economy.
    22. Bhattacharya, Rudrani & Kapoor, Mrigankshi, 2020. "Forecasting Consumer Price Index Inflation in India: Vector Error Correction Mechanism Vs. Dynamic Factor Model Approach for Non-Stationary Time Series," Working Papers 20/323, National Institute of Public Finance and Policy.
    23. Barnett, William A. & Chauvet, Marcelle & Leiva-Leon, Danilo, 2016. "Real-time nowcasting of nominal GDP with structural breaks," Journal of Econometrics, Elsevier, vol. 191(2), pages 312-324.
    24. David Coble & Pablo Pincheira, 2021. "Forecasting building permits with Google Trends," Empirical Economics, Springer, vol. 61(6), pages 3315-3345, December.
    25. Chauvet, Marcelle & Potter, Simon, 2013. "Forecasting Output," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 141-194, Elsevier.
    26. Máximo Camacho & Danilo Leiva-León & Gabriel Pérez-Quiros, 2015. "Country Shocks, Monetary Policy Expectations and ECB Decisions. A Dynamic Non-Linear Approach," Working Papers Central Bank of Chile 764, Central Bank of Chile.
    27. Kihwan Kim & Hyun Hak Kim & Norman R. Swanson, 2023. "Mixing mixed frequency and diffusion indices in good times and in bad: an assessment based on historical data around the great recession of 2008," Empirical Economics, Springer, vol. 64(3), pages 1421-1469, March.
    28. S. Boragan Aruoba & Francis X. Diebold & Jeremy J. Nalewaik & Frank Schorfheide & Dongho Song, 2013. "Improving GDP measurement: a measurement-error perspective," Working Papers 13-16, Federal Reserve Bank of Philadelphia.
    29. Leiva-Leon Danilo, 2014. "Real vs. nominal cycles: a multistate Markov-switching bi-factor approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(5), pages 557-580, December.
    30. Stona, Filipe & Morais, Igor A.C. & Triches, Divanildo, 2018. "Economic dynamics during periods of financial stress: Evidences from Brazil," International Review of Economics & Finance, Elsevier, vol. 55(C), pages 130-144.
    31. Abel Rodríguez Tirado & Marcelo Delajara & Federico Hernández Álvarez, 2016. "Nowcasting Mexico’s Short-Term GDP Growth in Real-Time: A Factor Model versus Professional Forecasters," Economía Journal, The Latin American and Caribbean Economic Association - LACEA, vol. 0(Fall 2016), pages 167-182, October.
    32. Rocio Alvarez & Maximo Camacho & Gabriel Perez-Quiros, 2012. "Finite sample performance of small versus large scale dynamic factor models," Working Papers 1204, Banco de España.
    33. Monica Defend & Aleksey Min & Lorenzo Portelli & Franz Ramsauer & Francesco Sandrini & Rudi Zagst, 2021. "Quantifying Drivers of Forecasted Returns Using Approximate Dynamic Factor Models for Mixed-Frequency Panel Data," Forecasting, MDPI, vol. 3(1), pages 1-35, February.
    34. Peter C.B. Phillips & Zhentao Shi, 2019. "Boosting the Hodrick-Prescott Filter," Cowles Foundation Discussion Papers 2192, Cowles Foundation for Research in Economics, Yale University.
    35. Baskot, Bojan & Orsag, Silvije & Mikerevic, Dejan, 2018. "Yield Curve In Bosnia And Herzegovina: Financial And Macroeconomic Framework," UTMS Journal of Economics, University of Tourism and Management, Skopje, Macedonia, vol. 9(1), pages 1-15.
    36. Máximo Camacho & Jaime Martínez-Martín, 2014. "Real-time forecasting us GDP from small-scale factor models," Working Papers 1425, Banco de España.
    37. Evren Erdogan Cosar & Sevim Kosem & Cagri Sarikaya, 2013. "Do We Really Need Filters In Estimating Output Gap? : Evidence From Turkey," Working Papers 1333, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    38. Germán López, 2015. "Forecast Accuracy of Small and Large Scale Dynamic Factor Models in Developing Economies," Working Papers. Serie AD 2015-03, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    39. Tuhkuri, Joonas, 2016. "ETLAnow: A Model for Forecasting with Big Data – Forecasting Unemployment with Google Searches in Europe," ETLA Reports 54, The Research Institute of the Finnish Economy.
    40. Maximo Camacho & Gabriel Perez-Quiros & Pilar Poncela, 2012. "Markov-switching dynamic factor models in real time," Working Papers 1205, Banco de España.
    41. Camacho, Maximo & Perez-Quiros, Gabriel & Pacce, Matías, 2020. "Spillover effects in international business cycles," Working Paper Series 2484, European Central Bank.
    42. Boragan Aruoba & Francis X. Diebold & Jeremy Nalewaik & Frank Schorfheide & Dongho Song, 2011. "Improving GDP Measurement: A Forecast Combination Perspective," PIER Working Paper Archive 11-028, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    43. Camacho, Maximo & Lopez-Buenache, German, 2023. "Factor models for large and incomplete data sets with unknown group structure," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1205-1220.
    44. Camba-Méndez, Gonzalo & Kapetanios, George & Papailias, Fotis & Weale, Martin R., 2015. "An automatic leading indicator, variable reduction and variable selection methods using small and large datasets: Forecasting the industrial production growth for euro area economies," Working Paper Series 1773, European Central Bank.
    45. Florin Paul Costel LILEA & Aurelian DIACONU & Radu Titus MARINESCU & Gyorgy BODO, 2017. "Structural Methods Used In Forecasting Studies," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 65(4), pages 66-74, April.
    46. Alvarez, Rocio & Camacho, Maximo & Perez-Quiros, Gabriel, 2016. "Aggregate versus disaggregate information in dynamic factor models," International Journal of Forecasting, Elsevier, vol. 32(3), pages 680-694.
    47. Chen, Ji & Yang, Xinglin & Liu, Xiliang, 2022. "Learning, disagreement and inflation forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    48. Zhang, Wei & He, Jie & Ge, Chanyuan & Xue, Rui, 2022. "Real-time macroeconomic monitoring using mixed frequency data: Evidence from China," Economic Modelling, Elsevier, vol. 117(C).
    49. Pérez-Quirós, Gabriel & Camacho, Máximo & Lovcha, Yuliya, 2012. "Can we use seasonally adjusted indicators in dynamic factor models?," CEPR Discussion Papers 9191, C.E.P.R. Discussion Papers.
    50. Andrzej Wojtyna, 2013. "Kontrowersje wokół charakteru ożywienia po kryzysie finansowym i recesji," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 11-12, pages 5-21.
    51. Camacho Maximo & Lovcha Yuliya & Quiros Gabriel Perez, 2015. "Can we use seasonally adjusted variables in dynamic factor models?," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(3), pages 377-391, June.
    52. Valentina Raponi & Cecilia Frale, 2014. "Revisions in official data and forecasting," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(3), pages 451-472, August.
    53. William A. Barnett & Marcelle Chauvetz & Danilo Leiva-Leonx, 2014. "Real-Time Nowcasting Nominal GDP Under Structural Break," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201313, University of Kansas, Department of Economics, revised Feb 2014.
    54. Kihwan Kim & Norman Swanson, 2013. "Diffusion Index Model Specification and Estimation Using Mixed Frequency Datasets," Departmental Working Papers 201315, Rutgers University, Department of Economics.
    55. Alexis Flageollet & Hamza Bahaji, 2016. "Monetary Policy and Risk-Based Asset Allocation," Open Economies Review, Springer, vol. 27(5), pages 851-870, November.
    56. Madalina-Gabriela Anghel & Georgeta Lixandru & Marius Popovici & Alina – Georgiana Solomon & Emilia Stanciu, 2017. "Theoretical Elements on the Use of Price Indices for Inflation Measurement," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 7(3), pages 38-47, July.
    57. Berger, Dave & Pukthuanthong, Kuntara, 2016. "Fragility, stress, and market returns," Journal of Banking & Finance, Elsevier, vol. 62(C), pages 152-163.
    58. Constantin Anghelache & Madalina-Gabriela Anghel & Alina-Georgiana Solomon, 2017. "National Accounts System: Source of Information in Macroeconomic Forecast," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 7(2), pages 76-82, April.
    59. Mr. Troy D Matheson, 2011. "New Indicators for Tracking Growth in Real Time," IMF Working Papers 2011/043, International Monetary Fund.
    60. Marcella Lucchetta & Mr. Gianni De Nicolo, 2012. "Systemic Real and Financial Risks: Measurement, Forecasting, and Stress Testing," IMF Working Papers 2012/058, International Monetary Fund.
    61. Florin Paul Costel LILEA & Andreea – Ioana MARINESCU, 2017. "Macroeconomic Forecast Models – Concepts And Theoretical Notions," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 65(6), pages 118-123, June.

  34. Francis X. Diebold & Georg H. Strasser, 2008. "On the Correlation Structure of Microstructure Noise: A Financial Economic Approach," Boston College Working Papers in Economics 693, Boston College Department of Economics, revised 24 Apr 2012.

    Cited by:

    1. Tim Bollerslev & Nour Meddahi & Serge Luther Nyawa Womo, 2019. "High-dimensional multivariate realized volatility estimation," Post-Print hal-04947294, HAL.
    2. Boudt, Kris & Raza, Muhammad Wajid & Wauters, Marjan, 2019. "Evaluating the Shariah-compliance of equity portfolios: The weighting method matters," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 406-417.
    3. Yafeng Shi & Tingting Ying & Yanlong Shi & Chunrong Ai, 2020. "A comparison of conditional predictive ability of implied volatility and realized measures in forecasting volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1025-1034, November.
    4. Hounyo, Ulrich, 2017. "Bootstrapping integrated covariance matrix estimators in noisy jump–diffusion models with non-synchronous trading," Journal of Econometrics, Elsevier, vol. 197(1), pages 130-152.
    5. Olkhov, Victor, 2022. "Introduction of the Market-Based Price Autocorrelation," MPRA Paper 112003, University Library of Munich, Germany.
    6. Christensen, K. & Podolskij, M. & Thamrongrat, N. & Veliyev, B., 2017. "Inference from high-frequency data: A subsampling approach," Journal of Econometrics, Elsevier, vol. 197(2), pages 245-272.
    7. Pierre Chausse & Dinghai Xu, 2012. "GMM Estimation of a Stochastic Volatility Model with Realized Volatility: A Monte Carlo Study," Working Papers 1203, University of Waterloo, Department of Economics, revised May 2012.
    8. Victor Olkhov, 2022. "Market-Based Price Autocorrelation," Papers 2202.09323, arXiv.org, revised Feb 2024.
    9. Andersen, Torben G. & Cebiroglu, Gökhan & Hautsch, Nikolaus, 2017. "Volatility, information feedback and market microstructure noise: A tale of two regimes," CFS Working Paper Series 569, Center for Financial Studies (CFS).
    10. Liu, Lily Y. & Patton, Andrew J. & Sheppard, Kevin, 2015. "Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes," Journal of Econometrics, Elsevier, vol. 187(1), pages 293-311.
    11. Rasmus Tangsgaard Varneskov, 2011. "Flat-Top Realized Kernel Estimation of Quadratic Covariation with Non-Synchronous and Noisy Asset Prices," CREATES Research Papers 2011-35, Department of Economics and Business Economics, Aarhus University.
    12. Bollerslev, Tim & Medeiros, Marcelo C. & Patton, Andrew J. & Quaedvlieg, Rogier, 2022. "From zero to hero: Realized partial (co)variances," Journal of Econometrics, Elsevier, vol. 231(2), pages 348-360.
    13. Shrestha, Keshab & Philip, Sheena & Peranginangin, Yessy, 2020. "Contributions of Crude Oil Exchange Traded Funds in Price Discovery Process," American Business Review, Pompea College of Business, University of New Haven, vol. 23(2), pages 393-407, November.
    14. Gustavo Fruet Dias & Karsten Schweiker, 2024. "Integrated Variance Estimation for Assets Traded in Multiple Venues," University of East Anglia School of Economics Working Paper Series 2024-04, School of Economics, University of East Anglia, Norwich, UK..
    15. Clinet, Simon & Potiron, Yoann, 2019. "Testing if the market microstructure noise is fully explained by the informational content of some variables from the limit order book," Journal of Econometrics, Elsevier, vol. 209(2), pages 289-337.
    16. Christensen, Kim & Oomen, Roel C.A. & Podolskij, Mark, 2014. "Fact or friction: Jumps at ultra high frequency," Journal of Financial Economics, Elsevier, vol. 114(3), pages 576-599.
    17. Li, Yifan & Nolte, Ingmar & Vasios, Michalis & Voev, Valeri & Xu, Qi, 2022. "Weighted Least Squares Realized Covariation Estimation," Journal of Banking & Finance, Elsevier, vol. 137(C).
    18. Zheng, Chengli & Su, Kuangxi & Yao, Yinhong, 2021. "Hedging futures performance with denoising and noise-assisted strategies," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    19. Olkhov, Victor, 2022. "Price and Payoff Autocorrelations in the Consumption-Based Asset Pricing Model," MPRA Paper 112255, University Library of Munich, Germany.
    20. Selma Chaker, 2013. "Volatility and Liquidity Costs," Staff Working Papers 13-29, Bank of Canada.
    21. Chu, Gang & Zhang, Yongjie & Zhang, Xiaotao, 2021. "An analysis of impact of cancellation activity on market quality: Evidence from China," Economic Modelling, Elsevier, vol. 102(C).
    22. Kim Christensen & Roel Oomen & Roberto Renò, 2016. "The Drift Burst Hypothesis," CREATES Research Papers 2016-28, Department of Economics and Business Economics, Aarhus University.
    23. Eric Jondeau & Jérôme Lahaye & Michael Rockinger, 2013. "Estimating the Price Impact of Trades in an High-Frequency Microstructure Model with Jumps," Swiss Finance Institute Research Paper Series 13-47, Swiss Finance Institute, revised Feb 2016.
    24. Simon Clinet & Yoann Potiron, 2021. "Estimation for high-frequency data under parametric market microstructure noise," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(4), pages 649-669, August.
    25. Bollerslev, Tim & Li, Jia & Li, Qiyuan, 2024. "Optimal nonparametric range-based volatility estimation," Journal of Econometrics, Elsevier, vol. 238(1).
    26. Sílvia Gonçalves & Ulrich Hounyo & Nour Meddahi, 2013. "Bootstrap inference for pre-averaged realized volatility based on non-overlapping returns," CREATES Research Papers 2013-07, Department of Economics and Business Economics, Aarhus University.
    27. Bian, Siyu & Serra, Teresa & Garcia, Philip & Irwin, Scott, 2022. "New evidence on market response to public announcements in the presence of microstructure noise," European Journal of Operational Research, Elsevier, vol. 298(2), pages 785-800.
    28. Christensen, Kim & Thyrsgaard, Martin & Veliyev, Bezirgen, 2019. "The realized empirical distribution function of stochastic variance with application to goodness-of-fit testing," Journal of Econometrics, Elsevier, vol. 212(2), pages 556-583.
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    34. Andersen, Torben G. & Archakov, Ilya & Cebiroglu, Gökhan & Hautsch, Nikolaus, 2022. "Local mispricing and microstructural noise: A parametric perspective," Journal of Econometrics, Elsevier, vol. 230(2), pages 510-534.
    35. P. Simmons & N. Tantisantiwong, 2014. "Equilibrium moment restrictions on asset returns: normal and crisis periods," The European Journal of Finance, Taylor & Francis Journals, vol. 20(11), pages 1064-1089, November.

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    Cited by:

    1. Rafael Barros de Rezende, 2011. "Giving Flexibility to the Nelson-Siegel Class of Term Structure Models," Brazilian Review of Finance, Brazilian Society of Finance, vol. 9(1), pages 27-49.
    2. Martins, Manuel M.F. & Afonso, António, 2010. "Level, slope, curvature of the sovereign yield curve, and fiscal behaviour," Working Paper Series 1276, European Central Bank.
    3. Laurini, Márcio Poletti & Hotta, Luiz Koodi, 2010. "Bayesian extensions to Diebold-Li term structure model," International Review of Financial Analysis, Elsevier, vol. 19(5), pages 342-350, December.
    4. Koeda, Junko & Sekine, Atsushi, 2022. "Nelson–Siegel decay factor and term premia in Japan," Journal of the Japanese and International Economies, Elsevier, vol. 64(C).
    5. Caldeira, João F. & Moura, Guilherme V. & Santos, André A.P., 2016. "Bond portfolio optimization using dynamic factor models," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 128-158.
    6. Eric Hillebrand & Huiyu Huang & Tae-Hwy Lee & Canlin Li, 2018. "Using the Entire Yield Curve in Forecasting Output and Inflation," Econometrics, MDPI, vol. 6(3), pages 1-27, August.
    7. Kim, Young Min & Kang, Kyu Ho & Ka, Kook, 2020. "Do bond markets find inflation targets credible? Evidence from five inflation-targeting countries," International Review of Economics & Finance, Elsevier, vol. 67(C), pages 66-84.
    8. Oleksandr Castello & Marina Resta, 2025. "Optimal Time Varying Parameters in Yield Curve Modeling and Forecasting: A Simulation Study on BRICS Countries," Computational Economics, Springer;Society for Computational Economics, vol. 65(4), pages 2081-2113, April.
    9. Mr. Aleš Bulíř & Mr. Jan Vlcek, 2020. "Monetary Policy Is Not Always Systematic and Data-Driven: Evidence from the Yield Curve," IMF Working Papers 2020/004, International Monetary Fund.
    10. Kaminska, Iryna & Mumtaz, Haroon & Šustek, Roman, 2021. "Monetary policy surprises and their transmission through term premia and expected interest rates," Journal of Monetary Economics, Elsevier, vol. 124(C), pages 48-65.
    11. Molenaars, Tomas K. & Reinerink, Nick H. & Hemminga, Marcus A., 2013. "Forecasting the yield curve - Forecast performance of the dynamic Nelson-Siegel model from 1971 to 2008," MPRA Paper 61862, University Library of Munich, Germany.
    12. Dauwe, Alexander & Moura, Marcelo L., 2011. "Forecasting the term structure of the Euro Market using Principal Component Analysis," Insper Working Papers wpe_233, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    13. Aguiar-Conraria, Luís & Martins, Manuel M.F. & Soares, Maria Joana, 2012. "The yield curve and the macro-economy across time and frequencies," Journal of Economic Dynamics and Control, Elsevier, vol. 36(12), pages 1950-1970.
    14. Januj Amar Juneja, 2021. "How do invariant transformations affect the calibration and optimization of the Kalman filtering algorithm used in the estimation of continuous-time affine term structure models?," Computational Management Science, Springer, vol. 18(1), pages 73-97, January.
    15. Laurini, Márcio P. & Caldeira, João F., 2016. "A macro-finance term structure model with multivariate stochastic volatility," International Review of Economics & Finance, Elsevier, vol. 44(C), pages 68-90.
    16. Lajos Horváth & Piotr Kokoszka & Jeremy VanderDoes & Shixuan Wang, 2022. "Inference in functional factor models with applications to yield curves," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(6), pages 872-894, November.
    17. A. Carriero & Sarah Mouabbi & E. Vangelista, 2016. "UK term structure decompositions at the zero lower bound," Working papers 589, Banque de France.
    18. Fabricio Tourrucôo & João F. Caldeira & Guilherme V. Moura & André A. P. Santos, 2016. "Forecasting The Yield Curve With The Arbitrage-Free Dynamic Nelson-Siegel Model: Brazilian Evidence," Anais do XLII Encontro Nacional de Economia [Proceedings of the 42nd Brazilian Economics Meeting] 028, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    19. Alessia Paccagnini, 2016. "The Macroeconomic Determinants of the US Term-Structure During The Great Moderation," Open Access publications 10197/7324, School of Economics, University College Dublin.
    20. Márcio Laurini, 2011. "Bayesian Factor Selection in Dynamic Term Structure Models," IBMEC RJ Economics Discussion Papers 2011-02, Economics Research Group, IBMEC Business School - Rio de Janeiro.
    21. Joslin, Scott & Le, Anh & Singleton, Kenneth J., 2013. "Why Gaussian macro-finance term structure models are (nearly) unconstrained factor-VARs," Journal of Financial Economics, Elsevier, vol. 109(3), pages 604-622.
    22. Linlin Niu & Gengming Zeng, 2013. "The Discrete-Time Framework of the Arbitrage-Free Nelson-Siegel Class of Term Structure Models," Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    23. Kei Imakubo & Haruki Kojima & Jouchi Nakajima, 2015. "The natural yield curve: its concept and measurement," Bank of Japan Working Paper Series 15-E-5, Bank of Japan.
    24. Jens H. E. Christensen & Jose A. Lopez & Glenn D. Rudebusch, 2009. "Inflation expectations and risk premiums in an arbitrage-free model of nominal and real bond yields," Proceedings, Federal Reserve Bank of San Francisco, issue Jan.
    25. Márcio Laurini & João Frois Caldeira, 2012. "Some Comments on a Macro-Finance Model with Stochastic Volatility," IBMEC RJ Economics Discussion Papers 2012-04, Economics Research Group, IBMEC Business School - Rio de Janeiro.
    26. Takamizawa, Hideyuki, 2022. "How arbitrage-free is the Nelson–Siegel model under stochastic volatility?," International Review of Economics & Finance, Elsevier, vol. 79(C), pages 205-223.
    27. Lorenčič Eva, 2016. "Testing the Performance of Cubic Splines and Nelson-Siegel Model for Estimating the Zero-coupon Yield Curve," Naše gospodarstvo/Our economy, Sciendo, vol. 62(2), pages 42-50, June.
    28. Härdle, Wolfgang Karl & Majer, Piotr, 2012. "Yield curve modeling and forecasting using semiparametric factor dynamics," SFB 649 Discussion Papers 2012-048, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    29. Balter, Anne G. & Pelsser, Antoon & Schotman, Peter C., 2021. "What does a term structure model imply about very long-term interest rates?," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 202-219.
    30. Leo Krippner, 2013. "A tractable framework for zero lower bound Gaussian term structure models," Reserve Bank of New Zealand Discussion Paper Series DP2013/02, Reserve Bank of New Zealand.
    31. Leo Krippner, 2012. "A theoretical foundation for the Nelson and Siegel class of yield curve models," CAMA Working Papers 2012-11, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    32. Sébastien Fries & Jean‐Stéphane Mésonnier & Sarah Mouabbi & Jean‐Paul Renne, 2018. "National natural rates of interest and the single monetary policy in the euro area," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(6), pages 763-779, September.
    33. Molenaars, Tomas K. & Reinerink, Nick H. & Hemminga, Marcus A., 2015. "Forecasting the yield curve: art or science?," MPRA Paper 61917, University Library of Munich, Germany.
    34. Leo Krippner, 2012. "Modifying Gaussian term structure models when interest rates are near the zero lower bound (this is a revised version of CAMA working paper 36/2011)," CAMA Working Papers 2012-05, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    35. Gzyl, Henryk & Mayoral, Silvia, 2016. "Determination of zero-coupon and spot rates from treasury data by maximum entropy methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 38-50.
    36. Christensen, Jens H.E. & Lopez, Jose A. & Mussche, Paul L., 2024. "International evidence on extending sovereign debt maturities," Journal of International Money and Finance, Elsevier, vol. 141(C).
    37. Glenn D. Rudebusch, 2010. "Macro-finance models of interest rates and the economy," Working Paper Series 2010-01, Federal Reserve Bank of San Francisco.
    38. Wali Ullah & Yasumasa Matsuda, 2014. "Generalized Nelson-Siegel Term Structure Model : Do the second slope and curvature factors improve the in-sample fit and out-of-sample forecast?," TERG Discussion Papers 312, Graduate School of Economics and Management, Tohoku University.
    39. Daniel Vela, 2013. "Forecasting Latin-American yield curves: An artificial neural network approach," Borradores de Economia 10502, Banco de la Republica.
    40. Wali Ullah, 2020. "The arbitrage-free generalized Nelson–Siegel term structure model: Does a good in-sample fit imply better out-of-sample forecasts?," Empirical Economics, Springer, vol. 59(3), pages 1243-1284, September.
    41. Leo Krippner, 2012. "Modifying Gaussian term structure models when interest rates are near the zero lower bound," Reserve Bank of New Zealand Discussion Paper Series DP2012/02, Reserve Bank of New Zealand.
    42. Robert Brooks & Pavel Teterin, 2020. "Samuelson hypothesis, arbitrage activity, and futures term premiums," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(9), pages 1420-1441, September.
    43. Hong, Zhiwu & Wang, Zhenhan & Li, Xinda, 2024. "Foreign trade and China’s yield curve during the COVID-19 pandemic: An analysis based on an extended arbitrage-free Nelson–Siegel model," Research in International Business and Finance, Elsevier, vol. 70(PB).
    44. Januj Amar Juneja, 2022. "A Computational Analysis of the Tradeoff in the Estimation of Different State Space Specifications of Continuous Time Affine Term Structure Models," Computational Economics, Springer;Society for Computational Economics, vol. 60(1), pages 173-220, June.
    45. Caldeira, João F. & Laurini, Márcio P. & Portugal, Marcelo S., 2010. "Bayesian Inference Applied to Dynamic Nelson-Siegel Model with Stochastic Volatility," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 30(1), October.
    46. Daniel Vela, 2013. "Forecasting Latin-American yield curves: An artificial neural network approach," Borradores de Economia 761, Banco de la Republica de Colombia.
    47. Vahidin Jeleskovic & Anastasios Demertzidis, 2018. "Comparing different methods for the estimation of interbank intraday yield curves," MAGKS Papers on Economics 201839, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    48. Levant, Jared & Ma, Jun, 2017. "A dynamic Nelson-Siegel yield curve model with Markov switching," Economic Modelling, Elsevier, vol. 67(C), pages 73-87.
    49. Bekker, Paul A., 2017. "Interpretable Parsimonious Arbitrage-free Modeling of the Yield Curve," Research Report 17009-EEF, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    50. Januj Juneja, 2025. "What is the Effect of Restrictions Imposed by Principal Components Analysis on the Empirical Performance of Dynamic Term Structure Models?," Computational Economics, Springer;Society for Computational Economics, vol. 65(5), pages 2505-2543, May.
    51. Caio Almeida & Kym Ardison & Daniela Kubudi & Axel Simonsen & José Vicente, 2018. "Forecasting Bond Yields with Segmented Term Structure Models," Journal of Financial Econometrics, Oxford University Press, vol. 16(1), pages 1-33.
    52. Tu, Anthony H. & Chen, Cathy Yi-Hsuan, 2016. "What derives the bond portfolio value-at-risk: Information roles of macroeconomic and financial stress factors," SFB 649 Discussion Papers 2016-006, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    53. Eran Raviv, 2013. "Prediction Bias Correction for Dynamic Term Structure Models," Tinbergen Institute Discussion Papers 13-041/III, Tinbergen Institute.
    54. Craig Blackburn & Michael Sherris, 2011. "Consistent Dynamic Affine Mortality Model for Longevity Risk Applications," Working Papers 201107, ARC Centre of Excellence in Population Ageing Research (CEPAR), Australian School of Business, University of New South Wales.
    55. Di Maggio, Marco, 2010. "The Political Economy of the Yield Curve," MPRA Paper 20697, University Library of Munich, Germany.
    56. Michał Brzoza-Brzezina & Jacek Kotłowski, 2012. "Measuring the natural yield curve," NBP Working Papers 108, Narodowy Bank Polski.
    57. Ranik Raaen Wahlstrøm & Florentina Paraschiv & Michael Schürle, 2022. "A Comparative Analysis of Parsimonious Yield Curve Models with Focus on the Nelson-Siegel, Svensson and Bliss Versions," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 967-1004, March.
    58. Juneja, Januj, 2017. "Invariance, observational equivalence, and identification: Some implications for the empirical performance of affine term structure models," The Quarterly Review of Economics and Finance, Elsevier, vol. 64(C), pages 292-305.
    59. Bredin, Don & O'Sullivan, Conall & Spencer, Simon, 2021. "Forecasting WTI crude oil futures returns: Does the term structure help?," Energy Economics, Elsevier, vol. 100(C).
    60. Eric Fischer, 2020. "Monetary Surprises and Global Financial Flows: A Case Study of Latin America," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 19(2), pages 189-225, August.
    61. Januj Juneja, 2015. "An evaluation of alternative methods used in the estimation of Gaussian term structure models," Review of Quantitative Finance and Accounting, Springer, vol. 44(1), pages 1-24, January.
    62. Hokuto Ishii, 2018. "Modeling and Predictability of Exchange Rate Changes by the Extended Relative Nelson–Siegel Class of Models," IJFS, MDPI, vol. 6(3), pages 1-15, August.
    63. Renata Tavanielli & Márcio Laurini, 2023. "Yield Curve Models with Regime Changes: An Analysis for the Brazilian Interest Rate Market," Mathematics, MDPI, vol. 11(11), pages 1-28, June.
    64. Andreasen, Martin M. & Christensen, Jens H.E. & Rudebusch, Glenn D., 2019. "Term Structure Analysis with Big Data: One-Step Estimation Using Bond Prices," Journal of Econometrics, Elsevier, vol. 212(1), pages 26-46.
    65. Albert K. Tsui & Junxiang Wu & Zhaoyong Zhang & Zhongxi Zheng, 2023. "Forecasting term structure of the Japanese bond yields in the presence of a liquidity trap," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1205-1227, August.
    66. Valentin Jouvanceau & Ieva Mikaliunaite, 2020. "Euro Area Monetary Communications: Excess Sensitivity and Perception Shocks," Bank of Lithuania Working Paper Series 79, Bank of Lithuania.
    67. Michael D. Bauer & Glenn D. Rudebusch & Jing Cynthia Wu, 2011. "Unbiased estimate of dynamic term structure models," Working Paper Series 2011-12, Federal Reserve Bank of San Francisco.
    68. Munch Grønlund, Asger & Jørgensen, Kasper & Schupp, Fabian, 2024. "Measuring market-based core inflation expectations," Working Paper Series 2908, European Central Bank.
    69. Rihab Bedoui & Islem Kedidi, 2018. "Modeling Longevity Risk using Consistent Dynamics Affine Mortality Models," Working Papers hal-01678050, HAL.
    70. Wali Ullah & Yasumasa Matsuda & Yoshihiko Tsukuda, 2015. "Generalized Nelson-Siegel term structure model: do the second slope and curvature factors improve the in-sample fit and out-of-sample forecasts?," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(4), pages 876-904, April.
    71. Lutz Kruschwitz, 2018. "Das Problem der Anschlussverzinsung," Schmalenbach Journal of Business Research, Springer, vol. 70(1), pages 9-45, March.
    72. Juneja, Januj, 2014. "Term structure estimation in the presence of autocorrelation," The North American Journal of Economics and Finance, Elsevier, vol. 28(C), pages 119-129.
    73. Hiroyuki Kawakatsu, 2020. "Recovering Yield Curves from Dynamic Term Structure Models with Time-Varying Factors," Stats, MDPI, vol. 3(3), pages 1-46, August.
    74. Takamizawa, Hideyuki & 高見澤, 秀幸, 2015. "Impact of No-arbitrage on Interest Rate Dynamics," Working Paper Series G-1-5, Hitotsubashi University Center for Financial Research.
    75. Blackburn, Craig & Sherris, Michael, 2013. "Consistent dynamic affine mortality models for longevity risk applications," Insurance: Mathematics and Economics, Elsevier, vol. 53(1), pages 64-73.
    76. Frank J. Fabozzi & Francesco A. Fabozzi & Diana Tunaru, 2023. "A comparison of multi-factor term structure models for interbank rates," Review of Quantitative Finance and Accounting, Springer, vol. 61(1), pages 323-356, July.
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  36. Francis X. Diebold & Kamil Yilmaz, 2008. "Measuring financial asset return and volatility spillovers, with application to global equity markets," Working Papers 08-16, Federal Reserve Bank of Philadelphia.

    Cited by:

    1. Chang, Hao-Wen & Chang, Tsangyao & Lee, Chien-Chiang, 2023. "Return and volatility connectedness among the BRICS stock and oil markets," Resources Policy, Elsevier, vol. 86(PA).
    2. Volker Seiler, 2024. "The relationship between Chinese and FOB prices of rare earth elements – Evidence in the time and frequency domain," Post-Print hal-04549980, HAL.
    3. Manish Kumar, 2011. "Return and volatility spillovers: evidence from Indian exchange rates," International Journal of Economics and Business Research, Inderscience Enterprises Ltd, vol. 3(4), pages 371-387.
    4. Naeem, Muhammad Abubakr & Yousaf, Imran & Karim, Sitara & Yarovaya, Larisa & Ali, Shoaib, 2023. "Tail-event driven NETwork dependence in emerging markets," Emerging Markets Review, Elsevier, vol. 55(C).
    5. Andrea Cipollini & Fabio Parla, 2018. "Housing Market Shocks in Italy: a GVAR approach," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0069, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    6. Shah, Adil Ahmad & Sahay, Arvind, 2024. "Is gold a preferable diversifier of cleaner equity risk across diverse scenarios? Evidence from multidimensional connectedness and spillover measures," Energy, Elsevier, vol. 305(C).
    7. Ioannis Chatziantoniou & David Gabauer, 2019. "EMU-Risk Synchronisation and Financial Fragility Through the Prism of Dynamic Connectedness," Working Papers in Economics & Finance 2019-07, University of Portsmouth, Portsmouth Business School, Economics and Finance Subject Group.
    8. Umar, Zaghum & Yousaf, Imran & Gubareva, Mariya & Vo, Xuan Vinh, 2022. "Spillover and risk transmission between the term structure of the US interest rates and Islamic equities," Pacific-Basin Finance Journal, Elsevier, vol. 72(C).
    9. Alla, Zineddine & Espinoza, Raphael & Li, Helen & Segoviano, Miguel, 2018. "Macroprudential stress tests: a reduced-form approach to quantifying systemic risk losses," LSE Research Online Documents on Economics 118930, London School of Economics and Political Science, LSE Library.
    10. Bouri, Elie & Cepni, Oguzhan & Gabauer, David & Gupta, Rangan, 2021. "Return connectedness across asset classes around the COVID-19 outbreak," International Review of Financial Analysis, Elsevier, vol. 73(C).
    11. Chen, Jing & Han, Qian & Ryu, Doojin & Tang, Jing, 2022. "Does the world smile together? A network analysis of global index option implied volatilities," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 77(C).
    12. Inekwe, John Nkwoma, 2020. "Liquidity connectedness and output synchronisation," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 66(C).
    13. Geng, Jiang-Bo & Du, Ya-Juan & Ji, Qiang & Zhang, Dayong, 2021. "Modeling return and volatility spillover networks of global new energy companies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    14. Fei Su & Lili Zhai & Yunyan Zhou & Zixi Zhuang & Feifan Wang, 2024. "Risk contagion in financial markets: A systematic review using bibliometric methods," Australian Economic Papers, Wiley Blackwell, vol. 63(1), pages 163-199, March.
    15. Daniel Stefan ARMEANU & Adrian ENCIU & Sorin-Iulian CIOACA, 2017. "How Important is the Contagion Effect for the Romanian Capital Market?," Economic Alternatives, University of National and World Economy, Sofia, Bulgaria, issue 2, pages 265-282, June.
    16. Imran Yousaf & Shoaib Ali, 2020. "Discovering interlinkages between major cryptocurrencies using high-frequency data: new evidence from COVID-19 pandemic," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-18, December.
    17. Chen, Yu-Lun & Mo, Wan-Shin & Qin, Rong-Ling & Yang, J. Jimmy, 2023. "Return spillover across China's financial markets," Pacific-Basin Finance Journal, Elsevier, vol. 80(C).
    18. Andrea Bucci, 2020. "Realized Volatility Forecasting with Neural Networks," Journal of Financial Econometrics, Oxford University Press, vol. 18(3), pages 502-531.
    19. Stilianos Fountas & Dimitra Kontana & Paraskevi Tzika, 2024. "Uncertainty and financial asset return spillovers: are they related? Empirical evidence from three continents," Empirical Economics, Springer, vol. 67(5), pages 1891-1918, November.
    20. Elsayed, Ahmed H. & Naifar, Nader & Nasreen, Samia & Tiwari, Aviral Kumar, 2022. "Dependence structure and dynamic connectedness between green bonds and financial markets: Fresh insights from time-frequency analysis before and during COVID-19 pandemic," Energy Economics, Elsevier, vol. 107(C).
    21. R. L�pez & E. Navarro, 2013. "Interest rate and stock return volatility indices for the Eurozone. Investors' gauges of fear during the recent financial crisis," Applied Financial Economics, Taylor & Francis Journals, vol. 23(18), pages 1419-1432, September.
    22. Dejan Živkov & Suzana Balaban & Marko Pećanac, 2021. "Assessing the multiscale “meteor shower” effect from oil to the central and eastern European stock indices," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 1855-1870, April.
    23. Zhu, Zongyuan & Luo, Qingtian, 2023. "Inter-industry risk spillover, role reversal, and economic stability," Finance Research Letters, Elsevier, vol. 57(C).
    24. Yabei Zhu & Xingguo Luo & Qi Xu, 2023. "Industry variance risk premium, cross‐industry correlation, and expected returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(1), pages 3-32, January.
    25. Chen-Yin Kuo & Shu-Mei Chiang, 2025. "Spillover effects and network connectedness among stock markets: evidence from the U.S. and Asia," Review of Quantitative Finance and Accounting, Springer, vol. 64(1), pages 1-52, January.
    26. Mayuri Mukherjee & Saumitra N. Bhaduri, 2015. "Spillover Effects of Quantitative Easing," Review of Market Integration, India Development Foundation, vol. 7(2), pages 117-132, August.
    27. Balli, Faruk & Balli, Hatice O. & Jean Louis, Rosmy & Vo, Tuan Kiet, 2015. "The transmission of market shocks and bilateral linkages: Evidence from emerging economies," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 349-357.
    28. Mardi Dungey & Renee Fry‐Mckibbin & Vladimir Volkov, 2020. "Transmission of a Resource Boom: The Case of Australia," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(3), pages 503-525, June.
    29. Shi, Huai-Long & Zhou, Wei-Xing, 2022. "Factor volatility spillover and its implications on factor premia," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
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    1638. Chu, Jeffrey & Chan, Stephen & Zhang, Yuanyuan, 2020. "High frequency momentum trading with cryptocurrencies," Research in International Business and Finance, Elsevier, vol. 52(C).
    1639. Apostolakis, Georgios N. & Giannellis, Nikolaos & Papadopoulos, Athanasios P., 2019. "Financial stress and asymmetric shocks transmission within the Eurozone. How fragile is the common monetary policy?," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    1640. Niu, Hongli & Hu, Wenwen, 2024. "Static and dynamic interdependencies among natural gas, stocks of global major economies and uncertainty," Resources Policy, Elsevier, vol. 94(C).
    1641. Barbaglia, Luca & Croux, Christophe & Wilms, Ines, 2020. "Volatility spillovers in commodity markets: A large t-vector autoregressive approach," Energy Economics, Elsevier, vol. 85(C).
    1642. Jouini, Jamel & Harrathi, Nizar, 2014. "Revisiting the shock and volatility transmissions among GCC stock and oil markets: A further investigation," Economic Modelling, Elsevier, vol. 38(C), pages 486-494.
    1643. Dirk G. Baur & Lai T. Hoang, 2024. "Cryptocurrency spillovers and correlations: inefficiency and co-movement," Digital Finance, Springer, vol. 6(2), pages 203-224, June.
    1644. Shen, Yiran & Feng, Qianqian & Sun, Xiaolei, 2024. "Stability and risk contagion in the global sovereign CDS market under Russia-Ukraine conflict," The North American Journal of Economics and Finance, Elsevier, vol. 74(C).
    1645. Chiang, Min-Hsien & Wang, Li-Min, 2011. "Volatility contagion: A range-based volatility approach," Journal of Econometrics, Elsevier, vol. 165(2), pages 175-189.
    1646. Younis, Ijaz & Gupta, Himani & Du, Anna Min & Shah, Waheed Ullah & Hanif, Waqas, 2024. "Spillover dynamics in DeFi, G7 banks, and equity markets during global crises: A TVP-VAR analysis," Research in International Business and Finance, Elsevier, vol. 70(PB).
    1647. Mensi, Walid & Hernandez, Jose Arroeola & Yoon, Seong-Min & Vo, Xuan Vinh & Kang, Sang Hoon, 2021. "Spillovers and connectedness between major precious metals and major currency markets: The role of frequency factor," International Review of Financial Analysis, Elsevier, vol. 74(C).
    1648. Lovcha, Yuliya & Pérez Laborda, Àlex, 2018. "Volatility Spillovers in a Long-Memory VAR: an Application to Energy Futures Returns," Working Papers 2072/307362, Universitat Rovira i Virgili, Department of Economics.
    1649. Bajo-Rubio, Oscar & Berke, Burcu & McMillan, David, 2017. "The behaviour of asset return and volatility spillovers in Turkey: A tale of two crises," Research in International Business and Finance, Elsevier, vol. 41(C), pages 577-589.
    1650. Wei Zhou & Jin Guo & Ning Chen & Shuai Lu, 2023. "Key market identification, mechanism transmission, and extreme shock during the risk spillover process: an empirical study of the G20 FOREX markets," Empirical Economics, Springer, vol. 65(6), pages 2549-2582, December.
    1651. Qian Wang & Yu Wei & Yifeng Zhang & Yuntong Liu, 2023. "Evaluating the Safe-Haven Abilities of Bitcoin and Gold for Crude Oil Market: Evidence During the COVID-19 Pandemic," Evaluation Review, , vol. 47(3), pages 391-432, June.
    1652. Zhang, Dayong, 2017. "Oil shocks and stock markets revisited: Measuring connectedness from a global perspective," Energy Economics, Elsevier, vol. 62(C), pages 323-333.
    1653. Syed Jawad Hussain Shahzad & Rangan Gupta & Riza Demirer & Christian Pierdzioch, 2022. "Oil shocks and directional predictability of macroeconomic uncertainties of developed economies: Evidence from high‐frequency data†," Scottish Journal of Political Economy, Scottish Economic Society, vol. 69(2), pages 169-185, May.
    1654. Kinkyo, Takuji, 2020. "Time-frequency dynamics of exchange rates in East Asia," Research in International Business and Finance, Elsevier, vol. 52(C).
    1655. Virginie Coudert & Cécile Couharde & Valérie Mignon, 2011. "L'impact des crises financières globales sur les marchés des changes des pays émergents," Revue économique, Presses de Sciences-Po, vol. 62(3), pages 451-460.
    1656. Nishimura, Yusaku & Tsutsui, Yoshiro & Hirayama, Kenjiro, 2018. "Do international investors cause stock market spillovers? Comparing responses of cross-listed stocks between accessible and inaccessible markets," Economic Modelling, Elsevier, vol. 69(C), pages 237-248.
    1657. Yongqi Feng & Tianshu Zhang, 2016. "Interest Rate Linkages between Offshore and Onshore Renminbi Markets," Australian Economic Papers, Wiley Blackwell, vol. 55(4), pages 434-450, December.
    1658. Lillie Lam & James Yetman, 2013. "Asia’s decoupling: fact, forecast or fiction?," BIS Working Papers 438, Bank for International Settlements.
    1659. Greg Clinch & Zi Wei, 2011. "The Association Between Earnings and Returns and Macroeconomic Performance: Evidence from Australia, the US and China," Australian Accounting Review, CPA Australia, vol. 21(1), pages 54-63, March.
    1660. Maria Elena Giuli & Alessandro Spelta, 2023. "Wasserstein barycenter regression for estimating the joint dynamics of renewable and fossil fuel energy indices," Computational Management Science, Springer, vol. 20(1), pages 1-17, December.
    1661. Al-Shboul, Mohammad & Assaf, Ata & Mokni, Khaled, 2022. "When bitcoin lost its position: Cryptocurrency uncertainty and the dynamic spillover among cryptocurrencies before and during the COVID-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 83(C).
    1662. Samet Gunay & Gokberk Can, 2022. "The source of financial contagion and spillovers: An evaluation of the covid-19 pandemic and the global financial crisis," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-20, January.
    1663. Zhang, Dayong & Lei, Lei & Ji, Qiang & Kutan, Ali M., 2019. "Economic policy uncertainty in the US and China and their impact on the global markets," Economic Modelling, Elsevier, vol. 79(C), pages 47-56.
    1664. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.
    1665. Tiwari, Aviral Kumar & Dam, Mehmet Metin & Altıntaş, Halil & Bekun, Festus Victor, 2025. "The dynamic connectedness between oil price shocks and emerging market economies stock markets: Evidence from new approaches," Energy Economics, Elsevier, vol. 141(C).
    1666. Grobys, Klaus & Heinonen, Jari-Pekka, 2017. "Option-implied volatility spillover indices for FX risk factors," Economics Letters, Elsevier, vol. 157(C), pages 83-87.
    1667. Yushi Xu & Baifan Chen & Jionghao Huang & Qingsha Hu & Shuning Kong, 2024. "Time–frequency connectedness between heterogeneous oil price shocks and inflation: a comparative analysis of developed and emerging economies," Economic Change and Restructuring, Springer, vol. 57(6), pages 1-42, December.
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    1671. Guo, Shanwen & Wang, Qibin & Hordofa, Tolassa Temesgen & Kaur, Prabjot & Nguyen, Ngoc Quynh & Maneengam, Apichit, 2022. "Does COVID-19 pandemic cause natural resources commodity prices volatility? Empirical evidence from China," Resources Policy, Elsevier, vol. 77(C).
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  37. Francis X. Diebold & Georg H. Strasser, 2008. "On the Correlation Structure of Microstructure Noise in Theory and Practice," PIER Working Paper Archive 08-038, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.

    Cited by:

    1. Kim Christensen & Roel Oomen & Mark Podolskij, 2009. "Realised Quantile-Based Estimation of the Integrated Variance," CREATES Research Papers 2009-27, Department of Economics and Business Economics, Aarhus University.
    2. E. Bacry & S. Delattre & M. Hoffmann & J. F. Muzy, 2011. "Modeling microstructure noise with mutually exciting point processes," Papers 1101.3422, arXiv.org.
    3. Kim Christensen & Silja Kinnebrock & Mark Podolskij, 2010. "Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data," Post-Print hal-00732537, HAL.
    4. Goettler, Ronald L. & Parlour, Christine A. & Rajan, Uday, 2009. "Informed traders and limit order markets," Journal of Financial Economics, Elsevier, vol. 93(1), pages 67-87, July.
    5. O. E. Barndorff-Nielsen & P. Reinhard Hansen & A. Lunde & N. Shephard, 2009. "Realized kernels in practice: trades and quotes," Econometrics Journal, Royal Economic Society, vol. 12(3), pages 1-32, November.
    6. P. Simmons & N. Tantisantiwong, 2014. "Equilibrium moment restrictions on asset returns: normal and crisis periods," The European Journal of Finance, Taylor & Francis Journals, vol. 20(11), pages 1064-1089, November.

  38. Frank Schorfheide & Francis X. Diebold & Marco Del Negro, 2008. "Priors from Frequency-Domain Dummy Observations," 2008 Meeting Papers 310, Society for Economic Dynamics.

    Cited by:

    1. Zhongjun Qu, 2018. "A Composite Likelihood Framework for Analyzing Singular DSGE Models," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 916-932, December.
    2. Zhongjun Qu, 2011. "Inference and Speci?cation Testing in DSGE Models with Possible Weak Identification," Boston University - Department of Economics - Working Papers Series WP2011-058, Boston University - Department of Economics.

  39. Jens H. E. Christensen & Francis X. Diebold & Glenn D. Rudebusch, 2007. "The affine arbitrage-free class of Nelson-Siegel term structure models," Working Paper Series 2007-20, Federal Reserve Bank of San Francisco.

    Cited by:

    1. Azamat Abdymomunov & Kyu Ho Kang & Ki Jeong Kim, 2014. "Forecasting the Term Structure of Government Bond Yields Using Credit Spreads and Structural Breaks," Working Papers 2014-19, Economic Research Institute, Bank of Korea.
    2. Laurini, Márcio Poletti & Hotta, Luiz Koodi, 2010. "Bayesian extensions to Diebold-Li term structure model," International Review of Financial Analysis, Elsevier, vol. 19(5), pages 342-350, December.
    3. Recchioni, M.C. & Sun, Y., 2016. "An explicitly solvable Heston model with stochastic interest rate," European Journal of Operational Research, Elsevier, vol. 249(1), pages 359-377.
    4. Zhou, Siwen, 2019. "Assessing the Macroeconomic Impact of the ECB’s Asset Purchase Programme in a Dynamic Nelson–Siegel Modelling Framework," MPRA Paper 92530, University Library of Munich, Germany.
    5. Koeda, Junko & Sekine, Atsushi, 2022. "Nelson–Siegel decay factor and term premia in Japan," Journal of the Japanese and International Economies, Elsevier, vol. 64(C).
    6. Caldeira, João F. & Moura, Guilherme V. & Santos, André A.P., 2016. "Bond portfolio optimization using dynamic factor models," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 128-158.
    7. Burban, Valentin & De Backer, Bruno & Vladu, Andreea Liliana, 2024. "Inflation (de-)anchoring in the euro area," Working Paper Series 2964, European Central Bank.
    8. Borochin, Paul & Chang, Hao & Wu, Yangru, 2020. "The information content of the term structure of risk-neutral skewness," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 247-274.
    9. Michael D. Bauer, 2018. "Restrictions on Risk Prices in Dynamic Term Structure Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(2), pages 196-211, April.
    10. Elizondo Rocío, 2013. "Forecasting the Term Structure of Interest Rates in Mexico Using an Affine Model," Working Papers 2013-03, Banco de México.
    11. Yajing Xu & Michael Sherris & Jonathan Ziveyi, 2020. "Market Price of Longevity Risk for a Multi‐Cohort Mortality Model With Application to Longevity Bond Option Pricing," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 87(3), pages 571-595, September.
    12. Jens H. E. Christensen & Jose A. Lopez & Glenn D. Rudebusch, 2013. "A Probability-Based Stress Test of Federal Reserve Assets and Income," Working Paper Series 2013-38, Federal Reserve Bank of San Francisco.
    13. Drew D. Creal & Jing Cynthia Wu, 2014. "Estimation of Affine Term Structure Models with Spanned or Unspanned Stochastic Volatility," NBER Working Papers 20115, National Bureau of Economic Research, Inc.
    14. Borus Jungbacker & Siem Jan Koopman & Michel van der Wel, 2009. "Smooth Dynamic Factor Analysis with an Application to the U.S. Term Structure of Interest Rates," CREATES Research Papers 2009-39, Department of Economics and Business Economics, Aarhus University.
    15. Kim, Young Min & Kang, Kyu Ho & Ka, Kook, 2020. "Do bond markets find inflation targets credible? Evidence from five inflation-targeting countries," International Review of Economics & Finance, Elsevier, vol. 67(C), pages 66-84.
    16. Michael D. Bauer, 2015. "Nominal Interest Rates and the News," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(2-3), pages 295-332, March.
    17. Laurini, Márcio Poletti & Mauad, Roberto Baltieri, 2012. "Non-Parametric Pricing of Interest Rates Options," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 32(2), April.
    18. Coroneo, Laura & Nyholm, Ken & Vidova-Koleva, Rositsa, 2008. "How arbitrage-free is the Nelson-Siegel Model?," Working Paper Series 874, European Central Bank.
    19. Daniela Osterrieder, 2013. "Interest Rates with Long Memory: A Generalized Affine Term-Structure Model," CREATES Research Papers 2013-17, Department of Economics and Business Economics, Aarhus University.
    20. Adam Kucera & Michal Dvorak & Zlatuse Komarkova, 2017. "Decomposition of the Czech government bond yield curve," Occasional Publications - Chapters in Edited Volumes, in: CNB Financial Stability Report 2016/2017, chapter 0, pages 125-134, Czech National Bank, Research and Statistics Department.
    21. Recchioni, Maria Cristina & Tedeschi, Gabriele, 2017. "From bond yield to macroeconomic instability: A parsimonious affine model," European Journal of Operational Research, Elsevier, vol. 262(3), pages 1116-1135.
    22. Minchul Shin & Molin Zhong, 2015. "Does Realized Volatility Help Bond Yield Density Prediction?," Finance and Economics Discussion Series 2015-115, Board of Governors of the Federal Reserve System (U.S.).
    23. Sarah Mouabbi, 2014. "An arbitrage-free Nelson-Siegel term structure model with stochastic volatility for the determination of currency risk premia," Working papers 527, Banque de France.
    24. Sarno, Lucio & Schneider, Paul & Wagner, Christian, 2012. "Properties of foreign exchange risk premiums," Journal of Financial Economics, Elsevier, vol. 105(2), pages 279-310.
    25. Byrne, Joseph & Cao, Shuo & Korobilis, Dimitris, 2015. "Term Structure Dynamics, Macro-Finance Factors and Model Uncertainty," MPRA Paper 63844, University Library of Munich, Germany.
    26. Juneja, Januj A., 2016. "Financial crises and estimation bias in international bond markets," Research in International Business and Finance, Elsevier, vol. 38(C), pages 593-607.
    27. von Borstel, Julia & Eickmeier, Sandra & Krippner, Leo, 2016. "The interest rate pass-through in the euro area during the sovereign debt crisis," Journal of International Money and Finance, Elsevier, vol. 68(C), pages 386-402.
    28. François Gourio & Phuong Ngo, 2024. "Downward Nominal Rigidities and Bond Premia," Working Paper Series WP 2024-09, Federal Reserve Bank of Chicago.
    29. Wali Ullah & Yoshihiko Tsukuda & Yasumasa Matsuda, 2012. "Term Structure Forecasting of Government Bond Yields with Latent and Macroeconomic Factors: Does Macroeconomic Factors Imply Better Out-of-Sample Forecasts?," TERG Discussion Papers 287, Graduate School of Economics and Management, Tohoku University.
    30. Michael D. Bauer & Glenn D. Rudebusch, 2014. "The Signaling Channel for Federal Reserve Bond Purchases," International Journal of Central Banking, International Journal of Central Banking, vol. 10(3), pages 233-289, September.
    31. Januj Amar Juneja, 2021. "How do invariant transformations affect the calibration and optimization of the Kalman filtering algorithm used in the estimation of continuous-time affine term structure models?," Computational Management Science, Springer, vol. 18(1), pages 73-97, January.
    32. Greg Duffee, 2011. "Forecasting with the term structure: The role of no-arbitrage restrictions," Economics Working Paper Archive 576, The Johns Hopkins University,Department of Economics.
    33. Laurini, Márcio P. & Caldeira, João F., 2016. "A macro-finance term structure model with multivariate stochastic volatility," International Review of Economics & Finance, Elsevier, vol. 44(C), pages 68-90.
    34. A. Carriero & Sarah Mouabbi & E. Vangelista, 2016. "UK term structure decompositions at the zero lower bound," Working papers 589, Banque de France.
    35. Dempster, M.A.H. & Tang, Ke, 2011. "Estimating exponential affine models with correlated measurement errors: Applications to fixed income and commodities," Journal of Banking & Finance, Elsevier, vol. 35(3), pages 639-652, March.
    36. Fabricio Tourrucôo & João F. Caldeira & Guilherme V. Moura & André A. P. Santos, 2016. "Forecasting The Yield Curve With The Arbitrage-Free Dynamic Nelson-Siegel Model: Brazilian Evidence," Anais do XLII Encontro Nacional de Economia [Proceedings of the 42nd Brazilian Economics Meeting] 028, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    37. Alessia Paccagnini, 2016. "The Macroeconomic Determinants of the US Term-Structure During The Great Moderation," Open Access publications 10197/7324, School of Economics, University College Dublin.
    38. Schupp, Fabian, 2020. "The (ir)relevance of the nominal lower bound for real yield curve analysis," Discussion Papers 32/2020, Deutsche Bundesbank.
    39. Márcio Laurini, 2011. "Bayesian Factor Selection in Dynamic Term Structure Models," IBMEC RJ Economics Discussion Papers 2011-02, Economics Research Group, IBMEC Business School - Rio de Janeiro.
    40. Márcio Laurini, 2012. "Dynamic Functional Data Analysis with Nonparametric State Space Models," IBMEC RJ Economics Discussion Papers 2012-01, Economics Research Group, IBMEC Business School - Rio de Janeiro.
    41. Michael Abrahams & Tobias Adrian & Richard K. Crump & Emanuel Moench, 2012. "Decomposing real and nominal yield curves," Staff Reports 570, Federal Reserve Bank of New York.
    42. Alfaro, Rodrigo A., 2011. "Affine Nelson-Siegel model," Economics Letters, Elsevier, vol. 110(1), pages 1-3, January.
    43. David O. Lucca & Jonathan H. Wright, 2022. "The Narrow Channel of Quantitative Easing: Evidence from YCC Down Under," Staff Reports 1013, Federal Reserve Bank of New York.
    44. Márcio Poletti Laurini & Armênio Westin Neto, 2014. "Arbitrage in the Term Structure of Interest Rates: a Bayesian Approach," International Econometric Review (IER), Econometric Research Association, vol. 6(2), pages 77-99, September.
    45. Jens H. E. Christensen & Francis X. Diebold & Glenn D. Rudebusch, 2008. "An Arbitrage-Free Generalized Nelson-Siegel Term Structure Model," PIER Working Paper Archive 08-030, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    46. Jens H. E. Christensen & Jose A. Lopez & Glenn D. Rudebusch, 2009. "Inflation expectations and risk premiums in an arbitrage-free model of nominal and real bond yields," Proceedings, Federal Reserve Bank of San Francisco, issue Jan.
    47. Shi, Yukun & Stasinakis, Charalampos & Xu, Yaofei & Yan, Cheng, 2022. "Market co-movement between credit default swap curves and option volatility surfaces," International Review of Financial Analysis, Elsevier, vol. 82(C).
    48. Lin, Mucai & Niu, Linlin, 2021. "Echo over the great wall: Spillover effects of QE announcements on Chinese yield curve," Journal of International Money and Finance, Elsevier, vol. 111(C).
    49. Rui Liu, 2019. "Forecasting Bond Risk Premia with Unspanned Macroeconomic Information," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 9(01), pages 1-62, March.
    50. Thomas Nitschka & Diego M. Hager, 2022. "Responses of Swiss bond yields and stock prices to ECB policy surprises," Working Papers 2022-08, Swiss National Bank.
    51. Takamizawa, Hideyuki, 2022. "How arbitrage-free is the Nelson–Siegel model under stochastic volatility?," International Review of Economics & Finance, Elsevier, vol. 79(C), pages 205-223.
    52. Brand, Claus & Goy, Gavin W & Lemke, Wolfgang, 2020. "Natural rate chimera and bond pricing reality," VfS Annual Conference 2020 (Virtual Conference): Gender Economics 224546, Verein für Socialpolitik / German Economic Association.
    53. Carlo A. Favero & Linlin Niu & Luca Sala, 2012. "Term Structure Forecasting: No‐Arbitrage Restrictions versus Large Information Set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 31(2), pages 124-156, March.
    54. Beauregard, Remy & Christensen, Jens H.E. & Fischer, Eric & Zhu, Simon, 2024. "Inflation expectations and risk premia in emerging bond markets: Evidence from Mexico," Journal of International Economics, Elsevier, vol. 151(C).
    55. Tobias Adrian & Daniel M. Covitz & J. Nellie Liang, 2013. "Financial stability monitoring," Staff Reports 601, Federal Reserve Bank of New York.
    56. Geiger, Felix & Schupp, Fabian, 2018. "With a little help from my friends: Survey-based derivation of euro area short rate expectations at the effective lower bound," Discussion Papers 27/2018, Deutsche Bundesbank.
    57. Balter, Anne G. & Pelsser, Antoon & Schotman, Peter C., 2021. "What does a term structure model imply about very long-term interest rates?," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 202-219.
    58. Gregory R. Duffee, 2012. "Forecasting interest rates," Economics Working Paper Archive 599, The Johns Hopkins University,Department of Economics.
    59. Zhou, Siwen, 2018. "Measuring the Signaling Effect of the ECB’s Asset Purchase Programme at the Effective Lower Bound," MPRA Paper 87084, University Library of Munich, Germany.
    60. Sophocles N. Brissimis & Evangelia A. Georgiou, 2022. "The effects of Federal Reserve's quantitative easing and balance sheet normalization policies on long-term interest rates," Working Papers 299, Bank of Greece.
    61. Jens H. E. Christensen & James M. Gillan, 2011. "A model-independent maximum range for the liquidity correction of TIPS yields," Working Paper Series 2011-16, Federal Reserve Bank of San Francisco.
    62. Almeida, Caio Ibsen Rodrigues de & Vicente, José, 2007. "The role of no-arbitrage on forecasting: lessons from a parametric term structure model," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 657, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    63. Kortela, Tomi & Nelimarkka, Jaakko, 2020. "The effects of conventional and unconventional monetary policy: Identification through the yield curve," Bank of Finland Research Discussion Papers 3/2020, Bank of Finland.
    64. Eo, Yunjong & Kang, Kyu Ho, 2020. "The effects of conventional and unconventional monetary policy on forecasting the yield curve," Journal of Economic Dynamics and Control, Elsevier, vol. 111(C).
    65. Leo Krippner, 2012. "A theoretical foundation for the Nelson and Siegel class of yield curve models," CAMA Working Papers 2012-11, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    66. Feng Guo, 2019. "Estimating yield curves of the U.S. Treasury securities: An interpolation approach," Review of Financial Economics, John Wiley & Sons, vol. 37(2), pages 297-321, April.
    67. Hager, Diego & Nitschka, Thomas, 2022. "The Impact of COVID-19 and other Crises on the Responses of Swiss Bond Yields and Stock Prices to ECB Policy Surprises," VfS Annual Conference 2022 (Basel): Big Data in Economics 264018, Verein für Socialpolitik / German Economic Association.
    68. Hitesh Doshi & Kris Jacobs & Rui Liu, 2021. "Information in the Term Structure: A Forecasting Perspective," Management Science, INFORMS, vol. 67(8), pages 5255-5277, August.
    69. Luis Ceballos & Jens H. E. Christensen & Damian Romero, 2024. "A Post-Pandemic New Normal for Interest Rates in Emerging Bond Markets? Evidence from Chile," Working Paper Series 2024-04, Federal Reserve Bank of San Francisco.
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    1. Andres Fernandez & Norman R. Swanson, 2009. "Real-time datasets really do make a difference: definitional change, data release, and forecasting," Working Papers 09-28, Federal Reserve Bank of Philadelphia.
    2. Willem Thorbecke, 2020. "The Impact of the COVID-19 Pandemic on the U.S. Economy: Evidence from the Stock Market," JRFM, MDPI, vol. 13(10), pages 1-30, October.
    3. Franco, Ray John Gabriel & Mapa, Dennis S., 2014. "The Dynamics of Inflation and GDP Growth: A Mixed Frequency Model Approach," MPRA Paper 55858, University Library of Munich, Germany.
    4. Martina Hengge & Seton Leonard, 2017. "Factor Models for Non-Stationary Series: Estimates of Monthly U.S. GDP," IHEID Working Papers 13-2017, Economics Section, The Graduate Institute of International Studies.
    5. Francisco Blasques & Siem Jan Koopman & Max Mallee, 2014. "Low Frequency and Weighted Likelihood Solutions for Mixed Frequency Dynamic Factor Models," Tinbergen Institute Discussion Papers 14-105/III, Tinbergen Institute.

  41. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling and Forecasting of Return Volatility," CREATES Research Papers 2007-18, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Eric Hillebrand & Gunther Schnabl & Yasemin Ulu, 2006. "Japanese Foreign Exchange Intervention and the Yen/Dollar Exchange Rate: A Simultaneous Equations Approach Using Realized Volatility," CESifo Working Paper Series 1766, CESifo.
    2. Yu-Min Yen, 2013. "Testing Jumps via False Discovery Rate Control," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-15, April.
    3. Oliver Linton & Yoon-Jae Whang & Yu-Min Yen, 2012. "A nonparametric test of the leverage hypothesis," CeMMAP working papers CWP24/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Viktor Todorov & Yang Zhang, 2022. "Information gains from using short‐dated options for measuring and forecasting volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(2), pages 368-391, March.
    5. David E. Allen & Michael McAleer & Marcel Scharth, 2014. "Asymmetric Realized Volatility Risk," JRFM, MDPI, vol. 7(2), pages 1-30, June.
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  44. Francis X. Diebold & Kamil Yılmaz, 2007. "Macroeconomic Volatility and Stock Market Volatility,World-Wide," Koç University-TUSIAD Economic Research Forum Working Papers 0711, Koc University-TUSIAD Economic Research Forum.

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    1. Chen, Qiang & Gong, Yuting, 2019. "The economic sources of China's CSI 300 spot and futures volatilities before and after the 2015 stock market crisis," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 102-121.
    2. Joel M. David & Ina Simonovska, 2015. "Correlated Beliefs, Returns, and Stock Market Volatility," NBER Working Papers 21480, National Bureau of Economic Research, Inc.
    3. Alessandro Paolo Rigamonti & Giulio Greco & Mariarita Pierotti & Alessandro Capocchi, 2024. "Macroeconomic uncertainty and earnings management: evidence from commodity firms," Review of Quantitative Finance and Accounting, Springer, vol. 62(4), pages 1615-1649, May.
    4. Bandi, F.M & Perron, B & Tamoni, Andrea & Tebaldi, C., 2018. "The scale of predictability," LSE Research Online Documents on Economics 85646, London School of Economics and Political Science, LSE Library.
    5. Mishra, Abinash & Srivastava, Pranjal & Chakrabarti, Anindya S., 2020. "'Too central to fail' firms in bi-layered financial networks: Evidence of linkages from the US corporate bond and stock markets," IIMA Working Papers WP 2020-06-02, Indian Institute of Management Ahmedabad, Research and Publication Department.
    6. Azad, A.S.M. Sohel & Fang, Victor & Hung, Chi-Hsiou, 2012. "Linking the interest rate swap markets to the macroeconomic risk: The UK and us evidence," International Review of Financial Analysis, Elsevier, vol. 22(C), pages 38-47.
    7. Charlotte Christiansen & Maik Schmeling & Andreas Schrimpf, 2012. "A Comprehensive Look at Financial Volatility Prediction by Economic Variables," BIS Working Papers 374, Bank for International Settlements.
    8. Karunanayake, Indika & Valadkhani, Abbas & O’Brien, Martin, 2012. "GDP Growth and the Interdependency of Volatility Spillovers," MPRA Paper 50398, University Library of Munich, Germany.
    9. Bevilacqua, Mattia & Morelli, David & Tunaru, Radu, 2019. "The determinants of the model-free positive and negative volatilities," Journal of International Money and Finance, Elsevier, vol. 92(C), pages 1-24.
    10. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2011. "Financial Risk Measurement for Financial Risk Management," PIER Working Paper Archive 11-037, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    11. Elie Bouri & Riza Demirer & Rangan Gupta & Xiaojin Sun, 2019. "The Predictability of Stock Market Volatility in Emerging Economies: Relative Roles of Local, Regional and Global Business Cycles," Working Papers 201938, University of Pretoria, Department of Economics.
    12. Taylor, Nick, 2014. "The rise and fall of technical trading rule success," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 286-302.
    13. Lieven Baele, 2010. "The Determinants of Stock and Bond Return Comovements," The Review of Financial Studies, Society for Financial Studies, vol. 23(6), pages 2374-2428, June.
    14. Andrea BASTIANIN & Matteo MANERA, 2015. "How Does Stock Market Volatility React to Oil Shocks?," Departmental Working Papers 2015-09, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    15. Andy Wui Wing Cheng & Iris Wing Han Yip, 2017. "China’s Macroeconomic Fundamentals on Stock Market Volatility: Evidence from Shanghai and Hong Kong," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 20(02), pages 1-57, June.
    16. Olkhov, Victor, 2022. "The Market-Based Asset Price Probability," MPRA Paper 115382, University Library of Munich, Germany, revised 16 Nov 2022.
    17. Prabhas Kumar Rath, 2023. "Nexus Between Indian Financial Markets and Macro-economic Shocks: A VAR Approach," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 30(1), pages 131-164, March.
    18. Lindblad, Annika, 2017. "Sentiment indicators and macroeconomic data as drivers for low-frequency stock market volatility," MPRA Paper 80266, University Library of Munich, Germany.
    19. Azariadis, Costas & Kaas, Leo, 2016. "Capital Misallocation And Aggregate Factor Productivity," Macroeconomic Dynamics, Cambridge University Press, vol. 20(2), pages 525-543, March.
    20. Andrew S. Duncan & Alain Kabundi, 2014. "Global Financial Crises and Time-Varying Volatility Comovement in World Equity Markets," South African Journal of Economics, Economic Society of South Africa, vol. 82(4), pages 531-550, December.
    21. Qin, Yiyi & Cai, Jun & Wang, James J.D. & Webb, Robert I., 2023. "Gold-mining stocks, risk factors, and tail patterns," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).
    22. Lieven Baele & Geert Bekaert & Koen Inghelbrecht, 2007. "The determinants of stock and bond return comovements," Working Paper Research 119, National Bank of Belgium.
    23. Alessandra Amendola & Marinella Boccia & Vincenzo Candila & Giampiero M. Gallo, 2020. "Energy and non–energy Commodities: Spillover Effects on African Stock Markets," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 9(4), pages 1-7.
    24. Stelios Bekiros & Syed Jawad Hussain Shahzad & Jose Arreola-Hernandez & Mobeen Ur Rehman, 2018. "Directional predictability and time-varying spillovers between stock markets and economic cycles," Post-Print hal-01996787, HAL.
    25. Becker, Christoph & Schmidt, Wolfgang M., 2013. "Stressing correlations and volatilities — A consistent modeling approach," Journal of Empirical Finance, Elsevier, vol. 21(C), pages 174-194.
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    27. Zeeshan, Mohammad, 2024. "Financial market volatility: Does banking concentration play a role?," Finance Research Letters, Elsevier, vol. 68(C).
    28. Yin Hong, 2011. "Positive feedback trading, institutional investors and securities price fluctuation," China Finance Review International, Emerald Group Publishing Limited, vol. 1(2), pages 120-132, January.
    29. Osazee Godwin Omorokunwa & Nosakhare Ikponmwosa, 2014. "Macroeconomic variables and stock price volatility in Nigeria," Annals of the University of Petrosani, Economics, University of Petrosani, Romania, vol. 14(1), pages 259-268.
    30. Muhammad Mansoor Baig & Waheed Aslam & Qaiser Malik & Muhammad Bilal, 2015. "Volatility of Stock Markets (an Analysis of South Asian and G8 Countries)," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 11(6), pages 58-70, December.
    31. Kumar, Sudarshan & Bansal, Avijit & Chakrabarti, Anindya S., 2019. "Ripples on financial networks," IIMA Working Papers WP 2019-10-01, Indian Institute of Management Ahmedabad, Research and Publication Department.
    32. Roberto Guimarães-Filho & Mr. Gee Hee Hong, 2016. "Dynamic Connectedness of Asian Equity Markets," IMF Working Papers 2016/057, International Monetary Fund.
    33. Kyoungsoo Yoon & Jayoung Kim, 2015. "Costs of Foreign Capital Flows in Emerging Market Economies: Unexpected Economic Growth and Increased Financial Market Volatility," Working Papers 2015-21, Economic Research Institute, Bank of Korea.
    34. Eric Girardin & Roselyne Joyeux, 2013. "Macro fundamentals as a source of stock market volatility in China: A GARCH-MIDAS approach," Post-Print hal-01499615, HAL.
    35. Roel Beetsma & Massimo Giuliodori, 2011. "The Changing Macroeconomic Response to Stock Market Volatility Shocks," CESifo Working Paper Series 3652, CESifo.
    36. Cumhur Şahin & Hüseyin Altay, 2016. "Examination of the Relationship between Turkey's Credit Default Swap (CDS) Points and Unemployment," Eurasian Business & Economics Journal, Eurasian Academy Of Sciences, vol. 4(4), pages 52-67, February.
    37. Li Rong Wang & Hsuan Fu & Xiuyi Fan, 2023. "Stock Price Predictability and the Business Cycle via Machine Learning," Papers 2304.09937, arXiv.org.
    38. King, Daniel & Botha, Ferdi, 2015. "Modelling stock return volatility dynamics in selected African markets," Economic Modelling, Elsevier, vol. 45(C), pages 50-73.
    39. Ali Chebbi, 2015. "Stochastic growth, taxation policy and welfare cost in an open emerging economy," International Review of Economics, Springer;Happiness Economics and Interpersonal Relations (HEIRS), vol. 62(1), pages 57-84, March.
    40. Cosset, Jean-Claude & Somé, Hyacinthe Y. & Valéry, Pascale, 2016. "Credible reforms and stock return volatility: Evidence from privatization," Journal of Banking & Finance, Elsevier, vol. 72(C), pages 99-120.
    41. Abbas Valadkhani & George Chen, 2014. "An empirical analysis of the US stock market and output growth volatility spillover effects on three Anglo-Saxon countries," International Review of Applied Economics, Taylor & Francis Journals, vol. 28(3), pages 323-335, May.
    42. Babajide Abiola Ayopo & Lawal Adedoyin Isola & Somoye Russel Olukayode, 2016. "Stock Market Volatility: Does Our Fundamentals Matter?," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 3, pages 33-42.
    43. Rufei Zhang & Haizhen Zhang & Qingzhu Fan & Wang Gao & Xue Luo & Shixiong Yang, 2022. "Partisan Conflict, National Security Policy Uncertainty and Tourism," Sustainability, MDPI, vol. 14(17), pages 1-22, August.
    44. Yum K. Kwan & Jinyue Dong, 2014. "Stock Price Dynamics of China: What Do the Asset Markets Tell Us About the Chinese Utility Function?," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 50(03), pages 77-108, May.
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    47. Syed Kamran Ali Haider & Shujahat Haider Hashmi & Ishtiaq Ahmed, 2017. "Systematic Risk Factors And Stock Return Volatility," APSTRACT: Applied Studies in Agribusiness and Commerce, AGRIMBA, vol. 11(01-2), September.

  45. S. Boragan Aruoba & Francis X. Diebold & Chiara Scotti, 2007. "Real-time measurement of business conditions," International Finance Discussion Papers 901, Board of Governors of the Federal Reserve System (U.S.).

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    1. Choi, Sangyup & Shin, Junhyeok & Yoo, Seung Yong, 2022. "Are government spending shocks inflationary at the zero lower bound? New evidence from daily data," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).
    2. Maria A. Arias & Charles S. Gascon & David E. Rapach, 2014. "Metro Business Cycles," Working Papers 2014-46, Federal Reserve Bank of St. Louis.
    3. Pete Richardson, 2018. "Nowcasting and the Use of Big Data in Short-Term Macroeconomic Forecasting: A Critical Review," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 505-506, pages 65-87.
    4. Allard, Anne-Florence & Iania, Leonardo & Smedts, Kristien, 2020. "Stock-bond return correlations: Moving away from "one-frequency-fits-all" by extending the DCC-MIDAS approach," LIDAM Reprints LFIN 2020005, Université catholique de Louvain, Louvain Finance (LFIN).
    5. Elsayed, Ahmed H. & Naifar, Nader & Nasreen, Samia & Tiwari, Aviral Kumar, 2022. "Dependence structure and dynamic connectedness between green bonds and financial markets: Fresh insights from time-frequency analysis before and during COVID-19 pandemic," Energy Economics, Elsevier, vol. 107(C).
    6. Noori, Mohammad, 2024. "Stock-oil comovements through fear, uncertainty, and expectations: Evidence from conditional comoments," International Review of Economics & Finance, Elsevier, vol. 93(PA), pages 529-551.
    7. Miranda Gualdrón, Karen Alejandra & Poncela, Pilar & Ruiz Ortega, Esther, 2021. "Dynamic factor models: does the specification matter?," DES - Working Papers. Statistics and Econometrics. WS 32210, Universidad Carlos III de Madrid. Departamento de Estadística.
    8. Banegas, Ayelen & Montes-Rojas, Gabriel & Siga, Lucas, 2022. "The effects of U.S. monetary policy shocks on mutual fund investing," Journal of International Money and Finance, Elsevier, vol. 123(C).
    9. Cecilia Frale, Serena Teobaldo, Marco Cacciotti, Alessandra Caretta, 2013. "A Quarterly Measure Of Potential Output In The New European Fiscal Framework," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 67(2), pages 181-197, April-Jun.
    10. Qian, Hang, 2012. "Essays on statistical inference with imperfectly observed data," ISU General Staff Papers 201201010800003618, Iowa State University, Department of Economics.
    11. Juan M. Londono & Mary Tian, 2014. "Bank Interventions and Options-based Systemic Risk: Evidence from the Global and Euro-area Crisis," International Finance Discussion Papers 1117, Board of Governors of the Federal Reserve System (U.S.).
    12. Hale, Galina & Lopez, Jose A., 2019. "Monitoring banking system connectedness with big data," Journal of Econometrics, Elsevier, vol. 212(1), pages 203-220.
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    14. Michele Modugno & Lucrezia Reichlin & Domenico Giannone & Marta Banbura, 2012. "Nowcasting with Daily Data," 2012 Meeting Papers 555, Society for Economic Dynamics.
    15. Antonello D'Agostino & Domenico Giannone & Michele Lenza & Michele Modugno, 2015. "Nowcasting Business Cycles: a Bayesian Approach to Dynamic Heterogeneous Factor Models," Finance and Economics Discussion Series 2015-66, Board of Governors of the Federal Reserve System (U.S.).
    16. Sebastian Rondeau, 2012. "Sources of Fluctuations in Emerging Markets: Structural Estimation with Mixed Frequency Data," 2012 Meeting Papers 1156, Society for Economic Dynamics.
    17. Mr. Maxym Kryshko, 2011. "Data-Rich DSGE and Dynamic Factor Models," IMF Working Papers 2011/216, International Monetary Fund.
    18. Xu, Yongdeng & Guan, Bo & Lu, Wenna & Heravi, Saeed, 2024. "Macroeconomic shocks and volatility spillovers between stock, bond, gold and crude oil markets," Energy Economics, Elsevier, vol. 136(C).
    19. Nicholas Taylor, 2014. "Economic forecast quality: information timeliness and data vintage effects," Empirical Economics, Springer, vol. 46(1), pages 145-174, February.
    20. Pham, Linh & Huynh, Toan Luu Duc & Hanif, Waqas, 2023. "Time-varying asymmetric spillovers among cryptocurrency, green and fossil-fuel investments," Global Finance Journal, Elsevier, vol. 58(C).
    21. Yanele Nyamela & Vasilios Plakandaras & Rangan Gupta, 2020. "Frequency-dependent real-time effects of uncertainty in the United States: evidence from daily data," Applied Economics Letters, Taylor & Francis Journals, vol. 27(19), pages 1562-1566, November.
    22. John Bagnall & David Bounie & Kim P. Huynh & Anneke Kosse & Tobias Schmidt & Scott Schuh, 2016. "Consumer Cash Usage: A Cross-Country Comparison with Payment Diary Survey Data," International Journal of Central Banking, International Journal of Central Banking, vol. 12(4), pages 1-61, December.
    23. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2010. "Should macroeconomic forecasters use daily financial data and how?," University of Cyprus Working Papers in Economics 09-2010, University of Cyprus Department of Economics.
    24. Faria, Gonçalo & Verona, Fabio, 2023. "Forecast combination in the frequency domain," Bank of Finland Research Discussion Papers 1/2023, Bank of Finland.
    25. Serena Ng & Jonathan H. Wright, 2013. "Facts and Challenges from the Great Recession for Forecasting and Macroeconomic Modeling," NBER Working Papers 19469, National Bureau of Economic Research, Inc.
    26. Wu, Nan & Wen, Fenghua & Gong, Xu, 2022. "Marionettes behind co-movement of commodity prices: Roles of speculative and hedging activities," Energy Economics, Elsevier, vol. 115(C).
    27. Bahattin Büyük şahin & Thomas K. Lee & James T. Moser & Michel A. Robe, 2013. "Physical Markets, Paper Markets and the WTI-Brent Spread," The Energy Journal, , vol. 34(3), pages 129-152, July.
    28. Giovannelli, Alessandro & Massacci, Daniele & Soccorsi, Stefano, 2021. "Forecasting stock returns with large dimensional factor models," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 252-269.
    29. Andres Fernandez & Norman R. Swanson, 2009. "Real-time datasets really do make a difference: definitional change, data release, and forecasting," Working Papers 09-28, Federal Reserve Bank of Philadelphia.
    30. Michael Funke & Aaron Mehrotra & Hao Yu, 2015. "Tracking Chinese CPI inflation in real time," Empirical Economics, Springer, vol. 48(4), pages 1619-1641, June.
    31. Kim Christensen & Mathias Siggaard & Bezirgen Veliyev, 2021. "A machine learning approach to volatility forecasting," CREATES Research Papers 2021-03, Department of Economics and Business Economics, Aarhus University.
    32. Faust, Jon & Wright, Jonathan H., 2009. "Comparing Greenbook and Reduced Form Forecasts Using a Large Realtime Dataset," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 468-479.
    33. Jesús Fernández-Villaverde & Pablo Guerrón-Quintana & Keith Kuester & Juan Rubio-Ramírez, 2015. "Fiscal Volatility Shocks and Economic Activity," American Economic Review, American Economic Association, vol. 105(11), pages 3352-3384, November.
    34. Helena Chuliá & Ignacio Garrón & Jorge M. Uribe, 2022. ""Daily Growth at Risk: financial or real drivers? The answer is not always the same"," IREA Working Papers 202208, University of Barcelona, Research Institute of Applied Economics, revised Jun 2022.
    35. Alex Edmans & Adrian Fernandez-Perez & Alexandre Garel & Ivan Indriawan, 2021. "Music Sentiment and Stock Returns Around the World," Post-Print hal-03324805, HAL.
    36. Gerson Javier Pérez-Valbuena & Diana Ricciulli-Marín & Jaime Bonet-Morón & Paula Barrios, 2021. "Reglas fiscales subnacionales en Colombia: desde su concepción hasta los resultados frente al COVID-19," Documentos de trabajo sobre Economía Regional y Urbana 297, Banco de la Republica de Colombia.
    37. Scott Brave & R. Andrew Butters & Alejandro Justiniano, 2016. "Forecasting Economic Activity with Mixed Frequency Bayesian VARs," Working Paper Series WP-2016-5, Federal Reserve Bank of Chicago.
    38. Cecilia Frale & Libero Monteforte, "undated". "FaMIDAS: A Mixed Frequency Factor Model with MIDAS structure," Working Papers 3, Department of the Treasury, Ministry of the Economy and of Finance.
    39. Nicholas Taylor, 2014. "The Economic Value of Volatility Forecasts: A Conditional Approach," Journal of Financial Econometrics, Oxford University Press, vol. 12(3), pages 433-478.
    40. Carlini, Federico & Farina, Vincenzo & Gufler, Ivan & Previtali, Daniele, 2024. "Do stress and overstatement in the news affect the stock market? Evidence from COVID-19 news in The Wall Street Journal," International Review of Financial Analysis, Elsevier, vol. 93(C).
    41. Luca Benzoni & Pierre Collin-Dufresne & Robert S. Goldstein & Jean Helwege, 2012. "Modeling credit contagion via the updating of fragile beliefs," Working Paper Series WP-2012-04, Federal Reserve Bank of Chicago.
    42. Galbraith, John W. & Tkacz, Greg, 2018. "Nowcasting with payments system data," International Journal of Forecasting, Elsevier, vol. 34(2), pages 366-376.
    43. Pettenuzzo, Davide & Timmermann, Allan & Valkanov, Rossen, 2016. "A MIDAS approach to modeling first and second moment dynamics," Journal of Econometrics, Elsevier, vol. 193(2), pages 315-334.
    44. Sieds, 2013. "Complete Volume LXVII n.2 2013," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 67(2), pages 1-197, April-Jun.
    45. Timmermann, Allan & Pettenuzzo, Davide & Sabbatucci, Riccardo, 2019. "Cash Flow News and Stock Price Dynamics," CEPR Discussion Papers 14117, C.E.P.R. Discussion Papers.
    46. Maximo Camacho & Rafael Domenech, 2010. "MICA-BBVA: A Factor Model of Economic and Financial Indicators for Short-term GDP Forecasting," Working Papers 1021, BBVA Bank, Economic Research Department.
    47. Baruník, Jozef & Bevilacqua, Mattia & Tunaru, Radu, 2022. "Asymmetric network connectedness of fears," LSE Research Online Documents on Economics 108199, London School of Economics and Political Science, LSE Library.
    48. Scott A. Brave & Charles S. Gascon & William Kluender & Thomas Walstrum, 2019. "Predicting Benchmarked US State Employment Data in Real Time," Working Papers 2019-037, Federal Reserve Bank of St. Louis, revised 11 Mar 2021.
    49. Massimo Guidolin & Manuela Pedio, 2019. "Does the Cost of Private Debt Respond to Monetary Policy? Heteroskedasticity-Based Identification in a Model with Regimes," BAFFI CAREFIN Working Papers 19118, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    50. Scott Brave & R. Andrew Butters, 2010. "Gathering insights on the forest from the trees: a new metric for financial conditions," Working Paper Series WP-2010-07, Federal Reserve Bank of Chicago.
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    53. Fornaro, Paolo & Luomaranta, Henri & Saarinen, Lauri, 2017. "Nowcasting Finnish Turnover Indexes Using Firm-Level Data," ETLA Working Papers 46, The Research Institute of the Finnish Economy.
    54. Fava, Santino Del & Gupta, Rangan & Pierdzioch, Christian & Rognone, Lavinia, 2024. "Forecasting international financial stress: The role of climate risks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 92(C).
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    57. Willem Thorbecke, 2020. "The Impact of the COVID-19 Pandemic on the U.S. Economy: Evidence from the Stock Market," JRFM, MDPI, vol. 13(10), pages 1-30, October.
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    349. Feng, Guanhao & He, Xin & Wang, Yanchu & Wu, Chunchi, 2025. "Predicting individual corporate bond returns," Journal of Banking & Finance, Elsevier, vol. 171(C).
    350. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    351. Faria, Gonçalo & Verona, Fabio, 2024. "Enhancing forecast accuracy through frequencydomain combination: Applications to financial and economic indicators," Bank of Finland Research Discussion Papers 14/2024, Bank of Finland.
    352. Conrad, Christian & Schienle, Melanie, 2015. "Misspecification Testing in GARCH-MIDAS Models," Working Papers 0597, University of Heidelberg, Department of Economics.
    353. M. Karanasos & S. Yfanti & J. Hunter, 2022. "Emerging stock market volatility and economic fundamentals: the importance of US uncertainty spillovers, financial and health crises," Annals of Operations Research, Springer, vol. 313(2), pages 1077-1116, June.
    354. Andres Algaba & Samuel Borms & Kris Boudt & Brecht Verbeken, 2021. "Daily news sentiment and monthly surveys: A mixed–frequency dynamic factor model for nowcasting consumer confidence," Working Paper Research 396, National Bank of Belgium.
    355. Timotheos Angelidis & Nikolaos Tessaromatis, 2014. "Global portfolio management under state dependent multiple risk premia," Proceedings of Economics and Finance Conferences 0400966, International Institute of Social and Economic Sciences.
    356. Taylor, Nicholas, 2012. "Testing forecasting model versatility," Economics Letters, Elsevier, vol. 117(3), pages 803-806.
    357. Matthes, Christian & Wang, Mu-Chun, 2012. "What drives inflation in New Keynesian models?," Economics Letters, Elsevier, vol. 114(3), pages 338-342.
    358. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2013. "Dynamic Factor Models: A review of the Literature ," Working papers 430, Banque de France.
    359. Deschamps, Bruno & Ioannidis, Christos & Ka, Kook, 2020. "High-frequency credit spread information and macroeconomic forecast revision," International Journal of Forecasting, Elsevier, vol. 36(2), pages 358-372.
    360. Ruey Yau & C. James Hueng, 2019. "Nowcasting GDP Growth for Small Open Economies with a Mixed-Frequency Structural Model," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 177-198, June.
    361. Chavleishvili, Sulkhan & Kremer, Manfred, 2023. "Measuring systemic financial stress and its risks for growth," Working Paper Series 2842, European Central Bank.
    362. Tjeerd M. Boonman & Jan P.A.M. Jacobs & Gerard H. Kuper, 2011. "Why didn't the Global Financial Crisis hit Latin America?," CIRANO Working Papers 2011s-63, CIRANO.
    363. Yfanti, Stavroula & Karanasos, Menelaos & Zopounidis, Constantin & Christopoulos, Apostolos, 2023. "Corporate credit risk counter-cyclical interdependence: A systematic analysis of cross-border and cross-sector correlation dynamics," European Journal of Operational Research, Elsevier, vol. 304(2), pages 813-831.
    364. Hasan, Md. Tanvir, 2022. "The sum of all SCARES COVID-19 sentiment and asset return," The Quarterly Review of Economics and Finance, Elsevier, vol. 86(C), pages 332-346.
    365. Bevilacqua, Mattia & Tunaru, Radu & Vioto, Davide, 2023. "Options-based systemic risk, financial distress, and macroeconomic downturns," Journal of Financial Markets, Elsevier, vol. 65(C).
    366. Sirio Aramonte & Matthew Carl, 2021. "Firm-level R&D after periods of intense technological innovation: the role of investor sentiment," BIS Working Papers 916, Bank for International Settlements.
    367. Fabian Kruger & Hendrik Plett, 2022. "Prediction intervals for economic fixed-event forecasts," Papers 2210.13562, arXiv.org, revised Mar 2024.
    368. Bouri, Elie & Quinn, Barry & Sheenan, Lisa & Tang, Yayan, 2024. "Investigating extreme linkage topology in the aerospace and defence industry," International Review of Financial Analysis, Elsevier, vol. 93(C).
    369. Daniel Wochner, 2020. "Dynamic Factor Trees and Forests – A Theory-led Machine Learning Framework for Non-Linear and State-Dependent Short-Term U.S. GDP Growth Predictions," KOF Working papers 20-472, KOF Swiss Economic Institute, ETH Zurich.
    370. Guglielmo Maria Caporale & Menelaos Karanasos & Stavroula Yfanti, 2019. "Macro-Financial Linkages in the High-Frequency Domain: The Effects of Uncertainty on Realized Volatility," CESifo Working Paper Series 8000, CESifo.
    371. Qadan, Mahmoud & Aharon, David Y. & Cohen, Gil, 2020. "Everybody likes shopping, including the US capital market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    372. Held, Matthias & Kapraun, Julia & Omachel, Marcel & Thimme, Julian, 2020. "Up- and downside variance risk premia in global equity markets," Journal of Banking & Finance, Elsevier, vol. 118(C).
    373. Boungou, Whelsy & Urom, Christian, 2023. "Climate change-related risks and bank stock returns," Economics Letters, Elsevier, vol. 224(C).
    374. Syed Jawad Hussain Shahzad & Rangan Gupta & Riza Demirer & Christian Pierdzioch, 2022. "Oil shocks and directional predictability of macroeconomic uncertainties of developed economies: Evidence from high‐frequency data†," Scottish Journal of Political Economy, Scottish Economic Society, vol. 69(2), pages 169-185, May.
    375. Tim A Kroencke & Maik Schmeling & Andreas Schrimpf, 2015. "Global Asset Allocation Shifts," BIS Working Papers 497, Bank for International Settlements.
    376. Blasques, F. & Koopman, S.J. & Mallee, M. & Zhang, Z., 2016. "Weighted maximum likelihood for dynamic factor analysis and forecasting with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 405-417.
    377. Beber, Alessandro & Brandt, Michael & Luisi, Maurizio, 2013. "Economic Cycles and Expected Stock Returns," CEPR Discussion Papers 9528, C.E.P.R. Discussion Papers.
    378. Aramonte, Sirio, 2014. "Macroeconomic uncertainty and the cross-section of option returns," Journal of Financial Markets, Elsevier, vol. 21(C), pages 25-49.
    379. Fatima Mboup, 2023. "Economic Activity by Race," Working Papers 23-16, Federal Reserve Bank of Philadelphia.
    380. Leippold, Markus & Yang, Hanlin, 2019. "Particle filtering, learning, and smoothing for mixed-frequency state-space models," Econometrics and Statistics, Elsevier, vol. 12(C), pages 25-41.
    381. Thomas J. Weinandy & Michael J. Ryan, 2021. "Flexible Ubers and Fixed Taxis: the Effect of Fuel Prices on Car Services," Journal of Industry, Competition and Trade, Springer, vol. 21(2), pages 139-168, June.
    382. Chollete, Loran & Ismailescu, Iuliana & Lu, Ching-Chih, 2014. "Dependence between Extreme Events in the Real and Financial Sectors," UiS Working Papers in Economics and Finance 2014/12, University of Stavanger.
    383. Živilė Tunčikienė & Rolandas Drejeris, 2017. "Assessment of business conditions benevolence: case of occupational safety and health services," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 18(3), pages 505-520, May.

  46. Francis X. Diebold & Lei Ji & Canlin Li, 2006. "A Three-Factor Yield Curve Model: Non-Affine Structure, Systematic Risk Sources, and Generalized Duration," PIER Working Paper Archive 06-017, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.

    Cited by:

    1. Cláudia Simões & Luís Oliveira & Jorge M. Bravo, 2021. "Immunization Strategies for Funding Multiple Inflation-Linked Retirement Income Benefits," Risks, MDPI, vol. 9(4), pages 1-28, March.
    2. Coroneo, Laura & Nyholm, Ken & Vidova-Koleva, Rositsa, 2008. "How arbitrage-free is the Nelson-Siegel Model?," Working Paper Series 874, European Central Bank.
    3. Baek, Seungho & Bilson, John F.O., 2015. "Size and value risk in financial firms," Journal of Banking & Finance, Elsevier, vol. 55(C), pages 295-326.
    4. Marc-Gregor Czaja & Hendrik Scholz & Marco Wilkens, 2009. "Interest rate risk of German financial institutions: the impact of level, slope, and curvature of the term structure," Review of Quantitative Finance and Accounting, Springer, vol. 33(1), pages 1-26, July.
    5. Leo Krippner, 2012. "A theoretical foundation for the Nelson and Siegel class of yield curve models," CAMA Working Papers 2012-11, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    6. Gimeno, Ricardo & Nave, Juan M., 2009. "A genetic algorithm estimation of the term structure of interest rates," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2236-2250, April.
    7. Francisco Ibáñez, 2016. "Calibrating the Dynamic Nelson-Siegel Model: A Practitioner Approach," Working Papers Central Bank of Chile 774, Central Bank of Chile.
    8. Sanjay Singh & Neeraj Hatekar, 2018. "Macroeconomic shocks and evolution of term structure of interest rate: A dynamic latent factor approach," Indian Economic Review, Springer, vol. 53(1), pages 245-262, December.
    9. Leo Krippner, 2008. "A Macroeconomic Foundation for the Nelson and Siegel Class of Yield Curve Models," Research Paper Series 226, Quantitative Finance Research Centre, University of Technology, Sydney.
    10. Ibanez, Francisco, 2015. "Calibrating the Dynamic Nelson-Siegel Model: A Practitioner Approach," MPRA Paper 68377, University Library of Munich, Germany.
    11. Md Akhtaruzzaman & Paul Docherty & Abul Shamsuddin, 2014. "Interest rate, size and book-to-market effects in Australian financial firms," Applied Economics, Taylor & Francis Journals, vol. 46(25), pages 3005-3020, September.
    12. Boril Šopov & Jakub Seidler, 2010. "Yield Curve Dynamics: Regional Common Factor Model," Working Papers IES 2010/17, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Aug 2010.
    13. Scott Mixon & Tugkan Tuzun, 2018. "Price Pressure and Price Discovery in the Term Structure of Interest Rates," Finance and Economics Discussion Series 2018-065, Board of Governors of the Federal Reserve System (U.S.).
    14. Md. Akhtaruzzaman & Abul Shamsuddin, 2017. "Australian financial firms’ exposures to the level, slope, and curvature of the interest rate term structure," Applied Economics, Taylor & Francis Journals, vol. 49(19), pages 1855-1874, April.

  47. Peter F. Christoffersen & Francis X. Diebold & Roberto S. Mariano & Anthony S. Tay & Yiu Kuen Tse, 2006. "Direction-of-Change Forecasts Based on Conditional Variance, Skewness and Kurtosis Dynamics : International Evidence," Finance Working Papers 22075, East Asian Bureau of Economic Research.

    Cited by:

    1. Stanislav Anatolyev & Nikolay Gospodinov, 2007. "Modeling Financial Return Dynamics by Decomposition," Working Papers w0095, New Economic School (NES).
    2. Papailias, Fotis & Liu, Jiadong & Thomakos, Dimitrios D., 2019. "Return Signal Momentum," QBS Working Paper Series 2019/04, Queen's University Belfast, Queen's Business School.
    3. Stelios Bekiros & Dimitris Georgoutsos, 2008. "Non-linear dynamics in financial asset returns: the predictive power of the CBOE volatility index," The European Journal of Finance, Taylor & Francis Journals, vol. 14(5), pages 397-408.
    4. M. Bigeco & E. Grosso & E. Otranto, 2008. "Recognizing and Forecasting the Sign of Financial Local Trends using Hidden Markov Models," Working Paper CRENoS 200803, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    5. Stanislav Anatolyev & Natalia Kryzhanovskaya, 2009. "Directional Prediction of Returns under Asymmetric Loss: Direct and Indirect Approaches," Working Papers w0136, Center for Economic and Financial Research (CEFIR).
    6. Luis H. R. Alvarez E. & Paavo Salminen, 2017. "Timing in the presence of directional predictability: optimal stopping of skew Brownian motion," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 86(2), pages 377-400, October.

  48. Diebold, F.X. & Kilian, L. & Nerlove, Marc, 2006. "Time Series Analysis," Working Papers 28556, University of Maryland, Department of Agricultural and Resource Economics.

    Cited by:

    1. Jeremy Berkowitz & Ionel Birgean & Lutz Kilian, 1999. "On the finite-sample accuracy of nonparametric resampling algorithms for economic time series," Finance and Economics Discussion Series 1999-04, Board of Governors of the Federal Reserve System (U.S.).
    2. Diana COCONOIU & Elena BUGUDUI, 2012. "Using Dynamic Series of Moments for Economic Analysis," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 60(4), pages 94-97, November.
    3. Thabo M. Mokoena & Gupta, R. & Van Eyden, R., 2009. "Half-Life Deviations from PPP in the South African Development Community (SADC)," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 9(1).
    4. Andersson, Fredrik N.G. & Edgerton, David L. & Opper, Sonja, 2013. "A Matter of Time: Revisiting Growth Convergence in China," World Development, Elsevier, vol. 45(C), pages 239-251.
    5. Hannes Leeb & Benedikt M. Potscher, 2003. "Can One Estimate the Conditional Distribution of Post-Model-Selection Estimators?," Cowles Foundation Discussion Papers 1444, Cowles Foundation for Research in Economics, Yale University.
    6. Grabowski, Daniel & Staszewska-Bystrova, Anna, 2018. "Skewness-Adjusted Bootstrap Confidence Intervals and Confidence Bands for Impulse Response Functions," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181590, Verein für Socialpolitik / German Economic Association.
    7. Hacker, Scott & Kim, Hyunjoo & Månsson, Kristofer, 2010. "The Relationship between Exchange Rates and Interest Rate Differentials: a Wavelet Approach," Working Paper Series in Economics and Institutions of Innovation 217, Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies.
    8. Brüggemann, Ralf, 2006. "Finite sample properties of impulse response intervals in SVECMs with long-run identifying restrictions," SFB 649 Discussion Papers 2006-021, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    9. Nyankori, James Cyprian Okuk & Wabukawo, Veronica & Sakyi-Dawson, Esther & Sefa-Dedeh, Sam, 2002. "Product Life Cycle Model Of Cowpea Based Products In Ghana," Working Papers 18803, Clemson University, Department of Agricultural and Applied Economics.
    10. Kilian, Lutz & Kim, Yun Jung, 2009. "Do Local Projections Solve the Bias Problem in Impulse Response Inference?," CEPR Discussion Papers 7266, C.E.P.R. Discussion Papers.
    11. Francis X. Diebold & Lutz Kilian, "undated". "Measuring Predictability: Theory and Macroeconomic Applications," CARESS Working Papres 97-19, University of Pennsylvania Center for Analytic Research and Economics in the Social Sciences.
    12. Atsushi Inoue & Lutz Kilian, 2002. "Bootstrapping Autoregressive Processes with Possible Unit Roots," Econometrica, Econometric Society, vol. 70(1), pages 377-391, January.
    13. Quinton Morris & Gary Van Vuuren & Paul Styger, 2009. "Further Evidence Of Long Memory In The South African Stock Market," South African Journal of Economics, Economic Society of South Africa, vol. 77(1), pages 81-101, March.
    14. Monika Blaszkiewicz-Schwartzman, 2007. "Explaining Exchange Rate Movements in New Member States of the European Union: Nominal and Real Convergence," Money Macro and Finance (MMF) Research Group Conference 2006 144, Money Macro and Finance Research Group.
    15. Madalina-Gabriela ANGHEL & Luminita Madalina CALOTA, 2016. "Statistical-econometric model used in performance analysis of the company," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 64(10), pages 33-40, October.
    16. Helmut Luetkepohl, 2007. "Econometric Analysis with Vector Autoregressive Models," Economics Working Papers ECO2007/11, European University Institute.

  49. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2005. "Volatility forecasting," CFS Working Paper Series 2005/08, Center for Financial Studies (CFS).

    Cited by:

    1. Courtenay, Roger & Clare, Andrew, 2001. "What can we learn about monetary policy transparency from financial market data?," Discussion Paper Series 1: Economic Studies 2001,06, Deutsche Bundesbank.
    2. Anthony S. Tay & Peter F. Christoffersen & Francis X. Diebold & Roberto S. Mariano & Yiu Kuen Tse, 2006. "Direction-of-Change Forecasts Based on Conditional Variance, Skewness and Kurtosis Dynamics : International Evidence," Finance Working Papers 22481, East Asian Bureau of Economic Research.
    3. Allen, P. Geoffrey & Morzuch, Bernard J., 2006. "Twenty-five years of progress, problems, and conflicting evidence in econometric forecasting. What about the next 25 years?," International Journal of Forecasting, Elsevier, vol. 22(3), pages 475-492.
    4. Olkhov, Victor, 2021. "Three Remarks On Asset Pricing," MPRA Paper 109238, University Library of Munich, Germany.
    5. David McMillan & Alan Speight, 2005. "Long-memory and heterogeneous components in high frequency Pacific-Basin exchange rate volatility," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 12(3), pages 199-226, September.
    6. Zareipour, Hamidreza & Bhattacharya, Kankar & Canizares, Claudio A., 2007. "Electricity market price volatility: The case of Ontario," Energy Policy, Elsevier, vol. 35(9), pages 4739-4748, September.
    7. Victor Olkhov, 2020. "Volatility Depends on Market Trades and Macro Theory," Papers 2008.07907, arXiv.org, revised Jun 2024.
    8. Gregory Bauer & Keith Vorkink, 2007. "Multivariate Realized Stock Market Volatility," Staff Working Papers 07-20, Bank of Canada.
    9. Lux, Thomas & Morales-Arias, Leonardo, 2010. "Relative forecasting performance of volatility models: Monte Carlo evidence," Kiel Working Papers 1582, Kiel Institute for the World Economy (IfW Kiel).
    10. Victoria Zinde-Walsh & Dongming Zhu, 2007. "Properties And Estimation Of Asymmetric Exponential Power Distribution," Departmental Working Papers 2007-11, McGill University, Department of Economics.
    11. Lux, Thomas & Morales-Arias, Leonardo, 2009. "Forecasting volatility under fractality, regime-switching, long memory and student-t innovations," Kiel Working Papers 1532, Kiel Institute for the World Economy (IfW Kiel).
    12. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Practical Volatility and Correlation Modeling for Financial Market Risk Management," NBER Working Papers 11069, National Bureau of Economic Research, Inc.
    13. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2001. "Modeling and Forecasting Realized Volatility," NBER Working Papers 8160, National Bureau of Economic Research, Inc.
    14. Eric Jacquier & Nicholas G. Polson & Peter E. Rossi, 1999. "Stochastic Volatility: Univariate and Multivariate Extensions," CIRANO Working Papers 99s-26, CIRANO.
    15. Suhejla Hoiti & Esfandiar Maasoumi & Michael McAleer & Daniel Slottje, 2005. "Measuring the Volatility in U.S. Treasury Benchmarks and Debt Instruments," DEA Working Papers 14, Universitat de les Illes Balears, Departament d'Economía Aplicada.
    16. Olkhov, Victor, 2022. "The Market-Based Asset Price Probability," MPRA Paper 115382, University Library of Munich, Germany, revised 16 Nov 2022.
    17. Andersen, Torben G. & Bollerslev, Tim & Huang, Xin, 2011. "A reduced form framework for modeling volatility of speculative prices based on realized variation measures," Journal of Econometrics, Elsevier, vol. 160(1), pages 176-189, January.
    18. Victor Olkhov, 2020. "Price, Volatility and the Second-Order Economic Theory," Papers 2009.14278, arXiv.org, revised Apr 2021.
    19. Théoret, Raymond & Racicot, François-Éric, 2010. "Forecasting stochastic Volatility using the Kalman filter: an application to Canadian Interest Rates and Price-Earnings Ratio," MPRA Paper 35911, University Library of Munich, Germany.
    20. Luisa Bisaglia & Silvano Bordignon & Francesco Lisi, 2003. "k -Factor GARMA models for intraday volatility forecasting," Applied Economics Letters, Taylor & Francis Journals, vol. 10(4), pages 251-254.
    21. Francois-Éric Racicot & Raymond Théoret, 2005. "Quelques applications du filtre de Kalman en finance: estimation et prévision de la volatilité stochastique et du rapport cours-bénéfices," RePAd Working Paper Series UQO-DSA-wp0312005, Département des sciences administratives, UQO.
    22. Matei, Marius, 2010. "Risk analysis in the evaluation of the international investment opportunities. Advances in modelling and forecasting volatility for risk assessment purposes," Working Papers of Institute for Economic Forecasting 100201, Institute for Economic Forecasting.
    23. Hao Zhou, 2003. "Itô conditional moment generator and the estimation of short rate processes," Finance and Economics Discussion Series 2003-32, Board of Governors of the Federal Reserve System (U.S.).
    24. Valeri Voev, 2009. "On the Economic Evaluation of Volatility Forecasts," CREATES Research Papers 2009-56, Department of Economics and Business Economics, Aarhus University.
    25. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Jin Wu, 2005. "A Framework for Exploring the Macroeconomic Determinants of Systematic Risk," American Economic Review, American Economic Association, vol. 95(2), pages 398-404, May.
    26. Yan-Leung Cheung & Yin-Wong Cheung & Alan T.K. Wan, 2008. "A High-Low Model of Daily Stock Price Ranges," CESifo Working Paper Series 2387, CESifo.
    27. Terasvirta, Timo, 2006. "Forecasting economic variables with nonlinear models," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 8, pages 413-457, Elsevier.
    28. Rime, Dagfinn & Sucarrat, Genaro, 2007. "Exchange rate variability, market activity and heterogeneity," UC3M Working papers. Economics we077039, Universidad Carlos III de Madrid. Departamento de Economía.
    29. Qinkai Chen & Christian-Yann Robert, 2021. "Multivariate Realized Volatility Forecasting with Graph Neural Network," Papers 2112.09015, arXiv.org, revised Dec 2021.
    30. Bauer, Gregory H. & Vorkink, Keith, 2011. "Forecasting multivariate realized stock market volatility," Journal of Econometrics, Elsevier, vol. 160(1), pages 93-101, January.
    31. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    32. Andrew Clare & Roger Courtenay, 2001. "Assessing the impact of macroeconomic news announcements on securities prices under different monetary policy regimes," Bank of England working papers 125, Bank of England.
    33. Guidolin, Massimo & Timmermann, Allan, 2006. "Term structure of risk under alternative econometric specifications," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 285-308.
    34. Milan Ficura & Jiri Witzany, 2016. "Estimating Stochastic Volatility and Jumps Using High-Frequency Data and Bayesian Methods," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 66(4), pages 278-301, August.
    35. SUCARRAT, Genaro, 2006. "The first stage in Hendry’s reduction theory revisited," LIDAM Discussion Papers CORE 2006082, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    36. Victor Olkhov, 2021. "To VaR, or Not to VaR, That is the Question," Papers 2101.08559, arXiv.org, revised Apr 2024.
    37. Torben G. Andersen & Tim Bollerslev & Per Houmann Frederiksen & Morten Ørregaard Nielsen, 2007. "Continuous-Time Models, Realized Volatilities, and Testable Distributional Implications for Daily Stock Returns," CREATES Research Papers 2007-21, Department of Economics and Business Economics, Aarhus University.
    38. Bistra Radeva, 2019. "Stock price fluctuations and GARCH modelling of stock market indexes," Economics and computer science, Publishing house "Knowledge and business" Varna, issue 3, pages 6-19.
    39. Wang, Weichen & An, Ran & Zhu, Ziwei, 2024. "Volatility prediction comparison via robust volatility proxies: An empirical deviation perspective," Journal of Econometrics, Elsevier, vol. 239(2).
    40. Sizova, Natalia, 2011. "Integrated variance forecasting: Model based vs. reduced form," Journal of Econometrics, Elsevier, vol. 162(2), pages 294-311, June.
    41. Juliusz Jabłecki & Ryszard Kokoszczyński & Paweł Sakowski & Robert Ślepaczuk & Piotr Wójcik, 2014. "Does historical volatility term structure contain valuable in-formation for predicting volatility index futures?," Working Papers 2014-18, Faculty of Economic Sciences, University of Warsaw.
    42. Veiga, Helena, 2006. "Volatility forecasts: a continuous time model versus discrete time models," DES - Working Papers. Statistics and Econometrics. WS ws062509, Universidad Carlos III de Madrid. Departamento de Estadística.
    43. Vyacheslav Abramov & Fima Klebaner, 2007. "Estimation and Prediction of a Non-Constant Volatility," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 14(1), pages 1-23, March.
    44. Robert Ślepaczuk & Grzegorz Zakrzewski, 2009. "High-Frequency and Model-Free Volatility Estimators," Working Papers 2009-13, Faculty of Economic Sciences, University of Warsaw.
    45. Ariño, Miguel A. & Canela, Miguel A., 2006. "Study of the dollar-euro exchange rate," IESE Research Papers D/620, IESE Business School, revised 30 Mar 2006.
    46. Abramov, Vyacheslav & Klebaner, Fima, 2006. "Forecasting and testing a non-constant volatility," MPRA Paper 207, University Library of Munich, Germany.
    47. Bretó, Carles & Veiga, Helena, 2011. "Forecasting volatility: does continuous time do better than discrete time?," DES - Working Papers. Statistics and Econometrics. WS ws112518, Universidad Carlos III de Madrid. Departamento de Estadística.

  50. Diebold, Francis X. & Piazzesi, Monica & Rudebusch, Glenn D., 2005. "Modeling bond yields in finance and macroeconomics," CFS Working Paper Series 2005/03, Center for Financial Studies (CFS).

    Cited by:

    1. Giuseppe Ferrero & Andrea Nobili, 2009. "Futures Contract Rates as Monetary Policy Forecasts," International Journal of Central Banking, International Journal of Central Banking, vol. 5(2), pages 109-145, June.
    2. Rafael Barros de Rezende, 2011. "Giving Flexibility to the Nelson-Siegel Class of Term Structure Models," Brazilian Review of Finance, Brazilian Society of Finance, vol. 9(1), pages 27-49.
    3. Martins, Manuel M.F. & Afonso, António, 2010. "Level, slope, curvature of the sovereign yield curve, and fiscal behaviour," Working Paper Series 1276, European Central Bank.
    4. Olesya Grishchenko & Sarah Mouabbi & Jean‐Paul Renne, 2019. "Measuring Inflation Anchoring and Uncertainty: A U.S. and Euro Area Comparison," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 51(5), pages 1053-1096, August.
    5. Hautsch, Nikolaus & Yang, Fuyu, 2010. "Bayesian inference in a stochastic volatility Nelson-Siegel Model," SFB 649 Discussion Papers 2010-004, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    6. Matteo Manera & Massimiliano Serati & Michele Plotegher, 2008. "Modeling Electricity Prices: From the State of the Art to a Draft of a New Proposal," Working Papers 2008.9, Fondazione Eni Enrico Mattei.
    7. Eric Hillebrand & Huiyu Huang & Tae-Hwy Lee & Canlin Li, 2018. "Using the Entire Yield Curve in Forecasting Output and Inflation," Econometrics, MDPI, vol. 6(3), pages 1-27, August.
    8. Stijn Claessens & M Ayhan Kose, 2018. "Frontiers of macrofinancial linkages," BIS Papers, Bank for International Settlements, number 95.
    9. René Garcia & Richard Luger, 2007. "The Canadian macroeconomy and the yield curve: an equilibrium-based approach," Canadian Journal of Economics, Canadian Economics Association, vol. 40(2), pages 561-583, May.
    10. Matsumura, Marco & Moreira, Ajax & Vicente, José, 2011. "Forecasting the yield curve with linear factor models," International Review of Financial Analysis, Elsevier, vol. 20(5), pages 237-243.
    11. Glenn D. Rudebusch & Eric T. Swanson, 2008. "Examining the bond premium puzzle with a DSGE model," Working Paper Series 2007-25, Federal Reserve Bank of San Francisco.
    12. Hautsch, Nikolaus & Ou, Yangguoyi, 2009. "Analyzing interest rate risk: Stochastic volatility in the term structure of government bond yields," CFS Working Paper Series 2009/03, Center for Financial Studies (CFS).
    13. Coroneo, Laura & Nyholm, Ken & Vidova-Koleva, Rositsa, 2008. "How arbitrage-free is the Nelson-Siegel Model?," Working Paper Series 874, European Central Bank.
    14. Carlos Garriga & Finn E. Kydland & Roman Šustek, 2019. "MoNK: Mortgages in a New-Keynesian Model," Working Papers 2019-32, Federal Reserve Bank of St. Louis.
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    2. Michael Ehrmann & Marcel Fratzscher & Roberto Rigobon, 2011. "Stocks, bonds, money markets and exchange rates: measuring international financial transmission," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 948-974, September.
    3. Mihaela NICOLAU, 2010. "Financial Markets Interactions between Economic Theory and Practice," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 2, pages 27-36.
    4. Yuko Hashimoto & Takatoshi Ito, 2009. "Effects of Japanese Macroeconomic Announcements on the Dollar/Yen Exchange Rate: High-Resolution Picture," NBER Working Papers 15020, National Bureau of Economic Research, Inc.
    5. Ielpo, Florian & Guégan, Dominique, 2006. "Further evidence on the impact of economic news on interest rates," MPRA Paper 3425, University Library of Munich, Germany, revised Jun 2007.
    6. Alenka Kavkler & Mejra Festić, 2011. "Modelling Stock Exchange Index Returns in Different GDP Growth Regimes," Prague Economic Papers, Prague University of Economics and Business, vol. 2011(1), pages 3-22.
    7. Bredin, Don & Hyde, Stuart & O'Reilly, Gerard, 2005. "European Monetary Policy Surprises: The Aggregate and Sectoral Stock Market Response," Research Technical Papers 10/RT/05, Central Bank of Ireland.
    8. Alessandro Beber & Michael W. Brandt, 2003. "The Effect of Macroeconomic News on Beliefs and Preferences: Evidence from the Options Market," NBER Working Papers 9914, National Bureau of Economic Research, Inc.
    9. Laurent E. Calvet & Adlai J. Fisher, 2005. "Multifrequency News and Stock Returns," NBER Working Papers 11441, National Bureau of Economic Research, Inc.
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    11. Mo, Henry & Wu, Liuren, 2007. "International capital asset pricing: Evidence from options," Journal of Empirical Finance, Elsevier, vol. 14(4), pages 465-498, September.
    12. Hanno Lustig & Stijn Van Nieuwerburgh, 2005. "The Returns on Human Capital: Good News on Wall Street is Bad News on Main Street," NBER Working Papers 11564, National Bureau of Economic Research, Inc.
    13. Saban Nazlioglu & Shawkat Hammoudeh & Rangan Gupta, 2015. "Volatility transmission between Islamic and conventional equity markets: evidence from causality-in-variance test," Applied Economics, Taylor & Francis Journals, vol. 47(46), pages 4996-5011, October.
    14. Victor Fang & Chien-Ting Lin & Kunaal Parbhoo, 2008. "Macroeconomic News, Business Cycles and Australian Financial Markets," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 15(3), pages 185-207, December.
    15. Fatih Ozatay & Erdal Ozmen & Gülbin Sahinbeyoglu, 2007. "Emerging Market Sovereign Spreads, Global Financial Conditions and U.S. Macroeconomic News," ERC Working Papers 0707, ERC - Economic Research Center, Middle East Technical University, revised Dec 2007.
    16. Faust, Jon & Rogers, John H. & Wang, Shing-Yi B. & Wright, Jonathan H., 2007. "The high-frequency response of exchange rates and interest rates to macroeconomic announcements," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1051-1068, May.
    17. Özer Karagedikli & Pierre L. Siklos, 2008. "Explaining Movements in the NZ Dollar - Central Bank Communication and the Surprise Element in Monetary Policy?," Reserve Bank of New Zealand Discussion Paper Series DP2008/02, Reserve Bank of New Zealand.
    18. Francis X. Diebold & Kamil Yilmaz, 2008. "Macroeconomic Volatility and Stock Market Volatility, World-Wide," PIER Working Paper Archive 08-031, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    19. Bruce Mizrach & Christopher J. Neely, 2007. "The microstructure of the U.S. treasury market," Working Papers 2007-052, Federal Reserve Bank of St. Louis.
    20. Bent Jesper Christensen & Morten Ø. Nielsen & Thomas Busch, 2008. "The Role Of Implied Volatility In Forecasting Future Realized Volatility And Jumps In Foreign Exchange, Stock, And Bond Markets," Working Paper 1181, Economics Department, Queen's University.
    21. George Tauchen & Hao Zhou, 2006. "Realized jumps on financial markets and predicting credit spreads," Finance and Economics Discussion Series 2006-35, Board of Governors of the Federal Reserve System (U.S.).
    22. Jon Wongswan, 2005. "The response of global equity indexes to U.S. monetary policy announcements," International Finance Discussion Papers 844, Board of Governors of the Federal Reserve System (U.S.).
    23. Bent Jesper Christensen & Morten Ø. Nielsen & Thomas Busch, 2006. "The Information Content Of Treasury Bond Options Concerning Future Volatility And Price Jumps," Working Paper 1188, Economics Department, Queen's University.
    24. Rui Albuquerque & Gregory Bauer & Martin Schneider, 2004. "Characterizing Asymmetric Information in International Equity Markets," International Finance 0405005, University Library of Munich, Germany.
    25. Schneider, Martin & Albuquerque, Rui & ,, 2006. "Global Private Information in International Equity Markets," CEPR Discussion Papers 5819, C.E.P.R. Discussion Papers.
    26. Patrice T. Robitaille & Jennifer E. Roush, 2006. "How do FOMC actions and U.S. macroeconomic data announcements move Brazilian sovereign yield spreads and stock prices?," International Finance Discussion Papers 868, Board of Governors of the Federal Reserve System (U.S.).
    27. Meredith J. Beechey & Jonathan H. Wright, 2008. "The high-frequency impact of news on long-term yields and forward rates: Is it real?," Finance and Economics Discussion Series 2008-39, Board of Governors of the Federal Reserve System (U.S.).
    28. Savaser, Tanseli, 2011. "Exchange rate response to macronews: Through the lens of microstructure," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 21(1), pages 107-126, February.
    29. Joshua Hausman & Jon Wongswan, 2006. "Global asset prices and FOMC announcements," International Finance Discussion Papers 886, Board of Governors of the Federal Reserve System (U.S.).
    30. Guglielmo Maria Caporale & Fabio Spagnolo & Nicola Spagnolo, 2017. "Macro News and Commodity Returns," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 22(1), pages 68-80, January.
    31. Marcel Fratzscher, 2011. "Capital Flows, Push versus Pull Factors and the Global Financial Crisis," NBER Working Papers 17357, National Bureau of Economic Research, Inc.
    32. Luc Arrondel & Hector Calvo Pardo & Xisco Oliver, 2007. "Temperant portfolio choice and background risk: evidence from France," PSE Working Papers halshs-00588069, HAL.
    33. Massimo Guidolin & Allan Timmerman, 2005. "An econometric model of nonlinear dynamics in the joint distribution of stock and bond returns," Working Papers 2005-003, Federal Reserve Bank of St. Louis.
    34. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Practical Volatility and Correlation Modeling for Financial Market Risk Management," NBER Working Papers 11069, National Bureau of Economic Research, Inc.
    35. Hashimoto, Yuko & Ito, Takatoshi, 2010. "Effects of Japanese macroeconomic statistic announcements on the dollar/yen exchange rate: High-resolution picture," Journal of the Japanese and International Economies, Elsevier, vol. 24(3), pages 334-354, September.
    36. Aron Drew & Özer Karagedikli, 2007. "Some Benefits of Monetary-Policy Transparency in New Zealand," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 57(11-12), pages 521-539, December.
    37. Christian Gourieroux & Alain Monfort & Razvan Sufana, 2005. "International Money and Stock Market Contingent Claims," Working Papers 2005-41, Center for Research in Economics and Statistics.
    38. Oikarinen, Elias, 2006. "Price Linkages between Stock, Bond and Housing Markets - Evidence from Finnish Data," Discussion Papers 1004, The Research Institute of the Finnish Economy.
    39. Alessandro Beber & Michael W. Brandt, 2009. "Resolving Macroeconomic Uncertainty in Stock and Bond Markets," Review of Finance, European Finance Association, vol. 13(1), pages 1-45.
    40. Laakkonen Helinä & Lanne Markku, 2009. "Asymmetric News Effects on Exchange Rate Volatility: Good vs. Bad News in Good vs. Bad Times," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(1), pages 1-38, December.
    41. Erenburg, Grigori & Kurov, Alexander & Lasser, Dennis J., 2006. "Trading around macroeconomic announcements: Are all traders created equal?," Journal of Financial Intermediation, Elsevier, vol. 15(4), pages 470-493, October.
    42. Christopher J. Neely, 2011. "A survey of announcement effects on foreign exchange volatility and jumps," Review, Federal Reserve Bank of St. Louis, vol. 93(Sep), pages 361-385.
    43. Tanseli Savaser, 2007. "Exchange Rate Response to Macro News: Through the Lens of Microstructure," Department of Economics Working Papers 2007-02, Department of Economics, Williams College.
    44. Christiansen, Charlotte & Ranaldo, Angelo, 2005. "Realized Bond-Stock Correlation: Macroeconomic Announcement Effects," Finance Research Group Working Papers F-2005-05, University of Aarhus, Aarhus School of Business, Department of Business Studies.
    45. Marc Simpson & Sanjay Ramchander & James Webb, 2007. "The Asymmetric Response of Equity REIT Returns to Inflation," The Journal of Real Estate Finance and Economics, Springer, vol. 34(4), pages 513-529, May.
    46. Ehrmann, Michael & Fratzscher, Marcel & Swanson, Eric & Gürkaynak, Refet S., 2007. "Convergence and anchoring of yield curves in the euro area," Working Paper Series 817, European Central Bank.
    47. P. Siklos, M. Bohl, 2006. "Policy Words and Policy Deeds: The ECB and the Euro," Working Papers eg0050, Wilfrid Laurier University, Department of Economics, revised 2006.
    48. Gropp, Reint E. & Kadareja, Arjan, 2007. "Stale information, shocks and volatility," ZEW Discussion Papers 07-012, ZEW - Leibniz Centre for European Economic Research.
    49. Andersen, Torben G. & Bollerslev, Tim & Huang, Xin, 2011. "A reduced form framework for modeling volatility of speculative prices based on realized variation measures," Journal of Econometrics, Elsevier, vol. 160(1), pages 176-189, January.
    50. Condie, Scott & Ganguli, Jayant, 2017. "The pricing effects of ambiguous private information," Journal of Economic Theory, Elsevier, vol. 172(C), pages 512-557.
    51. Guo, Hui & Savickas, Robert, 2008. "Forecasting foreign exchange rates using idiosyncratic volatility," Journal of Banking & Finance, Elsevier, vol. 32(7), pages 1322-1332, July.
    52. Stefano d'Addona & Axel H. Kind, 2005. "International Stock-Bond Correlations in a Simple Affine Asset Pricing Model," Finance 0502018, University Library of Munich, Germany.
    53. Neil Shephard & Torben G. Andersen, 2008. "Stochastic Volatility: Origins and Overview," Economics Series Working Papers 389, University of Oxford, Department of Economics.
    54. Hui Guo & Robert Savickas, 2006. "Idiosyncratic volatility, economic fundamentals, and foreign exchange rates," Working Papers 2005-025, Federal Reserve Bank of St. Louis.
    55. Bollerslev, Tim & Law, Tzuo Hann & Tauchen, George, 2008. "Risk, jumps, and diversification," Journal of Econometrics, Elsevier, vol. 144(1), pages 234-256, May.
    56. Celine Gimet & Sandra Montchaud, 2016. "What Drives European Football Clubs’ Stock Returns and Volatility?," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 23(3), pages 351-390, September.
    57. Enzo Weber, 2010. "Volatility and causality in Asia Pacific financial markets," Applied Financial Economics, Taylor & Francis Journals, vol. 20(16), pages 1269-1292.
    58. Troy Davig & Jeffrey R. Gerlach, 2006. "Monetary Policy, the Bond Market, and Changes in FOMC Communication Policy," Working Papers 31, Department of Economics, College of William and Mary.
    59. Michael Ehrmann & Marcel Fratzscher, 2009. "Global Financial Transmission of Monetary Policy Shocks," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(6), pages 739-759, December.
    60. Pietro Cova & Mr. Alessandro Rebucci & Mr. Akito Matsumoto & Massimiliano Pisani, 2008. "New Shocks, Exchange Rates and Equity Prices," IMF Working Papers 2008/284, International Monetary Fund.
    61. Paola Paiardini, 2010. "The Price Impact of Economic News, Private Information and Trading Intensity," Birkbeck Working Papers in Economics and Finance 1011, Birkbeck, Department of Economics, Mathematics & Statistics.
    62. Andersson, Magnus & Hansen, Lars Jul & Sebestyén, Szabolcs, 2006. "Which news moves the euro area bond market?," Working Paper Series 631, European Central Bank.
    63. Mizrach, Bruce & Neely, Christopher J., 2008. "Information shares in the US Treasury market," Journal of Banking & Finance, Elsevier, vol. 32(7), pages 1221-1233, July.
    64. Robert F. Engle & Jose Gonzalo Rangel, 2005. "The Spline GARCH Model for Unconditional Volatility and its Global Macroeconomic Causes," Working Papers 2005/13, Czech National Bank, Research and Statistics Department.
    65. Alexandr Èerný & Michal Koblas, 2008. "Stock Market Integration and the Speed of Information Transmission," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 58(01-02), pages 2-20, January.
    66. Lindner Axel, 2009. "Evaluating Communication Strategies for Public Agencies: Transparency, Opacity, and Secrecy," The B.E. Journal of Macroeconomics, De Gruyter, vol. 9(1), pages 1-18, July.
    67. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 701-720, November.
    68. Torben G. Andersen & Luca Benzoni, 2010. "Do Bonds Span Volatility Risk in the U.S. Treasury Market? A Specification Test for Affine Term Structure Models," Journal of Finance, American Finance Association, vol. 65(2), pages 603-653, April.
    69. Helena Chuliá & Hipòlit Torró, 2008. "The economic value of volatility transmission between the stock and bond markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 28(11), pages 1066-1094, November.
    70. Weihua Shi & Larry Eisenberg & Cheng-few Lee, 2009. "Intraday Patterns, Announcement Effects, and Volatility Persistence in the Japanese Government Bond Futures Market," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 12(01), pages 63-85.
    71. George Jiang & Ingrid Lo & Adrien Verdelhan, 2008. "Information Shocks, Jumps, and Price Discovery -- Evidence from the U.S. Treasury Market," Staff Working Papers 08-22, Bank of Canada.
    72. Albuquerque, Rui & Vega, Clara, 2006. "Asymmetric Information in the Stock Market: Economic News and Co-movement," CEPR Discussion Papers 5598, C.E.P.R. Discussion Papers.
    73. Tamim Bayoumi & Andrew Swiston, 2010. "The Ties that Bind: Measuring International Bond Spillovers Using Inflation-Indexed Bond Yields," IMF Staff Papers, Palgrave Macmillan, vol. 57(2), pages 366-406, June.
    74. Herrmann, Klaus & Teis, Stefan & Yu, Weijun, 2014. "Components of intraday volatility and their prediction at different sampling frequencies with application to DAX and BUND futures," FAU Discussion Papers in Economics 15/2014, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    75. Gregory H. Bauer & Clara Vega, 2006. "The monetary origins of asymmetric information in international equity markets," International Finance Discussion Papers 872, Board of Governors of the Federal Reserve System (U.S.).
    76. Paolo Pasquariello & Clara Vega, 2006. "Informed and strategic order flow in the bond markets," International Finance Discussion Papers 874, Board of Governors of the Federal Reserve System (U.S.).
    77. Lindner, Axel, 2008. "Evaluating communication strategies for public agencies: transparency, opacity, and secrecy," IWH Discussion Papers 8/2008, Halle Institute for Economic Research (IWH).
    78. Apergis, Nicholas, 2015. "The role of FOMC minutes for US asset prices before and after the 2008 crisis: Evidence from GARCH volatility modeling," The Quarterly Review of Economics and Finance, Elsevier, vol. 55(C), pages 100-107.
    79. Viceira, Luis M., 2012. "Bond risk, bond return volatility, and the term structure of interest rates," International Journal of Forecasting, Elsevier, vol. 28(1), pages 97-117.
    80. Tuysuz, Sukriye, 2007. "The asymmetric impact of macroeconomic announcements on U.S. Government bond rate level and volatility," MPRA Paper 5381, University Library of Munich, Germany.
    81. Bent Jesper Christensen & Morten Ørregaard Nielsen, 2007. "The Effect of Long Memory in Volatility on Stock Market Fluctuations," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 684-700, November.
    82. Andersen, Torben G. & Bollerslev, Tim & Dobrev, Dobrislav, 2007. "No-arbitrage semi-martingale restrictions for continuous-time volatility models subject to leverage effects, jumps and i.i.d. noise: Theory and testable distributional implications," Journal of Econometrics, Elsevier, vol. 138(1), pages 125-180, May.
    83. Laakkonen, Helinä, 2004. "The impact of macroeconomic news on exchange rate volatility," Bank of Finland Research Discussion Papers 24/2004, Bank of Finland.
    84. Bent Jesper Christensen & Morten Ø. Nielsen, 2005. "The Implied-realized Volatility Relation With Jumps In Underlying Asset Prices," Working Paper 1186, Economics Department, Queen's University.
    85. Ghent, Andra, 2007. "Why do markets react badly to good news? Evidence from Fed Funds Futures," MPRA Paper 1708, University Library of Munich, Germany.
    86. Jon Faust & Jonathan H. Wright, 2008. "Efficient Prediction of Excess Returns," NBER Working Papers 14169, National Bureau of Economic Research, Inc.
    87. Jeffrey R. Gerlach, 2007. "Macroeconomic News And Stock Market Calendar And Weather Anomalies," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 30(2), pages 283-300, June.

  52. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2005. "Practical volatility and correlation modeling for financial market risk management," CFS Working Paper Series 2005/02, Center for Financial Studies (CFS).

    Cited by:

    1. Jean-Paul Laurent & Hassan Omidi Firouzi, 2022. "Market Risk and Volatility Weighted Historical Simulation After Basel III," Working Papers hal-03679434, HAL.
    2. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    3. Olkhov, Victor, 2022. "Introduction of the Market-Based Price Autocorrelation," MPRA Paper 112003, University Library of Munich, Germany.
    4. Li, Leon, 2017. "Testing and comparing the performance of dynamic variance and correlation models in value-at-risk estimation," The North American Journal of Economics and Finance, Elsevier, vol. 40(C), pages 116-135.
    5. Sofiane Aboura & Julien Chevallier, 2014. "Cross-Market Spillovers with ‘Volatility Surprise’," EconomiX Working Papers 2014-46, University of Paris Nanterre, EconomiX.
    6. Gregory Bauer & Keith Vorkink, 2007. "Multivariate Realized Stock Market Volatility," Staff Working Papers 07-20, Bank of Canada.
    7. Norman R. Swanson & Valentina Corradi & Walter Distaso, 2011. "Predictive Inference for Integrated Volatility," Departmental Working Papers 201109, Rutgers University, Department of Economics.
    8. Francis X. Diebold & Kamil Yilmaz, 2008. "Macroeconomic Volatility and Stock Market Volatility, World-Wide," PIER Working Paper Archive 08-031, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    9. Othmar M. Lehner, 2013. "Crowdfunding social ventures: a model and research agenda," Venture Capital, Taylor & Francis Journals, vol. 15(4), pages 289-311, October.
    10. Erie Febrian & Aldrin Herwany, 2010. "Volatility Forecasting Models and Market Co-Integration: A Study on South-East Asian Markets," Working Papers in Business, Management and Finance 201005, Department of Management and Business, Padjadjaran University, revised May 2010.
    11. O’Brien, James & Szerszeń, Paweł J., 2017. "An evaluation of bank measures for market risk before, during and after the financial crisis," Journal of Banking & Finance, Elsevier, vol. 80(C), pages 215-234.
    12. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2005. "Volatility forecasting," CFS Working Paper Series 2005/08, Center for Financial Studies (CFS).
    13. Christophe Perignon & D. Smith, 2009. "The Level and Quality of Value-at-Risk Disclosure by Commercial Banks," Post-Print hal-00496102, HAL.
    14. James M. O'Brien & Pawel J. Szerszen, 2014. "An Evaluation of Bank VaR Measures for Market Risk During and Before the Financial Crisis," Finance and Economics Discussion Series 2014-21, Board of Governors of the Federal Reserve System (U.S.).
    15. Francis X. Diebold & Kamil Yilmaz, 2010. "Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers," Koç University-TUSIAD Economic Research Forum Working Papers 1001, Koc University-TUSIAD Economic Research Forum, revised Mar 2010.
    16. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Jin Wu, 2005. "A Framework for Exploring the Macroeconomic Determinants of Systematic Risk," American Economic Review, American Economic Association, vol. 95(2), pages 398-404, May.
    17. Erie Febrian & Aldrin Herwany, 2010. "Forecasting Stocks of Government Owned Companies (GOCS):Volatility Modeling," Working Papers in Business, Management and Finance 201002, Department of Management and Business, Padjadjaran University, revised Feb 2010.
    18. David S. Bates, 2009. "U.S. Stock Market Crash Risk, 1926-2006," NBER Working Papers 14913, National Bureau of Economic Research, Inc.
    19. Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), 2008. "Handbook on Information Technology in Finance," International Handbooks on Information Systems, Springer, number 978-3-540-49487-4, June.
    20. Gaisser, Sandra & Memmel, Christoph & Schmidt, Rafael & Wehn, Carsten, 2009. "Time dynamic and hierarchical dependence modelling of an aggregated portfolio of trading books: a multivariate nonparametric approach," Discussion Paper Series 2: Banking and Financial Studies 2009,07, Deutsche Bundesbank.
    21. Erie Febrian & Aldrin Herwany, 2009. "Volatility Model for Financial Market Risk Management : An Analysis on JSX Index Return Covariance Matrix," Working Papers in Economics and Development Studies (WoPEDS) 200907, Department of Economics, Padjadjaran University, revised Sep 2009.
    22. Bibinger, Markus & Winkelmann, Lars, 2014. "Common price and volatility jumps in noisy high-frequency data," SFB 649 Discussion Papers 2014-037, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    23. de Almeida, Daniel & Hotta, Luiz K. & Ruiz, Esther, 2018. "MGARCH models: Trade-off between feasibility and flexibility," International Journal of Forecasting, Elsevier, vol. 34(1), pages 45-63.
    24. McAleer, Michael & Medeiros, Marcelo C., 2008. "A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries," Journal of Econometrics, Elsevier, vol. 147(1), pages 104-119, November.
    25. Yoontae Jeon & Thomas H. McCurdy, 2017. "Time-Varying Window Length for Correlation Forecasts," Econometrics, MDPI, vol. 5(4), pages 1-29, December.
    26. Marc S. Paolella, 2017. "The Univariate Collapsing Method for Portfolio Optimization," Econometrics, MDPI, vol. 5(2), pages 1-33, May.
    27. Zhou, Xinmiao & Qian, Huanhuan & Pérez-Rodríguez, Jorge. V. & González López-Valcárcel, Beatriz, 2020. "Risk dependence and cointegration between pharmaceutical stock markets: The case of China and the USA," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    28. Jondeau, Eric, 2015. "The dynamics of squared returns under contemporaneous aggregation of GARCH models," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 80-93.
    29. Ghorbel, Ahmed & Trabelsi, Abdelwahed, 2007. "Predictive Performance of Conditional Extreme Value Theory and Conventional Methods in Value at Risk Estimation," MPRA Paper 3963, University Library of Munich, Germany.
    30. Faheem Aslam & Paulo Ferreira & Khurrum Shahzad Mughal & Beenish Bashir, 2021. "Intraday Volatility Spillovers among European Financial Markets during COVID-19," IJFS, MDPI, vol. 9(1), pages 1-19, January.
    31. Bates, David S., 2012. "U.S. stock market crash risk, 1926–2010," Journal of Financial Economics, Elsevier, vol. 105(2), pages 229-259.
    32. Barbara Bedowska-Sojka, 2017. "Evaluating the Accuracy of Time-varying Beta. The Evidence from Poland," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 17, pages 161-176.
    33. Krahnen, Jan-Pieter & Wilde, Christian, 2006. "Risk Transfer with CDOs and Systemic Risk in Banking," CEPR Discussion Papers 5618, C.E.P.R. Discussion Papers.
    34. Eric Hillebrand & Marcelo Cunha Medeiros, 2010. "Asymmetries, breaks, and long-range dependence: An estimation framework for daily realized volatility," Textos para discussão 578, Department of Economics PUC-Rio (Brazil).
    35. Gao, Jun & Gao, Xiang & Gu, Chen, 2023. "Forecasting European stock volatility: The role of the UK," International Review of Financial Analysis, Elsevier, vol. 89(C).
    36. Eric Jondeau, 2008. "Contemporaneous Aggregation of GARCH Models and Evaluation of the Aggregation Bias," Swiss Finance Institute Research Paper Series 08-06, Swiss Finance Institute.

  53. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2005. "Practical volatility and correlation modeling for financial market risk management," CFS Working Paper Series 2005/02, Center for Financial Studies (CFS).

    Cited by:

    1. Jean-Paul Laurent & Hassan Omidi Firouzi, 2022. "Market Risk and Volatility Weighted Historical Simulation After Basel III," Working Papers hal-03679434, HAL.
    2. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    3. Olkhov, Victor, 2022. "Introduction of the Market-Based Price Autocorrelation," MPRA Paper 112003, University Library of Munich, Germany.
    4. Li, Leon, 2017. "Testing and comparing the performance of dynamic variance and correlation models in value-at-risk estimation," The North American Journal of Economics and Finance, Elsevier, vol. 40(C), pages 116-135.
    5. Sofiane Aboura & Julien Chevallier, 2014. "Cross-Market Spillovers with ‘Volatility Surprise’," EconomiX Working Papers 2014-46, University of Paris Nanterre, EconomiX.
    6. Gregory Bauer & Keith Vorkink, 2007. "Multivariate Realized Stock Market Volatility," Staff Working Papers 07-20, Bank of Canada.
    7. Norman R. Swanson & Valentina Corradi & Walter Distaso, 2011. "Predictive Inference for Integrated Volatility," Departmental Working Papers 201109, Rutgers University, Department of Economics.
    8. Francis X. Diebold & Kamil Yilmaz, 2008. "Macroeconomic Volatility and Stock Market Volatility, World-Wide," PIER Working Paper Archive 08-031, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    9. Othmar M. Lehner, 2013. "Crowdfunding social ventures: a model and research agenda," Venture Capital, Taylor & Francis Journals, vol. 15(4), pages 289-311, October.
    10. Erie Febrian & Aldrin Herwany, 2010. "Volatility Forecasting Models and Market Co-Integration: A Study on South-East Asian Markets," Working Papers in Business, Management and Finance 201005, Department of Management and Business, Padjadjaran University, revised May 2010.
    11. O’Brien, James & Szerszeń, Paweł J., 2017. "An evaluation of bank measures for market risk before, during and after the financial crisis," Journal of Banking & Finance, Elsevier, vol. 80(C), pages 215-234.
    12. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2005. "Volatility forecasting," CFS Working Paper Series 2005/08, Center for Financial Studies (CFS).
    13. Christophe Perignon & D. Smith, 2009. "The Level and Quality of Value-at-Risk Disclosure by Commercial Banks," Post-Print hal-00496102, HAL.
    14. James M. O'Brien & Pawel J. Szerszen, 2014. "An Evaluation of Bank VaR Measures for Market Risk During and Before the Financial Crisis," Finance and Economics Discussion Series 2014-21, Board of Governors of the Federal Reserve System (U.S.).
    15. Francis X. Diebold & Kamil Yilmaz, 2010. "Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers," Koç University-TUSIAD Economic Research Forum Working Papers 1001, Koc University-TUSIAD Economic Research Forum, revised Mar 2010.
    16. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Jin Wu, 2005. "A Framework for Exploring the Macroeconomic Determinants of Systematic Risk," American Economic Review, American Economic Association, vol. 95(2), pages 398-404, May.
    17. Erie Febrian & Aldrin Herwany, 2010. "Forecasting Stocks of Government Owned Companies (GOCS):Volatility Modeling," Working Papers in Business, Management and Finance 201002, Department of Management and Business, Padjadjaran University, revised Feb 2010.
    18. David S. Bates, 2009. "U.S. Stock Market Crash Risk, 1926-2006," NBER Working Papers 14913, National Bureau of Economic Research, Inc.
    19. Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), 2008. "Handbook on Information Technology in Finance," International Handbooks on Information Systems, Springer, number 978-3-540-49487-4, June.
    20. Gaisser, Sandra & Memmel, Christoph & Schmidt, Rafael & Wehn, Carsten, 2009. "Time dynamic and hierarchical dependence modelling of an aggregated portfolio of trading books: a multivariate nonparametric approach," Discussion Paper Series 2: Banking and Financial Studies 2009,07, Deutsche Bundesbank.
    21. Erie Febrian & Aldrin Herwany, 2009. "Volatility Model for Financial Market Risk Management : An Analysis on JSX Index Return Covariance Matrix," Working Papers in Economics and Development Studies (WoPEDS) 200907, Department of Economics, Padjadjaran University, revised Sep 2009.
    22. Bibinger, Markus & Winkelmann, Lars, 2014. "Common price and volatility jumps in noisy high-frequency data," SFB 649 Discussion Papers 2014-037, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    23. de Almeida, Daniel & Hotta, Luiz K. & Ruiz, Esther, 2018. "MGARCH models: Trade-off between feasibility and flexibility," International Journal of Forecasting, Elsevier, vol. 34(1), pages 45-63.
    24. McAleer, Michael & Medeiros, Marcelo C., 2008. "A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries," Journal of Econometrics, Elsevier, vol. 147(1), pages 104-119, November.
    25. Yoontae Jeon & Thomas H. McCurdy, 2017. "Time-Varying Window Length for Correlation Forecasts," Econometrics, MDPI, vol. 5(4), pages 1-29, December.
    26. Marc S. Paolella, 2017. "The Univariate Collapsing Method for Portfolio Optimization," Econometrics, MDPI, vol. 5(2), pages 1-33, May.
    27. Zhou, Xinmiao & Qian, Huanhuan & Pérez-Rodríguez, Jorge. V. & González López-Valcárcel, Beatriz, 2020. "Risk dependence and cointegration between pharmaceutical stock markets: The case of China and the USA," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    28. Jondeau, Eric, 2015. "The dynamics of squared returns under contemporaneous aggregation of GARCH models," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 80-93.
    29. Ghorbel, Ahmed & Trabelsi, Abdelwahed, 2007. "Predictive Performance of Conditional Extreme Value Theory and Conventional Methods in Value at Risk Estimation," MPRA Paper 3963, University Library of Munich, Germany.
    30. Faheem Aslam & Paulo Ferreira & Khurrum Shahzad Mughal & Beenish Bashir, 2021. "Intraday Volatility Spillovers among European Financial Markets during COVID-19," IJFS, MDPI, vol. 9(1), pages 1-19, January.
    31. Bates, David S., 2012. "U.S. stock market crash risk, 1926–2010," Journal of Financial Economics, Elsevier, vol. 105(2), pages 229-259.
    32. Barbara Bedowska-Sojka, 2017. "Evaluating the Accuracy of Time-varying Beta. The Evidence from Poland," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 17, pages 161-176.
    33. Krahnen, Jan-Pieter & Wilde, Christian, 2006. "Risk Transfer with CDOs and Systemic Risk in Banking," CEPR Discussion Papers 5618, C.E.P.R. Discussion Papers.
    34. Eric Hillebrand & Marcelo Cunha Medeiros, 2010. "Asymmetries, breaks, and long-range dependence: An estimation framework for daily realized volatility," Textos para discussão 578, Department of Economics PUC-Rio (Brazil).
    35. Gao, Jun & Gao, Xiang & Gu, Chen, 2023. "Forecasting European stock volatility: The role of the UK," International Review of Financial Analysis, Elsevier, vol. 89(C).
    36. Eric Jondeau, 2008. "Contemporaneous Aggregation of GARCH Models and Evaluation of the Aggregation Bias," Swiss Finance Institute Research Paper Series 08-06, Swiss Finance Institute.

  54. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2005. "Volatility forecasting," CFS Working Paper Series 2005/08, Center for Financial Studies (CFS).

    Cited by:

    1. Courtenay, Roger & Clare, Andrew, 2001. "What can we learn about monetary policy transparency from financial market data?," Discussion Paper Series 1: Economic Studies 2001,06, Deutsche Bundesbank.
    2. Anthony S. Tay & Peter F. Christoffersen & Francis X. Diebold & Roberto S. Mariano & Yiu Kuen Tse, 2006. "Direction-of-Change Forecasts Based on Conditional Variance, Skewness and Kurtosis Dynamics : International Evidence," Finance Working Papers 22481, East Asian Bureau of Economic Research.
    3. Allen, P. Geoffrey & Morzuch, Bernard J., 2006. "Twenty-five years of progress, problems, and conflicting evidence in econometric forecasting. What about the next 25 years?," International Journal of Forecasting, Elsevier, vol. 22(3), pages 475-492.
    4. Olkhov, Victor, 2021. "Three Remarks On Asset Pricing," MPRA Paper 109238, University Library of Munich, Germany.
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    5. Hautsch, Nikolaus & Yang, Fuyu, 2010. "Bayesian inference in a stochastic volatility Nelson-Siegel Model," SFB 649 Discussion Papers 2010-004, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
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    97. Zeno Rotondi, 2006. "The Macroeconomy and the Yield Curve: A Review of the Literature with Some New Evidence," Giornale degli Economisti, GDE (Giornale degli Economisti e Annali di Economia), Bocconi University, vol. 65(2), pages 193-224, November.
    98. Timmermann, Allan & Guidolin, Massimo, 2007. "Forecasts of US Short-term Interest Rates: A Flexible Forecast Combination Approach," CEPR Discussion Papers 6188, C.E.P.R. Discussion Papers.
    99. Markus Demary, 2017. "Yield curve responses to market sentiments and monetary policy," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 12(2), pages 309-344, July.
    100. Jean-Paul Renne, 2009. "Frequency-domain analysis of debt service in a macro-finance model for the euro area," Working papers 261, Banque de France.
    101. Leite, André Luís & Filho, Romeu Braz Pereira Gomes & Vicente, José Valentim Machado, 2010. "Forecasting the yield curve: A statistical model with market survey data," International Review of Financial Analysis, Elsevier, vol. 19(2), pages 108-112, March.
    102. Bernaschi, Massimo & Tacconi, Elisa & Vergni, Davide, 2008. "A parametric study of the term structure dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(5), pages 1264-1272.
    103. Mr. Carlos I. Medeiros & Ying He, 2011. "An Assessment of Estimates of Term Structure Models for the United States," IMF Working Papers 2011/247, International Monetary Fund.
    104. Bennouna, Hicham, 2019. "Interest rate pass-through in Morocco: Evidence from bank-level survey data," Economic Modelling, Elsevier, vol. 80(C), pages 142-157.
    105. Koo, Bonsoo & La Vecchia, Davide & Linton, Oliver, 2021. "Estimation of a nonparametric model for bond prices from cross-section and time series information," Journal of Econometrics, Elsevier, vol. 220(2), pages 562-588.
    106. Emrah Ahi & Vedat Akgiray & Emrah Sener, 2018. "Robust term structure estimation in developed and emerging markets," Annals of Operations Research, Springer, vol. 260(1), pages 23-49, January.
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    108. Jing Yuan & Yan Peng & Zongwu Cai & Zhengyi Zhang, 2022. "A Quantitative Evaluation of Interest Rate Liberalization Reform in China," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202214, University of Kansas, Department of Economics.
    109. Bansal, Naresh & Connolly, Robert A. & Stivers, Chris, 2015. "Equity volatility as a determinant of future term-structure volatility," Journal of Financial Markets, Elsevier, vol. 25(C), pages 33-51.
    110. Marcos S. Matsumura & Ajax R. B. Moreira, 2006. "Macro Factors and the Brazilian Yield Curve With no Arbitrage Models," Discussion Papers 1210, Instituto de Pesquisa Econômica Aplicada - IPEA.
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    112. J.Marcelo Ochoa, 2006. "An interpretation of an affine term structure model of Chile," Estudios de Economia, University of Chile, Department of Economics, vol. 33(2 Year 20), pages 155-184, December.
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    117. Alexey Akimov & Simon Stevenson & Maxim Zagonov, 2015. "Public Real Estate and the Term Structure of Interest Rates: A Cross-Country Study," The Journal of Real Estate Finance and Economics, Springer, vol. 51(4), pages 503-540, November.
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    121. Marcos S. Matsumura & Ajax R. B. Moreira, 2015. "Macro Factors and the Brazilian Yield Curve with no Arbitrage Models," Discussion Papers 0171, Instituto de Pesquisa Econômica Aplicada - IPEA.
    122. Oliver Blaskowitz & Helmut Herwartz, 2009. "Adaptive forecasting of the EURIBOR swap term structure," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(7), pages 575-594.
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  56. Campbell, Sean D. & Diebold, Francis X., 2005. "Stock returns and expected business conditions: Half a century of direct evidence," CFS Working Paper Series 2005/22, Center for Financial Studies (CFS).

    Cited by:

    1. Park, Sunjin, 2022. "Heterogeneous beliefs in macroeconomic growth prospects and the carry risk premium," Journal of Banking & Finance, Elsevier, vol. 136(C).
    2. Riccardo Colacito & Eric Ghysels & Jinghan Meng & Wasin Siwasarit, 2016. "Skewness in Expected Macro Fundamentals and the Predictability of Equity Returns: Evidence and Theory," The Review of Financial Studies, Society for Financial Studies, vol. 29(8), pages 2069-2109.
    3. Paul Ehling & Christian Heyerdahl-Larsen, 2017. "Correlations," Management Science, INFORMS, vol. 63(6), pages 1919-1937, June.
    4. M. Barari & Brian Lucey & S. Voronkova, 2008. "Reassessing co-movements among G7 equity markets: evidence from iShares," Applied Financial Economics, Taylor & Francis Journals, vol. 18(11), pages 863-877.
    5. Conrad, Christian & Loch, Karin, 2012. "Anticipating Long-Term Stock Market Volatility," Working Papers 0535, University of Heidelberg, Department of Economics.
    6. Krainer, Robert E., 2017. "Economic stability under alternative banking systems: Theory and policy," Journal of Financial Stability, Elsevier, vol. 31(C), pages 107-118.
    7. Mikael C. Bergbrant & Patrick J. Kelly, 2015. "Macroeconomic Expectations and the Size, Value and Momentum Factors," Working Papers w0214, New Economic School (NES).
    8. Morana, Claudio, 2014. "Insights on the global macro-finance interface: Structural sources of risk factor fluctuations and the cross-section of expected stock returns," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 64-79.
    9. Conrad, Christian & Schoelkopf, Julius Theodor & Tushteva, Nikoleta, 2023. "Long-Term Volatility Shapes the Stock Market’s Sensitivity to News," Working Papers 0739, University of Heidelberg, Department of Economics.
    10. Diebold, Francis X. & Yilmaz, Kamil, 2015. "Financial and Macroeconomic Connectedness: A Network Approach to Measurement and Monitoring," OUP Catalogue, Oxford University Press, number 9780199338306, Decembrie.
    11. Bevilacqua, Mattia & Morelli, David & Tunaru, Radu, 2019. "The determinants of the model-free positive and negative volatilities," Journal of International Money and Finance, Elsevier, vol. 92(C), pages 1-24.
    12. Chava, Sudheer & Gallmeyer, Michael & Park, Heungju, 2015. "Credit conditions and stock return predictability," Journal of Monetary Economics, Elsevier, vol. 74(C), pages 117-132.
    13. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2011. "Financial Risk Measurement for Financial Risk Management," PIER Working Paper Archive 11-037, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    14. Turan G. Bali & Hao Zhou, 2011. "Risk, uncertainty, and expected returns," Finance and Economics Discussion Series 2011-45, Board of Governors of the Federal Reserve System (U.S.).
    15. van den Hauwe, Sjoerd & Paap, Richard & van Dijk, Dick, 2013. "Bayesian forecasting of federal funds target rate decisions," Journal of Macroeconomics, Elsevier, vol. 37(C), pages 19-40.
    16. Aleksandar M. Velkoski, 2015. "Restaurant Consumption as an Economic Indicator," Tourism Economics, , vol. 21(2), pages 325-339, April.
    17. Sousa, João & Sousa, Ricardo M., 2017. "Predicting risk premium under changes in the conditional distribution of stock returns," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 50(C), pages 204-218.
    18. Shamsi Zamenjani, Azam, 2021. "Do financial variables help predict the conditional distribution of the market portfolio?," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 327-345.
    19. Cem Cakmakli & Dick van Dijk, 2010. "Getting the Most out of Macroeconomic Information for Predicting Stock Returns and Volatility," Tinbergen Institute Discussion Papers 10-115/4, Tinbergen Institute.
    20. Prabheesh, K.P. & Vidya, C.T., 2018. "Do business cycles, investment-specific technology shocks matter for stock returns?," Economic Modelling, Elsevier, vol. 70(C), pages 511-524.
    21. Safari, Meysam & TahmooresPour, Reza, 2011. "Moderation Effect of Market Condition on the Relationship between Dividend Yield and Stock Return," MPRA Paper 28913, University Library of Munich, Germany.
    22. Golinski, Adam & Madeira, Joao & Rambaccussing, Dooruj, 2014. "Fractional Integration of the Price-Dividend Ratio in a Present-Value Model of Stock Prices," SIRE Discussion Papers 2015-79, Scottish Institute for Research in Economics (SIRE).
    23. Arjan Berkelaar & Roy Kouwenberg, 2011. "A Liability-Relative Drawdown Approach to Pension Asset Liability Management," Palgrave Macmillan Books, in: Gautam Mitra & Katharina Schwaiger (ed.), Asset and Liability Management Handbook, chapter 14, pages 352-382, Palgrave Macmillan.
    24. Kirby, Chris, 2019. "The value premium and expected business conditions," Finance Research Letters, Elsevier, vol. 30(C), pages 360-366.
    25. Lindblad, Annika, 2017. "Sentiment indicators and macroeconomic data as drivers for low-frequency stock market volatility," MPRA Paper 80266, University Library of Munich, Germany.
    26. Serhan Cevik & João Tovar Jalles, 2020. "This Changes Everything: Climate Shocks and Sovereign Bonds," Working Papers REM 2020/0132, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    27. Chelikani, Surya & Marks, Joseph M. & Nam, Kiseok, 2024. "State-dependent intertemporal risk-return tradeoff: Further evidence," Journal of Economics and Business, Elsevier, vol. 130(C).
    28. Lustig, Hanno & Verdelhan, Adrien, 2012. "Business cycle variation in the risk-return trade-off," Journal of Monetary Economics, Elsevier, vol. 59(S), pages 35-49.
    29. Nonejad, Nima, 2017. "Forecasting aggregate stock market volatility using financial and macroeconomic predictors: Which models forecast best, when and why?," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 131-154.
    30. Reitz, Stefan & Pierdzioch, Christian & Rülke, Jan-Christoph, 2015. "Nonlinear Expectation Formation in the U.S. Stock Market," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113210, Verein für Socialpolitik / German Economic Association.
    31. John G Powell & Sirimon Treepongkaruna, 2012. "Recession fears as self-fulfilling prophecies? Influence on stock returns and output," Australian Journal of Management, Australian School of Business, vol. 37(2), pages 231-260, August.
    32. Abdulilah Ibrahim Alsheikhmubarak & Evangelos Giouvris, 2018. "A Comparative GARCH Analysis of Macroeconomic Variables and Returns on Modelling the Kurtosis of FTSE 100 Implied Volatility Index," Multinational Finance Journal, Multinational Finance Journal, vol. 22(3-4), pages 119-172, September.
    33. Bonga-Bonga, Lumengo & Mwamba, Muteba, 2015. "A multivariate model for the prediction of stock returns in an emerging market: A comparison of parametric and non-parametric models," MPRA Paper 62028, University Library of Munich, Germany.
    34. Pierdzioch, Christian & Reitz, Stefan & Ruelke, Jan-Christoph, 2015. "Nonlinear expectation formation in the U.S. stock market: Empirical evidence from the Livingston survey," Kiel Working Papers 1947 [rev.], Kiel Institute for the World Economy (IfW Kiel).
    35. Conrad, Christian & Loch, Karin, 2015. "The variance risk premium and fundamental uncertainty," Economics Letters, Elsevier, vol. 132(C), pages 56-60.
    36. Diego Fresoli, 2022. "Bootstrap VAR forecasts: The effect of model uncertainties," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 279-293, March.
    37. Baetje, Fabian & Menkhoff, Lukas, 2013. "Macro determinants of U.S. stock market risk premia in bull and bear markets," Hannover Economic Papers (HEP) dp-520, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    38. Fong, Wai Mun, 2012. "Do expected business conditions explain the value premium?," Journal of Financial Markets, Elsevier, vol. 15(2), pages 181-206.
    39. Chuxuan Xiao & Winifred Huang & David P. Newton, 2024. "Predicting expected idiosyncratic volatility: Empirical evidence from ARFIMA, HAR, and EGARCH models," Review of Quantitative Finance and Accounting, Springer, vol. 63(3), pages 979-1006, October.
    40. Jungyeon Yoon & Juanjuan Fan, 2024. "Forecasting the direction of the Fed's monetary policy decisions using random forest," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2848-2859, November.
    41. Metiu, Norbert & Prieto, Esteban, 2023. "Time-varying stock return correlation, news shocks, and business cycles," Discussion Papers 05/2023, Deutsche Bundesbank.
    42. Marc Joëts, 2012. "Energy price transmissions during extreme movements," Working Papers hal-04141047, HAL.
    43. Cunha, Ronan & Pereira, Pedro L. Valls, 2015. "Automatic model selection for forecasting Brazilian stock returns," Textos para discussão 398, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    44. Eriksen, Jonas N., 2017. "Expected Business Conditions and Bond Risk Premia," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(4), pages 1667-1703, August.
    45. Martin PAŽICKÃ, 2017. "Stock Price Simulation Using Bootstrap And Monte Carlo," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 64(2), pages 155-170, June.
    46. Hirshleifer, David & Li, Jun & Yu, Jianfeng, 2015. "Asset pricing in production economies with extrapolative expectations," Journal of Monetary Economics, Elsevier, vol. 76(C), pages 87-106.
    47. Joëts, Marc, 2014. "Energy price transmissions during extreme movements," Economic Modelling, Elsevier, vol. 40(C), pages 392-399.
    48. Mönch, Emanuel & Stein, Tobias, 2021. "Equity premium predictability over the business cycle," Discussion Papers 25/2021, Deutsche Bundesbank.
    49. Ana Sofia Monteiro & Helder Sebastião & Nuno Silva, 2025. "Prediction and Allocation of Stocks, Bonds, and REITs in the US Market," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1191-1230, March.
    50. Pakoš, Michal, 2013. "Long-run risk and hidden growth persistence," Journal of Economic Dynamics and Control, Elsevier, vol. 37(9), pages 1911-1928.
    51. Çakmaklı, Cem & van Dijk, Dick, 2016. "Getting the most out of macroeconomic information for predicting excess stock returns," International Journal of Forecasting, Elsevier, vol. 32(3), pages 650-668.
    52. Markku Lanne & Henri Nyberg, 2015. "Nonlinear dynamic interrelationships between real activity and stock returns," CREATES Research Papers 2015-36, Department of Economics and Business Economics, Aarhus University.
    53. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Working Papers 23-04, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Nov 2023.
    54. Song, Wonho & Park, Sung Y. & Ryu, Doojin, 2018. "Dynamic conditional relationships between developed and emerging markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 534-543.
    55. Liu, Yang, 2023. "Government debt and risk premia," Journal of Monetary Economics, Elsevier, vol. 136(C), pages 18-34.
    56. Harri Pönkä, 2018. "Sentiment and sign predictability of stock returns," Economics Bulletin, AccessEcon, vol. 38(3), pages 1676-1684.
    57. Le, Trung H., 2021. "International portfolio allocation: The role of conditional higher moments," International Review of Economics & Finance, Elsevier, vol. 74(C), pages 33-57.
    58. Chelikani, Surya & Marks, Joseph M. & Nam, Kiseok, 2023. "Volatility feedback effect and risk-return tradeoff," The Quarterly Review of Economics and Finance, Elsevier, vol. 92(C), pages 49-65.
    59. Li, Jiaqi & Ahn, Hee-Joon, 2024. "Sensitivity of Chinese stock markets to individual investor sentiment: An analysis of Sina Weibo mood related to COVID-19," Journal of Behavioral and Experimental Finance, Elsevier, vol. 41(C).
    60. Mihai, Marius M., 2022. "The commercial bank leverage factor in U.S. asset prices," The Quarterly Review of Economics and Finance, Elsevier, vol. 86(C), pages 156-171.
    61. Paye, Bradley S., 2012. "‘Déjà vol’: Predictive regressions for aggregate stock market volatility using macroeconomic variables," Journal of Financial Economics, Elsevier, vol. 106(3), pages 527-546.
    62. Cheng, Hang & Shi, Yongdong, 2020. "Forecasting China's stock market variance," Pacific-Basin Finance Journal, Elsevier, vol. 64(C).
    63. Park, Sung Y. & Ryu, Doojin & Song, Jeongseok, 2017. "The dynamic conditional relationship between stock market returns and implied volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 638-648.
    64. Goliński, Adam & Madeira, João & Rambaccussing, Dooruj, 2025. "Return predictability, dividend growth, and the persistence of the price–dividend ratio," International Journal of Forecasting, Elsevier, vol. 41(1), pages 92-110.
    65. Golinski, Adam & Madeira, Joao & Rambaccussing, Dooruj, 2014. "Fractional Integration of the Price-Dividend Ratio in a Present-Value Model," MPRA Paper 58554, University Library of Munich, Germany.
    66. Pierdzioch, Christian & Reitz, Stefan & Ruelke, Jan-Christoph, 2014. "Heterogeneous Forecasters and Nonlinear Expectation Formation in the U.S. Stock Market," FinMaP-Working Papers 11, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    67. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 328-383, Elsevier.
    68. Chien, Mei-Se & Lee, Chien-Chiang & Hu, Te-Chung & Hu, Hui-Ting, 2015. "Dynamic Asian stock market convergence: Evidence from dynamic cointegration analysis among China and ASEAN-5," Economic Modelling, Elsevier, vol. 51(C), pages 84-98.
    69. Liu, Jingzhen & Kemp, Alexander, 2019. "Forecasting the sign of U.S. oil and gas industry stock index excess returns employing macroeconomic variables," Energy Economics, Elsevier, vol. 81(C), pages 672-686.
    70. Mordecai Kurz & Maurizio Motolese, 2011. "Diverse beliefs and time variability of risk premia," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 47(2), pages 293-335, June.
    71. Nuno Silva, 2013. "Equity Premia Predictability in the EuroZone," GEMF Working Papers 2013-22, GEMF, Faculty of Economics, University of Coimbra.
    72. Metiu, Norbert & Prieto, Esteban, 2025. "Time-varying stock return correlation, news shocks, and business cycles," European Economic Review, Elsevier, vol. 172(C).
    73. Kim Chang-Jin & Kim Yunmi, 2019. "A unified framework jointly explaining business conditions, stock returns, volatility and “volatility feedback news” effects," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 23(2), pages 1-14, April.
    74. Schmeling, Maik & Schrimpf, Andreas, 2011. "Expected inflation, expected stock returns, and money illusion: What can we learn from survey expectations?," European Economic Review, Elsevier, vol. 55(5), pages 702-719, June.
    75. Kroencke, Tim A., 2022. "Recessions and the stock market," Journal of Monetary Economics, Elsevier, vol. 131(C), pages 61-77.
    76. Conrad, Christian & Glas, Alexander, 2018. "‘Déjà vol’ revisited: Survey forecasts of macroeconomic variables predict volatility in the cross-section of industry portfolios," Working Papers 0655, University of Heidelberg, Department of Economics.

  57. Campbell, Sean D. & Diebold, Francis X., 2005. "Stock returns and expected business conditions: Half a century of direct evidence," CFS Working Paper Series 2005/22, Center for Financial Studies (CFS).

    Cited by:

    1. Park, Sunjin, 2022. "Heterogeneous beliefs in macroeconomic growth prospects and the carry risk premium," Journal of Banking & Finance, Elsevier, vol. 136(C).
    2. Riccardo Colacito & Eric Ghysels & Jinghan Meng & Wasin Siwasarit, 2016. "Skewness in Expected Macro Fundamentals and the Predictability of Equity Returns: Evidence and Theory," The Review of Financial Studies, Society for Financial Studies, vol. 29(8), pages 2069-2109.
    3. Paul Ehling & Christian Heyerdahl-Larsen, 2017. "Correlations," Management Science, INFORMS, vol. 63(6), pages 1919-1937, June.
    4. M. Barari & Brian Lucey & S. Voronkova, 2008. "Reassessing co-movements among G7 equity markets: evidence from iShares," Applied Financial Economics, Taylor & Francis Journals, vol. 18(11), pages 863-877.
    5. Conrad, Christian & Loch, Karin, 2012. "Anticipating Long-Term Stock Market Volatility," Working Papers 0535, University of Heidelberg, Department of Economics.
    6. Krainer, Robert E., 2017. "Economic stability under alternative banking systems: Theory and policy," Journal of Financial Stability, Elsevier, vol. 31(C), pages 107-118.
    7. Mikael C. Bergbrant & Patrick J. Kelly, 2015. "Macroeconomic Expectations and the Size, Value and Momentum Factors," Working Papers w0214, New Economic School (NES).
    8. Morana, Claudio, 2014. "Insights on the global macro-finance interface: Structural sources of risk factor fluctuations and the cross-section of expected stock returns," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 64-79.
    9. Conrad, Christian & Schoelkopf, Julius Theodor & Tushteva, Nikoleta, 2023. "Long-Term Volatility Shapes the Stock Market’s Sensitivity to News," Working Papers 0739, University of Heidelberg, Department of Economics.
    10. Diebold, Francis X. & Yilmaz, Kamil, 2015. "Financial and Macroeconomic Connectedness: A Network Approach to Measurement and Monitoring," OUP Catalogue, Oxford University Press, number 9780199338306, Decembrie.
    11. Bevilacqua, Mattia & Morelli, David & Tunaru, Radu, 2019. "The determinants of the model-free positive and negative volatilities," Journal of International Money and Finance, Elsevier, vol. 92(C), pages 1-24.
    12. Chava, Sudheer & Gallmeyer, Michael & Park, Heungju, 2015. "Credit conditions and stock return predictability," Journal of Monetary Economics, Elsevier, vol. 74(C), pages 117-132.
    13. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2011. "Financial Risk Measurement for Financial Risk Management," PIER Working Paper Archive 11-037, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    14. Turan G. Bali & Hao Zhou, 2011. "Risk, uncertainty, and expected returns," Finance and Economics Discussion Series 2011-45, Board of Governors of the Federal Reserve System (U.S.).
    15. van den Hauwe, Sjoerd & Paap, Richard & van Dijk, Dick, 2013. "Bayesian forecasting of federal funds target rate decisions," Journal of Macroeconomics, Elsevier, vol. 37(C), pages 19-40.
    16. Aleksandar M. Velkoski, 2015. "Restaurant Consumption as an Economic Indicator," Tourism Economics, , vol. 21(2), pages 325-339, April.
    17. Sousa, João & Sousa, Ricardo M., 2017. "Predicting risk premium under changes in the conditional distribution of stock returns," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 50(C), pages 204-218.
    18. Shamsi Zamenjani, Azam, 2021. "Do financial variables help predict the conditional distribution of the market portfolio?," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 327-345.
    19. Cem Cakmakli & Dick van Dijk, 2010. "Getting the Most out of Macroeconomic Information for Predicting Stock Returns and Volatility," Tinbergen Institute Discussion Papers 10-115/4, Tinbergen Institute.
    20. Prabheesh, K.P. & Vidya, C.T., 2018. "Do business cycles, investment-specific technology shocks matter for stock returns?," Economic Modelling, Elsevier, vol. 70(C), pages 511-524.
    21. Safari, Meysam & TahmooresPour, Reza, 2011. "Moderation Effect of Market Condition on the Relationship between Dividend Yield and Stock Return," MPRA Paper 28913, University Library of Munich, Germany.
    22. Golinski, Adam & Madeira, Joao & Rambaccussing, Dooruj, 2014. "Fractional Integration of the Price-Dividend Ratio in a Present-Value Model of Stock Prices," SIRE Discussion Papers 2015-79, Scottish Institute for Research in Economics (SIRE).
    23. Arjan Berkelaar & Roy Kouwenberg, 2011. "A Liability-Relative Drawdown Approach to Pension Asset Liability Management," Palgrave Macmillan Books, in: Gautam Mitra & Katharina Schwaiger (ed.), Asset and Liability Management Handbook, chapter 14, pages 352-382, Palgrave Macmillan.
    24. Kirby, Chris, 2019. "The value premium and expected business conditions," Finance Research Letters, Elsevier, vol. 30(C), pages 360-366.
    25. Lindblad, Annika, 2017. "Sentiment indicators and macroeconomic data as drivers for low-frequency stock market volatility," MPRA Paper 80266, University Library of Munich, Germany.
    26. Serhan Cevik & João Tovar Jalles, 2020. "This Changes Everything: Climate Shocks and Sovereign Bonds," Working Papers REM 2020/0132, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    27. Chelikani, Surya & Marks, Joseph M. & Nam, Kiseok, 2024. "State-dependent intertemporal risk-return tradeoff: Further evidence," Journal of Economics and Business, Elsevier, vol. 130(C).
    28. Lustig, Hanno & Verdelhan, Adrien, 2012. "Business cycle variation in the risk-return trade-off," Journal of Monetary Economics, Elsevier, vol. 59(S), pages 35-49.
    29. Nonejad, Nima, 2017. "Forecasting aggregate stock market volatility using financial and macroeconomic predictors: Which models forecast best, when and why?," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 131-154.
    30. Reitz, Stefan & Pierdzioch, Christian & Rülke, Jan-Christoph, 2015. "Nonlinear Expectation Formation in the U.S. Stock Market," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113210, Verein für Socialpolitik / German Economic Association.
    31. John G Powell & Sirimon Treepongkaruna, 2012. "Recession fears as self-fulfilling prophecies? Influence on stock returns and output," Australian Journal of Management, Australian School of Business, vol. 37(2), pages 231-260, August.
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    49. Ana Sofia Monteiro & Helder Sebastião & Nuno Silva, 2025. "Prediction and Allocation of Stocks, Bonds, and REITs in the US Market," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1191-1230, March.
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    53. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Working Papers 23-04, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Nov 2023.
    54. Song, Wonho & Park, Sung Y. & Ryu, Doojin, 2018. "Dynamic conditional relationships between developed and emerging markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 534-543.
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    1. Hassan, Gazi & Hisham, Al refai, 2010. "Can Macroeconomic Factors Explain Equity Returns in the Long Run? The Case of Jordan," MPRA Paper 22713, University Library of Munich, Germany.
    2. Fredj Jawadi & Wael Louhichi & Abdoulkarim Idi Cheffou & Hachmi Ben Ameur, 2019. "Modeling time-varying beta in a sustainable stock market with a three-regime threshold GARCH model," Annals of Operations Research, Springer, vol. 281(1), pages 275-295, October.
    3. Samet Günay, 2017. "Risk Configuration of S&P 500 Industries: Sigma-risk and Alpha-risk Approximation," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 11(2), pages 196-221, May.
    4. González, Mariano & Nave, Juan & Rubio, Gonzalo, 2018. "Macroeconomic determinants of stock market betas," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 26-44.
    5. Radosław Kurach, 2013. "Does Beta Explain Global Equity Market Volatility – Some Empirical Evidence," Contemporary Economics, Vizja University, vol. 7(2), June.
    6. Hautsch, Nikolaus & Hess, Dieter E. & Veredas, David, 2010. "The impact of macroeconomic news on quote adjustments, noise, and informational volatility," CFS Working Paper Series 2010/01, Center for Financial Studies (CFS).
    7. Roberto Pascual & David Veredas, 2009. "Does the open limit order book matter in explaining informational volatility?," ULB Institutional Repository 2013/183777, ULB -- Universite Libre de Bruxelles.
    8. Hooper, Vincent J. & Ng, Kevin & Reeves, Jonathan J., 2008. "Quarterly beta forecasting: An evaluation," International Journal of Forecasting, Elsevier, vol. 24(3), pages 480-489.
    9. Golosnoy, Vasyl & Hamid, Alain & Okhrin, Yarema, 2014. "The empirical similarity approach for volatility prediction," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 321-329.
    10. Dungey, Mardi & Henry, Olan T & Hvodzdyk, Lyudmyla, 2013. "The impact of jumps and thin trading on realized hedge ratios," Working Papers 2013-02, University of Tasmania, Tasmanian School of Business and Economics, revised 28 Mar 2013.
    11. Phoebe Koundouri & Nikolaos Kourogenis & Nikitas Pittis & Panagiotis Samartzis, 2015. "Factor Models as 'Explanatory Unifiers' versus 'Explanatory Ideals' of Empirical Regularities of Stock Returns," DEOS Working Papers 1507, Athens University of Economics and Business.
    12. Zhou, Dong-hai & Liu, Xiao-xing, 2024. "Does systemic risk in the fund markets predict future economic downturns?," International Review of Financial Analysis, Elsevier, vol. 92(C).
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    14. Degiannakis, Stavros & Filis, George & Kizys, Renatas, 2014. "The effects of oil price shocks on stock market volatility: Evidence from European data," MPRA Paper 96296, University Library of Munich, Germany.
    15. Marshall, Andrew & Maulana, Tubagus & Tang, Leilei, 2009. "The estimation and determinants of emerging market country risk and the dynamic conditional correlation GARCH model," International Review of Financial Analysis, Elsevier, vol. 18(5), pages 250-259, December.
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    37. Janik, Bogna & Płuciennik, Piotr, 2024. "Corporate ESG indices and stability during periods of deep concerns in financial markets," Finance Research Letters, Elsevier, vol. 70(C).
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    39. Sascha Mergner & Jan Bulla, 2008. "Time-varying beta risk of Pan-European industry portfolios: A comparison of alternative modeling techniques," The European Journal of Finance, Taylor & Francis Journals, vol. 14(8), pages 771-802.
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    42. Ciner, Cetin, 2015. "Time variation in systematic risk, returns and trading volume: Evidence from precious metals mining stocks," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 277-283.
    43. Bollerslev, Tim & Li, Sophia Zhengzi & Todorov, Viktor, 2016. "Roughing up beta: Continuous versus discontinuous betas and the cross section of expected stock returns," Journal of Financial Economics, Elsevier, vol. 120(3), pages 464-490.
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    45. Drobetz, Wolfgang & Menzel, Christina & Schröder, Henning, 2016. "Systematic risk behavior in cyclical industries: The case of shipping," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 88(C), pages 129-145.
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    47. Hwang, Young-Soon & Min, Hong-Ghi & McDonald, Judith A. & Kim, Hwagyun & Kim, Bong-Han, 2010. "Using the credit spread as an option-risk factor: Size and value effects in CAPM," Journal of Banking & Finance, Elsevier, vol. 34(12), pages 2995-3009, December.
    48. Dimitrios Dadakas & Christos Karpetis & Athanasios Fassas & Erotokritos Varelas, 2016. "Sectoral Differences in the Choice of the Time Horizon during Estimation of the Unconditional Stock Beta," IJFS, MDPI, vol. 4(4), pages 1-13, December.
    49. Cosemans, M. & Frehen, R.G.P. & Schotman, P.C. & Bauer, R.M.M.J., 2009. "Efficient Estimation of Firm-Specific Betas and its Benefits for Asset Pricing Tests and Portfolio Choice," MPRA Paper 23557, University Library of Munich, Germany.
    50. Richard Mawulawoe Ahadzie & Nagaratnam Jeyasreedharan, 2024. "Higher‐order moments and asset pricing in the Australian stock market," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 64(1), pages 75-128, March.
    51. Panagiotis Samartzis & Nikitas Pittis & Nikolaos Kourogenis & Phoebe Koundouri, 2013. "Factor Models of Stock Returns: GARCH Errors versus Autoregressive Betas," DEOS Working Papers 1318, Athens University of Economics and Business.
    52. Mohammad Abu Sayeed & Mardi Dungey & Wenying Yao, 2018. "High-frequency Characterisation of Indian Banking Stocks," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 17(2_suppl), pages 213-238, August.
    53. Ortas, E. & Salvador, M. & Moneva, J.M., 2015. "Improved beta modeling and forecasting: An unobserved component approach with conditional heteroscedastic disturbances," The North American Journal of Economics and Finance, Elsevier, vol. 31(C), pages 27-51.
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    55. Ray Ball & Gil Sadka & Ayung Tseng, 2022. "Using accounting earnings and aggregate economic indicators to estimate firm-level systematic risk," Review of Accounting Studies, Springer, vol. 27(2), pages 607-646, June.
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    58. Zhu, Lin & Jiang, Fuwei & Tang, Guohao & Jin, Fujing, 2024. "From macro to micro: Sparse macroeconomic risks and the cross-section of stock returns," International Review of Financial Analysis, Elsevier, vol. 95(PB).
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    63. Andrew Phin & Todd Prono & Jonathan J. Reeves & Konark Saxena, 2018. "Level Shifts in Beta, Spurious Abnormal Returns and the TARP Announcement," Finance and Economics Discussion Series 2018-081, Board of Governors of the Federal Reserve System (U.S.).
    64. Donggyu Kim & Minseok Shin, 2024. "Nonconvex High-Dimensional Time-Varying Coefficient Estimation for Noisy High-Frequency Observations with a Factor Structure," Working Papers 202418, University of California at Riverside, Department of Economics.
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    66. Todorov, Viktor & Bollerslev, Tim, 2010. "Jumps and betas: A new framework for disentangling and estimating systematic risks," Journal of Econometrics, Elsevier, vol. 157(2), pages 220-235, August.
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    Cited by:

    1. Turvey, Calum G. & Norton, Michael, 2008. "An Internet-Based Tool for Weather Risk Management," Agricultural and Resource Economics Review, Cambridge University Press, vol. 37(1), pages 63-78, April.
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    Cited by:

    1. Jin-Huei Yeh & Jying-Nan Wang & Chung-Ming Kuan, 2014. "A noise-robust estimator of volatility based on interquantile ranges," Review of Quantitative Finance and Accounting, Springer, vol. 43(4), pages 751-779, November.
    2. Andrey Kudryavtsev, 2012. "Short-Term Stock Price Reversals May Be Reversed," International Journal of Business and Economic Sciences Applied Research (IJBESAR), Democritus University of Thrace (DUTH), Kavala Campus, Greece, vol. 5(3), pages 129-146, December.
    3. Mattiussi, V. & Iori, G., 2006. "Currency futures volatility during the 1997 East Asian crisis: an application of Fourier analysis," Working Papers 06/09, Department of Economics, City University London.
    4. Yoann Potiron & Per Mykland, 2015. "Estimation of integrated quadratic covariation with endogenous sampling times," Papers 1507.01033, arXiv.org, revised Nov 2016.
    5. Eric Jondeau & Michael Rockinger, 2006. "Optimal Portfolio Allocation under Higher Moments," European Financial Management, European Financial Management Association, vol. 12(1), pages 29-55, January.
    6. Ole E. Barndorff-Nielsen & Neil Shephard, 2005. "Variation, jumps, market frictions and high frequency data in financial econometrics," OFRC Working Papers Series 2005fe08, Oxford Financial Research Centre.
    7. Lakshmi Padmakumari & S. Maheswaran, 2018. "Covariance estimation using random permutations," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 5(01), pages 1-21, March.
    8. Aharon, David Y. & Qadan, Mahmoud, 2020. "When do retail investors pay attention to their trading platforms?," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
    9. Yin-Wong Cheung, 2007. "An empirical model of daily highs and lows," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 12(1), pages 1-20.
    10. Chiu, Ching-Wai (Jeremy) & Harris, Richard & Stoja, Evarist & Chin, Michael, 2016. "Financial market volatility, macroeconomic fundamentals and investor sentiment," Bank of England working papers 608, Bank of England.
    11. Mueller, Philippe & Stathopoulos, Andreas & Vedolin, Andrea, 2017. "International correlation risk," LSE Research Online Documents on Economics 84140, London School of Economics and Political Science, LSE Library.
    12. Redouane Elkamhia & Denitsa Stefanova, 2011. "Dynamic Correlation or Tail Dependence Hedging for Portfolio Selection," Tinbergen Institute Discussion Papers 11-028/2/DSF10, Tinbergen Institute.
    13. LAURENT, Sébastien & VIOLANTE, Francesco, 2012. "Volatility forecasts evaluation and comparison," LIDAM Reprints CORE 2414, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    14. Yeh, Jin-Huei & Wang, Jying-Nan, 2010. "Correcting microstructure comovement biases for integrated covariance," Finance Research Letters, Elsevier, vol. 7(3), pages 184-191, September.
    15. Theodoros Diasakos, 2008. "Comparative Statics of Asset Prices," Carlo Alberto Notebooks 72, Collegio Carlo Alberto, revised 2011.
    16. Walid M.A. Ahmed, 2016. "Cross-border equity flows and market volatility: the case of Qatar Exchange," International Journal of Emerging Markets, Emerald Group Publishing Limited, vol. 11(3), pages 395-418, July.
    17. Vetter, Mathias & Podolskij, Mark, 2006. "Estimation of Volatility Functionals in the Simultaneous Presence of Microstructure Noise and Jumps," Technical Reports 2006,51, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    18. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "The role of high frequency intra-daily data, daily range and implied volatility in multi-period Value-at-Risk forecasting," MPRA Paper 35252, University Library of Munich, Germany.
    19. Silja Kinnebrock & Mark Podolskij, 2008. "An Econometric Analysis of Modulated Realised Covariance, Regression and Correlation in Noisy Diffusion Models," CREATES Research Papers 2008-23, Department of Economics and Business Economics, Aarhus University.
    20. Galli, Fausto, 2014. "Stochastic conditonal range, a latent variable model for financial volatility," MPRA Paper 54030, University Library of Munich, Germany.
    21. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2002. "Parametric and Nonparametric Volatility Measurement," Center for Financial Institutions Working Papers 02-27, Wharton School Center for Financial Institutions, University of Pennsylvania.
    22. Pagano, Michael S. & Peng, Lin & Schwartz, Robert A., 2013. "A call auction's impact on price formation and order routing: Evidence from the NASDAQ stock market," Journal of Financial Markets, Elsevier, vol. 16(2), pages 331-361.
    23. Eduardo Rossi & Paolo Santucci de Magistris, 2013. "A No‐Arbitrage Fractional Cointegration Model for Futures and Spot Daily Ranges," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 33(1), pages 77-102, January.
    24. S. Sanfelici & M. E. Mancino, 2008. "Covariance estimation via Fourier method in the presence of asynchronous trading and microstructure noise," Economics Department Working Papers 2008-ME01, Department of Economics, Parma University (Italy).
    25. Bertrand Maillet & Jean-Philippe Médecin & Thierry Michel, 2009. "High Watermarks of Market Risks," Post-Print halshs-00425585, HAL.
    26. Capelle-Blancard, Gunther & Havrylchyk, Olena, 2016. "The impact of the French securities transaction tax on market liquidity and volatility," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 166-178.
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    28. Gnabo, Jean-Yves & Hvozdyk, Lyudmyla & Lahaye, Jérôme, 2014. "System-wide tail comovements: A bootstrap test for cojump identification on the S&P 500, US bonds and currencies," Journal of International Money and Finance, Elsevier, vol. 48(PA), pages 147-174.
    29. Yin Liao & Heather M. Anderson, 2011. "Testing for co-jumps in high-frequency financial data: an approach based on first-high-low-last prices," Monash Econometrics and Business Statistics Working Papers 9/11, Monash University, Department of Econometrics and Business Statistics.
    30. Paulo M.M. Rodrigues & Matei Demetrescu, 2018. "Testing the fractionally integrated hypothesis using M estimation: With an application to stock market volatility," Working Papers w201817, Banco de Portugal, Economics and Research Department.
    31. Philippe Raimbourg & Paul Zimmermann, 2022. "Is normal backwardation normal? Valuing financial futures with a local index-rate covariance," Post-Print hal-04011013, HAL.
    32. Fausti, Scott W. & Qasmi, Bashir A. & Diersen, Matthew A., 2007. "Public Reporting of Fed Cattle Grid Prices: Policy Reform Consequences," Economics Staff Papers 7297, South Dakota State University, Department of Economics.
    33. Dovonon, Prosper, 2008. "Conditionally heteroskedastic factor models with skewness and leverage effects," MPRA Paper 40206, University Library of Munich, Germany, revised Feb 2012.
    34. Yeh, Jin-Huei & Yun, Mu-Shu, 2023. "Assessing jump and cojumps in financial asset returns with applications in futures markets," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).
    35. Leandro Maciel, 2020. "Technical analysis based on high and low stock prices forecasts: evidence for Brazil using a fractionally cointegrated VAR model," Empirical Economics, Springer, vol. 58(4), pages 1513-1540, April.
    36. Martin Becker & Ralph Friedmann & Stefan Klößner & Walter Sanddorf-Köhle, 2007. "A Hausman test for Brownian motion," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 91(1), pages 3-21, March.
    37. Pagano, Michael S. & Peng, Lin & Schwartz, Robert A., 2008. "The quality of price formation at market openings and closings: Evidence from the Nasdaq stock market," CFS Working Paper Series 2008/45, Center for Financial Studies (CFS).
    38. Härdle, Wolfgang Karl & Chen, Ying & Schulz, Rainer, 2004. "Prognose mit nichtparametrischen Verfahren," Papers 2004,07, Humboldt University of Berlin, Center for Applied Statistics and Economics (CASE).
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    41. Ozgur (Ozzy) Akay & Mark D. Griffiths & Drew B. Winters, 2010. "On The Robustness Of Range‐Based Volatility Estimators," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 33(2), pages 179-199, June.
    42. Sin, Chor-Yiu (CY), 2013. "Using CARRX models to study factors affecting the volatilities of Asian equity markets," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 552-564.
    43. Ben Ammar, Imen & Hellara, Slaheddine, 2022. "High-frequency trading, stock volatility, and intraday crashes," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 337-344.
    44. Vu Tran & Rasha Alsakka & Owain ap Gwilym, 2018. "Multiple credit ratings and market heterogeneity," Working Papers 2018-26, Swansea University, School of Management.
    45. Raimbourg, Philippe & Zimmermann, Paul, 2022. "Is normal backwardation normal? Valuing financial futures with a local index-rate covariance," European Journal of Operational Research, Elsevier, vol. 298(1), pages 351-367.
    46. Nicole Abruzzo & Yang-Ho Park, 2014. "An Empirical Analysis of Futures Margin Changes: Determinants and Policy Implications," Finance and Economics Discussion Series 2014-86, Board of Governors of the Federal Reserve System (U.S.).
    47. Hou, Kewei & Loh, Roger K., 2016. "Have we solved the idiosyncratic volatility puzzle?," Journal of Financial Economics, Elsevier, vol. 121(1), pages 167-194.
    48. Chazi, Abdelaziz & Samet, Anis & Azad, A.S.M. Sohel, 2023. "Volatility and correlation of Islamic and conventional indices during crises," Global Finance Journal, Elsevier, vol. 55(C).
    49. Gunther Capelle-Blancard, 2017. "Curbing the Growth of Stock Trading? Order-to-Trade Ratios and Financial Transaction Taxes," Post-Print hal-01441828, HAL.
    50. Tseng Tseng-Chan & Chung Huimin & Huang Chin-Sheng, 2009. "Modeling Jump and Continuous Components in the Volatility of Oil Futures," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 13(3), pages 1-30, May.
    51. Massimiliano Caporin & Angelo Ranaldo & Paolo Santucci de Magistris, 2011. "On the Predictability of Stock Prices: A Case for High and Low Prices," "Marco Fanno" Working Papers 0136, Dipartimento di Scienze Economiche "Marco Fanno".
    52. Bayraci, Selcuk & Demiralay, Sercan, 2013. "Conditional Autoregregressive Range (CARR) Based Volatility Spillover Index For the Eurozone Markets," MPRA Paper 51909, University Library of Munich, Germany.
    53. Castrén, Olli & Mazzotta, Stefano, 2005. "Foreign exchange option and returns based correlation forecasts: evaluation and two applications," Working Paper Series 447, European Central Bank.
    54. Lin Peng & Turan G. Bali, 2006. "Is there a risk-return trade-off? Evidence from high-frequency data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(8), pages 1169-1198.
    55. Cronin, David, 2014. "The interaction between money and asset markets: A spillover index approach," Journal of Macroeconomics, Elsevier, vol. 39(PA), pages 185-202.
    56. Yaya, OaOluwa S & Vo, Xuan Vinh & Ogbonna, Ahamuefula E & Adewuyi, Adeolu O, 2020. "Modelling Cryptocurrency High-Low Prices using Fractional Cointegrating VAR," MPRA Paper 102190, University Library of Munich, Germany, revised 02 Aug 2020.
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    58. Harris, Richard D.F. & Yilmaz, Fatih, 2010. "Estimation of the conditional variance-covariance matrix of returns using the intraday range," International Journal of Forecasting, Elsevier, vol. 26(1), pages 180-194, January.
    59. Yan-Leung Cheung & Yin-Wong Cheung & Alan T.K. Wan, 2008. "A High-Low Model of Daily Stock Price Ranges," CESifo Working Paper Series 2387, CESifo.
    60. Liu, Xinyi & Margaritis, Dimitris & Wang, Peiming, 2012. "Stock market volatility and equity returns: Evidence from a two-state Markov-switching model with regressors," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 483-496.
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    63. Wu, Chih-Chiang & Liang, Shin-Shun, 2011. "The economic value of range-based covariance between stock and bond returns with dynamic copulas," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 711-727, September.
    64. Henning Fischer & Ángela Blanco‐FERNÁndez & Peter Winker, 2016. "Predicting Stock Return Volatility: Can We Benefit from Regression Models for Return Intervals?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(2), pages 113-146, March.
    65. Vortelinos, Dimitrios I., 2014. "Optimally sampled realized range-based volatility estimators," Research in International Business and Finance, Elsevier, vol. 30(C), pages 34-50.
    66. L. C. G. Rogers & Fanyin Zhou, 2008. "Estimating correlation from high, low, opening and closing prices," Papers 0804.0162, arXiv.org.
    67. Vortelinos, Dimitrios I., 2016. "Incremental information of stock indicators," International Review of Economics & Finance, Elsevier, vol. 41(C), pages 79-97.
    68. Piotr Fiszeder, 2018. "Low and high prices can improve covariance forecasts: The evidence based on currency rates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(6), pages 641-649, September.
    69. González-Rivera, Gloria & Luo, Yun & Ruiz Ortega, Esther, 2019. "Prediction regions for interval-valued time series," DES - Working Papers. Statistics and Econometrics. WS 29054, Universidad Carlos III de Madrid. Departamento de Estadística.
    70. Awartani, Basel & Maghyereh, Aktham Issa, 2013. "Dynamic spillovers between oil and stock markets in the Gulf Cooperation Council Countries," Energy Economics, Elsevier, vol. 36(C), pages 28-42.
    71. Bandi, Federico M. & Russell, Jeffrey R., 2006. "Separating microstructure noise from volatility," Journal of Financial Economics, Elsevier, vol. 79(3), pages 655-692, March.
    72. Abdollahi, Hooman & Fjesme, Sturla L. & Sirnes, Espen, 2024. "Measuring market volatility connectedness to media sentiment," The North American Journal of Economics and Finance, Elsevier, vol. 71(C).
    73. Molnár, Peter, 2012. "Properties of range-based volatility estimators," International Review of Financial Analysis, Elsevier, vol. 23(C), pages 20-29.
    74. Peter Christoffersen & Kris Jacobs & Gregory Vainberg, 2007. "Forward-Looking Betas," CREATES Research Papers 2007-39, Department of Economics and Business Economics, Aarhus University.
    75. Eduardo Rossi & Paolo Santucci de Magistris, 2009. "A No Arbitrage Fractional Cointegration Analysis Of The Range Based Volatility," CREATES Research Papers 2009-31, Department of Economics and Business Economics, Aarhus University.
    76. Ben Sita, Bernard & Abdallah, Wissam, 2014. "Volatility links between the home and the host market for U.K. dual-listed stocks on U.S. markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 33(C), pages 183-199.
    77. Martens, Martin & van Dijk, Dick, 2007. "Measuring volatility with the realized range," Journal of Econometrics, Elsevier, vol. 138(1), pages 181-207, May.
    78. Lovcha, Yuliya & Perez-Laborda, Alejandro, 2022. "Long-memory and volatility spillovers across petroleum futures," Energy, Elsevier, vol. 243(C).
    79. Kumar, Dilip & Maheswaran, S., 2013. "Detecting sudden changes in volatility estimated from high, low and closing prices," Economic Modelling, Elsevier, vol. 31(C), pages 484-491.
    80. Sensoy, Ahmet & Uzun, Sevcan & Lucey, Brian M., 2021. "Commonality in FX liquidity: High-frequency evidence," Finance Research Letters, Elsevier, vol. 39(C).
    81. Li, Shaoyu & Zheng, Tingguo, 2017. "Modeling spot rate using a realized stochastic volatility model with level effect and dynamic drift☆," The North American Journal of Economics and Finance, Elsevier, vol. 40(C), pages 200-221.
    82. Vortelinos, Dimitrios I., 2010. "The properties of realized correlation: Evidence from the French, German and Greek equity markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 50(3), pages 273-290, August.
    83. Joan Jasiak & R. Sufana & C. Gourieroux, 2005. "The Wishart Autoregressive Process of Multivariate Stochastic Volatility," Working Papers 2005_2, York University, Department of Economics.
    84. Robert F. Engle & Giampiero M. Gallo, 2003. "A Multiple Indicators Model For Volatility Using Intra-Daily Data," Econometrics Working Papers Archive wp2003_07, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    85. Frankovic, Jozo & Liu, Bin & Suardi, Sandy, 2022. "On spillover effects between cryptocurrency-linked stocks and the cryptocurrency market: Evidence from Australia," Global Finance Journal, Elsevier, vol. 54(C).
    86. D’Amato, Valeria & Levantesi, Susanna & Piscopo, Gabriella, 2022. "Deep learning in predicting cryptocurrency volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    87. Padmakumari, Lakshmi & S., Maheswaran, 2017. "A new statistic to capture the level dependence in stock price volatility," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 355-362.
    88. Martin Becker, 2010. "Exact simulation of final, minimal and maximal values of Brownian motion and jump-diffusions with applications to option pricing," Computational Management Science, Springer, vol. 7(1), pages 1-17, January.
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    90. Bannouh, K. & van Dijk, D.J.C. & Martens, M.P.E., 2008. "Range-based covariance estimation using high-frequency data: The realized co-range," Econometric Institute Research Papers EI 2007-53, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    91. Galli, Fausto, 2014. "Stochastic conditonal range, a latent variable model for financial volatility," MPRA Paper 54841, University Library of Munich, Germany.
    92. Vallois, Pierre & Tapiero, Charles S., 2008. "Volatility estimators and the inverse range process in a random volatility random walk and Wiener processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(11), pages 2565-2574.
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    94. Kim Christensen & Mark Podolskij & Mathias Vetter, 2009. "Bias-correcting the realized range-based variance in the presence of market microstructure noise," Finance and Stochastics, Springer, vol. 13(2), pages 239-268, April.
    95. Christensen, Kim & Podolski, Mark, 2005. "Asymptotic theory for range-based estimation of integrated variance of a continuous semi-martingale," Technical Reports 2005,18, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    96. Matei, Marius, 2011. "Non-Linear Volatility Modeling of Economic and Financial Time Series Using High Frequency Data," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 116-141, June.
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    99. Ng, Kok Haur & Peiris, Shelton & Chan, Jennifer So-kuen & Allen, David & Ng, Kooi Huat, 2017. "Efficient modelling and forecasting with range based volatility models and its application," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 448-460.
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    108. Chen, Wei-Peng & Choudhry, Taufiq & Wu, Chih-Chiang, 2013. "The extreme value in crude oil and US dollar markets," Journal of International Money and Finance, Elsevier, vol. 36(C), pages 191-210.
    109. Angelo Ranaldo & Paolo Santucci de Magistris, 2018. "Trading Volume, Illiquidity and Commonalities in FX Markets," Working Papers on Finance 1823, University of St. Gallen, School of Finance, revised Oct 2019.

  61. Campbell, Sean D. & Diebold, Francis X., 2004. "Weather forecasting for weather derivatives," CFS Working Paper Series 2004/10, Center for Financial Studies (CFS).

    Cited by:

    1. Turvey, Calum G. & Norton, Michael, 2008. "An Internet-Based Tool for Weather Risk Management," Agricultural and Resource Economics Review, Cambridge University Press, vol. 37(1), pages 63-78, April.
    2. C. Vladimir Rodr'iguez-Caballero & Esther Ruiz, 2024. "Temperature in the Iberian Peninsula: Trend, seasonality, and heterogeneity," Papers 2406.14145, arXiv.org.
    3. Lunina, Veronika, 2016. "Joint Modelling of Power Price, Temperature, and Hydrological Balance with a View towards Scenario Analysis," Working Papers 2016:30, Lund University, Department of Economics.
    4. Nairobi Nairobi & Edwin Russel & Ambya Ambya & Arif Darmawan & Mustofa Usman & Wamiliana Wamiliana, 2020. "Dynamic Modeling Data Export Oil and Gas and Non-Oil and Gas by ARMA(2,1)-GARCH(1,1) Model: Study of Indonesian s Export over the Years 2008-2019," International Journal of Energy Economics and Policy, Econjournals, vol. 10(6), pages 175-184.
    5. Francis X. Diebold & Glenn D. Rudebusch, 2019. "On the Evolution of U.S. Temperature Dynamics," Papers 1907.06303, arXiv.org, revised Jan 2021.
    6. Campbell, Sean D. & Diebold, Francis X., 2004. "Weather forecasting for weather derivatives," CFS Working Paper Series 2004/10, Center for Financial Studies (CFS).
    7. Wei Yuan & Ahmet Göncü & Giray Ökten, 2015. "Estimating sensitivities of temperature-based weather derivatives," Applied Economics, Taylor & Francis Journals, vol. 47(19), pages 1942-1955, April.
    8. Groll, Andreas & López-Cabrera, Brenda & Meyer-Brandis, Thilo, 2014. "A consistent two-factor model for pricing temperature derivatives," SFB 649 Discussion Papers 2014-006, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    9. Bigerna, Simona, 2018. "Estimating temperature effects on the Italian electricity market," Energy Policy, Elsevier, vol. 118(C), pages 257-269.
    10. Cao, Xiaofeng & Okhrin, Ostap & Odening, Martin & Ritter, Matthias, 2014. "Modelling spatiotemporal variability of temperature," SFB 649 Discussion Papers 2014-020, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    11. Fred Espen Benth & Anca Pircalabu, 2018. "A non-Gaussian Ornstein–Uhlenbeck model for pricing wind power futures," Applied Mathematical Finance, Taylor & Francis Journals, vol. 25(1), pages 36-65, January.
    12. Benth, Fred Espen & Taib, Che Mohd Imran Che, 2013. "On the speed towards the mean for continuous time autoregressive moving average processes with applications to energy markets," Energy Economics, Elsevier, vol. 40(C), pages 259-268.
    13. Fred Espen Benth & Jūratė Šaltytė Benth, 2012. "Modeling and Pricing in Financial Markets for Weather Derivatives," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 8457, April.
    14. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    15. Jeongin Lee & Jongwoo Choi & Wanki Park & Ilwoo Lee, 2023. "A Dual-Stage Solar Power Prediction Model That Reflects Uncertainties in Weather Forecasts," Energies, MDPI, vol. 16(21), pages 1-19, October.
    16. Benth, Fred & Härdle, Wolfgang Karl & López Cabrera, Brenda, 2009. "Pricing of Asian temperature risk," SFB 649 Discussion Papers 2009-046, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    17. Július Bemš & Caner Aydin, 2022. "Introduction to weather derivatives," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 11(3), May.
    18. Yeny E. Rodríguez & Miguel A. Pérez-Uribe & Javier Contreras, 2021. "Wind Put Barrier Options Pricing Based on the Nordix Index," Energies, MDPI, vol. 14(4), pages 1-14, February.
    19. Zhang, Li, 2008. "Three essays on agricultural risk and insurance," ISU General Staff Papers 2008010108000016857, Iowa State University, Department of Economics.
    20. Atak, Alev & Linton, Oliver B. & Xiao, Zhijie, 2010. "A Semiparametric Panel Model for Unbalanced Data with Application to Climate Change in the United Kingdom," MPRA Paper 22079, University Library of Munich, Germany.
    21. Ollech, Daniel, 2018. "Seasonal adjustment of daily time series," Discussion Papers 41/2018, Deutsche Bundesbank.
    22. Meng, Xiaochun & Taylor, James W., 2022. "Comparing probabilistic forecasts of the daily minimum and maximum temperature," International Journal of Forecasting, Elsevier, vol. 38(1), pages 267-281.
    23. Šaltytė Benth, Jūratė & Benth, Fred Espen, 2012. "A critical view on temperature modelling for application in weather derivatives markets," Energy Economics, Elsevier, vol. 34(2), pages 592-602.
    24. Qibin Duan & Clare A McGrory & Glenn Brown & Kerrie Mengersen & You-Gan Wang, 2022. "Spatio-temporal quantile regression analysis revealing more nuanced patterns of climate change: A study of long-term daily temperature in Australia," PLOS ONE, Public Library of Science, vol. 17(8), pages 1-16, August.
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    2. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
    3. Donna J. Bergenstock & Mary E. Deily & Larry W. Taylor, 2006. "A Cartel's Response to Cheating: An Empirical Investigation of the De Beers Diamond Empire," Southern Economic Journal, John Wiley & Sons, vol. 73(1), pages 173-189, July.
    4. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    5. Joshua Aizenman & Brian Pinto, 2004. "Managing Volatility and Crises: A Practitioner's Guide Overview," NBER Working Papers 10602, National Bureau of Economic Research, Inc.
    6. Michele Vodret & Iacopo Mastromatteo & Bence Tóth & Michael Benzaquen, 2023. "Microfounding GARCH models and beyond: a Kyle-inspired model with adaptive agents," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 18(3), pages 599-625, July.
    7. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2005. "Volatility forecasting," CFS Working Paper Series 2005/08, Center for Financial Studies (CFS).
    8. Masih, Mansur & Algahtani, Ibrahim & De Mello, Lurion, 2010. "Price dynamics of crude oil and the regional ethylene markets," Energy Economics, Elsevier, vol. 32(6), pages 1435-1444, November.
    9. Vargas, Gregorio A., 2006. "An Asymmetric Block Dynamic Conditional Correlation Multivariate GARCH Model," MPRA Paper 189, University Library of Munich, Germany, revised Aug 2006.
    10. Stavros Degiannakis & Alexandra Livada & Epaminondas Panas, 2008. "Rolling-sampled parameters of ARCH and Levy-stable models," Applied Economics, Taylor & Francis Journals, vol. 40(23), pages 3051-3067.
    11. Thomas C. Chiang & Jiandong Li, 2012. "Stock Returns and Risk: Evidence from Quantile," JRFM, MDPI, vol. 5(1), pages 1-39, December.
    12. Eduardo Rossi, 2010. "Univariate GARCH models: a survey (in Russian)," Quantile, Quantile, issue 8, pages 1-67, July.
    13. Mohamed BELHEDI & Ines SLAMA & Amine LAHIANI, 2015. "Tranmission Of International Shocks To An Emerging Small Open-Economy: Evidence From Tunisia," Region et Developpement, Region et Developpement, LEAD, Universite du Sud - Toulon Var, vol. 42, pages 231-258.

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    2. Valadkhani, Abbas, 2023. "Asymmetric downside risk across different sectors of the US equity market," Global Finance Journal, Elsevier, vol. 57(C).
    3. Dovern, Jonas, 2006. "Predicting GDP components: do leading indicators increase predictability?," Kiel Advanced Studies Working Papers 436, Kiel Institute for the World Economy (IfW Kiel).
    4. F. Blasques & Christian Francq & Sébastien Laurent, 2024. "Autoregressive conditional betas," Post-Print hal-04676069, HAL.
    5. Gregory Bauer & Keith Vorkink, 2007. "Multivariate Realized Stock Market Volatility," Staff Working Papers 07-20, Bank of Canada.
    6. Doan, Bao & Jayasuriya, Dulani & Lee, John B. & Reeves, Jonathan J., 2024. "Cryptocurrency systematic risk dynamics," Economics Letters, Elsevier, vol. 241(C).
    7. David W. Berger & Alain P. Chaboud & Erik Hjalmarsson & Edward Howorka, 2006. "What drives volatility persistence in the foreign exchange market?," International Finance Discussion Papers 862, Board of Governors of the Federal Reserve System (U.S.).
    8. A. Mbairadjim Moussa & J. Sadefo Kamdem & A.F. Shapiro & M. Terraza, 2014. "CAPM with fuzzy returns and hypothesis testing," Post-Print hal-02901727, HAL.
    9. Helmut Herwartz, 2006. "Econometric analysis of high frequency data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 89-104, March.
    10. Alfred Mbairadjim Moussa & Jules Sadefo Kamdem & Arnold F. Shapiro & Michel Terraza, 2012. "Capital asset pricing model with fuzzy returns and hypothesis testing," Working Papers 12-33, LAMETA, Universtiy of Montpellier, revised Sep 2012.
    11. Papavassiliou, Vassilios G., 2013. "A new method for estimating liquidity risk: Insights from a liquidity-adjusted CAPM framework," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 24(C), pages 184-197.
    12. Quaye, Enoch & Tunaru, Diana & Tunaru, Radu, 2024. "Green-adjusted share prices: A comparison between standard investors and investors with green preferences," Journal of Financial Stability, Elsevier, vol. 74(C).
    13. Stoja, Evarist & Polanski, Arnold & Nguyen, Linh H. & Pereverzin, Aleksandr, 2023. "Does systematic tail risk matter?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).
    14. Richard M. Levich & Valerio Poti, 2008. "Predictability and 'Good Deals' in Currency Markets," NBER Working Papers 14597, National Bureau of Economic Research, Inc.
    15. Donggyu Kim & Minseok Shin, 2024. "Robust High-Dimensional Time-Varying Coefficient Estimation," Working Papers 202417, University of California at Riverside, Department of Economics.
    16. Luo, Jiawen & Chen, Zhenbiao & Cheng, Mingmian, 2025. "Forecasting realized betas using predictors indicating structural breaks and asymmetric risk effects," Journal of Empirical Finance, Elsevier, vol. 80(C).
    17. Robert Ślepaczuk & Grzegorz Zakrzewski, 2009. "High-Frequency and Model-Free Volatility Estimators," Working Papers 2009-13, Faculty of Economic Sciences, University of Warsaw.
    18. Haselmann, Rainer & Herwartz, Helmut, 2008. "Portfolio performance and the Euro: Prospects for new potential EMU members," Journal of International Money and Finance, Elsevier, vol. 27(2), pages 314-330, March.
    19. Nekhili, Ramzi & Bouri, Elie, 2023. "Higher-order moments and co-moments' contribution to spillover analysis and portfolio risk management," Energy Economics, Elsevier, vol. 119(C).

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    Cited by:

    1. Jin-Huei Yeh & Jying-Nan Wang & Chung-Ming Kuan, 2014. "A noise-robust estimator of volatility based on interquantile ranges," Review of Quantitative Finance and Accounting, Springer, vol. 43(4), pages 751-779, November.
    2. Andrey Kudryavtsev, 2012. "Short-Term Stock Price Reversals May Be Reversed," International Journal of Business and Economic Sciences Applied Research (IJBESAR), Democritus University of Thrace (DUTH), Kavala Campus, Greece, vol. 5(3), pages 129-146, December.
    3. Mattiussi, V. & Iori, G., 2006. "Currency futures volatility during the 1997 East Asian crisis: an application of Fourier analysis," Working Papers 06/09, Department of Economics, City University London.
    4. Yoann Potiron & Per Mykland, 2015. "Estimation of integrated quadratic covariation with endogenous sampling times," Papers 1507.01033, arXiv.org, revised Nov 2016.
    5. Eric Jondeau & Michael Rockinger, 2006. "Optimal Portfolio Allocation under Higher Moments," European Financial Management, European Financial Management Association, vol. 12(1), pages 29-55, January.
    6. Ole E. Barndorff-Nielsen & Neil Shephard, 2005. "Variation, jumps, market frictions and high frequency data in financial econometrics," OFRC Working Papers Series 2005fe08, Oxford Financial Research Centre.
    7. Lakshmi Padmakumari & S. Maheswaran, 2018. "Covariance estimation using random permutations," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 5(01), pages 1-21, March.
    8. Aharon, David Y. & Qadan, Mahmoud, 2020. "When do retail investors pay attention to their trading platforms?," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
    9. Yin-Wong Cheung, 2007. "An empirical model of daily highs and lows," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 12(1), pages 1-20.
    10. Chiu, Ching-Wai (Jeremy) & Harris, Richard & Stoja, Evarist & Chin, Michael, 2016. "Financial market volatility, macroeconomic fundamentals and investor sentiment," Bank of England working papers 608, Bank of England.
    11. Mueller, Philippe & Stathopoulos, Andreas & Vedolin, Andrea, 2017. "International correlation risk," LSE Research Online Documents on Economics 84140, London School of Economics and Political Science, LSE Library.
    12. Redouane Elkamhia & Denitsa Stefanova, 2011. "Dynamic Correlation or Tail Dependence Hedging for Portfolio Selection," Tinbergen Institute Discussion Papers 11-028/2/DSF10, Tinbergen Institute.
    13. LAURENT, Sébastien & VIOLANTE, Francesco, 2012. "Volatility forecasts evaluation and comparison," LIDAM Reprints CORE 2414, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    14. Yeh, Jin-Huei & Wang, Jying-Nan, 2010. "Correcting microstructure comovement biases for integrated covariance," Finance Research Letters, Elsevier, vol. 7(3), pages 184-191, September.
    15. Theodoros Diasakos, 2008. "Comparative Statics of Asset Prices," Carlo Alberto Notebooks 72, Collegio Carlo Alberto, revised 2011.
    16. Walid M.A. Ahmed, 2016. "Cross-border equity flows and market volatility: the case of Qatar Exchange," International Journal of Emerging Markets, Emerald Group Publishing Limited, vol. 11(3), pages 395-418, July.
    17. Vetter, Mathias & Podolskij, Mark, 2006. "Estimation of Volatility Functionals in the Simultaneous Presence of Microstructure Noise and Jumps," Technical Reports 2006,51, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    18. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "The role of high frequency intra-daily data, daily range and implied volatility in multi-period Value-at-Risk forecasting," MPRA Paper 35252, University Library of Munich, Germany.
    19. Silja Kinnebrock & Mark Podolskij, 2008. "An Econometric Analysis of Modulated Realised Covariance, Regression and Correlation in Noisy Diffusion Models," CREATES Research Papers 2008-23, Department of Economics and Business Economics, Aarhus University.
    20. Galli, Fausto, 2014. "Stochastic conditonal range, a latent variable model for financial volatility," MPRA Paper 54030, University Library of Munich, Germany.
    21. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2002. "Parametric and Nonparametric Volatility Measurement," Center for Financial Institutions Working Papers 02-27, Wharton School Center for Financial Institutions, University of Pennsylvania.
    22. Pagano, Michael S. & Peng, Lin & Schwartz, Robert A., 2013. "A call auction's impact on price formation and order routing: Evidence from the NASDAQ stock market," Journal of Financial Markets, Elsevier, vol. 16(2), pages 331-361.
    23. Eduardo Rossi & Paolo Santucci de Magistris, 2013. "A No‐Arbitrage Fractional Cointegration Model for Futures and Spot Daily Ranges," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 33(1), pages 77-102, January.
    24. S. Sanfelici & M. E. Mancino, 2008. "Covariance estimation via Fourier method in the presence of asynchronous trading and microstructure noise," Economics Department Working Papers 2008-ME01, Department of Economics, Parma University (Italy).
    25. Bertrand Maillet & Jean-Philippe Médecin & Thierry Michel, 2009. "High Watermarks of Market Risks," Post-Print halshs-00425585, HAL.
    26. Capelle-Blancard, Gunther & Havrylchyk, Olena, 2016. "The impact of the French securities transaction tax on market liquidity and volatility," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 166-178.
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    28. Gnabo, Jean-Yves & Hvozdyk, Lyudmyla & Lahaye, Jérôme, 2014. "System-wide tail comovements: A bootstrap test for cojump identification on the S&P 500, US bonds and currencies," Journal of International Money and Finance, Elsevier, vol. 48(PA), pages 147-174.
    29. Yin Liao & Heather M. Anderson, 2011. "Testing for co-jumps in high-frequency financial data: an approach based on first-high-low-last prices," Monash Econometrics and Business Statistics Working Papers 9/11, Monash University, Department of Econometrics and Business Statistics.
    30. Paulo M.M. Rodrigues & Matei Demetrescu, 2018. "Testing the fractionally integrated hypothesis using M estimation: With an application to stock market volatility," Working Papers w201817, Banco de Portugal, Economics and Research Department.
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    32. Fausti, Scott W. & Qasmi, Bashir A. & Diersen, Matthew A., 2007. "Public Reporting of Fed Cattle Grid Prices: Policy Reform Consequences," Economics Staff Papers 7297, South Dakota State University, Department of Economics.
    33. Dovonon, Prosper, 2008. "Conditionally heteroskedastic factor models with skewness and leverage effects," MPRA Paper 40206, University Library of Munich, Germany, revised Feb 2012.
    34. Yeh, Jin-Huei & Yun, Mu-Shu, 2023. "Assessing jump and cojumps in financial asset returns with applications in futures markets," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).
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    37. Pagano, Michael S. & Peng, Lin & Schwartz, Robert A., 2008. "The quality of price formation at market openings and closings: Evidence from the Nasdaq stock market," CFS Working Paper Series 2008/45, Center for Financial Studies (CFS).
    38. Härdle, Wolfgang Karl & Chen, Ying & Schulz, Rainer, 2004. "Prognose mit nichtparametrischen Verfahren," Papers 2004,07, Humboldt University of Berlin, Center for Applied Statistics and Economics (CASE).
    39. Vladimir Tsenkov, 2009. "Financial Markets Modelling," Economic Thought journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 5, pages 87-96.
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    41. Ozgur (Ozzy) Akay & Mark D. Griffiths & Drew B. Winters, 2010. "On The Robustness Of Range‐Based Volatility Estimators," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 33(2), pages 179-199, June.
    42. Sin, Chor-Yiu (CY), 2013. "Using CARRX models to study factors affecting the volatilities of Asian equity markets," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 552-564.
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    44. Vu Tran & Rasha Alsakka & Owain ap Gwilym, 2018. "Multiple credit ratings and market heterogeneity," Working Papers 2018-26, Swansea University, School of Management.
    45. Raimbourg, Philippe & Zimmermann, Paul, 2022. "Is normal backwardation normal? Valuing financial futures with a local index-rate covariance," European Journal of Operational Research, Elsevier, vol. 298(1), pages 351-367.
    46. Nicole Abruzzo & Yang-Ho Park, 2014. "An Empirical Analysis of Futures Margin Changes: Determinants and Policy Implications," Finance and Economics Discussion Series 2014-86, Board of Governors of the Federal Reserve System (U.S.).
    47. Hou, Kewei & Loh, Roger K., 2016. "Have we solved the idiosyncratic volatility puzzle?," Journal of Financial Economics, Elsevier, vol. 121(1), pages 167-194.
    48. Chazi, Abdelaziz & Samet, Anis & Azad, A.S.M. Sohel, 2023. "Volatility and correlation of Islamic and conventional indices during crises," Global Finance Journal, Elsevier, vol. 55(C).
    49. Gunther Capelle-Blancard, 2017. "Curbing the Growth of Stock Trading? Order-to-Trade Ratios and Financial Transaction Taxes," Post-Print hal-01441828, HAL.
    50. Tseng Tseng-Chan & Chung Huimin & Huang Chin-Sheng, 2009. "Modeling Jump and Continuous Components in the Volatility of Oil Futures," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 13(3), pages 1-30, May.
    51. Massimiliano Caporin & Angelo Ranaldo & Paolo Santucci de Magistris, 2011. "On the Predictability of Stock Prices: A Case for High and Low Prices," "Marco Fanno" Working Papers 0136, Dipartimento di Scienze Economiche "Marco Fanno".
    52. Bayraci, Selcuk & Demiralay, Sercan, 2013. "Conditional Autoregregressive Range (CARR) Based Volatility Spillover Index For the Eurozone Markets," MPRA Paper 51909, University Library of Munich, Germany.
    53. Castrén, Olli & Mazzotta, Stefano, 2005. "Foreign exchange option and returns based correlation forecasts: evaluation and two applications," Working Paper Series 447, European Central Bank.
    54. Lin Peng & Turan G. Bali, 2006. "Is there a risk-return trade-off? Evidence from high-frequency data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(8), pages 1169-1198.
    55. Cronin, David, 2014. "The interaction between money and asset markets: A spillover index approach," Journal of Macroeconomics, Elsevier, vol. 39(PA), pages 185-202.
    56. Yaya, OaOluwa S & Vo, Xuan Vinh & Ogbonna, Ahamuefula E & Adewuyi, Adeolu O, 2020. "Modelling Cryptocurrency High-Low Prices using Fractional Cointegrating VAR," MPRA Paper 102190, University Library of Munich, Germany, revised 02 Aug 2020.
    57. Filip Zikes, 2017. "Measuring Transaction Costs in the Absence of Timestamps," Finance and Economics Discussion Series 2017-045, Board of Governors of the Federal Reserve System (U.S.).
    58. Harris, Richard D.F. & Yilmaz, Fatih, 2010. "Estimation of the conditional variance-covariance matrix of returns using the intraday range," International Journal of Forecasting, Elsevier, vol. 26(1), pages 180-194, January.
    59. Yan-Leung Cheung & Yin-Wong Cheung & Alan T.K. Wan, 2008. "A High-Low Model of Daily Stock Price Ranges," CESifo Working Paper Series 2387, CESifo.
    60. Liu, Xinyi & Margaritis, Dimitris & Wang, Peiming, 2012. "Stock market volatility and equity returns: Evidence from a two-state Markov-switching model with regressors," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 483-496.
    61. Laurent, Sébastien & Rombouts, Jeroen V.K. & Violante, Francesco, 2013. "On loss functions and ranking forecasting performances of multivariate volatility models," Journal of Econometrics, Elsevier, vol. 173(1), pages 1-10.
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    63. Wu, Chih-Chiang & Liang, Shin-Shun, 2011. "The economic value of range-based covariance between stock and bond returns with dynamic copulas," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 711-727, September.
    64. Henning Fischer & Ángela Blanco‐FERNÁndez & Peter Winker, 2016. "Predicting Stock Return Volatility: Can We Benefit from Regression Models for Return Intervals?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(2), pages 113-146, March.
    65. Vortelinos, Dimitrios I., 2014. "Optimally sampled realized range-based volatility estimators," Research in International Business and Finance, Elsevier, vol. 30(C), pages 34-50.
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    67. Vortelinos, Dimitrios I., 2016. "Incremental information of stock indicators," International Review of Economics & Finance, Elsevier, vol. 41(C), pages 79-97.
    68. Piotr Fiszeder, 2018. "Low and high prices can improve covariance forecasts: The evidence based on currency rates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(6), pages 641-649, September.
    69. González-Rivera, Gloria & Luo, Yun & Ruiz Ortega, Esther, 2019. "Prediction regions for interval-valued time series," DES - Working Papers. Statistics and Econometrics. WS 29054, Universidad Carlos III de Madrid. Departamento de Estadística.
    70. Awartani, Basel & Maghyereh, Aktham Issa, 2013. "Dynamic spillovers between oil and stock markets in the Gulf Cooperation Council Countries," Energy Economics, Elsevier, vol. 36(C), pages 28-42.
    71. Bandi, Federico M. & Russell, Jeffrey R., 2006. "Separating microstructure noise from volatility," Journal of Financial Economics, Elsevier, vol. 79(3), pages 655-692, March.
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    75. Eduardo Rossi & Paolo Santucci de Magistris, 2009. "A No Arbitrage Fractional Cointegration Analysis Of The Range Based Volatility," CREATES Research Papers 2009-31, Department of Economics and Business Economics, Aarhus University.
    76. Ben Sita, Bernard & Abdallah, Wissam, 2014. "Volatility links between the home and the host market for U.K. dual-listed stocks on U.S. markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 33(C), pages 183-199.
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    81. Li, Shaoyu & Zheng, Tingguo, 2017. "Modeling spot rate using a realized stochastic volatility model with level effect and dynamic drift☆," The North American Journal of Economics and Finance, Elsevier, vol. 40(C), pages 200-221.
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    84. Robert F. Engle & Giampiero M. Gallo, 2003. "A Multiple Indicators Model For Volatility Using Intra-Daily Data," Econometrics Working Papers Archive wp2003_07, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    85. Frankovic, Jozo & Liu, Bin & Suardi, Sandy, 2022. "On spillover effects between cryptocurrency-linked stocks and the cryptocurrency market: Evidence from Australia," Global Finance Journal, Elsevier, vol. 54(C).
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    90. Bannouh, K. & van Dijk, D.J.C. & Martens, M.P.E., 2008. "Range-based covariance estimation using high-frequency data: The realized co-range," Econometric Institute Research Papers EI 2007-53, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    91. Galli, Fausto, 2014. "Stochastic conditonal range, a latent variable model for financial volatility," MPRA Paper 54841, University Library of Munich, Germany.
    92. Vallois, Pierre & Tapiero, Charles S., 2008. "Volatility estimators and the inverse range process in a random volatility random walk and Wiener processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(11), pages 2565-2574.
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    96. Matei, Marius, 2011. "Non-Linear Volatility Modeling of Economic and Financial Time Series Using High Frequency Data," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 116-141, June.
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    98. Robert Ślepaczuk & Grzegorz Zakrzewski, 2009. "High-Frequency and Model-Free Volatility Estimators," Working Papers 2009-13, Faculty of Economic Sciences, University of Warsaw.
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    Cited by:

    1. Geert Bekaert & Seonghoon Cho & Antonio Moreno, 2005. "New-Keynesian Macroeconomics and the Term Structure," NBER Working Papers 11340, National Bureau of Economic Research, Inc.
    2. Matteo Barigozzi & Lorenzo Trapani, 2018. "Determining the dimension of factor structures in non-stationary large datasets," Papers 1806.03647, arXiv.org.
    3. Ken Nyholm, 2007. "A New Approach to Predicting Recessions," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 36(1), pages 27-42, February.
    4. Martins, Manuel M.F. & Afonso, António, 2010. "Level, slope, curvature of the sovereign yield curve, and fiscal behaviour," Working Paper Series 1276, European Central Bank.
    5. Moench, Emanuel & Soofi-Siavash, Soroosh, 2022. "What moves treasury yields?," Journal of Financial Economics, Elsevier, vol. 146(3), pages 1016-1043.
    6. Hautsch, Nikolaus & Yang, Fuyu, 2010. "Bayesian inference in a stochastic volatility Nelson-Siegel Model," SFB 649 Discussion Papers 2010-004, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    7. Sowmya, Subramaniam & Prasanna, Krishna & Bhaduri, Saumitra, 2016. "Linkages in the term structure of interest rates across sovereign bond markets," Emerging Markets Review, Elsevier, vol. 27(C), pages 118-139.
    8. Zhou, Siwen, 2019. "Assessing the Macroeconomic Impact of the ECB’s Asset Purchase Programme in a Dynamic Nelson–Siegel Modelling Framework," MPRA Paper 92530, University Library of Munich, Germany.
    9. Koeda, Junko & Sekine, Atsushi, 2022. "Nelson–Siegel decay factor and term premia in Japan," Journal of the Japanese and International Economies, Elsevier, vol. 64(C).
    10. Caldeira, João F. & Moura, Guilherme V. & Santos, André A.P., 2016. "Bond portfolio optimization using dynamic factor models," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 128-158.
    11. Michael Pfarrhofer, 2019. "Measuring international uncertainty using global vector autoregressions with drifting parameters," Papers 1908.06325, arXiv.org, revised Dec 2019.
    12. RUGE-MURCIA, Francisco J., 2012. "Skewness Risk and Bond Prices," Cahiers de recherche 2012-14, Universite de Montreal, Departement de sciences economiques.
    13. Shu WU & Rende Li, 2025. "Estimating an affine term structure model of interest rates with correlated noise," PLOS ONE, Public Library of Science, vol. 20(2), pages 1-21, February.
    14. Peng Huang & C. James Hueng, 2009. "Interest-rate risk factor and stock returns: a time-varying factor-loadings model," Applied Financial Economics, Taylor & Francis Journals, vol. 19(22), pages 1813-1824.
    15. Eric Hillebrand & Huiyu Huang & Tae-Hwy Lee & Canlin Li, 2018. "Using the Entire Yield Curve in Forecasting Output and Inflation," Econometrics, MDPI, vol. 6(3), pages 1-27, August.
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    440. Michele Campolieti & Deborah Gefang & Gary Koop, 2013. "A new look at variation in employment growth in Canada," Working Papers 26145565, Lancaster University Management School, Economics Department.
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    442. Ricardo Gimeno & Eva Ortega, 2016. "The evolution of inflation expectations in euro area markets," Working Papers 1627, Banco de España.
    443. Loechel, Horst & Packham, Natalie & Walisch, Fabian, 2013. "Determinants of the onshore and offshore Chinese Government yield curves," Frankfurt School - Working Paper Series 202, Frankfurt School of Finance and Management.
    444. Martin M. Andreasen, 2019. "Explaining Bond Return Predictability in an Estimated New Keynesian Model," CREATES Research Papers 2019-11, Department of Economics and Business Economics, Aarhus University.
    445. Francesca Biagini & Alessandro Gnoatto & Maximilian Hartel, 2013. "Affine HJM Framework on $S_{d}^{+}$ and Long-Term Yield," Papers 1311.0688, arXiv.org, revised Aug 2015.
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    447. Efthymios Argyropoulos & Nikolaos Elias & Dimitris Smyrnakis & Elias Tzavalis, 2021. "Can country-specific interest rate factors explain the forward premium anomaly?," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 45(2), pages 252-269, April.
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    450. Chauvet, Marcelle & Senyuz, Zeynep, 2016. "A dynamic factor model of the yield curve components as a predictor of the economy," International Journal of Forecasting, Elsevier, vol. 32(2), pages 324-343.
    451. Gaus, Eric & Sinha, Arunima, 2018. "What does the yield curve imply about investor expectations?," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 248-265.
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    454. Borus Jungbacker & Siem Jan Koopman, 2015. "Likelihood‐based dynamic factor analysis for measurement and forecasting," Econometrics Journal, Royal Economic Society, vol. 18(2), pages 1-21, June.
    455. Memmel, Christoph & Heckmann, Lotta, 2025. "Modeling the term structure," Discussion Papers 07/2025, Deutsche Bundesbank.
    456. Ghent, Andra, 2007. "Why do markets react badly to good news? Evidence from Fed Funds Futures," MPRA Paper 1708, University Library of Munich, Germany.
    457. Jamie L. Cross & Aubrey Poon & Wenying Yao & Dan Zhu, 2024. "A Constrained Dynamic Nelson-Siegel Model for Monetary Policy Analysis," Working Papers No 06/2024, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    458. Oguzhan Cepni & Ibrahim Ethem Guney & Doruk Kucuksarac & Muhammed Hasan Yilmaz, 2018. "The Interaction between Yield Curve and Macroeconomic Factors," CBT Research Notes in Economics 1802, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    459. Guidolin, Massimo & Pedio, Manuela, 2019. "Forecasting and trading monetary policy effects on the riskless yield curve with regime switching Nelson–Siegel models," Journal of Economic Dynamics and Control, Elsevier, vol. 107(C), pages 1-1.
    460. Tronzano, Marco, 2015. "The Expectations Hypothesis of the Term Structure in Emerging Financial Markets: Some Evidence from Malaysia (1999-2015) - La struttura a termine dei tassi di interesse nei paesi emergenti: alcune evi," Economia Internazionale / International Economics, Camera di Commercio Industria Artigianato Agricoltura di Genova, vol. 68(4), pages 521-550.
    461. Takamizawa, Hideyuki & 高見澤, 秀幸, 2015. "Impact of No-arbitrage on Interest Rate Dynamics," Working Paper Series G-1-5, Hitotsubashi University Center for Financial Research.
    462. Rhea Choudhary, 2022. "AnalysingthespillovereffectsoftheSouthAfricanReserveBanksbondpurchaseprogramme," Working Papers 11039, South African Reserve Bank.
    463. Oliver Blaskowitz & Helmut Herwartz, 2009. "Adaptive forecasting of the EURIBOR swap term structure," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(7), pages 575-594.
    464. Wali ULLAH & Khadija Malik BARI, 2018. "The Term Structure of Government Bond Yields in an Emerging Market," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 5-28, September.
    465. Polat, Onur & Ozkan, Ibrahim, 2019. "Transmission mechanisms of financial stress into economic activity in Turkey," Journal of Policy Modeling, Elsevier, vol. 41(2), pages 395-415.
    466. Borus Jungbacker & Siem Jan Koopman, 2008. "Likelihood-based Analysis for Dynamic Factor Models," Tinbergen Institute Discussion Papers 08-007/4, Tinbergen Institute, revised 20 Mar 2014.
    467. SYED, Sarfaraz Ali Shah, 2021. "Heterogeneous consumers in the Euro-Area, facing homogeneous monetary policy: Tale of two large economies," The Journal of Economic Asymmetries, Elsevier, vol. 24(C).
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    469. Nagano, Teppei & Baba, Naohiko, 2008. "Extracting market expectations from yield curves augmented by money market interest rates: the case of Japan," Working Paper Series 980, European Central Bank.
    470. Wali Ullah & Yasumasa Matsuda & Yoshihiko Tsukuda, 2013. "Term Structure Modeling and Forecasting of Government Bond Yields," Economic Papers, The Economic Society of Australia, vol. 32(4), pages 535-560, December.
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    472. Sang-Heon Lee, 2025. "An Alternative Approach for Determining the Time-Varying Decay Parameter of the Nelson-Siegel Model," Computational Economics, Springer;Society for Computational Economics, vol. 65(5), pages 2965-2990, May.
    473. Vides, José Carlos & Golpe, Antonio A. & Iglesias, Jesús, 2020. "The EHTS and the persistence in the spread reconsidered. A fractional cointegration approach," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 124-137.
    474. ZHU Xiaoneng & Shahidur RAHMAN, 2009. "A Regime Switching Macro-finance Model of the Term Structure," Economic Growth Centre Working Paper Series 0901, Nanyang Technological University, School of Social Sciences, Economic Growth Centre.
    475. Tsz-Kin Chung & Cho-Hoi Hui & Ka-Fai Li, 2015. "Term-Structure Modelling at the Zero Lower Bound: Implications for Estimating the Term Premium," Working Papers 212015, Hong Kong Institute for Monetary Research.
    476. Fernandes, Marcelo & Vieira, Fausto, 2019. "A dynamic Nelson–Siegel model with forward-looking macroeconomic factors for the yield curve in the US," Journal of Economic Dynamics and Control, Elsevier, vol. 106(C), pages 1-1.
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    478. Christos Ioannidis & Kook Ka, 2021. "Economic Policy Uncertainty and Bond Risk Premia," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(6), pages 1479-1522, September.
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    480. Argyropoulos, Efthymios & Tzavalis, Elias, 2021. "The influence of real interest rates and risk premium effects on the ability of the nominal term structure to forecast inflation," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 785-796.
    481. Reyna Cerecero Mario & Salazar Cavazos Diana & Salgado Banda Héctor, 2008. "The Yield Curve and its Relation with Economic Activity: The Mexican Case," Working Papers 2008-15, Banco de México.

  66. Andersen, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Wu, Jin, 2004. "Realized beta: Persistence and predictability," CFS Working Paper Series 2004/16, Center for Financial Studies (CFS).

    Cited by:

    1. Jose Fernandes & Augusto Hasman & Juan Ignacio Pena, 2007. "Risk premium: insights over the threshold," Applied Financial Economics, Taylor & Francis Journals, vol. 18(1), pages 41-59.
    2. Valadkhani, Abbas, 2023. "Asymmetric downside risk across different sectors of the US equity market," Global Finance Journal, Elsevier, vol. 57(C).
    3. Dovern, Jonas, 2006. "Predicting GDP components: do leading indicators increase predictability?," Kiel Advanced Studies Working Papers 436, Kiel Institute for the World Economy (IfW Kiel).
    4. F. Blasques & Christian Francq & Sébastien Laurent, 2024. "Autoregressive conditional betas," Post-Print hal-04676069, HAL.
    5. Gregory Bauer & Keith Vorkink, 2007. "Multivariate Realized Stock Market Volatility," Staff Working Papers 07-20, Bank of Canada.
    6. Doan, Bao & Jayasuriya, Dulani & Lee, John B. & Reeves, Jonathan J., 2024. "Cryptocurrency systematic risk dynamics," Economics Letters, Elsevier, vol. 241(C).
    7. David W. Berger & Alain P. Chaboud & Erik Hjalmarsson & Edward Howorka, 2006. "What drives volatility persistence in the foreign exchange market?," International Finance Discussion Papers 862, Board of Governors of the Federal Reserve System (U.S.).
    8. A. Mbairadjim Moussa & J. Sadefo Kamdem & A.F. Shapiro & M. Terraza, 2014. "CAPM with fuzzy returns and hypothesis testing," Post-Print hal-02901727, HAL.
    9. Helmut Herwartz, 2006. "Econometric analysis of high frequency data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 89-104, March.
    10. Alfred Mbairadjim Moussa & Jules Sadefo Kamdem & Arnold F. Shapiro & Michel Terraza, 2012. "Capital asset pricing model with fuzzy returns and hypothesis testing," Working Papers 12-33, LAMETA, Universtiy of Montpellier, revised Sep 2012.
    11. Papavassiliou, Vassilios G., 2013. "A new method for estimating liquidity risk: Insights from a liquidity-adjusted CAPM framework," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 24(C), pages 184-197.
    12. Quaye, Enoch & Tunaru, Diana & Tunaru, Radu, 2024. "Green-adjusted share prices: A comparison between standard investors and investors with green preferences," Journal of Financial Stability, Elsevier, vol. 74(C).
    13. Stoja, Evarist & Polanski, Arnold & Nguyen, Linh H. & Pereverzin, Aleksandr, 2023. "Does systematic tail risk matter?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).
    14. Richard M. Levich & Valerio Poti, 2008. "Predictability and 'Good Deals' in Currency Markets," NBER Working Papers 14597, National Bureau of Economic Research, Inc.
    15. Donggyu Kim & Minseok Shin, 2024. "Robust High-Dimensional Time-Varying Coefficient Estimation," Working Papers 202417, University of California at Riverside, Department of Economics.
    16. Luo, Jiawen & Chen, Zhenbiao & Cheng, Mingmian, 2025. "Forecasting realized betas using predictors indicating structural breaks and asymmetric risk effects," Journal of Empirical Finance, Elsevier, vol. 80(C).
    17. Robert Ślepaczuk & Grzegorz Zakrzewski, 2009. "High-Frequency and Model-Free Volatility Estimators," Working Papers 2009-13, Faculty of Economic Sciences, University of Warsaw.
    18. Haselmann, Rainer & Herwartz, Helmut, 2008. "Portfolio performance and the Euro: Prospects for new potential EMU members," Journal of International Money and Finance, Elsevier, vol. 27(2), pages 314-330, March.
    19. Nekhili, Ramzi & Bouri, Elie, 2023. "Higher-order moments and co-moments' contribution to spillover analysis and portfolio risk management," Energy Economics, Elsevier, vol. 119(C).

  67. S. Boragan Aruoba & Francis X. Diebold & Glenn D. Rudebusch, 2003. "The macroeconomy and the yield curve: a nonstructural analysis," Working Paper Series 2003-18, Federal Reserve Bank of San Francisco.

    Cited by:

    1. Marco Lyrio & Hans Dewachter, 2004. "Filtering Long-Run Inflation Expectations with a Structural Macro Model of the Yield Curve," Computing in Economics and Finance 2004 188, Society for Computational Economics.
    2. Beechey, Meredith, 2004. "Excess Sensitivity and Volatility of Long Interest Rates: The Role of Limited Information in Bond Markets," Working Paper Series 173, Sveriges Riksbank (Central Bank of Sweden).
    3. GlennD. Rudebusch & Tao Wu, 2008. "A Macro-Finance Model of the Term Structure, Monetary Policy and the Economy," Economic Journal, Royal Economic Society, vol. 118(530), pages 906-926, July.
    4. David Bolder & Grahame Johnson & Adam Metzler, 2004. "An Empirical Analysis of the Canadian Term Structure of Zero-Coupon Interest Rates," Staff Working Papers 04-48, Bank of Canada.
    5. Michael R. Wickens & Chiona Balfoussia, 2004. "Macroeconomic Sources of Risk in the Term Structure," CEIS Research Paper 61, Tor Vergata University, CEIS.
    6. Greg Duffee, 2005. "Term structure estimation without using latent factors," Computing in Economics and Finance 2005 103, Society for Computational Economics.
    7. Daniel L. Thornton, 2005. "Predictions of short-term rates and the expectations hypothesis of the term structure of interest rates," Working Papers 2004-010, Federal Reserve Bank of St. Louis.
    8. Kanjilal, Kakali, 2013. "Factors causing movements of yield curve in India," Economic Modelling, Elsevier, vol. 31(C), pages 739-751.
    9. Leo Krippner, 2005. "An Intertemporally-Consistent and Arbitrage-Free Version of the Nelson and Siegel Class of Yield Curve Models," Working Papers in Economics 05/01, University of Waikato.
    10. Leo Krippner, 2005. "Investigating the Relationships between the Yield Curve, Output and Inflation using an Arbitrage-Free Version of the Nelson and Siegel Class of Yield Curve Models," Working Papers in Economics 05/02, University of Waikato.
    11. Peter Spencer, 2007. "Macro volatility in a model of the UK Gilt edged bond market," Money Macro and Finance (MMF) Research Group Conference 2006 73, Money Macro and Finance Research Group.

  68. Diebold, Francis X. & Li, Canlin, 2003. "Forecasting the term structure of government bond yields," CFS Working Paper Series 2004/09, Center for Financial Studies (CFS).

    Cited by:

    1. Driffill, John & Sola, Martin & Kenc, Turalay & Spagnolo, Fabio, 2004. "On Model Selection and Markov Switching: A Empirical Examination of Term Structure Models with Regime Shifts," CEPR Discussion Papers 4165, C.E.P.R. Discussion Papers.
    2. Andres Vesilind, 2003. "Application Of Fundamental Models To Money And Exchange Rate Markets," University of Tartu - Faculty of Economics and Business Administration Working Paper Series 22, Faculty of Economics and Business Administration, University of Tartu (Estonia).
    3. Bhattarai, Saroj & Chatterjee, Arpita & Park, Woong Yong, 2018. "Effects of US Quantitative Easing on Emerging Market Economies," ADBI Working Papers 803, Asian Development Bank Institute.
    4. Dennis Philip & Chihwa Kao & Giovanni Urga, 2007. "Testing for Instability in Factor Structure of Yield Curves," Center for Policy Research Working Papers 96, Center for Policy Research, Maxwell School, Syracuse University.
    5. Julien Vedani & Fabien Ramaharobandro, 2013. "Continuous compliance: a proxy-based monitoring framework," Papers 1309.7222, arXiv.org, revised Dec 2013.
    6. Umar, Zaghum & Yousaf, Imran & Gubareva, Mariya & Vo, Xuan Vinh, 2022. "Spillover and risk transmission between the term structure of the US interest rates and Islamic equities," Pacific-Basin Finance Journal, Elsevier, vol. 72(C).
    7. Azamat Abdymomunov & Kyu Ho Kang & Ki Jeong Kim, 2014. "Forecasting the Term Structure of Government Bond Yields Using Credit Spreads and Structural Breaks," Working Papers 2014-19, Economic Research Institute, Bank of Korea.
    8. Muhammad Yasir & Sitara Afzal & Khalid Latif & Ghulam Mujtaba Chaudhary & Nazish Yameen Malik & Farhan Shahzad & Oh-young Song, 2020. "An Efficient Deep Learning Based Model to Predict Interest Rate Using Twitter Sentiment," Sustainability, MDPI, vol. 12(4), pages 1-16, February.
    9. Lee, Tae-Hwy & Long, Xiangdong, 2009. "Copula-based multivariate GARCH model with uncorrelated dependent errors," Journal of Econometrics, Elsevier, vol. 150(2), pages 207-218, June.
    10. Rafael Barros de Rezende, 2011. "Giving Flexibility to the Nelson-Siegel Class of Term Structure Models," Brazilian Review of Finance, Brazilian Society of Finance, vol. 9(1), pages 27-49.
    11. Owadally, Iqbal & Jang, Chul & Clare, Andrew, 2021. "Optimal investment for a retirement plan with deferred annuities," Insurance: Mathematics and Economics, Elsevier, vol. 98(C), pages 51-62.
    12. Matteo Barigozzi & Lorenzo Trapani, 2018. "Determining the dimension of factor structures in non-stationary large datasets," Papers 1806.03647, arXiv.org.
    13. Ken Nyholm, 2007. "A New Approach to Predicting Recessions," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 36(1), pages 27-42, February.
    14. Martins, Manuel M.F. & Afonso, António, 2010. "Level, slope, curvature of the sovereign yield curve, and fiscal behaviour," Working Paper Series 1276, European Central Bank.
    15. Laurini, Márcio Poletti & Hotta, Luiz Koodi, 2010. "Bayesian extensions to Diebold-Li term structure model," International Review of Financial Analysis, Elsevier, vol. 19(5), pages 342-350, December.
    16. Jeimy Lorena Martínez Arroyo & Nini Johana Marín Rodríguez, 2021. "Relación dinámica entre los Credit Default Swaps y la deuda pública. Análisis en el contexto latinoamericano," Revista Cuadernos de Economia, Universidad Nacional de Colombia, FCE, CID, vol. 40(83), pages 583-608, August.
    17. Moench, Emanuel & Soofi-Siavash, Soroosh, 2022. "What moves treasury yields?," Journal of Financial Economics, Elsevier, vol. 146(3), pages 1016-1043.
    18. Hautsch, Nikolaus & Yang, Fuyu, 2010. "Bayesian inference in a stochastic volatility Nelson-Siegel Model," SFB 649 Discussion Papers 2010-004, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    19. H. Peter Boswijk & Giuseppe Cavaliere & Anders Rahbek & A. M. Robert Taylor, 2013. "Inference on Co-integration Parameters in Heteroskedastic Vector Autoregressions," Tinbergen Institute Discussion Papers 13-187/III, Tinbergen Institute.
    20. Fulvio Pegoraro & Siegel, A. F. & Tiozzo Pezzoli, L., 2014. "Specification Analysis of International Treasury Yield Curve Factors," Working papers 490, Banque de France.
    21. Fearghal Kearney & Han Lin Shang & Lisa Sheenan, 2019. "Implied volatility surface predictability: the case of commodity markets," Papers 1909.11009, arXiv.org.
    22. Sowmya, Subramaniam & Prasanna, Krishna & Bhaduri, Saumitra, 2016. "Linkages in the term structure of interest rates across sovereign bond markets," Emerging Markets Review, Elsevier, vol. 27(C), pages 118-139.
    23. Zhou, Siwen, 2019. "Assessing the Macroeconomic Impact of the ECB’s Asset Purchase Programme in a Dynamic Nelson–Siegel Modelling Framework," MPRA Paper 92530, University Library of Munich, Germany.
    24. Koeda, Junko & Sekine, Atsushi, 2022. "Nelson–Siegel decay factor and term premia in Japan," Journal of the Japanese and International Economies, Elsevier, vol. 64(C).
    25. Fernando Broner & Guido Lorenzoni & Sergio Schmuckler, 2006. "Why Do Emerging Economies Borrow Short Term?," 2006 Meeting Papers 841, Society for Economic Dynamics.
    26. Caldeira, João F. & Moura, Guilherme V. & Santos, André A.P., 2016. "Bond portfolio optimization using dynamic factor models," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 128-158.
    27. Siem Jan Koopman & Rutger Lit & Andre Lucas, 2015. "Intraday Stochastic Volatility in Discrete Price Changes: the Dynamic Skellam Model," Tinbergen Institute Discussion Papers 15-076/IV/DSF94, Tinbergen Institute.
    28. Michael Pfarrhofer, 2019. "Measuring international uncertainty using global vector autoregressions with drifting parameters," Papers 1908.06325, arXiv.org, revised Dec 2019.
    29. Helmut Herwartz & Helmut Lütkepohl, 2011. "Generalized least squares estimation for cointegration parameters under conditional heteroskedasticity," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(3), pages 281-291, May.
    30. Jose Vicente & Benjamin M. Tabak, 2007. "Forecasting Bonds Yields in the Brazilian Fixed Income Market," Working Papers Series 141, Central Bank of Brazil, Research Department.
    31. Comelli, Fabio, 2012. "Emerging market sovereign bond spreads: Estimation and back-testing," Emerging Markets Review, Elsevier, vol. 13(4), pages 598-625.
    32. Krüger Fabian & Pohlmeier Winfried & Mokinski Frieder, 2011. "Combining Survey Forecasts and Time Series Models: The Case of the Euribor," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 63-81, February.
    33. Eric Hillebrand & Huiyu Huang & Tae-Hwy Lee & Canlin Li, 2018. "Using the Entire Yield Curve in Forecasting Output and Inflation," Econometrics, MDPI, vol. 6(3), pages 1-27, August.
    34. Leo Krippner, 2003. "Modelling the Yield Curve with Orthonormalised Laguerre Polynomials: A Consistent Cross-Sectional and Inter-Temporal Approach," Working Papers in Economics 03/02, University of Waikato.
    35. Chalamandaris, Georgios & Tsekrekos, Andrianos E., 2010. "Predictable dynamics in implied volatility surfaces from OTC currency options," Journal of Banking & Finance, Elsevier, vol. 34(6), pages 1175-1188, June.
    36. Elizondo Rocío, 2013. "Forecasting the Term Structure of Interest Rates in Mexico Using an Affine Model," Working Papers 2013-03, Banco de México.
    37. Matsumura, Marco & Moreira, Ajax & Vicente, José, 2011. "Forecasting the yield curve with linear factor models," International Review of Financial Analysis, Elsevier, vol. 20(5), pages 237-243.
    38. Charles Goodhart, 2009. "The Interest Rate Conditioning Assumption," International Journal of Central Banking, International Journal of Central Banking, vol. 5(2), pages 85-108, June.
    39. Giacomini, Raffaella & Ragusa, Giuseppe & Altavilla, Carlo, 2013. "Anchoring the Yield Curve Using Survey Expectations," CEPR Discussion Papers 9738, C.E.P.R. Discussion Papers.
    40. Yajing Xu & Michael Sherris & Jonathan Ziveyi, 2020. "Market Price of Longevity Risk for a Multi‐Cohort Mortality Model With Application to Longevity Bond Option Pricing," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 87(3), pages 571-595, September.
    41. Chen, Steven Shu-Hsiu, 2024. "Volatility feedback and dealership position: Evidence from the CDS Index, Corporate Bonds, and Government Bonds," Finance Research Letters, Elsevier, vol. 69(PB).
    42. Oh, Dong Hwan & Patton, Andrew J., 2024. "Better the devil you know: Improved forecasts from imperfect models," Journal of Econometrics, Elsevier, vol. 242(1).
    43. Nath, Golaka, 2012. "Indian corporate bonds market –an analytical prospective," MPRA Paper 38992, University Library of Munich, Germany.
    44. Richard K. Crump & Stefano Eusepi & Emanuel Moench, 2016. "The term structure of expectations and bond yields," Staff Reports 775, Federal Reserve Bank of New York.
    45. van Landschoot, A., 2003. "The Term Structure of Credit Spreads on Euro Corporate Bonds," Discussion Paper 2003-046, Tilburg University, Center for Economic Research.
    46. Francis X. Diebold & Monika Piazzesi & Glenn D. Rudebusch, 2005. "Modeling Bond Yields in Finance and Macroeconomics," PIER Working Paper Archive 05-008, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
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  69. Andersen, Torben G. & Bollerslev, Tim & Francis X. Diebold,, 2003. "Some Like it Smooth, and Some Like it Rough: Untangling Continuous and Jump Components in Measuring, Modeling, and Forecasting Asset Return Volatility," CFS Working Paper Series 2003/35, Center for Financial Studies (CFS).

    Cited by:

    1. Ole E. Barndorff-Nielsen & Neil Shephard, 2003. "Econometrics of testing for jumps in financial economics using bipower variation," Economics Papers 2003-W21, Economics Group, Nuffield College, University of Oxford.
    2. Nielsen, Morten Ørregaard & Frederiksen, Per, 2008. "Finite sample accuracy and choice of sampling frequency in integrated volatility estimation," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 265-286, March.
    3. Ole E. Barndorff-Nielsen & Neil Shephard, 2005. "Variation, jumps, market frictions and high frequency data in financial econometrics," OFRC Working Papers Series 2005fe08, Oxford Financial Research Centre.
    4. Alexander Mende, 2006. "09/11 on the USD/EUR foreign exchange market," Applied Financial Economics, Taylor & Francis Journals, vol. 16(3), pages 213-222.
    5. Dominique Guegan & Florian Ielpo, 2007. "Further evidence on the impact of economic news on interest rates," Post-Print halshs-00188331, HAL.
    6. Laurent E. Calvet & Adlai J. Fisher, 2005. "Multifrequency News and Stock Returns," NBER Working Papers 11441, National Bureau of Economic Research, Inc.
    7. Gregory Bauer & Keith Vorkink, 2007. "Multivariate Realized Stock Market Volatility," Staff Working Papers 07-20, Bank of Canada.
    8. Ole E. Barndorff-Nielsen & Sven Erik Graversen & Jean Jacod & Neil Shephard, 2005. "Limit theorems for bipower variation in financial econometrics," OFRC Working Papers Series 2005fe09, Oxford Financial Research Centre.
    9. Audrino, Francesco & Camponovo, Lorenzo & Roth, Constantin, 2015. "Testing the lag structure of assets’ realized volatility dynamics," Economics Working Paper Series 1501, University of St. Gallen, School of Economics and Political Science.
    10. Dufour, Jean-Marie & García, René & Taamouti, Abderrahim, 2008. "Measuring causality between volatility and returns with high-frequency data," UC3M Working papers. Economics we084422, Universidad Carlos III de Madrid. Departamento de Economía.
    11. Zheng, Tingguo & Zuo, Haomiao, 2013. "Reexamining the time-varying volatility spillover effects: A Markov switching causality approach," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 643-662.
    12. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Practical Volatility and Correlation Modeling for Financial Market Risk Management," NBER Working Papers 11069, National Bureau of Economic Research, Inc.
    13. Andrew Ang & Robert J. Hodrick & Yuhang Xing & Xiaoyan Zhang, 2004. "The Cross-Section of Volatility and Expected Returns," NBER Working Papers 10852, National Bureau of Economic Research, Inc.
    14. Basel Awartani & Valentina Corradi, 2004. "Testing and Modelling Market Microstructure Effects with an Application to the Dow Jones Industrial Average," Econometric Society 2004 North American Summer Meetings 487, Econometric Society.
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    17. Heather Anderson & Fashid Vahid, 2005. "Forecasting the Volatility of Australian Stock Returns: Do Common Factors Help?," ANU Working Papers in Economics and Econometrics 2005-451, Australian National University, College of Business and Economics, School of Economics.
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    19. Álvaro Cartea & Dimitrios Karyampas, 2009. "The Relationship Between the Volatility of Returns and the Number of Jumps in Financial Markets," Birkbeck Working Papers in Economics and Finance 0914, Birkbeck, Department of Economics, Mathematics & Statistics.
    20. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies," CIRANO Working Papers 2004s-19, CIRANO.
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    23. Ysusi Carla, 2006. "Detecting Jumps in High-Frequency Financial Series Using Multipower Variation," Working Papers 2006-10, Banco de México.
    24. Hassan Zada & Arshad Hassan & Wing-Keung Wong, 2021. "Do Jumps Matter in Both Equity Market Returns and Integrated Volatility: A Comparison of Asian Developed and Emerging Markets," Economies, MDPI, vol. 9(2), pages 1-26, June.
    25. Çelik, Sibel & Ergin, Hüseyin, 2014. "Volatility forecasting using high frequency data: Evidence from stock markets," Economic Modelling, Elsevier, vol. 36(C), pages 176-190.
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    30. Tao, Minjing & Wang, Yahzen & Yao, Qiwei & Zou, Jian, 2011. "Large volatility matrix inference via combining low-frequency and high-frequency approaches," LSE Research Online Documents on Economics 39321, London School of Economics and Political Science, LSE Library.
    31. Antoine Bouveret & Martin Haferkorn & Gaetano Marseglia & Onofrio Panzarino, 2022. "Flash crashes on sovereign bond markets – EU evidence," Mercati, infrastrutture, sistemi di pagamento (Markets, Infrastructures, Payment Systems) 20, Bank of Italy, Directorate General for Markets and Payment System.
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    34. Tim Leung & Theodore Zhao, 2024. "A Noisy Fractional Brownian Motion Model for Multiscale Correlation Analysis of High-Frequency Prices," Mathematics, MDPI, vol. 12(6), pages 1-21, March.

  70. Diebold, Francis X. & Li, Canlin, 2003. "Forecasting the term structure of government bond yields," CFS Working Paper Series 2004/09, Center for Financial Studies (CFS).

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    1. Driffill, John & Sola, Martin & Kenc, Turalay & Spagnolo, Fabio, 2004. "On Model Selection and Markov Switching: A Empirical Examination of Term Structure Models with Regime Shifts," CEPR Discussion Papers 4165, C.E.P.R. Discussion Papers.
    2. Andres Vesilind, 2003. "Application Of Fundamental Models To Money And Exchange Rate Markets," University of Tartu - Faculty of Economics and Business Administration Working Paper Series 22, Faculty of Economics and Business Administration, University of Tartu (Estonia).
    3. Bhattarai, Saroj & Chatterjee, Arpita & Park, Woong Yong, 2018. "Effects of US Quantitative Easing on Emerging Market Economies," ADBI Working Papers 803, Asian Development Bank Institute.
    4. Dennis Philip & Chihwa Kao & Giovanni Urga, 2007. "Testing for Instability in Factor Structure of Yield Curves," Center for Policy Research Working Papers 96, Center for Policy Research, Maxwell School, Syracuse University.
    5. Julien Vedani & Fabien Ramaharobandro, 2013. "Continuous compliance: a proxy-based monitoring framework," Papers 1309.7222, arXiv.org, revised Dec 2013.
    6. Umar, Zaghum & Yousaf, Imran & Gubareva, Mariya & Vo, Xuan Vinh, 2022. "Spillover and risk transmission between the term structure of the US interest rates and Islamic equities," Pacific-Basin Finance Journal, Elsevier, vol. 72(C).
    7. Azamat Abdymomunov & Kyu Ho Kang & Ki Jeong Kim, 2014. "Forecasting the Term Structure of Government Bond Yields Using Credit Spreads and Structural Breaks," Working Papers 2014-19, Economic Research Institute, Bank of Korea.
    8. Muhammad Yasir & Sitara Afzal & Khalid Latif & Ghulam Mujtaba Chaudhary & Nazish Yameen Malik & Farhan Shahzad & Oh-young Song, 2020. "An Efficient Deep Learning Based Model to Predict Interest Rate Using Twitter Sentiment," Sustainability, MDPI, vol. 12(4), pages 1-16, February.
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    11. Owadally, Iqbal & Jang, Chul & Clare, Andrew, 2021. "Optimal investment for a retirement plan with deferred annuities," Insurance: Mathematics and Economics, Elsevier, vol. 98(C), pages 51-62.
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    35. Chalamandaris, Georgios & Tsekrekos, Andrianos E., 2010. "Predictable dynamics in implied volatility surfaces from OTC currency options," Journal of Banking & Finance, Elsevier, vol. 34(6), pages 1175-1188, June.
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    38. Charles Goodhart, 2009. "The Interest Rate Conditioning Assumption," International Journal of Central Banking, International Journal of Central Banking, vol. 5(2), pages 85-108, June.
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    40. Yajing Xu & Michael Sherris & Jonathan Ziveyi, 2020. "Market Price of Longevity Risk for a Multi‐Cohort Mortality Model With Application to Longevity Bond Option Pricing," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 87(3), pages 571-595, September.
    41. Chen, Steven Shu-Hsiu, 2024. "Volatility feedback and dealership position: Evidence from the CDS Index, Corporate Bonds, and Government Bonds," Finance Research Letters, Elsevier, vol. 69(PB).
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    43. Nath, Golaka, 2012. "Indian corporate bonds market –an analytical prospective," MPRA Paper 38992, University Library of Munich, Germany.
    44. Richard K. Crump & Stefano Eusepi & Emanuel Moench, 2016. "The term structure of expectations and bond yields," Staff Reports 775, Federal Reserve Bank of New York.
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    47. Minsu Chang & Xiaohong Chen & Frank Schorfheide, 2021. "Heterogeneity and Aggregate Fluctuations," Cowles Foundation Discussion Papers 2289, Cowles Foundation for Research in Economics, Yale University.
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    Cited by:

    1. Ole E. Barndorff-Nielsen & Neil Shephard, 2003. "Econometrics of testing for jumps in financial economics using bipower variation," Economics Papers 2003-W21, Economics Group, Nuffield College, University of Oxford.
    2. Nielsen, Morten Ørregaard & Frederiksen, Per, 2008. "Finite sample accuracy and choice of sampling frequency in integrated volatility estimation," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 265-286, March.
    3. Ole E. Barndorff-Nielsen & Neil Shephard, 2005. "Variation, jumps, market frictions and high frequency data in financial econometrics," OFRC Working Papers Series 2005fe08, Oxford Financial Research Centre.
    4. Alexander Mende, 2006. "09/11 on the USD/EUR foreign exchange market," Applied Financial Economics, Taylor & Francis Journals, vol. 16(3), pages 213-222.
    5. Dominique Guegan & Florian Ielpo, 2007. "Further evidence on the impact of economic news on interest rates," Post-Print halshs-00188331, HAL.
    6. Laurent E. Calvet & Adlai J. Fisher, 2005. "Multifrequency News and Stock Returns," NBER Working Papers 11441, National Bureau of Economic Research, Inc.
    7. Gregory Bauer & Keith Vorkink, 2007. "Multivariate Realized Stock Market Volatility," Staff Working Papers 07-20, Bank of Canada.
    8. Ole E. Barndorff-Nielsen & Sven Erik Graversen & Jean Jacod & Neil Shephard, 2005. "Limit theorems for bipower variation in financial econometrics," OFRC Working Papers Series 2005fe09, Oxford Financial Research Centre.
    9. Audrino, Francesco & Camponovo, Lorenzo & Roth, Constantin, 2015. "Testing the lag structure of assets’ realized volatility dynamics," Economics Working Paper Series 1501, University of St. Gallen, School of Economics and Political Science.
    10. Dufour, Jean-Marie & García, René & Taamouti, Abderrahim, 2008. "Measuring causality between volatility and returns with high-frequency data," UC3M Working papers. Economics we084422, Universidad Carlos III de Madrid. Departamento de Economía.
    11. Zheng, Tingguo & Zuo, Haomiao, 2013. "Reexamining the time-varying volatility spillover effects: A Markov switching causality approach," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 643-662.
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    13. Andrew Ang & Robert J. Hodrick & Yuhang Xing & Xiaoyan Zhang, 2004. "The Cross-Section of Volatility and Expected Returns," NBER Working Papers 10852, National Bureau of Economic Research, Inc.
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    17. Heather Anderson & Fashid Vahid, 2005. "Forecasting the Volatility of Australian Stock Returns: Do Common Factors Help?," ANU Working Papers in Economics and Econometrics 2005-451, Australian National University, College of Business and Economics, School of Economics.
    18. Talpsepp, Tõnn & Rieger, Marc Oliver, 2010. "Explaining asymmetric volatility around the world," Journal of Empirical Finance, Elsevier, vol. 17(5), pages 938-956, December.
    19. Álvaro Cartea & Dimitrios Karyampas, 2009. "The Relationship Between the Volatility of Returns and the Number of Jumps in Financial Markets," Birkbeck Working Papers in Economics and Finance 0914, Birkbeck, Department of Economics, Mathematics & Statistics.
    20. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies," CIRANO Working Papers 2004s-19, CIRANO.
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    24. Hassan Zada & Arshad Hassan & Wing-Keung Wong, 2021. "Do Jumps Matter in Both Equity Market Returns and Integrated Volatility: A Comparison of Asian Developed and Emerging Markets," Economies, MDPI, vol. 9(2), pages 1-26, June.
    25. Çelik, Sibel & Ergin, Hüseyin, 2014. "Volatility forecasting using high frequency data: Evidence from stock markets," Economic Modelling, Elsevier, vol. 36(C), pages 176-190.
    26. Ghorbel, Ahmed & Trabelsi, Abdelwahed, 2007. "Predictive Performance of Conditional Extreme Value Theory and Conventional Methods in Value at Risk Estimation," MPRA Paper 3963, University Library of Munich, Germany.
    27. Wagner, Niklas & Szimayer, Alexander, 2004. "Local and spillover shocks in implied market volatility: evidence for the U.S. and Germany," Research in International Business and Finance, Elsevier, vol. 18(3), pages 237-251, September.
    28. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
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    30. Tao, Minjing & Wang, Yahzen & Yao, Qiwei & Zou, Jian, 2011. "Large volatility matrix inference via combining low-frequency and high-frequency approaches," LSE Research Online Documents on Economics 39321, London School of Economics and Political Science, LSE Library.
    31. Antoine Bouveret & Martin Haferkorn & Gaetano Marseglia & Onofrio Panzarino, 2022. "Flash crashes on sovereign bond markets – EU evidence," Mercati, infrastrutture, sistemi di pagamento (Markets, Infrastructures, Payment Systems) 20, Bank of Italy, Directorate General for Markets and Payment System.
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    34. Tim Leung & Theodore Zhao, 2024. "A Noisy Fractional Brownian Motion Model for Multiscale Correlation Analysis of High-Frequency Prices," Mathematics, MDPI, vol. 12(6), pages 1-21, March.

  73. Anderson, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Labys, Paul, 2002. "Modeling and Forecasting Realized Volatility," Working Papers 02-12, Duke University, Department of Economics.

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    1. Eric Hillebrand & Gunther Schnabl & Yasemin Ulu, 2006. "Japanese Foreign Exchange Intervention and the Yen/Dollar Exchange Rate: A Simultaneous Equations Approach Using Realized Volatility," CESifo Working Paper Series 1766, CESifo.
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    3. Volker Seiler, 2024. "The relationship between Chinese and FOB prices of rare earth elements – Evidence in the time and frequency domain," Post-Print hal-04549980, HAL.
    4. Jin-Huei Yeh & Jying-Nan Wang & Chung-Ming Kuan, 2014. "A noise-robust estimator of volatility based on interquantile ranges," Review of Quantitative Finance and Accounting, Springer, vol. 43(4), pages 751-779, November.
    5. Jui-Cheng Hung & Ren-Xi Ni & Matthew C. Chang, 2009. "The Information Contents of VIX Index and Range-based Volatility on Volatility Forecasting Performance of S&P 500," Economics Bulletin, AccessEcon, vol. 29(4), pages 2592-2604.
    6. Viktor Todorov & Yang Zhang, 2022. "Information gains from using short‐dated options for measuring and forecasting volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(2), pages 368-391, March.
    7. Consuela-Elena Popescu & Georgiana Vrinceanu & Alexandra Horobet & Lucian Belascu, 2020. "Managing Exchange Rate Risk with Derivatives: An Application of the Hedge Ratio," Business & Management Compass, University of Economics Varna, issue 3, pages 316-327.
    8. Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2013. "Semiparametric Vector Mem," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(7), pages 1067-1086, November.
    9. Zhang, Yuan-Yuan & Zhang, Yue-Jun, 2022. "The impact of institutional analyst forecast divergence on crude oil market: Evidence from the mixed frequency models," International Review of Financial Analysis, Elsevier, vol. 84(C).
    10. Audrino, Francesco & Fengler, Matthias, 2013. "Are classical option pricing models consistent with observed option second-order moments? Evidence from high-frequency data," Economics Working Paper Series 1311, University of St. Gallen, School of Economics and Political Science.
    11. Clements, Michael P., 2008. "Explanations of the inconsistencies in survey respondents'forecasts," The Warwick Economics Research Paper Series (TWERPS) 870, University of Warwick, Department of Economics.
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    1672. Wesselhöfft, Niels & Härdle, Wolfgang Karl, 2019. "Estimating low sampling frequency risk measure by high-frequency data," IRTG 1792 Discussion Papers 2019-003, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    1673. Cenesizoglu, Tolga & de Oliveira Ferrazoli Ribeiro, Fabio & Reeves, Jonathan J., 2017. "Beta forecasting at long horizons," International Journal of Forecasting, Elsevier, vol. 33(4), pages 936-957.
    1674. Naimoli, Antonio, 2023. "The information content of sentiment indices in forecasting Value at Risk and Expected Shortfall: a Complete Realized Exponential GARCH-X approach," International Economics, Elsevier, vol. 176(C).
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    1676. Pawel Janus & André Lucas & Anne Opschoor & Dick J.C. van Dijk, 2014. "New HEAVY Models for Fat-Tailed Returns and Realized Covariance Kernels," Tinbergen Institute Discussion Papers 14-073/IV, Tinbergen Institute, revised 19 Aug 2015.
    1677. Haugom, Erik & Westgaard, Sjur & Solibakke, Per Bjarte & Lien, Gudbrand, 2011. "Realized volatility and the influence of market measures on predictability: Analysis of Nord Pool forward electricity data," Energy Economics, Elsevier, vol. 33(6), pages 1206-1215.
    1678. Torben G. Andersen & Luca Benzoni, 2008. "Realized volatility," Working Paper Series WP-08-14, Federal Reserve Bank of Chicago.
    1679. Mala Dutt & Sanjay Sehgal, 2018. "Domestic and International Information Linkages between Gold Spot and Futures Markets: An Empirical Study for India," Metamorphosis: A Journal of Management Research, , vol. 17(1), pages 1-17, June.
    1680. Chen, Wei-Peng & Choudhry, Taufiq & Wu, Chih-Chiang, 2013. "The extreme value in crude oil and US dollar markets," Journal of International Money and Finance, Elsevier, vol. 36(C), pages 191-210.
    1681. Wang, Yudong & Wei, Yu & Wu, Chongfeng & Yin, Libo, 2018. "Oil and the short-term predictability of stock return volatility," Journal of Empirical Finance, Elsevier, vol. 47(C), pages 90-104.
    1682. Kyungsub Lee, 2013. "Probabilistic and statistical properties of moment variations and their use in inference and estimation based on high frequency return data," Papers 1311.5036, arXiv.org, revised Jul 2015.
    1683. Chun-Hung Chen & Wei-Choun Yu & Eric Zivot, 2009. "Predicting Stock Volatility Using After-Hours Information," Working Papers UWEC-2009-01, University of Washington, Department of Economics.
    1684. María García Centeno & Román Mínguez Salido, 2009. "Estimation of Asymmetric Stochastic Volatility Models for Stock-Exchange Index Returns," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 15(1), pages 71-87, February.
    1685. Lee, Hyunchul & Kim, Heeho, 2020. "Time varying integration of European stock markets and monetary drivers," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 369-385.
    1686. Todorova, Neda & Souček, Michael, 2014. "The impact of trading volume, number of trades and overnight returns on forecasting the daily realized range," Economic Modelling, Elsevier, vol. 36(C), pages 332-340.
    1687. Lovcha, Yuliya & Pérez Laborda, Àlex, 2018. "Volatility Spillovers in a Long-Memory VAR: an Application to Energy Futures Returns," Working Papers 2072/307362, Universitat Rovira i Virgili, Department of Economics.
    1688. Ulrich Hounyo & Zhi Liu & Rasmus T. Varneskov, 2023. "Bootstrapping Laplace transforms of volatility," Quantitative Economics, Econometric Society, vol. 14(3), pages 1059-1103, July.
    1689. Zhenwei Li & Jing Han & Yuping Song, 2020. "On the forecasting of high‐frequency financial time series based on ARIMA model improved by deep learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1081-1097, November.
    1690. Per Mykland, 2012. "A Gaussian calculus for inference from high frequency data," Annals of Finance, Springer, vol. 8(2), pages 235-258, May.
    1691. Eleonora Iachini & Stefano Nobili, 2014. "An indicator of systemic liquidity risk in the Italian financial markets," Questioni di Economia e Finanza (Occasional Papers) 217, Bank of Italy, Economic Research and International Relations Area.
    1692. Ashwin, Julian, 2024. "Financial news media and volatility: Is there more to newspapers than news?," Journal of Financial Markets, Elsevier, vol. 69(C).
    1693. Wang, Yudong & Ma, Feng & Wei, Yu & Wu, Chongfeng, 2016. "Forecasting realized volatility in a changing world: A dynamic model averaging approach," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 136-149.
    1694. David Allen & Stephen Satchell & Colin Lizieri, 2024. "Quantifying the non-Gaussian gain," Journal of Asset Management, Palgrave Macmillan, vol. 25(1), pages 1-18, February.
    1695. Christophe HURLIN & Sessi TOKPAVI, 2006. "Backtesting VaR Accuracy: A Simple and Powerful Test," LEO Working Papers / DR LEO 268, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    1696. Grønborg, Niels S. & Lunde, Asger & Olesen, Kasper V. & Vander Elst, Harry, 2022. "Realizing correlations across asset classes," Journal of Financial Markets, Elsevier, vol. 59(PA).
    1697. Qiang Liu & Zhi Liu & Chuanhai Zhang, 2020. "Heteroscedasticity test of high-frequency data with jumps and microstructure noise," Papers 2010.07659, arXiv.org.
    1698. Adam Clements & Yin Liao, 2013. "The dynamics of co-jumps, volatility and correlation," NCER Working Paper Series 91, National Centre for Econometric Research.
    1699. Richard Gerlach & Chao Wang, 2016. "Forecasting risk via realized GARCH, incorporating the realized range," Quantitative Finance, Taylor & Francis Journals, vol. 16(4), pages 501-511, April.
    1700. Rafiq, Shuddhasawtta & Salim, Ruhul & Bloch, Harry, 2009. "Impact of crude oil price volatility on economic activities: An empirical investigation in the Thai economy," Resources Policy, Elsevier, vol. 34(3), pages 121-132, September.
    1701. Nishimura, Yusaku & Tsutsui, Yoshiro & Hirayama, Kenjiro, 2018. "Do international investors cause stock market spillovers? Comparing responses of cross-listed stocks between accessible and inaccessible markets," Economic Modelling, Elsevier, vol. 69(C), pages 237-248.
    1702. Wamg, Jianxin, 2011. "Forecasting Volatility in Asian Stock Markets: Contributions of Local, Regional, and Global Factors," Asian Development Review, Asian Development Bank, vol. 28(2), pages 32-57.
    1703. Brenner, Menachem & Pasquariello, Paolo & Subrahmanyam, Marti, 2009. "On the Volatility and Comovement of U.S. Financial Markets around Macroeconomic News Announcements," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 44(6), pages 1265-1289, December.
    1704. Selma Chaker & Nour Meddahi, 2013. "Volatility Forecasting when the Noise Variance Is Time-Varying," Staff Working Papers 13-48, Bank of Canada.
    1705. Jose Faias & Pedro Santa-Clara, 2011. "Optimal Option Portfolio Strategies," EcoMod2011 3041, EcoMod.
    1706. Fildes, Robert, 2006. "The forecasting journals and their contribution to forecasting research: Citation analysis and expert opinion," International Journal of Forecasting, Elsevier, vol. 22(3), pages 415-432.
    1707. Marcel Dettling & Peter Buhlmann, 2004. "Volatility and risk estimation with linear and nonlinear methods based on high frequency data," Applied Financial Economics, Taylor & Francis Journals, vol. 14(10), pages 717-729.
    1708. Ciner, Cetin, 2025. "Forecasting the aggregate market volatility by boosted neural networks," Finance Research Letters, Elsevier, vol. 72(C).
    1709. Loïc Maréchal, 2021. "Do economic variables forecast commodity futures volatility?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(11), pages 1735-1774, November.
    1710. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.
    1711. Ahmed A. A. Khalifa & Hong Miao & Sanjay Ramchander, 2011. "Return distributions and volatility forecasting in metal futures markets: Evidence from gold, silver, and copper," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 31(1), pages 55-80, January.
    1712. Anshul Verma & Riccardo Junior Buonocore & Tiziana di Matteo, 2017. "A cluster driven log-volatility factor model: a deepening on the source of the volatility clustering," Papers 1712.02138, arXiv.org, revised May 2018.
    1713. Ané, Thierry & Métais, Carole, 2009. "The distribution of realized variances: Marginal behaviors, asymmetric dependence and contagion effects," International Review of Financial Analysis, Elsevier, vol. 18(3), pages 134-150, June.
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    1716. Yang, Minxian, 2019. "The risk return relationship: Evidence from index returns and realised variances," Journal of Economic Dynamics and Control, Elsevier, vol. 107(C), pages 1-1.
    1717. Thomakos, Dimitrios D. & Wang, Tao & Wille, Luc T., 2002. "Modeling daily realized futures volatility with singular spectrum analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 312(3), pages 505-519.
    1718. Bo Yu & Bruce Mizrach & Norman R. Swanson, 2020. "New Evidence of the Marginal Predictive Content of Small and Large Jumps in the Cross-Section," Econometrics, MDPI, vol. 8(2), pages 1-52, May.
    1719. Jayawardena, Nirodha I. & Todorova, Neda & Li, Bin & Su, Jen-Je & Gau, Yin-Feng, 2022. "Risk-return trade-off in the Australian Securities Exchange: Accounting for overnight effects, realized higher moments, long-run relations, and fractional cointegration," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 384-401.

  74. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2002. "Parametric and Nonparametric Volatility Measurement," NBER Technical Working Papers 0279, National Bureau of Economic Research, Inc.

    Cited by:

    1. Tim Bollerslev & Michael Gibson & Hao Zhou, 2007. "Dynamic Estimation of Volatility Risk Premia and Investor Risk Aversion from Option-Implied and Realized Volatilities," CREATES Research Papers 2007-16, Department of Economics and Business Economics, Aarhus University.
    2. Anisha Ghosh & Oliver Linton, 2019. "Estimation with Mixed Data Frequencies: A Bias-Correction Approach," CeMMAP working papers CWP65/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Campbell, Sean D. & Diebold, Francis X., 2004. "Weather forecasting for weather derivatives," CFS Working Paper Series 2004/10, Center for Financial Studies (CFS).
    4. Degiannakis, Stavros & Floros, Christos, 2013. "Modeling CAC40 volatility using ultra-high frequency data," Research in International Business and Finance, Elsevier, vol. 28(C), pages 68-81.
    5. Zagaglia, Paolo, 2008. "Money-market segmentation in the euro area: what has changed during the turmoil?," Bank of Finland Research Discussion Papers 23/2008, Bank of Finland.
    6. Haselmann, Rainer & Helmut, Herwartz, 2005. "The Introduction of the Euro and its Effects on Investment Decisions," Economics Working Papers 2005-15, Christian-Albrechts-University of Kiel, Department of Economics.
    7. Ole E. Barndorff-Nielsen & Neil Shephard, 2001. "Econometric Analysis of Realised Covariation: High Frequency Covariance, Regression and Correlation in Financial Economics," Economics Papers 2002-W13, Economics Group, Nuffield College, University of Oxford, revised 18 Mar 2002.
    8. Athanasia Gavala & Nikolay Gospodinov & Deming Jiang, 2006. "Forecasting volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(6), pages 381-400.
    9. Torben G. Andersen & Tim Bollerslev & Nour Meddahi, 2002. "Correcting the Errors: A Note on Volatility Forecast Evaluation Based on High-Frequency Data and Realized Volatilities," CIRANO Working Papers 2002s-91, CIRANO.
    10. Laarni Bulan & Christopher Mayer & C. Tsuriel Somerville, "undated". "Irreversible Investment, Real Options, and Competition: Evidence from Real Estate Development," Zell/Lurie Center Working Papers 391, Wharton School Samuel Zell and Robert Lurie Real Estate Center, University of Pennsylvania.
    11. Victor Olkhov, 2020. "Volatility Depends on Market Trades and Macro Theory," Papers 2008.07907, arXiv.org, revised Jun 2024.
    12. Sabrina Camargo & Silvio M. Duarte Queiros & Celia Anteneodo, 2013. "Bridging stylized facts in finance and data non-stationarities," Papers 1302.3197, arXiv.org, revised May 2013.
    13. George Tauchen & Hao Zhou, 2006. "Realized jumps on financial markets and predicting credit spreads," Finance and Economics Discussion Series 2006-35, Board of Governors of the Federal Reserve System (U.S.).
    14. Ole E. Barndorff-Nielsen & Neil Shephard, 2002. "Estimating quadratic variation using realized variance," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 457-477.
    15. Brousseau, Vincent & Durré, Alain, 2013. "Interest rate volatility: a consol rate-based measure," Working Paper Series 1505, European Central Bank.
    16. Turgut Kısınbay, 2010. "Predictive ability of asymmetric volatility models at medium-term horizons," Applied Economics, Taylor & Francis Journals, vol. 42(30), pages 3813-3829.
    17. Christensen, Kim & Podolskij, Mark, 2006. "Range-Based Estimation of Quadratic Variation," Technical Reports 2006,37, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    18. Martin Martens & Dick van Dijk & Michiel de Pooter, 2004. "Modeling and Forecasting S&P 500 Volatility: Long Memory, Structural Breaks and Nonlinearity," Tinbergen Institute Discussion Papers 04-067/4, Tinbergen Institute.
    19. Michael W. Brandt & Francis X. Diebold, 2006. "A No-Arbitrage Approach to Range-Based Estimation of Return Covariances and Correlations," The Journal of Business, University of Chicago Press, vol. 79(1), pages 61-74, January.
    20. Emese Lazar & Carol Alexander, 2006. "Normal mixture GARCH(1,1): applications to exchange rate modelling," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(3), pages 307-336.
    21. Henker, Thomas & Husodo, Zaäfri A., 2010. "Noise and efficient variance in the Indonesia Stock Exchange," Pacific-Basin Finance Journal, Elsevier, vol. 18(2), pages 199-216, April.
    22. Ole E. Barndorff-Nielsen & Elisa Nicolato & Neil Shephard, 2001. "Some recent developments in stochastic volatility modelling," Economics Papers 2001-W25, Economics Group, Nuffield College, University of Oxford.
    23. Frowin Schulz & Karl Mosler, 2011. "The effect of infrequent trading on detecting price jumps," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(1), pages 27-58, March.
    24. Ravi Bansal & Varoujan Khatchatrian & Amir Yaron, 2004. "Interpretable Asset Markets?," 2004 Meeting Papers 136b, Society for Economic Dynamics.
    25. Sydney C. Ludvigson & Serena Ng, 2005. "The Empirical Risk-Return Relation: A Factor Analysis Approach," NBER Working Papers 11477, National Bureau of Economic Research, Inc.
    26. Nadhem Selmi & Nejib Hachicha, 2014. "Were Oil Price Markets the Source of Credit Crisis in European Countries? Evidence Using a VAR-MGARCH-DCC Model," International Journal of Energy Economics and Policy, Econjournals, vol. 4(2), pages 169-177.
    27. Lars Forsberg & Tim Bollerslev, 2002. "Bridging the gap between the distribution of realized (ECU) volatility and ARCH modelling (of the Euro): the GARCH-NIG model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 535-548.
    28. Ole E. Barndorff-Nielsen & Bent Nielsen & Neil Shephard & Carla Ysusi, 2002. "Measuring and forecasting financial variability using realised variance with and without a model," Economics Papers 2002-W21, Economics Group, Nuffield College, University of Oxford.
    29. Li, Handong & Cao, Shi-Nan & Wang, Yan, 2010. "The properties and mechanism of long-term memory in nonparametric volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3254-3259.
    30. Bollerslev, Tim & Law, Tzuo Hann & Tauchen, George, 2008. "Risk, jumps, and diversification," Journal of Econometrics, Elsevier, vol. 144(1), pages 234-256, May.
    31. Peter Christoffersen & Francis X. Diebold, 2002. "Financial Asset Returns, Market Timing, and Volatility Dynamics," CIRANO Working Papers 2002s-02, CIRANO.
    32. Oliver Linton & Anisha Ghosh, 2007. "Consistent Estimation of the Risk-Return Tradeoff in the Presence of Measurement Error," FMG Discussion Papers dp605, Financial Markets Group.
    33. René Garcia & Eric Ghysels & Eric Renault, 2004. "The Econometrics of Option Pricing," CIRANO Working Papers 2004s-04, CIRANO.
    34. Werner, Thomas & Stapf, Jelena, 2003. "How wacky is the DAX? The changing structure of German stock market volatility," Discussion Paper Series 1: Economic Studies 2003,18, Deutsche Bundesbank.
    35. Sean D. Campbell & Canlin Li, 2004. "Alternative estimates of the presidential premium," Finance and Economics Discussion Series 2004-69, Board of Governors of the Federal Reserve System (U.S.).
    36. Jeremy Large, 2005. "Estimating Quadratic Variation When Quoted Prices Jump by a Constant Increment," Economics Series Working Papers 2005-FE-05, University of Oxford, Department of Economics.
    37. B. Jungbacker & S.J. Koopman, 2005. "Model-based Measurement of Actual Volatility in High-Frequency Data," Tinbergen Institute Discussion Papers 05-002/4, Tinbergen Institute.
    38. Jianqing Fan & Yingying Fan & Jinchi Lv, 0. "Aggregation of Nonparametric Estimators for Volatility Matrix," Journal of Financial Econometrics, Oxford University Press, vol. 5(3), pages 321-357.
    39. Hellström, Jörgen & Lönnbark, Carl, 2011. "Identi�cation of jumps in �financial price series," MPRA Paper 30977, University Library of Munich, Germany.
    40. Yi, Chae-Deug, 2020. "Jump probability using volatility periodicity filters in US Dollar/Euro exchange rates," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
    41. Torben G. Andersen & Dobrislav Dobrev & Ernst Schaumburg, 2010. "Jump-robust volatility estimation using nearest neighbor truncation," Staff Reports 465, Federal Reserve Bank of New York.
    42. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies," CIRANO Working Papers 2004s-19, CIRANO.
    43. Fornari, Fabio, 2010. "Assessing the compensation for volatility risk implicit in interest rate derivatives," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 722-743, September.
    44. Fornari, Fabio, 2008. "Assessing the compensation for volatility risk implicit in interest rate derivatives," Working Paper Series 859, European Central Bank.
    45. Juan Manuel Julio & Norberto Rodríguez & Hector Zárate, 2005. "Estimating the COP Exchange Rate Volatility Smile and the Market Effect of Central Bank Interventions: A CHARN Approach," Borradores de Economia 347, Banco de la Republica de Colombia.
    46. Tim Bollerslev & Hao Zhou, 2003. "Volatility puzzles: a unified framework for gauging return-volatility regressions," Finance and Economics Discussion Series 2003-40, Board of Governors of the Federal Reserve System (U.S.).
    47. Amir Safari & Detlef Seese, 2009. "Non-parametric estimation of a multiscale CHARN model using SVR," Quantitative Finance, Taylor & Francis Journals, vol. 9(1), pages 105-121.
    48. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    49. Stavros Degiannakis, 2004. "Volatility forecasting: evidence from a fractional integrated asymmetric power ARCH skewed-t model," Applied Financial Economics, Taylor & Francis Journals, vol. 14(18), pages 1333-1342.
    50. E. C. Brechmann & M. Heiden & Y. Okhrin, 2018. "A multivariate volatility vine copula model," Econometric Reviews, Taylor & Francis Journals, vol. 37(4), pages 281-308, April.
    51. Chae-Deug, Yi, 2024. "Realized normal volatility and maximum outlying jumps in high frequency returns for Korean won–US Dollar," International Review of Financial Analysis, Elsevier, vol. 95(PA).
    52. Robert F. Engle & Giampiero M. Gallo, 2003. "A Multiple Indicators Model For Volatility Using Intra-Daily Data," Econometrics Working Papers Archive wp2003_07, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    53. Daniel Jubinski & Amy F. Lipton, 2012. "Equity volatility, bond yields, and yield spreads," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 32(5), pages 480-503, May.
    54. Christensen, Kim & Podolskij, Mark, 2007. "Realized range-based estimation of integrated variance," Journal of Econometrics, Elsevier, vol. 141(2), pages 323-349, December.
    55. Hyeong-Ohk Bae & Seung-Yeal Ha & Yongsik Kim & Hyuncheul Lim & Jane Yoo, 2020. "Volatility Flocking by Cucker–Smale Mechanism in Financial Markets," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 27(3), pages 387-414, September.
    56. Ivo Arnold & Evert Vrugt, 2008. "Fundamental uncertainty and stock market volatility," Applied Financial Economics, Taylor & Francis Journals, vol. 18(17), pages 1425-1440.
    57. Ceylan, Ozcan, 2012. "Time-Varying Volatility Asymmetry: A Conditioned HAR-RV(CJ) EGARCH-M Model," GIAM Working Papers 12-4, Galatasaray University Economic Research Center.
    58. Degiannakis, Stavros & Xekalaki, Evdokia, 2004. "Autoregressive Conditional Heteroskedasticity (ARCH) Models: A Review," MPRA Paper 80487, University Library of Munich, Germany.
    59. Fornari, Fabio & Mele, Antonio, 2006. "Approximating volatility diffusions with CEV-ARCH models," Journal of Economic Dynamics and Control, Elsevier, vol. 30(6), pages 931-966, June.
    60. Elena Andreou & Eric Ghysels, 2004. "Monitoring for Disruptions in Financial Markets," CIRANO Working Papers 2004s-26, CIRANO.
    61. James Chong, 2004. "Options trading profits from correlation forecasts," Applied Financial Economics, Taylor & Francis Journals, vol. 14(15), pages 1075-1085.
    62. Chun-Hung Chen & Wei-Choun Yu & Eric Zivot, 2009. "Predicting Stock Volatility Using After-Hours Information," Working Papers UWEC-2009-01, University of Washington, Department of Economics.
    63. YI, Chae-Deug, 2023. "Exchange rate volatility and intraday jump probability with periodicity filters using a local robust variance," Finance Research Letters, Elsevier, vol. 55(PA).
    64. Ole E. Barndorff-Nielsen & Neil Shephard, 2001. "How accurate is the asymptotic approximation to the distribution of realised volatility?," Economics Papers 2001-W16, Economics Group, Nuffield College, University of Oxford.

  75. Peter Christoffersen & Francis X. Diebold, 2002. "Financial Asset Returns, Market Timing, and Volatility Dynamics," CIRANO Working Papers 2002s-02, CIRANO.

    Cited by:

    1. Oliver Linton & Yoon-Jae Whang, 2003. "A Quantilogram Approach to Evaluating Directional Predictability," STICERD - Econometrics Paper Series 463, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    2. Jaehun Chung & Yongmiao Hong, 2007. "Model-free evaluation of directional predictability in foreign exchange markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(5), pages 855-889.
    3. Ayedi Ahmed & Marjène Gana & Stéphane Goutte & Khaled Guesmi, 2023. "Managing Portfolio Risk During the BREXIT Crisis: A Cross-Quantilogram Analysis of Stock Markets and Commodities Across European Countries, the US, and BRICS," Working Papers halshs-04068651, HAL.
    4. Heejoon Han & Oliver Linton & Tatsushi Oka & Yoon-Jae Whang, 2014. "The Cross-Quantilogram: Measuring Quantile Dependence and Testing Directional Predictability between Time Series," Cambridge Working Papers in Economics 1452, Faculty of Economics, University of Cambridge.
    5. Alex Maynard, 2006. "The forward premium anomaly: statistical artefact or economic puzzle? New evidence from robust tests," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 39(4), pages 1244-1281, November.
    6. Hansen, Peter Reinhard & Lunde, Asger, 2006. "Consistent ranking of volatility models," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 97-121.
    7. Aviral Kumar Tiwari & Muhammad Shahbaz & Rabeh Khalfaoui & Rizwan Ahmed & Shawkat Hammoudeh, 2024. "Directional predictability from energy markets to exchange rates and stock markets in the emerging market countries (E7 + 1): New evidence from cross‐quantilogram approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(1), pages 719-789, January.
    8. Linton, O. & Whang, Yoon-Jae, 2007. "The quantilogram: With an application to evaluating directional predictability," Journal of Econometrics, Elsevier, vol. 141(1), pages 250-282, November.

  76. Andersen, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Vega, Clara, 2002. "Micro Effects of Macro Announcements: Real-Time Price Discovery in Foreign Exchange," Working Papers 02-16, Duke University, Department of Economics.

    Cited by:

    1. Döpke, Jörg & Hartmann, Daniel & Pierdzioch, Christian, 2006. "Forecasting stock market volatility with macroeconomic variables in real time," Discussion Paper Series 2: Banking and Financial Studies 2006,01, Deutsche Bundesbank.
    2. Stefan Mittnik & Nikolay Robinzonov & Klaus Wohlrabe, 2013. "The Micro Dynamics of Macro Announcements," CESifo Working Paper Series 4421, CESifo.
    3. Mykola Pinchuk, 2023. "Bitcoin Does Not Hedge Inflation," Papers 2301.10117, arXiv.org.
    4. Rasmus Fatum & Michael M. Hutchison & Thomas Wu, 2010. "Asymmetries and state dependence: the impact of macro surprises on intraday exchange rates," Globalization Institute Working Papers 49, Federal Reserve Bank of Dallas.
    5. Martin D. D. Evans & Richard K. Lyons, 2017. "Meese-Rogoff Redux: Micro-Based Exchange-Rate Forecasting," World Scientific Book Chapters, in: Studies in Foreign Exchange Economics, chapter 11, pages 457-475, World Scientific Publishing Co. Pte. Ltd..
    6. Wu, Zhen-Xing & Gau, Yin-Feng, 2022. "Informativeness of trades around macroeconomic announcements in the foreign exchange market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 78(C).
    7. Lucio Sarno & Giorgio Valente, 2009. "Exchange Rates and Fundamentals: Footloose or Evolving Relationship?," Journal of the European Economic Association, MIT Press, vol. 7(4), pages 786-830, June.
    8. Evans, Kevin P. & Speight, Alan E.H., 2010. "Intraday periodicity, calendar and announcement effects in Euro exchange rate volatility," Research in International Business and Finance, Elsevier, vol. 24(1), pages 82-101, January.
    9. Mehmet Ivrendi & Douglas K. Pearce, 2014. "Asset prices and expected monetary policy: evidence from daily data," Applied Economics, Taylor & Francis Journals, vol. 46(9), pages 985-995, March.
    10. Imane El Ouadghiri & Remzi Uctum, 2016. "Jumps in equilibrium prices and asymmetric news in foreign exchange markets," Post-Print hal-01386027, HAL.
    11. Ole E. Barndorff-Nielsen & Neil Shephard, 2003. "Econometrics of testing for jumps in financial economics using bipower variation," Economics Papers 2003-W21, Economics Group, Nuffield College, University of Oxford.
    12. Yuriy Gorodnichenko & Viacheslav Sheremirov & Oleksandr Talavera, 2018. "Price Setting in Online Markets: Does IT Click?," Journal of the European Economic Association, European Economic Association, vol. 16(6), pages 1764-1811.
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    1. Ignacio Arango & Diego A. Agudelo, 2017. "How does information disclosure affect liquidity?Evidence from an Emerging Market," Documentos de Trabajo de Valor Público 16990, Universidad EAFIT.
    2. Wang, Jianxin, 2007. "Foreign equity trading and emerging market volatility: Evidence from Indonesia and Thailand," Journal of Development Economics, Elsevier, vol. 84(2), pages 798-811, November.
    3. Galina Hale & Assaf Razin & Hui Tong, 2011. "The impact of creditor protection on stock prices in the presence of credit crunches," Working Paper Series 2011-13, Federal Reserve Bank of San Francisco.
    4. Diego A. Agudelo & Ignacio Arango, 2017. "How does information disclosure affect liquidity? Evidence from an Emerging Market," Documentos de Trabajo de Valor Público 16944, Universidad EAFIT.
    5. Arango, Ignacio & Agudelo, Diego A., 2019. "How does information disclosure affect liquidity? Evidence from an emerging market," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    6. Pan, Jun, 2002. "The jump-risk premia implicit in options: evidence from an integrated time-series study," Journal of Financial Economics, Elsevier, vol. 63(1), pages 3-50, January.
    7. Siddique, Akhtar R., 2003. "Common asset pricing factors in volatilities and returns in futures markets," Journal of Banking & Finance, Elsevier, vol. 27(12), pages 2347-2368, December.

  78. Francis X. Diebold & Atsushi Inoue, 2000. "Long Memory and Regime Switching," NBER Technical Working Papers 0264, National Bureau of Economic Research, Inc.

    Cited by:

    1. Eric Hillebrand & Gunther Schnabl & Yasemin Ulu, 2006. "Japanese Foreign Exchange Intervention and the Yen/Dollar Exchange Rate: A Simultaneous Equations Approach Using Realized Volatility," CESifo Working Paper Series 1766, CESifo.
    2. Gil-Alana, Luis A. & Aye, Goodness C. & Gupta, Rangan, 2015. "Trends and cycles in historical gold and silver prices," Journal of International Money and Finance, Elsevier, vol. 58(C), pages 98-109.
    3. Carlos P. Barros & Guglielmo Maria Caporale & Luis A. Gil-Alana, 2012. "Long Memory in German Energy Price Indices," Discussion Papers of DIW Berlin 1186, DIW Berlin, German Institute for Economic Research.
    4. David E. Allen & Michael McAleer & Marcel Scharth, 2014. "Asymmetric Realized Volatility Risk," JRFM, MDPI, vol. 7(2), pages 1-30, June.
    5. Zeynel Abidin Ozdemir & Mehmet Balcilar & Aysit Tansel, 2014. "Are Labor Force Participation Rates Really Non-Stationary? Evidence from Three OECD Countries," Working Papers 15-25, Eastern Mediterranean University, Department of Economics.
    6. Kapetanios, George, 2006. "Nonlinear autoregressive models and long memory," Economics Letters, Elsevier, vol. 91(3), pages 360-368, June.
    7. Baillie, Richard T. & Kapetanios, George, 2007. "Testing for Neglected Nonlinearity in Long-Memory Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 447-461, October.
    8. David McMillan & Mark Wohar, 2011. "Structural breaks in volatility: the case of UK sector returns," Applied Financial Economics, Taylor & Francis Journals, vol. 21(15), pages 1079-1093.
    9. Guglielmo Maria Caporale & Luis A. Gil-Alana & Carlos Poza, 2020. "Inflation in the G7 Countries: Persistence and Structural Breaks," CESifo Working Paper Series 8349, CESifo.
    10. Chen, Xiaohong & Hansen, Lars Peter & Carrasco, Marine, 2008. "Nonlinearity and Temporal Dependence," Working Papers 48, Yale University, Department of Economics.
    11. J. Cuñado & L. Gil-Alana & F. Gracia, 2009. "US stock market volatility persistence: evidence before and after the burst of the IT bubble," Review of Quantitative Finance and Accounting, Springer, vol. 33(3), pages 233-252, October.
    12. Abi Morshed, Alaa & Andreou, E. & Boldea, Otilia, 2016. "Structural Break Tests Robust to Regression Misspecification," Discussion Paper 2016-019, Tilburg University, Center for Economic Research.
    13. Lu, Yang K. & Perron, Pierre, 2010. "Modeling and forecasting stock return volatility using a random level shift model," Journal of Empirical Finance, Elsevier, vol. 17(1), pages 138-156, January.
    14. Gabriel Rodríguez & Dennis Alvaro & Ángel Guillén, 2016. "Modelling the Volatility of Commodities Prices using a Stochastic Volatility Model with Random Level Shifts," Documentos de Trabajo / Working Papers 2016-414, Departamento de Economía - Pontificia Universidad Católica del Perú.
    15. Chin Wen Cheong, 2010. "Estimating the Hurst parameter in financial time series via heuristic approaches," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(2), pages 201-214.
    16. Elena Andreou, 2004. "The Impact of Sampling Frequency and Volatility Estimators on Change-Point Tests," Journal of Financial Econometrics, Oxford University Press, vol. 2(2), pages 290-318.
    17. Gürtler, Marc & Rauh, Ronald, 2012. "Challenging traditional risk models by a non-stationary approach with nonparametric heteroscedasticity," Working Papers IF41V1, Technische Universität Braunschweig, Institute of Finance.
    18. Kyongwook Choi & Wei-Choun Yu & Eric Zivot, 2008. "Long Memory versus Structural Breaks in Modeling and Forecasting Realized Volatility," Working Papers UWEC-2008-20-FC, University of Washington, Department of Economics.
    19. Morten Ø. Nielsen & Per Houmann Frederiksen, 2005. "Finite Sample Comparison Of Parametric, Semiparametric, And Wavelet Estimators Of Fractional Integration," Working Paper 1189, Economics Department, Queen's University.
    20. Haldrup, Niels & Nielsen, Frank S. & Nielsen, Morten Ørregaard, 2010. "A vector autoregressive model for electricity prices subject to long memory and regime switching," Energy Economics, Elsevier, vol. 32(5), pages 1044-1058, September.
    21. Geoffrey Ngene & Charles Lambert & Ali Darrat, 2015. "Testing Long Memory in the Presence of Structural Breaks: An Application to Regional and National Housing Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 50(4), pages 465-483, May.
    22. Monge, Manuel & Gil-Alana, Luis A. & Pérez de Gracia, Fernando, 2017. "U.S. shale oil production and WTI prices behaviour," Energy, Elsevier, vol. 141(C), pages 12-19.
    23. Claudio Morana, 2010. "Heteroskedastic Factor Vector Autoregressive Estimation of Persistent and Non Persistent Processes Subject to Structural Breaks," ICER Working Papers - Applied Mathematics Series 36-2010, ICER - International Centre for Economic Research.
    24. John Dawson & John Seater, 2013. "Federal regulation and aggregate economic growth," Journal of Economic Growth, Springer, vol. 18(2), pages 137-177, June.
    25. Carlos Barros & Luis Gil-Alana, 2012. "Inflation forecasting in Angola: a fractional approach," CEsA Working Papers 103, CEsA - Centre for African and Development Studies.
    26. Luis A Gil-Alana & Christophe André & Rangan Gupta & Tsangyao Chang & Omid Ranjbar, 2015. "The Feldstein-Horioka Puzzle in South Africa: A Fractional Cointegration Approach," Working Papers 201501, University of Pretoria, Department of Economics.
    27. Luis Gil-Alana, 2008. "Real GDP growth rates across countries: long memory and mean shifts," Applied Economics Letters, Taylor & Francis Journals, vol. 15(6), pages 449-455.
    28. N. Antonakakis & J. Darby, 2013. "Forecasting volatility in developing countries' nominal exchange returns," Applied Financial Economics, Taylor & Francis Journals, vol. 23(21), pages 1675-1691, November.
    29. Charfeddine, Lanouar & Guégan, Dominique, 2012. "Breaks or long memory behavior: An empirical investigation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5712-5726.
    30. Jensen, Mark J. & Liu, Ming, 2006. "Do long swings in the business cycle lead to strong persistence in output?," Journal of Monetary Economics, Elsevier, vol. 53(3), pages 597-611, April.
    31. Claudio Morana, 2014. "Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks," Working Papers 273, University of Milano-Bicocca, Department of Economics, revised May 2014.
    32. Luis A. Gil-Alana & Sakiru Adebola Solarin & Mehmet Balcilar & Rangan Gupta, 2023. "Productivity and GDP: international evidence of persistence and trends over 130 years of data," Empirical Economics, Springer, vol. 64(3), pages 1219-1246, March.
    33. Pierre Perron & Zhongjun Qu, 2008. "Long-Memory and Level Shifts in the Volatility of Stock Market Return Indices," Boston University - Department of Economics - Working Papers Series wp2008-004, Boston University - Department of Economics.
    34. Ngene, Geoffrey & Tah, Kenneth A. & Darrat, Ali F., 2017. "Long memory or structural breaks: Some evidence for African stock markets," Review of Financial Economics, Elsevier, vol. 34(C), pages 61-73.
    35. Valadkhani, Abbas, 2023. "Asymmetric downside risk across different sectors of the US equity market," Global Finance Journal, Elsevier, vol. 57(C).
    36. S. Bordignon & D. Raggi, 2010. "Long memory and nonlinearities in realized volatility: a Markov switching approach," Working Papers 694, Dipartimento Scienze Economiche, Universita' di Bologna.
    37. Becker, Janis & Leschinski, Christian & Sibbertsen, Philipp, 2019. "Robust Multivariate Local Whittle Estimation and Spurious Fractional Cointegration," Hannover Economic Papers (HEP) dp-660, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    38. Adam McCloskey, 2012. "Estimation of the Long-Memory Stochastic Volatility Model Parameters that is Robust to Level Shifts and Deterministic Trends," Working Papers 2012-17, Brown University, Department of Economics.
    39. Maria Caporale, Guglielmo & A. Gil-Alana, Luis, 2011. "Multi-Factor Gegenbauer Processes and European Inflation Rates," Journal of Economic Integration, Center for Economic Integration, Sejong University, vol. 26, pages 386-409.
    40. Dark Jonathan Graeme, 2010. "Estimation of Time Varying Skewness and Kurtosis with an Application to Value at Risk," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(2), pages 1-50, March.
    41. Gil-Alana, Luis Alberiko & Poza, Carlos, 2024. "Volatility persistence in metal prices," Resources Policy, Elsevier, vol. 88(C).
    42. Chaim, Pedro & Laurini, Márcio P., 2018. "Volatility and return jumps in bitcoin," Economics Letters, Elsevier, vol. 173(C), pages 158-163.
    43. Tobias Hartl & Rolf Tschernig & Enzo Weber, 2020. "Fractional trends and cycles in macroeconomic time series," Papers 2005.05266, arXiv.org, revised May 2020.
    44. Gil-Alana, Luis A. & Moreno, Antonio, 2012. "Uncovering the US term premium: An alternative route," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 1181-1193.
    45. Torben G. Andersen & Rasmus T. Varneskov, 2021. "Testing for Parameter Instability and Structural Change in Persistent Predictive Regressions," NBER Working Papers 28570, National Bureau of Economic Research, Inc.
    46. Claudio Morana & Fabio Cesare Bagliano, 2007. "Inflation and monetary dynamics in the USA: a quantity-theory approach," Applied Economics, Taylor & Francis Journals, vol. 39(2), pages 229-244.
    47. Mstislav Elagin, 2008. "Locally adaptive estimation methods with application to univariate time series," Papers 0812.0449, arXiv.org.
    48. Mccloskey, Adam & Perron, Pierre, 2013. "Memory Parameter Estimation In The Presence Of Level Shifts And Deterministic Trends," Econometric Theory, Cambridge University Press, vol. 29(6), pages 1196-1237, December.
    49. Luis Alberiko Gil-Alaña & Olanrewaju L. Shittu & OlaOluwa S. Yaya, 2011. "Long memory, strcutural breaks and mean shifts in the inflation rates in Nigeria," NCID Working Papers 04/2011, Navarra Center for International Development, University of Navarra.
    50. Luis A. Gil-Alana & Juncal Cunado & Rangan Gupta, 2015. "Persistence, Mean-Reversion and Non-Linearities in Infant Mortality Rates," Working Papers 201574, University of Pretoria, Department of Economics.
    51. Mohamed Boutahar & Gilles Dufrénot & Anne Péguin-Feissolle, 2008. "A Simple Fractionally Integrated Model with a Time-varying Long Memory Parameter d t," Computational Economics, Springer;Society for Computational Economics, vol. 31(3), pages 225-241, April.
    52. Joseph P. Byrne & Roger Perman, 2006. "Unit Roots and Structural Breaks: A Survey of the Literature," Working Papers 2006_10, Business School - Economics, University of Glasgow.
    53. João Ricardo Faria & Juan Carlos Cuestas & Luis Gil-Alana & Estefania Mourelle, 2021. "Self-employment by gender in the EU: convergence and clusters," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 48(3), pages 717-741, August.
    54. Gürtler, Marc & Rauh, Ronald, 2009. "Shortcomings of a parametric VaR approach and nonparametric improvements based on a non-stationary return series model," Working Papers IF32V2, Technische Universität Braunschweig, Institute of Finance.
    55. Pal, Debdatta, 2022. "Does hospitality industry stock volatility react asymmetrically to health and economic crises?," Economic Modelling, Elsevier, vol. 108(C).
    56. Smith, Aaron, 2005. "Level Shifts and the Illusion of Long Memory in Economic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 321-335, July.
    57. Uwe Hassler & Paulo M.M. Rodrigues & Antonio Rubia, 2016. "Quantile Regression for Long Memory Testing: A Case of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 14(4), pages 693-724.
    58. Daniela Osterrieder, 2013. "Interest Rates with Long Memory: A Generalized Affine Term-Structure Model," CREATES Research Papers 2013-17, Department of Economics and Business Economics, Aarhus University.
    59. Claudio Morana & Giacomo Sbrana, 2017. "Temperature Anomalies, Radiative Forcing and ENSO," Working Papers 2017.09, Fondazione Eni Enrico Mattei.
    60. Guillaume Chevillon & Alain Hecq & Sébastien Laurent, 2018. "Generating Univariate Fractional Integration within a Large VAR(1)," AMSE Working Papers 1844, Aix-Marseille School of Economics, France.
    61. Luis A. Gil-Alana, 2009. "Warming break trends and fractional integration in the northern, southern and global temperature anomaly series," Faculty Working Papers 09/09, School of Economics and Business Administration, University of Navarra.
    62. Aaron Smallwood, 2004. "Joint Tests for Long Memory and Non-linearity: The Case of Purchasing Power Parity," Computing in Economics and Finance 2004 23, Society for Computational Economics.
    63. Chen, Ying & Han, Qian & Niu, Linlin, 2018. "Forecasting the Term Structure of Option Implied Volatility: The Power of an Adaptive Method," IRTG 1792 Discussion Papers 2018-046, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    64. Shaher Al-Gounmeein Remal & Ismail Mohd Tahir, 2021. "Modelling and forecasting monthly Brent crude oil prices: a long memory and volatility approach," Statistics in Transition New Series, Statistics Poland, vol. 22(1), pages 29-54, March.
    65. Athanasia Gavala & Nikolay Gospodinov & Deming Jiang, 2006. "Forecasting volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(6), pages 381-400.
    66. Kreye, Tom Jannik & Sibbertsen, Philipp, 2024. "Testing for a Forecast Accuracy Breakdown under Long Memory," Hannover Economic Papers (HEP) dp-729, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    67. Simone Alfarano & Thomas Lux, 2007. "A Minimal Noise Trader Model with Realistic Time Series Properties," Springer Books, in: Gilles Teyssière & Alan P. Kirman (ed.), Long Memory in Economics, pages 345-361, Springer.
    68. Berna Kirkulak Uludag & Zorikto Lkhamazhapov, 2014. "Long memory and structural breaks in the returns and volatility of gold: evidence from Turkey," Applied Economics, Taylor & Francis Journals, vol. 46(31), pages 3777-3787, November.
    69. Bos, Charles S. & Koopman, Siem Jan & Ooms, Marius, 2014. "Long memory with stochastic variance model: A recursive analysis for US inflation," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 144-157.
    70. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    71. Christophe Andre & Luis A. Gil-Alana & Rangan Gupta, 2013. "Testing for Persistence in Housing Price-to-Income and Price-to-Rent Ratios in 16 OECD Countries," Working Papers 201321, University of Pretoria, Department of Economics.
    72. Lean, Hooi Hooi & Smyth, Russell, 2009. "Long memory in US disaggregated petroleum consumption: Evidence from univariate and multivariate LM tests for fractional integration," Energy Policy, Elsevier, vol. 37(8), pages 3205-3211, August.
    73. Fabrizio Iacone & Peter M Robinson, 2004. "Cointegration in Fractional Systems with Deterministic Trends," STICERD - Econometrics Paper Series 476, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    74. Silvestro Di Sanzo, 2007. "Forecasting Time Series with Long Memory and Level Shifts, A Bayesian Approach," Working Papers 2007_03, Department of Economics, University of Venice "Ca' Foscari".
    75. Jerry Coakley & Jian Dollery & Neil Kellard, 2011. "Long memory and structural breaks in commodity futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 31(11), pages 1076-1113, November.
    76. Million, N., 2008. "Test simultan de la non-stationnarit et de la non-lin arit : une application au taux d.intérêt r el am ricain," Working papers 201, Banque de France.
    77. Marcel Aloy & Mohamed Boutahar & Karine Gente & Anne Peguin-Feissolle, 2011. "Purchasing power parity and the long memory properties of real exchange rates: does one size fit all?," Working Papers halshs-00559170, HAL.
    78. David E. Rapach & Jack K. Strauss, 2008. "Structural breaks and GARCH models of exchange rate volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(1), pages 65-90.
    79. Casini, Alessandro, 2023. "Theory of evolutionary spectra for heteroskedasticity and autocorrelation robust inference in possibly misspecified and nonstationary models," Journal of Econometrics, Elsevier, vol. 235(2), pages 372-392.
    80. Gil-Alana, Luis A. & Shittu, Olanrewaju I. & Yaya, OlaOluwa S., 2014. "On the persistence and volatility in European, American and Asian stocks bull and bear markets," Journal of International Money and Finance, Elsevier, vol. 40(C), pages 149-162.
    81. Chen, Fei & Diebold, Francis X. & Schorfheide, Frank, 2013. "A Markov-switching multifractal inter-trade duration model, with application to US equities," Journal of Econometrics, Elsevier, vol. 177(2), pages 320-342.
    82. Gbaguidi DAVID, 2011. "Expectations Impact On The Effectiveness Of The Inflation-Real Activity Trade-Off," Theoretical and Practical Research in the Economic Fields, ASERS Publishing, vol. 2(2), pages 141-181.
    83. Härdle, Wolfgang Karl & Hautsch, Nikolaus & Pigorsch, Uta, 2008. "Measuring and modeling risk using high-frequency data," SFB 649 Discussion Papers 2008-045, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    84. Lux, Thomas & Kaizoji, Taisei, 2006. "Forecasting volatility and volume in the Tokyo stock market: Long memory, fractality and regime switching," Economics Working Papers 2006-13, Christian-Albrechts-University of Kiel, Department of Economics.
    85. Robinson Kruse & Philipp Sibbertsen, 2010. "Long memory and changing persistence," CREATES Research Papers 2010-42, Department of Economics and Business Economics, Aarhus University.
    86. Wenger, Kai & Leschinski, Christian & Sibbertsen, Philipp, 2017. "A Simple Test on Structural Change in Long-Memory Time Series," Hannover Economic Papers (HEP) dp-592, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    87. Matteo Luciani & David Veredas, 2012. "A model for vast panels of volatilities," Working Papers 1230, Banco de España.
    88. John J. Seater & John W. Dawson, 2008. "The Macroeconomic Effects of Federal Regulation," 2008 Meeting Papers 1035, Society for Economic Dynamics.
    89. Gustavo Cabrera González, 2019. "Modeling and Projection of the Mexican Exchange Rate (Peso/Dollar): a Bayesian Approach for Model Selection," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 14(2), pages 203-219, Abril-Jun.
    90. Gil-Alana, Luis A. & Infante, Juan & Martín-Valmayor, Miguel Angel, 2023. "Persistence and long run co-movements across stock market prices," The Quarterly Review of Economics and Finance, Elsevier, vol. 89(C), pages 347-357.
    91. Charfeddine, Lanouar, 2016. "Breaks or long range dependence in the energy futures volatility: Out-of-sample forecasting and VaR analysis," Economic Modelling, Elsevier, vol. 53(C), pages 354-374.
    92. McMillan, David G. & Wohar, Mark E., 2010. "Persistence and time-varying coefficients," Economics Letters, Elsevier, vol. 108(1), pages 85-88, July.
    93. Davide Delle Monache & Stefano Grassi & Paolo Santucci de Magistris, 2015. "Testing for Level Shifts in Fractionally Integrated Processes: a State Space Approach," CREATES Research Papers 2015-30, Department of Economics and Business Economics, Aarhus University.
    94. Cuestas, Juan C. & Gil-Alana, Luís A., 2009. "Further evidence on the PPP analysis of the Australian dollar: Non-linearities, fractional integration and structural changes," Economic Modelling, Elsevier, vol. 26(6), pages 1184-1192, November.
    95. Yanlin Shi & Lingbing Feng & Tong Fu, 2020. "Markov Regime-Switching in-Mean Model with Tempered Stable Distribution," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1275-1299, April.
    96. Dominique Guegan & Philippe de Peretti, 2011. "Tests of structural changes in conditional distributions with unknown changepoints," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00611932, HAL.
    97. Pascalau, Razvan & Thomann, Christian & Gregoriou, Greg N., 2010. "Unconditional mean, Volatility and the Fourier-Garch representation," MPRA Paper 35932, University Library of Munich, Germany.
    98. Andrea Beltratti & Claudio Morana, 2005. "Structural Breaks and Common Factors in the Volatility of the Fama-French Factor Portfolios," ICER Working Papers 23-2005, ICER - International Centre for Economic Research.
    99. Goliński, Adam & Zaffaroni, Paolo, 2016. "Long memory affine term structure models," Journal of Econometrics, Elsevier, vol. 191(1), pages 33-56.
    100. Lima, Luiz Renato & Notini, Hilton Hostalácio & Reis Gomes, Fábio Augusto, 2010. "Empirical Evidence on Convergence Across Brazilian States," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 64(2), June.
    101. Dominique Guegan, 2007. "Global and local stationary modelling in finance: theory and empirical evidence," Post-Print halshs-00187875, HAL.
    102. Gabriel Rodríguez, 2015. "Modeling Latin-American Stock Markets Volatility: Varying Probabilities and Mean Reversion in a Random Level Shifts Model," Documentos de Trabajo / Working Papers 2015-403, Departamento de Economía - Pontificia Universidad Católica del Perú.
    103. Monge, Manuel & Gil-Alana, Luis A. & Pérez de Gracia, Fernando, 2017. "Crude oil price behaviour before and after military conflicts and geopolitical events," Energy, Elsevier, vol. 120(C), pages 79-91.
    104. Hou, Jie & Perron, Pierre, 2014. "Modified local Whittle estimator for long memory processes in the presence of low frequency (and other) contaminations," Journal of Econometrics, Elsevier, vol. 182(2), pages 309-328.
    105. Dominique Guegan, 2007. "La persistance dans les marchés financiers," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00179269, HAL.
    106. Dominique Guegan, 2005. "How can we Define the Concept of Long Memory? An Econometric Survey," Econometric Reviews, Taylor & Francis Journals, vol. 24(2), pages 113-149.
    107. Guglielmo Maria Caporale & Juncal Cunado & Luis A. Gil-Alana & Rangan Gupta, 2015. "The Relationship between Healthcare Expenditure and Disposable Personal Income in the US States: A Fractional Integration and Cointegration Analysis," Discussion Papers of DIW Berlin 1486, DIW Berlin, German Institute for Economic Research.
    108. Bertram, Philip & Sibbertsen, Philipp & Stahl, Gerhard, 2011. "About the Impact of Model Risk on Capital Reserves: A Quantitative Analysis," Hannover Economic Papers (HEP) dp-469, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    109. Guglielmo Maria Caporale & Silvia García Tapia & Luis Alberiko Gil-Alana, 2024. "Persistence in Tax Revenues: Evidence from Some OECD Countries," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 22(2), pages 475-491, June.
    110. Laurent Calvet & Adlai Fisher, 2003. "Regime-Switching and the Estimation of Multifractal Processes," Harvard Institute of Economic Research Working Papers 1999, Harvard - Institute of Economic Research.
    111. Fabrizio Iacone & Stephen J. Leybourne & A. M. Robert Taylor, 2014. "A FIXED- b TEST FOR A BREAK IN LEVEL AT AN UNKNOWN TIME UNDER FRACTIONAL INTEGRATION," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(1), pages 40-54, January.
    112. Charfeddine, Lanouar & Ajmi, Ahdi Noomen, 2013. "The Tunisian stock market index volatility: Long memory vs. switching regime," Emerging Markets Review, Elsevier, vol. 16(C), pages 170-182.
    113. Sabiou Inoua, 2016. "The Random Walk behind Volatility Clustering," Papers 1612.09344, arXiv.org.
    114. Emmanuel Joel Aikins Abakah & Guglielmo Maria Caporale & Luis A. Gil-Alana, 2020. "Economic Policy Uncertainty: Persistence and Cross-Country Linkages," CESifo Working Paper Series 8289, CESifo.
    115. Kyongwook Choi & Eric Zivot, 2003. "Long Memory and Structural Changes in the Forward Discount: An Empirical Investigation," EERI Research Paper Series EERI_RP_2003_02, Economics and Econometrics Research Institute (EERI), Brussels.
    116. Aikins Abakah, Emmanuel Joel & Gil-Alana, Luis A. & Tripathy, Trilochan, 2022. "Stochastic structure of metal prices: Evidence from fractional integration non-linearities and breaks," Resources Policy, Elsevier, vol. 78(C).
    117. Philippe Charlot & Vêlayoudom Marimoutou, 2014. "On the relationship between the prices of oil and the precious metals: Revisiting with a multivariate regime-switching decision tree," Working Papers hal-00980125, HAL.
    118. Wang, Cindy Shin-Huei & Bauwens, Luc & Hsiao, Cheng, 2013. "Forecasting a long memory process subject to structural breaks," Journal of Econometrics, Elsevier, vol. 177(2), pages 171-184.
    119. Katsumi Shimotsu, 2006. "Simple (but Effective) Tests Of Long Memory Versus Structural Breaks," Working Paper 1101, Economics Department, Queen's University.
    120. Luigi Spezia & Andy Vinten & Roberta Paroli & Marc Stutter, 2021. "An evolutionary Monte Carlo method for the analysis of turbidity high‐frequency time series through Markov switching autoregressive models," Environmetrics, John Wiley & Sons, Ltd., vol. 32(8), December.
    121. Caporale, Guglielmo Maria & Gil-Alana, Luis A. & Poza, Carlos, 2020. "Persistence, non-linearities and structural breaks in European stock market indices," The Quarterly Review of Economics and Finance, Elsevier, vol. 77(C), pages 50-61.
    122. Javier Hualde & Morten {O}rregaard Nielsen, 2022. "Fractional integration and cointegration," Papers 2211.10235, arXiv.org.
    123. Haldrup, Niels & Nielsen, Morten Orregaard, 2007. "Estimation of fractional integration in the presence of data noise," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 3100-3114, March.
    124. Hadhri, Sinda & Ftiti, Zied, 2019. "Commonality in liquidity among Middle East and North Africa emerging stock markets: Does it really matter?," Economic Systems, Elsevier, vol. 43(3).
    125. Benjamin M. Tabak, 2007. "Estimating the Fractional Order of Integration of Yields in the Brazilian Fixed Income Market," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 36(3), pages 231-246, November.
    126. Ralf Becker & Adam Clements, 2007. "Forecasting stock market volatility conditional on macroeconomic conditions," NCER Working Paper Series 18, National Centre for Econometric Research.
    127. Marie Busch & Philipp Sibbertsen, 2018. "An Overview of Modified Semiparametric Memory Estimation Methods," Econometrics, MDPI, vol. 6(1), pages 1-21, March.
    128. Sibbertsen, Philipp & Leschinski, Christian & Busch, Marie, 2018. "A multivariate test against spurious long memory," Journal of Econometrics, Elsevier, vol. 203(1), pages 33-49.
    129. Choi, Kyongwook & Zivot, Eric, 2007. "Long memory and structural changes in the forward discount: An empirical investigation," Journal of International Money and Finance, Elsevier, vol. 26(3), pages 342-363, April.
    130. Minford, Patrick & Peel, David, 2006. "On the Equality of Real Interest Rates Across Borders in Integrated Capital Markets," CEPR Discussion Papers 5611, C.E.P.R. Discussion Papers.
    131. Kruse, Robinson, 2015. "A modified test against spurious long memory," Economics Letters, Elsevier, vol. 135(C), pages 34-38.
    132. Amisano, Gianni & Geweke, John, 2007. "Hierarchical Markov normal mixture models with applications to financial asset returns," Working Paper Series 831, European Central Bank.
    133. Kuswanto, Heri & Sibbertsen, Philipp, 2008. "A Study on "Spurious Long Memory in Nonlinear Time Series Models"," Hannover Economic Papers (HEP) dp-410, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    134. Han, Heejoon & Park, Joon Y., 2006. "Time series properties of ARCH processes with persistent covariates," MPRA Paper 5199, University Library of Munich, Germany.
    135. Jérôme Coulon & Yannick Malevergne, 2010. "Heterogeneous expectations and long range correlation of the volatility of asset returns," Working Papers halshs-00541953, HAL.
    136. Juan Carlos Cuestas & Luis A. Gil-Alana, 2012. "A Non-Linear Approach with Long Range Dependence Based on Chebyshev Polynomials," Working Papers 2012013, The University of Sheffield, Department of Economics.
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    191. Luis Alberiko Gil-Alaña & Shinhye Chang & Mehmet Balcilar & Goodness C. Aye & Rangan Gupta, 2015. "Persistence of precious metal prices: a fractional integration approach with structural breaks," NCID Working Papers 06/2015, Navarra Center for International Development, University of Navarra.
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    198. Harry Vander Elst, 2015. "FloGARCH : Realizing long memory and asymmetries in returns volatility," Working Paper Research 280, National Bank of Belgium.
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    212. Beran, Jan, 2007. "On parameter estimation for locally stationary long-memory processes," CoFE Discussion Papers 07/13, University of Konstanz, Center of Finance and Econometrics (CoFE).
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    217. Luis A. Gil-Alana, 2004. "Modelling the Japanese Exchange Rate in Terms of I(d) Statistical Models with Parametric and Semiparametric Techniques," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 3(2), pages 123-138, August.
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    224. Aye, Goodness C. & Gil-Alana, Luis A. & Gupta, Rangan & Wohar, Mark E., 2017. "The efficiency of the art market: Evidence from variance ratio tests, linear and nonlinear fractional integration approaches," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 283-294.
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    231. Li, Ziran & Sun, Jiajing & Wang, Shouyang, 2013. "Amplitude-Duration-Persistence Trade-off Relationship for Long Term Bear Stock Markets," MPRA Paper 54177, University Library of Munich, Germany.
    232. Mateo Isoardi & Luis A. Gil-Alana, 2019. "Inflation in Argentina: Analysis of Persistence Using Fractional Integration," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 45(2), pages 204-223, April.
    233. Luis A. Gil-Alana & Jiang Liang, 2011. "The PPP hypothesis in the US/China relationship. Fractional integration, time variation and data frequency," Faculty Working Papers 13/11, School of Economics and Business Administration, University of Navarra.
    234. Dark, Jonathan, 2024. "An adaptive long memory conditional correlation model," Journal of Empirical Finance, Elsevier, vol. 75(C).
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    236. Härdle, Wolfgang Karl & López Cabrera, Brenda & Okhrin, Ostap & Wang, Weining, 2010. "Localising temperature risk," SFB 649 Discussion Papers 2011-001, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
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    241. Solarin, Sakiru Adebola & Gil-Alana, Luis A., 2021. "The persistence of economic policy uncertainty: Evidence of long range dependence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 568(C).
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    263. Caporale, Guglielmo Maria & Gil-Alana, Luis A., 2017. "Persistence and cycles in the us federal funds rate," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 1-8.
    264. Guglielmo Maria Caporale & Luis A. Gil-Alana & Robert Mudida, 2012. "Testing the Marshall-Lerner Condition in Kenya," Discussion Papers of DIW Berlin 1247, DIW Berlin, German Institute for Economic Research.
    265. Zeynel Abidin Ozdemir & Mehmet Balcilar & Aysit Tansel, 2011. "International Labour Force Participation Rates by Gender: Unit Root or Structural Breaks?," ERC Working Papers 1105, ERC - Economic Research Center, Middle East Technical University, revised Oct 2011.
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    269. Hyung, N. & Franses, Ph.H.B.F., 2002. "Inflation rates; long-memoray, level shifts, or both?," Econometric Institute Research Papers EI 2002-08, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    270. Goliński, Adam & Spencer, Peter, 2017. "The advantages of using excess returns to model the term structure," Journal of Financial Economics, Elsevier, vol. 125(1), pages 163-181.
    271. Luis A. Gil-Alana & Juncal Cunado & Fernando Perez de Gracia, 2008. "Tourism in the Canary Islands: forecasting using several seasonal time series models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(7), pages 621-636.
    272. Charfeddine, Lanouar & Maouchi, Youcef, 2019. "Are shocks on the returns and volatility of cryptocurrencies really persistent?," Finance Research Letters, Elsevier, vol. 28(C), pages 423-430.
    273. Gil-Alana, Luis A. & Yaya, OlaOluwa S. & Shittu, Olanrewaju I., 2014. "Global temperatures and sunspot numbers. Are they related?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 42-50.
    274. Lai, Kon S., 2008. "The puzzling unit root in the real interest rate and its inconsistency with intertemporal consumption behavior," Journal of International Money and Finance, Elsevier, vol. 27(1), pages 140-155, February.
    275. Kobayashi, Kiyoshi & Kaito, Kiyoyuki & Lethanh, Nam, 2012. "A statistical deterioration forecasting method using hidden Markov model for infrastructure management," Transportation Research Part B: Methodological, Elsevier, vol. 46(4), pages 544-561.
    276. Luis A. Gil-Alana & Antonio Moreno & Seonghoon Cho, 2009. "The Deaton paradox in a long memory context with structural breaks," Faculty Working Papers 03/09, School of Economics and Business Administration, University of Navarra.
    277. Mohamed El Hedi Arouri & Shawkat Hammoudeh & Amine Lahiani & Duc Khuong Nguyen, 2013. "Long memory and structural breaks in modeling the return and volatility dynamics of precious metals," Working Papers hal-00798033, HAL.
    278. Augustine Arize & John Malindretos & Kiseok Nam, 2005. "Inflation and Structural Change in 50 Developing Countries," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 33(4), pages 461-471, December.
    279. Tommaso Proietti, 2015. "Exponential Smoothing, Long Memory and Volatility Prediction," CREATES Research Papers 2015-51, Department of Economics and Business Economics, Aarhus University.
    280. Davide Delle Monache & Stefano Grassi & Paolo Santucci de Magistris, 2017. "Does the ARFIMA really shift?," CREATES Research Papers 2017-16, Department of Economics and Business Economics, Aarhus University.
    281. Andreou, Elena & Ghysels, Eric, 2006. "Monitoring disruptions in financial markets," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 77-124.
    282. Gil-Alana, Luis A. & Mudida, Robert & Zerbo, Eleazar, 2021. "GDP per capita IN SUB-SAHARAN Africa: A time series approach using long memory," International Review of Economics & Finance, Elsevier, vol. 72(C), pages 175-190.
    283. Arturo Leccadito & Omar Rachedi & Giovanni Urga, 2015. "True Versus Spurious Long Memory: Some Theoretical Results and a Monte Carlo Comparison," Econometric Reviews, Taylor & Francis Journals, vol. 34(4), pages 452-479, April.
    284. Chevillon, Guillaume, 2016. "Multistep forecasting in the presence of location shifts," International Journal of Forecasting, Elsevier, vol. 32(1), pages 121-137.
    285. Walter Kramer & Philipp Sibbertsen, 2002. "Testing for Structural Changes in the Presence of Long Memory," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 1(3), pages 235-242, December.
    286. Lahiani, A. & Scaillet, O., 2009. "Testing for threshold effect in ARFIMA models: Application to US unemployment rate data," International Journal of Forecasting, Elsevier, vol. 25(2), pages 418-428.
    287. Fabio Trojani & Francesco Audrino, 2006. "Estimating and predicting multivariate volatility thresholds in global stock markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(3), pages 345-369.
    288. Audrino, Francesco & Corsi, Fulvio, 2010. "Modeling tick-by-tick realized correlations," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2372-2382, November.
    289. Kellard, Neil M. & Jiang, Ying & Wohar, Mark, 2015. "Spurious long memory, uncommon breaks and the implied–realized volatility puzzle," Journal of International Money and Finance, Elsevier, vol. 56(C), pages 36-54.
    290. Paulo M.M. Rodrigues & Uwe Hassler, 2014. "Persistence in the Banking Industry: Fractional integration and breaks in memory," Working Papers w201406, Banco de Portugal, Economics and Research Department.
    291. Daniela Osterrieder & Peter C. Schotman, 2012. "The Volatility of Long-term Bond Returns: Persistent Interest Shocks and Time-varying Risk Premiums," CREATES Research Papers 2012-35, Department of Economics and Business Economics, Aarhus University.
    292. Gil-Alana, Luis A. & Fischer, Christian, 2007. "International traveling and trade: further evidence for the case of Spanish wine based on fractional VAR specifications," 105th Seminar, March 8-10, 2007, Bologna, Italy 7859, European Association of Agricultural Economists.
    293. Davis, Richard A. & Hancock, Stacey A. & Yao, Yi-Ching, 2016. "On consistency of minimum description length model selection for piecewise autoregressions," Journal of Econometrics, Elsevier, vol. 194(2), pages 360-368.
    294. Maheu John, 2005. "Can GARCH Models Capture Long-Range Dependence?," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(4), pages 1-43, December.
    295. Niels Haldrup & Robinson Kruse, 2014. "Discriminating between fractional integration and spurious long memory," CREATES Research Papers 2014-19, Department of Economics and Business Economics, Aarhus University.
    296. Smallwood, Aaron D., 2008. "Measuring the persistence of deviations from purchasing power parity with a fractionally integrated STAR model," Journal of International Money and Finance, Elsevier, vol. 27(7), pages 1161-1176, November.
    297. McMillan, David G. & Ruiz, Isabel, 2009. "Volatility persistence, long memory and time-varying unconditional mean: Evidence from 10 equity indices," The Quarterly Review of Economics and Finance, Elsevier, vol. 49(2), pages 578-595, May.
    298. Bildirici, Melike & Ersin, Özgür, 2012. "Nonlinear volatility models in economics: smooth transition and neural network augmented GARCH, APGARCH, FIGARCH and FIAPGARCH models," MPRA Paper 40330, University Library of Munich, Germany, revised May 2012.
    299. Al-Shboul, Mohammad & Anwar, Sajid, 2016. "Fractional integration in daily stock market indices at Jordan's Amman stock exchange," The North American Journal of Economics and Finance, Elsevier, vol. 37(C), pages 16-37.
    300. Chen, Ying & Niu, Linlin, 2014. "Adaptive dynamic Nelson–Siegel term structure model with applications," Journal of Econometrics, Elsevier, vol. 180(1), pages 98-115.
    301. Elie Bouri & Luis A. Gil-Alana & Rangan Gupta & David Roubaud, 2016. "Modelling Long Memory Volatility in the Bitcoin Market: Evidence of Persistence and Structural Breaks," Working Papers 201654, University of Pretoria, Department of Economics.
    302. Gil-Alana, Luis A. & Gupta, Rangan & Shittu, Olanrewaju I. & Yaya, OlaOluwa S., 2018. "Market efficiency of Baltic stock markets: A fractional integration approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 511(C), pages 251-262.
    303. Gadea, Maria Dolores & Sabate, Marcela & Serrano, Jose Maria, 2004. "Structural breaks and their trace in the memory: Inflation rate series in the long-run," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 14(2), pages 117-134, April.
    304. Karanasos, Menelaos & Paraskevopoulos, Alexandros & Magdalinos, Anastasios & Canepa, Alessandra, 2024. "A Unified Theory for Arma Models with Varying Coefficients: One Solution Fits All," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202413, University of Turin.
    305. Omane-Adjepong, Maurice & Boako, Gideon, 2017. "Long-range dependence in returns and volatility of global gold market amid financial crises," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 472(C), pages 188-202.
    306. Baillie, Richard T. & Kongcharoen, Chaleampong & Kapetanios, George, 2012. "Prediction from ARFIMA models: Comparisons between MLE and semiparametric estimation procedures," International Journal of Forecasting, Elsevier, vol. 28(1), pages 46-53.
    307. Christis Katsouris, 2023. "Structural Analysis of Vector Autoregressive Models," Papers 2312.06402, arXiv.org, revised Feb 2024.
    308. Kyongwook Choi & Eric Zivot, 2003. "Long Memory and Structural Changes in the Forward Discount: An Empirical Investigation," Econometrics 0307001, University Library of Munich, Germany, revised 23 Jul 2003.
    309. Paul De Grauwe & Zhaoyong Zhang & Kin-Yip Ho & Yanlin Shi & Zhaoyong Zhang, 2016. "It takes two to tango: A regime-switching analysis of the correlation dynamics between the mainland Chinese and Hong Kong stock markets," Scottish Journal of Political Economy, Scottish Economic Society, vol. 63(1), pages 41-65, February.
    310. Dufrénot, Gilles & Lardic, Sandrine & Mathieu, Laurent & Mignon, Valérie & Péguin-Feissolle, Anne, 2008. "Explaining the European exchange rates deviations: Long memory or non-linear adjustment?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 18(3), pages 207-215, July.
    311. Andrés Herrera Aramburú & Gabriel Rodríguez, 2016. "Volatility of stock market and exchange rate returns in Peru: Long memory or short memory with level shifts?," International Journal of Monetary Economics and Finance, Inderscience Enterprises Ltd, vol. 9(1), pages 45-66.
    312. Beran, Jan & Shumeyko, Yevgen, 2012. "Bootstrap testing for discontinuities under long-range dependence," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 322-347.
    313. Lovcha, Yuliya & Perez-Laborda, Alejandro, 2018. "Monetary policy shocks, inflation persistence, and long memory," Journal of Macroeconomics, Elsevier, vol. 55(C), pages 117-127.
    314. Christian Fischer & Luis Alberiko Gil-Alana, 2005. "The Nature of the Relationship between International Tourism and International Trade: The Case of Ge," Faculty Working Papers 15/05, School of Economics and Business Administration, University of Navarra.
    315. Dooruj Rambaccussing & Murat Mazibas, 2020. "True versus Spurious Long Memory in Cryptocurrencies," JRFM, MDPI, vol. 13(9), pages 1-11, August.
    316. Lutz, Benjamin Johannes & Pigorsch, Uta & Rotfuß, Waldemar, 2013. "Nonlinearity in cap-and-trade systems: The EUA price and its fundamentals," Energy Economics, Elsevier, vol. 40(C), pages 222-232.
    317. Saban Nazlioglu & Rangan Gupta & Elie Bouri, 2019. "Movements in International Bond Markets: The Role of Oil Prices," Working Papers 201935, University of Pretoria, Department of Economics.
    318. Matei Demetrescu & Mehdi Hosseinkouchack, 2022. "Autoregressive spectral estimates under ignored changes in the mean," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(2), pages 329-340, March.
    319. Dittmann, Ingolf & Granger, Clive W. J., 2000. "Properties of nonlinear transformations of fractionally integrated processes," Technical Reports 2000,25, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    320. Baek, Changryong & Fortuna, Natércia & Pipiras, Vladas, 2014. "Can Markov switching model generate long memory?," Economics Letters, Elsevier, vol. 124(1), pages 117-121.
    321. Laura Mayoral, 2005. "Is the observed persistence spurious? A test for fractional integration versus short memory and structural breaks," Economics Working Papers 956, Department of Economics and Business, Universitat Pompeu Fabra.
    322. Junior A. Ojeda Cunya & Gabriel Rodríguez, 2016. "An application of a random level shifts model to the volatility of Peruvian stock and exchange rate returns," Macroeconomics and Finance in Emerging Market Economies, Taylor & Francis Journals, vol. 9(1), pages 34-55, March.
    323. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2011. "Fractional Integration and Cointegration in US Financial Time Series Data," Discussion Papers of DIW Berlin 1116, DIW Berlin, German Institute for Economic Research.
    324. Richard T. Baillie & Fabio Calonaci & Dooyeon Cho & Seunghwa Rho, 2019. "Long Memory, Realized Volatility and HAR Models," Working Papers 881, Queen Mary University of London, School of Economics and Finance.
    325. Luis A. Gil-Alana, 2006. "Fractional integration and structural breaks at unknown periods of time," Faculty Working Papers 16/06, School of Economics and Business Administration, University of Navarra.
    326. Wenger, Kai & Leschinski, Christian & Sibbertsen, Philipp, 2017. "The Memory of Volatility," Hannover Economic Papers (HEP) dp-601, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    327. Tapas K. Mishra, 2006. "A Further Look into the Demography-based GDP Forecasting Method," Working Papers of BETA 2006-17, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    328. Gawon Yoon, 2010. "Long memory in return volatility," Applied Economics Letters, Taylor & Francis Journals, vol. 17(4), pages 345-349.
    329. Juan J. Dolado & Jesús Gonzalo & Laura Mayoral, 2005. "What is What? A Simple Time-Domain Test of Long-memory vs. Structural Breaks," Working Papers 258, Barcelona School of Economics.
    330. Jose Maria Fernandez-Crehuet & Luis Alberiko Gil-Alana & Cristina Martí Barco, 2020. "Unemployment and Fertility: A Long Run Relationship," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 152(3), pages 1177-1196, December.
    331. Richard Paap & Philip Hans Franses & Marco Van Der Leij, 2002. "Modelling and forecasting level shifts in absolute returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 601-616.
    332. Oomen, Roel C. A., 2004. "Modelling realized variance when returns are serially correlated [Modellierung realisierter Varianz bei autokorrelierten Erträgen]," Discussion Papers, Research Unit: Market Processes and Governance SP II 2004-11, WZB Berlin Social Science Center.
    333. Biage, Milton & Nelcide, Pierre Joseph, 2020. "Effects of asset frequency components on value-at-risk in emerging and developed markets," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 40(1), August.
    334. Saban Nazlioglu & Rangan Gupta & Alper Gormus & Ugur Soytas, 2019. "Price and Volatility Linkages between International REITs and Oil Markets," Working Papers 201954, University of Pretoria, Department of Economics.
    335. Francis W. Ahking, 2004. "Non-Parametric Tests of Real Exchange rates in the Post-Bretton Woods Era," Working papers 2004-05, University of Connecticut, Department of Economics.
    336. Altissimo, F. & Corradi, V., 2000. "Strong Rules for Detecting the Number of Breaks in a Time Series," Discussion Papers 0011, University of Exeter, Department of Economics.
    337. Leonardo Ieracitano Vieira & Márcio Poletti Laurini, 2023. "Time-varying higher moments in Bitcoin," Digital Finance, Springer, vol. 5(2), pages 231-260, June.
    338. Haldrup; Niels & Morten Oerregaard Nielsen, 2005. "Directional Congestion and Regime Switching in a Long Memory Model for Electricity Prices," Economics Working Papers 2005-18, Department of Economics and Business Economics, Aarhus University.
    339. Abdollahi, Hooman & Ebrahimi, Seyed Babak, 2020. "A new hybrid model for forecasting Brent crude oil price," Energy, Elsevier, vol. 200(C).
    340. Eric Hillebrand & Marcelo C. Medeiros, 2012. "Nonlinearity, Breaks, and Long-Range Dependence in Time-Series Models," CREATES Research Papers 2012-30, Department of Economics and Business Economics, Aarhus University.
    341. Achour Maha & Trabelsi Abdelwahed, 2011. "Markov Switching and State-Space Approaches for Investigating the Link between Egyptian Inflation Level and Uncertainty," Review of Middle East Economics and Finance, De Gruyter, vol. 6(3), pages 46-62, February.
    342. Guglielmo Maria Caporale & Juncal Cunado & Luis A. Gil-Alana, 2007. "Deterministic versus Stochastic Seasonal Fractional Integration and Structural Breaks," CESifo Working Paper Series 1989, CESifo.
    343. Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), 2008. "Handbook on Information Technology in Finance," International Handbooks on Information Systems, Springer, number 978-3-540-49487-4, June.
    344. Derek Bond & Michael J. Harrison & Edward J. O'Brien, 2006. "Purchasing Power Parity: The Irish Experience Re-visited," Trinity Economics Papers tep200615, Trinity College Dublin, Department of Economics.
    345. Eric Hillebrand, 2003. "Overlaying Time Scales and Persistence Estimation in GARCH(1,1) Models," Econometrics 0301003, University Library of Munich, Germany.
    346. Juan Carlos Cuestas & Luis A. Gil-Alana & Paulo José Regis, 2015. "The Sustainability of European External Debt: What have We Learned?," Review of International Economics, Wiley Blackwell, vol. 23(3), pages 445-468, August.
    347. Dark, Jonathan, 2018. "Multivariate models with long memory dependence in conditional correlation and volatility," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 162-180.
    348. Burcu Kiran, 2010. "The Structure of Tourism Revenues in Turkey: Evidence from Fractional Integration under Multiple Structural Breaks," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 4, pages 85-96.
    349. Rea, William & Oxley, Les & Reale, Marco & Brown, Jennifer, 2013. "Not all estimators are born equal: The empirical properties of some estimators of long memory," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 93(C), pages 29-42.
    350. Gil-Alana, Luis Alberiko & Dettoni, Robinson & Costamagna, Rodrigo & Valenzuela, Mario, 2019. "Rational bubbles in the real housing stock market: Empirical evidence from Santiago de Chile," Research in International Business and Finance, Elsevier, vol. 49(C), pages 269-281.
    351. L.A. Gil-Alana, 2005. "Fractional Cyclical Structures & Business Cycles in the Specification of the US Real Output," European Research Studies Journal, European Research Studies Journal, vol. 0(1-2), pages 99-126.
    352. Cliff L. F. Attfield & Jonathan R. W. Temple, 2006. "Balanced growth and the great ratios: new evidence for the US and UK," Centre for Growth and Business Cycle Research Discussion Paper Series 75, Economics, The University of Manchester.
    353. Fillol, Jerome, 2007. "Estimating long memory: Scaling function vs Andrews and Guggenberger GPH," Economics Letters, Elsevier, vol. 95(2), pages 309-314, May.
    354. Chen, Ying & Härdle, Wolfgang Karl & Pigorsch, Uta, 2010. "Localized Realized Volatility Modeling," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1376-1393.
    355. Andrea Monticini & Francesco Ravazzolo, 2014. "Forecasting the intraday market price of money," DISCE - Working Papers del Dipartimento di Economia e Finanza def010, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    356. Gao, Guangyuan & Ho, Kin-Yip & Shi, Yanlin, 2020. "Long memory or regime switching in volatility? Evidence from high-frequency returns on the U.S. stock indices," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
    357. Granger, Clive W. J. & Hyung, Namwon, 2004. "Occasional structural breaks and long memory with an application to the S&P 500 absolute stock returns," Journal of Empirical Finance, Elsevier, vol. 11(3), pages 399-421, June.
    358. Luis Gil-Alana & Rolando Peláez, 2008. "The persistence of earnings per share," Review of Quantitative Finance and Accounting, Springer, vol. 31(4), pages 425-439, November.
    359. Gil-Alana, Luis A., 2002. "A mean shift break in the US interest rate," Economics Letters, Elsevier, vol. 77(3), pages 357-363, November.
    360. Gianluca Cubadda & Alain Hecq & Antonio Riccardo, 2018. "Forecasting Realized Volatility Measures with Multivariate and Univariate Models: The Case of The US Banking Sector," CEIS Research Paper 445, Tor Vergata University, CEIS, revised 30 Oct 2018.
    361. Adebola, Solarin Sakiru & Gil-Alana, Luis A. & Madigu, Godfrey, 2019. "Gold prices and the cryptocurrencies: Evidence of convergence and cointegration," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1227-1236.
    362. Shikta Sing & Supun Chandrasena & Yue Shi & Abdullah Alhussain & Claude DIEBOLT & Martin Enilov & Tapas Mishra, 2024. "A Learning Model with Memory in the Financial Markets," Working Papers 06-24, Association Française de Cliométrie (AFC).
    363. Kuswanto, Heri & Sibbertsen, Philipp, 2009. "Testing for Long Memory Against ESTAR Nonlinearities," Hannover Economic Papers (HEP) dp-427, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    364. Sibbertsen, Philipp, 2001. "Long-memory versus structural breaks: An overview," Technical Reports 2001,28, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    365. Renzo Pardo Figueroa & Gabriel Rodríguez, 2014. "Distinguishing between True and Spurious Long Memory in the Volatility of Stock Market Returns in Latin America," Documentos de Trabajo / Working Papers 2014-395, Departamento de Economía - Pontificia Universidad Católica del Perú.
    366. Pierre Perron & Wendong Shi, 2020. "Temporal Aggregation and Long Memory for Asset Price Volatility," JRFM, MDPI, vol. 13(8), pages 1-18, August.
    367. Luisa Bisaglia & Margherita Gerolimetto, 2009. "Testing structural breaks versus long memory with the Box–Pierce statistics: a Monte Carlo study," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 18(4), pages 543-553, November.
    368. Chen, Shengming & Bouteska, Ahmed & Sharif, Taimur & Abedin, Mohammad Zoynul, 2023. "The Russia–Ukraine war and energy market volatility: A novel application of the volatility ratio in the context of natural gas," Resources Policy, Elsevier, vol. 85(PA).
    369. Chen, Runquan, 2009. "Regime switching in volatilities and correlation between stock and bond markets," LSE Research Online Documents on Economics 29306, London School of Economics and Political Science, LSE Library.
    370. Young Wook Han, 2010. "The Effects of US Macroeconomic Surprises on the Intraday Movements of Foreign Exchange Rates: Cases of USD-EUR and USD-JPY Exchange Rates," International Economic Journal, Taylor & Francis Journals, vol. 24(3), pages 375-396.
    371. Barros, Carlos Pestana & Gil-Alana, Luis A. & Payne, James E., 2011. "An analysis of oil production by OPEC countries: Persistence, breaks, and outliers," Energy Policy, Elsevier, vol. 39(1), pages 442-453, January.
    372. Heejoon Han & Shen Zhang, 2012. "Non‐stationary non‐parametric volatility model," Econometrics Journal, Royal Economic Society, vol. 15(2), pages 204-225, June.
    373. Barros, Carlos Pestana & Gil-Alana, Luis A. & Payne, James E., 2012. "Comovements among U.S. state housing prices: Evidence from fractional cointegration," Economic Modelling, Elsevier, vol. 29(3), pages 936-942.
    374. Shi, Yanlin & Ho, Kin-Yip, 2015. "Long memory and regime switching: A simulation study on the Markov regime-switching ARFIMA model," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 189-204.
    375. Corbet, Shaen & Goodell, John W. & Günay, Samet, 2020. "Co-movements and spillovers of oil and renewable firms under extreme conditions: New evidence from negative WTI prices during COVID-19," Energy Economics, Elsevier, vol. 92(C).
    376. A. M. M. Shahiduzzaman Quoreshi & Reaz Uddin & Viroj Jienwatcharamongkhol, 2019. "Equity Market Contagion in Return Volatility during Euro Zone and Global Financial Crises: Evidence from FIMACH Model," JRFM, MDPI, vol. 12(2), pages 1-18, June.
    377. Belkhouja, Mustapha & Mootamri, Imene, 2016. "Long memory and structural change in the G7 inflation dynamics," Economic Modelling, Elsevier, vol. 54(C), pages 450-462.
    378. Walter Kraemer, 2008. "Long Memory with Markov-Switching GARCH," CESifo Working Paper Series 2225, CESifo.
    379. McAleer, Michael & Medeiros, Marcelo C., 2008. "A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries," Journal of Econometrics, Elsevier, vol. 147(1), pages 104-119, November.
    380. Aloui, Chaker & Hamida, Hela ben, 2014. "Modelling and forecasting value at risk and expected shortfall for GCC stock markets: Do long memory, structural breaks, asymmetry, and fat-tails matter?," The North American Journal of Economics and Finance, Elsevier, vol. 29(C), pages 349-380.
    381. Caporale, Guglielmo Maria & Gil-Alana, Luis A., 2008. "Modelling the US, UK and Japanese unemployment rates: Fractional integration and structural breaks," Computational Statistics & Data Analysis, Elsevier, vol. 52(11), pages 4998-5013, July.
    382. Bent Jesper Christensen & Nabanita Datta Gupta & Paolo Santucci de Magistris, 2021. "Measuring the impact of clean energy production on CO2 abatement in Denmark: Upper bound estimation and forecasting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 118-149, January.
    383. Abanto-Valle, Carlos A. & Rodríguez, Gabriel & Garrafa-Aragón, Hernán B., 2021. "Stochastic Volatility in Mean: Empirical evidence from Latin-American stock markets using Hamiltonian Monte Carlo and Riemann Manifold HMC methods," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 272-286.
    384. Luis A. Gil-Alana, 2015. "Linear and segmented trends in sea surface temperature data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(7), pages 1531-1546, July.
    385. Gil-Alana, Luis A., 2002. "Structural breaks and fractional integration in the US output and unemployment rate," Economics Letters, Elsevier, vol. 77(1), pages 79-84, September.
    386. Sibbertsen, Philipp & Willert, Juliane, 2012. "Estimating the number of mean shifts under long memory," Hannover Economic Papers (HEP) dp-496, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    387. Yaya O. S. & Akintande O. J. & Ogbonna A. E. & Adegoke H. M., 2019. "Cpi Inflation In Africa: Fractional Persistence, Mean Reversion And Nonlinearity," Statistics in Transition New Series, Statistics Poland, vol. 20(3), pages 119-132, September.
    388. Alessandro Casini, 2021. "Theory of Evolutionary Spectra for Heteroskedasticity and Autocorrelation Robust Inference in Possibly Misspecified and Nonstationary Models," Papers 2103.02981, arXiv.org, revised Aug 2024.
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    390. Dietmar Bauer & Alex Maynard, 2010. "Persistence-robust Granger causality testing," Working Papers 1011, University of Guelph, Department of Economics and Finance.
    391. Laurent-Emmanuel Calvet & Adlai J. Fisher & Samuel B. Thompson, 2006. "Volatility Comovement: a multifrequency approach," Post-Print hal-00459667, HAL.
    392. Khurshid M. Kiani, 2009. "Asymmetries in Macroeconomic Time Series in Eleven Asian Economies," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 8(1), pages 37-54, April.
    393. Dominique Guegan & Philippe de Peretti, 2011. "Tests of structural changes in conditional distributions with unknown changepoints," Post-Print halshs-00611932, HAL.
    394. Bond, Derek & Harrison, Michael J & Hession, Niall & O’Brien, Edward J., 2006. "Some Empirical Observations on the Forward Exchange Rate Anomaly," Research Technical Papers 3/RT/06, Central Bank of Ireland.
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    525. Luis Alberiko Gil-Alana, 2005. "Unit and Fractional Roots in the Presence of Abrupt Changes with an Application to the Brazilian Inf," Faculty Working Papers 19/05, School of Economics and Business Administration, University of Navarra.
    526. Banerjee, Anindya & Urga, Giovanni, 2005. "Modelling structural breaks, long memory and stock market volatility: an overview," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 1-34.
    527. Luis A. Gil‐Alana, 2004. "A joint test of fractional integration and structural breaks at a known period of time," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(5), pages 691-700, September.
    528. Yalama, Abdullah & Celik, Sibel, 2013. "Real or spurious long memory characteristics of volatility: Empirical evidence from an emerging market," Economic Modelling, Elsevier, vol. 30(C), pages 67-72.
    529. Eric Hillebrand & Marcelo Cunha Medeiros, 2010. "Asymmetries, breaks, and long-range dependence: An estimation framework for daily realized volatility," Textos para discussão 578, Department of Economics PUC-Rio (Brazil).
    530. Cunado, J. & Gil-Alana, L. A. & Perez de Gracia, F., 2004. "Is the US fiscal deficit sustainable?: A fractionally integrated approach," Journal of Economics and Business, Elsevier, vol. 56(6), pages 501-526.
    531. Feng, Lingbing & Fu, Tong & Shi, Yanlin, 2022. "How does news sentiment affect the states of Japanese stock return volatility?," International Review of Financial Analysis, Elsevier, vol. 84(C).
    532. Laura Mayoral, 2003. "Further Evidence on the Uncertain (Fractional) Unit Root in Real GNP," Working Papers 82, Barcelona School of Economics.
    533. Mengmeng Guo & Wolfgang Härdle, 2012. "Simultaneous confidence bands for expectile functions," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(4), pages 517-541, October.
    534. Agata Kliber, 2013. "Influence of the Greek Crisis on the Risk Perception of European Economies," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 5(2), pages 125-161, June.
    535. Cheong, Chin Wen, 2008. "Time-varying volatility in Malaysian stock exchange: An empirical study using multiple-volatility-shift fractionally integrated model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(4), pages 889-898.
    536. Abakah, Emmanuel Joel Aikins & Gil-Alana, Luis Alberiko & Madigu, Godfrey & Romero-Rojo, Fatima, 2020. "Volatility persistence in cryptocurrency markets under structural breaks," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 680-691.
    537. Asai, Manabu & Chang, Chia-Lin & McAleer, Michael, 2022. "Realized matrix-exponential stochastic volatility with asymmetry, long memory and higher-moment spillovers," Journal of Econometrics, Elsevier, vol. 227(1), pages 285-304.
    538. Chaim, Pedro & Laurini, Márcio P., 2019. "Nonlinear dependence in cryptocurrency markets," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 32-47.
    539. Canarella, Giorgio & Miller, Stephen M., 2017. "Inflation targeting and inflation persistence: New evidence from fractional integration and cointegration," Journal of Economics and Business, Elsevier, vol. 92(C), pages 45-62.
    540. Giorgio Canarella & Luis A. Gil‐Alana & Rangan Gupta & Stephen M. Miller, 2022. "The behaviour of real interest rates: New evidence from a 'suprasecular' perspective," International Finance, Wiley Blackwell, vol. 25(1), pages 46-64, April.
    541. Leschinski, Christian & Sibbertsen, Philipp, 2017. "Origins of Spurious Long Memory," Hannover Economic Papers (HEP) dp-595, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    542. Kang, Sang Hoon & Cho, Hwan-Gue & Yoon, Seong-Min, 2009. "Modeling sudden volatility changes: Evidence from Japanese and Korean stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(17), pages 3543-3550.
    543. Duan, Kun & Gao, Yang & Mishra, Tapas & Satchell, Stephen, 2023. "Efficiency dynamics across segmented Bitcoin Markets: Evidence from a decomposition strategy," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 83(C).
    544. Huang, Yirong & Luo, Yi, 2024. "Forecasting conditional volatility based on hybrid GARCH-type models with long memory, regime switching, leverage effect and heavy-tail: Further evidence from equity market," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
    545. Leschinski, Christian & Sibbertsen, Philipp, 2018. "The Periodogram of Spurious Long-Memory Processes," Hannover Economic Papers (HEP) dp-632, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    546. José M. Belbute & Alfredo Marvão Pereira, 2016. "Updated Reference Forecasts for Global CO2 Emissions from Fossil-Fuel Consumption," Working Papers 170, Department of Economics, College of William and Mary.
    547. Apergis, Nicholas & Tsoumas, Chris, 2011. "Integration properties of disaggregated solar, geothermal and biomass energy consumption in the U.S," Energy Policy, Elsevier, vol. 39(9), pages 5474-5479, September.
    548. Vasco J. Gabriel & Martin Sola & Zacharias Psaradakis, 2002. "Residual-based tests for cointegration and multiple regime shifts," NIPE Working Papers 7/2002, NIPE - Universidade do Minho.
    549. Dominique Guegan & Zhiping Lu, 2007. "A note on self-similarity for discrete time series," Post-Print halshs-00187910, HAL.
    550. Terence C. Mills, 2007. "Time series modelling of two millennia of northern hemisphere temperatures: long memory or shifting trends?," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(1), pages 83-94, January.
    551. Zanetti Chini, Emilio, 2025. "Judgment can spur long memory," Journal of Economic Dynamics and Control, Elsevier, vol. 170(C).
    552. Axioglou, Christos & Skouras, Spyros, 2011. "Markets change every day: Evidence from the memory of trade direction," Journal of Empirical Finance, Elsevier, vol. 18(3), pages 423-446, June.
    553. Zegadło, Piotr, 2022. "Identifying bull and bear market regimes with a robust rule-based method," Research in International Business and Finance, Elsevier, vol. 60(C).
    554. Cedric Okou & Eric Jacquier, 2014. "Horizon Effect in the Term Structure of Long-Run Risk-Return Trade-Offs," CIRANO Working Papers 2014s-36, CIRANO.
    555. Steven Clark & T. Coggin, 2011. "Are U.S. stock prices mean reverting? Some new tests using fractional integration models with overlapping data and structural breaks," Empirical Economics, Springer, vol. 40(2), pages 373-391, April.

  79. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Heiko Ebens, 2000. "The Distribution of Stock Return Volatility," NBER Working Papers 7933, National Bureau of Economic Research, Inc.

    Cited by:

    1. Hau, Harald, 2002. "The Role of Transaction Costs for Financial Volatility: Evidence from the Paris Bourse," CEPR Discussion Papers 3651, C.E.P.R. Discussion Papers.
    2. Adam Wu, 2017. "Systematic Noise: Micro-movements in Equity Options Markets," Papers 1708.06855, arXiv.org.
    3. Magdalena Vorzsak & Carmen Maria Gut, 2008. "Constraints Concerning Investment And Participation In Professional Training In The Companies From The Romanian Manufacturing Industry," JOURNAL STUDIA UNIVERSITATIS BABES-BOLYAI NEGOTIA, Babes-Bolyai University, Faculty of Business.
    4. Theodoros Diasakos, 2008. "Comparative Statics of Asset Prices," Carlo Alberto Notebooks 72, Collegio Carlo Alberto, revised 2011.
    5. M. Illueca & J. A. LaFuente, 2006. "New evidence on expiration‐day effects using realized volatility: An intraday analysis for the Spanish stock exchange," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 26(9), pages 923-938, September.
    6. Dimitrios D. Thomakos & Michail S. Koubouros, 2005. "Realized Volatility and Asymmetries in the A.S.E. Returns," Finance 0504009, University Library of Munich, Germany, revised 17 Jan 2006.
    7. Özer Karagedikli & Pierre L. Siklos, 2008. "Explaining Movements in the NZ Dollar - Central Bank Communication and the Surprise Element in Monetary Policy?," Reserve Bank of New Zealand Discussion Paper Series DP2008/02, Reserve Bank of New Zealand.
    8. Sam Howison & David Lamper, 2001. "Trading volume in models of financial derivatives," Applied Mathematical Finance, Taylor & Francis Journals, vol. 8(2), pages 119-135.
    9. John M. Maheu & Thomas McCurdy, 2001. "Nonlinear Features of Realized FX Volatility," CIRANO Working Papers 2001s-42, CIRANO.
    10. Tiberiu Cristian Avramescu, 2008. "Romanian Tourism: A Regional Approach," JOURNAL STUDIA UNIVERSITATIS BABES-BOLYAI NEGOTIA, Babes-Bolyai University, Faculty of Business.
    11. John Cotter, 2004. "Realized volatility and minimum capital requirements," Money Macro and Finance (MMF) Research Group Conference 2003 20, Money Macro and Finance Research Group.
    12. Šárka Brychtová, 2008. "Spa Healing Sources In Czech Republic," JOURNAL STUDIA UNIVERSITATIS BABES-BOLYAI NEGOTIA, Babes-Bolyai University, Faculty of Business.
    13. Chang, Yoosoon & Isaac Miller, J. & Park, Joon Y., 2009. "Extracting a common stochastic trend: Theory with some applications," Journal of Econometrics, Elsevier, vol. 150(2), pages 231-247, June.
    14. Mihaela Dragan & Zenovia Cristiana Pop, 2008. "CRITERIA FOR PRODUCT QUALITY IN THE FRAME OF INTERCULTURAL MARKET STRATEGIES OF SMALL AND MEDIUM SIZED ENTERPRISES - a brief review of literature -," JOURNAL STUDIA UNIVERSITATIS BABES-BOLYAI NEGOTIA, Babes-Bolyai University, Faculty of Business.
    15. Hayette Gatfaoui, 2006. "Idiosyncratic Risk, Systematic Risk and Stochastic Volatility: An Implementation of Merton's Credit Risk Valuation," Post-Print hal-00589918, HAL.
    16. Manole Velicanu & Gheorghe Matei, 2008. "Decision Support Systems: Present And Future Trends," JOURNAL STUDIA UNIVERSITATIS BABES-BOLYAI NEGOTIA, Babes-Bolyai University, Faculty of Business.
    17. Borgsen, Sina & Glaser, Markus, 2005. "Diversifikationseffekte durch Small und Mid Caps?," Sonderforschungsbereich 504 Publications 05-10, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
    18. Rasmus T. Varneskov & Pierre Perron, 2017. "Combining Long Memory and Level Shifts in Modeling and Forecasting the Volatility of Asset Returns," Boston University - Department of Economics - Working Papers Series WP2017-006, Boston University - Department of Economics.
    19. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Heiko Ebens, 2000. "The Distribution of Stock Return Volatility," NBER Working Papers 7933, National Bureau of Economic Research, Inc.
    20. Cristina Silvia Nistor & Crina Ioana Filip & Adela Deaconu, 2008. "Derivative Instruments – Alternatives To Cover The Foreign Exchange Rate In The Case Of Import-Export Operations - Accounting Approach For Romania," JOURNAL STUDIA UNIVERSITATIS BABES-BOLYAI NEGOTIA, Babes-Bolyai University, Faculty of Business.
    21. David S. Bates, 2003. "Maximum Likelihood Estimation of Latent Affine Processes," NBER Working Papers 9673, National Bureau of Economic Research, Inc.
    22. Asger Lunde & Peter Reinhard Hansen, 2001. "A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH(1,1)?," Working Papers 2001-04, Brown University, Department of Economics.
    23. Barucci, Emilio & Reno, Roberto, 2002. "On measuring volatility and the GARCH forecasting performance," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 12(3), pages 183-200, July.
    24. Cristina Curutiu, 2008. "Methods Of Portfolio Management - A Review Of Literature -," JOURNAL STUDIA UNIVERSITATIS BABES-BOLYAI NEGOTIA, Babes-Bolyai University, Faculty of Business.
    25. Partenie Dumbrava & Ioan Pop & Eniko Fazakas & Jozsef Fazakas & Ludovica Breban, 2008. "The Environmental Impact Of Beer Production," JOURNAL STUDIA UNIVERSITATIS BABES-BOLYAI NEGOTIA, Babes-Bolyai University, Faculty of Business.
    26. Michael W. Brandt & Qiang Kang, 2002. "On the Relationship Between the Conditional Mean and Volatility of Stock Returns: A Latent VAR Approach," NBER Working Papers 9056, National Bureau of Economic Research, Inc.
    27. Bollerslev, Tim, 2001. "Financial econometrics: Past developments and future challenges," Journal of Econometrics, Elsevier, vol. 100(1), pages 41-51, January.
    28. Srečko Devjak & Andraž Grum, 2006. "Third Moment of Yield Probability Distributions for Instruments on Slovenian Financial Markets," Prague Economic Papers, Prague University of Economics and Business, vol. 2006(4), pages 364-373.
    29. Stavros Degiannakis, 2004. "Volatility forecasting: evidence from a fractional integrated asymmetric power ARCH skewed-t model," Applied Financial Economics, Taylor & Francis Journals, vol. 14(18), pages 1333-1342.
    30. Su, Ender & Wong, Kai Wen, 2018. "Measuring bank downside systemic risk in Taiwan," The Quarterly Review of Economics and Finance, Elsevier, vol. 70(C), pages 172-193.
    31. Stavros Degiannakis & Alexandra Livada & Epaminondas Panas, 2008. "Rolling-sampled parameters of ARCH and Levy-stable models," Applied Economics, Taylor & Francis Journals, vol. 40(23), pages 3051-3067.
    32. Srecko Devjak & Andraz Grum, 2005. "Market Risk Control In Stable Paretian Markets," Economic Thought and Practice, Department of Economics and Business, University of Dubrovnik, vol. 14(2), pages 147-160, december.
    33. Borgsen, Sina & Glaser, Markus, 2005. "Diversifikationseffekte durch small und mid caps? : Eine empirische Untersuchung basierend auf europäischen Aktienindizes," Papers 05-10, Sonderforschungsbreich 504.
    34. Adina Negrusa & Oana Adriana Gica, 2008. "Analysis Of Potential Sme’S Role For Developing Tourism In Transylvania," JOURNAL STUDIA UNIVERSITATIS BABES-BOLYAI NEGOTIA, Babes-Bolyai University, Faculty of Business.
    35. Eugenie Hol & Siem Jan Koopman, 2000. "Forecasting the Variability of Stock Index Returns with Stochastic Volatility Models and Implied Volatility," Tinbergen Institute Discussion Papers 00-104/4, Tinbergen Institute.
    36. Høg, Esben, 2008. "Volatility and realized quadratic variation of differenced returns : A wavelet method approach," Finance Research Group Working Papers F-2008-06, University of Aarhus, Aarhus School of Business, Department of Business Studies.
    37. Ivanovski, Zoran & Stojanovski, Toni & Narasanov, Zoran, 2015. "Volatility And Kurtosis Of Daily Stock Returns At Mse," UTMS Journal of Economics, University of Tourism and Management, Skopje, Macedonia, vol. 6(2), pages 209-221.
    38. Adrian Grosanu & Paula Ramona Rachisan, 2008. "The Implementation Of Profit Centres Inside An Economic Entity," JOURNAL STUDIA UNIVERSITATIS BABES-BOLYAI NEGOTIA, Babes-Bolyai University, Faculty of Business.
    39. Haugom, Erik & Westgaard, Sjur & Solibakke, Per Bjarte & Lien, Gudbrand, 2011. "Realized volatility and the influence of market measures on predictability: Analysis of Nord Pool forward electricity data," Energy Economics, Elsevier, vol. 33(6), pages 1206-1215.

  80. Anil Bangia & Francis X. Diebold & Til Schuermann, 2000. "Ratings Migration and the Business Cycle, With Application to Credit Portfolio Stress Testing," Center for Financial Institutions Working Papers 00-26, Wharton School Center for Financial Institutions, University of Pennsylvania.

    Cited by:

    1. Samu Peura & Esa Jokivuolle, 2004. "Simulation-based stress testing of banks’ regulatory capital adequacy," Finance 0405003, University Library of Munich, Germany.
    2. Pesaran, M. Hashem & Schuermann, Til & Treutler, Bjorn-Jakob & Weiner, Scott M., 2006. "Macroeconomic Dynamics and Credit Risk: A Global Perspective," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(5), pages 1211-1261, August.
    3. Siem Jan Koopman & André Lucas, 2003. "Business and Default Cycles for Credit Risk," Tinbergen Institute Discussion Papers 03-062/2, Tinbergen Institute, revised 09 Jan 2003.
    4. Guillermo Ordonez, 2008. "Fragility of Reputation and Clustering in Risk Taking," 2008 Meeting Papers 441, Society for Economic Dynamics.
    5. Liuren Wu & Frank X. Zhang, 2005. "A no-arbitrage analysis of economic determinants of the credit spread term structure," Finance and Economics Discussion Series 2005-59, Board of Governors of the Federal Reserve System (U.S.).
    6. Dimitris Gavalas & Theodore Syriopoulos, 2014. "Bank Credit Risk Management and Migration Analysis; Conditioning Transition Matrices on the Stage of the Business Cycle," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 20(2), pages 151-166, May.
    7. Siem Jan Koopman & André Lucas & André Monteiro, 2005. "The Multi-State Latent Factor Intensity Model for Credit Rating Transitions," Tinbergen Institute Discussion Papers 05-071/4, Tinbergen Institute, revised 04 Jul 2005.
    8. Koopman, Siem Jan & Lucas, André, 2008. "A Non-Gaussian Panel Time Series Model for Estimating and Decomposing Default Risk," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 510-525.
    9. Nicolas Jannone Bellot, MaLuisa Marti Selva, Leandro Garcia Menendez, 2017. "Herding Behaviour among Credit Rating Agencies," Journal of Finance and Economics Research, Geist Science, Iqra University, Faculty of Business Administration, vol. 2(1), pages 56-83, March.
    10. Radu Tunaru, 2015. "Model Risk in Financial Markets:From Financial Engineering to Risk Management," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 9524, April.
    11. Alain Monfort & Jean-Paul Renne, 2010. "Default, Liquidity and Crises : An Econometric Framework," Working Papers 2010-46, Center for Research in Economics and Statistics.
    12. Bonfim, Diana, 2009. "Credit risk drivers: Evaluating the contribution of firm level information and of macroeconomic dynamics," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 281-299, February.
    13. Wiem Ben Jabra & Zouheir Mighri & Faysal Mansouri, 2017. "Determinants of European bank risk during financial crisis," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1298420-129, January.
    14. Tatiana Cesaroni, 2015. "Procyclicality of credit rating systems: how to manage it," Temi di discussione (Economic working papers) 1034, Bank of Italy, Economic Research and International Relations Area.
    15. Dmitri Boreiko & Serguei Kaniovski & Yuri Kaniovski & Georg Pflug, 2017. "Traces of business cycles in credit-rating migrations," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-29, April.
    16. Phillips, Jill & Katchova, Ani L., 2004. "Credit Score Migration Analysis Of Farm Businesses: Conditioning On Business Cycles And Migration Trends," 2004 Annual meeting, August 1-4, Denver, CO 20136, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    17. Lee, Yongwoong & Poon, Ser-Huang, 2014. "Forecasting and decomposition of portfolio credit risk using macroeconomic and frailty factors," Journal of Economic Dynamics and Control, Elsevier, vol. 41(C), pages 69-92.
    18. Schuermann, Til, 2013. "Stress Testing Banks," Working Papers 12-08, University of Pennsylvania, Wharton School, Weiss Center.
    19. Pastor Monsálvez José Manuel & Fernández de Guevara Radoselovics Juan & Salvador Muñoz Carlos, 2012. "Impact of the Subprime Crisis on Bank Ratings: The Effect of the Hardening of Rating Policies and Worsening of Solvency," Working Papers 2012120, Fundacion BBVA / BBVA Foundation.
    20. Jafry, Yusuf & Schuermann, Til, 2004. "Measurement, estimation and comparison of credit migration matrices," Journal of Banking & Finance, Elsevier, vol. 28(11), pages 2603-2639, November.
    21. Pederzoli, Chiara & Torricelli, Costanza, 2005. "Capital requirements and business cycle regimes: Forward-looking modelling of default probabilities," Journal of Banking & Finance, Elsevier, vol. 29(12), pages 3121-3140, December.
    22. Yi Jiang & Stewart Jones, 2018. "Corporate distress prediction in China: a machine learning approach," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(4), pages 1063-1109, December.
    23. Philip Lowe, 2002. "Credit risk measurement and procyclicality," BIS Working Papers 116, Bank for International Settlements.
    24. Valerio Vacca, 2011. "An unexpected crisis? Looking at pricing effectiveness of different banks," Temi di discussione (Economic working papers) 814, Bank of Italy, Economic Research and International Relations Area.
    25. Kiefer, Nicholas M. & Larson, C. Erik, 2007. "A simulation estimator for testing the time homogeneity of credit rating transitions," Journal of Empirical Finance, Elsevier, vol. 14(5), pages 818-835, December.
    26. Wu, Yang-Che & Chung, San-Lin, 2010. "Catastrophe risk management with counterparty risk using alternative instruments," Insurance: Mathematics and Economics, Elsevier, vol. 47(2), pages 234-245, October.
    27. Claudio Borio, 2011. "Rediscovering the Macroeconomic Roots of Financial Stability Policy: Journey, Challenges, and a Way Forward," Annual Review of Financial Economics, Annual Reviews, vol. 3(1), pages 87-117, December.
    28. Biase di Giuseppe & Guglielmo D'Amico & Jacques Janssen & Raimondo Manca, 2014. "A Duration Dependent Rating Migration Model: Real Data Application and Cost of Capital Estimation," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 64(3), pages 233-245, June.
    29. T H Moon & Y Kim & S Y Sohn, 2011. "Technology credit rating system for funding SMEs," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(4), pages 608-615, April.
    30. Agostino, Mariarosaria & Errico, Lucia & Rondinella, Sandro & Trivieri, Francesco, 2023. "Enduring lending relationships and european firms default," Research in Economics, Elsevier, vol. 77(4), pages 459-477.
    31. Rasha Alsakka & Owain ap Gwilym, 2010. "Sovereign Ratings and Migrations: Emerging Markets," Working Papers 10009, Bangor Business School, Prifysgol Bangor University (Cymru / Wales).
    32. T. Gärtner & S. Kaniovski & Y. Kaniovski, 2021. "Numerical estimates of risk factors contingent on credit ratings," Computational Management Science, Springer, vol. 18(4), pages 563-589, October.
    33. Weißbach, Rafael & Walter, Ronja, 2010. "A likelihood ratio test for stationarity of rating transitions," Journal of Econometrics, Elsevier, vol. 155(2), pages 188-194, April.
    34. Zhang, Tianwei & Katchova, Ani L., 2005. "Credit Risk Migration Analysis of Illinois Farm Business: Possible Impacts of Farm Business Cycle," 2005 Annual meeting, July 24-27, Providence, RI 19292, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    35. Brian BARNARD, 2017. "Rating Migration and Bond Valuation: Decomposing Rating Migration Matrices from Market Data via Default Probability Term Structures," Expert Journal of Finance, Sprint Investify, vol. 5, pages 49-72.
    36. Hakimi, Abdelaziz & Hamdi, Helmi, 2013. "Credit Information, Guarantees and Non-Performing Loans," MPRA Paper 55750, University Library of Munich, Germany, revised 2014.
    37. Wang, Zheqi & Crook, Jonathan & Andreeva, Galina, 2020. "Reducing estimation risk using a Bayesian posterior distribution approach: Application to stress testing mortgage loan default," European Journal of Operational Research, Elsevier, vol. 287(2), pages 725-738.
    38. Valerio Vacca, 2017. "An Unexpected Crisis? Looking at Pricing Effectiveness of Heterogeneous Banks," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 46(2), pages 171-206, July.
    39. Ferrer, Alex & Casals, José & Sotoca, Sonia, 2015. "Sample dependency during unconditional credit capital estimation," Finance Research Letters, Elsevier, vol. 15(C), pages 175-186.
    40. Koopman, Siem Jan & Lucas, Andre & Klaassen, Pieter, 2005. "Empirical credit cycles and capital buffer formation," Journal of Banking & Finance, Elsevier, vol. 29(12), pages 3159-3179, December.
    41. Schechtman, Ricardo, 2013. "Default matrices: A complete measurement of banks’ consumer credit delinquency," Journal of Financial Stability, Elsevier, vol. 9(4), pages 460-474.
    42. Peura, Samu & Jokivuolle, Esa, 2003. "Simulation-based stress testing of banks' regulatory capital adequacy," Bank of Finland Research Discussion Papers 4/2003, Bank of Finland.
    43. M. Hashem Pesaran & Til Schuermann & Bjorn-Jakob Treutler, 2007. "Global Business Cycles and Credit Risk," NBER Chapters, in: The Risks of Financial Institutions, pages 419-469, National Bureau of Economic Research, Inc.
    44. Linda Allen & Anthony Saunders, 2003. "A survey of cyclical effects in credit risk measurement model," BIS Working Papers 126, Bank for International Settlements.
    45. Sanvi Avouyi-Dovi & Bardos, M. & Caroline Jardet & Kendaoui, L. & Moquet , J., 2009. "Macro stress testing with a macroeconomic credit risk model: Application to the French manufacturing sector," Working papers 238, Banque de France.
    46. Philip Lowe & Miguel Angel Segoviano, 2002. "Internal ratings, the business cycle, and capital requirements: some evidence from an emerging market economy," Conference Series ; [Proceedings], Federal Reserve Bank of Boston.
    47. Myriam Ben Ayed & Adel Karaa & Jean‐Luc Prigent, 2018. "Duration Models For Credit Rating Migration: Evidence From The Financial Crisis," Economic Inquiry, Western Economic Association International, vol. 56(3), pages 1870-1886, July.
    48. Sumon Bhaumik & John S. Landon-Lane, 2007. "Directional Mobility of Ratings," William Davidson Institute Working Papers Series wp900, William Davidson Institute at the University of Michigan.
    49. Arnildo Da Silva Correa & Jaqueline Terra Moura Marins & Myrian Beatriz Eiras Das Neves & Antonio Carlos Magalhes Da Silva, 2014. "Credit Default And Business Cycles: Anempirical Investigation Of Brazilian Retail Loans," Anais do XL Encontro Nacional de Economia [Proceedings of the 40th Brazilian Economics Meeting] 028, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    50. Suarez, Javier & ,, 2018. "The Procyclicality of Expected Credit Loss Provisions," CEPR Discussion Papers 13135, C.E.P.R. Discussion Papers.
    51. Marc Saidenberg & Til Schuermann & May, "undated". "The New Basel Capital Accord and Questions for Research," Center for Financial Institutions Working Papers 03-14, Wharton School Center for Financial Institutions, University of Pennsylvania.
    52. Konrad Banachewicz & André Lucas, 2008. "Quantile forecasting for credit risk management using possibly misspecified hidden Markov models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(7), pages 566-586.
    53. Faiçal Belaid, 2014. "Loan quality determinants: evaluating the contribution of bank-specific variables, macroeconomic factors and firm level information," IHEID Working Papers 04-2014, Economics Section, The Graduate Institute of International Studies.
    54. Al-Sakka, Rasha & ap Gwilym, Owain, 2009. "Heterogeneity of sovereign rating migrations in emerging countries," Emerging Markets Review, Elsevier, vol. 10(2), pages 151-165, June.
    55. Miroslav Plasil & Tomas Konecny & Jakub Seidler & Petr Hlavac, 2015. "In the Quest of Measuring the Financial Cycle," Working Papers 2015/05, Czech National Bank, Research and Statistics Department.
    56. Carlos González-Aguado & Max Bruche, 2006. "Recovery Rates, Default Probabilities and the Credit Cycle," FMG Discussion Papers dp572, Financial Markets Group.
    57. Ebnother, Silvan & Vanini, Paolo, 2007. "Credit portfolios: What defines risk horizons and risk measurement?," Journal of Banking & Finance, Elsevier, vol. 31(12), pages 3663-3679, December.
    58. Hayette Gatfaoui, 2006. "Idiosyncratic Risk, Systematic Risk and Stochastic Volatility: An Implementation of Merton's Credit Risk Valuation," Post-Print hal-00589918, HAL.
    59. Kim, Young Il & Kim, Hyoung Chan & Yoo, Joo Hee, 2016. "Household Over-indebtedness and Financial Vulnerability in Korea: Evidence from Credit Bureau Data," KDI Journal of Economic Policy, Korea Development Institute (KDI), vol. 38(3), pages 53-77.
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    198. Simona Castellani & Chiara Pederzoli & Costanza Torricelli, 2008. "Indebtedness, macroeconomic conditions and banks’ loan losses: evidence from Italy," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0009, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    199. Weißbach, Rafael & Walter, Ronja, 2008. "A likelihood ratio test for stationarity of rating transitions," Technical Reports 2008,27, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    200. Lando, David & Mortensen, Allan, 2004. "On the Pricing of Step-Up Bonds in the European Telecom Sector," Working Papers 2004-9, Copenhagen Business School, Department of Finance.
    201. Güttler, André & Raupach, Peter, 2008. "The impact of downward rating momentum on credit portfolio risk," Discussion Paper Series 2: Banking and Financial Studies 2008,16, Deutsche Bundesbank.
    202. Gatzert, Nadine & Martin, Michael, 2012. "Quantifying credit and market risk under Solvency II: Standard approach versus internal model," Insurance: Mathematics and Economics, Elsevier, vol. 51(3), pages 649-666.
    203. André Lucas & Pieter Klaassen, 2003. "Discrete versus Continuous State Switching Models for Portfolio Credit Risk," Tinbergen Institute Discussion Papers 03-075/2, Tinbergen Institute, revised 30 Sep 2003.
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    205. Qin, Zhaohui & Wang, Xiaowan & Chen, Yijie & Fan, Yali & Andrianarimanana, Mihasina Harinaivo & Gai, Dhornor Tarir Duok, 2024. "Time-varying default risk of Chinese-listed companies: From empirical test to theoretical conjecture," Finance Research Letters, Elsevier, vol. 67(PA).
    206. D’Amico, Guglielmo & Scocchera, Stefania & Storchi, Loriano, 2018. "Financial risk distribution in European Union," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 252-267.
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    208. Nikolas Stege & Christoph Wegener & Tobias Basse & Frederik Kunze, 2021. "Mapping swap rate projections on bond yields considering cointegration: an example for the use of neural networks in stress testing exercises," Annals of Operations Research, Springer, vol. 297(1), pages 309-321, February.
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    210. Kerem Tuzcuoglu, 2019. "Composite Likelihood Estimation of an Autoregressive Panel Probit Model with Random Effects," Staff Working Papers 19-16, Bank of Canada.
    211. Issouf Soumaré & Ernest Tafolong, 2017. "Risk-based capital for credit insurers with business cycles and dynamic leverage," Quantitative Finance, Taylor & Francis Journals, vol. 17(4), pages 597-612, April.
    212. Manzoni, Katiuscia, 2004. "Modeling Eurobond credit ratings and forecasting downgrade probability," International Review of Financial Analysis, Elsevier, vol. 13(3), pages 277-300.
    213. Marcucci, Juri & Quagliariello, Mario, 2009. "Asymmetric effects of the business cycle on bank credit risk," Journal of Banking & Finance, Elsevier, vol. 33(9), pages 1624-1635, September.
    214. Areski Cousin & Jérôme Lelong & Tom Picard, 2023. "Rating transitions forecasting: a filtering approach," Post-Print hal-03347521, HAL.
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    225. Ruichang Lu & Yao Luo & Ruli Xiao, 2023. "Estimation of misreporting probability in corporate credit rating: A nonparametric approach," International Studies of Economics, John Wiley & Sons, vol. 18(3), pages 260-276, September.
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    1. Capistrán, Carlos & López-Moctezuma, Gabriel, 2014. "Forecast revisions of Mexican inflation and GDP growth," International Journal of Forecasting, Elsevier, vol. 30(2), pages 177-191.
    2. Michael Clements, 2016. "Are Macroeconomic Density Forecasts Informative?," ICMA Centre Discussion Papers in Finance icma-dp2016-02, Henley Business School, University of Reading.
    3. Jeremy Berkowitz & Ionel Birgean & Lutz Kilian, 1999. "On the finite-sample accuracy of nonparametric resampling algorithms for economic time series," Finance and Economics Discussion Series 1999-04, Board of Governors of the Federal Reserve System (U.S.).
    4. Ionel Birgean & Lutz Kilian, 2002. "Data-Driven Nonparametric Spectral Density Estimators For Economic Time Series: A Monte Carlo Study," Econometric Reviews, Taylor & Francis Journals, vol. 21(4), pages 449-476.
    5. Osmani Teixeira de Carvalho Guillén & João Victor Issler & George Athanasopoulos, 2005. "Forecasting Accuracy and Estimation Uncertainty Using VAR Models with Short- and Long-Term Economic Restrictions: A Monte-Carlo Study," Monash Econometrics and Business Statistics Working Papers 15/05, Monash University, Department of Econometrics and Business Statistics.
    6. Lütkepohl, Helmut & Proietti, Tommaso, 2011. "Does the Box-Cox transformation help in forecasting macroeconomic time series?," Working Papers 08/2011, University of Sydney Business School, Discipline of Business Analytics.
    7. Breitung, Jörg & Knüppel, Malte, 2018. "How far can we forecast? Statistical tests of the predictive content," Discussion Papers 07/2018, Deutsche Bundesbank.
    8. Isiklar, Gultekin & Lahiri, Kajal, 2007. "How far ahead can we forecast? Evidence from cross-country surveys," International Journal of Forecasting, Elsevier, vol. 23(2), pages 167-187.
    9. Zhang, Gang & Yang, Dazhi & Galanis, George & Androulakis, Emmanouil, 2022. "Solar forecasting with hourly updated numerical weather prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    10. Dovern, Jonas, 2006. "Predicting GDP components: do leading indicators increase predictability?," Kiel Advanced Studies Working Papers 436, Kiel Institute for the World Economy (IfW Kiel).
    11. António Brandão Moniz, 2008. "Assessing scenarios on the future of work," Enterprise and Work Innovation Studies, Universidade Nova de Lisboa, IET/CICS.NOVA-Interdisciplinary Centre on Social Sciences, Faculty of Science and Technology, vol. 4(4), pages 91-106, November.
    12. Yang, Dazhi, 2022. "Correlogram, predictability error growth, and bounds of mean square error of solar irradiance forecasts," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    13. Lucey, Brian M & Zhao, Shelly, 2008. "Halloween or January? Yet another puzzle," International Review of Financial Analysis, Elsevier, vol. 17(5), pages 1055-1069, December.
    14. Kilian, Lutz & Zhou, Xiaoqing, 2018. "Modeling fluctuations in the global demand for commodities," Journal of International Money and Finance, Elsevier, vol. 88(C), pages 54-78.
    15. Chen, Qiwei & Costantini, Mauro & Deschamps, Bruno, 2016. "How accurate are professional forecasts in Asia? Evidence from ten countries," International Journal of Forecasting, Elsevier, vol. 32(1), pages 154-167.
    16. John W. Galbraith, 1999. "Content Horizons For Forecasts Of Economic Time Series," Departmental Working Papers 1999-01, McGill University, Department of Economics.
    17. Lahiri, Kajal & Sheng, Xuguang, 2010. "Learning and heterogeneity in GDP and inflation forecasts," International Journal of Forecasting, Elsevier, vol. 26(2), pages 265-292, April.
    18. Rosa, Carlo & Verga, Giovanni, 2007. "On the consistency and effectiveness of central bank communication: Evidence from the ECB," European Journal of Political Economy, Elsevier, vol. 23(1), pages 146-175, March.
    19. Jamie L. Cross & Bao H. Nguyen & Trung Duc Tran, 2021. "The Role of Precautionary and Speculative Demand in the Global Market for Crude Oil," Working Papers No 06/2021, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    20. Hendry, David F. & Hubrich, Kirstin, 2006. "Forecasting economic aggregates by disaggregates," Working Paper Series 589, European Central Bank.
    21. Hofer Helmut & Weyerstraß Klaus & Schmidt Torsten, 2011. "Practice and Prospects of Medium-term Economic Forecasting," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 153-171, February.
    22. John W. Galbraith & Greg Tkacz, 2007. "Forecast Content And Content Horizons For Some Important Macroeconomic Time Series," Departmental Working Papers 2007-01, McGill University, Department of Economics.
    23. Monica Jain, 2013. "Perceived Inflation Persistence," Staff Working Papers 13-43, Bank of Canada.
    24. Marc Brisson & Bryan Campbell & John W. Galbraith, 2001. "Forecasting Some Low-Predictability Time Series Using Diffusion Indices," CIRANO Working Papers 2001s-46, CIRANO.
    25. Marc-Oliver Pohle, 2020. "The Murphy Decomposition and the Calibration-Resolution Principle: A New Perspective on Forecast Evaluation," Papers 2005.01835, arXiv.org.
    26. S. Boragan Aruoba & Francis X. Diebold & Jeremy J. Nalewaik & Frank Schorfheide & Dongho Song, 2013. "Improving GDP measurement: a measurement-error perspective," Working Papers 13-16, Federal Reserve Bank of Philadelphia.
    27. Dovern, Jonas, 2024. "Eliciting expectation uncertainty from private households," International Journal of Forecasting, Elsevier, vol. 40(1), pages 113-123.
    28. John G. Galbraith & Greg Tkacz, 2006. "How Far Can We Forecast? Forecast Content Horizons For Some Important Macroeconomic Time Series," Departmental Working Papers 2006-13, McGill University, Department of Economics.
    29. Francis X. Diebold, 1997. "The Past, Present, and Future of Macroeconomic Forecasting," NBER Working Papers 6290, National Bureau of Economic Research, Inc.
    30. Glenn D. Rudebusch, 2001. "Term structure evidence on interest rate smoothing and monetary policy inertia," Working Paper Series 2001-02, Federal Reserve Bank of San Francisco.
    31. Oller, Lars-Erik & Teterukovsky, Alex, 2007. "Quantifying the quality of macroeconomic variables," International Journal of Forecasting, Elsevier, vol. 23(2), pages 205-217.
    32. Romeo-Catalin CRETU & Irina-Daniela CISMASU & Adrian ANICA-POPA & Petrica STEFAN, 2021. "Education In Digital Era Between Analysis Of Predictability And Consolidation Of Resiliance," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 15(1), pages 274-289, November.
    33. Ruan, Xinfeng & Zhang, Jin E., 2018. "Risk-neutral moments in the crude oil market," Energy Economics, Elsevier, vol. 72(C), pages 583-600.
    34. Thomas A. Knetsch, 2007. "Forecasting the price of crude oil via convenience yield predictions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(7), pages 527-549.
    35. Edoardo Otrano & Umberto Triacca, 2007. "Testing for Equal Predictability of Stationary ARMA Processes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(9), pages 1091-1108.
    36. Yang, Dazhi & Wang, Wenting & Gueymard, Christian A. & Hong, Tao & Kleissl, Jan & Huang, Jing & Perez, Marc J. & Perez, Richard & Bright, Jamie M. & Xia, Xiang’ao & van der Meer, Dennis & Peters, Ian , 2022. "A review of solar forecasting, its dependence on atmospheric sciences and implications for grid integration: Towards carbon neutrality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    37. Luati, Alessandra & Proietti, Tommaso & Reale, Marco, 2011. "The Variance Profile," MPRA Paper 30378, University Library of Munich, Germany.
    38. Dufour, Jean-Marie & Taamouti, Abderrahim, 2010. "Short and long run causality measures: Theory and inference," Journal of Econometrics, Elsevier, vol. 154(1), pages 42-58, January.
    39. Sridevi Narayanan & Chee Keong Choong & Lin Sea Lau, 2020. "An investigation on the role of good governance as a mediating factor in the FDI-Growth nexus: An ASEAN Perspective," Economics Bulletin, AccessEcon, vol. 40(4), pages 2769-2779.
    40. Fanelli, Luca & Paruolo, Paolo, 2007. "Speed of Adjustment in Cointegrated Systems," MPRA Paper 9174, University Library of Munich, Germany.
    41. Konstantin A. Kholodilin & Boriss Siliverstovs, 2009. "Do forecasters inform or reassure?," KOF Working papers 09-215, KOF Swiss Economic Institute, ETH Zurich.
    42. Degenhardt, Thomas & Auer, Benjamin R., 2018. "The “Sell in May” effect: A review and new empirical evidence," The North American Journal of Economics and Finance, Elsevier, vol. 43(C), pages 169-205.
    43. Kieran Mc Morrow & Werner Roeger & Valerie Vandermeulen, 2017. "Evaluating Medium Term Forecasting Methods and their Implications for EU Output Gap Calculations," European Economy - Discussion Papers 070, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    44. Czudaj, Robert L., 2022. "Heterogeneity of beliefs and information rigidity in the crude oil market: Evidence from survey data," European Economic Review, Elsevier, vol. 143(C).
    45. David McMillan & Isabel Ruiz & Alan Speight, 2010. "Correlations and spillovers among three euro rates: evidence using realised variance," The European Journal of Finance, Taylor & Francis Journals, vol. 16(8), pages 753-767.
    46. Yoshua Bengio & François Gingras & Claude Nadeau, 2002. "On Out-of-Sample Statistics for Time-Series," CIRANO Working Papers 2002s-51, CIRANO.
    47. Timothy Cogley & Giorgio E. Primiceri & Thomas J. Sargent, 2010. "Inflation-Gap Persistence in the US," American Economic Journal: Macroeconomics, American Economic Association, vol. 2(1), pages 43-69, January.
    48. Kirstin Hubrich & David F. Hendry, 2005. "Forecasting Aggregates by Disaggregates," Computing in Economics and Finance 2005 270, Society for Computational Economics.
    49. Paulo Esteves, 2003. "Uncertainty and Risk Analysis: na Application to the Projections for the Portuguese Economy in 2004," Economic Bulletin and Financial Stability Report Articles and Banco de Portugal Economic Studies, Banco de Portugal, Economics and Research Department.
    50. Liu, Bai & Yang, Dazhi & Mayer, Martin János & Coimbra, Carlos F.M. & Kleissl, Jan & Kay, Merlinde & Wang, Wenting & Bright, Jamie M. & Xia, Xiang’ao & Lv, Xin & Srinivasan, Dipti & Wu, Yan & Beyer, H, 2023. "Predictability and forecast skill of solar irradiance over the contiguous United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    51. Barnett, Alina & Groen, Jan J J & Mumtaz, Haroon, 2010. "Time-varying inflation expectations and economic fluctuations in the United Kingdom: a structural VAR analysis," Bank of England working papers 392, Bank of England.
    52. Potì, Valerio & Siddique, Akhtar, 2013. "What drives currency predictability?," Journal of International Money and Finance, Elsevier, vol. 36(C), pages 86-106.
    53. Vardhan, Harsh & Sinha, Pankaj, 2015. "Influence of Macroeconomic Variable on Indian Stock Movement: Cointegration Approach," MPRA Paper 64369, University Library of Munich, Germany, revised 10 May 2015.
    54. Lahiri, Kajal & Sheng, Xuguang, 2008. "Evolution of forecast disagreement in a Bayesian learning model," Journal of Econometrics, Elsevier, vol. 144(2), pages 325-340, June.

  82. Sassan Alizadeh & Michael W. Brandt & Francis X. Diebold, 1999. "Range-Based Estimation of Stochastic Volatility Models or Exchange Rate Dynamics are More Interesting Than You Think," Center for Financial Institutions Working Papers 00-28, Wharton School Center for Financial Institutions, University of Pennsylvania.

    Cited by:

    1. Gregory Bauer & Keith Vorkink, 2007. "Multivariate Realized Stock Market Volatility," Staff Working Papers 07-20, Bank of Canada.
    2. John M. Maheu & Thomas McCurdy, 2001. "Nonlinear Features of Realized FX Volatility," CIRANO Working Papers 2001s-42, CIRANO.
    3. Chan, Kam C. & Fung, Hung-Gay & Leung, Wai K., 2004. "Daily volatility behavior in Chinese futures markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 14(5), pages 491-505, December.
    4. Tsiotas, Georgios, 2012. "On generalised asymmetric stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 151-172, January.
    5. Sin, Chor-Yiu (CY), 2013. "Using CARRX models to study factors affecting the volatilities of Asian equity markets," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 552-564.
    6. Bollerslev, Tim, 2001. "Financial econometrics: Past developments and future challenges," Journal of Econometrics, Elsevier, vol. 100(1), pages 41-51, January.
    7. Venetis, Ioannis A. & Peel, David, 2005. "Non-linearity in stock index returns: the volatility and serial correlation relationship," Economic Modelling, Elsevier, vol. 22(1), pages 1-19, January.
    8. Chen, Cathy W.S. & Gerlach, Richard & Lin, Edward M.H., 2008. "Volatility forecasting using threshold heteroskedastic models of the intra-day range," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 2990-3010, February.
    9. Chan, J.S.K. & Lam, C.P.Y. & Yu, P.L.H. & Choy, S.T.B. & Chen, C.W.S., 2012. "A Bayesian conditional autoregressive geometric process model for range data," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3006-3019.
    10. Chan, Leo & Lien, Donald, 2003. "Using high, low, open, and closing prices to estimate the effects of cash settlement on futures prices," International Review of Financial Analysis, Elsevier, vol. 12(1), pages 35-47.

  83. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 1999. "The Distribution of Exchange Rate Volatility," New York University, Leonard N. Stern School Finance Department Working Paper Seires 99-059, New York University, Leonard N. Stern School of Business-.

    Cited by:

    1. Sassan Alizadeh & Michael W. Brandt & Francis X. Diebold, 1999. "Range-Based Estimation of Stochastic Volatility Models or Exchange Rate Dynamics are More Interesting Than You Think," Center for Financial Institutions Working Papers 00-28, Wharton School Center for Financial Institutions, University of Pennsylvania.
    2. Ralf Becker & Adam Clements, 2007. "Are combination forecasts of S&P 500 volatility statistically superior?," NCER Working Paper Series 17, National Centre for Econometric Research.
    3. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 1999. "The Distribution of Exchange Rate Volatility," Center for Financial Institutions Working Papers 99-08, Wharton School Center for Financial Institutions, University of Pennsylvania.
    4. Meddahi, N., 2001. "A Theoretical Comparison Between Integrated and Realized Volatilies," Cahiers de recherche 2001-26, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    5. Dmitri Koulikov, 2002. "Modeling Sequences of Long Memory Positive Weakly Stationary Random Variables," William Davidson Institute Working Papers Series 493, William Davidson Institute at the University of Michigan.
    6. Adusei Jumah, 2001. "The effects of dollar-sterling exchange rate volatility on futures markets for coffee and cocoa," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 28(3), pages 307-328, October.
    7. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2000. "Exchange Rate Returns Standardized by Realized Volatility are (Nearly) Gaussian," NBER Working Papers 7488, National Bureau of Economic Research, Inc.
    8. Ole E. Barndorff-Nielsen & Neil Shephard, 2001. "Econometric Analysis of Realised Covariation: High Frequency Covariance, Regression and Correlation in Financial Economics," Economics Papers 2002-W13, Economics Group, Nuffield College, University of Oxford, revised 18 Mar 2002.
    9. Torben G. Andersen & Tim Bollerslev & Nour Meddahi, 2002. "Correcting the Errors: A Note on Volatility Forecast Evaluation Based on High-Frequency Data and Realized Volatilities," CIRANO Working Papers 2002s-91, CIRANO.
    10. Klaassen, F.J.G.M., 1999. "Why is it so Difficult to Find An Effect of Exchange Rate Risk on Trade?," Discussion Paper 1999-73, Tilburg University, Center for Economic Research.
    11. Elena Andreou & Eric Ghysels, 2000. "Rolling-Sample Volatility Estimators: Some New Theoretical, Simulation and Empirical Results," CIRANO Working Papers 2000s-19, CIRANO.
    12. Michel Beine, 2004. "Conditional covariance and direct Central Bank intervention in the foreign exchange markets," ULB Institutional Repository 2013/10431, ULB -- Universite Libre de Bruxelles.
    13. Dimitrios D. Thomakos & Michail S. Koubouros, 2005. "Realized Volatility and Asymmetries in the A.S.E. Returns," Finance 0504009, University Library of Munich, Germany, revised 17 Jan 2006.
    14. Campbell, John Y & Kim, Sangjoon & Lettau, Martin, 1998. "Dispersion and Volatility in Stock Returns: An Empirical Investigation," CEPR Discussion Papers 1923, C.E.P.R. Discussion Papers.
    15. Ole E. Barndorff-Nielsen & Neil Shephard, 2002. "Estimating quadratic variation using realized variance," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 457-477.
    16. Perez, Ana & Ruiz, Esther, 2001. "Finite sample properties of a QML estimator of stochastic volatility models with long memory," Economics Letters, Elsevier, vol. 70(2), pages 157-164, February.
    17. Eric Hillebrand & Marcelo Cunha Medeiros, 2007. "Forecasting realized volatility models:the benefits of bagging and nonlinear specifications," Textos para discussão 547, Department of Economics PUC-Rio (Brazil).
    18. John M. Maheu & Thomas McCurdy, 2001. "Nonlinear Features of Realized FX Volatility," CIRANO Working Papers 2001s-42, CIRANO.
    19. Laurent Calvet, 2000. "Forecasting Multifractal Volatility," Harvard Institute of Economic Research Working Papers 1902, Harvard - Institute of Economic Research.
    20. John Cotter, 2004. "Realized volatility and minimum capital requirements," Money Macro and Finance (MMF) Research Group Conference 2003 20, Money Macro and Finance Research Group.
    21. Ole E. Barndorff-Nielsen & Elisa Nicolato & Neil Shephard, 2001. "Some recent developments in stochastic volatility modelling," Economics Papers 2001-W25, Economics Group, Nuffield College, University of Oxford.
    22. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 1999. "(Understanding, Optimizing, Using and Forecasting) Realized Volatility and Correlation," New York University, Leonard N. Stern School Finance Department Working Paper Seires 99-061, New York University, Leonard N. Stern School of Business-.
    23. Soosung Hwang & Steve Satchell, 2005. "GARCH model with cross-sectional volatility: GARCHX models," Applied Financial Economics, Taylor & Francis Journals, vol. 15(3), pages 203-216.
    24. Ait-Sahalia, Yacine & Mykland, Per A. & Zhang, Lan, 2005. "Ultra high frequency volatility estimation with dependent microstructure noise," Discussion Paper Series 1: Economic Studies 2005,30, Deutsche Bundesbank.
    25. Murat Körs & Mehmet Baha Karan, 2023. "Stock exchange volatility forecasting under market stress with MIDAS regression," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 295-306, January.
    26. Moorad Choudhry, 2010. "Measuring bond market liquidity: devising a composite aggregate liquidity score," Applied Financial Economics, Taylor & Francis Journals, vol. 20(12), pages 955-973.
    27. Laura Mayoral, 2005. "The persistence of inflation in OECD countries: A fractionally integrated approach," Economics Working Papers 958, Department of Economics and Business, Universitat Pompeu Fabra, revised Oct 2005.
    28. Nuno Cassola & Claudio Morana, 2006. "Volatility of interest rates in the euro area: Evidence from high frequency data," The European Journal of Finance, Taylor & Francis Journals, vol. 12(6-7), pages 513-528.
    29. John Y. Campbell & Martin Lettau & Burton G. Malkiel & Yexiao Xu, 2001. "Have Individual Stocks Become More Volatile? An Empirical Exploration of Idiosyncratic Risk," Journal of Finance, American Finance Association, vol. 56(1), pages 1-43, February.
    30. Elena Andreou & Eric Ghysels, 2001. "Detecting Mutiple Breaks in Financial Market Volatility Dynamics," CIRANO Working Papers 2001s-65, CIRANO.
    31. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Heiko Ebens, 2000. "The Distribution of Stock Return Volatility," NBER Working Papers 7933, National Bureau of Economic Research, Inc.
    32. Nour Meddahi, 2003. "ARMA representation of integrated and realized variances," Econometrics Journal, Royal Economic Society, vol. 6(2), pages 335-356, December.
    33. Ole E. Barndorff-Nielsen & Bent Nielsen & Neil Shephard & Carla Ysusi, 2002. "Measuring and forecasting financial variability using realised variance with and without a model," Economics Papers 2002-W21, Economics Group, Nuffield College, University of Oxford.
    34. Ole E. Barndorff‐Nielsen & Neil Shephard, 2002. "Econometric analysis of realized volatility and its use in estimating stochastic volatility models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 253-280, May.
    35. Sylvain Friederich & Richard Payne, 2007. "Dealer Liquidity in an Auction Market: Evidence from the London Stock Exchange," Economic Journal, Royal Economic Society, vol. 117(522), pages 1168-1191, July.
    36. Nour Meddahi & Eric Renault, 2000. "Temporal Aggregation of Volatility Models," CIRANO Working Papers 2000s-22, CIRANO.
    37. Jarl G. Kallberg & Paolo Pasquariello, 2005. "An Examination of the Asian Crisis: Regime Shifts in Currency and Equity Markets," The Journal of Business, University of Chicago Press, vol. 78(1), pages 169-212, January.
    38. Barndorff-Nielsen, Ole E. & Shephard, Neil, 2006. "Impact of jumps on returns and realised variances: econometric analysis of time-deformed Levy processes," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 217-252.
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    49. Christian Bontemps & Nour Meddahi, 2005. "Testing normality: a GMM approach," Post-Print hal-02875105, HAL.
    50. Gerhard, Frank & Hautsch, Nikolaus, 2002. "Volatility estimation on the basis of price intensities," Journal of Empirical Finance, Elsevier, vol. 9(1), pages 57-89, January.
    51. Claudio Morana, 2004. "Frequency domain principal components estimation of fractionally cointegrated processes," Applied Economics Letters, Taylor & Francis Journals, vol. 11(13), pages 837-842.
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    56. Carnero, María Ángeles & Peña, Daniel & Ruiz Ortega, Esther, 2001. "Outliers and conditional autoregressive heteroscedasticity in time series," DES - Working Papers. Statistics and Econometrics. WS ws010704, Universidad Carlos III de Madrid. Departamento de Estadística.
    57. Bollerslev, Tim, 2001. "Financial econometrics: Past developments and future challenges," Journal of Econometrics, Elsevier, vol. 100(1), pages 41-51, January.
    58. Christian Schittenkopf & Peter Tino & Georg Dorffner, 2002. "The benefit of information reduction for trading strategies," Applied Economics, Taylor & Francis Journals, vol. 34(7), pages 917-930.
    59. Elena Andreou & Eric Ghysels, 2003. "Test for Breaks in the Conditional Co-Movements of Asset Returns," University of Cyprus Working Papers in Economics 3-2003, University of Cyprus Department of Economics.
    60. John Cotter, 2011. "Uncovering Long Memory in High Frequency UK Futures," Papers 1103.5651, arXiv.org.
    61. Stavros Degiannakis, 2004. "Volatility forecasting: evidence from a fractional integrated asymmetric power ARCH skewed-t model," Applied Financial Economics, Taylor & Francis Journals, vol. 14(18), pages 1333-1342.
    62. Brusco, Sandro & Manzano, Carolina & Tapia, Mikel, 2003. "Price discovery in the pre-opening period. theory and evidence from the madrid stock exchange," DEE - Working Papers. Business Economics. WB wb035814, Universidad Carlos III de Madrid. Departamento de Economía de la Empresa.
    63. Moosa, Imad A. & Bollen, Bernard, 2002. "A benchmark for measuring bias in estimated daily value at risk," International Review of Financial Analysis, Elsevier, vol. 11(1), pages 85-100.
    64. Stanley, H.E. & Gopikrishnan, P. & Plerou, V. & Amaral, L.A.N., 2000. "Quantifying fluctuations in economic systems by adapting methods of statistical physics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 287(3), pages 339-361.
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    66. Zhang, Lan & Mykland, Per A. & Ait-Sahalia, Yacine, 2005. "A Tale of Two Time Scales: Determining Integrated Volatility With Noisy High-Frequency Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1394-1411, December.
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    68. Muhammad Anas & Syed Jawad Hussain Shahzad & Larisa Yarovaya, 2024. "The use of high-frequency data in cryptocurrency research: a meta-review of literature with bibliometric analysis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-31, December.
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    Cited by:

    1. Terence D.Agbeyegbe, 2003. "The tail behavior of stock index return on the Jamaican Stock Exchange," Economics Working Paper Archive at Hunter College 305, Hunter College Department of Economics.
    2. Andrew Kuritzkes & Til Schuermann & Scott M. Weiner, 2002. "Risk Measurement, Risk Management and Capital Adequacy in Financial Conglomerates," Center for Financial Institutions Working Papers 03-02, Wharton School Center for Financial Institutions, University of Pennsylvania.
    3. David L. Eckles & Anthony M. Santomero, 2000. "The determinants of success in the new financial services environment: now that firms can do everything, what should they do and why should regulators care?," Economic Policy Review, Federal Reserve Bank of New York, issue Oct, pages 11-23.
    4. Anthony M Santomero & David L. Eckles, 2000. "The Determinants Of Success In the New Financial Services Environment: Now That Firms Can Do Everything, What Should They Do And Why Should Regulators Care?," Center for Financial Institutions Working Papers 00-32, Wharton School Center for Financial Institutions, University of Pennsylvania.
    5. Lee, Tae-Hwy & Saltoglu, Burak, 2002. "Assessing the risk forecasts for Japanese stock market," Japan and the World Economy, Elsevier, vol. 14(1), pages 63-85, January.

  85. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 1999. "Exchange Rate Returns Standardized by Realized Volatility are (Nearly) Gaussian," New York University, Leonard N. Stern School Finance Department Working Paper Seires 99-060, New York University, Leonard N. Stern School of Business-.

    Cited by:

    1. Takaishi, Tetsuya, 2018. "Bias correction in the realized stochastic volatility model for daily volatility on the Tokyo Stock Exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 500(C), pages 139-154.
    2. Márcio Gomes Pinto Garcia & Marcelo Cunha Medeiros & Francisco Eduardo de Luna e Almeida Santos, 2014. "Economic gains of realized volatility in the Brazilian stock market," Brazilian Review of Finance, Brazilian Society of Finance, vol. 12(3), pages 319-349.
    3. Degiannakis, Stavros & Floros, Christos, 2014. "Intra-Day Realized Volatility for European and USA Stock Indices," MPRA Paper 64940, University Library of Munich, Germany, revised Jan 2015.
    4. Cotter, John, 2004. "Varying the VaR for Unconditional and Conditional Environments," MPRA Paper 3483, University Library of Munich, Germany.
    5. Mueller, Philippe & Stathopoulos, Andreas & Vedolin, Andrea, 2017. "International correlation risk," LSE Research Online Documents on Economics 84140, London School of Economics and Political Science, LSE Library.
    6. Ferland, Rene & Lalancette, Simon, 2006. "Dynamics of realized volatilities and correlations: An empirical study," Journal of Banking & Finance, Elsevier, vol. 30(7), pages 2109-2130, July.
    7. Mihaela Craioveanu & Eric Hillebrand, 2012. "Why It Is Ok To Use The Har-Rv(1,5,21) Model," Working Papers 1201, University of Central Missouri, Department of Economics & Finance, revised Aug 2012.
    8. Hooper, Vincent J. & Ng, Kevin & Reeves, Jonathan J., 2008. "Quarterly beta forecasting: An evaluation," International Journal of Forecasting, Elsevier, vol. 24(3), pages 480-489.
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    10. Chortareas, Georgios & Jiang, Ying & Nankervis, John. C., 2011. "Forecasting exchange rate volatility using high-frequency data: Is the euro different?," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1089-1107, October.
    11. Gilles Zumbach, 2021. "On the short term stability of financial ARCH price processes," Papers 2107.06758, arXiv.org.
    12. Rim Ammar Lamouchi & Ruba Khalid Shira, 2023. "Heterogeneous Behavior and Volatility Transmission in the Forex Market using High-Frequency Data," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 13(3), pages 1-3.
    13. Filip Žikeš & Jozef Baruník, 2016. "Semi-parametric Conditional Quantile Models for Financial Returns and Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 14(1), pages 185-226.
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    27. Dimitrios Dimitriou & Dimitris Kenourgios & Theodore Simos & Alexandros Tsioutsios, 2025. "The implications of non‐synchronous trading in G‐7 financial markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 30(1), pages 689-709, January.
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    50. Sattarhoff, Cristina & Lux, Thomas, 2021. "Forecasting the Variability of Stock Index Returns with the Multifractal Random Walk Model for Realized Volatilities," Economics Working Papers 2021-02, Christian-Albrechts-University of Kiel, Department of Economics.
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    1. Lenarčič, Črt & Zorko, Robert & Herman, Uroš & Savšek, Simon, 2016. "A Primer on Slovene House Prices Forecast," MPRA Paper 103552, University Library of Munich, Germany.
    2. Kilian, Lutz & Alquist, Ron, 2007. "What Do We Learn from the Price of Crude Oil Futures?," CEPR Discussion Papers 6548, C.E.P.R. Discussion Papers.
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    8. Mohitosh Kejriwal & Xuewen Yu, 2019. "Generalized Forecasr Averaging in Autoregressions with a Near Unit Root," Purdue University Economics Working Papers 1318, Purdue University, Department of Economics.
    9. Manish K. Singh & Marta Gómez-Puig & Simón Sosvilla-Rivero, 2024. "Quantifying sovereign risk in the euro area," IREA Working Papers 202403, University of Barcelona, Research Institute of Applied Economics, revised Feb 2024.
    10. Tsangyao Chang & Wen-Chi Liu & Goodness C. Aye & Rangan Gupta, 2016. "Are there housing bubbles in South Africa? Evidence from SPSM-based panel KSS test with a Fourier function," Global Business and Economics Review, Inderscience Enterprises Ltd, vol. 18(5), pages 517-532.
    11. Alberto Baffigi & Roberto Golinelli & Giuseppe Parigi, 2002. "Real-time GDP forecasting in the euro area," Temi di discussione (Economic working papers) 456, Bank of Italy, Economic Research and International Relations Area.
    12. Massimiliano Marcellino, "undated". "Instability and non-linearity in the EMU," Working Papers 211, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
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    36. Duarte, Claudia & Rua, Antonio, 2007. "Forecasting inflation through a bottom-up approach: How bottom is bottom?," Economic Modelling, Elsevier, vol. 24(6), pages 941-953, November.
    37. Blerina Vika & Ilir Vika, 2021. "Forecasting Albanian Time Series with Linear and Nonlinear Univariate Models," Academic Journal of Interdisciplinary Studies, Richtmann Publishing Ltd, vol. 10, September.
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    39. Junsoo Lee & John List, 2004. "Examining Trends of Criteria Air Pollutants: Are the Effects of Governmental Intervention Transitory?," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 29(1), pages 21-37, September.
    40. Van Heerden, Dorathea & Rodrigues, Jose & Hockly, Dale & Lambert, Bongani & Taljard, Tjaart & Phiri, Andrew, 2013. "Efficient Market Hypothesis in South Africa: Evidence from a threshold autoregressive (TAR) model," MPRA Paper 50544, University Library of Munich, Germany.
    41. In Choi & Hanbat Jeong, 2020. "Differencing versus nondifferencing in factor‐based forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(6), pages 728-750, September.
    42. Ted Peterson & Zachary Bair, 2022. "United States Tax Rates and Economic Growth," SAGE Open, , vol. 12(3), pages 21582440221, July.
    43. E Pavlidis & I Paya & D Peel, 2009. "Forecasting the Real Exchange Rate using a Long Span of Data. A Rematch: Linear vs Nonlinear," Working Papers 601190, Lancaster University Management School, Economics Department.
    44. Peter C.B. Phillips & Zhentao Shi, 2019. "Boosting the Hodrick-Prescott Filter," Cowles Foundation Discussion Papers 2192, Cowles Foundation for Research in Economics, Yale University.
    45. Sarno, Lucio & Daniel l Thornton & Giorgio Valente, 2003. "Federal Funds Rate Prediction," Royal Economic Society Annual Conference 2003 183, Royal Economic Society.
    46. Shujie Shen & Gang Li & Haiyan Song, 2009. "Effect of Seasonality Treatment on the Forecasting Performance of Tourism Demand Models," Tourism Economics, , vol. 15(4), pages 693-708, December.
    47. Tsangyao CHANG & Yifei CAI & Wen-Yi CHEN, 2017. "Are Suicide Rate Fluctuations Transitory or Permanent? Panel KSS Unit Root Test with a Fourier Function through the Sequential Panel Selection Method," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 5-17, September.
    48. Severin Borenstein & James Bushnell & Frank A. Wolak & Matthew Zaragoza-Watkins, 2015. "Expecting the Unexpected: Emissions Uncertainty and Environmental Market Design," NBER Working Papers 20999, National Bureau of Economic Research, Inc.
    49. Khalaf, Lynda & Saunders, Charles J., 2017. "Monte Carlo forecast evaluation with persistent data," International Journal of Forecasting, Elsevier, vol. 33(1), pages 1-10.
    50. David Griffiths, 2004. "The big problem of forecasting small change," Applied Economics, Taylor & Francis Journals, vol. 36(19), pages 2195-2207.
    51. Hubrich, Kirstin, 2005. "Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy?," International Journal of Forecasting, Elsevier, vol. 21(1), pages 119-136.
    52. Mohitosh Kejriwal & Linh Nguyen & Xuewen Yu, 2023. "Multistep Forecast Averaging with Stochastic and Deterministic Trends," Econometrics, MDPI, vol. 11(4), pages 1-44, December.
    53. Manuel Landajo & María Presno, 2013. "Nonparametric pseudo-Lagrange multiplier stationarity testing," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(1), pages 125-147, February.
    54. Rambeli, Norimah & Awang Marikan, Dayang Affizah & Hashim, Emilda & Mohd. Ariffin, Siti Zubaidah & Hashim, Asmawi & M. Podivinsky, Jan, 2021. "The Determinants of Carbon Dioxide Emissions in Malaysia and Singapore," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 55(2), pages 107-119.
    55. Lee, Cheng-Feng & Tsong, Ching-Chuan, 2009. "Bootstrapping covariate stationarity tests for inflation rates," Economic Modelling, Elsevier, vol. 26(6), pages 1443-1448, November.
    56. Michael W. McCracken & Serena Ng, 2021. "FRED-QD: A Quarterly Database for Macroeconomic Research," Review, Federal Reserve Bank of St. Louis, vol. 103(1), pages 1-44, January.
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    61. Paul Newbold & Stephan Pfaffenzeller & Anthony Rayner, 2005. "How well are long-run commodity price series characterized by trend components?," Journal of International Development, John Wiley & Sons, Ltd., vol. 17(4), pages 479-494.
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    64. Wagatha, Matthias, 2007. "Integration, Kointegration und die Langzeitprognose von Kreditausfallzyklen [Integration, Cointegration and Long-Horizont Forecasting of Credit-Default-Cycles]," MPRA Paper 8602, University Library of Munich, Germany.
    65. Barakchian , Seyed Mahdi & Bayat , Saeed & Karami , Hooman, 2013. "Common Factors of CPI Sub-aggregates and Forecast of Inflation," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 8(4), pages 1-17, October.
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    Cited by:

    1. Ralf Becker & Adam Clements, 2007. "Are combination forecasts of S&P 500 volatility statistically superior?," NCER Working Paper Series 17, National Centre for Econometric Research.
    2. N. Antonakakis & J. Darby, 2013. "Forecasting volatility in developing countries' nominal exchange returns," Applied Financial Economics, Taylor & Francis Journals, vol. 23(21), pages 1675-1691, November.
    3. Benavides, Guillermo & Capistrán, Carlos, 2012. "Forecasting exchange rate volatility: The superior performance of conditional combinations of time series and option implied forecasts," Journal of Empirical Finance, Elsevier, vol. 19(5), pages 627-639.
    4. Elisa Alòs & Maria Elvira Mancino & Tai-Ho Wang, 2019. "Volatility and volatility-linked derivatives: estimation, modeling, and pricing," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 42(2), pages 321-349, December.
    5. Long H. Vo, 2017. "Estimating Financial Volatility with High-Frequency Returns," Journal of Finance and Economics Research, Geist Science, Iqra University, Faculty of Business Administration, vol. 2(2), pages 84-114, October.
    6. Aldrich, Eric M. & Heckenbach, Indra & Laughlin, Gregory, 2016. "A compound duration model for high-frequency asset returns," Journal of Empirical Finance, Elsevier, vol. 39(PA), pages 105-128.
    7. Karthik Raju & Saravanan Rangaswamy, 2017. "Forecasting volatility in the Indian equity market using return and range-based models," Applied Economics, Taylor & Francis Journals, vol. 49(49), pages 5027-5039, October.
    8. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    9. Selma Chaker & Nour Meddahi, 2013. "A Distributional Approach to Realized Volatility," Staff Working Papers 13-49, Bank of Canada.
    10. Karmakar, Madhusudan & Paul, Samit, 2016. "Intraday risk management in International stock markets: A conditional EVT approach," International Review of Financial Analysis, Elsevier, vol. 44(C), pages 34-55.
    11. Amir Safari & Detlef Seese, 2010. "Behavior of realized volatility and correlation in exchange markets," International Econometric Review (IER), Econometric Research Association, vol. 2(2), pages 73-96, September.
    12. Becker, Ralf & Clements, Adam E. & White, Scott I., 2007. "Does implied volatility provide any information beyond that captured in model-based volatility forecasts?," Journal of Banking & Finance, Elsevier, vol. 31(8), pages 2535-2549, August.
    13. Katarzyna Bien & Ingmar Nolte & Winfried Pohlmeier, 2011. "An inflated multivariate integer count hurdle model: an application to bid and ask quote dynamics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(4), pages 669-707, June.
    14. Bandi, Federico M. & Russell, Jeffrey R. & Yang, Chen, 2008. "Realized volatility forecasting and option pricing," Journal of Econometrics, Elsevier, vol. 147(1), pages 34-46, November.
    15. Maria Elvira Mancino & Simona Sanfelici, 2011. "Estimation of Quarticity with High Frequency Data," Working Papers - Mathematical Economics 2011-06, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa, revised Jan 2012.
    16. Daniel Djupsjobacka, 2010. "Implications of market microstructure for realized variance measurement," The European Journal of Finance, Taylor & Francis Journals, vol. 16(1), pages 27-43.
    17. Chu, Carlin C.F. & Lam, K.P., 2011. "Modeling intraday volatility: A new consideration," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 21(3), pages 388-418, July.
    18. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2005. "Volatility forecasting," CFS Working Paper Series 2005/08, Center for Financial Studies (CFS).
    19. Sébastien Laurent & Jeroen V. K. Rombouts & Francesco Violante, 2012. "On the forecasting accuracy of multivariate GARCH models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(6), pages 934-955, September.
    20. Byun, Suk Joon & Cho, Hangjun, 2013. "Forecasting carbon futures volatility using GARCH models with energy volatilities," Energy Economics, Elsevier, vol. 40(C), pages 207-221.
    21. Ai-ru (Meg) Cheng & Kuntal Das & Takeshi Shimatani, 2013. "Central Bank Intervention and Exchange Rate Volatility: Evidence from Japan Using Realized Volatility," Working Papers in Economics 13/19, University of Canterbury, Department of Economics and Finance.
    22. Ingmar Nolte & Valeri Voev, 2008. "Estimating High-Frequency Based (Co-) Variances: A Unified Approach," CREATES Research Papers 2008-31, Department of Economics and Business Economics, Aarhus University.
    23. George Tzagkarakis & Frantz Maurer & John P. Nolan, 2024. "Taming impulsive high-frequency data using optimal sampling periods," Annals of Operations Research, Springer, vol. 333(1), pages 393-415, February.
    24. Giorgio Mirone, 2017. "Inference from the futures: ranking the noise cancelling accuracy of realized measures," CREATES Research Papers 2017-24, Department of Economics and Business Economics, Aarhus University.
    25. Eugenie Hol & Siem Jan Koopman, 2002. "Stock Index Volatility Forecasting with High Frequency Data," Tinbergen Institute Discussion Papers 02-068/4, Tinbergen Institute.
    26. Becker Ralf & Clements Adam E & Hurn Stan, 2011. "Semi-Parametric Forecasting of Realized Volatility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(3), pages 1-23, May.
    27. Laurent, Sébastien & Rombouts, Jeroen V.K. & Violante, Francesco, 2013. "On loss functions and ranking forecasting performances of multivariate volatility models," Journal of Econometrics, Elsevier, vol. 173(1), pages 1-10.
    28. Becker, Ralf & Clements, Adam E. & McClelland, Andrew, 2009. "The jump component of S&P 500 volatility and the VIX index," Journal of Banking & Finance, Elsevier, vol. 33(6), pages 1033-1038, June.
    29. Linlan Xiao, 2013. "Realized volatility forecasting: empirical evidence from stock market indices and exchange rates," Applied Financial Economics, Taylor & Francis Journals, vol. 23(1), pages 57-69, January.
    30. Katarzyna Bien & Ingmar Nolte & Winfried Pohlmeier, 2008. "A multivariate integer count hurdle model: theory and application to exchange rate dynamics," Studies in Empirical Economics, in: Luc Bauwens & Winfried Pohlmeier & David Veredas (ed.), High Frequency Financial Econometrics, pages 31-48, Springer.
    31. Becker, Ralf & Clements, Adam E. & White, Scott I., 2006. "On the informational efficiency of S&P500 implied volatility," The North American Journal of Economics and Finance, Elsevier, vol. 17(2), pages 139-153, August.
    32. Long, Xiangdong & Su, Liangjun & Ullah, Aman, 2011. "Estimation and Forecasting of Dynamic Conditional Covariance: A Semiparametric Multivariate Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 109-125.
    33. Mancino, M.E. & Sanfelici, S., 2008. "Robustness of Fourier estimator of integrated volatility in the presence of microstructure noise," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 2966-2989, February.
    34. E. C. Brechmann & M. Heiden & Y. Okhrin, 2018. "A multivariate volatility vine copula model," Econometric Reviews, Taylor & Francis Journals, vol. 37(4), pages 281-308, April.
    35. Ghorbel, Ahmed & Trabelsi, Abdelwahed, 2007. "Predictive Performance of Conditional Extreme Value Theory and Conventional Methods in Value at Risk Estimation," MPRA Paper 3963, University Library of Munich, Germany.
    36. Gang-Zhi Fan & Zsuzsa Huszár & Weina Zhang, 2013. "The Relationships between Real Estate Price and Expected Financial Asset Risk and Return: Theory and Empirical Evidence," The Journal of Real Estate Finance and Economics, Springer, vol. 46(4), pages 568-595, May.
    37. Fei Su, 2018. "Essays on Price Discovery and Volatility Dynamics in the Foreign Exchange Market," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 2-2018, January-A.
    38. Roland Füss & Ferdinand Mager & Michael Stein & Lu Zhao, 2018. "Financial crises, price discovery, and information transmission: a high-frequency perspective," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 32(4), pages 333-365, November.
    39. Scott I. White & Adam E. Clements & Stan Hurn, 2004. "Discretised Non-Linear Filtering for Dynamic Latent Variable Models: with Application to Stochastic Volatility," Econometric Society 2004 Australasian Meetings 46, Econometric Society.
    40. Adam Clements & Annastiina Silvennoinen, 2009. "On the economic benefit of utility based estimation of a volatility model," NCER Working Paper Series 44, National Centre for Econometric Research.
    41. Małgorzata Just & Aleksandra Łuczak, 2020. "Assessment of Conditional Dependence Structures in Commodity Futures Markets Using Copula-GARCH Models and Fuzzy Clustering Methods," Sustainability, MDPI, vol. 12(6), pages 1-22, March.
    42. Su, Fei & Zhang, Jingjing, 2018. "Global price discovery in the Australian dollar market and its determinants," Pacific-Basin Finance Journal, Elsevier, vol. 48(C), pages 35-55.
    43. Dimpfl, Thomas & Peter, Franziska J., 2021. "Nothing but noise? Price discovery across cryptocurrency exchanges," Journal of Financial Markets, Elsevier, vol. 54(C).
    44. Georgios Chortareas & John Nankervis & Ying Jiang, 2007. "Forecasting Exchange Rate Volatility with High Frequency Data: Is the Euro Different?," Money Macro and Finance (MMF) Research Group Conference 2006 79, Money Macro and Finance Research Group.
    45. Scott I White & Ralf Becker & Adam E Clements, 2004. "Forward looking information in S&P 500 options," Econometric Society 2004 Australasian Meetings 233, Econometric Society.
    46. Adam Clements & Ralf Becker, 2009. "A nonparametric approach to forecasting realized volatility," NCER Working Paper Series 43, National Centre for Econometric Research.
    47. Yalama, Abdullah & Celik, Sibel, 2013. "Real or spurious long memory characteristics of volatility: Empirical evidence from an emerging market," Economic Modelling, Elsevier, vol. 30(C), pages 67-72.
    48. Linlan Xiao & Vigdis Boasson & Sergey Shishlenin & Victoria Makushina, 2018. "Volatility forecasting: combinations of realized volatility measures and forecasting models," Applied Economics, Taylor & Francis Journals, vol. 50(13), pages 1428-1441, March.
    49. Wang, Yuanfang & Roberts, Matthew C., 2005. "Realized Volatility in the Agricultural Futures Market," 2005 Annual meeting, July 24-27, Providence, RI 19211, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    50. A. Saichev & D. Sornette, 2014. "A simple microstructure return model explaining microstructure noise and Epps effects," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 25(06), pages 1-36.
    51. André Schöne, 2010. "Zum Informationsgehalt der Volatilitätsindizes VDAX und VDAX-New der Deutsche Börse AG," Schmalenbach Journal of Business Research, Springer, vol. 62(6), pages 625-661, September.
    52. Fei, Tianlun & Liu, Xiaoquan & Wen, Conghua, 2019. "Cross-sectional return dispersion and volatility prediction," Pacific-Basin Finance Journal, Elsevier, vol. 58(C).

  88. Francis X. Diebold & Til Schuermann & John D. Stroughair, 1998. "Pitfalls and Opportunities in the Use of Extreme Value Theory in Risk Management," New York University, Leonard N. Stern School Finance Department Working Paper Seires 98-081, New York University, Leonard N. Stern School of Business-.

    Cited by:

    1. Jose Fernandes & Augusto Hasman & Juan Ignacio Pena, 2007. "Risk premium: insights over the threshold," Applied Financial Economics, Taylor & Francis Journals, vol. 18(1), pages 41-59.
    2. Cotter, John, 2004. "Varying the VaR for Unconditional and Conditional Environments," MPRA Paper 3483, University Library of Munich, Germany.
    3. Turan G. Bali, 2007. "An Extreme Value Approach to Estimating Interest-Rate Volatility: Pricing Implications for Interest-Rate Options," Management Science, INFORMS, vol. 53(2), pages 323-339, February.
    4. Odening, Martin & Hinrichs, Jan, 2003. "Die Quantifizierung von Marktrisiken in der Tierproduktion mittels Value-at-Risk und Extreme-Value-Theory," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 52(02), pages 1-11.
    5. Turan G. Bali, 2007. "A Generalized Extreme Value Approach to Financial Risk Measurement," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1613-1649, October.
    6. Hammoudeh, Shawkat & Araújo Santos, Paulo & Al-Hassan, Abdullah, 2013. "Downside risk management and VaR-based optimal portfolios for precious metals, oil and stocks," The North American Journal of Economics and Finance, Elsevier, vol. 25(C), pages 318-334.
    7. Loriano Mancini & Fabio Trojani, 2011. "Robust Value at Risk Prediction," Journal of Financial Econometrics, Oxford University Press, vol. 9(2), pages 281-313, Spring.
    8. Luca Erzegovesi, 2002. "VaR and Liquidity Risk.Impact on Market Behaviour and Measurement Issues," Alea Tech Reports 014, Department of Computer and Management Sciences, University of Trento, Italy, revised 14 Jun 2008.
    9. Jimenez-Martin, Juan-Angel & McAleer, Michael & Pérez-Amaral, Teodosio & Santos, Paulo Araújo, 2013. "GFC-robust risk management under the Basel Accord using extreme value methodologies," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 223-237.
    10. Dimitrakopoulos, Dimitris N. & Kavussanos, Manolis G. & Spyrou, Spyros I., 2010. "Value at risk models for volatile emerging markets equity portfolios," The Quarterly Review of Economics and Finance, Elsevier, vol. 50(4), pages 515-526, November.
    11. Jobst, Andreas A., 2002. "The Pricing puzzle: The default term structure of collateralised loan obligations," CFS Working Paper Series 2002/14, Center for Financial Studies (CFS).
    12. Ozaki, Vitor Augusto & Olinda, Ricardo & Faria, Priscila Neves & Campos, Rogerio Costa, 2014. "Estimation of the Agricultural Probability of Loss: evidence for soybean in Paraná Stats," Brazilian Journal of Rural Economy and Sociology (Revista de Economia e Sociologia Rural-RESR), Sociedade Brasileira de Economia e Sociologia Rural, vol. 52(01), pages 1-16, March.
    13. Suarez, R, 2001. "Improving Modeling of Extreme Events using Generalized Extreme Value Distribution or Generalized Pareto Distribution with Mixing Unconditional Disturbances," MPRA Paper 17443, University Library of Munich, Germany.
    14. Marco Rocco, 2011. "Extreme value theory for finance: a survey," Questioni di Economia e Finanza (Occasional Papers) 99, Bank of Italy, Economic Research and International Relations Area.
    15. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Practical Volatility and Correlation Modeling for Financial Market Risk Management," NBER Working Papers 11069, National Bureau of Economic Research, Inc.
    16. Sarafrazi, Soodabeh & Hammoudeh, Shawkat & AraújoSantos, Paulo, 2014. "Downside risk, portfolio diversification and the financial crisis in the euro-zone," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 32(C), pages 368-396.
    17. Wagner, Niklas & Marsh, Terry A., 2005. "Measuring tail thickness under GARCH and an application to extreme exchange rate changes," Journal of Empirical Finance, Elsevier, vol. 12(1), pages 165-185, January.
    18. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2011. "Financial Risk Measurement for Financial Risk Management," PIER Working Paper Archive 11-037, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    19. Marc Saidenberg & Til Schuermann & May, "undated". "The New Basel Capital Accord and Questions for Research," Center for Financial Institutions Working Papers 03-14, Wharton School Center for Financial Institutions, University of Pennsylvania.
    20. Ozaki, Vitor Augusto & Olinda, Ricardo & Faria, Priscila Neves & Campos, Rogério Costa, 2014. "Estimation of the Agricultural Probability of Loss: evidence for soybean in Paraná State," Revista de Economia e Sociologia Rural (RESR), Sociedade Brasileira de Economia e Sociologia Rural, vol. 52(01), January.
    21. Bystrom, Hans N. E., 2004. "Managing extreme risks in tranquil and volatile markets using conditional extreme value theory," International Review of Financial Analysis, Elsevier, vol. 13(2), pages 133-152.
    22. Suarez, Ronny, 2009. "Improving Modeling of Extreme Events using Generalized Extreme Value Distribution or Generalized Pareto Distribution with Mixing Unconditional Disturbances," MPRA Paper 17482, University Library of Munich, Germany.
    23. Daniele Massacci, 2017. "Tail Risk Dynamics in Stock Returns: Links to the Macroeconomy and Global Markets Connectedness," Management Science, INFORMS, vol. 63(9), pages 3072-3089, September.
    24. Jobst, Andreas A., 2002. "Loan securitisation: default term structure and asset pricing based on loss prioritisation," LSE Research Online Documents on Economics 24941, London School of Economics and Political Science, LSE Library.
    25. Haque, Mahfuzul & Varela, Oscar & Hassan, M. Kabir, 2007. "Safety-first and extreme value bilateral U.S.-Mexican portfolio optimization around the peso crisis and NAFTA in 1994," The Quarterly Review of Economics and Finance, Elsevier, vol. 47(3), pages 449-469, July.
    26. Marco Moscadelli, 2004. "The modelling of operational risk: experience with the analysis of the data collected by the Basel Committee," Temi di discussione (Economic working papers) 517, Bank of Italy, Economic Research and International Relations Area.
    27. Lu, Xunfa & He, Pengchao & Zhang, Zhengjun & Apergis, Nicholas & Roubaud, David, 2024. "Extreme co-movements between decomposed oil price shocks and sustainable investments," Energy Economics, Elsevier, vol. 134(C).
    28. Candia, Claudio & Herrera, Rodrigo, 2024. "An empirical review of dynamic extreme value models for forecasting value at risk, expected shortfall and expectile," Journal of Empirical Finance, Elsevier, vol. 77(C).
    29. Anthony Tay & Kenneth F. Wallis, 2000. "Density Forecasting: A Survey," Econometric Society World Congress 2000 Contributed Papers 0370, Econometric Society.
    30. Sasa Zikovic & Randall Filer, 2009. "Hybrid Historical Simulation VaR and ES: Performance in Developed and Emerging Markets," CESifo Working Paper Series 2820, CESifo.
    31. Peter F. Christoffersen & Francis X. Diebold, 1998. "How Relevant is Volatility Forecasting for Financial Risk Management?," New York University, Leonard N. Stern School Finance Department Working Paper Seires 98-080, New York University, Leonard N. Stern School of Business-.
    32. Singh, Abhay K. & Allen, David E. & Robert, Powell J., 2013. "Extreme market risk and extreme value theory," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 310-328.
    33. Odening, Martin & Hinrichs, Jan, 2002. "Assessment Of Market Risk In Hog Production Using Value-At-Risk And Extreme Value Theory," 2002 Annual meeting, July 28-31, Long Beach, CA 19907, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    34. POON, Ser-Huang & ROCKINGER, Michael & TAWN, Jonathan, 2001. "New Extreme-Value Dependance Measures and Finance Applications," HEC Research Papers Series 719, HEC Paris.
    35. Ghosh, Bikramaditya & Bouri, Elie & Wee, Jung Bum & Zulfiqar, Noshaba, 2023. "Return and volatility properties: Stylized facts from the universe of cryptocurrencies and NFTs," Research in International Business and Finance, Elsevier, vol. 65(C).
    36. Garcia-Jorcano, Laura & Sanchis-Marco, Lidia, 2024. "Forecasting the effect of extreme sea-level rise on financial market risk," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 1-27.
    37. Timotheos Angelidis & Alexandros Benos & Stavros Degiannakis, 2007. "A robust VaR model under different time periods and weighting schemes," Review of Quantitative Finance and Accounting, Springer, vol. 28(2), pages 187-201, February.
    38. Chang, Chia-Lin & González-Serrano, Lydia & Jimenez-Martin, Juan-Angel, 2013. "Currency hedging strategies using dynamic multivariate GARCH," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 164-182.
    39. Bali, Turan G. & Weinbaum, David, 2007. "A conditional extreme value volatility estimator based on high-frequency returns," Journal of Economic Dynamics and Control, Elsevier, vol. 31(2), pages 361-397, February.
    40. Araújo Santos, Paulo & Fraga Alves, Isabel & Hammoudeh, Shawkat, 2013. "High quantiles estimation with Quasi-PORT and DPOT: An application to value-at-risk for financial variables," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 487-496.
    41. Manfred Gilli & Evis këllezi, 2006. "An Application of Extreme Value Theory for Measuring Financial Risk," Computational Economics, Springer;Society for Computational Economics, vol. 27(2), pages 207-228, May.
    42. Cotter, John, 2004. "Downside Risk for European Equity Markets," MPRA Paper 3537, University Library of Munich, Germany.
    43. Huang, Wei & Liu, Qianqiu & Ghon Rhee, S. & Wu, Feng, 2012. "Extreme downside risk and expected stock returns," Journal of Banking & Finance, Elsevier, vol. 36(5), pages 1492-1502.
    44. John Cotter, 2006. "Extreme Value Estimation of Boom and Crash Statistics," The European Journal of Finance, Taylor & Francis Journals, vol. 12(6-7), pages 553-566.
    45. Antonio Díaz & Gonzalo García-Donato & Andrés Mora-Valencia, 2017. "Risk quantification in turmoil markets," Risk Management, Palgrave Macmillan, vol. 19(3), pages 202-224, August.
    46. Ghorbel, Ahmed & Trabelsi, Abdelwahed, 2007. "Predictive Performance of Conditional Extreme Value Theory and Conventional Methods in Value at Risk Estimation," MPRA Paper 3963, University Library of Munich, Germany.
    47. Evis Këllezi & Manfred Gilli, 2000. "Extreme Value Theory for Tail-Related Risk Measures," FAME Research Paper Series rp18, International Center for Financial Asset Management and Engineering.
    48. Peter F. Christoffersen & Francis X. Diebold & Til Schuermann, 1998. "Horizon problems and extreme events in financial risk management," Economic Policy Review, Federal Reserve Bank of New York, vol. 4(Oct), pages 109-118.
    49. Carlo Marinelli & Stefano D'Addona & Svetlozar T. Rachev, 2007. "A Comparison Of Some Univariate Models For Value-At-Risk And Expected Shortfall," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 10(06), pages 1043-1075.
    50. Martins-Filho Carlos & Yao Feng, 2006. "Estimation of Value-at-Risk and Expected Shortfall based on Nonlinear Models of Return Dynamics and Extreme Value Theory," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(2), pages 1-43, May.
    51. Zhang, Zhengjun & Huang, James, 2006. "Extremal financial risk models and portfolio evaluation," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2313-2338, December.
    52. Chavez-Demoulin, V. & Embrechts, P. & Sardy, S., 2014. "Extreme-quantile tracking for financial time series," Journal of Econometrics, Elsevier, vol. 181(1), pages 44-52.
    53. Alfonso Novales & Laura Garcia-Jorcano, 2019. "Backtesting Extreme Value Theory models of expected shortfall," Documentos de Trabajo del ICAE 2019-24, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    54. Aboura, Sofiane & Wagner, Niklas, 2016. "Extreme asymmetric volatility: Stress and aggregate asset prices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 41(C), pages 47-59.
    55. Ardakani, Omid M., 2023. "Capturing information in extreme events," Economics Letters, Elsevier, vol. 231(C).
    56. Zhao, Zifeng & Zhang, Zhengjun & Chen, Rong, 2018. "Modeling maxima with autoregressive conditional Fréchet model," Journal of Econometrics, Elsevier, vol. 207(2), pages 325-351.
    57. Riedel, Christoph & Wagner, Niklas, 2015. "Is risk higher during non-trading periods? The risk trade-off for intraday versus overnight market returns," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 39(C), pages 53-64.
    58. Alfredo Calderon Vela & Gabriel Rodríguez, 2014. "Extreme Value Theory: An Application to the Peruvian Stock Market Returns," Documentos de Trabajo / Working Papers 2014-394, Departamento de Economía - Pontificia Universidad Católica del Perú.

  89. Francis X. Diebold & Jinyong Hahn & Anthony S. Tay, 1998. "Real-Time Multivariate Density Forecast Evaluation and Calibration: Monitoring the Risk of High-Frequency Returns on Foreign Exchange," New York University, Leonard N. Stern School Finance Department Working Paper Seires 98-079, New York University, Leonard N. Stern School of Business-.

    Cited by:

    1. Jose A. Lopez & Marc R. Saidenberg, 1999. "Evaluating credit risk models," Working Papers in Applied Economic Theory 99-06, Federal Reserve Bank of San Francisco.
    2. Stefan Jaschke & Gerhard Stahl & Richard Stehle, 2007. "Value-at-risk forecasts under scrutiny—the German experience," Quantitative Finance, Taylor & Francis Journals, vol. 7(6), pages 621-636.

  90. Peter F. Christoffersen & Francis X. Diebold & Til Schuermann, 1998. "Horizon Problems and Extreme Events in Financial Risk Management," Center for Financial Institutions Working Papers 98-16, Wharton School Center for Financial Institutions, University of Pennsylvania.

    Cited by:

    1. Antonio Rubia & Trino-Manuel Ñíguez, 2006. "Forecasting the conditional covariance matrix of a portfolio under long-run temporal dependence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(6), pages 439-458.
    2. Radu Tunaru, 2015. "Model Risk in Financial Markets:From Financial Engineering to Risk Management," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 9524, April.
    3. Wolff, Christian & Bams, Dennis & Lehnert, Thorsten, 2002. "An Evaluation Framework for Alternative VaR Models," CEPR Discussion Papers 3403, C.E.P.R. Discussion Papers.
    4. Luca Erzegovesi, 2002. "VaR and Liquidity Risk.Impact on Market Behaviour and Measurement Issues," Alea Tech Reports 014, Department of Computer and Management Sciences, University of Trento, Italy, revised 14 Jun 2008.
    5. Durán Santomil, Pablo & Otero González, Luís & Martorell Cunill, Onofre & Merigó Lindahl, José M., 2018. "Backtesting an equity risk model under Solvency II," Journal of Business Research, Elsevier, vol. 89(C), pages 216-222.
    6. Andreas Lehnert & Wayne Passmore, 1999. "Pricing systemic crises: monetary and fiscal policy when savers are uncertain," Finance and Economics Discussion Series 1999-33, Board of Governors of the Federal Reserve System (U.S.).
    7. Gregory, Allan W. & Reeves, Jonathan J., 2008. "Interpreting Value at Risk (VaR) forecasts," Economic Systems, Elsevier, vol. 32(2), pages 167-176, June.
    8. Douglas D. Evanoff & Larry D. Wall, 2000. "Subordinated debt and bank capital reform," Working Paper Series WP-00-7, Federal Reserve Bank of Chicago.
    9. D. Schneller & S. Heiden & M. Heiden & A. Hamid, 2018. "Home is Where You Know Your Volatility – Local Investor Sentiment and Stock Market Volatility," German Economic Review, Verein für Socialpolitik, vol. 19(2), pages 209-236, May.
    10. Gonzalez-Rivera, Gloria & Lee, Tae-Hwy & Mishra, Santosh, 2004. "Forecasting volatility: A reality check based on option pricing, utility function, value-at-risk, and predictive likelihood," International Journal of Forecasting, Elsevier, vol. 20(4), pages 629-645.
    11. Cyril Caillault & Dominique Guegan, 2009. "Forecasting VaR and Expected Shortfall using Dynamical Systems: A Risk Management Strategy," PSE-Ecole d'économie de Paris (Postprint) halshs-00375765, HAL.
    12. Li, Xingyi & Zakamulin, Valeriy, 2020. "The term structure of volatility predictability," International Journal of Forecasting, Elsevier, vol. 36(2), pages 723-737.
    13. Zoia, Maria Grazia & Biffi, Paola & Nicolussi, Federica, 2018. "Value at risk and expected shortfall based on Gram-Charlier-like expansions," Journal of Banking & Finance, Elsevier, vol. 93(C), pages 92-104.
    14. Christoffersen, Peter & Errunza, Vihang, 2000. "Towards a global financial architecture: capital mobility and risk management issues," Emerging Markets Review, Elsevier, vol. 1(1), pages 3-20, May.
    15. Guidolin, Massimo & Timmermann, Allan, 2006. "Term structure of risk under alternative econometric specifications," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 285-308.
    16. Wang, Jying-Nan & Du, Jiangze & Hsu, Yuan-Teng, 2018. "Measuring long-term tail risk: Evaluating the performance of the square-root-of-time rule," Journal of Empirical Finance, Elsevier, vol. 47(C), pages 120-138.
    17. Flavio Bazzana, 2001. "I modelli interni per la valutazione del rischio di mercato secondo l'approccio del Value at Risk," Alea Tech Reports 011, Department of Computer and Management Sciences, University of Trento, Italy, revised 14 Jun 2008.
    18. Kevin Dowd & David Blake & Andrew Cairns, 2004. "Long‐Term Value at Risk," Journal of Risk Finance, Emerald Group Publishing Limited, vol. 5(2), pages 52-57, February.
    19. Wang, Cheng & Bouri, Elie & Xu, Yahua & Zhang, Dingsheng, 2023. "Intraday and overnight tail risks and return predictability in the crude oil market: Evidence from oil-related regular news and extreme shocks," Energy Economics, Elsevier, vol. 127(PB).
    20. Aboura, Sofiane & Chevallier, Julien, 2018. "Tail risk and the return-volatility relation," Research in International Business and Finance, Elsevier, vol. 46(C), pages 16-29.
    21. Luca Spadafora & Marco Dubrovich & Marcello Terraneo, 2014. "Value-at-Risk time scaling for long-term risk estimation," Papers 1408.2462, arXiv.org.
    22. Beverly Hirtle, 2003. "What market risk capital reporting tells us about bank risk," Economic Policy Review, Federal Reserve Bank of New York, issue Sep, pages 37-54.
    23. Kavussanos, Manolis G. & Dimitrakopoulos, Dimitris N., 2011. "Market risk model selection and medium-term risk with limited data: Application to ocean tanker freight markets," International Review of Financial Analysis, Elsevier, vol. 20(5), pages 258-268.
    24. da Costa, B. Freitas Paulo & Pesenti, Silvana M. & Targino, Rodrigo S., 2023. "Risk budgeting portfolios from simulations," European Journal of Operational Research, Elsevier, vol. 311(3), pages 1040-1056.
    25. Ho, Lan-Chih & Burridge, Peter & Cadle, John & Theobald, Michael, 2000. "Value-at-risk: Applying the extreme value approach to Asian markets in the recent financial turmoil," Pacific-Basin Finance Journal, Elsevier, vol. 8(2), pages 249-275, May.
    26. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.

  91. Peter F. Christoffersen & Francis X. Diebold, 1998. "How Relevant is Volatility Forecasting for Financial Risk Management?," New York University, Leonard N. Stern School Finance Department Working Paper Seires 98-080, New York University, Leonard N. Stern School of Business-.

    Cited by:

    1. Bordignon, Silvano & Lisi, Francesco, 2001. "Predictive accuracy for chaotic economic models," Economics Letters, Elsevier, vol. 70(1), pages 51-58, January.
    2. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 1999. "The Distribution of Exchange Rate Volatility," Center for Financial Institutions Working Papers 99-08, Wharton School Center for Financial Institutions, University of Pennsylvania.
    3. Egelkraut, Thorsten M. & Garcia, Philip, 2005. "Intermediate Volatility Forecasts Using Implied Forward Volatility: The Performance of Selected Agricultural Commodity Options," 2005 Conference, April 18-19, 2005, St. Louis, Missouri 19033, NCR-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    4. Cotter, John, 2004. "Varying the VaR for Unconditional and Conditional Environments," MPRA Paper 3483, University Library of Munich, Germany.
    5. Lyu, Yongjian & Wang, Peng & Wei, Yu & Ke, Rui, 2017. "Forecasting the VaR of crude oil market: Do alternative distributions help?," Energy Economics, Elsevier, vol. 66(C), pages 523-534.
    6. Demiralay, Sercan & Ulusoy, Veysel, 2014. "Value-at-risk Predictions of Precious Metals with Long Memory Volatility Models," MPRA Paper 53229, University Library of Munich, Germany.
    7. Yi Yang & Kunpeng Zhang & Yangyang Fan, 2022. "Analyzing Firm Reports for Volatility Prediction: A Knowledge-Driven Text-Embedding Approach," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 522-540, January.
    8. Coronado, Semei & Rojas, Omar & Venegas-Martínez, Francisco (ed.), 2018. "Recent Topics in Time Series and Finance: Theory and Applications in Emerging Markets," Sección de Estudios de Posgrado e Investigación de la Escuela Superios de Economía del Instituto Politécnico Nacional, Escuela Superior de Economía, Instituto Politécnico Nacional, edition 1, volume 1, number 022, January.
    9. Yutong Chen & Paul Bilokon & Conan Hales & Laura Kerr, 2023. "Real-time VaR Calculations for Crypto Derivatives in kdb+/q," Papers 2309.06393, arXiv.org.
    10. Jean-Paul Laurent & Hassan Omidi Firouzi, 2022. "Market Risk and Volatility Weighted Historical Simulation After Basel III," Working Papers hal-03679434, HAL.
    11. Odening, Martin & Hinrichs, Jan, 2003. "Die Quantifizierung von Marktrisiken in der Tierproduktion mittels Value-at-Risk und Extreme-Value-Theory," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 52(02), pages 1-11.
    12. J. Q. Smith & António Santos, 2003. "Second Order Filter Distribution Approximations for Financial Time Series with Extreme Outlier," GEMF Working Papers 2003-03, GEMF, Faculty of Economics, University of Coimbra.
    13. Hung, Jui-Cheng & Lee, Ming-Chih & Liu, Hung-Chun, 2008. "Estimation of value-at-risk for energy commodities via fat-tailed GARCH models," Energy Economics, Elsevier, vol. 30(3), pages 1173-1191, May.
    14. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    15. Turan G. Bali, 2007. "A Generalized Extreme Value Approach to Financial Risk Measurement," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1613-1649, October.
    16. Tang, Ta-Lun & Shieh, Shwu-Jane, 2006. "Long memory in stock index futures markets: A value-at-risk approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 366(C), pages 437-448.
    17. Florian Ielpo & Benoît Sévi, 2014. "Forecasting the density of oil futures," Working Papers 2014-601, Department of Research, Ipag Business School.
    18. Valentina Corradi & Norman Swanson, 2013. "A Survey of Recent Advances in Forecast Accuracy Comparison Testing, with an Extension to Stochastic Dominance," Departmental Working Papers 201309, Rutgers University, Department of Economics.
    19. Wolff, Christian & Lehnert, Thorsten, 2001. "Modelling Scale-Consistent VaR with the Truncated Lévy Flight," CEPR Discussion Papers 2711, C.E.P.R. Discussion Papers.
    20. DAVID G. McMILLAN & ALAN E. H. SPEIGHT, 2007. "Value‐at‐Risk in Emerging Equity Markets: Comparative Evidence for Symmetric, Asymmetric, and Long‐Memory GARCH Models," International Review of Finance, International Review of Finance Ltd., vol. 7(1‐2), pages 1-19, March.
    21. BOUADDI, Mohammed & ROMBOUTS, Jeroen V.K., 2007. "Mixed exponential power asymmetric conditional heteroskedasticity," LIDAM Discussion Papers CORE 2007097, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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  93. Jeremy Berkowitz & Francis X. Diebold & Lee E. Ohanian, 1997. "Dynamic equilibrium economies: a framework for comparing models and data," Finance and Economics Discussion Series 1997-23, Board of Governors of the Federal Reserve System (U.S.).

    Cited by:

    1. Takashi Kano & James M. Nason, 2014. "Business Cycle Implications of Internal Consumption Habit for New Keynesian Models," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(2-3), pages 519-544, March.
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    4. Jeremy Berkowitz & Ionel Birgean & Lutz Kilian, 1999. "On the finite-sample accuracy of nonparametric resampling algorithms for economic time series," Finance and Economics Discussion Series 1999-04, Board of Governors of the Federal Reserve System (U.S.).
    5. Canova, Fabio, 2002. "Validating Monetary DSGE Models through VARs," CEPR Discussion Papers 3442, C.E.P.R. Discussion Papers.
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    7. Gianfreda, Angelica & Maranzano, Paolo & Parisio, Lucia & Pelagatti, Matteo, 2023. "Testing for integration and cointegration when time series are observed with noise," Economic Modelling, Elsevier, vol. 125(C).
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    9. Michael Reiter & Ulrich Woitek, 1999. "Are these classical business cycles?," Economics Working Papers 398, Department of Economics and Business, Universitat Pompeu Fabra.
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    11. Li, Y. & Donkers, A.C.D. & Melenberg, B., 2006. "The Non- and Semiparametric Analysis of MS Models : Some Applications," Other publications TiSEM c14adc9f-f490-40d6-81b7-8, Tilburg University, School of Economics and Management.
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    57. Franz Hamann Salcedo & Juan Manuel Julio & Paulina Restrepo & Alvaro Riascos, 2004. "Inflation Targeting in a Samll Open Economy: The Colombian Case," Borradores de Economia 308, Banco de la Republica de Colombia.
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    63. Francis X. Diebold & Lutz Kilian, "undated". "Measuring Predictability: Theory and Macroeconomic Applications," CARESS Working Papres 97-19, University of Pennsylvania Center for Analytic Research and Economics in the Social Sciences.
    64. Jaromir Benes & Tibor Hledik & Michael Kumhof & David Vavra, 2005. "An Economy in Transition and DSGE: What the Czech National Bank's New Projection Model Needs," Working Papers 2005/12, Czech National Bank, Research and Statistics Department.
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    Cited by:

    1. Christian Hutter & Enzo Weber, 2015. "Constructing a new leading indicator for unemployment from a survey among German employment agencies," Applied Economics, Taylor & Francis Journals, vol. 47(33), pages 3540-3558, July.
    2. Athanasopoulos, George & Guillen, Osmani Teixeira Carvalho & Issler, João Victor & Vahid, Farshid, 2011. "Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 713, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    3. Osmani Teixeira de Carvalho Guillén & João Victor Issler & George Athanasopoulos, 2005. "Forecasting Accuracy and Estimation Uncertainty Using VAR Models with Short- and Long-Term Economic Restrictions: A Monte-Carlo Study," Monash Econometrics and Business Statistics Working Papers 15/05, Monash University, Department of Econometrics and Business Statistics.
    4. Kelly Burns, 2016. "A Reconsideration of the Meese-Rogoff Puzzle: An Alternative Approach to Model Estimation and Forecast Evaluation," Multinational Finance Journal, Multinational Finance Journal, vol. 20(1), pages 41-83, March.
    5. Barakchian , Seyed Mahdi, 2012. "Implications of Cointegration for Forecasting: A Review and an Empirical Analysis," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 7(1), pages 87-118, October.
    6. Torben G. Andersen & Rasmus T. Varneskov, 2021. "Testing for Parameter Instability and Structural Change in Persistent Predictive Regressions," NBER Working Papers 28570, National Bureau of Economic Research, Inc.
    7. Francis X. Diebold & Todd A. Gunther & Anthony S. Tay, 1997. "Evaluating Density Forecasts," Center for Financial Institutions Working Papers 97-37, Wharton School Center for Financial Institutions, University of Pennsylvania.
    8. Koop, Gary & Korobilis, Dimitris & Pettenuzzo, Davide, 2019. "Bayesian compressed vector autoregressions," Journal of Econometrics, Elsevier, vol. 210(1), pages 135-154.
    9. Inske Pirschel & Maik H. Wolters, 2018. "Forecasting with large datasets: compressing information before, during or after the estimation?," Empirical Economics, Springer, vol. 55(2), pages 573-596, September.
    10. Cheung, Yin-Wong & Chinn, Menzie David & Garcia Pascual, Antonio, 2003. "What Do We Know about Recent Exchange Rate Models? In-Sample Fit and Out-of-Sample Performance Evaluated," Santa Cruz Department of Economics, Working Paper Series qt8ds2g7qg, Department of Economics, UC Santa Cruz.
    11. Pino, Gabriel & Tena Horrillo, Juan de Dios & Espasa, Antoni, 2013. "Forecasting disaggregates by sectors and regions : the case of inflation in the euro area and Spain," DES - Working Papers. Statistics and Econometrics. WS ws130807, Universidad Carlos III de Madrid. Departamento de Estadística.
    12. Michiel De Pooter & Francesco Ravazzolo & Dick van Dijk, 2010. "Term structure forecasting using macro factors and forecast combination," International Finance Discussion Papers 993, Board of Governors of the Federal Reserve System (U.S.).
    13. Alberto Baffigi & Roberto Golinelli & Giuseppe Parigi, 2002. "Real-time GDP forecasting in the euro area," Temi di discussione (Economic working papers) 456, Bank of Italy, Economic Research and International Relations Area.
    14. Richard Harrison & George Kapetanios, 2004. "Forecasting with Measurement Errors in Dynamic Models," Working Papers 521, Queen Mary University of London, School of Economics and Finance.
    15. Korobilis, Dimitris & Pettenuzzo, Davide, 2019. "Adaptive hierarchical priors for high-dimensional vector autoregressions," Journal of Econometrics, Elsevier, vol. 212(1), pages 241-271.
    16. Donal Mac Géidigh & Friedrich Schneider & Matthias Blum, 2016. "Grey Matters: Charting the Development of the Shadow Economy," CESifo Working Paper Series 6234, CESifo.
    17. Carlos Carvalho & Jared D. Fisher & Davide Pettenuzzo, 2018. "Optimal Asset Allocation with Multivariate Bayesian Dynamic Linear Models," Working Papers 123, Brandeis University, Department of Economics and International Business School.
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    19. Christiane Baumeister & Lutz Kilian & Thomas K. Lee, 2016. "Inside the Crystal Ball: New Approaches to Predicting the Gasoline Price at the Pump," CESifo Working Paper Series 5759, CESifo.
    20. Yin-Wong Cheung & Menzie D. Chinn & Antonio Garcia-Pascual, 2005. "Empirical Exchange Rate Models of the Nineties: Are Any Fit to Survive?," Working Papers 122005, Hong Kong Institute for Monetary Research.
    21. Theologos Pantelidis & Nikitas Pittis, 2009. "Estimation and forecasting in first-order vector autoregressions with near to unit roots and conditional heteroscedasticity," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(7), pages 612-630.
    22. Francis X. Diebold & Lutz Kilian, 1999. "Unit Root Tests Are Useful for Selecting Forecasting Models," NBER Working Papers 6928, National Bureau of Economic Research, Inc.
    23. Villani, Mattias & Warne, Anders, 2003. "Monetary Policy Analysis in a Small Open Economy using Bayesian Cointegrated Structural VARs," Working Paper Series 156, Sveriges Riksbank (Central Bank of Sweden).
    24. Grégory Levieuge, 2017. "Explaining and forecasting bank loans. Good times and crisis," Post-Print hal-03529226, HAL.
    25. Goulet Coulombe, Philippe & Leroux, Maxime & Stevanovic, Dalibor & Surprenant, Stéphane, 2021. "Macroeconomic data transformations matter," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1338-1354.
    26. Peter Fuleky & Carl S. Bonham, 2011. "Forecasting Based on Common Trends in Mixed Frequency Samples," Working Papers 201110, University of Hawaii at Manoa, Department of Economics.
    27. Jennifer Castle & Takamitsu Kurita, 2019. "Modelling and forecasting the dollar-pound exchange rate in the presence of structural breaks," Economics Series Working Papers 866, University of Oxford, Department of Economics.
    28. Eran Raviv & Kees E. Bouwman & Dick van Dijk, 2013. "Forecasting Day-Ahead Electricity Prices: Utilizing Hourly Prices," Tinbergen Institute Discussion Papers 13-068/III, Tinbergen Institute.
    29. Clements, M.C. & Krolzig, H.-M., 2001. "Modelling Business Cycle Features Using Switching Regime Models," Economics Series Working Papers 9958, University of Oxford, Department of Economics.
    30. Dennis L. Hoffman & Robert H. Rasche, 1997. "STLS/US-VECM6.1: a vector error-correction forecasting model of the U. S. economy," Working Papers 1997-008, Federal Reserve Bank of St. Louis.
    31. Guo, Zi-Yi, 2017. "Comparison of Error Correction Models and First-Difference Models in CCAR Deposits Modeling," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 17(4).
    32. De Gooijer, Jan G. & Vidiella-i-Anguera, Antoni, 2004. "Forecasting threshold cointegrated systems," International Journal of Forecasting, Elsevier, vol. 20(2), pages 237-253.
    33. Juan de Dios Tena & Antoni Espasa & Gabriel Pino, 2010. "Forecasting Spanish Inflation Using the Maximum Disaggregation Level by Sectors and Geographical Areas," International Regional Science Review, , vol. 33(2), pages 181-204, April.
    34. Koo, Bonsoo & Anderson, Heather M. & Seo, Myung Hwan & Yao, Wenying, 2020. "High-dimensional predictive regression in the presence of cointegration," Journal of Econometrics, Elsevier, vol. 219(2), pages 456-477.
    35. Neri, Marcelo Côrtes & Soares, Wagner Lopes, 2008. "Turismo sustentável e alivio a pobreza: avaliação de impacto," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 689, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
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    1. von der Gracht, Heiko A. & Hommel, Ulrich & Prokesch, Tobias & Wohlenberg, Holger, 2016. "Testing weighting approaches for forecasting in a Group Wisdom Support System environment," Journal of Business Research, Elsevier, vol. 69(10), pages 4081-4094.
    2. G. Boero & J. Smith & KF. Wallis, 2002. "The properties of some goodness-of-fit tests," Working Paper CRENoS 200209, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    3. Norman R. Swanson & Nii Ayi Armah, 2011. "Predictive Inference Under Model Misspecification with an Application to Assessing the Marginal Predictive Content of Money for Output," Departmental Working Papers 201103, Rutgers University, Department of Economics.
    4. Clements, Michael P., 2008. "Explanations of the inconsistencies in survey respondents'forecasts," The Warwick Economics Research Paper Series (TWERPS) 870, University of Warwick, Department of Economics.
    5. Michal Franta & Jozef Barunik & Roman Horvath & Katerina Smidkova, 2011. "Are Bayesian Fan Charts Useful for Central Banks? Uncertainty, Forecasting, and Financial Stability Stress Tests," Working Papers 2011/10, Czech National Bank, Research and Statistics Department.
    6. Campbell, Sean D. & Diebold, Francis X., 2004. "Weather forecasting for weather derivatives," CFS Working Paper Series 2004/10, Center for Financial Studies (CFS).
    7. Alquier Pierre & Li Xiaoyin & Wintenberger Olivier, 2013. "Prediction of time series by statistical learning: general losses and fast rates," Dependence Modeling, De Gruyter, vol. 1(2013), pages 65-93, January.
    8. Robert R. Bliss & Nikolaos Panigirtzoglou, 2001. "Recovering risk aversion from options," Working Paper Series WP-01-15, Federal Reserve Bank of Chicago.
    9. Richhild Moessner & Feng Zhu & Colin Ellis, 2011. "Measuring disagreement in UK consumer and central bank inflation forecasts," BIS Working Papers 339, Bank for International Settlements.
    10. Tsyplakov, Alexander, 2010. "The links between inflation and inflation uncertainty at the longer horizon," MPRA Paper 26908, University Library of Munich, Germany.
    11. Bharat Barot, 2004. "How accurate are the Swedish forecasters on GDB-Growth, CPI-inflation and unemployment? (1993 - 2001)," Brussels Economic Review, ULB -- Universite Libre de Bruxelles, vol. 47(2), pages 249-278.
    12. Refet Gürkaynak & Justin Wolfers, 2005. "Macroeconomic Derivatives: An Initial Analysis of Market-Based Macro Forecasts, Uncertainty, and Risk," NBER Chapters, in: NBER International Seminar on Macroeconomics 2005, pages 11-50, National Bureau of Economic Research, Inc.
    13. Di Nino, Virginia & Aprigliano, Valentina, 2024. "How income expectations adjust to inflation – a consumers’ expectations-revealed pass-through," Working Paper Series 2986, European Central Bank.
    14. Francis X. Diebold & Jinyong Hahn & Anthony S. Tay, 1998. "Real-Time Multivariate Density Forecast Evaluation and Calibration: Monitoring the Risk of High-Frequency Returns on Foreign Exchange," New York University, Leonard N. Stern School Finance Department Working Paper Seires 98-079, New York University, Leonard N. Stern School of Business-.
    15. Diep Duong & Norman Swanson, 2013. "Density and Conditional Distribution Based Specification Analysis," Departmental Working Papers 201312, Rutgers University, Department of Economics.
    16. Cees Diks & Valentyn Panchenko & Dick van Dijk, 2008. "Partial Likelihood-Based Scoring Rules for Evaluating Density Forecasts in Tails," Discussion Papers 2008-10, School of Economics, The University of New South Wales.
    17. Shamiri, Ahmed & Shaari, Abu Hassan & Isa, Zaidi, 2007. "Practical Volatility Modeling for Financial Market Risk Management," MPRA Paper 9790, University Library of Munich, Germany, revised 15 May 2008.
    18. Michael P. Clements, 2014. "US Inflation Expectations and Heterogeneous Loss Functions, 1968–2010," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(1), pages 1-14, January.
    19. Giordani, Paolo & Soderlind, Paul, 2003. "Inflation forecast uncertainty," European Economic Review, Elsevier, vol. 47(6), pages 1037-1059, December.
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    21. Patrick T. Brandt & John R. Freeman & Philip A. Schrodt, 2011. "Real Time, Time Series Forecasting of Inter- and Intra-State Political Conflict," Conflict Management and Peace Science, Peace Science Society (International), vol. 28(1), pages 41-64, February.
    22. Christian Grimme & Steffen Henzel & Elisabeth Wieland, 2014. "Inflation uncertainty revisited: a proposal for robust measurement," Empirical Economics, Springer, vol. 47(4), pages 1497-1523, December.
    23. Wang, Yudong & Zhang, Bing & Diao, Xundi & Wu, Chongfeng, 2015. "Commodity price changes and the predictability of economic policy uncertainty," Economics Letters, Elsevier, vol. 127(C), pages 39-42.
    24. Raunig, Burkhard & de Raaij, Gabriela, 2002. "Evaluating Density Forecasts with an Application to Stock Market Returns," Discussion Paper Series 1: Economic Studies 2002,08, Deutsche Bundesbank.
    25. Dean Croushore, 2010. "Philadelphia Fed forecasting surveys: their value for research," Business Review, Federal Reserve Bank of Philadelphia, issue Q3, pages 1-11.
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    27. G. Ascari & E. Marrocu, 2003. "Forecasting inflation: a comparison of linear Phillips curve models and nonlinear time serie models," Working Paper CRENoS 200307, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
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    29. Arrieta-Prieto, Mario & Schell, Kristen R., 2022. "Spatio-temporal probabilistic forecasting of wind power for multiple farms: A copula-based hybrid model," International Journal of Forecasting, Elsevier, vol. 38(1), pages 300-320.
    30. Mestre, Ricardo, 2007. "Are survey-based inflation expections in the euro area informative?," Working Paper Series 721, European Central Bank.
    31. Clements, Michael P., 2006. "Internal consistency of survey respondentsíforecasts: Evidence based on the Survey of Professional Forecasters," Economic Research Papers 269742, University of Warwick - Department of Economics.
    32. Clements, Michael P., 2008. "Rounding of probability forecasts: The SPF forecast probabilities of negative output growth," Economic Research Papers 269880, University of Warwick - Department of Economics.
    33. Valentina Corradi & Norman R. Swanson, 2003. "A Test for Comparing Multiple Misspecified Conditional Distributions," Departmental Working Papers 200314, Rutgers University, Department of Economics.
    34. Casey, Eddie, 2021. "Are professional forecasters overconfident?," International Journal of Forecasting, Elsevier, vol. 37(2), pages 716-732.
    35. Corradi, Valentina & Swanson, Norman R., 2006. "Bootstrap conditional distribution tests in the presence of dynamic misspecification," Journal of Econometrics, Elsevier, vol. 133(2), pages 779-806, August.
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    37. Amisano, Gianni & Giacomini, Raffaella, 2007. "Comparing Density Forecasts via Weighted Likelihood Ratio Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 177-190, April.
    38. Deniz Igan & Thomas Lambert & Prachi Mishra & Eden Zhang, 2024. "The Politics of the Paycheck Protection Program," Working Papers 133, Ashoka University, Department of Economics.
    39. Warne, Anders & Coenen, Günter & Christoffel, Kai, 2010. "Forecasting with DSGE models," Working Paper Series 1185, European Central Bank.
    40. Shamiri, Ahmed & Shaari, Abu Hassan & Isa, Zaidi, 2008. "Comparing the accuracy of density forecasts from competing GARCH models," MPRA Paper 13662, University Library of Munich, Germany.
    41. Barot, Bharat, 2007. "Empirical Studies in Consumption, House Prices and the Accuracy of European Growth and Inflation Forecasts," Working Papers 98, National Institute of Economic Research.
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  96. Francis X. Diebold & Todd A. Gunther & Anthony S. Tay, 1997. "Evaluating density forecasts," Working Papers 97-6, Federal Reserve Bank of Philadelphia.

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    1. von der Gracht, Heiko A. & Hommel, Ulrich & Prokesch, Tobias & Wohlenberg, Holger, 2016. "Testing weighting approaches for forecasting in a Group Wisdom Support System environment," Journal of Business Research, Elsevier, vol. 69(10), pages 4081-4094.
    2. G. Boero & J. Smith & KF. Wallis, 2002. "The properties of some goodness-of-fit tests," Working Paper CRENoS 200209, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    3. Luc Bauwens & Pierre Giot & Joachim Grammig & David Veredas, 2000. "A Comparison of Financial Duration Models via Density Forecasts," Econometric Society World Congress 2000 Contributed Papers 0810, Econometric Society.
    4. Mr. Tamim Bayoumi & Ms. Silvia Sgherri, 2004. "Deconstructing the Art of Central Banking," IMF Working Papers 2004/195, International Monetary Fund.
    5. Taillardat, Maxime & Fougères, Anne-Laure & Naveau, Philippe & de Fondeville, Raphaël, 2023. "Evaluating probabilistic forecasts of extremes using continuous ranked probability score distributions," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1448-1459.
    6. Clive G. Bowsher, 2005. "Modelling Security Market Events in Continuous Time: Intensity Based, Multivariate Point Process Models," Economics Papers 2005-W26, Economics Group, Nuffield College, University of Oxford.
    7. Campbell, Sean D. & Diebold, Francis X., 2004. "Weather forecasting for weather derivatives," CFS Working Paper Series 2004/10, Center for Financial Studies (CFS).
    8. Jose A. Lopez, 1998. "Methods for evaluating value-at-risk estimates," Economic Policy Review, Federal Reserve Bank of New York, vol. 4(Oct), pages 119-124.
    9. Jeremy Berkowitz, 1999. "Evaluating the forecasts of risk models," Finance and Economics Discussion Series 1999-11, Board of Governors of the Federal Reserve System (U.S.).
    10. Hashem Pesaran & Paolo Zaffaroni & Banca d'Italia), 2004. "Model Averaging and Value-at-Risk based Evaluation of Large Multi Asset Volatility Models for Risk Management," Money Macro and Finance (MMF) Research Group Conference 2004 101, Money Macro and Finance Research Group.
    11. Alquier Pierre & Li Xiaoyin & Wintenberger Olivier, 2013. "Prediction of time series by statistical learning: general losses and fast rates," Dependence Modeling, De Gruyter, vol. 1(2013), pages 65-93, January.
    12. Massimo Guidolin & Allan Timmerman, 2006. "Asset allocation under multivariate regime switching," Working Papers 2005-002, Federal Reserve Bank of St. Louis.
    13. Jean-Paul Laurent & Hassan Omidi Firouzi, 2022. "Market Risk and Volatility Weighted Historical Simulation After Basel III," Working Papers hal-03679434, HAL.
    14. Fuchun Li & Greg Tkacz, 2001. "A Consistent Bootstrap Test for Conditional Density Functions with Time-Dependent Data," Staff Working Papers 01-21, Bank of Canada.
    15. Casciaro, Gabriele & Ferrari, Francesco & Lagomarsino-Oneto, Daniele & Lira-Loarca, Andrea & Mazzino, Andrea, 2022. "Increasing the skill of short-term wind speed ensemble forecasts combining forecasts and observations via a new dynamic calibration," Energy, Elsevier, vol. 251(C).
    16. Gabriela De Raaij & Burkhard Raunig, 2005. "Evaluating density forecasts from models of stock market returns," The European Journal of Finance, Taylor & Francis Journals, vol. 11(2), pages 151-166.
    17. Jose A. Lopez, 1997. "Regulatory evaluation of value-at-risk models," Staff Reports 33, Federal Reserve Bank of New York.
    18. Li, Rui & Reich, Brian J. & Bondell, Howard D., 2021. "Deep distribution regression," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    19. Allayioti, Anastasia & Venditti, Fabrizio, 2024. "The role of comovement and time-varying dynamics in forecasting commodity prices," Working Paper Series 2901, European Central Bank.
    20. M. Hashem Pesaran, 2000. "Forecast Uncertainties in Macroeconometric Modelling: An Application to the UK Economy," CESifo Working Paper Series 345, CESifo.
    21. Jeremy Berkowitz & James M. O'Brien, 2001. "How accurate are Value-at-Risk models at commercial banks?," Finance and Economics Discussion Series 2001-31, Board of Governors of the Federal Reserve System (U.S.).
    22. Corradi, Valentina & Swanson, Norman R., 2006. "Predictive density and conditional confidence interval accuracy tests," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 187-228.
    23. Bharat Barot, 2004. "How accurate are the Swedish forecasters on GDB-Growth, CPI-inflation and unemployment? (1993 - 2001)," Brussels Economic Review, ULB -- Universite Libre de Bruxelles, vol. 47(2), pages 249-278.
    24. Marc Saidenberg & Til Schuermann & May, "undated". "The New Basel Capital Accord and Questions for Research," Center for Financial Institutions Working Papers 03-14, Wharton School Center for Financial Institutions, University of Pennsylvania.
    25. Tara Sinclair & Herman O. Stekler & Warren Carnow, 2012. "A New Approach For Evaluating Economic Forecasts," Working Papers 2012-2, The George Washington University, Institute for International Economic Policy.
    26. Keller, Joachim & Glatzer, Ernst & Craig, Ben R. & Scheicher, Martin, 2003. "The Forecasting Performance of German Stock Option Densities," Discussion Paper Series 1: Economic Studies 2003,17, Deutsche Bundesbank.
    27. Francis X. Diebold & Jinyong Hahn & Anthony S. Tay, 1998. "Real-Time Multivariate Density Forecast Evaluation and Calibration: Monitoring the Risk of High-Frequency Returns on Foreign Exchange," New York University, Leonard N. Stern School Finance Department Working Paper Seires 98-079, New York University, Leonard N. Stern School of Business-.
    28. Vahidin Jeleskovic & Mirko Meloni & Zahid Irshad Younas, 2020. "Cryptocurrencies: A Copula Based Approach for Asymmetric Risk Marginal Allocations," MAGKS Papers on Economics 202034, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    29. Peter Christoffersen, 2004. "Backtesting Value-at-Risk: A Duration-Based Approach," Journal of Financial Econometrics, Oxford University Press, vol. 2(1), pages 84-108.
    30. Valentina Corradi & Norman Swanson, 2003. "The Block Bootstrap for Parameter Estimation Error In Recursive Estimation Schemes, With Applications to Predictive Evaluation," Departmental Working Papers 200313, Rutgers University, Department of Economics.
    31. Jose A. Lopez & Marc R. Saidenberg, 1999. "Evaluating credit risk models," Working Papers in Applied Economic Theory 99-06, Federal Reserve Bank of San Francisco.
    32. Daniel F. Waggoner & Tao Zha, 1998. "Conditional forecasts in dynamic multivariate models," FRB Atlanta Working Paper 98-22, Federal Reserve Bank of Atlanta.
    33. Jones, David & Mingo, John, 1999. "Credit risk modeling and internal capital allocation processes: implications for a models-based regulatory bank capital standard," Journal of Economics and Business, Elsevier, vol. 51(2), pages 79-108, March.
    34. Massimo Guidolin & Carrie Fangzhou Na, 2007. "The economic and statistical value of forecast combinations under regime switching: an application to predictable U.S. returns," Working Papers 2006-059, Federal Reserve Bank of St. Louis.
    35. Sancetta, A. & Nikanrova, A., 2005. "Forecasting and Prequential Validation for Time Varying Meta-Elliptical Distributions with a Study of Commodity Futures Prices," Cambridge Working Papers in Economics 0516, Faculty of Economics, University of Cambridge.
    36. Wang, Yudong & Zhang, Bing & Diao, Xundi & Wu, Chongfeng, 2015. "Commodity price changes and the predictability of economic policy uncertainty," Economics Letters, Elsevier, vol. 127(C), pages 39-42.
    37. Silvano Bordignon & Francesco Lisi, 2001. "Interval prediction for chaotic time series," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3-4), pages 117-140.
    38. Raunig, Burkhard & de Raaij, Gabriela, 2002. "Evaluating Density Forecasts with an Application to Stock Market Returns," Discussion Paper Series 1: Economic Studies 2002,08, Deutsche Bundesbank.
    39. Burkhard Raunig, 2003. "Testing for Longer Horizon Predictability of Return Volatility with an Application to the German," Working Papers 86, Oesterreichische Nationalbank (Austrian Central Bank).
    40. Leopoldo Catania & Alessandra Luati & Pierluigi Vallarino, 2021. "Economic vulnerability is state dependent," CREATES Research Papers 2021-09, Department of Economics and Business Economics, Aarhus University.
    41. Jordi Pons, 2001. "The rationality of price forecasts: a directional analysis," Applied Financial Economics, Taylor & Francis Journals, vol. 11(3), pages 287-290.
    42. Heinen, Andreas, 2003. "Modelling Time Series Count Data: An Autoregressive Conditional Poisson Model," MPRA Paper 8113, University Library of Munich, Germany.
    43. Stefania D'Amico, 2004. "Density Estimation and Combination under Model Ambiguity," Computing in Economics and Finance 2004 273, Society for Computational Economics.
    44. Matthew Pritsker, 2001. "The hidden dangers of historical simulation," Finance and Economics Discussion Series 2001-27, Board of Governors of the Federal Reserve System (U.S.).
    45. Vahidin Jeleskovic & Claudio Latini & Zahid I. Younas & Mamdouh A. S. Al-Faryan, 2023. "Optimization of portfolios with cryptocurrencies: Markowitz and GARCH-Copula model approach," Papers 2401.00507, arXiv.org.
    46. Gourieroux, Christian & Jasiak, Joanna, 1999. "Nonlinear innovations and impulse responses," CEPREMAP Working Papers (Couverture Orange) 9906, CEPREMAP.
    47. Arrieta-Prieto, Mario & Schell, Kristen R., 2022. "Spatio-temporal probabilistic forecasting of wind power for multiple farms: A copula-based hybrid model," International Journal of Forecasting, Elsevier, vol. 38(1), pages 300-320.
    48. Giovanni De Luca & Paola Zuccolotto, 2003. "Finite and infinite mixtures for financial durations," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3), pages 431-455.
    49. Guidolin, Massimo & Timmermann, Allan, 2006. "Term structure of risk under alternative econometric specifications," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 285-308.
    50. Tomás Marinozzi, 2023. "Forecasting Inflation in Argentina: A Probabilistic Approach," Ensayos Económicos, Central Bank of Argentina, Economic Research Department, vol. 1(81), pages 81-110, May.
    51. Ioannis Anagnostou & Drona Kandhai, 2019. "Risk Factor Evolution for Counterparty Credit Risk under a Hidden Markov Model," Risks, MDPI, vol. 7(2), pages 1-22, June.
    52. Massimo Guidolin & Allan Timmerman, 2005. "Optimal portfolio choice under regime switching, skew and kurtosis preferences," Working Papers 2005-006, Federal Reserve Bank of St. Louis.
    53. Heather M. Anderson & Chin Nam Low, 2006. "Random Walk Smooth Transition Autoregressive Models," Contributions to Economic Analysis, in: Nonlinear Time Series Analysis of Business Cycles, pages 247-281, Emerald Group Publishing Limited.
    54. Mestre, Ricardo, 2007. "Are survey-based inflation expections in the euro area informative?," Working Paper Series 721, European Central Bank.
    55. Clements, Michael P., 2006. "Internal consistency of survey respondentsíforecasts: Evidence based on the Survey of Professional Forecasters," Economic Research Papers 269742, University of Warwick - Department of Economics.
    56. Valentina Corradi & Norman R. Swanson, 2003. "A Test for Comparing Multiple Misspecified Conditional Distributions," Departmental Working Papers 200314, Rutgers University, Department of Economics.
    57. Li, Fuchun, 2007. "Testing The Parametric Specification Of The Diffusion Function In A Diffusion Process," Econometric Theory, Cambridge University Press, vol. 23(2), pages 221-250, April.
    58. Chib, Siddhartha & Nardari, Federico & Shephard, Neil, 2002. "Markov chain Monte Carlo methods for stochastic volatility models," Journal of Econometrics, Elsevier, vol. 108(2), pages 281-316, June.
    59. Corradi, Valentina & Swanson, Norman R., 2006. "Bootstrap conditional distribution tests in the presence of dynamic misspecification," Journal of Econometrics, Elsevier, vol. 133(2), pages 779-806, August.
    60. Enrique Sentana & Giorgio Calzolari & Gabriele Fiorentini, 2004. "Indirect Estimation of Conditionally Heteroskedastic Factor Models," Working Papers wp2004_0409, CEMFI.
    61. Michael F. Bryan & Brent Meyer & Nicholas B. Parker, 2014. "The inflation expectations of firms: what do they look like, are they accurate, and do they matter?," FRB Atlanta Working Paper 2014-27, Federal Reserve Bank of Atlanta.
    62. Amisano, Gianni & Giacomini, Raffaella, 2007. "Comparing Density Forecasts via Weighted Likelihood Ratio Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 177-190, April.
    63. Baggio, Roberta & Muzy, Jean-François, 2024. "Improving probabilistic wind speed forecasting using M-Rice distribution and spatial data integration," Applied Energy, Elsevier, vol. 360(C).
    64. Barot, Bharat, 2007. "Empirical Studies in Consumption, House Prices and the Accuracy of European Growth and Inflation Forecasts," Working Papers 98, National Institute of Economic Research.
    65. Öller, Lars-Erik & Barot, Bharat, 2000. "The Accuracy of European Growth and Inflation Forecasts," Working Papers 72, National Institute of Economic Research.
    66. Kreye, M.E. & Goh, Y.M. & Newnes, L.B. & Goodwin, P., 2012. "Approaches to displaying information to assist decisions under uncertainty," Omega, Elsevier, vol. 40(6), pages 682-692.
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    68. Manner, H., 2007. "Estimation and model selection of copulas with an application to exchange rates," Research Memorandum 056, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    69. Stefania D'Amico, 2005. "Density selection and combination under model ambiguity: an application to stock returns," Finance and Economics Discussion Series 2005-09, Board of Governors of the Federal Reserve System (U.S.).

  97. Antulio N. Bomfim & Francis X. Diebold, 1997. "Bounded rationality and strategic complementarity in a macroeconomic model: policy effects, persistence, and multipliers," Working Papers 97-18, Federal Reserve Bank of Philadelphia.

    Cited by:

    1. Francois R. Velde, 2006. "Chronicles of a deflation unforetold," Working Paper Series WP-06-12, Federal Reserve Bank of Chicago.
    2. Anderlini, Luca & Canning, David, 2001. "Structural Stability Implies Robustness to Bounded Rationality," Journal of Economic Theory, Elsevier, vol. 101(2), pages 395-422, December.
    3. Di Bartolomeo, Giovanni & Beqiraj, Elton & Di Pietro, Marco, 2017. "Beliefs formation and the puzzle of forward guidance power," EconStor Preprints 175198, ZBW - Leibniz Information Centre for Economics.
    4. SOOREEA, Rajeev, 2007. "Are Taylor-Based Monetary Policy Rules Forward-Looking?. An Investigation Using Superexogeneity Tests," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 7(2), pages 87-94.
    5. Ripamonti, Alexandre, 2013. "Rational Valuation Formula (RVF) and Time Variability in Asset Rates of Return," MPRA Paper 79460, University Library of Munich, Germany.
    6. Seonghwan Oh & Michael Waldman, 2005. "The Index of Leading Economic Indicators as a Source of Expectational Shocks," Eastern Economic Journal, Eastern Economic Association, vol. 31(1), pages 75-95, Winter.
    7. Tobias Rötheli, 2005. "Applied Welfare Economics with Bounded Rationality: Public Policies Toward Remote Sensing," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 11(1), pages 39-47, February.
    8. Weder, Mark, 2004. "Near-rational expectations in animal spirits models of aggregate fluctuations," Economic Modelling, Elsevier, vol. 21(2), pages 249-265, March.
    9. Thijssen, J.J.J., 2003. "Investment under uncertainty, market evolution and coalition spillovers in a game theoretic perspective," Other publications TiSEM 672073a6-492e-4621-8d4a-0, Tilburg University, School of Economics and Management.

  98. Francis X. Diebold, 1997. "The past, present, and future of macroeconomic forecasting," Working Papers 97-20, Federal Reserve Bank of Philadelphia.

    Cited by:

    1. Haider, Adnan & Khan, Safdar Ullah, 2008. "A Small Open Economy DSGE Model for Pakistan," MPRA Paper 12977, University Library of Munich, Germany, revised 17 Jan 2009.
    2. Fildes, Robert & Stekler, Herman, 2002. "The state of macroeconomic forecasting," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 435-468, December.
    3. Keck, Alexander & Raubold, Alexander, 2006. "Forecasting trade," WTO Staff Working Papers ERSD-2006-05, World Trade Organization (WTO), Economic Research and Statistics Division.
    4. KAMKOUM, Arnaud Cedric, 2023. "The Federal Reserve’s Response to the Global Financial Crisis and its Effects: An Interrupted Time-Series Analysis of the Impact of its Quantitative Easing Programs," Thesis Commons d7pvg, Center for Open Science.
    5. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    6. Stekler, H.O., 2007. "The future of macroeconomic forecasting: Understanding the forecasting process," International Journal of Forecasting, Elsevier, vol. 23(2), pages 237-248.
    7. Valadkhani, Abbas, 2004. "History of macroeconometric modelling: lessons from past experience," Journal of Policy Modeling, Elsevier, vol. 26(2), pages 265-281, February.
    8. Barakchian , Seyed Mahdi, 2012. "Implications of Cointegration for Forecasting: A Review and an Empirical Analysis," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 7(1), pages 87-118, October.
    9. Allen, P. Geoffrey & Morzuch, Bernard J., 2006. "Twenty-five years of progress, problems, and conflicting evidence in econometric forecasting. What about the next 25 years?," International Journal of Forecasting, Elsevier, vol. 22(3), pages 475-492.
    10. Andrea Kunnert, 2013. "Baubewilligungen für Wohneinheiten in Österreich: Prognose 2012/13 und regionale Entwicklung 2006/2011. Teilbericht 2," WIFO Studies, WIFO, number 67110, August.
    11. Michael Klien & Andrea Kunnert, 2018. "Baubewilligungen für neue Wohneinheiten in Österreich. Prognose Sommer 2018 und regionale Entwicklung 2012/2017," WIFO Studies, WIFO, number 62399, August.
    12. Viviana Fernández, 2006. "Forecasting crude oil and natural gas spot prices by classification methods," Documentos de Trabajo 229, Centro de Economía Aplicada, Universidad de Chile.
    13. Thomas R. Cook & Aaron Smalter Hall, 2017. "Macroeconomic Indicator Forecasting with Deep Neural Networks," Research Working Paper RWP 17-11, Federal Reserve Bank of Kansas City.
    14. Michael Klien & Andrea Kunnert, 2017. "Baubewilligungen für neue Wohneinheiten in Österreich. Prognose Sommer 2017 und regionale Entwicklung 2011/2016," WIFO Studies, WIFO, number 65636, August.
    15. Hampel, Katharina & Kunz, Marcus & Schanne, Norbert & Wapler, Rüdiger & Weyh, Antje, 2007. "Regional employment forecasts with spatial interdependencies," IAB-Discussion Paper 200702, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    16. Tursoy, Turgut, 2013. "Structural Modelling for North Cyprus’ Economic Growth," MPRA Paper 98519, University Library of Munich, Germany.
    17. Fabio Bacchini & Cristina Brandimarte & Piero Crivelli & Roberta De Santis & Marco Fioramanti & Alessandro Girardi & Roberto Golinelli & Cecilia Jona-Lasinio & Massimo Mancini & Carmine Pappalardo & D, 2013. "Building the core of the Istat system of models for forecasting the Italian economy: MeMo-It," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 15(1), pages 17-45.
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    41. Christian Glocker & Serguei Kaniovski, 2020. "Macroeconometric Forecasting Using a Cluster of Dynamic Factor Models," WIFO Working Papers 614, WIFO.
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    46. D. Tutberidze & D. Japaridze, 2017. "Macroeconomic Forecasting Using Bayesian Vector Autoregressive Approach," Вестник Киевского национального университета имени Тараса Шевченко. Экономика., Socionet;Киевский национальный университет имени Тараса Шевченко, vol. 2(191), pages 42-49.
    47. Barrie, Mohamed Samba & Jackson, Emerson Abraham, 2022. "Impact of Technological Shock on the Sierra Leone Economy: A Dynamic Stochastic General Equilibrium (DSGE) Approach," MPRA Paper 113636, University Library of Munich, Germany, revised 10 Jun 2022.
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    53. Afshin Amiraslany & Hari S. Luitel & Gerry J. Mahar, 2019. "Structural Breaks, Biased Estimations, and Forecast Errors in a GDP Series of Canada versus the United States," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 25(2), pages 235-244, May.
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    67. Nicholson, William B. & Matteson, David S. & Bien, Jacob, 2017. "VARX-L: Structured regularization for large vector autoregressions with exogenous variables," International Journal of Forecasting, Elsevier, vol. 33(3), pages 627-651.
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    1. Clements, Michael P., 2008. "Explanations of the inconsistencies in survey respondents'forecasts," The Warwick Economics Research Paper Series (TWERPS) 870, University of Warwick, Department of Economics.
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    144. Rockie U Kei Kuok & Tay T.R. Koo & Christine Lim, 2024. "Air transport capacity and tourism demand: A panel cointegration approach with cross-sectionally augmented autoregressive distributed lag (CS-ARDL) model," Tourism Economics, , vol. 30(3), pages 702-727, May.
    145. Ulu, Yasemin, 2013. "Multivariate test for forecast rationality under asymmetric loss functions: Recent evidence from MMS survey of inflation–output forecasts," Economics Letters, Elsevier, vol. 119(2), pages 168-171.
    146. Anatolyev, Stanislav, 2006. "Kernel estimation under linear-exponential loss," Economics Letters, Elsevier, vol. 91(1), pages 39-43, April.
    147. Manfred Deistler & Klaus Neusser, 2004. "Prognose uni- und multivariater Zeitreihen," Diskussionsschriften dp0401, Universitaet Bern, Departement Volkswirtschaft.
    148. Matei Demetrescu, 2007. "Optimal forecast intervals under asymmetric loss," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(4), pages 227-238.
    149. Frenkel, Michael & Rülke, Jan-Christoph & Zimmermann, Lilli, 2013. "Do private sector forecasters chase after IMF or OECD forecasts?," Journal of Macroeconomics, Elsevier, vol. 37(C), pages 217-229.
    150. Heike Hans-Dieter & Demetrescu Matei, 2006. "Loss Reduction in Point Estimation Problems," Stochastics and Quality Control, De Gruyter, vol. 21(2), pages 209-217, January.
    151. Lehmann Robert & Wollmershäuser Timo, 2020. "The macroeconomic projections of the German government: A comparison to an independent forecasting institution," German Economic Review, De Gruyter, vol. 21(2), pages 235-270, June.
    152. Robert Jarrow & Feng Zhao, 2006. "Downside Loss Aversion and Portfolio Management," Management Science, INFORMS, vol. 52(4), pages 558-566, April.
    153. Batchelor, Roy & Peel, David A., 1998. "Rationality testing under asymmetric loss," Economics Letters, Elsevier, vol. 61(1), pages 49-54, October.
    154. Bollerslev, Tim & Zhang, Benjamin Y. B., 2003. "Measuring and modeling systematic risk in factor pricing models using high-frequency data," Journal of Empirical Finance, Elsevier, vol. 10(5), pages 533-558, December.
    155. Capistrán Carlos, 2007. "Optimality Tests for Multi-Horizon Forecasts," Working Papers 2007-14, Banco de México.
    156. Alexander, Marcus & Christakis, Nicholas A., 2008. "Bias and asymmetric loss in expert forecasts: A study of physician prognostic behavior with respect to patient survival," Journal of Health Economics, Elsevier, vol. 27(4), pages 1095-1108, July.
    157. Henri Karttunen, 2020. "An autoregressive model based on the generalized hyperbolic distribution," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(3), pages 787-816, September.

  100. Francis X. Diebold & Andrew Hickman & Atsushi Inoue & Til Schuermann, 1997. "Converting 1-Day Volatility to h-Day Volatitlity: Scaling by Root-h is Worse Than You Think," Center for Financial Institutions Working Papers 97-34, Wharton School Center for Financial Institutions, University of Pennsylvania.

    Cited by:

    1. Odening, Martin & Hinrichs, Jan, 2003. "Die Quantifizierung von Marktrisiken in der Tierproduktion mittels Value-at-Risk und Extreme-Value-Theory," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 52(02), pages 1-11.
    2. J. Q. Smith & António Santos, 2003. "Second Order Filter Distribution Approximations for Financial Time Series with Extreme Outlier," GEMF Working Papers 2003-03, GEMF, Faculty of Economics, University of Coimbra.
    3. Wolff, Christian & Lehnert, Thorsten, 2001. "Modelling Scale-Consistent VaR with the Truncated Lévy Flight," CEPR Discussion Papers 2711, C.E.P.R. Discussion Papers.
    4. Del Brio, Esther B. & Mora-Valencia, Andrés & Perote, Javier, 2014. "VaR performance during the subprime and sovereign debt crises: An application to emerging markets," Emerging Markets Review, Elsevier, vol. 20(C), pages 23-41.
    5. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Practical Volatility and Correlation Modeling for Financial Market Risk Management," NBER Working Papers 11069, National Bureau of Economic Research, Inc.
    6. Danielsson, Jon & Zigrand, Jean-Pierre, 2006. "On time-scaling of risk and the square-root-of-time rule," Journal of Banking & Finance, Elsevier, vol. 30(10), pages 2701-2713, October.
    7. Erik Kole & Thijs Markwat & Anne Opschoor & Dick van Dijk, 2017. "Forecasting Value-at-Risk under Temporal and Portfolio Aggregation," Journal of Financial Econometrics, Oxford University Press, vol. 15(4), pages 649-677.
    8. Gonzalez-Rivera, Gloria & Lee, Tae-Hwy & Mishra, Santosh, 2004. "Forecasting volatility: A reality check based on option pricing, utility function, value-at-risk, and predictive likelihood," International Journal of Forecasting, Elsevier, vol. 20(4), pages 629-645.
    9. Fritzsch, Simon & Timphus, Maike & Weiß, Gregor, 2024. "Marginals versus copulas: Which account for more model risk in multivariate risk forecasting?," Journal of Banking & Finance, Elsevier, vol. 158(C).
    10. Saadi, Samir & Rahman, Abdul, 2008. "Evidence of non-stationary bias in scaling by square root of time: Implications for Value-at-Risk," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 18(3), pages 272-289, July.
    11. Peter F. Christoffersen & Francis X. Diebold, 1998. "How Relevant is Volatility Forecasting for Financial Risk Management?," New York University, Leonard N. Stern School Finance Department Working Paper Seires 98-080, New York University, Leonard N. Stern School of Business-.
    12. Amy S. K. Wong, 2006. "Basel II and the Risk Management of Basket Options with Time-Varying Correlations," International Journal of Central Banking, International Journal of Central Banking, vol. 2(4), December.
    13. Odening, Martin & Hinrichs, Jan, 2002. "Assessment Of Market Risk In Hog Production Using Value-At-Risk And Extreme Value Theory," 2002 Annual meeting, July 28-31, Long Beach, CA 19907, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    14. Mark R. Manfredo & Raymond M. Leuthold, 1998. "Agricultural Applications of Value-at-Risk Analysis: A Perspective," Finance 9805002, University Library of Munich, Germany.
    15. Th'eophile Griveau-Billion & Ben Calderhead, 2019. "A Dynamic Bayesian Model for Interpretable Decompositions of Market Behaviour," Papers 1904.08153, arXiv.org, revised Jan 2020.
    16. Kam Fong Chan & Christopher Gan & Patricia A. McGraw, 2003. "A Hedging Strategy for New Zealand’s Exporters in Transaction Exposure to Currency Risk," Multinational Finance Journal, Multinational Finance Journal, vol. 7(1-2), pages 25-54, March-Jun.
    17. Wang, Jying-Nan & Du, Jiangze & Hsu, Yuan-Teng, 2018. "Measuring long-term tail risk: Evaluating the performance of the square-root-of-time rule," Journal of Empirical Finance, Elsevier, vol. 47(C), pages 120-138.
    18. Peter F. Christoffersen & Francis X. Diebold & Til Schuermann, 1998. "Horizon problems and extreme events in financial risk management," Economic Policy Review, Federal Reserve Bank of New York, vol. 4(Oct), pages 109-118.
    19. Wang, Jying-Nan & Yeh, Jin-Huei & Cheng, Nick Ying-Pin, 2011. "How accurate is the square-root-of-time rule in scaling tail risk: A global study," Journal of Banking & Finance, Elsevier, vol. 35(5), pages 1158-1169, May.
    20. Kavussanos, Manolis G. & Dimitrakopoulos, Dimitris N., 2011. "Market risk model selection and medium-term risk with limited data: Application to ocean tanker freight markets," International Review of Financial Analysis, Elsevier, vol. 20(5), pages 258-268.
    21. Agnieszka Borowska & Lennart Hoogerheide & Siem Jan Koopman, 2019. "Bayesian Risk Forecasting for Long Horizons," Tinbergen Institute Discussion Papers 19-018/III, Tinbergen Institute.
    22. Simon Fritzsch & Maike Timphus & Gregor Weiss, 2021. "Marginals Versus Copulas: Which Account For More Model Risk In Multivariate Risk Forecasting?," Papers 2109.10946, arXiv.org.

  101. Francis X. Diebold & Til Schuermann, 1996. "Exact Maximum Likelihood Estimation of Observation-Driven Econometric Models," NBER Technical Working Papers 0194, National Bureau of Economic Research, Inc.

    Cited by:

    1. Martin Browning & Mette Ejrnaes & Javier Alvarez, 2006. "Modelling income processes with lots of heterogeneity," Economics Series Working Papers 285, University of Oxford, Department of Economics.
    2. Gabriele Fiorentini & Enrique Sentana & Neil Shephard, 2002. "Likelihood-based estimation of latent generalised ARCH structures," Economics Papers 2002-W19, Economics Group, Nuffield College, University of Oxford.
    3. Pagan, Adrian, 1996. "The econometrics of financial markets," Journal of Empirical Finance, Elsevier, vol. 3(1), pages 15-102, May.
    4. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
    5. Bollerslev, Tim & Ole Mikkelsen, Hans, 1996. "Modeling and pricing long memory in stock market volatility," Journal of Econometrics, Elsevier, vol. 73(1), pages 151-184, July.

  102. Francis X. Diebold & Abdelhak S. Senhadji, 1996. "Deterministic vs. Stochastic Trend in U.S. GNP, Yet Again," NBER Working Papers 5481, National Bureau of Economic Research, Inc.

    Cited by:

    1. Guillaume Chevillon, 2004. ""Weak" trends for inference and forecasting in finite samples," Documents de Travail de l'OFCE 2004-12, Observatoire Francais des Conjonctures Economiques (OFCE).
    2. Zarnowitz, Victor & Ozyildirim, Ataman, 2006. "Time series decomposition and measurement of business cycles, trends and growth cycles," Journal of Monetary Economics, Elsevier, vol. 53(7), pages 1717-1739, October.
    3. Prodan, Ruxandra, 2008. "Potential Pitfalls in Determining Multiple Structural Changes With an Application to Purchasing Power Parity," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 50-65, January.
    4. Freitas, Paulo S.A. & Rodrigues, Antonio J.L., 2006. "Model combination in neural-based forecasting," European Journal of Operational Research, Elsevier, vol. 173(3), pages 801-814, September.
    5. Junsoo Lee & John List, 2004. "Examining Trends of Criteria Air Pollutants: Are the Effects of Governmental Intervention Transitory?," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 29(1), pages 21-37, September.
    6. Foster, Neil & Stehrer, Robert, 2007. "Modeling transformation in CEECs using smooth transitions," Journal of Comparative Economics, Elsevier, vol. 35(1), pages 57-86, March.
    7. Valentina Corradi & Norman R. Swanson, 2003. "The Effect of Data Transformation on Common Cycle, Cointegration and Unit Root Tests: Monte Carlo Results and a Simple Test," Departmental Working Papers 200322, Rutgers University, Department of Economics.
    8. Dan Tortorice, 2016. "The Business Cycles Implications of Fluctuating Long Run Expectations," Working Papers 100, Brandeis University, Department of Economics and International Business School.

  103. Francis X. Diebold & Jose A. Lopez, 1995. "Modeling volatility dynamics," Research Paper 9522, Federal Reserve Bank of New York.

    Cited by:

    1. Bodart, Vincent & Reding, Paul, 1999. "Exchange rate regime, volatility and international correlations on bond and stock markets," Journal of International Money and Finance, Elsevier, vol. 18(1), pages 133-151, January.
    2. Dungey, Mardi & McKenzie, Michael & Tambakis, Demosthenes N., 2009. "Flight-to-quality and asymmetric volatility responses in US Treasuries," Global Finance Journal, Elsevier, vol. 19(3), pages 252-267.
    3. Agnolucci, Paolo, 2009. "Volatility in crude oil futures: A comparison of the predictive ability of GARCH and implied volatility models," Energy Economics, Elsevier, vol. 31(2), pages 316-321, March.
    4. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
    5. Pedro J. F. de Lima & Michelle L. Barnes, 2000. "Modeling Financial Volatility: Extreme Observations, Nonlinearities and Nonstationarities," School of Economics and Public Policy Working Papers 2000-05, University of Adelaide, School of Economics and Public Policy.
    6. Friedmann, Ralph & Sanddorf-Kohle, Walter G., 2002. "Volatility clustering and nontrading days in Chinese stock markets," Journal of Economics and Business, Elsevier, vol. 54(2), pages 193-217.
    7. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Clara Vega, 2003. "Micro Effects of Macro Announcements: Real-Time Price Discovery in Foreign Exchange," American Economic Review, American Economic Association, vol. 93(1), pages 38-62, March.
    8. J. Q. Smith & António Santos, 2003. "Second Order Filter Distribution Approximations for Financial Time Series with Extreme Outlier," GEMF Working Papers 2003-03, GEMF, Faculty of Economics, University of Coimbra.
    9. Prasad Bidarkota & J Huston Mcculloch, 2004. "Testing for persistence in stock returns with GARCH-stable shocks," Quantitative Finance, Taylor & Francis Journals, vol. 4(3), pages 256-265.
    10. Francis X. Diebold & Til Schuermann, 1996. "Exact Maximum Likelihood Estimation of Observation-Driven Econometric Models," NBER Technical Working Papers 0194, National Bureau of Economic Research, Inc.
    11. He, Changli & Teräsvirta, Timo, 1997. "Properties of Moments of a Family of GARCH Processes," SSE/EFI Working Paper Series in Economics and Finance 198, Stockholm School of Economics.
    12. Satheesh Aradhyula & A. Tolga Ergun, 2004. "Trading collar, intraday periodicity and stock market volatility," Applied Financial Economics, Taylor & Francis Journals, vol. 14(13), pages 909-913.
    13. Eric Ghysels & Joann Jasiak, 1997. "GARCH for Irregularly Spaced Data: The ACD-GARCH Model," CIRANO Working Papers 97s-06, CIRANO.
    14. Menelaos Karanasos, "undated". "The Covariance Structure of Component and Multivariate Garch Models," Discussion Papers 99/12, Department of Economics, University of York.
    15. Menelaos Karanasos, "undated". "Some Exact Formulae for the Constant Correlation and Diagonal M - Garch Models," Discussion Papers 00/14, Department of Economics, University of York.
    16. Siem Jan Koopman & Eugenie Hol Uspensky, 2002. "The stochastic volatility in mean model: empirical evidence from international stock markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(6), pages 667-689.
    17. Lopez, Jose A, 2001. "Evaluating the Predictive Accuracy of Volatility Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(2), pages 87-109, March.
    18. Aradhyula, Satheesh V. & Ergun, A. Tolga, 2002. "Trading Collar, Intraday, Periodicity, And Stock Market Volatility," 2002 Annual meeting, July 28-31, Long Beach, CA 19630, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    19. Ghassan, Hassan Belkacem & AlHajhoj, Hassan Rafdan, 2016. "Long run dynamic volatilities between OPEC and non-OPEC crude oil prices," Applied Energy, Elsevier, vol. 169(C), pages 384-394.
    20. de Souza Vasconcelos, Camila & Hadad Júnior, Eli, 2023. "Forecasting exchange rate: A bibliometric and content analysis," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 607-628.
    21. Berument, M. Hakan & Yalcin, Yeliz & Yildirim, Julide, 2012. "Inflation and inflation uncertainty: A dynamic framework," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(20), pages 4816-4826.
    22. Francis X. Diebold, 2004. "The Nobel Memorial Prize for Robert F. Engle," PIER Working Paper Archive 04-010, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    23. Verbeek, Marno, 2007. "A Guide to Modern Econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 8(4), pages 125-132.
    24. KIANI, Khurshid M., 2007. "Determination Of Volatility And Mean Returns: An Evidence From An Emerging Stock Market," International Journal of Applied Econometrics and Quantitative Studies, Euro-American Association of Economic Development, vol. 4(1), pages 103-118.
    25. Nour Meddahi & Eric Renault, 2000. "Temporal Aggregation of Volatility Models," CIRANO Working Papers 2000s-22, CIRANO.
    26. Peter Christoffersen & Redouane Elkamhi & Bruno Feunou & Kris Jacobs, 2009. "Option Valuation with Conditional Heteroskedasticity and Non-Normality," CIRANO Working Papers 2009s-32, CIRANO.
    27. Mr. Gene L. Leon & Mr. Rupert D Worrell, 2001. "Price Volatility and Financial Instability," IMF Working Papers 2001/060, International Monetary Fund.
    28. Carmen PINTILESCU & Mircea ASANDULUI & Elena-Daniela VIORICA & Danut-Vasile JEMNA, 2016. "Investigation On The Causal Relationship Between Inflation, Output Growth And Their Uncertainties In Romania," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 17, pages 71-89, June.
    29. GIOT, Pierre & PETITJEAN, Mikael, 2006. "The information content of the Bond-Equity Yield Ratio: better than a random walk?," LIDAM Discussion Papers CORE 2006089, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    30. Antypas, Antonios & Koundouri, Phoebe & Kourogenis, Nikolaos, 2013. "Aggregational Gaussianity and barely infinite variance in financial returns," Journal of Empirical Finance, Elsevier, vol. 20(C), pages 102-108.
    31. Leung, Pui-Lam & Wong, Wing-Keung, 2008. "Three-factor profile analysis with GARCH innovations," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 77(1), pages 1-8.
    32. Nikitopoulos, Christina Sklibosios & Thomas, Alice Carole & Wang, Jianxin, 2023. "The economic impact of daily volatility persistence on energy markets," Journal of Commodity Markets, Elsevier, vol. 30(C).
    33. Teräsvirta, Timo, 2006. "An introduction to univariate GARCH models," SSE/EFI Working Paper Series in Economics and Finance 646, Stockholm School of Economics.
    34. Berument, Hakan & Yalcin, Yeliz & Yildirim, Julide, 2009. "The effect of inflation uncertainty on inflation: Stochastic volatility in mean model within a dynamic framework," Economic Modelling, Elsevier, vol. 26(6), pages 1201-1207, November.
    35. Bali, Turan G. & Weinbaum, David, 2007. "A conditional extreme value volatility estimator based on high-frequency returns," Journal of Economic Dynamics and Control, Elsevier, vol. 31(2), pages 361-397, February.
    36. Shaun Bond & Stephen Satchell, 2006. "Asymmetry and downside risk in foreign exchange markets," The European Journal of Finance, Taylor & Francis Journals, vol. 12(4), pages 313-332.
    37. Tim Bollerslev & Jonathan H. Wright, 1999. "High frequency data, frequency domain inference and volatility forecasting," International Finance Discussion Papers 649, Board of Governors of the Federal Reserve System (U.S.).
    38. Abanto-Valle, Carlos A. & Rodríguez, Gabriel & Garrafa-Aragón, Hernán B., 2021. "Stochastic Volatility in Mean: Empirical evidence from Latin-American stock markets using Hamiltonian Monte Carlo and Riemann Manifold HMC methods," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 272-286.
    39. Y. Liu & B. Wang & H. Zhan & Y. Fan & Y. Zha & Y. Hao, 2017. "Simulation of Nonstationary Spring Discharge Using Time Series Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4875-4890, December.
    40. Francis X. Diebold & Til Schuermann & John D. Stroughair, 1998. "Pitfalls and Opportunities in the Use of Extreme Value Theory in Risk Management," New York University, Leonard N. Stern School Finance Department Working Paper Seires 98-081, New York University, Leonard N. Stern School of Business-.
    41. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    42. Zambujal-Oliveira, João & Mouta-Lopes, Manuel & Bangueses, Ricardo, 2021. "Real options appraisal of forestry investments under information scarcity in biomass markets," Resources Policy, Elsevier, vol. 74(C).
    43. Christian Bordes & Samuel Maveyraud, 2008. "The Friedman's and Mishkin's Hypotheses (Re)Considered," Post-Print hal-00308571, HAL.
    44. Kearney, Colm & Daly, Kevin, 1997. "Monetary volatility and real output volatility: An empirical model of the financial transmission mechanism in Australia," International Review of Financial Analysis, Elsevier, vol. 6(2), pages 77-95.
    45. Peter F. Christoffersen & Francis X. Diebold & Til Schuermann, 1998. "Horizon problems and extreme events in financial risk management," Economic Policy Review, Federal Reserve Bank of New York, vol. 4(Oct), pages 109-118.
    46. Gebka, Bartosz, 2008. "Volume- and size-related lead-lag effects in stock returns and volatility: An empirical investigation of the Warsaw Stock Exchange," International Review of Financial Analysis, Elsevier, vol. 17(1), pages 134-155.
    47. Eugenie Hol & Siem Jan Koopman, 2000. "Forecasting the Variability of Stock Index Returns with Stochastic Volatility Models and Implied Volatility," Tinbergen Institute Discussion Papers 00-104/4, Tinbergen Institute.
    48. Siem Jan Koopman & Eugenie Hol Uspensky, 2000. "The Stochastic Volatility in Mean Model," Tinbergen Institute Discussion Papers 00-024/4, Tinbergen Institute.
    49. Okay, Nesrin, 1998. "Asymmetric Volatility Dynamics: Evidence From the Istanbul Stock Exchange," MPRA Paper 52812, University Library of Munich, Germany.
    50. Ameni Ben Salem & Imene Safer & Islem Khefacha, 2021. "Value at Risk Estimation For the BRICS Countries : A Comparative Study," Post-Print hal-03502428, HAL.
    51. Hassan Ghassan & Prashanta Banerjee, 2015. "A threshold cointegration analysis of asymmetric adjustment of OPEC and non-OPEC monthly crude oil prices," Empirical Economics, Springer, vol. 49(1), pages 305-323, August.
    52. Francis X. Diebold & Andrew Hickman & Atsushi Inoue & Til Schuermann, 1997. "Converting 1-Day Volatility to h-Day Volatitlity: Scaling by Root-h is Worse Than You Think," Center for Financial Institutions Working Papers 97-34, Wharton School Center for Financial Institutions, University of Pennsylvania.
    53. Andreas Krause, 2000. "Microstructure Effects on Daily Return Volatility in Financial Markets," Papers cond-mat/0011295, arXiv.org.
    54. Khurshid M. Kiani, 2006. "Predictability in Stock Returns in an Emerging Market: Evidence from KSE 100 Stock Price Index," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 45(3), pages 369-381.

  104. Francis X. Diebold & Jose A. Lopez, 1995. "Forecast evaluation and combination," Research Paper 9525, Federal Reserve Bank of New York.

    Cited by:

    1. Wang, Yudong & Hao, Xianfeng, 2023. "Forecasting the real prices of crude oil: What is the role of parameter instability?," Energy Economics, Elsevier, vol. 117(C).
    2. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
    3. Jose A. Lopez & Christian Walter, 2000. "Is implied correlation worth calculating? Evidence from foreign exchange options and historical data," Working Paper Series 2000-02, Federal Reserve Bank of San Francisco.
    4. Kajal Lahiri & Huaming Peng & Xuguang Simon Sheng, 2021. "Measuring Uncertainty of a Combined Forecast and Some Tests for Forecaster Heterogeneity," Working Papers 2021-005, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    5. Tu, Yundong & Yi, Yanping, 2017. "Forecasting cointegrated nonstationary time series with time-varying variance," Journal of Econometrics, Elsevier, vol. 196(1), pages 83-98.
    6. Bofinger, Peter & Schmidt, Robert, 2003. "Should one rely on professional exchange rate forecasts: An empirical analysis of professional forecasts for the €/US-$ rate," W.E.P. - Würzburg Economic Papers 38, University of Würzburg, Department of Economics.
    7. Anthony S. Tay & Peter F. Christoffersen & Francis X. Diebold & Roberto S. Mariano & Yiu Kuen Tse, 2006. "Direction-of-Change Forecasts Based on Conditional Variance, Skewness and Kurtosis Dynamics : International Evidence," Finance Working Papers 22481, East Asian Bureau of Economic Research.
    8. Christoffersen, Peter F & Diebold, Francis X, 1998. "Cointegration and Long-Horizon Forecasting," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(4), pages 450-458, October.
    9. Ke Yang & Langnan Chen & Fengping Tian, 2015. "Realized Volatility Forecast of Stock Index Under Structural Breaks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(1), pages 57-82, January.
    10. Jose A. Lopez, 1998. "Methods for evaluating value-at-risk estimates," Economic Policy Review, Federal Reserve Bank of New York, vol. 4(Oct), pages 119-124.
    11. Wang, Yudong & Wu, Chongfeng, 2012. "Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?," Energy Economics, Elsevier, vol. 34(6), pages 2167-2181.
    12. Jeremy Berkowitz, 1999. "Evaluating the forecasts of risk models," Finance and Economics Discussion Series 1999-11, Board of Governors of the Federal Reserve System (U.S.).
    13. Shahzad Ahmad & Farooq Pasha, 2015. "A Pragmatic Model for Monetary Policy Analysis I: The Case of Pakistan," SBP Research Bulletin, State Bank of Pakistan, Research Department, vol. 11, pages 1-42.
    14. Hashem Pesaran & Paolo Zaffaroni & Banca d'Italia), 2004. "Model Averaging and Value-at-Risk based Evaluation of Large Multi Asset Volatility Models for Risk Management," Money Macro and Finance (MMF) Research Group Conference 2004 101, Money Macro and Finance Research Group.
    15. Hansen, Bruce E., 2010. "Averaging estimators for autoregressions with a near unit root," Journal of Econometrics, Elsevier, vol. 158(1), pages 142-155, September.
    16. Francis X. Diebold & Todd A. Gunther & Anthony S. Tay, 1997. "Evaluating Density Forecasts," Center for Financial Institutions Working Papers 97-37, Wharton School Center for Financial Institutions, University of Pennsylvania.
    17. Dopke, Jorg & Fritsche, Ulrich, 2006. "When do forecasters disagree? An assessment of German growth and inflation forecast dispersion," International Journal of Forecasting, Elsevier, vol. 22(1), pages 125-135.
    18. Pong, Shiuyan & Shackleton, Mark B. & Taylor, Stephen J. & Xu, Xinzhong, 2004. "Forecasting currency volatility: A comparison of implied volatilities and AR(FI)MA models," Journal of Banking & Finance, Elsevier, vol. 28(10), pages 2541-2563, October.
    19. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    20. Hilde C. Bjørnland & Karsten Gerdrup & Anne Sofie Jore & Christie Smith & Leif Anders Thorsrud, 2012. "Does Forecast Combination Improve Norges Bank Inflation Forecasts?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 74(2), pages 163-179, April.
    21. Patton, Andrew J. & Timmermann, Allan, 2007. "Properties of optimal forecasts under asymmetric loss and nonlinearity," Journal of Econometrics, Elsevier, vol. 140(2), pages 884-918, October.
    22. Zhang, Gang & Yang, Dazhi & Galanis, George & Androulakis, Emmanouil, 2022. "Solar forecasting with hourly updated numerical weather prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    23. No, Sung Chul & Salassi, Michael E., 2006. "Dynamic Analysis and Forecasts of Rough Rice Price under Government Price Support Program: An Application of Bayesian VAR," 2006 Annual Meeting, February 5-8, 2006, Orlando, Florida 35279, Southern Agricultural Economics Association.
    24. Michael P. Clements & David I. Harvey, 2010. "Forecast encompassing tests and probability forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(6), pages 1028-1062.
    25. Kitchen, John, 2003. "Observed Relationships Between Economic and Technical Receipts Revisions in Federal Budget Projections," National Tax Journal, National Tax Association;National Tax Journal, vol. 56(2), pages 337-353, June.
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    6. Peter AUER & Sandrine CAZES, 2000. "The resilience of the long-term employment relationship: Evidence from the industrialized countries," International Labour Review, International Labour Organization, vol. 139(4), pages 379-408, December.
    7. Xiao, Hongyu & Wu, Andy & Kim, Jaeho, 2021. "Commuting and innovation: Are closer inventors more productive?," Journal of Urban Economics, Elsevier, vol. 121(C).
    8. Bergemann, Annette & Mertens, Antje, 2004. "Job Stability Trends, Layoffs, and Transitions to Unemployment: An Empirical Analysis for West Germany," IZA Discussion Papers 1368, Institute of Labor Economics (IZA).
    9. Henry S. Farber, 1996. "The Changing Face of Job Loss in the United States, 1981-1993," NBER Working Papers 5596, National Bureau of Economic Research, Inc.
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    13. Henry S. Farber, 2007. "Is the Company Man an Anachronism? Trends in Long Term Employment in the U.S., 1973-2006," Working Papers 1039, Princeton University, Department of Economics, Industrial Relations Section..
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    40. Mertens, Antje, 1999. "Job stability trends and labor market (re-)entry in West Germany 1984 - 1997," SFB 373 Discussion Papers 1999,60, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    41. Kandel, Eugene & Pearson, Neil D., 2001. "Flexibility versus Commitment in Personnel Management," Journal of the Japanese and International Economies, Elsevier, vol. 15(4), pages 515-556, December.
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    48. Statt, A.L., 1998. "Great Prospects: Employer Provided Training as a Credible Screening Device," Working Papers Series 9802, University of Stirling, Division of Economics.
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    51. David Neumark, 2000. "Changes in Job Stability and Job Security: A Collective Effort to Untangle, Reconcile, and Interpret the Evidence," NBER Working Papers 7472, National Bureau of Economic Research, Inc.
    52. John M. Fitzgerald, 1999. "Job Instability and Earnings and Income Consequences: Evidence from SIPP: 1983-1995," JCPR Working Papers 99, Northwestern University/University of Chicago Joint Center for Poverty Research.
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    55. Hoang, Ha & Rascher, Dan, 1999. "The NBA, Exit Discrimination, and Career Earnings," MPRA Paper 3542, University Library of Munich, Germany.
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    Cited by:

    1. De Leon, Marycruz & Fullerton, Thomas M., Jr. & Kelley, Brian W., 2009. "Tolls, Exchange Rates, and Borderplex International Bridge Traffic," MPRA Paper 19861, University Library of Munich, Germany.
    2. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2016. "Dynamic Predictive Density Combinations for Large Data Sets in Economics and Finance," Tinbergen Institute Discussion Papers 15-084/III, Tinbergen Institute, revised 03 Jul 2017.
    3. Döpke, Jörg & Hartmann, Daniel & Pierdzioch, Christian, 2006. "Forecasting stock market volatility with macroeconomic variables in real time," Discussion Paper Series 2: Banking and Financial Studies 2006,01, Deutsche Bundesbank.
    4. Driffill, John & Sola, Martin & Kenc, Turalay & Spagnolo, Fabio, 2004. "On Model Selection and Markov Switching: A Empirical Examination of Term Structure Models with Regime Shifts," CEPR Discussion Papers 4165, C.E.P.R. Discussion Papers.
    5. Frédérick Demers & Annie De Champlain, 2005. "Forecasting Core Inflation in Canada: Should We Forecast the Aggregate or the Components?," Staff Working Papers 05-44, Bank of Canada.
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    6. Gerard O'Reilly & Karl Whelan, 2005. "Has Euro-Area Inflation Persistence Changed Over Time?," The Review of Economics and Statistics, MIT Press, vol. 87(4), pages 709-720, November.
    7. Tan, B. & Yilmaz, K., 1999. "Markov Chain Test for Time Dependence and Homogeneity: An Analytical and Empirical Evaluation," Papers 99/03, Koc University.
    8. Benati, Luca & Surico, Paolo, 2007. "Vector Autoregression Analysis and the Great Moderation," Discussion Papers 18, Monetary Policy Committee Unit, Bank of England.
    9. Torben G. Andersen & Rasmus T. Varneskov, 2021. "Testing for Parameter Instability and Structural Change in Persistent Predictive Regressions," NBER Working Papers 28570, National Bureau of Economic Research, Inc.
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    14. Gerlach, Stefan & Tillmann, Peter, 2012. "Inflation targeting and inflation persistence in Asia–Pacific," Journal of Asian Economics, Elsevier, vol. 23(4), pages 360-373.
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    61. du Plessis, S. A., 2004. "Stretching the South African business cycle," Economic Modelling, Elsevier, vol. 21(4), pages 685-701, July.
    62. Harman, Yvette S. & Zuehlke, Thomas W., 2007. "Nonlinear duration dependence in stock market cycles," Review of Financial Economics, Elsevier, vol. 16(4), pages 350-362.
    63. Agnello, Luca & Castro, Vítor & Hammoudeh, Shawkat & Sousa, Ricardo M., 2020. "Global factors, uncertainty, weather conditions and energy prices: On the drivers of the duration of commodity price cycle phases," Energy Economics, Elsevier, vol. 90(C).
    64. Agnello, Luca & Castro, Vitor & Hammoudeh, Shawkat & Sousa, Ricardo M., 2017. "Spillovers from the oil sector to the housing market cycle," Energy Economics, Elsevier, vol. 61(C), pages 209-220.
    65. Michael J. Dueker & Andreas M. Fischer, 1997. "The FOMC in 1996: \\"watchful waiting\\"," Review, Federal Reserve Bank of St. Louis, issue Jul, pages 7-23.
    66. Luca Agnello & Vitor Castro & Ricardo M. Sousa, 2015. "Booms, Busts, and Normal Times in the Housing Market," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 25-45, January.
    67. Iiboshi, Hirokuni, 2007. "Duration dependence of the business cycle in Japan: A Bayesian analysis of extended Markov switching model," Japan and the World Economy, Elsevier, vol. 19(1), pages 86-111, January.
    68. Douglas Sutherland & Peter Hoeller, 2013. "Growth-promoting Policies and Macroeconomic Stability," OECD Economics Department Working Papers 1091, OECD Publishing.
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    70. Luca Agnello & Vítor Castro & Ricardo M. Sousa, 2023. "Interest rate gaps in an uncertain global context: why “too” low (high) for “so” long?," Empirical Economics, Springer, vol. 64(2), pages 539-565, February.
    71. Terence Tai-Leung Chong & Zimu Li & Haiqiang Chen & Melvin Hinich, 2010. "An investigation of duration dependence in the American stock market cycle," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(8), pages 1407-1416.
    72. Taamouti, Abderrahim, 2012. "Moments of multivariate regime switching with application to risk-return trade-off," Journal of Empirical Finance, Elsevier, vol. 19(2), pages 292-308.
    73. George Koutsoumanis & Vítor Castro, 2023. "The duration of acceleration cycle downturns: duration dependence, international dynamics and synchronisation," Empirical Economics, Springer, vol. 64(4), pages 1667-1698, April.
    74. Shyh-Wei Chen & Chung-Hua Shen, 2006. "Is there a duration dependence in Taiwan's business cycles?," International Economic Journal, Taylor & Francis Journals, vol. 20(1), pages 109-128.
    75. Billio Monica & Casarin Roberto, 2011. "Beta Autoregressive Transition Markov-Switching Models for Business Cycle Analysis," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(4), pages 1-32, September.
    76. Di Guilmi, C. & Gaffeo, E. & Gallegati, M. & Palestrini, A., 2005. "International Evidence on Business Cycle Magnitude Dependence: An Analyisis of 16 Industrialized Countries, 1881-2000," International Journal of Applied Econometrics and Quantitative Studies, Euro-American Association of Economic Development, vol. 2(1), pages 5-16.
    77. Di Guilmi, Corrado & Gaffeo, Edoardo & Gallegati, Mauro, 2004. "Empirical results on the size distribution of business cycle phases," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 333(C), pages 325-334.

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    1. Kenneth D. West & Dongchul Cho, 1994. "The Predictive Ability of Several Models of Exchange Rate Volatility," NBER Technical Working Papers 0152, National Bureau of Economic Research, Inc.
    2. Havranek, Tomas & Zeynalov, Ayaz, 2018. "Forecasting Tourist Arrivals: Google Trends Meets Mixed Frequency Data," MPRA Paper 90205, University Library of Munich, Germany.
    3. Lin, Wen-Ling, 1995. "Market closure and predictability of intradaily stock returns in the United States and Japan," Journal of Empirical Finance, Elsevier, vol. 2(1), pages 19-44, March.
    4. Werner Kristjanpoller & Kevin Michell & Cristian Llanos & Marcel C. Minutolo, 2025. "Incorporating causal notions to forecasting time series: a case study," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-22, December.
    5. Chan Guk Huh, 1998. "Forecasting industrial production using models with business cycle asymmetry," Economic Review, Federal Reserve Bank of San Francisco, pages 29-41.
    6. Francis X. Diebold, 2012. "Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests," PIER Working Paper Archive 12-035, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    7. Tazwell S. Rowe & Roy H. Webb, 1995. "An index of leading indicators for inflation," Economic Quarterly, Federal Reserve Bank of Richmond, issue Spr, pages 75-96.
    8. Meyer-Gohde, Alexander & Shabalina, Ekaterina, 2022. "Estimation and forecasting using mixed-frequency DSGE models," IMFS Working Paper Series 175, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    9. Gerdesmeier Dieter & Roffia Barbara & Reimers Hans-Eggert, 2017. "Forecasting Euro Area Inflation Using Single-Equation and Multivariate VAR–Models," Folia Oeconomica Stetinensia, Sciendo, vol. 17(2), pages 19-34, December.

  116. Francis X. Diebold & Glenn D. Rudebusch, 1990. "On the power of Dickey-Fuller tests against fractional alternatives," Finance and Economics Discussion Series 119, Board of Governors of the Federal Reserve System (U.S.).

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    1. Ingolf Dittmann, 2000. "Residual‐Based Tests For Fractional Cointegration: A Monte Carlo Study," Journal of Time Series Analysis, Wiley Blackwell, vol. 21(6), pages 615-647, November.
    2. Marcelo Mello & Roberto Guimaraes-Filho, 2007. "A note on fractional stochastic convergence," Economics Bulletin, AccessEcon, vol. 3(16), pages 1-14.
    3. OlaOluwa S. Yaya & Ahamuefula E. Ogbonna & Robert Mudida & Nuruddeen Abu, 2021. "Market efficiency and volatility persistence of cryptocurrency during pre‐ and post‐crash periods of Bitcoin: Evidence based on fractional integration," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 1318-1335, January.
    4. Oluwasegun B. Adekoya, 2021. "Persistence and efficiency of OECD stock markets: linear and nonlinear fractional integration approaches," Empirical Economics, Springer, vol. 61(3), pages 1415-1433, September.
    5. Efe Çağlar Çağli & Pinar Evrim Mandaci & Pinar Hakan Kahyaoğlu, 2011. "Volatility Shifts and Persistence in Variance: Evidence from the Sector Indices of Istanbul Stock Exchange," International Journal of Business and Economic Sciences Applied Research (IJBESAR), Democritus University of Thrace (DUTH), Kavala Campus, Greece, vol. 4(3), pages 119-140, December.
    6. Baillie, Richard T & Bollerslev, Tim, 1994. "Cointegration, Fractional Cointegration, and Exchange Rate Dynamics," Journal of Finance, American Finance Association, vol. 49(2), pages 737-745, June.
    7. Luis A. Gil-Alana & Sakiru Adebola Solarin & Mehmet Balcilar & Rangan Gupta, 2023. "Productivity and GDP: international evidence of persistence and trends over 130 years of data," Empirical Economics, Springer, vol. 64(3), pages 1219-1246, March.
    8. Luis A. Gil-Alana & James Payne & David Loomis, 2010. "Does energy consumption by the US electric power secto exhibit long memory behaviour?," Faculty Working Papers 04/10, School of Economics and Business Administration, University of Navarra.
    9. Luis A. Gil-Alana & Rangan Gupta, 2013. "Persistence and Cycles in Historical Oil Prices Data," Working Papers 201375, University of Pretoria, Department of Economics.
    10. Waldemar Florczak, 2012. "Instytucjonalne uwarunkowania przestępczości w Polsce," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 10, pages 97-126.
    11. Galán-Gutiérrez, Juan Antonio & Martín-García, Rodrigo, 2021. "Cointegration between the structure of copper futures prices and Brexit," Resources Policy, Elsevier, vol. 71(C).
    12. Uwe Hassler & Matei Demetrescu & Adina Tarcolea, 2011. "Asymptotic normal tests for integration in panels with cross-dependent units," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(2), pages 187-204, June.
    13. Juan Infante & Marta Rio & Luis A. Gil-Alana, 2023. "Measuring Persistence in the US Equity Gender Diversity Index," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 167(1), pages 175-182, June.
    14. Joseph Ross, 2021. "Stationarity Statistics on Rolling Windows," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 655-691, February.
    15. Luis A. Gil-Alana & Juncal Cunado & Rangan Gupta, 2015. "Persistence, Mean-Reversion and Non-Linearities in Infant Mortality Rates," Working Papers 201574, University of Pretoria, Department of Economics.
    16. Luis Alberiko Gil-Alana & Antonio Moreno, 2006. "Technology Shocks and Hours Worked: A Fractional Integration Perspective," Faculty Working Papers 03/06, School of Economics and Business Administration, University of Navarra.
    17. João Ricardo Faria & Juan Carlos Cuestas & Luis Gil-Alana & Estefania Mourelle, 2021. "Self-employment by gender in the EU: convergence and clusters," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 48(3), pages 717-741, August.
    18. Mirko Abbritti & Luis A. Gil-Alana & Yuliya Lovcha & Antonio Moreno, 2016. "Term Structure Persistence," Journal of Financial Econometrics, Oxford University Press, vol. 14(2), pages 331-352.
    19. Curi, Andréa Zaitune & Menezes Filho, N. A., 2006. "A Relação entre o Desempenho Escolar e os Salários no Brasil," Insper Working Papers wpe_51, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    20. Krämer, Walter, 1997. "Kointegration von Aktienkursen," Technical Reports 1997,11, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    21. Cuñado, J. & Gil-Alana, L.A. & Perez de Gracia, F., 2012. "Testing for persistent deviations of stock prices to dividends in the Nasdaq index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(20), pages 4675-4685.
    22. Geetha Mayadunne & Merran Evans & Brett Inder, 1995. "An Empirical Investigation of Shock Persistence in Economic Time Series," The Economic Record, The Economic Society of Australia, vol. 71(2), pages 145-156, June.
    23. Gonzalo, Jesús & Lee, Tae-Hwy, 1996. "On the robustness of cointegration tests when series are fractionally integrated," DES - Working Papers. Statistics and Econometrics. WS 4542, Universidad Carlos III de Madrid. Departamento de Estadística.
    24. Manabu Asai & Shelton Peiris & Michael McAleer & David E. Allen, 2018. "Cointegrated Dynamics for A Generalized Long Memory Process: An Application to Interest Rates," Documentos de Trabajo del ICAE 2018-22, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    25. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
    26. Aaron Smallwood & Stefan C. Norrbin, 2008. "An Encompassing Test of Real Interest Rate Equalization," Review of International Economics, Wiley Blackwell, vol. 16(1), pages 114-126, February.
    27. Wang, Dabin & Tomek, William G., 2004. "Commodity Prices And Unit Root Tests," 2004 Annual meeting, August 1-4, Denver, CO 20141, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    28. Lee, Yun Shin, 2014. "Management of a periodic-review inventory system using Bayesian model averaging when new marketing efforts are made," International Journal of Production Economics, Elsevier, vol. 158(C), pages 278-289.
    29. Gil-Alana, Luis A. & Infante, Juan & Martín-Valmayor, Miguel Angel, 2023. "Persistence and long run co-movements across stock market prices," The Quarterly Review of Economics and Finance, Elsevier, vol. 89(C), pages 347-357.
    30. Ji, In Bae & Chung, Chanjin, 2012. "Causality Between Captive Supplies and Cash Market Prices in the U.S. Cattle Procurement Market," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 41(3), pages 1-11, December.
    31. Ata Assaf & Luis Alberiko Gil-Alana & Khaled Mokni, 2022. "True or spurious long memory in the cryptocurrency markets: evidence from a multivariate test and other Whittle estimation methods," Empirical Economics, Springer, vol. 63(3), pages 1543-1570, September.
    32. Ramos, Francisco F. Ribeiro, 2001. "Exports, imports, and economic growth in Portugal: evidence from causality and cointegration analysis," Economic Modelling, Elsevier, vol. 18(4), pages 613-623, December.
    33. Aloui, Chaker & Mabrouk, Samir, 2010. "Value-at-risk estimations of energy commodities via long-memory, asymmetry and fat-tailed GARCH models," Energy Policy, Elsevier, vol. 38(5), pages 2326-2339, May.
    34. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2015. "Infant mortality rates: time trends and fractional integration," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(3), pages 589-602, March.
    35. Jesús Tomás Monge Moreno & Manuel Monge, 2023. "Coronavirus, Vaccination and the Reaction of Consumer Sentiment in The United States: Time Trends and Persistence Analysis," Mathematics, MDPI, vol. 11(8), pages 1-8, April.
    36. Belbute, José M. & Pereira, Alfredo M., 2020. "Reference forecasts for CO2 emissions from fossil-fuel combustion and cement production in Portugal," Energy Policy, Elsevier, vol. 144(C).
    37. Lima, Luiz Renato & Notini, Hilton Hostalácio & Reis Gomes, Fábio Augusto, 2010. "Empirical Evidence on Convergence Across Brazilian States," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 64(2), June.
    38. Guglielmo Maria Caporale & Hector Carcel & Luis A. Gil-Alana, 2017. "Central Bank Policy Rates: Are they Cointegrated?," CESifo Working Paper Series 6389, CESifo.
    39. Michelacci, C. & Zaffaroni, P., 2000. "(Fractional) Beta Convergence," Papers 383, Banca Italia - Servizio di Studi.
    40. Dolado, Juan José & Mármol, Francesc, 1997. "On the properties of the Dickey-Pantula test against fractional alternatives," DES - Working Papers. Statistics and Econometrics. WS 4549, Universidad Carlos III de Madrid. Departamento de Estadística.
    41. Monge, Manuel & Gil-Alana, Luis A. & Pérez de Gracia, Fernando, 2017. "Crude oil price behaviour before and after military conflicts and geopolitical events," Energy, Elsevier, vol. 120(C), pages 79-91.
    42. Basma Bekdache & Christopher F. Baum, 2000. "A re-evaluation of empirical tests of the Fisher hypothesis," Boston College Working Papers in Economics 472, Boston College Department of Economics.
    43. Arielle Beyaert, 2004. "Fractional Output Convergence, with an Application to Nine Developed Countries," Econometric Society 2004 Australasian Meetings 280, Econometric Society.
    44. Guglielmo Maria Caporale & Juncal Cunado & Luis A. Gil-Alana & Rangan Gupta, 2015. "The Relationship between Healthcare Expenditure and Disposable Personal Income in the US States: A Fractional Integration and Cointegration Analysis," Discussion Papers of DIW Berlin 1486, DIW Berlin, German Institute for Economic Research.
    45. W.H. Buiter & U Patel, 1995. "Budgetary Aspects of Stabilization and Strucutral Adjustment in India: The Painful Road to a Sustainable Fiscal-Financial-Monetary Plan," CEP Discussion Papers dp0247, Centre for Economic Performance, LSE.
    46. Diebold, Francis X & Husted, Steven & Rush, Mark, 1991. "Real Exchange Rates under the Gold Standard," Journal of Political Economy, University of Chicago Press, vol. 99(6), pages 1252-1271, December.
    47. Peter C. B. Phillips & Zhijie Xiao, 1998. "A Primer on Unit Root Testing," Journal of Economic Surveys, Wiley Blackwell, vol. 12(5), pages 423-470, December.
    48. Batuo Enowbi, Michael & Guidi, Francesco & Mlambo, Kupukile, 2009. "Testing the weak-form market efficiency and the day of the week effects of some African countries," MPRA Paper 19116, University Library of Munich, Germany.
    49. J. D. Byers & D. A. Peel, 2001. "Volatility persistence in asset markets: long memory in high/low prices," Applied Financial Economics, Taylor & Francis Journals, vol. 11(3), pages 253-260.
    50. Carlos P. Barros & Luis A. Gil-Alana, 2013. "The Housing Markets in Spain and Portugal: Evidence of Persistence," Review of Economics & Finance, Better Advances Press, Canada, vol. 3, pages 19-32, November.
    51. SangKun Bae & Mark J. Jensen, 1998. "Long-Run Neutrality in a Long-Memory Model," Macroeconomics 9809006, University Library of Munich, Germany, revised 21 Apr 1999.
    52. Javier Hualde & Morten {O}rregaard Nielsen, 2022. "Fractional integration and cointegration," Papers 2211.10235, arXiv.org.
    53. Johan Lyhagen, 2006. "The seasonal KPSS statistic," Economics Bulletin, AccessEcon, vol. 3(13), pages 1-9.
    54. Yarovaya, Larisa & Matkovskyy, Roman & Jalan, Akanksha, 2022. "The COVID-19 black swan crisis: Reaction and recovery of various financial markets," Research in International Business and Finance, Elsevier, vol. 59(C).
    55. Greg Tkacz, 2000. "Estimating the Fractional Order of Integration of Interest Rates Using a Wavelet OLS Estimator," Staff Working Papers 00-5, Bank of Canada.
    56. Guglielmo Maria Caporale & Luis A. Gil-Alana & OlaOluwa Simon Yaya, 2022. "Modelling Persistence and Non-Linearities in the US Treasury 10-Year Bond Yields," CESifo Working Paper Series 9554, CESifo.
    57. Shih-Cheng Lee & Chien-Ting Lin & Min-Teh Yu, 2013. "A fractional cointegration approach to testing the Ohlson accounting based valuation model," Review of Quantitative Finance and Accounting, Springer, vol. 41(3), pages 535-547, October.
    58. Salah A. Nusair, 2003. "Testing The Validity Of Purchasing Power Parity For Asian Countries During The Current Float," Journal of Economic Development, Chung-Ang Unviersity, Department of Economics, vol. 28(2), pages 129-147, December.
    59. Guglielmo Caporale & Luis Gil-Alana, 2016. "Persistence and cyclical dependence in the monthly euribor rate," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 40(1), pages 157-171, January.
    60. Luis Alberiko Gil-Alana, 2024. "All Road User Casualties (Killed) in Great Britain from 1926. Linear and Nonlinear Trends with Persistent Data," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 22(3), pages 631-640, September.
    61. Monge, Manuel & Cristóbal, Enrique, 2021. "Terrorism and the behavior of oil production and prices in OPEC," Resources Policy, Elsevier, vol. 74(C).
    62. Monge, Manuel & Gil-Alana, Luis A., 2021. "Lithium industry and the U.S. crude oil prices. A fractional cointegration VAR and a Continuous Wavelet Transform analysis," Resources Policy, Elsevier, vol. 72(C).
    63. Greg Tkacz, 2002. "Inflation Changes, Yield Spreads, and Threshold Effects," Staff Working Papers 02-40, Bank of Canada.
    64. Härdle, Wolfgang Karl & Mungo, Julius, 2007. "Long memory persistence in the factor of Implied volatility dynamics," SFB 649 Discussion Papers 2007-027, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    65. Abul Masih & Rumi Masih, 1998. "A multivariate cointegrated modelling approach in testing temporal causality between energy consumption, real income and prices with an application to two Asian LDCs," Applied Economics, Taylor & Francis Journals, vol. 30(10), pages 1287-1298.
    66. Guglielmo Maria Caporale & Gloria Claudio-Quiroga & Luis A. Gil-Alana, 2021. "The Relationship between Prices and Output in the UK and the US," CESifo Working Paper Series 8970, CESifo.
    67. Strauß, Hubert, 2002. "Multivariate Cointegration Analysis of Aggregate Exports: Empirical Evidence for the United States, Canada, and Germany," Kiel Working Papers 1101, Kiel Institute for the World Economy (IfW Kiel).
    68. Bhardwaj, Geetesh & Swanson, Norman R., 2006. "An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 539-578.
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    70. Chevillon, Guillaume & Mavroeidis, Sophocles, 2011. "Learning generates Long Memory," ESSEC Working Papers WP1113, ESSEC Research Center, ESSEC Business School.
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    72. Hassler, Uwe & Breitung, Jörg, 2009. "A Residual-Based LM Test for Fractional Cointegration," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 77555, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    73. Cho, Cheol-Keun & Amsler, Christine & Schmidt, Peter, 2015. "A test of the null of integer integration against the alternative of fractional integration," Journal of Econometrics, Elsevier, vol. 187(1), pages 217-237.
    74. Christian Fischer & Luis Gil-Alana, 2009. "The nature of the relationship between international tourism and international trade: the case of German imports of Spanish wine," Applied Economics, Taylor & Francis Journals, vol. 41(11), pages 1345-1359.
    75. Sakiru Adebola Solarin & Luis A. Gil-Alana & Carmen Lafuente, 2020. "Persistence of the Misery Index in African Countries," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 147(3), pages 825-841, February.
    76. Laura Mayoral, 2005. "The persistence of inflation in OECD countries: A fractionally integrated approach," Economics Working Papers 958, Department of Economics and Business, Universitat Pompeu Fabra, revised Oct 2005.
    77. Bond, Derek & Harrison, Michael J & O’Brien, Edward J., 2006. "Testing for Long Memory and Nonlinear Time Series: A Demand for Money Study," Research Technical Papers 2/RT/06, Central Bank of Ireland.
    78. Carlos Pestana Barros & Luis Gil-Alana, 2006. "Eta: A Persistent Phenomenon," Defence and Peace Economics, Taylor & Francis Journals, vol. 17(2), pages 95-116.
    79. McElroy, Tucker S & Politis, D N, 2011. "Distribution Theory for the Studentized Mean for Long, Short, and Negative Memory Time Series," University of California at San Diego, Economics Working Paper Series qt0dr145dt, Department of Economics, UC San Diego.
    80. Luis Alberiko Gil-Alaña & Shinhye Chang & Mehmet Balcilar & Goodness C. Aye & Rangan Gupta, 2015. "Persistence of precious metal prices: a fractional integration approach with structural breaks," NCID Working Papers 06/2015, Navarra Center for International Development, University of Navarra.
    81. Martin-Valmayor, Miguel A. & Gil-Alana, Luis A. & Infante, Juan, 2023. "Energy prices in Europe. Evidence of persistence across markets," Resources Policy, Elsevier, vol. 82(C).
    82. Adekoya, Oluwasegun B. & Oliyide, Johnson A., 2022. "Commodity and financial markets’ fear before and during COVID-19 pandemic: Persistence and causality analyses," Resources Policy, Elsevier, vol. 76(C).
    83. Robinson, Peter M. & Yajima, Yoshihiro, 2002. "Determination of cointegrating rank in fractional systems," Journal of Econometrics, Elsevier, vol. 106(2), pages 217-241, February.
    84. Michael KUEHL, 2008. "Strong Comovements of Exchange Rates: Theoretical and Empirical Cases when Currencies Become the Same Asset," EcoMod2008 23800071, EcoMod.
    85. Luis A. Gil-Alana & Rangan Gupta & Fernando Perez de Gracia, 2016. "Persistence, mean reversion and non-linearities in the US housing prices over 1830--2013," Applied Economics, Taylor & Francis Journals, vol. 48(34), pages 3244-3252, July.
    86. Noor Ghazali & Shamshubariah Ramlee, 2003. "A long memory test of the long-run Fisher effect in the G7 countries," Applied Financial Economics, Taylor & Francis Journals, vol. 13(10), pages 763-769.
    87. Gil-Alana, Luis A., 2004. "Long memory in the U.S. interest rate," International Review of Financial Analysis, Elsevier, vol. 13(3), pages 265-276.
    88. Berta Marcos Ceron & Manuel Monge, 2023. "Consumer Sentiment and Luxury Behavior in the United States before and after COVID-19: Time Trends and Persistence Analysis," Mathematics, MDPI, vol. 11(16), pages 1-14, August.
    89. Aye, Goodness C. & Gil-Alana, Luis A. & Gupta, Rangan & Wohar, Mark E., 2017. "The efficiency of the art market: Evidence from variance ratio tests, linear and nonlinear fractional integration approaches," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 283-294.
    90. Cristofaro, Lorenzo & Gil-Alana, Luis A. & Chen, Zhongfei & Wanke, Peter, 2021. "Modelling stock market data in China: Crisis and Coronavirus," Finance Research Letters, Elsevier, vol. 41(C).
    91. Giorgio Canarella & Stephen M Miller, 2017. "Inflation Persistence Before and After Inflation Targeting: A Fractional Integration Approach," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 43(1), pages 78-103, January.
    92. Onour, Ibrahim, 2009. "Rational bubbles and volatility persistence in India stock market," MPRA Paper 18545, University Library of Munich, Germany.
    93. Yaya, OlaOluwa S. & Gil-Alana, Luis A., 2014. "The persistence and asymmetric volatility in the Nigerian stock bull and bear markets," Economic Modelling, Elsevier, vol. 38(C), pages 463-469.
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    95. Guglielmo Maria Caporale & Luis Alberiko Gil-Alana, 2023. "Gold and silver as safe havens: A fractional integration and cointegration analysis," PLOS ONE, Public Library of Science, vol. 18(3), pages 1-9, March.
    96. Somvang PHIMMAVONG & Ian FERGUSON & Barbara OZARSKA, 2010. "Economy-Wide Impact of Forest Plantation Development in Laos Using a Dynamic General Equilibrium Approach," EcoMod2010 259600131, EcoMod.
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  117. Yin-Wong Cheung & Francis X. Diebold, 1990. "On maximum-likelihood estimation of the differencing parameter of fractionally integrated noise with unknown mean," Discussion Paper / Institute for Empirical Macroeconomics 34, Federal Reserve Bank of Minneapolis.

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    1. Baillie, Richard T. & Kapetanios, George, 2007. "Testing for Neglected Nonlinearity in Long-Memory Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 447-461, October.
    2. Ghysels, E. & Harvey, A. & Renault, E., 1996. "Stochastic Volatility," Cahiers de recherche 9613, Universite de Montreal, Departement de sciences economiques.
    3. Morten Ø. Nielsen & Per Houmann Frederiksen, 2005. "Finite Sample Comparison Of Parametric, Semiparametric, And Wavelet Estimators Of Fractional Integration," Working Paper 1189, Economics Department, Queen's University.
    4. Koop, G. & Ley, E. & Osiewalski, J. & Steel, M. F. J., 1997. "Bayesian analysis of long memory and persistence using ARFIMA models," LIDAM Reprints CORE 1246, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. Martin, Gael M. & Nadarajah, K. & Poskitt, D.S., 2020. "Issues in the estimation of mis-specified models of fractionally integrated processes," Journal of Econometrics, Elsevier, vol. 215(2), pages 559-573.
    6. Paul M. Beaumont & Aaron D. Smallwood, 2024. "Conditional sum of squares estimation of k-factor GARMA models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 108(3), pages 501-543, September.
    7. Aaron D. Smallwood & Paul M. Beaumont, 2002. "An Asymptotic MLE Approach to Modelling Multiple Frequency GARMA Models," Computing in Economics and Finance 2002 285, Society for Computational Economics.
    8. Nilss Olekalns & Nigel Wilkins, 1998. "Re‐examining the Evidence for Long‐Run Purchasing Power Parity," The Economic Record, The Economic Society of Australia, vol. 74(224), pages 54-61, March.
    9. Gonzalo, Jesús & Lee, Tae-Hwy, 1996. "On the robustness of cointegration tests when series are fractionally integrated," DES - Working Papers. Statistics and Econometrics. WS 4542, Universidad Carlos III de Madrid. Departamento de Estadística.
    10. Yin-Wong Cheung & Kon S. Lai, 1999. "On Cross-Country Differences in the Persistence of Real Exchange Rates," CESifo Working Paper Series 218, CESifo.
    11. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
    12. S. Lardic & V. Mignon, 2003. "The exact minimum likelihood estimation of ARFIMA processes and model selection criteria: A Monte Carlo study," THEMA Working Papers 2003-06, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    13. Diebold, Francis X & Husted, Steven & Rush, Mark, 1991. "Real Exchange Rates under the Gold Standard," Journal of Political Economy, University of Chicago Press, vol. 99(6), pages 1252-1271, December.
    14. Perez, Ana & Ruiz, Esther, 2001. "Finite sample properties of a QML estimator of stochastic volatility models with long memory," Economics Letters, Elsevier, vol. 70(2), pages 157-164, February.
    15. Stelios Arvanitis & Antonis Demos, 2014. "A Class of Indirect Inference Estimators: Higher Order Asymptotics and Approximate Bias Correction (Revised)," DEOS Working Papers 1411, Athens University of Economics and Business, revised 23 Sep 2014.
    16. John W. Galbraith & Victoria Zinde-Walsh, 2001. "Autoregression-Based Estimators for ARFIMA Models," CIRANO Working Papers 2001s-11, CIRANO.
    17. Davidson, James & Hashimzade, Nigar, 2009. "Type I and type II fractional Brownian motions: A reconsideration," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2089-2106, April.
    18. Bhardwaj, Geetesh & Swanson, Norman R., 2006. "An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 539-578.
    19. John Goddard & Enrico Onali, 2014. "Self-affinity in financial asset returns," Papers 1401.7170, arXiv.org.
    20. Ila M. Semenick Alam & Robin C. Sickles, 1997. "Long Run Properties of Technical Efficiency in the U.S. Airline Industry," CIG Working Papers FS IV 97-25, Wissenschaftszentrum Berlin (WZB), Research Unit: Competition and Innovation (CIG).
    21. Mauro Coli & Lara Fontanella & Mariagrazia Granturco, 2005. "Parametric estimation for ARFIMA models via spectral methods," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 14(1), pages 11-27, February.
    22. E. Dubois & S. Lardic & V. Mignon, 2003. "The exact maximum likelihood-based test for fractional cointegration: critical values, power and size," THEMA Working Papers 2003-26, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    23. Yin-Wong Cheung & Kon S. Lai, 2005. "Nominal Exchange Rate Flexibility and Real Exchange Rate Adjustment: Evidence from Dual Exchange Rates in Developing Countries," CESifo Working Paper Series 1512, CESifo.
    24. Liudas Giraitis & Peter M Robinson, 2001. "Parametric Estimation under Long-Range Dependence," STICERD - Econometrics Paper Series 416, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    25. Donald W.K. Andrews & Offer Lieberman, 2002. "Higher-order Improvements of the Parametric Bootstrap for Long-memory Gaussian Processes," Cowles Foundation Discussion Papers 1378, Cowles Foundation for Research in Economics, Yale University.
    26. Kraicova, Lucie & Barunik, Jozef, 2015. "Estimation of long memory in volatility using wavelets," FinMaP-Working Papers 33, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    27. Mark J. Jensen, 1998. "An Approximate Wavelet MLE of Short and Long Memory Parameters," Econometrics 9802003, University Library of Munich, Germany, revised 21 Jun 1999.
    28. Pérez, Ana & Ruiz Ortega, Esther, 2001. "Modelos de memoria larga para series económicas y financieras," DES - Documentos de Trabajo. Estadística y Econometría. DS ds010101, Universidad Carlos III de Madrid. Departamento de Estadística.
    29. Michael A. Hauser, 1998. "Maximum Likelihood Estimators for ARMA and ARFIMA Models: A Monte Carlo Study," Econometrics 9809001, University Library of Munich, Germany.
    30. Bae, Sang-Kun & Jensen, Mark J. & Murdock, Scott G., 2005. "Long-run neutrality in a fractionally integrated model," Journal of Macroeconomics, Elsevier, vol. 27(2), pages 257-274, June.
    31. Christopher F. Baum & John Barkoulas & Mustafa Caglayan, 1996. "Persistence in International Inflation Rates," Boston College Working Papers in Economics 333., Boston College Department of Economics.
    32. Gadea, Maria Dolores & Sabate, Marcela & Serrano, Jose Maria, 2004. "Structural breaks and their trace in the memory: Inflation rate series in the long-run," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 14(2), pages 117-134, April.
    33. Francis In & Sangbae Kim, 2012. "An Introduction to Wavelet Theory in Finance:A Wavelet Multiscale Approach," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 8431, April.
    34. Maharaj, E.A., 1999. "A Test for the Difference Parameter of the ARFIMA Model Using the Moving Blocks Bootstrap," Monash Econometrics and Business Statistics Working Papers 11/99, Monash University, Department of Econometrics and Business Statistics.
    35. Gonzalo, Jesus & Lee, Tae-Hwy, 1998. "Pitfalls in testing for long run relationships," Journal of Econometrics, Elsevier, vol. 86(1), pages 129-154, June.
    36. Cheung, Yin-Wong & Lai, Kon S., 2008. "Nominal exchange rate flexibility and real exchange rate adjustment: New evidence from dual exchange rates in developing countries," Japan and the World Economy, Elsevier, vol. 20(3), pages 415-434, August.
    37. J. Hidalgo & Y. Yajima, 2003. "Semiparametric estimation of the long-range parameter," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 55(4), pages 705-736, December.
    38. Zevallos, Mauricio & Palma, Wilfredo, 2013. "Minimum distance estimation of ARFIMA processes," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 242-256.
    39. Giraitis, Liudas & Robinson, Peter M., 2001. "Parametric estimation under long-range dependence," LSE Research Online Documents on Economics 2227, London School of Economics and Political Science, LSE Library.
    40. Arteche, Josu, 2004. "Gaussian semiparametric estimation in long memory in stochastic volatility and signal plus noise models," Journal of Econometrics, Elsevier, vol. 119(1), pages 131-154, March.
    41. Martin, V.L. & Wilkins, N.P., 1997. "Indirect Estimation of Arfima and Varfima Models," Department of Economics - Working Papers Series 547, The University of Melbourne.
    42. Jensen, Mark J., 2000. "An alternative maximum likelihood estimator of long-memory processes using compactly supported wavelets," Journal of Economic Dynamics and Control, Elsevier, vol. 24(3), pages 361-387, March.
    43. Nuno Crato & Philip Rothman, 1994. "A reappraisal of parity reversion for UK real exchange rates," Applied Economics Letters, Taylor & Francis Journals, vol. 1(9), pages 139-141.
    44. Valérie Mignon, 1998. "Méthodes d'estimation de l'exposant de Hurst. Application aux rentabilités boursières," Économie et Prévision, Programme National Persée, vol. 132(1), pages 193-214.
    45. Zaffaroni, Paolo, 2009. "Whittle estimation of EGARCH and other exponential volatility models," Journal of Econometrics, Elsevier, vol. 151(2), pages 190-200, August.
    46. Breidt, F. Jay & Crato, Nuno & de Lima, Pedro, 1998. "The detection and estimation of long memory in stochastic volatility," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 325-348.
    47. Chih-Chiang Hsu, 2000. "Long Memory or Structural Change: Testing Method and Empirical Examination," Econometric Society World Congress 2000 Contributed Papers 0867, Econometric Society.
    48. Richard T. Baillie & George Kapetanios, 2006. "Nonlinear Models with Strongly Dependent Processes and Applications to Forward Premia and Real Exchange Rates," Working Papers 570, Queen Mary University of London, School of Economics and Finance.
    49. Davidson, James, 2002. "A model of fractional cointegration, and tests for cointegration using the bootstrap," Journal of Econometrics, Elsevier, vol. 110(2), pages 187-212, October.
    50. So, Mike K.P. & Kwok, Susanna W.Y., 2006. "A multivariate long memory stochastic volatility model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 362(2), pages 450-464.
    51. Kavasseri, Rajesh G. & Seetharaman, Krithika, 2009. "Day-ahead wind speed forecasting using f-ARIMA models," Renewable Energy, Elsevier, vol. 34(5), pages 1388-1393.
    52. Dominique Guegan & Abdou Kâ Diongue & Bertrand Vignal, 2004. "A k- factor GIGARCH process : estimation and application to electricity market spot prices," Post-Print halshs-00188533, HAL.
    53. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
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    58. Jaesik Jeong & Marina Vannucci & Kyungduk Ko, 2013. "A Wavelet-Based Bayesian Approach to Regression Models with Long Memory Errors and Its Application to fMRI Data," Biometrics, The International Biometric Society, vol. 69(1), pages 184-196, March.
    59. Beaumont, Paul & Smallwood, Aaron, 2019. "Conditional Sum of Squares Estimation of Multiple Frequency Long Memory Models," MPRA Paper 96314, University Library of Munich, Germany.
    60. Beran, Jan & Ghosh, Sucharita & Schell, Dieter, 2009. "On least squares estimation for long-memory lattice processes," Journal of Multivariate Analysis, Elsevier, vol. 100(10), pages 2178-2194, November.

  118. Francis X. Diebold & Glenn D. Rudebusch & Daniel E. Sichel, 1990. "International evidence on business cycle duration dependence," Discussion Paper / Institute for Empirical Macroeconomics 31, Federal Reserve Bank of Minneapolis.

    Cited by:

    1. Eric Ghysels, 1992. "Christmas, Spring and the Dawning of Economic Recovery," Cowles Foundation Discussion Papers 1027, Cowles Foundation for Research in Economics, Yale University.
    2. Vítor Castro, 2010. "The duration of business cycle expansions and contractions: Are there change-points in duration dependence?," NIPE Working Papers 24/2010, NIPE - Universidade do Minho.
    3. Vitor Castro, 2015. "The Portuguese business cycle: chronology and duration dependence," Empirical Economics, Springer, vol. 49(1), pages 325-342, August.
    4. Vítor Castro, 2008. "The duration of economic expansions and recessions: More than duration dependence," NIPE Working Papers 18/2008, NIPE - Universidade do Minho.

  119. Francis X. Diebold & Steven Husted & Mark Rush, 1990. "Real exchange rates under the gold standard," Discussion Paper / Institute for Empirical Macroeconomics 32, Federal Reserve Bank of Minneapolis.

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    1. JamesR. Lothian & MarkP. Taylor, 2008. "Real Exchange Rates Over the Past Two Centuries: How Important is the Harrod-Balassa-Samuelson Effect?," Economic Journal, Royal Economic Society, vol. 118(532), pages 1742-1763, October.
    2. Jean-Francois Villeneuve & Jagdish Handa, 2006. "Purchasing Power Parity as a long-term memory process: evidence from Canada," Applied Financial Economics, Taylor & Francis Journals, vol. 16(1-2), pages 109-117.
    3. Hali J. Edison & Joseph E. Gagnon & William R. Melick, 1994. "Understanding the empirical literature on purchasing power parity: the post-Bretton Woods era," International Finance Discussion Papers 465, Board of Governors of the Federal Reserve System (U.S.).
    4. Param Silvapulle & Titi Kanti Lestari & Jae Kim, 2004. "Nonlinear Modelling of Purchasing Power Parity in Indonesia," Econometric Society 2004 Australasian Meetings 316, Econometric Society.
    5. Joseph H. Davis & Christopher Hanes & Paul W. Rhode, 2009. "Harvests and Business Cycles in Nineteenth-Century America," NBER Working Papers 14686, National Bureau of Economic Research, Inc.
    6. Okimoto, Tatsuyoshi & Shimotsu, Katsumi, 2010. "Decline in the persistence of real exchange rates, but not sufficient for purchasing power parity," Journal of the Japanese and International Economies, Elsevier, vol. 24(3), pages 395-411, September.
    7. Qian Chen & David E. Giles, 2007. "A Saddlepoint Approximation to the Distribution of the Half-Life Estimator in an Autoregressive Model: New Insights Into the PPP Puzzle," Econometrics Working Papers 0703, Department of Economics, University of Victoria.
    8. Mollick, Andre Varella, 1999. "The real exchange rate in Brazil Mean reversion or random walk in the long run?," International Review of Economics & Finance, Elsevier, vol. 8(1), pages 115-126, January.
    9. Morten Ø. Nielsen & Per Houmann Frederiksen, 2005. "Finite Sample Comparison Of Parametric, Semiparametric, And Wavelet Estimators Of Fractional Integration," Working Paper 1189, Economics Department, Queen's University.
    10. Richard T. Baillie & George Kapetanios & Fotis Papailias, 2017. "Inference for impulse response coefficients from multivariate fractionally integrated processes," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 60-84, March.
    11. Lothian, James R., 1997. "Multi-country evidence on the behavior of purchasing power parity under the current float," Journal of International Money and Finance, Elsevier, vol. 16(1), pages 19-35, February.
    12. Ivan Paya & David A. Peel, 2004. "Nonlinear Ppp Under The Gold Standard," Working Papers. Serie AD 2004-24, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    13. Baillie, Richard T & Bollerslev, Tim, 1994. "Cointegration, Fractional Cointegration, and Exchange Rate Dynamics," Journal of Finance, American Finance Association, vol. 49(2), pages 737-745, June.
    14. Luis Gil-Alana, 2008. "Real GDP growth rates across countries: long memory and mean shifts," Applied Economics Letters, Taylor & Francis Journals, vol. 15(6), pages 449-455.
    15. Kilian, L. & Zha, T., 1999. "Quantifying the Half-Life of Deviations from PPP: The Role of Economic Priors," Papers 99-08, Michigan - Center for Research on Economic & Social Theory.
    16. Serletis, Apostolos & Shahmoradi, Asghar, 2007. "Chaos, self-organized criticality, and SETAR nonlinearity: An analysis of purchasing power parity between Canada and the United States," Chaos, Solitons & Fractals, Elsevier, vol. 33(5), pages 1437-1444.
    17. Husted, Steven & MacDonald, Ronald, 1999. "The Asian currency crash: were badly driven fundamentals to blame?," Journal of Asian Economics, Elsevier, vol. 10(4), pages 537-550.
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    21. Thomas L Bradley & Paul B Eberle, 2023. "Purchasing Power Parity In Russia And The Transitioning Economy 1990-1995," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 31, pages 85-111, June.
    22. Kenneth A. Froot, 2019. "Currency Hedging Over Long Horizons," Annals of Economics and Finance, Society for AEF, vol. 20(1), pages 37-66, May.
    23. Mototsugu Shintani, 2006. "A nonparametric measure of convergence towards purchasing power parity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(5), pages 589-604.
    24. Masao Ogaki & Jaebeom Kim, 2004. "Purchasing Power Parity for Traded and Non-traded Goods: A Structural Error Correction Model Approach," Econometric Society 2004 Far Eastern Meetings 515, Econometric Society.
    25. Aaron Smallwood, 2004. "Joint Tests for Long Memory and Non-linearity: The Case of Purchasing Power Parity," Computing in Economics and Finance 2004 23, Society for Computational Economics.
    26. Nilss Olekalns & Nigel Wilkins, 1998. "Re‐examining the Evidence for Long‐Run Purchasing Power Parity," The Economic Record, The Economic Society of Australia, vol. 74(224), pages 54-61, March.
    27. Belbute, José, 2013. "Does final demand for energy in Portugal exhibit long memory?," MPRA Paper 45717, University Library of Munich, Germany.
    28. Karim M. Abadir & Gabriel Talmain, 2008. "Macro and Financial Markets: The Memory of an Elephant?," Working Paper series 17_08, Rimini Centre for Economic Analysis.
    29. Kanchana Nadarajah & Gael M Martin & Donald S Poskitt, 2019. "Optimal Bias Correction of the Log-periodogram Estimator of the Fractional Parameter: A Jackknife Approach," Monash Econometrics and Business Statistics Working Papers 7/19, Monash University, Department of Econometrics and Business Statistics.
    30. Lean, Hooi Hooi & Smyth, Russell, 2009. "Long memory in US disaggregated petroleum consumption: Evidence from univariate and multivariate LM tests for fractional integration," Energy Policy, Elsevier, vol. 37(8), pages 3205-3211, August.
    31. Lopez, Claude & Murray, Chris & Papell, David, 2009. "Median-Unbiased Estimation in DF-GLS Regressions and the PPP Puzzle," MPRA Paper 26091, University Library of Munich, Germany.
    32. Cheung, Yin-Wong & Lai, Kon S., 2001. "Long memory and nonlinear mean reversion in Japanese yen-based real exchange rates," Journal of International Money and Finance, Elsevier, vol. 20(1), pages 115-132, February.
    33. Yin-Wong Cheung & Francis X. Diebold, 1993. "On maximum-likelihood estimation of the differencing parameter of fractionally integrated noise with unknown mean," Working Papers 93-5, Federal Reserve Bank of Philadelphia.
    34. Yin-Wong Cheung & Kon S. Lai, 1999. "On Cross-Country Differences in the Persistence of Real Exchange Rates," CESifo Working Paper Series 218, CESifo.
    35. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
    36. Giorgio Canarella & Luis A. Gil-Alana & Rangan Gupta & Stephen M. Miller, 2022. "Globalization, long memory, and real interest rate convergence: a historical perspective," Empirical Economics, Springer, vol. 63(5), pages 2331-2355, November.
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    42. Cuestas, Juan C. & Gil-Alana, Luís A., 2009. "Further evidence on the PPP analysis of the Australian dollar: Non-linearities, fractional integration and structural changes," Economic Modelling, Elsevier, vol. 26(6), pages 1184-1192, November.
    43. Christina Anderl & Guglielmo Maria Caporale, 2022. "Exchange rate parities and Taylor rule deviations," Empirical Economics, Springer, vol. 63(4), pages 1809-1835, October.
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    1. Kanungo, Rama Prasad, 2021. "Uncertainty of M&As under asymmetric estimation," Journal of Business Research, Elsevier, vol. 122(C), pages 774-793.
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  126. Francis X. Diebold, 1988. "Random walks versus fractional integration: power comparisons of scalar and joint tests of the variance-time function," Finance and Economics Discussion Series 41, Board of Governors of the Federal Reserve System (U.S.).

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    1. Quoreshi, Shahiduzzaman, 2006. "Time Series Modelling Of High Frequency Stock Transaction Data," Umeå Economic Studies 675, Umeå University, Department of Economics.
    2. Quoreshi, Shahiduzzaman, 2006. "LongMemory, Count Data, Time Series Modelling for Financial Application," Umeå Economic Studies 673, Umeå University, Department of Economics.
    3. Mohamed Boutahar, 2002. "General Autoregressive Models with Long-Memory Noise," Statistical Inference for Stochastic Processes, Springer, vol. 5(3), pages 321-333, October.

  127. Francis X. Diebold & Marc Nerlove, 1988. "Unit roots in economic time series: a selective survey," Finance and Economics Discussion Series 49, Board of Governors of the Federal Reserve System (U.S.).

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    1. Fred C. White, 1992. "Dynamic relationships between agribusiness sales and research," Agribusiness, John Wiley & Sons, Ltd., vol. 8(3), pages 233-241.
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    3. Aslan, Alper, 2008. "Testing Gibrat’s law: empirical evidence from panel unit root tests of turkish firms," MPRA Paper 10594, University Library of Munich, Germany.
    4. Diebold, Francis X & Husted, Steven & Rush, Mark, 1991. "Real Exchange Rates under the Gold Standard," Journal of Political Economy, University of Chicago Press, vol. 99(6), pages 1252-1271, December.
    5. Candelon, Bertrand & Gil-Alaña, Luis A., 2001. "Fractional integration and business cycle features," SFB 373 Discussion Papers 2001,46, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    6. Marcelo Resende & Marcos Lima, 2005. "Market share instability in Brazilian industry: a dynamic panel data analysis," Applied Economics, Taylor & Francis Journals, vol. 37(6), pages 713-718.
    7. Mukhtar Ali, 2002. "Distribution Of The Least Squares Estimator In A First-Order Autoregressive Model," Econometric Reviews, Taylor & Francis Journals, vol. 21(1), pages 89-119.
    8. Luciano Gutierrez, 2003. "Common and idiosyncratic shocks to labor productivity across sectors and countries: Is climate relevant?," Macroeconomics 0311008, University Library of Munich, Germany.
    9. Kari Takala & Pekka Pere, 1991. "Testing the cointegration of house and stock prices in Finland," Finnish Economic Papers, Finnish Economic Association, vol. 4(1), pages 33-51, Spring.
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    13. N. Vijayamohanan Pillai, 2001. "Electricity demand analysis and forecasting: The tradition is questioned," Centre for Development Studies, Trivendrum Working Papers 312, Centre for Development Studies, Trivendrum, India.
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    15. Francis X. Diebold & Lutz Kilian, "undated". "Measuring Predictability: Theory and Macroeconomic Applications," CARESS Working Papres 97-19, University of Pennsylvania Center for Analytic Research and Economics in the Social Sciences.
    16. Dufour, Jean-Marie & Neifar, Malika, 2002. "Méthodes d’inférence exactes pour des processus autorégressifs : une approche fondée sur des tests induits," L'Actualité Economique, Société Canadienne de Science Economique, vol. 78(1), pages 19-40, Mars.
    17. Mukhtar M. Ali, 1996. "Distribution of the Least Squares Estimator in a First-Order Autoregressive Model," Econometrics 9610004, University Library of Munich, Germany.
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    19. Saatci, Mustafa & Aslan, Alper, 2007. "TÜRKİYE İMALAT SANAYİNDE İTHALATIN PİYASAYI DİSİPLİNE ETME HİPOTEZİNİN TESTİ: PANEL VERi YAKLASIMI," MPRA Paper 10604, University Library of Munich, Germany.
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  128. Francis X. Diebold, 1988. "State space modeling of time series: a review essay," Finance and Economics Discussion Series 9, Board of Governors of the Federal Reserve System (U.S.).

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    1. E. Skordilis & R. Moghaddass, 2017. "A condition monitoring approach for real-time monitoring of degrading systems using Kalman filter and logistic regression," International Journal of Production Research, Taylor & Francis Journals, vol. 55(19), pages 5579-5596, October.
    2. Najand, Mohammad & Noronha, Gregory, 1998. "Causal relations among stock returns, inflation, real activity, and interest rates: Evidence from Japan," Global Finance Journal, Elsevier, vol. 9(1), pages 71-80.
    3. Alkulaib, Yaser A. & Najand, Mohammad & Mashayekh, Ahmad, 2009. "Dynamic linkages among equity markets in the Middle East and North African countries," Journal of Multinational Financial Management, Elsevier, vol. 19(1), pages 43-53, February.
    4. Author, A., 1999. "Book review," International Journal of Forecasting, Elsevier, vol. 15(4), pages 449-450, October.

  129. Francis X. Diebold & Glenn D. Rudebusch, 1988. "A nonparametric investigation of duration dependence in the American business cycle," Working Paper Series / Economic Activity Section 90, Board of Governors of the Federal Reserve System (U.S.).

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    5. João Victor Issler & Hilton Hostalacio Notini & Claudia Fontoura Rodrigues, 2013. "Constructing coincident and leading indices of economic activity for the Brazilian economy," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2012(2), pages 43-65.
    6. Crentsil, Christian & Gschwandtner, Adelina & Wahhaj, Zaki, 2020. "The effects of risk and ambiguity aversion on technology adoption: Evidence from aquaculture in Ghana," Journal of Economic Behavior & Organization, Elsevier, vol. 179(C), pages 46-68.
    7. Akdi, Yilmaz & Varlik, Serdar & Berument, M. Hakan, 2020. "Duration of Global Financial Cycles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    8. Vitor Castro & Boris Fisera, 2022. "Determinants of the Duration of Economic Recoveries: The Role of ´Too Much Finance´," Working Papers IES 2022/33, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Dec 2022.
    9. Travis J. Berge & Damjan Pfajfar, 2019. "Duration Dependence, Monetary Policy Asymmetries, and the Business Cycle," Finance and Economics Discussion Series 2019-020, Board of Governors of the Federal Reserve System (U.S.).
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    277. Hafner, Christian M. & Preminger, Arie, 2009. "On asymptotic theory for multivariate GARCH models," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 2044-2054, October.
    278. Gabriele Fiorentini & Enrique Sentana, 2012. "Tests for Serial Dependence in Static, Non-Gaussian Factor Models," Working Papers wp2012_1211, CEMFI.
    279. Jon Wongswan, 2003. "Contagion: an empirical test," International Finance Discussion Papers 775, Board of Governors of the Federal Reserve System (U.S.).
    280. Matteo Barigozzi & Marc Hallin & Stefano Soccorsi, 2017. "Identification of Global and National Shocks in International Financial Markets via General Dynamic Factor Models," Working Papers ECARES ECARES 2017-10, ULB -- Universite Libre de Bruxelles.
    281. Pandey, Ajay, 2003. "Modeling and Forecasting Volatility in Indian Capital Markets," IIMA Working Papers WP2003-08-03, Indian Institute of Management Ahmedabad, Research and Publication Department.
    282. S. M. Abdullah & Salina Siddiqua & Muhammad Shahadat Hossain Siddiquee & Nazmul Hossain, 2017. "Modeling and forecasting exchange rate volatility in Bangladesh using GARCH models: a comparison based on normal and Student’s t-error distribution," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 3(1), pages 1-19, December.
    283. Mohamadou L. Fadi & Yongsheng Wang, 2014. "Common Stochastic Volatility in International Real Estate Market," Journal of Reviews on Global Economics, Lifescience Global, vol. 3, pages 131-139.
    284. Gulcay Tuna PAYASLIOGLU, 2001. "The Effectiveness Of Central Bank Intervention on Exchange Rate Volatility: Evidence from Turkey," Middle East and North Africa 330400050, EcoMod.
    285. Bodnar, Olha & Bodnar, Taras & Gupta, Arjun K., 2010. "Estimation and inference for dependence in multivariate data," Journal of Multivariate Analysis, Elsevier, vol. 101(4), pages 869-881, April.
    286. Camiel de Koning & Stefan Straetmans, 1997. "Variation in the Slope Coefficient of the Fama Regression for Testing Uncovered Interest Rate Parity: Evidence from Fixed and Time-varying Coefficient Approaches," Tinbergen Institute Discussion Papers 97-014/2, Tinbergen Institute.

  135. Francis X. Diebold, 1986. "Temporal aggregation of ARCH processes and the distribution of asset returns," Special Studies Papers 200, Board of Governors of the Federal Reserve System (U.S.).

    Cited by:

    1. Bahram Pesaran & Gary Robinson, 1993. "The Statistical Distribution of Short-Term Libor Rates Under Two Monetary Regimes," Bank of England working papers 16, Bank of England.
    2. Murinde V. & Poshakwala S., 2001. "Volatility in the Emerging Stock Markets in Central and Eastern Europe: Evidence on Croatia, Czech Republic, Hungary, Poland, Russia and Slovakia," European Research Studies Journal, European Research Studies Journal, vol. 0(3-4), pages 73-102, July - De.
    3. Geert Bekaert, 1994. "The Time Variation of Risk and Return in Foreign Exchange Markets: A General Equilibrium Perspective," NBER Working Papers 4818, National Bureau of Economic Research, Inc.
    4. Gary Robinson, 1993. "The Effect of Futures Trading on Cash Market Volatility: Evidence from the London Stock Exchange," Bank of England working papers 19, Bank of England.
    5. Michail Karoglou, 2010. "Breaking down the non-normality of stock returns," The European Journal of Finance, Taylor & Francis Journals, vol. 16(1), pages 79-95.
    6. Sunil Poshakwale & Victor Murinde, 2001. "Modelling the volatility in East European emerging stock markets: evidence on Hungary and Poland," Applied Financial Economics, Taylor & Francis Journals, vol. 11(4), pages 445-456.
    7. Richard Harmon, 1988. "The simultaneous equations model with generalized autoregressive conditional heteroskedasticity: the SEM-GRACH model," International Finance Discussion Papers 322, Board of Governors of the Federal Reserve System (U.S.).

  136. Francis X. Diebold & Peter Pauly, 1986. "Structural change and the combination of forecasts," Special Studies Papers 201, Board of Governors of the Federal Reserve System (U.S.).

    Cited by:

    1. Francis X. Diebold & Jose A. Lopez, 1996. "Forecast Evaluation and Combination," NBER Technical Working Papers 0192, National Bureau of Economic Research, Inc.
    2. Francis X. Diebold & Peter Pauly, 1986. "Structural change and the combination of forecasts," Special Studies Papers 201, Board of Governors of the Federal Reserve System (U.S.).
    3. Sun, Yuying & Hong, Yongmiao & Wang, Shouyang & Zhang, Xinyu, 2023. "Penalized time-varying model averaging," Journal of Econometrics, Elsevier, vol. 235(2), pages 1355-1377.
    4. Xi Wu & Adam Blake, 2023. "Does the combination of models with different explanatory variables improve tourism demand forecasting performance?," Tourism Economics, , vol. 29(8), pages 2032-2056, December.
    5. Heng, Jiani & Wang, Jianzhou & Xiao, Liye & Lu, Haiyan, 2017. "Research and application of a combined model based on frequent pattern growth algorithm and multi-objective optimization for solar radiation forecasting," Applied Energy, Elsevier, vol. 208(C), pages 845-866.
    6. Ekaterina V. Astafyeva & Maria Yu. Turuntseva, 2023. "Analysis of Opportunities to Improve the Quality of Natural Resource Price by Combining Forecasts Resulting from Methods Based on Regression Estimates of Weights [Анализ Возможностей Улучшения Каче," Russian Economic Development, Gaidar Institute for Economic Policy, issue 12, pages 24-33, December.
    7. Yi Ding & Peng Wu & Jie Zhao & Ligang Zhou, 2025. "Forecasting product sales using text mining: a case study in new energy vehicle," Electronic Commerce Research, Springer, vol. 25(1), pages 495-527, February.
    8. Chen, Bin & Maung, Kenwin, 2023. "Time-varying forecast combination for high-dimensional data," Journal of Econometrics, Elsevier, vol. 237(2).
    9. Fuchun Li & Greg Tkacz, 2001. "Evaluating Linear and Non-Linear Time-Varying Forecast-Combination Methods," Staff Working Papers 01-12, Bank of Canada.
    10. Mekelburg, Erik & Strauss, Jack, 2024. "Pooling and winsorizing machine learning forecasts to predict stock returns with high-dimensional data," Journal of Empirical Finance, Elsevier, vol. 79(C).
    11. David E. Rapach & Jack K. Strauss, 2005. "Forecasting employment growth in Missouri with many potentially relevant predictors: an analysis of forecast combining methods," Regional Economic Development, Federal Reserve Bank of St. Louis, issue Nov, pages 97-112.
    12. Ekaterina V. Astafyeva & Maria Yu. Turuntseva, 2023. "Анализ Возможностей Улучшения Качества Прогнозов Цен На Природные Ресурсы Методами Комбинирования На Основе Регрессионных Оценок Весов," Russian Economic Development (in Russian), Gaidar Institute for Economic Policy, issue 12, pages 24-33, December.
    13. Timmermann, Allan & Guidolin, Massimo, 2007. "Forecasts of US Short-term Interest Rates: A Flexible Forecast Combination Approach," CEPR Discussion Papers 6188, C.E.P.R. Discussion Papers.
    14. Luis Fernando Melo & Héctor Núñez, 2004. "Combinación de Pronósticos de la Inflación en Presencia de cambios Estructurales," Borradores de Economia 286, Banco de la Republica de Colombia.
    15. Elkin Castaño & Luis Fernando Melo, 1998. "Métodos de Combinación de Pronósticos: Una Aplicación a la Inflación Colombiana," Borradores de Economia 109, Banco de la Republica de Colombia.
    16. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    17. Ekrem Kilic, 2005. "Forecasting Volatility of Turkish Markets: A Comparison of Thin and Thick Models," Econometrics 0510007, University Library of Munich, Germany.
    18. Dejian Yu & Libo Sheng & Shunshun Shi, 2023. "A retrospective analysis of Journal of Forecasting: From 1982 to 2019," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 1008-1035, July.

  137. Christoffersen & Diebold, "undated". "Further Results on Forecasting and Model Selection Under Asymmetric Loss," Home Pages _059, University of Pennsylvania.

    Cited by:

    1. Emilio Zanetti Chini, 2018. "Forecaster’s utility and forecasts coherence," CREATES Research Papers 2018-01, Department of Economics and Business Economics, Aarhus University.
    2. Peter F. Christoffersen & Francis X. Diebold, 1994. "Optimal Prediction Under Asymmetric Loss," NBER Technical Working Papers 0167, National Bureau of Economic Research, Inc.
    3. Christoffersen, Peter F & Diebold, Francis X, 1998. "Cointegration and Long-Horizon Forecasting," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(4), pages 450-458, October.
    4. Goodwin, Paul, 2005. "Providing support for decisions based on time series information under conditions of asymmetric loss," European Journal of Operational Research, Elsevier, vol. 163(2), pages 388-402, June.
    5. Campbell, Sean D. & Diebold, Francis X., 2004. "Weather forecasting for weather derivatives," CFS Working Paper Series 2004/10, Center for Financial Studies (CFS).
    6. Francis X. Diebold & Todd A. Gunther & Anthony S. Tay, 1997. "Evaluating Density Forecasts," Center for Financial Institutions Working Papers 97-37, Wharton School Center for Financial Institutions, University of Pennsylvania.
    7. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    8. Patton, Andrew J. & Timmermann, Allan, 2007. "Properties of optimal forecasts under asymmetric loss and nonlinearity," Journal of Econometrics, Elsevier, vol. 140(2), pages 884-918, October.
    9. M A Clatworthy & D Peel & P F Pope, 2005. "Are analysts' loss functions asymmetric?," Working Papers 574124, Lancaster University Management School, Economics Department.
    10. Francis X. Diebold & Jose A. Lopez, 1996. "Forecast Evaluation and Combination," NBER Technical Working Papers 0192, National Bureau of Economic Research, Inc.
    11. Matteo Iacopini & Francesco Ravazzolo & Luca Rossini, 2020. "Proper scoring rules for evaluating asymmetry in density forecasting," Working Papers No 06/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    12. Valentina Corradi & Norman Swanson, 2013. "A Survey of Recent Advances in Forecast Accuracy Comparison Testing, with an Extension to Stochastic Dominance," Departmental Working Papers 201309, Rutgers University, Department of Economics.
    13. Philip Hans Franses & Rianne Legerstee & Richard Paap, 2011. "Estimating Loss Functions of Experts," Tinbergen Institute Discussion Papers 11-177/4, Tinbergen Institute.
    14. Peter F. Christoffersen & Francis X. Diebold, 2006. "Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics," Management Science, INFORMS, vol. 52(8), pages 1273-1287, August.
    15. Timmermann Allan & Capistrán Carlos, 2006. "Disagreement and Biases in Inflation Expectations," Working Papers 2006-07, Banco de México.
    16. Berk, K. & Hoffmann, A. & Müller, A., 2018. "Probabilistic forecasting of industrial electricity load with regime switching behavior," International Journal of Forecasting, Elsevier, vol. 34(2), pages 147-162.
    17. Wolfgang Polasek, 2013. "Forecast Evaluations for Multiple Time Series: A Generalized Theil Decomposition," Working Paper series 23_13, Rimini Centre for Economic Analysis.
    18. Kai-Chao Yao & Hsiu-Wen Hsueh & Ming-Hsiang Huang & Tsung-Che Wu, 2022. "The Role of GARCH Effect on the Prediction of Air Pollution," Sustainability, MDPI, vol. 14(8), pages 1-20, April.
    19. Demetrescu, Matei & Hacioglu Hoke, Sinem, 2018. "Predictive regressions under asymmetric loss: factor augmentation and model selection," Bank of England working papers 723, Bank of England.
    20. Anatolyev, Stanislav, 2009. "Dynamic modeling under linear-exponential loss," Economic Modelling, Elsevier, vol. 26(1), pages 82-89, January.
    21. Franses,Philip Hans & Dijk,Dick van & Opschoor,Anne, 2014. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521817707, June.
    22. Valentina Corradi & Norman Swanson, 2003. "The Block Bootstrap for Parameter Estimation Error In Recursive Estimation Schemes, With Applications to Predictive Evaluation," Departmental Working Papers 200313, Rutgers University, Department of Economics.
    23. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2005. "Volatility forecasting," CFS Working Paper Series 2005/08, Center for Financial Studies (CFS).
    24. Hitesh Doshi & Kris Jacobs & Rui Liu, 2021. "Information in the Term Structure: A Forecasting Perspective," Management Science, INFORMS, vol. 67(8), pages 5255-5277, August.
    25. Sun, Yuying & Wang, Shouyang & Zhang, Xun, 2018. "How efficient are China's macroeconomic forecasts? Evidences from a new forecasting evaluation approach," Economic Modelling, Elsevier, vol. 68(C), pages 506-513.
    26. Behrens, Christoph & Pierdzioch, Christian & Risse, Marian, 2018. "Testing the optimality of inflation forecasts under flexible loss with random forests," Economic Modelling, Elsevier, vol. 72(C), pages 270-277.
    27. Alp, Tansel & Demetrescu, Matei, 2010. "Joint forecasts of Dow Jones stocks under general multivariate loss function," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2360-2371, November.
    28. Gonzalez-Rivera, Gloria & Lee, Tae-Hwy & Mishra, Santosh, 2004. "Forecasting volatility: A reality check based on option pricing, utility function, value-at-risk, and predictive likelihood," International Journal of Forecasting, Elsevier, vol. 20(4), pages 629-645.
    29. Corradi, Valentina & Swanson, Norman R., 2002. "A consistent test for nonlinear out of sample predictive accuracy," Journal of Econometrics, Elsevier, vol. 110(2), pages 353-381, October.
    30. Hashem Pesaran, M., 2003. "Aggregation of linear dynamic models: an application to life-cycle consumption models under habit formation," Economic Modelling, Elsevier, vol. 20(2), pages 383-415, March.
    31. Carlos Capistrán & Allan Timmermann, 2009. "Disagreement and Biases in Inflation Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(2‐3), pages 365-396, March.
    32. Peter Christoffersen & Francis X. Diebold, 2002. "Financial Asset Returns, Market Timing, and Volatility Dynamics," CIRANO Working Papers 2002s-02, CIRANO.
    33. E. Mamatzakis, 2014. "Revealing asymmetries in the loss function of WTI oil futures market," Empirical Economics, Springer, vol. 47(2), pages 411-426, September.
    34. Granger, C.W.J. & Pesaran, M. H., 1999. "Economic and Statistical Measures of Forecast Accuracy," Cambridge Working Papers in Economics 9910, Faculty of Economics, University of Cambridge.
    35. Demetrescu, Matei, 2006. "An extension of the Gauss-Newton algorithm for estimation under asymmetric loss," Computational Statistics & Data Analysis, Elsevier, vol. 50(2), pages 379-401, January.
    36. Gneiting, Tilmann, 2011. "Quantiles as optimal point forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 197-207, April.
    37. Christoph Behrens, 2019. "A Nonparametric Evaluation of the Optimality of German Export and Import Growth Forecasts under Flexible Loss," Economies, MDPI, vol. 7(3), pages 1-23, September.
    38. M A Clatworthy & D Peel & P F Pope, 2006. "Are analysts’ loss functions asymmetric?," Working Papers 574591, Lancaster University Management School, Economics Department.
    39. Marcella Niglio, 2007. "Multi-step forecasts from threshold ARMA models using asymmetric loss functions," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 16(3), pages 395-410, November.
    40. Brownlees, Christian T. & Gallo, Giampiero M., 2011. "Shrinkage estimation of semiparametric multiplicative error models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 365-378.
    41. Trapani, Lorenzo & Urga, Giovanni, 2009. "Optimal forecasting with heterogeneous panels: A Monte Carlo study," International Journal of Forecasting, Elsevier, vol. 25(3), pages 567-586, July.
    42. Dell'Aquila, Rosario & Ronchetti, Elvezio, 2006. "Stock and bond return predictability: the discrimination power of model selection criteria," Computational Statistics & Data Analysis, Elsevier, vol. 50(6), pages 1478-1495, March.
    43. Michael P. Clements & Philip Hans Franses & Norman R. Swanson, 2003. "Forecasting economic and financial time-series with non-linear models," Departmental Working Papers 200309, Rutgers University, Department of Economics.
    44. Francis X. Diebold & Lutz Kilian, "undated". "Measuring Predictability: Theory and Macroeconomic Applications," CARESS Working Papres 97-19, University of Pennsylvania Center for Analytic Research and Economics in the Social Sciences.
    45. Higgins, Matthew L. & Mishra, Sagarika, 2012. "State dependent asymmetric loss and the consensus forecast of real U.S. GDP growth," Working Papers fe_2012_10, Deakin University, Department of Economics.
    46. Bruzda, Joanna, 2019. "Quantile smoothing in supply chain and logistics forecasting," International Journal of Production Economics, Elsevier, vol. 208(C), pages 122-139.
    47. Skouras, Spyros, 2001. "Financial returns and efficiency as seen by an artificial technical analyst," Journal of Economic Dynamics and Control, Elsevier, vol. 25(1-2), pages 213-244, January.
    48. Siddhartha S. Bora & Ani L. Katchova & Todd H. Kuethe, 2021. "The Rationality of USDA Forecasts under Multivariate Asymmetric Loss," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 1006-1033, May.
    49. Corradi, Valentina & Swanson, Norman R. & Olivetti, Claudia, 2001. "Predictive ability with cointegrated variables," Journal of Econometrics, Elsevier, vol. 104(2), pages 315-358, September.
    50. Cherif Guermat & Richard D. F. Harris, 2006. "Bias in the estimation of non-linear transformations of the integrated variance of returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(7), pages 481-494.
    51. Blaskowitz, Oliver J. & Herwartz, Helmut, 2009. "On economic evaluation of directional forecasts," SFB 649 Discussion Papers 2009-052, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    52. Valentina Corradi & Norman Swanson, 2004. "Bootstrap Procedures for Recursive Estimation Schemes With Applications to Forecast Model Selection," Departmental Working Papers 200418, Rutgers University, Department of Economics.
    53. Gneiting, Tilmann, 2011. "Making and Evaluating Point Forecasts," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 746-762.
    54. Stanislav Anatolyev & Natalia Kryzhanovskaya, 2009. "Directional Prediction of Returns under Asymmetric Loss: Direct and Indirect Approaches," Working Papers w0136, Center for Economic and Financial Research (CEFIR).
    55. Ulu, Yasemin, 2007. "Optimal prediction under LINLIN loss: Empirical evidence," International Journal of Forecasting, Elsevier, vol. 23(4), pages 707-715.
    56. Chan, Kam C. & Chan, Leo H. & Nguyen, Chi M., 2020. "Forecasting oil futures market volatility in a financialized world: Why speculative activities matter," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    57. Schorfheide, Frank, 2005. "VAR forecasting under misspecification," Journal of Econometrics, Elsevier, vol. 128(1), pages 99-136, September.
    58. Diebold, F.X. & Kilian, L. & Nerlove, Marc, 2006. "Time Series Analysis," Working Papers 28556, University of Maryland, Department of Agricultural and Resource Economics.
    59. Georgios Tsiotas, 2022. "Regression Analysis Using Asymmetric Losses: A Bayesian Approach," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(2), pages 311-327, June.
    60. María Clara Aristizábal Restrepo, 2006. "Evaluación asimétrica de una red neuronal artificial:Aplicación al caso de la inflación en Colombia," Borradores de Economia 377, Banco de la Republica de Colombia.
    61. Gneiting, Tilmann, 2011. "Quantiles as optimal point forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 197-207.
    62. Kloek, T., 1998. "Loss development forecasting models: an econometrician's view," Insurance: Mathematics and Economics, Elsevier, vol. 23(3), pages 251-261, December.
    63. Ulu, Yasemin, 2013. "Multivariate test for forecast rationality under asymmetric loss functions: Recent evidence from MMS survey of inflation–output forecasts," Economics Letters, Elsevier, vol. 119(2), pages 168-171.
    64. Matei Demetrescu, 2007. "Optimal forecast intervals under asymmetric loss," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(4), pages 227-238.
    65. Pesaran, M. H., 1999. "On Aggregation of Linear Dynamic Models," Cambridge Working Papers in Economics 9919, Faculty of Economics, University of Cambridge.
    66. Demosthenes N Tambakis, 2000. "On The Informational Content Of Asset Prices," Computing in Economics and Finance 2000 101, Society for Computational Economics.

  138. Francis X. Diebold & Russell L. Lamb, "undated". "Why Are Estimates of Agricultural Supply Response so Variable?," Finance and Economics Discussion Series 1996-08, Board of Governors of the Federal Reserve System (U.S.), revised 04 Dec 2019.

    Cited by:

    1. Keeney, Roman & Hertel, Thomas W., 2008. "Yield Response To Prices: Implications For Policy Modeling," Working papers 45969, Purdue University, Department of Agricultural Economics.
    2. Stigler, Matthieu M., 2018. "Supply response at the field-level: disentangling area and yield effects," 2018 Annual Meeting, August 5-7, Washington, D.C. 274343, Agricultural and Applied Economics Association.
    3. Frank Kleibergen & Eric Zivot, 2003. "Bayesian and Classical Approaches to Instrumental Variable Regression," Working Papers UWEC-2002-21-P, University of Washington, Department of Economics.
    4. Amina Al Naabi & Shekar Bose, 2020. "Do Regulatory Measures Necessarily Affect Oman’s Seafood Export-Supply?," SAGE Open, , vol. 10(3), pages 21582440209, August.
    5. Arnold Zellner, 2003. "Some Recent Developments in Econometric Inference," Econometric Reviews, Taylor & Francis Journals, vol. 22(2), pages 203-215.
    6. Lamb, Russell L., 2000. "Food crops, exports, and the short-run policy response of agriculture in Africa," Agricultural Economics, Blackwell, vol. 22(3), pages 271-298, April.
    7. Traoré, Fousseini, 2013. "Estimating the supply elasticity of cotton in Mali with the Nerlove Model: A bayesian method of moments approach," Review of Agricultural and Environmental Studies - Revue d'Etudes en Agriculture et Environnement (RAEStud), Institut National de la Recherche Agronomique (INRA), vol. 94(3).
    8. Shen, Edward Z. & Perloff, Jeffrey M., 2001. "Maximum entropy and Bayesian approaches to the ratio problem," Journal of Econometrics, Elsevier, vol. 104(2), pages 289-313, September.
    9. Demont, Matty & Tollens, Eric, 2001. "Uncertainties Of Estimating The Welfare Effects Of Agricultural Biotechnology In The European Union," Working Papers 31828, Katholieke Universiteit Leuven, Centre for Agricultural and Food Economics.
    10. Jae H Kim & Iain Fraser & Rob J. Hyndman, 2010. "Improved Interval Estimation of Long Run Response from a Dynamic Linear Model: A Highest Density Region Approach," Working Papers 2010.06, School of Economics, La Trobe University.
    11. Roman Keeney & Thomas W. Hertel, 2008. "U.S. Market Potential For Dried Distillers Grain With Solubles," Working Papers 08-13, Purdue University, College of Agriculture, Department of Agricultural Economics.
    12. Hee Mok Park & Puneet Manchanda, 2015. "When Harry Bet with Sally: An Empirical Analysis of Multiple Peer Effects in Casino Gambling Behavior," Marketing Science, INFORMS, vol. 34(2), pages 179-194, March.
    13. Psaradakis, Zacharias & Sola, Martin, 1998. "Finite-sample properties of the maximum likelihood estimator in autoregressive models with Markov switching," Journal of Econometrics, Elsevier, vol. 86(2), pages 369-386, June.
    14. Anbes Tenaye, 2020. "New Evidence Using a Dynamic Panel Data Approach: Cereal Supply Response in Smallholder Agriculture in Ethiopia," Economies, MDPI, vol. 8(3), pages 1-24, July.
    15. Tobias, Justin & Zellner, Arnold, 1998. "Further Results on Bayesian Method of Moments Analysis of the Multiple Regression Model," CUDARE Working Papers 198659, University of California, Berkeley, Department of Agricultural and Resource Economics.
    16. Ball, V. Eldon & Moss, Charles B. & Erickson, Kenneth W. & Nehring, Richard F., 2003. "Modeling Supply Response In A Multiproduct Framework Revisited: The Nexus Of Empirics And Economics," 2003 Annual meeting, July 27-30, Montreal, Canada 21981, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    17. Zellner, Arnold, 1998. "The finite sample properties of simultaneous equations' estimates and estimators Bayesian and non-Bayesian approaches," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 185-212.
    18. Andres Ramirez-Hassan & Manuel Correa-Giraldo, 2018. "Focused econometric estimation for noisy and small datasets: A Bayesian Minimum Expected Loss estimator approach," Papers 1809.06996, arXiv.org.

Articles

  1. Diebold, Francis X. & Shin, Minchul & Zhang, Boyuan, 2023. "On the aggregation of probability assessments: Regularized mixtures of predictive densities for Eurozone inflation and real interest rates," Journal of Econometrics, Elsevier, vol. 237(2).
    See citations under working paper version above.
  2. Diebold, Francis X. & Rudebusch, Glenn D., 2023. "Climate models underestimate the sensitivity of Arctic sea ice to carbon emissions," Energy Economics, Elsevier, vol. 126(C).
    See citations under working paper version above.
  3. Diebold, Francis X. & Göbel, Maximilian & Goulet Coulombe, Philippe, 2023. "Assessing and comparing fixed-target forecasts of Arctic sea ice: Glide charts for feature-engineered linear regression and machine learning models," Energy Economics, Elsevier, vol. 124(C).
    See citations under working paper version above.
  4. Diebold, Francis X. & Rudebusch, Glenn D. & Göbel, Maximilian & Goulet Coulombe, Philippe & Zhang, Boyuan, 2023. "When will Arctic sea ice disappear? Projections of area, extent, thickness, and volume," Journal of Econometrics, Elsevier, vol. 236(2).
    See citations under working paper version above.
  5. Baillie, Richard T. & Diebold, Francis X. & Kapetanios, George & Kim, Kun Ho, 2023. "A new test for market efficiency and uncovered interest parity," Journal of International Money and Finance, Elsevier, vol. 130(C).
    See citations under working paper version above.
  6. Diebold, Francis X. & Göbel, Maximilian, 2022. "A benchmark model for fixed-target Arctic sea ice forecasting," Economics Letters, Elsevier, vol. 215(C).
    See citations under working paper version above.
  7. Diebold, Francis X. & Rudebusch, Glenn D., 2022. "Probability assessments of an ice-free Arctic: Comparing statistical and climate model projections," Journal of Econometrics, Elsevier, vol. 231(2), pages 520-534.
    See citations under working paper version above.
  8. Diebold, Francis X. & Göbel, Maximilian & Goulet Coulombe, Philippe & Rudebusch, Glenn D. & Zhang, Boyuan, 2021. "Optimal combination of Arctic sea ice extent measures: A dynamic factor modeling approach," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1509-1519.
    See citations under working paper version above.
  9. Diebold, Francis X. & Shin, Minchul, 2019. "Machine learning for regularized survey forecast combination: Partially-egalitarian LASSO and its derivatives," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1679-1691.
    See citations under working paper version above.
  10. Mert Demirer & Francis X. Diebold & Laura Liu & Kamil Yilmaz, 2018. "Estimating global bank network connectedness," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(1), pages 1-15, January.
    See citations under working paper version above.
  11. Ross Askanazi & Francis X. Diebold & Frank Schorfheide & Minchul Shin, 2018. "On the Comparison of Interval Forecasts," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(6), pages 953-965, November.
    See citations under working paper version above.
  12. Diebold, Francis X. & Schorfheide, Frank & Shin, Minchul, 2017. "Real-time forecast evaluation of DSGE models with stochastic volatility," Journal of Econometrics, Elsevier, vol. 201(2), pages 322-332.
    See citations under working paper version above.
  13. Francis X. Diebold & Minchul Shin, 2017. "Assessing point forecast accuracy by stochastic error distance," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 588-598, October.
    See citations under working paper version above.
  14. Aruoba, S. Borağan & Diebold, Francis X. & Nalewaik, Jeremy & Schorfheide, Frank & Song, Dongho, 2016. "Improving GDP measurement: A measurement-error perspective," Journal of Econometrics, Elsevier, vol. 191(2), pages 384-397.
    See citations under working paper version above.
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    1. Nucera, Federico & Schwaab, Bernd & Koopman, Siem Jan & Lucas, André, 2016. "The information in systemic risk rankings," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 461-475.
    2. Ki-Hong Choi & Ron P. McIver & Salvatore Ferraro & Lei Xu & Sang Hoon Kang, 2021. "Dynamic volatility spillover and network connectedness across ASX sector markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 45(4), pages 677-691, October.
    3. Okorie, David Iheke & Lin, Boqiang, 2022. "Givers never lack: Nigerian oil & gas asymmetric network analyses," Energy Economics, Elsevier, vol. 108(C).
    4. Clausen Volker & Schlösser Alexander & Thiem Christopher, 2019. "Economic Policy Uncertainty in the Euro Area: Cross-Country Spillovers and Macroeconomic Impact," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 239(5-6), pages 957-981, October.
    5. Rehman, Mobeen Ur & Nautiyal, Neeraj & Vo, Xuan Vinh & Ghardallou, Wafa & Kang, Sang Hoon, 2023. "Is the impact of oil shocks more pronounced during extreme market conditions?," Resources Policy, Elsevier, vol. 85(PA).
    6. Aslanidis, Nektarios & Bariviera, Aurelio F. & Perez-Laborda, Alejandro, 2021. "Are cryptocurrencies becoming more interconnected?," Economics Letters, Elsevier, vol. 199(C).
    7. Aslan, Aydin & Posch, Peter N., 2022. "Does carbon price volatility affect European stock market sectors? A connectedness network analysis," Finance Research Letters, Elsevier, vol. 50(C).
    8. Hung Do & Rabindra Nepal & Tooraj Jamasb, 2020. "Electricity Market Integration, Decarbonisation and Security of Supply: Dynamic Volatility Connectedness in the Irish and Great Britain Markets," CAMA Working Papers 2020-42, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    9. João Henrique G. Mazzeu & Gloria González-Rivera & Esther Ruiz & Helena Veiga, 2020. "A bootstrap approach for generalized Autocontour testing Implications for VIX forecast densities," Econometric Reviews, Taylor & Francis Journals, vol. 39(10), pages 971-990, November.
    10. Mensi, Walid & Shafiullah, Muhammad & Vo, Xuan Vinh & Kang, Sang Hoon, 2021. "Volatility spillovers between strategic commodity futures and stock markets and portfolio implications: Evidence from developed and emerging economies," Resources Policy, Elsevier, vol. 71(C).
    11. Christian Manicaro, 2022. "The link between regional CDS spreads and equity returns: a multivariate GARCH approach," SN Business & Economics, Springer, vol. 2(2), pages 1-15, February.
    12. Ji, Qiang & Bahloul, Walid & Geng, Jiang-Bo & Gupta, Rangan, 2020. "Trading behaviour connectedness across commodity markets: Evidence from the hedgers’ sentiment perspective," Research in International Business and Finance, Elsevier, vol. 52(C).
    13. Thiem, Christopher, 2018. "Cross-category spillovers of economic policy uncertainty," Ruhr Economic Papers 744, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    14. Awartani, Basel & Aktham, Maghyereh & Cherif, Guermat, 2016. "The connectedness between crude oil and financial markets: Evidence from implied volatility indices," Journal of Commodity Markets, Elsevier, vol. 4(1), pages 56-69.
    15. Borjigin, Sumuya & Gao, Ting & Sun, Yafei & An, Biao, 2020. "For evil news rides fast, while good news baits later?—A network based analysis in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    16. Everett Grant & Julieta Yung, 2019. "Upstream, Downstream & Common Firm Shocks," Globalization Institute Working Papers 360, Federal Reserve Bank of Dallas.
    17. Chiang, Shu-hen & Chen, Chien-Fu, 2022. "From systematic to systemic risk among G7 members: Do the stock or real estate markets matter?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).
    18. Honghai Yu & Wangyu Chu & Yu’ang Ding & Xuezhou Zhao, 2021. "Risk contagion of global stock markets under COVID‐19:A network connectedness method," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(4), pages 5745-5782, December.
    19. Mensi, Walid & Sensoy, Ahmet & Aslan, Aylin & Kang, Sang Hoon, 2019. "High-frequency asymmetric volatility connectedness between Bitcoin and major precious metals markets," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    20. Jozef Baruník & Evžen Kocenda & Lukáš Vácha, 2015. "Asymmetric Connectedness on the U.S. Stock Market: Bad and Good Volatility Spillover," CESifo Working Paper Series 5305, CESifo.
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    27. Mensi, Walid & Hammoudeh, Shawkat & Rehman, Mobeen Ur & Al-Maadid, Alanoud Ali S. & Hoon Kang, Sang, 2020. "Dynamic risk spillovers and portfolio risk management between precious metals and global foreign exchange markets," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
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    30. Zubair Ahmad Parrey & Arif Billah Dar & Manas Paul, 2025. "Revisiting precious metal mining stocks and precious metals as hedge, diversifiers and safe-havens: a multidimensional scaling and wavelet quantile correlation perspective," Empirical Economics, Springer, vol. 68(2), pages 511-533, February.
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    32. Korobilis, D & Yilmaz, K, 2018. "Measuring Dynamic Connectedness with Large Bayesian VAR Models," Essex Finance Centre Working Papers 20937, University of Essex, Essex Business School.
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    51. Julián Andrada-Félix & Adrian Fernandez-Perez & Simón Sosvilla-Rivero, 2018. "Fear connectedness among asset classes," Applied Economics, Taylor & Francis Journals, vol. 50(39), pages 4234-4249, August.
    52. Mensi, Walid & Boubaker, Ferihane Zaraa & Al-Yahyaee, Khamis Hamed & Kang, Sang Hoon, 2018. "Dynamic volatility spillovers and connectedness between global, regional, and GIPSI stock markets," Finance Research Letters, Elsevier, vol. 25(C), pages 230-238.
    53. Cao, Li & Jiang, Junhua & Piljak, Vanja, 2023. "Did mega-regional trade agreements reshuffle the financial influence of the US, China, and Japan in ASEAN? Evidence from the volatility-spillover effects," Research in International Business and Finance, Elsevier, vol. 65(C).
    54. Dahl, Roy Endré & Jonsson, Erlendur, 2018. "Volatility spillover in seafood markets," Journal of Commodity Markets, Elsevier, vol. 12(C), pages 44-59.
    55. Sheheryar Malik & Ms. TengTeng Xu, 2017. "Interconnectedness of Global Systemically-Important Banks and Insurers," IMF Working Papers 2017/210, International Monetary Fund.
    56. Fabrizio Cipollini & Giampiero Gallo & Andrea Ugolini, 2016. "Median Response to Shocks: A Model for VaR Spillovers in East Asia," Econometrics Working Papers Archive 2016_01, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    57. Mensi, Walid & Hammoudeh, Shawkat & Al-Jarrah, Idries Mohammad Wanas & Sensoy, Ahmet & Kang, Sang Hoon, 2017. "Dynamic risk spillovers between gold, oil prices and conventional, sustainability and Islamic equity aggregates and sectors with portfolio implications," Energy Economics, Elsevier, vol. 67(C), pages 454-475.
    58. Chen, Wang & Ho, Kung-Cheng & Yang, Lu, 2020. "Network structures and idiosyncratic contagion in the European sovereign credit default swap market," International Review of Financial Analysis, Elsevier, vol. 72(C).
    59. Andrada-Félix, Julián & Fernandez-Perez, Adrian & Sosvilla-Rivero, Simón, 2020. "Distant or close cousins: Connectedness between cryptocurrencies and traditional currencies volatilities," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 67(C).
    60. González-Urteaga, Ana & Rubio, Gonzalo, 2022. "Guarantee requirements by European central counterparties and international volatility spillovers," Research in International Business and Finance, Elsevier, vol. 62(C).
    61. Dungey, Mardi & Matei, Marius & Treepongkaruna, Sirimon, 2020. "Examining stress in Asian currencies: A perspective offered by high frequency financial market data," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 67(C).
    62. Klaucke, Konstantin, 2025. "Text Spillover: Measuring connectedness of financial institutions based on news text data," The North American Journal of Economics and Finance, Elsevier, vol. 75(PA).
    63. Boakye, Robert Owusu & Mensah, Lord Kwaku & Kang, Sang Hoon & Osei, Kofi Acheampong, 2023. "Foreign exchange market return spillovers and connectedness among African countries," International Review of Financial Analysis, Elsevier, vol. 86(C).
    64. Muñoz Mendoza, Jorge A. & Veloso Ramos, Carmen L. & Delgado Fuentealba, Carlos L. & Araya Gómez, Iván E. & Sepúlveda Yelpo, Sandra M. & Cornejo Saavedra, Edinson E., 2024. "Connectedness in the global banking market network: Implications for risk management and financial policy," International Review of Financial Analysis, Elsevier, vol. 95(PB).
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    67. Gang-Jin Wang & Chi Xie & Kaijian He & H. Eugene Stanley, 2017. "Extreme risk spillover network: application to financial institutions," Quantitative Finance, Taylor & Francis Journals, vol. 17(9), pages 1417-1433, September.
    68. Damianov, Damian S. & Elsayed, Ahmed H., 2020. "Does Bitcoin add value to global industry portfolios?," Economics Letters, Elsevier, vol. 191(C).
    69. Dungey, Mardi & Harvey, John & Volkov, Vladimir, 2017. "The changing international network of sovereign debt and financial institutions," Working Papers 2017-04, University of Tasmania, Tasmanian School of Business and Economics.
    70. Muñoz Mendoza, Jorge A. & Veloso Ramos, Carmen L. & Delgado Fuentealba, Carlos L. & Cornejo Saavedra, Edinson E. & Sepúlveda Yelpo, Sandra M., 2024. "Stock, foreign exchange and commodity markets linkages: Implications for risk diversification and portfolio management," Global Finance Journal, Elsevier, vol. 63(C).
    71. Nishimura, Yusaku & Sun, Bianxia, 2018. "The intraday volatility spillover index approach and an application in the Brexit vote," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 55(C), pages 241-253.
    72. Tihana ŠKRINJARIĆ & Lidija DEDI & Boško ŠEGO, 2021. "Return and Volatility Spillover between Stock Prices and Exchange Rates in Croatia: A Spillover Methodology Approach," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 93-108, December.
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    74. Lin, Sihan & Chen, Shoudong, 2021. "Dynamic connectedness of major financial markets in China and America," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 646-656.
    75. Zhang, Wenting & He, Xie & Hamori, Shigeyuki, 2023. "The impact of the COVID-19 pandemic and Russia-Ukraine war on multiscale spillovers in green finance markets: Evidence from lower and higher order moments," International Review of Financial Analysis, Elsevier, vol. 89(C).
    76. Rosenkranz, Peter & Melchor, Monica, 2022. "Asia’s financial interconnectedness: Evolution, implications, and insights from past crises," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 685-707.
    77. Zhang, Weiping & Zhuang, Xintian & Lu, Yang, 2020. "Spatial spillover effects and risk contagion around G20 stock markets based on volatility network," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    78. Gonçalves Mazzeu, Joao Henrique & González-Rivera, Gloria & Ruiz Ortega, Esther & Veiga, Helena, 2016. "A Bootstrap Approach for Generalized Autocontour Testing," DES - Working Papers. Statistics and Econometrics. WS 23457, Universidad Carlos III de Madrid. Departamento de Estadística.
    79. Mensi, Walid & Al Rababa'a, Abdel Razzaq & Vo, Xuan Vinh & Kang, Sang Hoon, 2021. "Asymmetric spillover and network connectedness between crude oil, gold, and Chinese sector stock markets," Energy Economics, Elsevier, vol. 98(C).
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    95. Mensi, Walid & Aslan, Aylin & Vo, Xuan Vinh & Kang, Sang Hoon, 2023. "Time-frequency spillovers and connectedness between precious metals, oil futures and financial markets: Hedge and safe haven implications," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 219-232.
    96. Okorie, David Iheke & Lin, Boqiang, 2023. "Cryptocurrency spectrum and 2020 pandemic: Contagion analysis," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 29-38.
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  16. Diebold, Francis X. & Shin, Minchul, 2015. "Assessing point forecast accuracy by stochastic loss distance," Economics Letters, Elsevier, vol. 130(C), pages 37-38.

    Cited by:

    1. Sainan Jin & Valentina Corradi & Norman Swanson, 2015. "Robust Forecast Comparison," Departmental Working Papers 201502, Rutgers University, Department of Economics.
    2. Hitesh Doshi & Kris Jacobs & Rui Liu, 2021. "Information in the Term Structure: A Forecasting Perspective," Management Science, INFORMS, vol. 67(8), pages 5255-5277, August.
    3. Norman R. Swanson & Weiqi Xiong, 2018. "Big data analytics in economics: What have we learned so far, and where should we go from here?," Canadian Journal of Economics, Canadian Economics Association, vol. 51(3), pages 695-746, August.
    4. Emilian Dobrescu, 2014. "Attempting to Quantify the Accuracy of Complex Macroeconomic Forecasts," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 5-21, December.
    5. Valentina Corradi & Sainan Jin & Norman R. Swanson, 2023. "Robust forecast superiority testing with an application to assessing pools of expert forecasters," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 596-622, June.
    6. Yen, Yu-Min & Yen, Tso-Jung, 2021. "Testing forecast accuracy of expectiles and quantiles with the extremal consistent loss functions," International Journal of Forecasting, Elsevier, vol. 37(2), pages 733-758.

  17. Francis X. Diebold, 2015. "Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 1-1, January.
    See citations under working paper version above.
  18. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    See citations under working paper version above.
  19. Francis X. Diebold & Georg Strasser, 2013. "On the Correlation Structure of Microstructure Noise: A Financial Economic Approach," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 80(4), pages 1304-1337.
    See citations under working paper version above.
  20. Chen, Fei & Diebold, Francis X. & Schorfheide, Frank, 2013. "A Markov-switching multifractal inter-trade duration model, with application to US equities," Journal of Econometrics, Elsevier, vol. 177(2), pages 320-342.
    See citations under working paper version above.
  21. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    See citations under working paper version above.
  22. S. Borağan Aruoba & Francis X. Diebold & M. Ayhan Kose & Marco E. Terrones, 2011. "Globalization, the Business Cycle, and Macroeconomic Monitoring," NBER International Seminar on Macroeconomics, University of Chicago Press, vol. 7(1), pages 245-286.
    See citations under working paper version above.
  23. Christensen, Jens H.E. & Diebold, Francis X. & Rudebusch, Glenn D., 2011. "The affine arbitrage-free class of Nelson-Siegel term structure models," Journal of Econometrics, Elsevier, vol. 164(1), pages 4-20, September.
    See citations under working paper version above.
  24. S. Boragan Aruoba & Francis X. Diebold, 2010. "Real-Time Macroeconomic Monitoring: Real Activity, Inflation, and Interactions," American Economic Review, American Economic Association, vol. 100(2), pages 20-24, May.
    See citations under working paper version above.
  25. FrancisX. Diebold & Kamil Yilmaz, 2009. "Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets," Economic Journal, Royal Economic Society, vol. 119(534), pages 158-171, January.
    See citations under working paper version above.
  26. Francis X. Diebold / Kamil Yilmaz, 2009. "Equity Market Spillovers in the Americas," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 12(2), pages 55-65, August.

    Cited by:

    1. Yilmaz, Kamil, 2010. "Return and volatility spillovers among the East Asian equity markets," Journal of Asian Economics, Elsevier, vol. 21(3), pages 304-313, June.
    2. Tihana Škrinjarić, 2019. "Time Varying Spillovers between the Online Search Volume and Stock Returns: Case of CESEE Markets," IJFS, MDPI, vol. 7(4), pages 1-30, October.
    3. Mehmet Balcilar & Rangan Gupta & Duc Khuong Nguyen & Mark E. Wohar, 2018. "Causal effects of the United States and Japan on Pacific-Rim stock markets: nonparametric quantile causality approach," Applied Economics, Taylor & Francis Journals, vol. 50(53), pages 5712-5727, November.
    4. Asai, Manabu & Brugal, Ivan, 2013. "Forecasting volatility via stock return, range, trading volume and spillover effects: The case of Brazil," The North American Journal of Economics and Finance, Elsevier, vol. 25(C), pages 202-213.
    5. Chen, Hongyi & Funke, Michael & Tsang, Andrew, 2016. "The diffusion and dynamics of producer prices, deflationary pressure across Asian countries, and the role of China," BOFIT Discussion Papers 11/2016, Bank of Finland Institute for Emerging Economies (BOFIT).
    6. Habibi, Hamidreza & Mohammadi, Hassan, 2022. "Return and volatility spillovers across the Western and MENA countries," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    7. Tihana Škrinjarić & Zrinka Orlović, 2020. "Economic Policy Uncertainty and Stock Market Spillovers: Case of Selected CEE Markets," Mathematics, MDPI, vol. 8(7), pages 1-33, July.
    8. Panda, Ajaya Kumar & Panda, Pradiptarathi & Nanda, Swagatika & Parad, Atul, 2021. "Information bias and its spillover effect on return volatility: A study on stock markets in the Asia-Pacific region," Pacific-Basin Finance Journal, Elsevier, vol. 69(C).
    9. Yavas, Burhan F. & Dedi, Lidija, 2016. "An investigation of return and volatility linkages among equity markets: A study of selected European and emerging countries," Research in International Business and Finance, Elsevier, vol. 37(C), pages 583-596.
    10. Zheng, Yao & Osmer, Eric, 2021. "Housing price dynamics: The impact of stock market sentiment and the spillover effect," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 854-867.
    11. Prasad, Nalin & Grant, Andrew & Kim, Suk-Joong, 2018. "Time varying volatility indices and their determinants: Evidence from developed and emerging stock markets," International Review of Financial Analysis, Elsevier, vol. 60(C), pages 115-126.
    12. Mauricio Calani C., 2012. "Spillovers of the Credit Default Swap Market," Working Papers Central Bank of Chile 678, Central Bank of Chile.
    13. Zhou, Xiangyi & Zhang, Weijin & Zhang, Jie, 2012. "Volatility spillovers between the Chinese and world equity markets," Pacific-Basin Finance Journal, Elsevier, vol. 20(2), pages 247-270.
    14. Iwanicz-Drozdowska, Małgorzata & Rogowicz, Karol & Kurowski, Łukasz & Smaga, Paweł, 2021. "Two decades of contagion effect on stock markets: Which events are more contagious?," Journal of Financial Stability, Elsevier, vol. 55(C).
    15. Farid, Saqib & Naeem, Muhammad Abubakr & Paltrinieri, Andrea & Nepal, Rabindra, 2022. "Impact of COVID-19 on the quantile connectedness between energy, metals and agriculture commodities," Energy Economics, Elsevier, vol. 109(C).
    16. Favero, Carlo A., 2013. "Modelling and forecasting government bond spreads in the euro area: A GVAR model," Journal of Econometrics, Elsevier, vol. 177(2), pages 343-356.
    17. Bayraci, Selcuk & Demiralay, Sercan, 2013. "Conditional Autoregregressive Range (CARR) Based Volatility Spillover Index For the Eurozone Markets," MPRA Paper 51909, University Library of Munich, Germany.
    18. Arčabić, Vladimir & Škrinjarić, Tihana, 2021. "Sharing is caring: Spillovers and synchronization of business cycles in the European Union," Economic Modelling, Elsevier, vol. 96(C), pages 25-39.
    19. Yavas, Burhan F. & Malladi, Rama K., 2020. "Foreign direct investment and financial markets influences: Results from the United States," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
    20. Zhang, Bing & Wang, Peijie, 2014. "Return and volatility spillovers between china and world oil markets," Economic Modelling, Elsevier, vol. 42(C), pages 413-420.
    21. Yen-Hsien Lee & Ting-Huei Liao & Ya-Ling Huang & Tzu-Ling Huang, 2015. "Dynamic Spillovers between Oil and Stock Markets: New Approaches at Spillover Index," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 6(2), pages 178-189, April.
    22. Awartani, Basel & Maghyereh, Aktham Issa, 2013. "Dynamic spillovers between oil and stock markets in the Gulf Cooperation Council Countries," Energy Economics, Elsevier, vol. 36(C), pages 28-42.
    23. Lidija Dedi & Burhan F. Yavas, 2016. "Return and volatility spillovers in equity markets: An investigation using various GARCH methodologies," Cogent Economics & Finance, Taylor & Francis Journals, vol. 4(1), pages 1266788-126, December.
    24. Fowowe, Babajide & Shuaibu, Mohammed, 2016. "Dynamic spillovers between Nigerian, South African and international equity markets," International Economics, Elsevier, vol. 148(C), pages 59-80.
    25. Luo, Jiawen & Marfatia, Hardik A. & Ji, Qiang & Klein, Tony, 2023. "Co-volatility and asymmetric transmission of risks between the global oil and China's futures markets," Energy Economics, Elsevier, vol. 117(C).
    26. Wiesen, Thomas F.P. & Adekoya, Oluwasegun Babatunde & Oliyide, Johnson & Afatsao, Richard, 2024. "Does high volatility increase connectedness? A study of Asian equity markets," International Review of Financial Analysis, Elsevier, vol. 96(PB).
    27. Zhang, Weiping & Zhuang, Xintian & Lu, Yang, 2020. "Spatial spillover effects and risk contagion around G20 stock markets based on volatility network," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    28. Shakya, Shishir & Li, Bingxin & Etienne, Xiaoli, 2022. "Shale revolution, oil and gas prices, and drilling activities in the United States," Energy Economics, Elsevier, vol. 108(C).
    29. Genc, Ismail H., 2022. "Are Indian Subcontinent remittance markets connected to each other?," Journal of Asian Economics, Elsevier, vol. 80(C).
    30. Takashi Miyazaki & Shigeyuki Hamori, 2013. "Testing for causality between the gold return and stock market performance: evidence for ‘gold investment in case of emergency’," Applied Financial Economics, Taylor & Francis Journals, vol. 23(1), pages 27-40, January.
    31. Zornitsa Todorova, 2020. "Network Risk in the European Sovereign CDS Market," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 12(2), pages 137-154, December.
    32. Corradi, Valentina & Distaso, Walter & Fernandes, Marcelo, 2012. "International market links and volatility transmission," Journal of Econometrics, Elsevier, vol. 170(1), pages 117-141.

  27. Campbell, Sean D. & Diebold, Francis X., 2009. "Stock Returns and Expected Business Conditions: Half a Century of Direct Evidence," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(2), pages 266-278.
    See citations under working paper version above.
  28. Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009. "Real-Time Measurement of Business Conditions," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
    See citations under working paper version above.
  29. Jens H. E. Christensen & Francis X. Diebold & Glenn D. Rudebusch, 2009. "An arbitrage-free generalized Nelson--Siegel term structure model," Econometrics Journal, Royal Economic Society, vol. 12(3), pages 33-64, November.
    See citations under working paper version above.
  30. Diebold, Francis X. & Li, Canlin & Yue, Vivian Z., 2008. "Global yield curve dynamics and interactions: A dynamic Nelson-Siegel approach," Journal of Econometrics, Elsevier, vol. 146(2), pages 351-363, October.
    See citations under working paper version above.
  31. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 701-720, November.
    See citations under working paper version above.
  32. Andersen, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Vega, Clara, 2007. "Real-time price discovery in global stock, bond and foreign exchange markets," Journal of International Economics, Elsevier, vol. 73(2), pages 251-277, November.
    See citations under working paper version above.
  33. Peter F. Christoffersen & Francis X. Diebold, 2006. "Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics," Management Science, INFORMS, vol. 52(8), pages 1273-1287, August.
    See citations under working paper version above.
  34. Diebold, Francis X. & Rudebusch, Glenn D. & Borag[caron]an Aruoba, S., 2006. "The macroeconomy and the yield curve: a dynamic latent factor approach," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 309-338.
    See citations under working paper version above.
  35. Diebold, Francis X. & Li, Canlin, 2006. "Forecasting the term structure of government bond yields," Journal of Econometrics, Elsevier, vol. 130(2), pages 337-364, February.
    See citations under working paper version above.
  36. Michael W. Brandt & Francis X. Diebold, 2006. "A No-Arbitrage Approach to Range-Based Estimation of Return Covariances and Correlations," The Journal of Business, University of Chicago Press, vol. 79(1), pages 61-74, January.
    See citations under working paper version above.
  37. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Jin Wu, 2005. "A Framework for Exploring the Macroeconomic Determinants of Systematic Risk," American Economic Review, American Economic Association, vol. 95(2), pages 398-404, May.
    See citations under working paper version above.
  38. Francis X. Diebold & Monika Piazzesi & Glenn D. Rudebusch, 2005. "Modeling Bond Yields in Finance and Macroeconomics," American Economic Review, American Economic Association, vol. 95(2), pages 415-420, May.
    See citations under working paper version above.
  39. Sean D. Campbell & Francis X. Diebold, 2005. "Weather Forecasting for Weather Derivatives," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 6-16, March.
    See citations under working paper version above.
  40. Gene Amromin & Steven A. Sharpe, 2005. "From the horse’s mouth: gauging conditional expected stock returns from investor surveys," Proceedings, Board of Governors of the Federal Reserve System (U.S.).

    Cited by:

    1. Didier, Tatiana & Lowenkron, Alexandre, 2009. "The current account as a dynamic portfolio choice problem," Policy Research Working Paper Series 4861, The World Bank.

  41. Francis X. Diebold, 2004. "The Nobel Memorial Prize for Robert F. Engle," Scandinavian Journal of Economics, Wiley Blackwell, vol. 106(2), pages 165-185, June.
    See citations under working paper version above.
  42. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Clara Vega, 2003. "Micro Effects of Macro Announcements: Real-Time Price Discovery in Foreign Exchange," American Economic Review, American Economic Association, vol. 93(1), pages 38-62, March.
    See citations under working paper version above.
  43. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
    See citations under working paper version above.
  44. Diebold, Francis X., 2003. "The Et Interview: Professor Robert F. Engle, January 2003," Econometric Theory, Cambridge University Press, vol. 19(6), pages 1159-1193, December.

    Cited by:

    1. Aurélien Goutsmedt & Alexandre Truc, 2023. "An Independent European Macroeconomics? A History of European Macroeconomics through the Lens of the European Economic Review [Une macroéconomie européenne indépendante? Une histoire de la macroéco," Post-Print hal-04181833, HAL.
    2. Peter Hans Matthews, 2004. "Paradise Lost and Found? The Econometric Contributions of Clive W.J. Granger and Robert F. Engle," Middlebury College Working Paper Series 0416, Middlebury College, Department of Economics.
    3. Francis X. Diebold, 2004. "The Nobel Memorial Prize for Robert F. Engle," PIER Working Paper Archive 04-010, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    4. Masih, Mansur & Algahtani, Ibrahim & De Mello, Lurion, 2010. "Price dynamics of crude oil and the regional ethylene markets," Energy Economics, Elsevier, vol. 32(6), pages 1435-1444, November.
    5. Stavros Degiannakis & Alexandra Livada & Epaminondas Panas, 2008. "Rolling-sampled parameters of ARCH and Levy-stable models," Applied Economics, Taylor & Francis Journals, vol. 40(23), pages 3051-3067.
    6. Shuping Shi & Jun Yu, 2023. "Volatility Puzzle: Long Memory or Antipersistency," Management Science, INFORMS, vol. 69(7), pages 3861-3883, July.

  45. Sassan Alizadeh & Michael W. Brandt & Francis X. Diebold, 2002. "Range‐Based Estimation of Stochastic Volatility Models," Journal of Finance, American Finance Association, vol. 57(3), pages 1047-1091, June.

    Cited by:

    1. Hau, Harald, 2002. "The Role of Transaction Costs for Financial Volatility: Evidence from the Paris Bourse," CEPR Discussion Papers 3651, C.E.P.R. Discussion Papers.
    2. Volker Seiler, 2024. "The relationship between Chinese and FOB prices of rare earth elements – Evidence in the time and frequency domain," Post-Print hal-04549980, HAL.
    3. Jin-Huei Yeh & Jying-Nan Wang & Chung-Ming Kuan, 2014. "A noise-robust estimator of volatility based on interquantile ranges," Review of Quantitative Finance and Accounting, Springer, vol. 43(4), pages 751-779, November.
    4. Paola Zerilli, 2005. "Option pricing and spikes in volatility: theoretical and empirical analysis," Money Macro and Finance (MMF) Research Group Conference 2005 76, Money Macro and Finance Research Group.
    5. Villani, Mattias & Kohn, Robert & Giordani, Paolo, 2009. "Regression density estimation using smooth adaptive Gaussian mixtures," Journal of Econometrics, Elsevier, vol. 153(2), pages 155-173, December.
    6. Parthajit Kayal & S. Maheswaran, 2017. "Is USD-INR Really an Excessively Volatile Currency Pair?," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 15(2), pages 329-342, June.
    7. Phuong Pham, Thu & Joakim Westerholm, P., 2013. "An international trend in market design: Endogenous effects of limit order book transparency on volatility, spreads, depth and volume," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 27(C), pages 202-223.
    8. Gloria Gonzalez‐Rivera & Yun Luo & Esther Ruiz, 2020. "Prediction regions for interval‐valued time series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(4), pages 373-390, June.
    9. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "Are realized volatility models good candidates for alternative Value at Risk prediction strategies?," MPRA Paper 30364, University Library of Munich, Germany.
    10. Elena Andreou, 2004. "The Impact of Sampling Frequency and Volatility Estimators on Change-Point Tests," Journal of Financial Econometrics, Oxford University Press, vol. 2(2), pages 290-318.
    11. Narayan, Paresh Kumar & Mishra, Sagarika & Sharma, Susan Sunila & Liu, Ruipeng, 2013. "Determinants of stock price bubbles," Working Papers fe_2013_06, Deakin University, Department of Economics.
    12. Suraj Kumar & Krishna Prasanna, 2019. "Global Financial Crisis: Dynamics of Liquidity Risk in Emerging Asia," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 18(3), pages 339-362, December.
    13. Muneer Shaik & S. Maheswaran, 2019. "Robust Volatility Estimation with and Without the Drift Parameter," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(1), pages 57-91, March.
    14. Díaz-Mendoza, Ana-Carmen & Pardo, Angel, 2020. "Holidays, weekends and range-based volatility," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    15. Francis X. Diebold & Glenn D. Rudebusch, 2019. "On the Evolution of U.S. Temperature Dynamics," Papers 1907.06303, arXiv.org, revised Jan 2021.
    16. Gifty Malhotra & R. Srivastava & H. C. Taneja, 2019. "Comparative Study of Two Extensions of Heston Stochastic Volatility Model," Papers 1912.10237, arXiv.org.
    17. Meng, Xiaochun & Taylor, James W., 2018. "An approximate long-memory range-based approach for value at risk estimation," International Journal of Forecasting, Elsevier, vol. 34(3), pages 377-388.
    18. Paolo Mazza & Mikael Petitjean, 2016. "On the usefulness of intraday price ranges to gauge liquidity in cap-based portfolios," Post-Print hal-01562991, HAL.
    19. Tobias Adrian & Joshua V. Rosenberg, 2006. "Stock returns and volatility: pricing the short-run and long-run components of market risk," Staff Reports 254, Federal Reserve Bank of New York.
    20. Christoffersen, Peter & Jacobs, Kris & Ornthanalai, Chayawat & Wang, Yintian, 2008. "Option valuation with long-run and short-run volatility components," Journal of Financial Economics, Elsevier, vol. 90(3), pages 272-297, December.
    21. Lakshmi Padmakumari & S. Maheswaran, 2018. "Covariance estimation using random permutations," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 5(01), pages 1-21, March.
    22. Yin-Wong Cheung, 2007. "An empirical model of daily highs and lows," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 12(1), pages 1-20.
    23. Petri Kyröläinen, 2008. "Day trading and stock price volatility," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 32(1), pages 75-89, January.
    24. Manabu Asai & Michael McAleer, 2013. "Leverage and Feedback Effects on Multifactor Wishart Stochastic Volatility for Option Pricing," Tinbergen Institute Discussion Papers 13-003/III, Tinbergen Institute.
    25. Chiu, Ching-Wai (Jeremy) & Harris, Richard & Stoja, Evarist & Chin, Michael, 2016. "Financial market volatility, macroeconomic fundamentals and investor sentiment," Bank of England working papers 608, Bank of England.
    26. Broto, Carmen & Ruiz Ortega, Esther, 2002. "Estimation methods for stochastic volatility models: a survey," DES - Working Papers. Statistics and Econometrics. WS ws025414, Universidad Carlos III de Madrid. Departamento de Estadística.
    27. Li, Hongquan & Hong, Yongmiao, 2011. "Financial volatility forecasting with range-based autoregressive volatility model," Finance Research Letters, Elsevier, vol. 8(2), pages 69-76, June.
    28. Bodnar, Taras & Hautsch, Nikolaus, 2016. "Dynamic conditional correlation multiplicative error processes," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 41-67.
    29. Uwe Helmes & Julia Henker & Thomas Henker & Tom Smith, 2017. "Effect of the ban on short selling on market prices and volatility," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 57(3), pages 727-757, September.
    30. Alicia Garcia-Herrero & Eric Girardin, 2013. "China's Monetary Policy Communication: Money Markets not only Listen, They also Understand," Working Papers 022013, Hong Kong Institute for Monetary Research.
    31. Hendershott, Terrence & Moulton, Pamela C., 2011. "Automation, speed, and stock market quality: The NYSE's Hybrid," Journal of Financial Markets, Elsevier, vol. 14(4), pages 568-604, November.
    32. Yuta Kurose, 2021. "Stochastic volatility model with range-based correction and leverage," Papers 2110.00039, arXiv.org, revised Oct 2021.
    33. Mert Demirer & Francis X. Diebold & Laura Liu & Kamil Yilmaz, 2015. "Estimating Global Bank Network Connectedness," PIER Working Paper Archive 15-025, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 25 Jul 2015.
    34. Sinda Hadhri, 2021. "Fear of the Coronavirus and Cryptocurrencies' returns," Economics Bulletin, AccessEcon, vol. 41(3), pages 2041-2054.
    35. Yin, Kedong & Liu, Zhe & Jin, Xue, 2020. "Interindustry volatility spillover effects in China’s stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    36. Pong, Shiuyan & Shackleton, Mark B. & Taylor, Stephen J. & Xu, Xinzhong, 2004. "Forecasting currency volatility: A comparison of implied volatilities and AR(FI)MA models," Journal of Banking & Finance, Elsevier, vol. 28(10), pages 2541-2563, October.
    37. Junni L. Zhang & Wolfgang Karl Hardle & Cathy Y. Chen & Elisabeth Bommes, 2020. "Distillation of News Flow into Analysis of Stock Reactions," Papers 2009.10392, arXiv.org.
    38. Elezovic, Suad, 2009. "Functional modelling of volatility in the Swedish limit order book," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2107-2118, April.
    39. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    40. Matteo Barigozzi & Christian Brownlees, 2015. "Nets: Network Estimation for Time Series," Working Papers 723, Barcelona School of Economics.
    41. Clancey-Shang, Danjue, 2023. "COVID lockdown, Robinhood traders, and liquidity in stock and option markets," International Review of Financial Analysis, Elsevier, vol. 90(C).
    42. Colacito, Riccardo & Engle, Robert F. & Ghysels, Eric, 2011. "A component model for dynamic correlations," Journal of Econometrics, Elsevier, vol. 164(1), pages 45-59, September.
    43. Cathy W. S. Chen & Richard Gerlach & Bruce B. K. Hwang & Michael McAleer, 2011. "Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intra-day Range," KIER Working Papers 775, Kyoto University, Institute of Economic Research.
    44. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "The role of high frequency intra-daily data, daily range and implied volatility in multi-period Value-at-Risk forecasting," MPRA Paper 35252, University Library of Munich, Germany.
    45. Chen, Fei & Diebold, Francis X. & Schorfheide, Frank, 2013. "A Markov-switching multifractal inter-trade duration model, with application to US equities," Journal of Econometrics, Elsevier, vol. 177(2), pages 320-342.
    46. Su, Xianfang, 2020. "Dynamic behaviors and contributing factors of volatility spillovers across G7 stock markets," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
    47. Fabrizio Cipollini & Giampiero M. Gallo & Edoardo Otranto, 2019. "Realized Volatility Forecasting: Robustness to Measurement Errors," Econometrics Working Papers Archive 2019_04, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    48. Chris Bardgett & Elise Gourier & Markus Leippold, 2016. "Inferring Volatility Dynamics and Risk Premia from the S&P 500 and VIX markets," Working Papers 780, Queen Mary University of London, School of Economics and Finance.
    49. Matteo Luciani & David Veredas, 2012. "A model for vast panels of volatilities," Working Papers 1230, Banco de España.
    50. Robert Azencott & Peng Ren & Ilya Timofeyev, 2017. "Realized volatility and parametric estimation of Heston SDEs," Papers 1706.04566, arXiv.org, revised Mar 2020.
    51. Kumar, Dilip, 2015. "Sudden changes in extreme value volatility estimator: Modeling and forecasting with economic significance analysis," Economic Modelling, Elsevier, vol. 49(C), pages 354-371.
    52. Luca Rossi, 2020. "Indicators of uncertainty: a brief user’s guide," Questioni di Economia e Finanza (Occasional Papers) 564, Bank of Italy, Economic Research and International Relations Area.
    53. Z. Sun & P. A. Hamill & Y. Li & Y. C. Yang & S. A. Vigne, 2019. "Did long-memory of liquidity signal the European sovereign debt crisis?," Annals of Operations Research, Springer, vol. 282(1), pages 355-377, November.
    54. Wang, Junbo & Wu, Chunchi, 2015. "Liquidity, credit quality, and the relation between volatility and trading activity: Evidence from the corporate bond market," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 183-203.
    55. Baig, Ahmed S. & Chen, Mengxi, 2022. "Did the COVID-19 pandemic (really) positively impact the IPO Market? An Analysis of information uncertainty," Finance Research Letters, Elsevier, vol. 46(PB).
    56. Oliver Pfante & Nils Bertschinger, 2019. "Volatility Inference And Return Dependencies In Stochastic Volatility Models," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(03), pages 1-44, May.
    57. Degiannakis, Stavros & Livada, Alexandra, 2013. "Realized volatility or price range: Evidence from a discrete simulation of the continuous time diffusion process," Economic Modelling, Elsevier, vol. 30(C), pages 212-216.
    58. Fiszeder, Piotr & Perczak, Grzegorz, 2016. "Low and high prices can improve volatility forecasts during periods of turmoil," International Journal of Forecasting, Elsevier, vol. 32(2), pages 398-410.
    59. Dilip Kumar, 2016. "Sudden changes in crude oil price volatility: an application of extreme value volatility estimator," American Journal of Finance and Accounting, Inderscience Enterprises Ltd, vol. 4(3/4), pages 215-234.
    60. Galli, Fausto, 2014. "Stochastic conditonal range, a latent variable model for financial volatility," MPRA Paper 54030, University Library of Munich, Germany.
    61. Peter Reinhard Hansen & Zhuo Huang, 2012. "Exponential GARCH Modeling with Realized Measures of Volatility," Economics Working Papers ECO2012/26, European University Institute.
    62. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2002. "Parametric and Nonparametric Volatility Measurement," Center for Financial Institutions Working Papers 02-27, Wharton School Center for Financial Institutions, University of Pennsylvania.
    63. Pagano, Michael S. & Peng, Lin & Schwartz, Robert A., 2013. "A call auction's impact on price formation and order routing: Evidence from the NASDAQ stock market," Journal of Financial Markets, Elsevier, vol. 16(2), pages 331-361.
    64. Miao, Ke & Phillips, Peter C.B. & Su, Liangjun, 2023. "High-dimensional VARs with common factors," Journal of Econometrics, Elsevier, vol. 233(1), pages 155-183.
    65. Alfred Lehar & Christine Parlour & Marius Zoican, 2023. "Fragmentation and optimal liquidity supply on decentralized exchanges," Papers 2307.13772, arXiv.org, revised May 2024.
    66. Duong, Huu Nhan & Kalev, Petko S. & Tian, Xiao, 2023. "Short selling, divergence of opinion and volatility in the corporate bond market," Journal of Economic Dynamics and Control, Elsevier, vol. 147(C).
    67. Lazar, Emese & Xue, Xiaohan, 2020. "Forecasting risk measures using intraday data in a generalized autoregressive score framework," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1057-1072.
    68. Eduardo Rossi & Paolo Santucci de Magistris, 2013. "A No‐Arbitrage Fractional Cointegration Model for Futures and Spot Daily Ranges," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 33(1), pages 77-102, January.
    69. Karmakar, Madhusudan & Paul, Samit, 2016. "Intraday risk management in International stock markets: A conditional EVT approach," International Review of Financial Analysis, Elsevier, vol. 44(C), pages 34-55.
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    1. Allen, P. Geoffrey & Morzuch, Bernard J., 2006. "Twenty-five years of progress, problems, and conflicting evidence in econometric forecasting. What about the next 25 years?," International Journal of Forecasting, Elsevier, vol. 22(3), pages 475-492.
    2. Taylor, James W., 2004. "Volatility forecasting with smooth transition exponential smoothing," International Journal of Forecasting, Elsevier, vol. 20(2), pages 273-286.

  49. Andersen T. G & Bollerslev T. & Diebold F. X & Labys P., 2001. "The Distribution of Realized Exchange Rate Volatility," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 42-55, March.

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    1. Eric Hillebrand & Gunther Schnabl & Yasemin Ulu, 2006. "Japanese Foreign Exchange Intervention and the Yen/Dollar Exchange Rate: A Simultaneous Equations Approach Using Realized Volatility," CESifo Working Paper Series 1766, CESifo.
    2. Ambrogio Cesa-Bianchi & M. Hashem Pesaran & Alessandro Rebucci, 2014. "Uncertainty and Economic Activity: A Global Perspective," CESifo Working Paper Series 4736, CESifo.
    3. Fernando Perez-cruz & Julio Afonso-rodriguez & Javier Giner, 2003. "Estimating GARCH models using support vector machines," Quantitative Finance, Taylor & Francis Journals, vol. 3(3), pages 163-172.
    4. Buckle, Mike & Chen, Jing & Guo, Qian & Li, Xiaoxi, 2023. "Does smile help detect the UK's price leadership change after MiFID?," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 756-769.
    5. Per Frederiksen & Morten Orregaard Nielsen, 2008. "Bias-Reduced Estimation of Long-Memory Stochastic Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 6(4), pages 496-512, Fall.
    6. Takaishi, Tetsuya, 2018. "Bias correction in the realized stochastic volatility model for daily volatility on the Tokyo Stock Exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 500(C), pages 139-154.
    7. Jurado Pedroza Wilfrido, 2021. "Around-the-Clock USD/MXN Volatility: Macroeconomic Announcement Spillovers and FX Market Intervention Mechanisms," Working Papers 2021-05, Banco de México.
    8. Wu, Zhen-Xing & Gau, Yin-Feng, 2022. "Informativeness of trades around macroeconomic announcements in the foreign exchange market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 78(C).
    9. Audrino, Francesco & Fengler, Matthias, 2013. "Are classical option pricing models consistent with observed option second-order moments? Evidence from high-frequency data," Economics Working Paper Series 1311, University of St. Gallen, School of Economics and Political Science.
    10. Cotter, John & Dowd, Kevin, 2007. "The tail risks of FX return distributions: A comparison of the returns associated with limit orders and market orders," Finance Research Letters, Elsevier, vol. 4(3), pages 146-154, September.
    11. Michel Beine & Oscar Bernal & Jean-Yves Gnabo & Christelle Lecourt, 2007. "Intervention Policy of the BoJ: a Unified Approach," LSF Research Working Paper Series 07-19, Luxembourg School of Finance, University of Luxembourg.
    12. Ahmed, Walid M.A., 2021. "How do Islamic equity markets respond to good and bad volatility of cryptocurrencies? The case of Bitcoin," Pacific-Basin Finance Journal, Elsevier, vol. 70(C).
    13. Baillie, Richard T. & Kapetanios, George, 2007. "Testing for Neglected Nonlinearity in Long-Memory Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 447-461, October.
    14. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2020. "The contribution of intraday jumps to forecasting the density of returns," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02505861, HAL.
    15. Nielsen, Morten Ørregaard & Frederiksen, Per, 2008. "Finite sample accuracy and choice of sampling frequency in integrated volatility estimation," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 265-286, March.
    16. Chen, Xiaohong & Hansen, Lars Peter & Carrasco, Marine, 2008. "Nonlinearity and Temporal Dependence," Working Papers 48, Yale University, Department of Economics.
    17. Ilze Kalnina & Natalia Sizova, 2015. "Estimation of volatility measures using high frequency data (in Russian)," Quantile, Quantile, issue 13, pages 3-14, May.
    18. Rombouts, Jeroen V.K. & Stentoft, Lars & Violante, Francesco, 2020. "Dynamics of variance risk premia: A new model for disentangling the price of risk," Journal of Econometrics, Elsevier, vol. 217(2), pages 312-334.
    19. Lu, Yang K. & Perron, Pierre, 2010. "Modeling and forecasting stock return volatility using a random level shift model," Journal of Empirical Finance, Elsevier, vol. 17(1), pages 138-156, January.
    20. Jensen Mark J., 2016. "Robust estimation of nonstationary, fractionally integrated, autoregressive, stochastic volatility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(4), pages 455-475, September.
    21. Thierry Ane & Carole Metais, 2010. "Jump Distribution Characteristics: Evidence from European Stock Markets," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 9(1), pages 1-22, April.
    22. Ulrich Hounyo & Sílvia Goncalves & Nour Meddahi, 2013. "Bootstrapping pre-averaged realized volatility under market microstructure noise," CREATES Research Papers 2013-28, Department of Economics and Business Economics, Aarhus University.
    23. Fengler, Matthias R. & Mammen, Enno & Vogt, Michael, 2013. "Additive modeling of realized variance: tests for parametric specifications and structural breaks," Economics Working Paper Series 1332, University of St. Gallen, School of Economics and Political Science.
    24. Ciarreta, Aitor & Zarraga, Ainhoa, 2016. "Modeling realized volatility on the Spanish intra-day electricity market," Energy Economics, Elsevier, vol. 58(C), pages 152-163.
    25. Ralf Becker & Adam Clements, 2007. "Are combination forecasts of S&P 500 volatility statistically superior?," NCER Working Paper Series 17, National Centre for Econometric Research.
    26. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "Are realized volatility models good candidates for alternative Value at Risk prediction strategies?," MPRA Paper 30364, University Library of Munich, Germany.
    27. G.M. Gallo & D. Lacava & E. Otranto, 2020. "On Classifying the Effects of Policy Announcements on Volatility," Working Paper CRENoS 202008, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    28. Adam Clements & Yin Liao, 2014. "The role in index jumps and cojumps in forecasting stock index volatility: Evidence from the Dow Jones index," NCER Working Paper Series 101, National Centre for Econometric Research.
    29. Mattiussi, V. & Iori, G., 2006. "Currency futures volatility during the 1997 East Asian crisis: an application of Fourier analysis," Working Papers 06/09, Department of Economics, City University London.
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    Cited by:

    1. Viv. B Hall & McDermott C. John, 2004. "Regional Business Cycles in New Zealand: Do they exist? What might drive them?," ERSA conference papers ersa04p200, European Regional Science Association.
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    1. Niyibizi, Bart & Brorsen, Wade & Park, Eunchun, 2018. "Using Bayesian Kriging for Spatial Smoothing of Trends in the Means and Variances of Crop Yield Densities," 2018 Annual Meeting, August 5-7, Washington, D.C. 274403, Agricultural and Applied Economics Association.
    2. Thi Huyen Tran & Robert Ślepaczuk, 2022. "Quantile regression analysis to predict GDP distribution using data from the US and UK," Working Papers 2022-30, Faculty of Economic Sciences, University of Warsaw.
    3. Felix Wick & Ulrich Kerzel & Martin Hahn & Moritz Wolf & Trapti Singhal & Daniel Stemmer & Jakob Ernst & Michael Feindt, 2021. "Demand Forecasting of Individual Probability Density Functions with Machine Learning," SN Operations Research Forum, Springer, vol. 2(3), pages 1-39, September.
    4. Bratu, Mihaela, 2013. "The Assessment And Improvement Of The Accuracy For The Forecast Intervals," Working Papers of Macroeconomic Modelling Seminar 132602, Institute for Economic Forecasting.
    5. TAKEDA Yosuke, 2014. "Quantifying the Beauty Contest: Density Inflation-Forecasts of Professional Japanese Forecasters," ESRI Discussion paper series 309, Economic and Social Research Institute (ESRI).

  60. Christoffersen, Peter F & Diebold, Francis X, 1998. "Cointegration and Long-Horizon Forecasting," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(4), pages 450-458, October.
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  61. Francis X. Diebold & Lee E. Ohanian & Jeremy Berkowitz, 1998. "Dynamic Equilibrium Economies: A Framework for Comparing Models and Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(3), pages 433-451.
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    1. Luca Benati, 2008. "The “Great Moderation” in the United Kingdom," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(1), pages 121-147, February.
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    3. Luca Benati, 2003. "Evolving Post-World War II U.K. Economic Performance," Computing in Economics and Finance 2003 171, Society for Computational Economics.
    4. Jeremy Berkowitz & Lutz Kilian, 2000. "Recent developments in bootstrapping time series," Econometric Reviews, Taylor & Francis Journals, vol. 19(1), pages 1-48.
    5. Luboš Hanus & Lukáš Vácha, 2020. "Growth cycle synchronization of the Visegrad Four and the European Union," Empirical Economics, Springer, vol. 58(4), pages 1779-1795, April.
    6. Baumeister, Christiane & Benati, Luca, 2010. "Unconventional monetary policy and the great recession - Estimating the impact of a compression in the yield spread at the zero lower bound," Working Paper Series 1258, European Central Bank.
    7. Adam Cagliarini & Fiona Price, 2017. "Exploring the Link between the Macroeconomic and Financial Cycles," RBA Annual Conference Volume (Discontinued), in: Jonathan Hambur & John Simon (ed.),Monetary Policy and Financial Stability in a World of Low Interest Rates, Reserve Bank of Australia.
    8. Assenmacher-Wesche, Katrin & Gerlach, Stefan, 2008. "Money growth, output gaps and inflation at low and high frequency: Spectral estimates for Switzerland," Journal of Economic Dynamics and Control, Elsevier, vol. 32(2), pages 411-435, February.
    9. Francis X. Diebold & Lee E. Ohanian & Jeremy Berkowitz, 1998. "Dynamic Equilibrium Economies: A Framework for Comparing Models and Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(3), pages 433-451.
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    11. Benati, Luca & Mumtaz, Haroon, 2007. "U.S. evolving macroeconomic dynamics: a structural investigation," Working Paper Series 746, European Central Bank.
    12. Schüler, Yves S. & Hiebert, Paul P. & Peltonen, Tuomas A., 2020. "Financial cycles: Characterisation and real-time measurement," Journal of International Money and Finance, Elsevier, vol. 100(C).
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    18. Luca Benati, 2018. "Money and Credit: A Long-Term View," Diskussionsschriften dp1811, Universitaet Bern, Departement Volkswirtschaft.
    19. Lovcha, Yuliya & Perez-Laborda, Alejandro, 2018. "Monetary policy shocks, inflation persistence, and long memory," Journal of Macroeconomics, Elsevier, vol. 55(C), pages 117-127.
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    23. Lovcha, Yuliya & Pérez Laborda, Àlex, 2016. "Structural shocks and dinamic elasticities in a long memory model of the US gasoline retail market," Working Papers 2072/261538, Universitat Rovira i Virgili, Department of Economics.
    24. Lovcha, Yuliya & Pérez Laborda, Alejandro, 2016. "Frequency-Domain Estimation as an Alternative to Pre-Filtering External Cycles in Structural VAR Analysis," Working Papers 2072/290743, Universitat Rovira i Virgili, Department of Economics.
    25. Luca Benati & Peter N. Ireland, 2017. "Money-Multiplier Shocks," Boston College Working Papers in Economics 933, Boston College Department of Economics.
    26. Christiane Baumeister & Luca Benati, 2013. "Unconventional Monetary Policy and the Great Recession: Estimating the Macroeconomic Effects of a Spread Compression at the Zero Lower Bound," International Journal of Central Banking, International Journal of Central Banking, vol. 9(2), pages 165-212, June.
    27. Liu, Yixuan & Kirch, Claudia & Lee, Jeong Eun & Meyer, Renate, 2024. "A nonparametrically corrected likelihood for Bayesian spectral analysis of multivariate time series," Computational Statistics & Data Analysis, Elsevier, vol. 199(C).
    28. Hangyong Lee and Jin Lee, 2019. "Inflation Co-Movement in the ASEAN Countries," Journal of Economic Development, Chung-Ang Unviersity, Department of Economics, vol. 44(4), pages 135-152, December.
    29. Gonzalo Camba-Mendez & George Kapetanios, 2005. "Statistical Tests of the Rank of a Matrix and Their Applications in Econometric Modelling," Working Papers 541, Queen Mary University of London, School of Economics and Finance.
    30. Bollerslev, Tim & Wright, Jonathan H., 2000. "Semiparametric estimation of long-memory volatility dependencies: The role of high-frequency data," Journal of Econometrics, Elsevier, vol. 98(1), pages 81-106, September.
    31. Shirin Nezampour & Alireza Nematollahi & Robert T. Krafty & Mehdi Maadooliat, 2024. "A new approach to nonparametric estimation of multivariate spectral density function using basis expansion," Computational Statistics, Springer, vol. 39(7), pages 3625-3641, December.

  63. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-883, November.

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    1. Leopoldo Catania & Nima Nonejad, 2016. "Density Forecasts and the Leverage Effect: Some Evidence from Observation and Parameter-Driven Volatility Models," Papers 1605.00230, arXiv.org, revised Nov 2016.
    2. Alla, Zineddine & Espinoza, Raphael & Li, Helen & Segoviano, Miguel, 2018. "Macroprudential stress tests: a reduced-form approach to quantifying systemic risk losses," LSE Research Online Documents on Economics 118930, London School of Economics and Political Science, LSE Library.
    3. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
    4. Dick van Dijk & Timo Terasvirta & Philip Hans Franses, 2002. "Smooth Transition Autoregressive Models — A Survey Of Recent Developments," Econometric Reviews, Taylor & Francis Journals, vol. 21(1), pages 1-47.
    5. Clements, Michael P., 2008. "Explanations of the inconsistencies in survey respondents'forecasts," The Warwick Economics Research Paper Series (TWERPS) 870, University of Warwick, Department of Economics.
    6. Gergely Ganics & Barbara Rossi & Tatevik Sekhposyan, 2019. "From fixed-event to fixed-horizon density forecasts: Obtaining measures of multi-horizon uncertainty from survey density forecasts," Economics Working Papers 1689, Department of Economics and Business, Universitat Pompeu Fabra.
    7. Zhongxian Men & Tony S. Wirjanto & Adam W. Kolkiewicz, 2021. "Multiscale Stochastic Volatility Model with Heavy Tails and Leverage Effects," JRFM, MDPI, vol. 14(5), pages 1-28, May.
    8. Lee, Tae-Hwy & Long, Xiangdong, 2009. "Copula-based multivariate GARCH model with uncorrelated dependent errors," Journal of Econometrics, Elsevier, vol. 150(2), pages 207-218, June.
    9. Buncic, Daniel & Müller, Oliver, 2017. "Measuring the output gap in Switzerland with linear opinion pools," Economic Modelling, Elsevier, vol. 64(C), pages 153-171.
    10. Corsi, Fulvio & Kretschmer, Uta & Mittnik, Stefan & Pigorsch, Christian, 2005. "The volatility of realized volatility," CFS Working Paper Series 2005/33, Center for Financial Studies (CFS).
    11. Isao Ishida, 2005. "Scanning Multivariate Conditional Densities with Probability Integral Transforms," CARF F-Series CARF-F-045, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    12. Friedmann, Ralph & Sanddorf-Kohle, Walter G., 2007. "A conditional distribution model for limited stock index returns," Journal of Economic Dynamics and Control, Elsevier, vol. 31(3), pages 721-741, March.
    13. VEREDAS, David & RODRIGUEZ-POO, Juan & ESPASA, Antoni, 2002. "On the (intradaily) seasonality and dynamics of a financial point process: a semiparametric approach," LIDAM Discussion Papers CORE 2002023, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    14. Markku Lanne, 2009. "Properties of Market-Based and Survey Macroeconomic Forecasts for Different Data Releases," Economics Bulletin, AccessEcon, vol. 29(3), pages 2231-2240.
    15. Jonas Dovern & Hans Manner, 2018. "Order Invariant Tests for Proper Calibration of Multivariate Density Forecasts," CESifo Working Paper Series 7023, CESifo.
    16. Delle Monache, Davide & Petrella, Ivan, 2017. "Adaptive models and heavy tails with an application to inflation forecasting," International Journal of Forecasting, Elsevier, vol. 33(2), pages 482-501.
    17. Michael Clements, 2016. "Are Macroeconomic Density Forecasts Informative?," ICMA Centre Discussion Papers in Finance icma-dp2016-02, Henley Business School, University of Reading.
    18. Stelios D. Bekiros & Alessia Paccagnini, 2014. "Estimating point and density forecasts for the US economy with a factor-augmented vector autoregressive DSGE model," Open Access publications 10197/7588, School of Economics, University College Dublin.
    19. Ng, Jason & Forbes, Catherine S. & Martin, Gael M. & McCabe, Brendan P.M., 2013. "Non-parametric estimation of forecast distributions in non-Gaussian, non-linear state space models," International Journal of Forecasting, Elsevier, vol. 29(3), pages 411-430.
    20. Memmel, Christoph & Wehn, Carsten, 2005. "The supervisor's portfolio: the market price risk of German banks from 2001 to 2003 - Analysis and models for risk aggregation," Discussion Paper Series 2: Banking and Financial Studies 2005,02, Deutsche Bundesbank.
    21. Fildes, Robert & Stekler, Herman, 2002. "The state of macroeconomic forecasting," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 435-468, December.
    22. Luc Bauwens & Pierre Giot & Joachim Grammig & David Veredas, 2000. "A Comparison of Financial Duration Models via Density Forecasts," Econometric Society World Congress 2000 Contributed Papers 0810, Econometric Society.
    23. Jacinto Marabel Romo, 2012. "Volatility Regimes For The Vix Index," Revista de Economia Aplicada, Universidad de Zaragoza, Departamento de Estructura Economica y Economia Publica, vol. 20(2), pages 111-134, Autumn.
    24. Carlos Capistrán-Carmona, 2005. "Bias in Federal Reserve Inflation Forecasts: Is the Federal Reserve Irrational or Just Cautious?," Computing in Economics and Finance 2005 127, Society for Computational Economics.
    25. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2019. "How is Machine Learning Useful for Macroeconomic Forecasting?," CIRANO Working Papers 2019s-22, CIRANO.
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  65. Diebold, Francis X & Neumark, David & Polsky, Daniel, 1997. "Job Stability in the United States," Journal of Labor Economics, University of Chicago Press, vol. 15(2), pages 206-233, April.
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  66. Diebold, Francis X. & Lamb, Russell L., 1997. "Why are estimates of agricultural supply response so variable?," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 357-373.
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  69. Diebold, Francis X & Rudebusch, Glenn D, 1996. "Measuring Business Cycles: A Modern Perspective," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 67-77, February.
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  70. Koehler, Anne & Diebold, Francis X. & Giogianni, Lorenzo & Inoue, Atsushi, 1996. "Software review," International Journal of Forecasting, Elsevier, vol. 12(2), pages 309-315, June.

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  71. Diebold, Francis X & Senhadji, Abdelhak S, 1996. "The Uncertain Unit Root in Real GNP: Comment," American Economic Review, American Economic Association, vol. 86(5), pages 1291-1298, December.

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    69. Darné, Olivier, 2009. "The uncertain unit root in real GNP: A re-examination," Journal of Macroeconomics, Elsevier, vol. 31(1), pages 153-166, March.
    70. Noriega Antonio E. & Rodríguez-Pérez Cid Alonso, 2011. "Stationarity, structural breaks, and economic growth in Mexico: 1895-2008," Working Papers 2011-11, Banco de México.

  72. Christoffersen, Peter F & Diebold, Francis X, 1996. "Further Results on Forecasting and Model Selection under Asymmetric Loss," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(5), pages 561-571, Sept.-Oct.
    See citations under working paper version above.
  73. Diebold, Francis X. & Lindner, Peter, 1996. "Fractional integration and interval prediction," Economics Letters, Elsevier, vol. 50(3), pages 305-313, March.

    Cited by:

    1. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 1999. "The Distribution of Exchange Rate Volatility," Center for Financial Institutions Working Papers 99-08, Wharton School Center for Financial Institutions, University of Pennsylvania.
    2. Gil-Alana, Luis A. & Moreno, Antonio, 2012. "Uncovering the US term premium: An alternative route," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 1181-1193.
    3. Francis X. Diebold & Jose A. Lopez, 1996. "Forecast Evaluation and Combination," NBER Technical Working Papers 0192, National Bureau of Economic Research, Inc.
    4. Papailias, Fotis & Fruet Dias, Gustavo, 2015. "Forecasting long memory series subject to structural change: A two-stage approach," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1056-1066.
    5. Barkoulas, John T. & Baum, Christopher F. & Chakraborty, Atreya, 2001. "Waves and persistence in merger and acquisition activity," Economics Letters, Elsevier, vol. 70(2), pages 237-243, February.
    6. Pérez, Ana & Ruiz Ortega, Esther, 2001. "Modelos de memoria larga para series económicas y financieras," DES - Documentos de Trabajo. Estadística y Econometría. DS ds010101, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. Baillie, Richard T. & Kongcharoen, Chaleampong & Kapetanios, George, 2012. "Prediction from ARFIMA models: Comparisons between MLE and semiparametric estimation procedures," International Journal of Forecasting, Elsevier, vol. 28(1), pages 46-53.
    8. M. Chudý & S. Karmakar & W. B. Wu, 2020. "Long-term prediction intervals of economic time series," Empirical Economics, Springer, vol. 58(1), pages 191-222, January.
    9. Eisinga, Rob & Franses, Philip Hans & Ooms, Marius, 1999. "Forecasting long memory left-right political orientations," International Journal of Forecasting, Elsevier, vol. 15(2), pages 185-199, April.
    10. John Barkoulas & Christopher Baum & Nickolaos Travlos, 2000. "Long memory in the Greek stock market," Applied Financial Economics, Taylor & Francis Journals, vol. 10(2), pages 177-184.
    11. Nakamura, H. Richard, 2002. "Mapping Out the Japanese Mergers & Acquisitions Patterns - The Influence of Macro Factors on M & As," EIJS Working Paper Series 164, Stockholm School of Economics, The European Institute of Japanese Studies.
    12. John Barkoulas & Christopher F. Baum, 2003. "Long-Memory Forecasting of U.S. Monetary Indices," Boston College Working Papers in Economics 558, Boston College Department of Economics.
    13. Nakamura, Richard, 2004. "To Merge And Acquire When The Times Are Good? The Influence Of Macro Factors On The Japanese M&A Pattern," EIJS Working Paper Series 197, Stockholm School of Economics, The European Institute of Japanese Studies.
    14. Town, R.J., "undated". "Merger," Instructional Stata datasets for econometrics merger, Boston College Department of Economics.

  74. Diebold, Francis X. & Chen, Celia, 1996. "Testing structural stability with endogenous breakpoint A size comparison of analytic and bootstrap procedures," Journal of Econometrics, Elsevier, vol. 70(1), pages 221-241, January.
    See citations under working paper version above.
  75. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-263, July.
    See citations under working paper version above.
  76. Cheung, Yin-Wong & Diebold, Francis X., 1994. "On maximum likelihood estimation of the differencing parameter of fractionally-integrated noise with unknown mean," Journal of Econometrics, Elsevier, vol. 62(2), pages 301-316, June.
    See citations under working paper version above.
  77. Diebold, Francis X & Gardeazabal, Javier & Yilmaz, Kamil, 1994. "On Cointegration and Exchange Rate Dynamics," Journal of Finance, American Finance Association, vol. 49(2), pages 727-735, June.
    See citations under working paper version above.
  78. Diebold, Francis X., 1993. "Discussion : The effect of seasonal adjustment filters on tests for a unit root," Journal of Econometrics, Elsevier, vol. 55(1-2), pages 99-103.

    Cited by:

    1. Daniel, Betty C., 1997. "International interdependence of national growth rates: A structural trends anakysis," Journal of Monetary Economics, Elsevier, vol. 40(1), pages 73-96, September.
    2. Stephen Pollock, 2014. "Econometric Filters," Discussion Papers in Economics 14/07, Division of Economics, School of Business, University of Leicester.
    3. Josef Arlt, 2023. "The problem of annual inflation rate indicator," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(3), pages 2772-2788, July.

  79. Francis X. Diebold, 1993. "Are long expansions followed by short contractions?," Business Review, Federal Reserve Bank of Philadelphia, issue Jul, pages 3-11.

    Cited by:

    1. Abderrezak, Ali, 1998. "On the Duration of Growth Cycles: An International Study," International Review of Economics & Finance, Elsevier, vol. 7(3), pages 343-355.

  80. Diebold, Francis X & Rudebusch, Glenn D, 1992. "Have Postwar Economic Fluctuations Been Stabilized?," American Economic Review, American Economic Association, vol. 82(4), pages 993-1005, September.
    See citations under working paper version above.
  81. Diebold, Francis X., 1992. "Forecasting, Structural Time Series Models and the Kalman Filter, Andrew C. Harvey Cambridge University Press, 1939 - Fore Casting, Structural Time Series Models and The Kalman FilterAdrew C. Harvey C," Econometric Theory, Cambridge University Press, vol. 8(2), pages 293-299, June.

    Cited by:

    1. Diebold, Giorgianni, & Inoue, "undated". "Stamp 5.0: A Review," Home Pages _058, University of Pennsylvania.
    2. Koehler, Anne & Diebold, Francis X. & Giogianni, Lorenzo & Inoue, Atsushi, 1996. "Software review," International Journal of Forecasting, Elsevier, vol. 12(2), pages 309-315, June.

  82. Diebold, Francis X. & Rudebusch, Glenn D., 1991. "On the power of Dickey-Fuller tests against fractional alternatives," Economics Letters, Elsevier, vol. 35(2), pages 155-160, February.
    See citations under working paper version above.
  83. Francis X. Diebold & Glenn D. Rudebusch, 1991. "Shorter recessions and longer expansions," Business Review, Federal Reserve Bank of Philadelphia, issue Nov, pages 13-20.

    Cited by:

    1. Gross, Marco, 2022. "Beautiful cycles: A theory and a model implying a curious role for interest," Economic Modelling, Elsevier, vol. 106(C).

  84. Diebold, Francis X & Husted, Steven & Rush, Mark, 1991. "Real Exchange Rates under the Gold Standard," Journal of Political Economy, University of Chicago Press, vol. 99(6), pages 1252-1271, December.
    See citations under working paper version above.
  85. Diebold, Francis X & Rudebusch, Glenn D, 1991. "Is Consumption Too Smooth? Long Memory and the Deaton Paradox," The Review of Economics and Statistics, MIT Press, vol. 73(1), pages 1-9, February.
    See citations under working paper version above.
  86. Diebold, Francis X. & Pauly, Peter, 1990. "The use of prior information in forecast combination," International Journal of Forecasting, Elsevier, vol. 6(4), pages 503-508, December.
    See citations under working paper version above.
  87. Diebold, Francis X & Rudebusch, Glenn D, 1990. "A Nonparametric Investigation of Duration Dependence in the American Business Cycle," Journal of Political Economy, University of Chicago Press, vol. 98(3), pages 596-616, June.
    See citations under working paper version above.
  88. Diebold, Francis X. & Nason, James A., 1990. "Nonparametric exchange rate prediction?," Journal of International Economics, Elsevier, vol. 28(3-4), pages 315-332, May.
    See citations under working paper version above.
  89. Diebold, Francis X & Sharpe, Steven A, 1990. "Post-deregulation Bank-Deposit-Rate Pricing: The Multivariate Dynamics," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(3), pages 281-291, July.

    Cited by:

    1. Charles M. Kahn & George Pennacchi & Ben Sopranzetti, 1996. "Bank deposit rate clustering: theory and empirical evidence," Working Papers (Old Series) 9604, Federal Reserve Bank of Cleveland.
    2. Itamar Drechsler & Alexi Savov & Philipp Schnabl, 2016. "The Deposits Channel of Monetary Policy," NBER Working Papers 22152, National Bureau of Economic Research, Inc.
    3. Roger Craine, 1996. "Fairly Priced Deposit Insurance and Bank Charter Policy," Finance 9605002, University Library of Munich, Germany.
    4. Francis X. Diebold & Canlin Li, 2002. "Forecasting the Term Structure of Government Bond Yields," Center for Financial Institutions Working Papers 02-34, Wharton School Center for Financial Institutions, University of Pennsylvania.
    5. Richard Sheehan, 2013. "Valuing Core Deposits," Journal of Financial Services Research, Springer;Western Finance Association, vol. 43(2), pages 197-220, April.
    6. Bredin, Don & Fitzpatrick, Trevor & O'Reilly, Gerard, 2001. "Retail Interest Rate Pass-Through: The Irish Experience," Research Technical Papers 6/RT/01, Central Bank of Ireland.
    7. Ambrogio Cesa-Bianchi & Mr. Alessandro Rebucci, 2015. "Does Easing Monetary Policy Increase Financial Instability?," IMF Working Papers 2015/139, International Monetary Fund.
    8. Alfaro, Rodrigo & Becerra, Juan Sebastian & Sagner, Andres, 2010. "Estimación de la estructura de tasas utilizando el modelo Dinámico Nelson Siegel: resultados para Chile y EEUU [The Dynamic Nelson-Siegel model: empirical results for Chile and US]," MPRA Paper 25912, University Library of Munich, Germany, revised 23 Jun 2010.
    9. Chu V. Nguyen, & Muhammad Mahboob Ali, & Alexandru Mircea Nedelea, 2017. "The Behaviors Of Lending, Deposit Rates And Intermediation Premium Of Pakistani Banks With Different Types Of Ownership Structures," EcoForum, "Stefan cel Mare" University of Suceava, Romania, Faculty of Economics and Public Administration - Economy, Business Administration and Tourism Department., vol. 6(1), pages 1-49, January.
    10. Francis X. Diebold, 2004. "The Nobel Memorial Prize for Robert F. Engle," PIER Working Paper Archive 04-010, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    11. Peter M. DeMarzo & Arvind Krishnamurthy & Stefan Nagel, 2024. "Interest Rate Risk in Banking," NBER Working Papers 33308, National Bureau of Economic Research, Inc.
    12. Guo, Lin & Prezas, Alexandros P., 2019. "Market monitoring and influence: evidence from deposit pricing and liability composition from 1986 to 2013," Journal of Financial Stability, Elsevier, vol. 43(C), pages 146-166.
    13. Yasuo Nishiyama, 2007. "Are Banks Risk-Averse?," Eastern Economic Journal, Eastern Economic Association, vol. 33(4), pages 471-490, Fall.
    14. Rodrigo Fuentes & Solange Berstein, 2004. "Concentration and Price Rigidity: Evidence for the deposit Market in Chile," Econometric Society 2004 Latin American Meetings 67, Econometric Society.
    15. John C. Driscoll & Ruth A. Judson, 2013. "Sticky deposit rates," Finance and Economics Discussion Series 2013-80, Board of Governors of the Federal Reserve System (U.S.).
    16. Raquel Lago-González & Vicente Salas-Fumás, 2005. "Market power and bank interest rate adjustments," Working Papers 0539, Banco de España.
    17. James Gilkeson & John List & Craig Ruff, 1999. "Evidence of Early Withdrawal in Time Deposit Portfolios," Journal of Financial Services Research, Springer;Western Finance Association, vol. 15(2), pages 103-122, March.
    18. Gerlach, Jeffrey R. & Mora, Nada & Uysal, Pinar, 2018. "Bank funding costs in a rising interest rate environment," Journal of Banking & Finance, Elsevier, vol. 87(C), pages 164-186.
    19. Vladimir Yankov, 2014. "In Search of a Risk-free Asset," Finance and Economics Discussion Series 2014-108, Board of Governors of the Federal Reserve System (U.S.).
    20. Su, Chi Wei & Chang, Hsu Ling, 2010. "Asymmetric Adjustment in the Lending-Deposit Rate Spread: Evidence from Eastern European Countries," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 165-175, July.
    21. Gary B. Gorton, 2019. "The Regulation of Private Money," NBER Working Papers 25891, National Bureau of Economic Research, Inc.
    22. Koont, Naz & Santos, Tano & Zingales, Luigi, 2023. "Destabilizing digital "bank walks"," Working Papers 328, The University of Chicago Booth School of Business, George J. Stigler Center for the Study of the Economy and the State.
    23. Birge, John R. & Júdice, Pedro, 2013. "Long-term bank balance sheet management: Estimation and simulation of risk-factors," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 4711-4720.
    24. Scholnick, Barry, 1996. "Asymmetric adjustment of commercial bank interest rates: evidence from Malaysia and Singapore," Journal of International Money and Finance, Elsevier, vol. 15(3), pages 485-496, June.
    25. Guo, Lin, 2003. "Inferring market information from the price and quantity of S&L deposits," Journal of Banking & Finance, Elsevier, vol. 27(11), pages 2177-2202, November.
    26. Nishiyama, Yasuo, 2011. "The term structure of CD rates and monetary policy transmission," Journal of Banking & Finance, Elsevier, vol. 35(1), pages 82-94, January.
    27. Hsu-Ling Chang & Chi-Wei Su, 2010. "The Lending-Deposit Rate Relationship in Eastern European Countries: Evidence from the Rank Test for Non-linear Cointegration," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 60(6), pages 534-544, December.
    28. Sjölander, Pär, 2013. "A ridge bootstrap method for analyzing APT effects on the mortgage loan market," Economic Modelling, Elsevier, vol. 30(C), pages 844-855.
    29. Thompson, Mark A., 2006. "Asymmetric adjustment in the prime lending-deposit rate spread," Review of Financial Economics, Elsevier, vol. 15(4), pages 323-329.
    30. Blöchlinger, Andreas, 2015. "Identifying, valuing and hedging of embedded options in non-maturity deposits," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 34-51.
    31. Michael J. Dueker, 2000. "Are prime rate changes asymmetric?," Review, Federal Reserve Bank of St. Louis, vol. 82(Sep), pages 33-40.

  90. Diebold, Francis X & Rudebusch, Glenn D, 1989. "Scoring the Leading Indicators," The Journal of Business, University of Chicago Press, vol. 62(3), pages 369-391, July.
    See citations under working paper version above.
  91. Diebold, Francis X. & Rudebusch, Glenn D., 1989. "Long memory and persistence in aggregate output," Journal of Monetary Economics, Elsevier, vol. 24(2), pages 189-209, September.
    See citations under working paper version above.
  92. Diebold, Francis X, 1989. "Structural Time Series Analysis and Modelling Package: A Review," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 4(2), pages 195-204, April-Jun.

    Cited by:

    1. Diebold, Giorgianni, & Inoue, "undated". "Stamp 5.0: A Review," Home Pages _058, University of Pennsylvania.
    2. Koehler, Anne & Diebold, Francis X. & Giogianni, Lorenzo & Inoue, Atsushi, 1996. "Software review," International Journal of Forecasting, Elsevier, vol. 12(2), pages 309-315, June.
    3. Patrick Wilson & Simon Stevenson & Ralf Zurbruegg, 2007. "Foreign Property Shocks and the Impact on Domestic Securitized Real Estate Markets: An Unobserved Components Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 34(3), pages 407-424, April.

  93. Diebold, Francis X., 1989. "State space modeling of time series : A review essay," Journal of Economic Dynamics and Control, Elsevier, vol. 13(4), pages 597-612, October.
    See citations under working paper version above.
  94. Diebold, Francis X & Nerlove, Marc, 1989. "The Dynamics of Exchange Rate Volatility: A Multivariate Latent Factor Arch Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 4(1), pages 1-21, Jan.-Mar..
    See citations under working paper version above.
  95. Diebold, Francis X., 1989. "Forecast combination and encompassing: Reconciling two divergent literatures," International Journal of Forecasting, Elsevier, vol. 5(4), pages 589-592.
    See citations under working paper version above.
  96. Diebold, Francis X., 1988. "Testing for bubbles, reflecting barriers and other anomalies," Journal of Economic Dynamics and Control, Elsevier, vol. 12(1), pages 63-70, March.

    Cited by:

    1. Jean-Pierre Laffargue & Sanvi Avouyi-Dovi, 1992. "Anticipations stabilisatrices dans un système de serpent monétaire. Théorie et application au système monétaire européen," Revue Économique, Programme National Persée, vol. 43(6), pages 1107-1128.
    2. Li, Minqiang, 2010. "A damped diffusion framework for financial modeling and closed-form maximum likelihood estimation," Journal of Economic Dynamics and Control, Elsevier, vol. 34(2), pages 132-157, February.

  97. Diebold, Francis X. & Pauly, Peter, 1988. "Endogenous risk in a portfolio-balance rational-expectations model of the Deutschemark-Dollar rate," European Economic Review, Elsevier, vol. 32(1), pages 27-53, January.

    Cited by:

    1. Malliaropulos, Dimitrios, 1997. "A multivariate GARCH model of risk premia in foreign exchange markets," Economic Modelling, Elsevier, vol. 14(1), pages 61-79, January.
    2. Richard Meese & Andrew K. Rose, 1989. "An empirical assessment of non-linearities in models of exchange rate determination," International Finance Discussion Papers 367, Board of Governors of the Federal Reserve System (U.S.).
    3. McKenzie, Andrew M. & Holt, Matthew T., 1998. "Market Efficiency In Agricultural Futures Markets," 1998 Annual meeting, August 2-5, Salt Lake City, UT 20933, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    4. Maggiori, Matteo, 2021. "International Macroeconomics With Imperfect Financial Markets," SocArXiv z8g6r, Center for Open Science.
    5. Daly, Kevin, 2008. "Financial volatility: Issues and measuring techniques," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(11), pages 2377-2393.
    6. Frederick Nieuwland & Willem Verschoor & Christian Wolff, 2000. "Exchange risk premia in the European monetary system," Applied Financial Economics, Taylor & Francis Journals, vol. 10(4), pages 351-360.
    7. James Rude & Yves Surry, 2014. "Canadian Hog Supply Response: A Provincial Level Analysis," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 62(2), pages 149-169, June.
    8. Morley, Bruce & Pentecost, Eric J., 1998. "Asset pricing and foreign exchange risk: econometric evidence for the G-7," Journal of International Money and Finance, Elsevier, vol. 17(2), pages 317-329, April.
    9. Holt, Matthew T. & Aradhyula, Satheesh V., 1998. "Endogenous risk in rational-expectations commodity models: A multivariate generalized ARCH-M approach," Journal of Empirical Finance, Elsevier, vol. 5(2), pages 99-129, June.
    10. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    11. Adrián Fernández-P�rez & Fernando Fernández-Rodr�guez & Simón Sosvilla-Rivero, 2012. "Exploiting trends in the foreign exchange markets," Applied Economics Letters, Taylor & Francis Journals, vol. 19(6), pages 591-597, April.
    12. Karnikaa Bhattacharyya & Kaveri Deb, 2025. "Analyzing a New Portfolio Balance Model with Micro and Macro Determinants of Exchange Rate when Expectations are Rational," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 23(1), pages 105-135, March.
    13. Alexakis, Panayotis & Apergis, Nicholas, 1996. "ARCH effects and cointegration: Is the foreign exchange market efficient?," Journal of Banking & Finance, Elsevier, vol. 20(4), pages 687-697, May.
    14. Tim Bollerslev & Ray Y. Chou & Narayanan Jayaraman & Kenneth F. Kroner - L, 1991. "es modéles ARCH en finance : un point sur la théorie et les résultats empiriques," Annals of Economics and Statistics, GENES, issue 24, pages 1-59.
    15. Degiannakis, Stavros & Xekalaki, Evdokia, 2004. "Autoregressive Conditional Heteroskedasticity (ARCH) Models: A Review," MPRA Paper 80487, University Library of Munich, Germany.
    16. Holt, Matthew T. & Aradhyula, Satheesh V., 1991. "Endogenous Risk in a Rational-Expectation Model of the U.S. Broiler Market: A Multivariate Arch-M Approach," Staff Papers 200538, University of Wisconsin-Madison, Department of Agricultural and Applied Economics.

  98. Diebold, F X & Pauly, P, 1988. "Has the EMS Reduced Member-Country Exchange Rate Volatility?," Empirical Economics, Springer, vol. 13(2), pages 81-102.

    Cited by:

    1. David Chappell & Lindsay Chant, 1998. "A non-linear time series model for the South Korean Won/British pound exchange rate : 1.1.97-9.30.98," Global Economic Review, Taylor & Francis Journals, vol. 27(3), pages 65-75.
    2. Yin-Wong Cheung & Hung-Gay Fung & Kon S. Lai & Wai-Chung Lo, 1995. "Purchasing power parity under the European Monetary System," Journal of International Money and Finance, Elsevier, vol. 14(2), pages 179-189, April.
    3. Hu, Michael Y. & Tsoukalas, Christos, 1999. "Combining conditional volatility forecasts using neural networks: an application to the EMS exchange rates," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 9(4), pages 407-422, November.
    4. Frederick Nieuwland & Willem Verschoor & Christian Wolff, 2000. "Exchange risk premia in the European monetary system," Applied Financial Economics, Taylor & Francis Journals, vol. 10(4), pages 351-360.
    5. Neely, Christopher J., 1999. "Target zones and conditional volatility: The role of realignments," Journal of Empirical Finance, Elsevier, vol. 6(2), pages 177-192, April.
    6. Hu, Michael Y. & Jiang, Christine X. & Tsoukalas, Christos, 1997. "The European exchange rates before and after the establishment of the European Monetary System," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 7(3), pages 235-253, October.
    7. Chihwa Kao, 2001. "Geography, Industrial Organization, and Agglomeration Heteroskedasticity Models with Estimates of the Variances of Foreign Exchange Rates," Center for Policy Research Working Papers 34, Center for Policy Research, Maxwell School, Syracuse University.
    8. Michael Hu & Christine Jiang & Christos Tsoukalas, 2004. "The volatility impact of the European monetary system on member and non-member currencies," Applied Financial Economics, Taylor & Francis Journals, vol. 14(5), pages 313-325.
    9. Tim Bollerslev & Ray Y. Chou & Narayanan Jayaraman & Kenneth F. Kroner - L, 1991. "es modéles ARCH en finance : un point sur la théorie et les résultats empiriques," Annals of Economics and Statistics, GENES, issue 24, pages 1-59.
    10. Beum-Jo Park, 2002. "Asymmetric Volatility of Exchange Rate Returns Under The EMS: Some Evidence From Quantile Regression Approach for Tgarch Models," International Economic Journal, Taylor & Francis Journals, vol. 16(1), pages 105-125.

  99. Diebold, Francis X, 1988. "Serial Correlation and the Combination of Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(1), pages 105-111, January.

    Cited by:

    1. J. M. Gil & L. M. Albisu, 1993. "Composite Forecasting Methods: An Application To Spanish Maize Prices," Journal of Agricultural Economics, Wiley Blackwell, vol. 44(2), pages 264-271, May.
    2. Francis X. Diebold & Jose A. Lopez, 1996. "Forecast Evaluation and Combination," NBER Technical Working Papers 0192, National Bureau of Economic Research, Inc.
    3. Bec, Frédérique & Mogliani, Matteo, 2015. "Nowcasting French GDP in real-time with surveys and “blocked” regressions: Combining forecasts or pooling information?," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1021-1042.
    4. Elliott, Graham & Timmermann, Allan, 2004. "Optimal forecast combinations under general loss functions and forecast error distributions," Journal of Econometrics, Elsevier, vol. 122(1), pages 47-79, September.
    5. Pablo Pincheira, 2012. "Are Forecast Combinations Efficient?," Working Papers Central Bank of Chile 661, Central Bank of Chile.
    6. M A Sánchez-Granero & J E Trinidad-Segovia & J Clara-Rahola & A M Puertas & F J De las Nieves, 2017. "A model for foreign exchange markets based on glassy Brownian systems," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-22, December.
    7. Yi-Ting Chen & Chu-An Liu, 2021. "Model Averaging for Asymptotically Optimal Combined Forecasts," IEAS Working Paper : academic research 21-A002, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    8. Ewa Ratuszny, 2015. "Risk Modeling of Commodities using CAViaR Models, the Encompassing Method and the Combined Forecasts," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 15, pages 129-156.
    9. Paul Soderlind, 2010. "Predicting stock price movements: regressions versus economists," Applied Economics Letters, Taylor & Francis Journals, vol. 17(9), pages 869-874.
    10. Ekaterina V. Astafyeva & Maria Yu. Turuntseva, 2023. "Analysis of Opportunities to Improve the Quality of Natural Resource Price by Combining Forecasts Resulting from Methods Based on Regression Estimates of Weights [Анализ Возможностей Улучшения Каче," Russian Economic Development, Gaidar Institute for Economic Policy, issue 12, pages 24-33, December.
    11. de Menezes, Lilian M. & W. Bunn, Derek & Taylor, James W., 2000. "Review of guidelines for the use of combined forecasts," European Journal of Operational Research, Elsevier, vol. 120(1), pages 190-204, January.
    12. Aiolfi Marco & Capistrán Carlos & Timmermann Allan, 2010. "Forecast Combinations," Working Papers 2010-04, Banco de México.
    13. Massimo Guidolin & Carrie Fangzhou Na, 2007. "The economic and statistical value of forecast combinations under regime switching: an application to predictable U.S. returns," Working Papers 2006-059, Federal Reserve Bank of St. Louis.
    14. Bin Chen & Kenwin Maung, 2020. "Time-varying Forecast Combination for High-Dimensional Data," Papers 2010.10435, arXiv.org.
    15. Chen, Bin & Maung, Kenwin, 2023. "Time-varying forecast combination for high-dimensional data," Journal of Econometrics, Elsevier, vol. 237(2).
    16. Giovanni De Luca & Alfonso Carfora, 2014. "Predicting U.S. recessions through a combination of probability forecasts," Empirical Economics, Springer, vol. 46(1), pages 127-144, February.
    17. Kenton K. Yee, 2007. "A Bayesian Framework for Combining Valuation Estimates," Papers 0707.3482, arXiv.org.
    18. Christopher Gibbs, 2015. "Forecast Combination, Non-linear Dynamics, and the Macroeconomy," Discussion Papers 2015-05, School of Economics, The University of New South Wales.
    19. David G. McMillan & Mark E. Wohar, 2010. "Stock return predictability and dividend-price ratio: a nonlinear approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 351-365.
    20. Konstantin Kuck & Karsten Schweikert, 2021. "Forecasting Baden‐Württemberg's GDP growth: MIDAS regressions versus dynamic mixed‐frequency factor models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 861-882, August.
    21. Ekaterina V. Astafyeva & Maria Yu. Turuntseva, 2023. "Анализ Возможностей Улучшения Качества Прогнозов Цен На Природные Ресурсы Методами Комбинирования На Основе Регрессионных Оценок Весов," Russian Economic Development (in Russian), Gaidar Institute for Economic Policy, issue 12, pages 24-33, December.
    22. David Hendry & Michael P. Clements, 2001. "Pooling of Forecasts," Economics Papers 2002-W9, Economics Group, Nuffield College, University of Oxford.
    23. Stuart, Rebecca, 2018. "A quarterly Phillips curve for Switzerland using interpolated data, 1963–2016," Economic Modelling, Elsevier, vol. 70(C), pages 78-86.
    24. Pietro Giorgio Lovaglio, 2025. "Cross‐Learning With Panel Data Modeling for Stacking and Forecast Time Series Employment in Europe," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 753-780, March.
    25. Pablo Pincheira-Brown & Andrea Bentancor & Nicolás Hardy, 2023. "An Inconvenient Truth about Forecast Combinations," Mathematics, MDPI, vol. 11(18), pages 1-24, September.
    26. Jiun-Hua Su, 2021. "No-Regret Forecasting with Egalitarian Committees," Papers 2109.13801, arXiv.org.
    27. Elkin Castaño & Luis Fernando Melo, 1998. "Métodos de Combinación de Pronósticos: Una Aplicación a la Inflación Colombiana," Borradores de Economia 109, Banco de la Republica de Colombia.
    28. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    29. Ekrem Kilic, 2005. "Forecasting Volatility of Turkish Markets: A Comparison of Thin and Thick Models," Econometrics 0510007, University Library of Munich, Germany.
    30. António Rua & Paulo Esteves, 2012. "Short-term forecasting for the portuguese economy: a methodological overview," Economic Bulletin and Financial Stability Report Articles and Banco de Portugal Economic Studies, Banco de Portugal, Economics and Research Department.
    31. Elkin Castaño Vélez & Luis Fernando Melo Velandia, 2000. "Metodos de combinacion de pronosticos: una aplicacion a la inflacion," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 52, pages 113-165, Enero Jun.
    32. de Menezes, Lilian M. & Bunn, Derek W., 1998. "The persistence of specification problems in the distribution of combined forecast errors," International Journal of Forecasting, Elsevier, vol. 14(3), pages 415-426, September.

  100. Francis X. Diebold & Peter Pauly, 1987. "Structural change and the combination of forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 6(1), pages 21-40.
    See citations under working paper version above.
  101. Diebold, Francis X., 1986. "Exact maximum-likelihood estimation of autoregressive models via the Kalman filter," Economics Letters, Elsevier, vol. 22(2-3), pages 197-201.

    Cited by:

    1. Stephen Pollock, 2002. "Recursive Estimation in Econometrics," Working Papers 462, Queen Mary University of London, School of Economics and Finance.
    2. Pollock, D. S. G., 2003. "Recursive estimation in econometrics," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 37-75, October.
    3. Manahov, Viktor & Hudson, Robert & Gebka, Bartosz, 2014. "Does high frequency trading affect technical analysis and market efficiency? And if so, how?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 28(C), pages 131-157.

  102. Diebold, Francis X., 1986. "The exact initial covariance matrix of the state vector of a general MA(q) process," Economics Letters, Elsevier, vol. 22(1), pages 27-31.

    Cited by:

    1. Stephen Pollock, 2002. "Recursive Estimation in Econometrics," Working Papers 462, Queen Mary University of London, School of Economics and Finance.
    2. Pollock, D. S. G., 2003. "Recursive estimation in econometrics," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 37-75, October.

Chapters

  1. Francis X. Diebold & Glenn D. Rudebusch, 2022. "On the Evolution of US Temperature Dynamics," Advances in Econometrics, in: Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling, volume 43, pages 9-28, Emerald Group Publishing Limited.
    See citations under working paper version above.
  2. Francis X. Diebold, 2022. "Real-Time Real Economic Activity: Entering and Exiting the Pandemic Recession of 2020," Advances in Econometrics, in: Essays in Honour of Fabio Canova, volume 44, pages 5-24, Emerald Group Publishing Limited.
    See citations under working paper version above.
  3. Francis X. Diebold & Laura Liu & Kamil Yilmaz, 2018. "Commodity Connectedness," Central Banking, Analysis, and Economic Policies Book Series, in: Enrique G. Mendoza & Ernesto Pastén & Diego Saravia (ed.),Monetary Policy and Global Spillovers: Mechanisms, Effects and Policy Measures, edition 1, volume 25, chapter 4, pages 097-136, Central Bank of Chile.
    See citations under working paper version above.
  4. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2013. "Financial Risk Measurement for Financial Risk Management," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 1127-1220, Elsevier.
    See citations under working paper version above.
  5. Francis X. Diebold & Kamil Yilmaz, 2011. "Equity Market Spillovers in the Americas," Central Banking, Analysis, and Economic Policies Book Series, in: Rodrigo Alfaro (ed.),Financial Stability, Monetary Policy, and Central Banking, edition 1, volume 15, chapter 7, pages 199-214, Central Bank of Chile.
    See citations under working paper version above.
  6. S. Borağan Aruoba & Francis X. Diebold & M. Ayhan Kose & Marco E. Terrones, 2010. "Globalization, the Business Cycle, and Macroeconomic Monitoring," NBER Chapters, in: NBER International Seminar on Macroeconomics 2010, pages 245-286, National Bureau of Economic Research, Inc.
    See citations under working paper version above.
  7. Torben G. Andersen & Tim Bollerslev & Peter Christoffersen & Francis X. Diebold, 2007. "Practical Volatility and Correlation Modeling for Financial Market Risk Management," NBER Chapters, in: The Risks of Financial Institutions, pages 513-544, National Bureau of Economic Research, Inc.
    See citations under working paper version above.
  8. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Ginger Wu, 2006. "Realized Beta: Persistence and Predictability," Advances in Econometrics, in: Econometric Analysis of Financial and Economic Time Series, pages 1-39, Emerald Group Publishing Limited.
    See citations under working paper version above.
  9. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.

    Cited by:

    1. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
    2. Francis X. Diebold & Georg Strasser, 2010. "On the Correlation Structure of Microstructure Noise: A Financial Economic Approach," NBER Working Papers 16469, National Bureau of Economic Research, Inc.
    3. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "Are realized volatility models good candidates for alternative Value at Risk prediction strategies?," MPRA Paper 30364, University Library of Munich, Germany.
    4. Márcio Gomes Pinto Garcia & Marcelo Cunha Medeiros & Francisco Eduardo de Luna e Almeida Santos, 2014. "Economic gains of realized volatility in the Brazilian stock market," Brazilian Review of Finance, Brazilian Society of Finance, vol. 12(3), pages 319-349.
    5. Sattarhoff, Cristina & Gronwald, Marc, 2022. "Measuring informational efficiency of the European carbon market — A quantitative evaluation of higher order dependence," International Review of Financial Analysis, Elsevier, vol. 84(C).
    6. Davide Pettenuzzo & Konstantinos Metaxoglou & Aaron Smith, 2016. "Option-Implied Equity Premium Predictions via Entropic TiltinG," Working Papers 99R, Brandeis University, Department of Economics and International Business School, revised Aug 2016.
    7. Degiannakis, Stavros & Floros, Christos, 2014. "Intra-Day Realized Volatility for European and USA Stock Indices," MPRA Paper 64940, University Library of Munich, Germany, revised Jan 2015.
    8. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil price realized volatility using information channels from other asset classes," MPRA Paper 96276, University Library of Munich, Germany.
    9. Davide Pettenuzzo & Rossen Valkanov & Allan Timmermann, 2014. "A Bayesian MIDAS Approach to Modeling First and Second Moment Dynamics," Working Papers 76, Brandeis University, Department of Economics and International Business School.
    10. Peter Christoffersen & Jeremy Berkowitz & Denis Pelletier, 2008. "Evaluating Value-at-Risk Models with Desk-Level Data," CREATES Research Papers 2009-35, Department of Economics and Business Economics, Aarhus University.
    11. Degiannakis, Stavros & Floros, Christos, 2013. "Modeling CAC40 volatility using ultra-high frequency data," Research in International Business and Finance, Elsevier, vol. 28(C), pages 68-81.
    12. Benavides, Guillermo & Capistrán, Carlos, 2012. "Forecasting exchange rate volatility: The superior performance of conditional combinations of time series and option implied forecasts," Journal of Empirical Finance, Elsevier, vol. 19(5), pages 627-639.
    13. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
    14. Gadea Rivas, María Dolores & Gonzalo, Jesús, 2017. "Trends in distributional characteristics : Existence of global warming," UC3M Working papers. Economics 24121, Universidad Carlos III de Madrid. Departamento de Economía.
    15. Campbell, John Y. & Giglio, Stefano & Polk, Christopher & Turley, Robert, 2018. "An intertemporal CAPM with stochastic volatility," Journal of Financial Economics, Elsevier, vol. 128(2), pages 207-233.
    16. Pal, Debdatta, 2022. "Does hospitality industry stock volatility react asymmetrically to health and economic crises?," Economic Modelling, Elsevier, vol. 108(C).
    17. Lucien Boulet, 2021. "Forecasting High-Dimensional Covariance Matrices of Asset Returns with Hybrid GARCH-LSTMs," Papers 2109.01044, arXiv.org.
    18. Jean-Paul Laurent & Hassan Omidi Firouzi, 2022. "Market Risk and Volatility Weighted Historical Simulation After Basel III," Working Papers hal-03679434, HAL.
    19. Dungey, Mardi & Henry, Olan T & Hvodzdyk, Lyudmyla, 2013. "The impact of jumps and thin trading on realized hedge ratios," Working Papers 2013-02, University of Tasmania, Tasmanian School of Business and Economics, revised 28 Mar 2013.
    20. Davide Pettenuzzo & Francesco Ravazzolo, 2014. "Optimal portfolio choice under decision-based model combinations," Working Paper 2014/15, Norges Bank.
    21. Singh, Manohar & Nejadmalayeri, Ali & Lucey, Brian, 2013. "Do U.S. macroeconomic surprises influence equity returns? An exploratory analysis of developed economies," The Quarterly Review of Economics and Finance, Elsevier, vol. 53(4), pages 476-485.
    22. Olkhov, Victor, 2022. "Introduction of the Market-Based Price Autocorrelation," MPRA Paper 112003, University Library of Munich, Germany.
    23. Pettenuzzo, Davide & Timmermann, Allan & Valkanov, Rossen, 2016. "A MIDAS approach to modeling first and second moment dynamics," Journal of Econometrics, Elsevier, vol. 193(2), pages 315-334.
    24. Christian T. Brownlees & Giampiero Gallo, 2006. "Financial Econometric Analysis at Ultra–High Frequency: Data Handling Concerns," Econometrics Working Papers Archive wp2006_03, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    25. David E. Rapach & Jack K. Strauss, 2008. "Structural breaks and GARCH models of exchange rate volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(1), pages 65-90.
    26. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "The role of high frequency intra-daily data, daily range and implied volatility in multi-period Value-at-Risk forecasting," MPRA Paper 35252, University Library of Munich, Germany.
    27. Fabrizio Cipollini & Giampiero M. Gallo & Edoardo Otranto, 2019. "Realized Volatility Forecasting: Robustness to Measurement Errors," Econometrics Working Papers Archive 2019_04, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    28. Dong Hwan Oh & Andrew J. Patton, 2015. "High-Dimensional Copula-Based Distributions with Mixed Frequency Data," Finance and Economics Discussion Series 2015-50, Board of Governors of the Federal Reserve System (U.S.).
    29. Chaker, Selma, 2019. "The signal and the noise volatilities," Research in International Business and Finance, Elsevier, vol. 50(C), pages 79-105.
    30. Timmermann, Allan & Pettenuzzo, Davide & Gargano, Antonio, 2014. "Bond Return Predictability: Economic Value and Links to the Macroeconomy," CEPR Discussion Papers 10104, C.E.P.R. Discussion Papers.
    31. Olkhov, Victor, 2018. "Expectations, Price Fluctuations and Lorenz Attractor," MPRA Paper 89105, University Library of Munich, Germany.
    32. Sabiwalsky, Ralf, 2012. "Does Basel II pillar 3 risk exposure data help to identify risky banks?," SFB 649 Discussion Papers 2012-008, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    33. Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2017. "Copula–Based vMEM Specifications versus Alternatives: The Case of Trading Activity," Econometrics, MDPI, vol. 5(2), pages 1-24, April.
    34. Christian T. Brownlees & Fabrizio Cipollini & Giampiero M. Gallo, 2011. "Multiplicative Error Models," Econometrics Working Papers Archive 2011_03, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Apr 2011.
    35. Florian Ielpo & Benoît Sévi, 2014. "Forecasting the density of oil futures," Working Papers 2014-601, Department of Research, Ipag Business School.
    36. LeBaron, Blake, 2012. "Heterogeneous gain learning and the dynamics of asset prices," Journal of Economic Behavior & Organization, Elsevier, vol. 83(3), pages 424-445.
    37. Tim Bollerslev & Hao Zhou, 2006. "Expected stock returns and variance risk premia," Finance and Economics Discussion Series 2007-11, Board of Governors of the Federal Reserve System (U.S.).
    38. Politis, Dimitris N & Thomakos, Dimitrios D, 2008. "NoVaS Transformations: Flexible Inference for Volatility Forecasting," University of California at San Diego, Economics Working Paper Series qt982208kx, Department of Economics, UC San Diego.
    39. Francis X. Diebold & Kamil Yilmaz, 2008. "Macroeconomic Volatility and Stock Market Volatility, World-Wide," PIER Working Paper Archive 08-031, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    40. Alizadeh, Amir H. & Huang, Chih-Yueh & Marsh, Ian W., 2021. "Modelling the volatility of TOCOM energy futures: A regime switching realised volatility approach," Energy Economics, Elsevier, vol. 93(C).
    41. Rizvi, Syed Kumail Abbas & Naqvi, Bushra, 2008. "Asymmetric Behavior of Inflation Uncertainty and Friedman-Ball Hypothesis: Evidence from Pakistan," MPRA Paper 19488, University Library of Munich, Germany.
    42. Christian Conrad, 2007. "Non-negativity Conditions for the Hyperbolic GARCH Model," KOF Working papers 07-162, KOF Swiss Economic Institute, ETH Zurich.
    43. Amir Safari & Detlef Seese, 2010. "Behavior of realized volatility and correlation in exchange markets," International Econometric Review (IER), Econometric Research Association, vol. 2(2), pages 73-96, September.
    44. Victor Olkhov, 2022. "Price and Payoff Autocorrelations in a Multi-Period Consumption-Based Asset Pricing Model," Papers 2204.07506, arXiv.org, revised Mar 2024.
    45. Victor Olkhov, 2022. "Market-Based Price Autocorrelation," Papers 2202.09323, arXiv.org, revised Feb 2024.
    46. Huisman, Ronald & Van der Sar, Nico L. & Zwinkels, Remco C.J., 2021. "Volatility expectations and disagreement," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 379-393.
    47. Markellos, Raphael N. & Psychoyios, Dimitris, 2018. "Interest rate volatility and risk management: Evidence from CBOE Treasury options," The Quarterly Review of Economics and Finance, Elsevier, vol. 68(C), pages 190-202.
    48. Catania, Leopoldo & Grassi, Stefano, 2022. "Forecasting cryptocurrency volatility," International Journal of Forecasting, Elsevier, vol. 38(3), pages 878-894.
    49. Charlotte Christiansen & Maik Schmeling & Andreas Schrimpf, 2012. "A Comprehensive Look at Financial Volatility Prediction by Economic Variables," BIS Working Papers 374, Bank for International Settlements.
    50. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107630024, June.
    51. Liu, Lily Y. & Patton, Andrew J. & Sheppard, Kevin, 2015. "Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes," Journal of Econometrics, Elsevier, vol. 187(1), pages 293-311.
    52. Haakon Kavli & Kevin Kotzé, 2014. "Spillovers in Exchange Rates and the Effects of Global Shocks on Emerging Market Currencies," South African Journal of Economics, Economic Society of South Africa, vol. 82(2), pages 209-238, June.
    53. Abadir, Karim M. & Luati, Alessandra & Paruolo, Paolo, 2023. "GARCH density and functional forecasts," Journal of Econometrics, Elsevier, vol. 235(2), pages 470-483.
    54. Irving Arturo De Lira Salvatierra & Andrew J. Patton, 2013. "Dynamic Copula Models and High Frequency Data," Working Papers 13-28, Duke University, Department of Economics.
    55. Liu, Jing & Ma, Feng & Zhang, Yaojie, 2019. "Forecasting the Chinese stock volatility across global stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 466-477.
    56. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2014. "Realized volatility models and alternative Value-at-Risk prediction strategies," Economic Modelling, Elsevier, vol. 40(C), pages 101-116.
    57. Luc, BAUWENS & Genaro, SUCARRAT, 2006. "General to Specific Modelling of Exchange Rate Volatility : a Forecast Evaluation," Discussion Papers (ECON - Département des Sciences Economiques) 2006013, Université catholique de Louvain, Département des Sciences Economiques.
    58. Marina Theodosiou, 2010. "Calendar Time Sampling of High Frequency Financial Asset Price and the Verdict on Jumps," Working Papers 2010-7, Central Bank of Cyprus.
    59. Bollerslev, Tim & Medeiros, Marcelo C. & Patton, Andrew J. & Quaedvlieg, Rogier, 2022. "From zero to hero: Realized partial (co)variances," Journal of Econometrics, Elsevier, vol. 231(2), pages 348-360.
    60. Timmermann, Allan, 2018. "Forecasting Methods in Finance," CEPR Discussion Papers 12692, C.E.P.R. Discussion Papers.
    61. Andres, P. & Harvey, A., 2012. "The Dyanamic Location/Scale Model: with applications to intra-day financial data," Cambridge Working Papers in Economics 1240, Faculty of Economics, University of Cambridge.
    62. Farmer, J. Doyne & Kleinnijenhuis, Alissa & Wetzer, Thom & Aymanns, Christopher, 2018. "Models of Financial Stability and Their Application in Stress Tests," INET Oxford Working Papers 2018-06, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
    63. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2011. "Financial Risk Measurement for Financial Risk Management," PIER Working Paper Archive 11-037, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    64. Becker, R. & Clements, A.E. & Doolan, M.B. & Hurn, A.S., 2015. "Selecting volatility forecasting models for portfolio allocation purposes," International Journal of Forecasting, Elsevier, vol. 31(3), pages 849-861.
    65. Wang, Zijin & Chen, Peimin & Liu, Peng & Wu, Chunchi, 2024. "Volatility forecasts by clustering: Applications for VaR estimation," International Review of Economics & Finance, Elsevier, vol. 94(C).
    66. Segnon, Mawuli & Lux, Thomas, 2013. "Multifractal models in finance: Their origin, properties, and applications," Kiel Working Papers 1860, Kiel Institute for the World Economy (IfW Kiel).
    67. Lux, Thomas & Morales-Arias, Leonardo & Sattarhoff, Cristina, 2011. "A Markov-switching multifractal approach to forecasting realized volatility," Kiel Working Papers 1737, Kiel Institute for the World Economy (IfW Kiel).
    68. Sévi, Benoît, 2014. "Forecasting the volatility of crude oil futures using intraday data," European Journal of Operational Research, Elsevier, vol. 235(3), pages 643-659.
    69. Cristina Sattarhoff & Marc Gronwald, 2018. "How to Measure Financial Market Efficiency? A Multifractality-Based Quantitative Approach with an Application to the European Carbon Market," CESifo Working Paper Series 7102, CESifo.
    70. Julien Chevallier & Benoît Sévi, 2013. "A Fear Index to Predict Oil Futures Returns," Working Papers 2013.62, Fondazione Eni Enrico Mattei.
    71. Steffen Henzel & Malte Rengel, 2013. "Dimensions of macroeconomic uncertainty: A common factor analysis," ifo Working Paper Series 167, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    72. Franses,Philip Hans & Dijk,Dick van & Opschoor,Anne, 2014. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521817707, June.
    73. Ewing, Bradley T. & Malik, Farooq, 2017. "Modelling asymmetric volatility in oil prices under structural breaks," Energy Economics, Elsevier, vol. 63(C), pages 227-233.
    74. Chatziantoniou, Ioannis & Degiannakis, Stavros & Filis, George, 2019. "Futures-based forecasts: How useful are they for oil price volatility forecasting?," Energy Economics, Elsevier, vol. 81(C), pages 639-649.
    75. Sucarrat, Genaro, 2009. "Forecast Evaluation of Explanatory Models of Financial Variability," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 3, pages 1-33.
    76. Cem Cakmakli & Dick van Dijk, 2010. "Getting the Most out of Macroeconomic Information for Predicting Stock Returns and Volatility," Tinbergen Institute Discussion Papers 10-115/4, Tinbergen Institute.
    77. Timmermann, Allan & Elliott, Graham, 2007. "Economic Forecasting," CEPR Discussion Papers 6158, C.E.P.R. Discussion Papers.
    78. Yuan Liao & Xiye Yang, 2017. "Uniform Inference for Characteristic Effects of Large Continuous-Time Linear Models," Papers 1711.04392, arXiv.org, revised Dec 2018.
    79. Kevin Sheppard & Andrew J. Patton, 2008. "Evaluating Volatility and Correlation Forecasts," Economics Series Working Papers 2008fe22, University of Oxford, Department of Economics.
    80. Tim Bollerslev & Benjamin Hood & John Huss & Lasse Heje Pedersen, 2018. "Risk Everywhere: Modeling and Managing Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2729-2773.
    81. Christoph Aymanns & J. Doyne Farmer, 2014. "The dynamics of the leverage cycle," Papers 1407.5305, arXiv.org, revised Aug 2014.
    82. Olkhov, Victor, 2022. "The Market-Based Asset Price Probability," MPRA Paper 115382, University Library of Munich, Germany, revised 16 Nov 2022.
    83. Dongming Zhu & John W. Galbraith, 2009. "Forecasting Expected Shortfall with a Generalized Asymmetric Student-t Distribution," CIRANO Working Papers 2009s-24, CIRANO.
    84. Ralf Becker & Adam Clements & Robert O'Neill, 2010. "A Cholesky-MIDAS model for predicting stock portfolio volatility," NCER Working Paper Series 60, National Centre for Econometric Research.
    85. Andersen, Torben G. & Bollerslev, Tim & Huang, Xin, 2011. "A reduced form framework for modeling volatility of speculative prices based on realized variation measures," Journal of Econometrics, Elsevier, vol. 160(1), pages 176-189, January.
    86. Erik Kole & Thijs Markwat & Anne Opschoor & Dick van Dijk, 2017. "Forecasting Value-at-Risk under Temporal and Portfolio Aggregation," Journal of Financial Econometrics, Oxford University Press, vol. 15(4), pages 649-677.
    87. Sébastien Laurent & Christelle Lecourt & Franz C. Palm, 2016. "Testing for jumps in conditionally Gaussian ARMA-GARCH models, a robust approach," Post-Print hal-01447861, HAL.
    88. Stanislav Anatolyev & Nikolay Gospodinov, 2007. "Modeling Financial Return Dynamics by Decomposition," Working Papers w0095, New Economic School (NES).
    89. Andersen, Torben G. & Bollerslev, Tim & Meddahi, Nour, 2011. "Realized volatility forecasting and market microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 220-234, January.
    90. Lindblad, Annika, 2017. "Sentiment indicators and macroeconomic data as drivers for low-frequency stock market volatility," MPRA Paper 80266, University Library of Munich, Germany.
    91. Stefano d'Addona & Axel H. Kind, 2005. "International Stock-Bond Correlations in a Simple Affine Asset Pricing Model," Finance 0502018, University Library of Munich, Germany.
    92. Syed Kumail Abbas Naqvi & Bushra Naqvi, 2010. "Asymmetric Behavior of Inflation Uncertainty and Friedman-Ball Hypothesis: Evidence from Pakistan," Lahore Journal of Economics, Department of Economics, The Lahore School of Economics, vol. 15(2), pages 1-33, Jul-Dec.
    93. Leopoldo Catania & Stefano Grassi, 2017. "Modelling Crypto-Currencies Financial Time-Series," CEIS Research Paper 417, Tor Vergata University, CEIS, revised 11 Dec 2017.
    94. Diebold, Francis X. & Strasser, Georg H., 2008. "On the correlation structure of microstructure noise in theory and practice," CFS Working Paper Series 2008/32, Center for Financial Studies (CFS).
    95. Rengel, Malte & Herwartz, Helmut & Xu, Fang, 2013. "Persistence in the price-to-dividend ratio and its macroeconomic fundamentals," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79860, Verein für Socialpolitik / German Economic Association.
    96. Palandri, Alessandro, 2015. "Do negative and positive equity returns share the same volatility dynamics?," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 486-505.
    97. Mittnik, Stefan & Robinzonov, Nikolay & Spindler, Martin, 2015. "Stock market volatility: Identifying major drivers and the nature of their impact," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 1-14.
    98. Goyal, Amit & Saretto, Alessio, 2009. "Cross-section of option returns and volatility," Journal of Financial Economics, Elsevier, vol. 94(2), pages 310-326, November.
    99. Shirota, Shinichiro & Hizu, Takayuki & Omori, Yasuhiro, 2014. "Realized stochastic volatility with leverage and long memory," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 618-641.
    100. Li, Jia & Patton, Andrew J., 2018. "Asymptotic inference about predictive accuracy using high frequency data," Journal of Econometrics, Elsevier, vol. 203(2), pages 223-240.
    101. R. P. Brito & H. Sebastião & P. Godinho, 2017. "Portfolio choice with high frequency data: CRRA preferences and the liquidity effect," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 16(2), pages 65-86, August.
    102. Escobar-Anel, Marcos & Rastegari, Javad & Stentoft, Lars, 2021. "Option pricing with conditional GARCH models," European Journal of Operational Research, Elsevier, vol. 289(1), pages 350-363.
    103. Patton, Andrew J. & Sheppard, Kevin, 2009. "Optimal combinations of realised volatility estimators," International Journal of Forecasting, Elsevier, vol. 25(2), pages 218-238.
    104. Degiannakis, Stavros & Kiohos, Apostolos, 2014. "Multivariate modelling of 10-day-ahead VaR and dynamic correlation for worldwide real estate and stock indices," MPRA Paper 80438, University Library of Munich, Germany.
    105. Carlos Pinho & Mara Madaleno, 2011. "Links between spot and futures allowances: ECX and EEX markets comparison," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 35(2/3/4), pages 101-131.
    106. Dooyeon Cho & Seunghwa Rho, 2022. "On asymmetric volatility effects in currency markets," Empirical Economics, Springer, vol. 62(5), pages 2149-2177, May.
    107. Olkhov, Victor, 2022. "Price and Payoff Autocorrelations in the Consumption-Based Asset Pricing Model," MPRA Paper 112255, University Library of Munich, Germany.
    108. Rexford Abaidoo & Elvis Kwame Agyapong, 2022. "Commodity price volatility, inflation uncertainty and political stability," International Review of Economics, Springer;Happiness Economics and Interpersonal Relations (HEIRS), vol. 69(3), pages 351-381, September.
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Books

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    Cited by:

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    3. Okorie, David Iheke & Lin, Boqiang, 2022. "Givers never lack: Nigerian oil & gas asymmetric network analyses," Energy Economics, Elsevier, vol. 108(C).
    4. Zhang, Xiaoyuan & Zhang, Tianqi, 2022. "Dynamic credit contagion and aggregate loss in networks," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    5. Hanif, Waqas & Arreola Hernandez, Jose & Kang, Sang Hoon & Boako, Gideon & Yoon, Seong-Min, 2024. "Interdependence and spillovers between big oil companies and regional and global energy equity markets," International Review of Economics & Finance, Elsevier, vol. 92(C), pages 451-469.
    6. Ha, Jongrim & Kose, M. Ayhan & Otrok, Christopher & Prasad, Eswar, 2020. "Global Macro-Financial Cycles and Spillovers," IZA Discussion Papers 13000, Institute of Labor Economics (IZA).
    7. Balcilar, Mehmet & Ozdemir, Zeynel Abidin & Ozdemir, Huseyin & Wohar, Mark E., 2020. "Spillover effects in oil-related CDS markets during and after the sub-prime crisis," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    8. Clausen Volker & Schlösser Alexander & Thiem Christopher, 2019. "Economic Policy Uncertainty in the Euro Area: Cross-Country Spillovers and Macroeconomic Impact," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 239(5-6), pages 957-981, October.
    9. Fernando Fernández-Rodríguez & Simón Sosvilla-Rivero, 2016. "Volatility transmission between stock and exchange-rate markets: A connectedness analysis," Working Papers del Instituto Complutense de Estudios Internacionales 1604, Universidad Complutense de Madrid, Instituto Complutense de Estudios Internacionales.
    10. Stijn Claessens & M Ayhan Kose, 2018. "Frontiers of macrofinancial linkages," BIS Papers, Bank for International Settlements, number 95.
    11. Áureo de Paula & Imran Rasul & Pedro CL Souza, 2023. "Identifying network ties from panel data: Theory and an application to tax competition," CeMMAP working papers 21/23, Institute for Fiscal Studies.
    12. Lyócsa, Štefan & Výrost, Tomáš & Baumöhl, Eduard, 2019. "Return spillovers around the globe: A network approach," Economic Modelling, Elsevier, vol. 77(C), pages 133-146.
    13. Zhang, Xingmin & Zhang, Shuai & Lu, Liping, 2022. "The banking instability and climate change: Evidence from China," Energy Economics, Elsevier, vol. 106(C).
    14. Xu, Yongdeng & Guan, Bo & Lu, Wenna & Heravi, Saeed, 2024. "Macroeconomic shocks and volatility spillovers between stock, bond, gold and crude oil markets," Energy Economics, Elsevier, vol. 136(C).
    15. Wen, Danyan & Wang, Gang-Jin & Ma, Chaoqun & Wang, Yudong, 2019. "Risk spillovers between oil and stock markets: A VAR for VaR analysis," Energy Economics, Elsevier, vol. 80(C), pages 524-535.
    16. Lovcha, Yuliya & Pérez Laborda, Àlex & Sikora, Iryna, 2019. "The Determinants of CO2 prices in the EU ETS System," Working Papers 2072/376031, Universitat Rovira i Virgili, Department of Economics.
    17. Magkonis, Georgios & Tsopanakis, Andreas, 2020. "The Financial Connectedness Between Eurozone Core And Periphery: A Disaggregated View," Macroeconomic Dynamics, Cambridge University Press, vol. 24(7), pages 1674-1699, October.
    18. Baumöhl, Eduard & Kočenda, Evžen & Lyócsa, Štefan & Výrost, Tomáš, 2018. "Networks of volatility spillovers among stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1555-1574.
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    150. Mehmet Balcilar & Ojonugwa Usman & Busra Agan, 2024. "On the connectedness of commodity markets: A critical and selective survey of empirical studies and bibliometric analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 38(1), pages 97-136, February.
    151. Paflioti, Persa & Vitsounis, Thomas K. & Teye, Collins & Bell, Michael G.H. & Tsamourgelis, Ioannis, 2017. "Box dynamics: A sectoral approach to analyse containerized port throughput interdependencies," Transportation Research Part A: Policy and Practice, Elsevier, vol. 106(C), pages 396-413.
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    154. Julián Andrada-Félix & Adrian Fernandez-Perez & Fernando Fernández-Rodríguez & Simón Sosvilla-Rivero, 2022. "Time connectedness of fear," Empirical Economics, Springer, vol. 62(3), pages 905-931, March.
      • Julián Andrada-Félixa & Adrian Fernandez-Perez & Fernando Fernández-Rodríguez & Simón Sosvilla-Rivero, 2018. "“Time connectedness of fear”," IREA Working Papers 201818, University of Barcelona, Research Institute of Applied Economics, revised Sep 2018.
    155. Jonathan E. Ogbuabor & Anthony Orji & Gladys C. Aneke & Oyun Erdene-Urnukh, 2016. "Measuring the Real and Financial Connectedness of Selected African Economies with the Global Economy," South African Journal of Economics, Economic Society of South Africa, vol. 84(3), pages 364-399, September.
    156. Karkowska, Renata & Urjasz, Szczepan, 2023. "How does the Russian-Ukrainian war change connectedness and hedging opportunities? Comparison between dirty and clean energy markets versus global stock indices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 85(C).
    157. Gao, Yang & Li, Yangyang & Wang, Yaojun, 2021. "Risk spillover and network connectedness analysis of China’s green bond and financial markets: Evidence from financial events of 2015–2020," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
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    159. Victor Pontines & Davaajargal Luvsannyam & Khuslen Batmunkh & Tsolmon Otgonbat, 2023. "The dynamics of business cycle connectedness and the decoupling of Asia‐Pacific," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1667-1692, April.
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    161. Rangan Gupta & Chi Keung Marco Lau & Jacobus A Nel & Xin Sheng, 2020. "Monetary Policy Uncertainty Spillovers in Time- and Frequency-Domains," Working Papers 202005, University of Pretoria, Department of Economics.
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    163. Zhang, Wenting & He, Xie & Hamori, Shigeyuki, 2023. "The impact of the COVID-19 pandemic and Russia-Ukraine war on multiscale spillovers in green finance markets: Evidence from lower and higher order moments," International Review of Financial Analysis, Elsevier, vol. 89(C).
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    169. De Ponti, Pietro & Romagnoli, Matteo, 2022. "Financial implications of the EU Emission Trading System: an analysis of wavelet coherence and volatility spillovers," FEEM Working Papers 323874, Fondazione Eni Enrico Mattei (FEEM).
    170. Sudarshan Kumar & Tiziana Di Matteo & Anindya S. Chakrabarti, 2020. "Disentangling shock diffusion on complex networks: Identification through graph planarity," Papers 2001.01518, arXiv.org.
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    175. Bhanja, Niyati & Shah, Adil Ahmad & Dar, Arif Billah, 2023. "Aggregate, asymmetric and frequency-based spillover among equity, precious metals, and cryptocurrency," Resources Policy, Elsevier, vol. 80(C).
    176. Aslanidis, Nektarios & Martinez, Oscar, 2021. "Correlation regimes in international equity and bond returns," Economic Modelling, Elsevier, vol. 97(C), pages 397-410.
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    178. Jan Sila & Evzen Kocenda & Ladislav Kristoufek & Jiri Kukacka, 2023. "Good vs. Bad Volatility in Major Cryptocurrencies: The Dichotomy and Drivers of Connectedness," Working Papers IES 2023/24, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Jul 2023.
    179. Shang, Jin & Hamori, Shigeyuki, 2024. "Quantile time-frequency connectedness analysis between crude oil, gold, financial markets, and macroeconomic indicators: Evidence from the US and EU," Energy Economics, Elsevier, vol. 132(C).
    180. Goczek, Łukasz & Witkowski, Bartosz, 2023. "Spillover effects of the unconventional monetary policy of the European Central Bank," The Quarterly Review of Economics and Finance, Elsevier, vol. 89(C), pages 82-104.
    181. Charfeddine, Lanouar & Al Refai, Hisham, 2019. "Political tensions, stock market dependence and volatility spillover: Evidence from the recent intra-GCC crises," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    182. Rodolfo C. Moura & Márcio P. Laurini, 2021. "Spillovers and jumps in global markets: A comparative analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5997-6013, October.
    183. Ana Inés Arbiza, 2018. "¿Es estable la demanda de dinero? Análisis del caso Uruguayo," Documentos de trabajo 2018009, Banco Central del Uruguay.
    184. Celani, Alessandro & Cerchiello, Paola & Pagnottoni, Paolo, 2024. "The topological structure of panel variance decomposition networks," Journal of Financial Stability, Elsevier, vol. 71(C).
    185. Vanessa Olakemi Dovonou, 2023. "Trilemma revisited with dollar dominance in trade and finance," Working Papers 2023.05, International Network for Economic Research - INFER.
    186. Yakup Ari & Hakan Kurt & Harun Uçak, 2025. "Volatility Spillovers Among EAGLE Economies: Insights from Frequency-Based TVP-VAR Connectedness," Mathematics, MDPI, vol. 13(8), pages 1-32, April.
    187. Li-Yang Guo & Chao Feng, 2022. "Measuring the Demand Connectedness among China’s Regional Carbon Markets," IJERPH, MDPI, vol. 19(21), pages 1-16, October.
    188. Dahl, Roy Endré & Oglend, Atle & Yahya, Muhammad, 2020. "Dynamics of volatility spillover in commodity markets: Linking crude oil to agriculture," Journal of Commodity Markets, Elsevier, vol. 20(C).
    189. Fernández-Rodríguez, Fernando & Gómez-Puig, Marta & Sosvilla-Rivero, Simón, 2016. "Using connectedness analysis to assess financial stress transmission in EMU sovereign bond market volatility," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 43(C), pages 126-145.
    190. Jose Arreola Hernandez & Sang Hoon Kang & Ron P. McIver & Seong-Min Yoon, 2021. "Network Interdependence and Optimization of Bank Portfolios from Developed and Emerging Asia Pacific Countries," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(4), pages 613-647, December.
    191. Wenting Zhang & Xie He & Tadahiro Nakajima & Shigeyuki Hamori, 2020. "How Does the Spillover among Natural Gas, Crude Oil, and Electricity Utility Stocks Change over Time? Evidence from North America and Europe," Energies, MDPI, vol. 13(3), pages 1-26, February.
    192. Mensi, Walid & Nekhili, Ramzi & Vo, Xuan Vinh & Suleman, Tahir & Kang, Sang Hoon, 2021. "Asymmetric volatility connectedness among U.S. stock sectors," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    193. Christopher Thiem, 2020. "Cross-Category, Trans-Pacific Spillovers of Policy Uncertainty and Financial Market Volatility," Open Economies Review, Springer, vol. 31(2), pages 317-342, April.
    194. Shah, Adil Ahmad & Dar, Arif Billah & Bhanumurthy, N.R., 2021. "Are precious metals and equities immune to monetary and fiscal policy uncertainties?," Resources Policy, Elsevier, vol. 74(C).
    195. Yakup Arı, 2022. "TVP-VAR Based CARR-Volatility Connectedness: Evidence from The Russian-Ukraine Conflict," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, vol. 7(3), pages 590-607.
    196. Casarin, Roberto & Costola, Michele & Yenerdag, Erdem, 2018. "Financial bridges and network communities," SAFE Working Paper Series 208, Leibniz Institute for Financial Research SAFE, revised 2018.
    197. Výrost, Tomas & Lyócsa, Štefan & Baumöhl, Eduard, 2019. "Network-based asset allocation strategies," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 516-536.
    198. Jose Arreola Hernandez & Sang Hoon Kang & Seong‐Min Yoon, 2022. "Nonlinear spillover and portfolio allocation characteristics of energy equity sectors: Evidence from the United States and Canada," Review of International Economics, Wiley Blackwell, vol. 30(1), pages 1-33, February.
    199. Jalshayin Bhachech & Arnab Chakrabarti & Taisei Kaizoji & Anindya S. Chakrabarti, 2022. "Instability of networks: effects of sampling frequency and extreme fluctuations in financial data," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 95(4), pages 1-14, April.
    200. Arı, Yakup, 2022. "USD/TRY and foreign banks in Turkey: Evidence by TVP-VAR," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 67, pages 5-26.
    201. Uddin, Ajim & Tao, Xinyuan & Yu, Dantong, 2023. "Attention based dynamic graph neural network for asset pricing," Global Finance Journal, Elsevier, vol. 58(C).
    202. Hamill, Philip A. & Li, Youwei & Pantelous, Athanasios A. & Vigne, Samuel A. & Waterworth, James, 2021. "Was a deterioration in ‘connectedness’ a leading indicator of the European sovereign debt crisis?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    203. Nikolay Iskrev, 2018. "Term premia dynamics in the US and Euro Area: who is leading whom?," Economic Bulletin and Financial Stability Report Articles and Banco de Portugal Economic Studies, Banco de Portugal, Economics and Research Department.
    204. Elsayed, Ahmed H. & Nasreen, Samia & Tiwari, Aviral Kumar, 2020. "Time-varying co-movements between energy market and global financial markets: Implication for portfolio diversification and hedging strategies," Energy Economics, Elsevier, vol. 90(C).
    205. Chuliá, Helena & Fernández, Julián & Uribe, Jorge M., 2018. "Currency downside risk, liquidity, and financial stability," Journal of International Money and Finance, Elsevier, vol. 89(C), pages 83-102.
    206. Cai, Jian & Eidam, Frederik & Saunders, Anthony & Steffen, Sascha, 2018. "Syndication, interconnectedness, and systemic risk," Journal of Financial Stability, Elsevier, vol. 34(C), pages 105-120.
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    208. Ogbuabor, Jonathan E. & Anthony-Orji, Onyinye I. & Manasseh, Charles O. & Orji, Anthony, 2020. "Measuring the dynamics of COMESA output connectedness with the global economy," The Journal of Economic Asymmetries, Elsevier, vol. 21(C).
    209. Duc Hong Vo & Minh Phuoc Bao Tran & Phuong Thi Ha Cao, 2025. "The spillover effects of military spending across superpowers using the TVP - VAR approach," HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - ECONOMICS AND BUSINESS ADMINISTRATION, HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE, HO CHI MINH CITY OPEN UNIVERSITY, vol. 15(1), pages 38-53.
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    212. Wei Zhou & Jin Guo & Ning Chen & Shuai Lu, 2023. "Key market identification, mechanism transmission, and extreme shock during the risk spillover process: an empirical study of the G20 FOREX markets," Empirical Economics, Springer, vol. 65(6), pages 2549-2582, December.
    213. Buchetti, Bruno & Bouteska, Ahmed & Harasheh, Murad & Santoni, Alessandro, 2025. "Investor sentiment and dynamic connectedness in European markets: insights from the covid-19 and Russia-Ukraine conflict," Working Paper Series 3050, European Central Bank.
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  2. Francis X. Diebold & Glenn D. Rudebusch, 2012. "Yield Curve Modeling and Forecasting: The Dynamic Nelson-Siegel Approach," Economics Books, Princeton University Press, edition 1, volume 1, number 9895.

    Cited by:

    1. Eyden Samunderu & Yvonne T. Murahwa, 2021. "Return Based Risk Measures for Non-Normally Distributed Returns: An Alternative Modelling Approach," JRFM, MDPI, vol. 14(11), pages 1-48, November.
    2. Drew D. Creal & Jing Cynthia Wu, 2014. "Estimation of Affine Term Structure Models with Spanned or Unspanned Stochastic Volatility," NBER Working Papers 20115, National Bureau of Economic Research, Inc.
    3. Atsushi Inoue & Barbara Rossi, 2019. "A New Approach to Measuring Economic Policy Shocks, with an Application to Conventional and Unconventional Monetary Policy," Working Papers 1082, Barcelona School of Economics.
    4. Minchul Shin & Molin Zhong, 2015. "Does Realized Volatility Help Bond Yield Density Prediction?," Finance and Economics Discussion Series 2015-115, Board of Governors of the Federal Reserve System (U.S.).
    5. Michelle Lewis & C. John McDermott, 2016. "New Zealand's experience with changing its inflation target and the impact on inflation expectations," New Zealand Economic Papers, Taylor & Francis Journals, vol. 50(3), pages 343-361, September.
    6. Bruno Feunou & Jean-Sébastien Fontaine & Anh Le & Christian Lundblad, 2022. "Tractable Term Structure Models," Management Science, INFORMS, vol. 68(11), pages 8411-8429, November.
    7. Machava, Agostinho & Brännäs, Kurt, 2015. "Mozambican Monetary Policy and the Yield Curve of Treasury Bills - An Empirical Study," Umeå Economic Studies 918, Umeå University, Department of Economics.
    8. Constantino Hevia & Martin Sola, 2018. "Bond Risk Premia and Restrictions on Risk Prices," JRFM, MDPI, vol. 11(4), pages 1-22, October.

  3. Francis X. Diebold (ed.), 2012. "Financial Risk Measurement and Management," Books, Edward Elgar Publishing, number 14102.

    Cited by:

    1. Serena Ng & Jonathan H. Wright, 2013. "Facts and Challenges from the Great Recession for Forecasting and Macroeconomic Modeling," NBER Working Papers 19469, National Bureau of Economic Research, Inc.
    2. Kyle Jurado & Sydney C. Ludvigson & Serena Ng, 2015. "Measuring Uncertainty," American Economic Review, American Economic Association, vol. 105(3), pages 1177-1216, March.
    3. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2011. "Financial Risk Measurement for Financial Risk Management," PIER Working Paper Archive 11-037, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    4. Olkhov, Victor, 2018. "The Business Cycle Model Beyond General Equilibrium," MPRA Paper 87204, University Library of Munich, Germany.
    5. F. Lilla, 2016. "High Frequency vs. Daily Resolution: the Economic Value of Forecasting Volatility Models," Working Papers wp1084, Dipartimento Scienze Economiche, Universita' di Bologna.
    6. Olkhov, Victor, 2018. "Economic and Financial Transactions Govern Business Cycles," MPRA Paper 93269, University Library of Munich, Germany.
    7. Victor Olkhov, 2018. "Econophysics Beyond General Equilibrium: the Business Cycle Model," Papers 1804.04721, arXiv.org.
    8. Olkhov, Victor, 2020. "Business Cycles as Collective Risk Fluctuations," MPRA Paper 104598, University Library of Munich, Germany.
    9. Olkhov, Victor, 2018. "Economic Transactions Govern Business Cycles," MPRA Paper 88531, University Library of Munich, Germany, revised 19 Aug 2018.
    10. F. Lilla, 2017. "High Frequency vs. Daily Resolution: the Economic Value of Forecasting Volatility Models - 2nd ed," Working Papers wp1099, Dipartimento Scienze Economiche, Universita' di Bologna.
    11. Massacci, Daniele, 2014. "A two-regime threshold model with conditional skewed Student t distributions for stock returns," Economic Modelling, Elsevier, vol. 43(C), pages 9-20.
    12. Victor Olkhov, 2018. "How Macro Transactions Describe the Evolution and Fluctuation of Financial Variables," IJFS, MDPI, vol. 6(2), pages 1-19, March.
    13. Dias, Alexandra, 2013. "Market capitalization and Value-at-Risk," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 5248-5260.
    14. Victor Olkhov, 2021. "To VaR, or Not to VaR, That is the Question," Papers 2101.08559, arXiv.org, revised Apr 2024.
    15. BALTES Nicolae & DRAGOE Alexandra-Gabriela-Maria, 2017. "Estimating The Return Of The Financial Titles Of The Companies From The Manufacturing Industry, Listed On The Bucharest Stock Exchange," Revista Economica, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 69(3), pages 19-28, August.
    16. Francis X. Diebold, 2012. "A Personal Perspective on the Origin(s) and Development of “Big Data": The Phenomenon, the Term, and the Discipline, Second Version," PIER Working Paper Archive 13-003, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 26 Nov 2012.

  4. Francis X. Diebold & Neil A. Doherty & Richard J. Herring, 2010. "The Known, the Unknown, and the Unknowable in Financial Risk Management: Measurement and Theory Advancing Practice," Economics Books, Princeton University Press, edition 1, number 9223.

    Cited by:

    1. W. Kip Viscusi & Richard J. Zeckhauser, 2015. "Regulating Ambiguous Risks: The Less than Rational Regulation of Pharmaceuticals," The Journal of Legal Studies, University of Chicago Press, vol. 44(S2), pages 387-422.
    2. Gordon L. Clark, 2014. "Information, knowledge, and investing in offshore financial markets," Journal of Sustainable Finance & Investment, Taylor & Francis Journals, vol. 4(4), pages 299-320, October.
    3. Michal Skorepa & Jakub Seidler, 2014. "Capital Buffers Based on Banks' Domestic Systemic Importance: Selected Issues," Research and Policy Notes 2014/01, Czech National Bank, Research and Statistics Department.
    4. Yuanrong Wang & Yinsen Miao & Alexander CY Wong & Nikita P Granger & Christian Michler, 2023. "Domain-adapted Learning and Interpretability: DRL for Gas Trading," Papers 2301.08359, arXiv.org, revised Sep 2023.
    5. Michal Skorepa, 2014. "Concurrent Capital Buffers in a Banking Group," Occasional Publications - Chapters in Edited Volumes, in: CNB Financial Stability Report 2013/2014, chapter 0, pages 128-136, Czech National Bank, Research and Statistics Department.
    6. Christian Hugo Hoffmann, 2017. "Towards Understanding Dynamic Complexity in Financial Systems Structure-based Explanatory Modelling of Risks," Systems Research and Behavioral Science, Wiley Blackwell, vol. 34(6), pages 728-745, November.
    7. Ross Brown & Ronald V Kalafsky & Suzanne Mawson & Lori Davies, 2020. "Shocks, uncertainty and regional resilience: The case of Brexit and Scottish SMEs," Local Economy, London South Bank University, vol. 35(7), pages 655-675, November.
    8. Stéphane Albert & Hervé Alexandre, 2013. "Banks’ Earnings: an empirical evidence of the influence of economic and financial markets factors," Post-Print hal-01622842, HAL.
    9. Oksana Hoshovska & Zhanna Poplavska & Jana Kajanova & Olena Trevoho, 2023. "Random Risk Factors Influencing Cash Flows: Modifying RADR," Mathematics, MDPI, vol. 11(2), pages 1-22, January.
    10. Chuliá, Helena & Guillén, Montserrat & Uribe, Jorge M., 2017. "Measuring uncertainty in the stock market," International Review of Economics & Finance, Elsevier, vol. 48(C), pages 18-33.
    11. Albert, Stéphane, 2015. "US bank holding companies: Structure of activities and performance through the cycles," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 253-269.
    12. Michal Skorepa & Jakub Seidler, 2013. "An Additional capital requirements based on the domestic systemic importance of a bank," Occasional Publications - Chapters in Edited Volumes, in: CNB Financial Stability Report 2012/2013, chapter 0, pages 96-102, Czech National Bank, Research and Statistics Department.
    13. Gabel Taggart, 2023. "Taking stock of systems for organizing existential and global catastrophic risks: Implications for policy," Global Policy, London School of Economics and Political Science, vol. 14(3), pages 489-499, June.
    14. Jaime Terceiro Lomba, 2019. "The energy transition and the financial system," Financial Stability Review, Banco de España, issue Autumn.
    15. Andreas Richter & Thomas C. Wilson, 2020. "Covid-19: implications for insurer risk management and the insurability of pandemic risk," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 45(2), pages 171-199, September.
    16. Amandha Ganegoda & John Evans, 2014. "A framework to manage the measurable, immeasurable and the unidentifiable financial risk," Australian Journal of Management, Australian School of Business, vol. 39(1), pages 5-34, February.
    17. Christian Hugo Hoffmann & Charles Djordjevic, 2020. "Complexity, Power Laws and a Humean Argument in Risk Management: The Fundamental Inadequacy of Probability Theory as a Foundation for Modeling Complex Risk in Banking," Homo Oeconomicus: Journal of Behavioral and Institutional Economics, Springer, vol. 37(3), pages 155-182, December.
    18. Michael Hanemann, 2010. "The Impact of Climate Change: An Economic Perspective," Chapters, in: Emilio Cerdá Tena & Xavier Labandeira (ed.), Climate Change Policies, chapter 2, Edward Elgar Publishing.
    19. PARYS, Wilfried, 2020. "David Ricardo, the Stock Exchange, and the Battle of Waterloo: Samuelsonian legends lack historical evidence," Working Papers 2020009, University of Antwerp, Faculty of Business and Economics.
    20. Gordon L Clark, 2012. "Pensions or Property?," Environment and Planning A, , vol. 44(5), pages 1185-1199, May.
    21. Jingnan Chen & Mark D. Flood & Richard B. Sowers, 2015. "Measuring the Unmeasurable: An Application of Uncertainty Quantification to Financial Portfolios," Working Papers 15-19, Office of Financial Research, US Department of the Treasury.
    22. Ekaterina Svetlova & Henk van Elst, 2012. "How is non-knowledge represented in economic theory?," Papers 1209.2204, arXiv.org.

  5. Francis X. Diebold & Glenn D. Rudebusch, 1999. "Business Cycles: Durations, Dynamics, and Forecasting," Economics Books, Princeton University Press, edition 1, number 6636.

    Cited by:

    1. Guilhem Bentoglio & Jacky Fayolle & Matthieu Lemoine, 2002. "Unity and Plurality of the European Cycle," Documents de Travail de l'OFCE 2002-03, Observatoire Francais des Conjonctures Economiques (OFCE).
    2. Gordon W. Crawford & Michael C. Fratantoni, 2003. "Assessing the Forecasting Performance of Regime‐Switching, ARIMA and GARCH Models of House Prices," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 31(2), pages 223-243, June.
    3. Bao, Te & Ma, Mengzhong & Wen, Yonggang, 2023. "Herding in the non-fungible token (NFT) market," Journal of Behavioral and Experimental Finance, Elsevier, vol. 39(C).
    4. Philip Lowe, 2002. "Credit risk measurement and procyclicality," BIS Working Papers 116, Bank for International Settlements.
    5. Gustavo Cabrera González, 2019. "Modeling and Projection of the Mexican Exchange Rate (Peso/Dollar): a Bayesian Approach for Model Selection," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 14(2), pages 203-219, Abril-Jun.
    6. Olkhov, Victor, 2019. "New Essentials of Economic Theory," MPRA Paper 95065, University Library of Munich, Germany.
    7. Viv. B Hall & McDermott C. John, 2004. "Regional Business Cycles in New Zealand: Do they exist? What might drive them?," ERSA conference papers ersa04p200, European Regional Science Association.
    8. Mr. Thomas Helbling & Mr. Tamim Bayoumi, 2003. "Are they All in the Same Boat? the 2000-2001 Growth Slowdown and the G-7 Business Cycle Linkages," IMF Working Papers 2003/046, International Monetary Fund.
    9. Zarnowitz, Victor & Ozyildirim, Ataman, 2006. "Time series decomposition and measurement of business cycles, trends and growth cycles," Journal of Monetary Economics, Elsevier, vol. 53(7), pages 1717-1739, October.
    10. Chang-Jin Kim & Charles Nelson, 1999. "A Bayesian Approach to Testing for Markov Switching in Univariate and Dynamic Factor Models," Working Papers 0035, University of Washington, Department of Economics.
    11. Olkhov, Victor, 2018. "The Business Cycle Model Beyond General Equilibrium," MPRA Paper 87204, University Library of Munich, Germany.
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