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Andrew Patton

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. Andrew Patton, 2006. "Volatility Forecast Comparison using Imperfect Volatility Proxies," Research Paper Series 175, Quantitative Finance Research Centre, University of Technology, Sydney.

    Mentioned in:

    1. Forecasting Volatility II
      by Clive Jones in Business Forecasting on 2013-04-10 22:31:01
  2. 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.

    Mentioned in:

    1. On Forecasting Variation and Covariation
      by Francis Diebold in No Hesitations on 2016-05-02 06:01:00

RePEc Biblio mentions

As found on the RePEc Biblio, the curated bibliography of Economics:
  1. Andrew Patton & Dimitris Politis & Halbert White, 2009. "Correction to “Automatic Block-Length Selection for the Dependent Bootstrap” by D. Politis and H. White," Econometric Reviews, Taylor & Francis Journals, vol. 28(4), pages 372-375.

    Mentioned in:

    1. > Econometrics > Econometric Theory > Bootstrap Methods

Working papers

  1. Andrew J. Patton & Yasin Simsek, 2023. "Generalized Autoregressive Score Trees and Forests," Papers 2305.18991, arXiv.org.

    Cited by:

    1. Siyu Bie & Francis X. Diebold & Jingyu He & Junye Li, 2024. "Machine Learning and the Yield Curve:Tree-Based Macroeconomic Regime Switching," PIER Working Paper Archive 24-028, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    2. Eric A. Beutner & Yicong Lin & Andre Lucas, 2023. "Consistency, distributional convergence, and optimality of score-driven filters," Tinbergen Institute Discussion Papers 23-051/III, Tinbergen Institute.

  2. Albert J. Menkveld & Anna Dreber & Felix Holzmeister & Jürgen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neusüss & Michael Razen & Utz Weitzel & David Abad-Dí­az & Menachem Abudy & Tobi, 2021. "Non-Standard Errors," Working Papers 2021-31, Faculty of Economics and Statistics, Universität Innsbruck.
    • Albert J. Menkveld & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neusüß & Michael Razen & Utz Weitzel & David Abad‐Díaz & Menachem (Meni) Abudy , 2024. "Nonstandard Errors," Journal of Finance, American Finance Association, vol. 79(3), pages 2339-2390, June.
    • Utz Weitzel & Michael Razen & Sebastian Neussüs & Michael Kirchler & Magnus Johannesson & Juergen Huber & Felix Holzmeister & Anna Dreber & Albert J. Menkveld & Javier Gil-Bazo, 2021. "Non-Standard Errors," Working Papers 1303, Barcelona School of Economics.
    • Menkveld, Albert J. & Dreber, Anna & Holzmeister, Felix & Huber, Jürgen & Johannesson, Magnus & Kirchler, Michael & Neusüss, Sebastian & Razen, Michael & Weitzel, Utz, 2021. "Non-standard errors," IWH Discussion Papers 11/2021, Halle Institute for Economic Research (IWH).
    • Albert J. et al. Menkveld, 2021. "Non-Standard Errors," CESifo Working Paper Series 9453, CESifo.
    • Albert J Menkveld & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neusüss & Michael Razen & Utz Weitzel & Gunther Capelle-Blancard & David Abad-Dí, 2021. "Non-Standard Errors," Post-Print halshs-03500882, HAL.
    • Menkveld, A. & Dreber, A. & Holzmeister, F. & Huber, J. & Johannesson, M. & Kirchler, M. & Neusüss, S. & Razen, M. & Neusüss, S. & Neusüss, S., 2021. "Non-Standard Errors," Cambridge Working Papers in Economics 2182, Faculty of Economics, University of Cambridge.
    • Menkveld, Albert J. & Dreber, Anna & Holzmeister, Felix & Huber, Juergen & Johannesson, Magnus & Hasse, Jean-Baptiste & e.a.,, 2023. "Non-Standard Errors," LIDAM Reprints LFIN 2023002, Université catholique de Louvain, Louvain Finance (LFIN).
    • Menkveld, Albert J. & Dreber, Anna & Holzmeister, Felix & Huber, Juergen & Johannesson, Magnus & Kirchler, Michael & Neusüß, Sebastian & Razen, Michael & Weitzel, Utz & Abad-Díaz, David & Abudy, Menac, 2024. "Nonstandard errors," LSE Research Online Documents on Economics 123002, London School of Economics and Political Science, LSE Library.
    • Menkveld, Albert J. & Dreber, Anna & Holzmeister, Felix & Huber, Jürgen & Johannesson, Magnus & Kirchler, Michael & Neusüss, Sebastian & Razen, Michael & Weitzel, Utz, 2021. "Non-standard errors," SAFE Working Paper Series 327, Leibniz Institute for Financial Research SAFE.
    • Albert Menkveld & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neusüß & Michael Razen & Utz Weitzel & David Abad-Díaz & Tobias Adrian & Yacine Ai, 2024. "Nonstandard Errors," Post-Print hal-05077550, HAL.
    • Wolff, Christian & Menkveld, Albert J. & Dreber, Anna & Holzmeister, Felix & Huber, Juergen & Johannesson, Magnus & Kirchler, Michael & Neusüess, Sebastian & Razen, Michael & Weitzel, Utz, 2021. "Non-Standard Errors," CEPR Discussion Papers 16751, C.E.P.R. Discussion Papers.
    • Albert J. Menkveld & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neussüs & Michael Razen & Utz Weitzel & Christian T. Brownlees & Javier Gil-Baz, 2021. "Non-standard errors," Economics Working Papers 1807, Department of Economics and Business, Universitat Pompeu Fabra.
    • Menkveld, Albert J. & Dreber, Anna & Holzmeister, Felix & Huber, Juergen & Johannesson, Magnus & Kirchler, Michael & Neusüss, Sebastian & Razen, Michael & Weitzel, Utz & Abad-Díaz, David & Abudy, Mena, 2021. "Non-Standard Errors," Working Papers 2021:17, Lund University, Department of Economics.
    • Albert J. Menkveld & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neus ss & Michael Razen & Utz Weitzel & Edwin Baidoo & Michael Fr mmel & et al, 2021. "Non-Standard Errors," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 21/1032, Ghent University, Faculty of Economics and Business Administration.
    • Francesco Franzoni & Roxana Mihet & Markus Leippold & Per Ostberg & Olivier Scaillet & Norman Schürhoff & Oksana Bashchenko & Nicola Mano & Michele Pelli, 2022. "Non-Standard Errors," Swiss Finance Institute Research Paper Series 22-09, Swiss Finance Institute.
    • Moinas, Sophie & Declerck, Fany & Menkveld, Albert J. & Dreber, Anna, 2023. "Non-Standard Errors," TSE Working Papers 23-1451, Toulouse School of Economics (TSE).
    • Albert Menkveld & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neusüß & Michael Razen & Utz Weitzel & David Abad-Díaz & Tobias Adrian & Yacine Ai, 2024. "Nonstandard Errors," Post-Print hal-04676112, HAL.
    • Gerardo Ferrara & Simon Jurkatis, 2021. "Non-standard errors," Bank of England working papers 955, Bank of England.
    • Albert J Menkveld & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neusüss & Michael Razen & Utz Weitzel & Gunther Capelle-Blancard & David Abad-Dí, 2021. "Non-Standard Errors," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-03500882, HAL.
    • Albert Menkveld & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neusüß & Michael Razen & Utz Weitzel & David Abad-Díaz & Tobias Adrian & Yacine Ai, 2024. "Nonstandard Errors," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-05077550, HAL.
    • Ciril Bosch-Rosa & Bernhard Kassner, 2023. "Non-Standard Errors," Rationality and Competition Discussion Paper Series 385, CRC TRR 190 Rationality and Competition.
    • Menkveld, A. & Dreber, A. & Holzmeister, F. & Huber, J. & Johannesson, M. & Kirchler, M. & Neusüss, S. & Razen, M. & Neusüss, S. & Neusüss, S., 2021. "Non-Standard Errors," Janeway Institute Working Papers 2112, Faculty of Economics, University of Cambridge.
    • Albert J. Menkveld & Anna Dreber & Félix Holzmeister & Juergen Huber & Magnus Johannesson & Michael Kirchler & Sebastian Neusüss & Michael Razen & Utz Weitzel & Gunther Capelle-Blancard, 2021. "Non-Standard Errors," Documents de travail du Centre d'Economie de la Sorbonne 21033, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.

    Cited by:

    1. Huntington-Klein, Nick & Pörtner, Claus C. & Acharya, Yubraj & Adamkovic, Matus & Adema, Joop & Agasa, Lameck Ondieki & Ahmad, Imtiaz & Akbulut-Yuksel, Mevlude & Andresen, Martin Eckhoff & Angenendt, , 2025. "The Sources of Researcher Variation in Economics," HEC Research Papers Series 1551, HEC Paris.
    2. Nate Breznau & Eike Mark Rinke & Alexander Wuttke & Hung H. V. Nguyen & Muna Adem & Jule Adriaans & Amalia Alvarez-Benjumea & Henrik K. Andersen & Daniel Auer & Flavio Azevedo & Oke Bahnsen & Dave Bal, 2022. "Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 119(44), pages 2203150119-, November.
    3. Guillaume Coqueret, 2023. "Forking paths in financial economics," Papers 2401.08606, arXiv.org.
    4. Dreber, Anna & Johannesson, Magnus, 2023. "A framework for evaluating reproducibility and replicability in economics," I4R Discussion Paper Series 38, The Institute for Replication (I4R).
    5. Huber, Christoph & König-Kersting, Christian & Marini, Matteo M., 2025. "Experimenting with financial professionals," Journal of Banking & Finance, Elsevier, vol. 170(C).
    6. Fišar, Miloš & Greiner, Ben & Huber, Christoph & Katok, Elena & Ozkes, Ali & Collaboration, Management Science Reproducibility, 2023. "Reproducibility in Management Science," OSF Preprints mydzv, Center for Open Science.
    7. Stephen A. Gorman & Frank J. Fabozzi, 2023. "Alternative risk premium: specification noise," Journal of Asset Management, Palgrave Macmillan, vol. 24(6), pages 459-473, October.
    8. Christophe Pérignon & Olivier Akmansoy & Christophe Hurlin & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johanneson & Michael Kirchler & Albert Menkveld & Michael Razen & Utz Weitzel, 2022. "Reproducibility of Empirical Results: Evidence from 1,000 Tests in Finance," Working Papers hal-03810013, HAL.
    9. Soebhag, Amar & Van Vliet, Bart & Verwijmeren, Patrick, 2024. "Non-standard errors in asset pricing: Mind your sorts," Journal of Empirical Finance, Elsevier, vol. 78(C).
    10. van Dolder, Dennie & Vandenbroucke, Jurgen, 2024. "Behavioral risk profiling: Measuring loss aversion of individual investors," Journal of Banking & Finance, Elsevier, vol. 168(C).

  3. Dong Hwan Oh & Andrew J. Patton, 2021. "Better the Devil You Know: Improved Forecasts from Imperfect Models," Finance and Economics Discussion Series 2021-071, Board of Governors of the Federal Reserve System (U.S.).

    Cited by:

    1. MAMATZAKIS, emmanuel & MAMATZAKIS, E, 2022. "Understanding the impact of travel on wellbeing: evidence for Great Britain during the pandemic," MPRA Paper 121782, University Library of Munich, Germany.
    2. Emmanuel Mamatzakis & Mike G. Tsionas & Steven Ongena, 2023. "Why do households repay their debt in UK during the COVID-19 crisis?," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 50(8), pages 1789-1823, April.

  4. Sander Barendse & Andrew J. Patton, 2020. "Comparing Predictive Accuracy in the Presence of a Loss Function Shape Parameter," Economics Series Working Papers 909, University of Oxford, Department of Economics.

    Cited by:

    1. Lazar, Emese & Wang, Shixuan & Xue, Xiaohan, 2023. "Loss function-based change point detection in risk measures," European Journal of Operational Research, Elsevier, vol. 310(1), pages 415-431.
    2. 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.

  5. Timo Dimitriadis & Andrew J. Patton & Patrick W. Schmidt, 2019. "Testing Forecast Rationality for Measures of Central Tendency," Papers 1910.12545, arXiv.org, revised Jul 2024.

    Cited by:

    1. Timo Dimitriadis & Tobias Fissler & Johanna Ziegel, 2022. "Characterizing M-estimators," Papers 2208.08108, arXiv.org.

  6. Andrew J. Patton & Johanna F. Ziegel & Rui Chen, 2017. "Dynamic Semiparametric Models for Expected Shortfall (and Value-at-Risk)," Papers 1707.05108, arXiv.org.

    Cited by:

    1. Hallin, Marc & Trucíos, Carlos, 2023. "Forecasting value-at-risk and expected shortfall in large portfolios: A general dynamic factor model approach," Econometrics and Statistics, Elsevier, vol. 27(C), pages 1-15.
    2. Oh, Dong Hwan & Patton, Andrew J., 2024. "Better the devil you know: Improved forecasts from imperfect models," Journal of Econometrics, Elsevier, vol. 242(1).
    3. Aktham Maghyereh & Salem Adel Ziadat, 2024. "Pattern and determinants of tail-risk transmission between cryptocurrency markets: new evidence from recent crisis episodes," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-34, December.
    4. Timo Dimitriadis & Yannick Hoga, 2023. "Regressions under Adverse Conditions," Papers 2311.13327, arXiv.org, revised Feb 2025.
    5. Pedro Isaac Chavez-Lopez & Tae-Hwy Lee, 2025. "Quantile-Covariance Three-Pass Regression Filter," Working Papers 202501, University of California at Riverside, Department of Economics.
    6. Janneke van Brummelen & Paolo Gorgi & Siem Jan Koopman, 2025. "Score-driven time-varying parameter models with splinebased densities," Tinbergen Institute Discussion Papers 25-011/III, Tinbergen Institute.
    7. Alexander Arimond & Damian Borth & Andreas Hoepner & Michael Klawunn & Stefan Weisheit, 2020. "Neural Networks and Value at Risk," Papers 2005.01686, arXiv.org, revised May 2020.
    8. Mario Cerrato & Danyang Li & Zhekai Zhang, 2020. "Factor Investing and forex Portfolio Management," Working Papers 2020_01, Business School - Economics, University of Glasgow.
    9. Fernanda Maria Müller & Marcelo Brutti Righi, 2024. "Comparison of Value at Risk (VaR) Multivariate Forecast Models," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 75-110, January.
    10. Catania, Leopoldo & Luati, Alessandra, 2023. "Semiparametric modeling of multiple quantiles," Journal of Econometrics, Elsevier, vol. 237(2).
    11. Taylor, James W., 2022. "Forecasting Value at Risk and expected shortfall using a model with a dynamic omega ratio," Journal of Banking & Finance, Elsevier, vol. 140(C).
    12. Christis Katsouris, 2021. "Optimal Portfolio Choice and Stock Centrality for Tail Risk Events," Papers 2112.12031, arXiv.org.
    13. Francq, Christian & Zakoïan, Jean-Michel, 2025. "Inference on dynamic systemic risk measures," Journal of Econometrics, Elsevier, vol. 247(C).
    14. 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.
    15. Xenxo Vidal-Llana & Carlos Salort Sánchez & Vincenzo Coia & Montserrat Guillen, 2022. ""Non-Crossing Dual Neural Network: Joint Value at Risk and Conditional Tail Expectation estimations with non-crossing conditions"," IREA Working Papers 202215, University of Barcelona, Research Institute of Applied Economics, revised Oct 2022.
    16. Marcellino, Massimiliano & Clark, Todd & Carriero, Andrea, 2021. "Nowcasting Tail Risk to Economic Activity at a Weekly Frequency," CEPR Discussion Papers 16496, C.E.P.R. Discussion Papers.
    17. Chen, Cathy W.S. & Hsu, Hsiao-Yun & Watanabe, Toshiaki, 2023. "Tail risk forecasting of realized volatility CAViaR models," Finance Research Letters, Elsevier, vol. 51(C).
    18. Qifa Xu & Lu Chen & Cuixia Jiang & Yezheng Liu, 2022. "Forecasting expected shortfall and value at risk with a joint elicitable mixed data sampling model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 407-421, April.
    19. Giovanni Bonaccolto & Massimiliano Caporin & Bertrand Maillet, 2022. "Dynamic Large Financial Networks via Conditional Expected Shortfalls," Post-Print hal-03287947, HAL.
    20. Catania, Leopoldo & Grassi, Stefano, 2022. "Forecasting cryptocurrency volatility," International Journal of Forecasting, Elsevier, vol. 38(3), pages 878-894.
    21. Jie Wang & Yongqiao Wang, 2025. "Forecasting Expected Shortfall and Value‐at‐Risk With Cross‐Sectional Aggregation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 391-423, March.
    22. Luca Merlo & Lea Petrella & Valentina Raponi, 2021. "Forecasting VaR and ES using a joint quantile regression and implications in portfolio allocation," Papers 2106.06518, arXiv.org.
    23. Michel Ferreira Cardia Haddad & Szabolcs Blazsek & Philip Arestis & Franz Fuerst & Hsia Hua Sheng, 2023. "The two-component Beta-t-QVAR-M-lev: a new forecasting model," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 37(4), pages 379-401, December.
    24. Zhengkun Li & Minh-Ngoc Tran & Chao Wang & Richard Gerlach & Junbin Gao, 2020. "A Bayesian Long Short-Term Memory Model for Value at Risk and Expected Shortfall Joint Forecasting," Papers 2001.08374, arXiv.org, revised May 2021.
    25. Leandro Maciel, 2021. "Cryptocurrencies value‐at‐risk and expected shortfall: Do regime‐switching volatility models improve forecasting?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4840-4855, July.
    26. Antonio Naimoli & Giuseppe Storti, 2021. "Forecasting Volatility and Tail Risk in Electricity Markets," JRFM, MDPI, vol. 14(7), pages 1-17, June.
    27. Reh, Laura & Krüger, Fabian & Liesenfeld, Roman, 2020. "Predicting the global minimum variance portfolio," Working Paper Series in Economics 141, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    28. Sullivan Hu'e & Christophe Hurlin & Yang Lu, 2024. "Backtesting Expected Shortfall: Accounting for both duration and severity with bivariate orthogonal polynomials," Papers 2405.02012, arXiv.org, revised May 2024.
    29. Christis Katsouris, 2023. "Quantile Time Series Regression Models Revisited," Papers 2308.06617, arXiv.org, revised Aug 2023.
    30. Santos, Douglas G. & Candido, Osvaldo & Tófoli, Paula V., 2022. "Forecasting risk measures using intraday and overnight information," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    31. Lazar, Emese & Wang, Shixuan & Xue, Xiaohan, 2023. "Loss function-based change point detection in risk measures," European Journal of Operational Research, Elsevier, vol. 310(1), pages 415-431.
    32. Anubha Goel & Puneet Pasricha & Juho Kanniainen, 2024. "Time-Series Foundation AI Model for Value-at-Risk Forecasting," Papers 2410.11773, arXiv.org, revised May 2025.
    33. 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.
    34. Sander Barendse & Erik Kole & Dick van Dijk, 2019. "Backtesting Value-at-Risk and Expected Shortfall in the Presence of Estimation Error," Tinbergen Institute Discussion Papers 19-058/III, Tinbergen Institute.
    35. Alain-Philippe Fortin & Jean-Guy Simonato & Georges Dionne, 2018. "Forecasting Expected Shortfall: Should we use a Multivariate Model for Stock Market Factors?," Working Papers 18-4, HEC Montreal, Canada Research Chair in Risk Management.
    36. Dong, Chaohua & Chen, Rong & Xiao, Zhijie & Liu, Weiyi, 2024. "Functional quantile autoregression," Journal of Econometrics, Elsevier, vol. 244(2).
    37. Storti, Giuseppe & Wang, Chao, 2022. "Nonparametric expected shortfall forecasting incorporating weighted quantiles," International Journal of Forecasting, Elsevier, vol. 38(1), pages 224-239.
    38. Creal, Drew & Koopman, Siem Jan & Lucas, André & Zamojski, Marcin, 2024. "Observation-driven filtering of time-varying parameters using moment conditions," Journal of Econometrics, Elsevier, vol. 238(2).
    39. Shawn McCarthy & Gita Alaghband, 2023. "The Emotion Magnitude Effect: Navigating Market Dynamics Amidst Supply Chain Events," JRFM, MDPI, vol. 16(12), pages 1-21, November.
    40. Catania, Leopoldo & Luati, Alessandra, 2025. "Quasi Maximum Likelihood Estimation of Value at Risk and Expected Shortfall," Econometrics and Statistics, Elsevier, vol. 33(C), pages 23-34.
    41. HONDA, Toshio & 本田, 敏雄 & PENG, Po-Hsiang, 2025. "Expected Shortfall Regression for High-Dimensional Additive Models," Discussion Papers 2025-01, Graduate School of Economics, Hitotsubashi University.
    42. Hong, Yun & Qu, Bo & Yang, Zhuohang & Jiang, Yanhui, 2023. "The contagion of fake news concern and extreme stock market risks during the COVID-19 period," Finance Research Letters, Elsevier, vol. 58(PA).
    43. 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).
    44. Joanna Bruzda, 2020. "Multistep quantile forecasts for supply chain and logistics operations: bootstrapping, the GARCH model and quantile regression based approaches," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(1), pages 309-336, March.
    45. Cui, Zhenyu & Kirkby, J. Lars & Nguyen, Duy, 2021. "A data-driven framework for consistent financial valuation and risk measurement," European Journal of Operational Research, Elsevier, vol. 289(1), pages 381-398.
    46. 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).
    47. Wang, Shuai & Wang, Qian & Lu, Helen & Zhang, Dongxue & Xing, Qianyi & Wang, Jianzhou, 2025. "Learning about tail risk: Machine learning and combination with regularization in market risk management," Omega, Elsevier, vol. 133(C).
    48. Hong Shaopeng, 2020. "Generalized Autoregressive Score asymmetric Laplace Distribution and Extreme Downward Risk Prediction," Papers 2008.01277, arXiv.org, revised Oct 2020.
    49. Timo Dimitriadis & iaochun Liu & Julie Schnaitmann, 2023. "Encompassing Tests for Value at Risk and Expected Shortfall Multistep Forecasts Based on Inference on the Boundary," Journal of Financial Econometrics, Oxford University Press, vol. 21(2), pages 412-444.
    50. Man Wang & Yihan Cheng, 2022. "Forecasting value at risk and expected shortfall using high‐frequency data of domestic and international stock markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1595-1607, December.
    51. Dimitriadis, Timo & Schnaitmann, Julie, 2021. "Forecast encompassing tests for the expected shortfall," International Journal of Forecasting, Elsevier, vol. 37(2), pages 604-621.
    52. Zhouwei Wang & Qicheng Zhao & Min Zhu & Tao Pang, 2020. "Jump Aggregation, Volatility Prediction, and Nonlinear Estimation of Banks’ Sustainability Risk," Sustainability, MDPI, vol. 12(21), pages 1-17, October.
    53. Ning Zhang & Yujing Gong & Xiaohan Xue, 2023. "Less disagreement, better forecasts: Adjusted risk measures in the energy futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(10), pages 1332-1372, October.
    54. Dong Hwan Oh & Andrew J. Patton, 2021. "Better the Devil You Know: Improved Forecasts from Imperfect Models," Finance and Economics Discussion Series 2021-071, Board of Governors of the Federal Reserve System (U.S.).
    55. Guglielmo Maria Caporale & Timur Zekokh, 2018. "Modelling Volatility of Cryptocurrencies Using Markov-Switching Garch Models," CESifo Working Paper Series 7167, CESifo.
    56. Richard Gerlach & Antonio Naimoli & Giuseppe Storti, 2025. "Using quantile time series and historical simulation to forecast financial risk multiple steps ahead," Papers 2502.20978, arXiv.org, revised Mar 2025.
    57. Jianzhou Wang & Shuai Wang & Mengzheng Lv & He Jiang, 2024. "Forecasting VaR and ES by using deep quantile regression, GANs-based scenario generation, and heterogeneous market hypothesis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-35, December.
    58. Sebastian Bayer & Timo Dimitriadis, 2018. "Regression Based Expected Shortfall Backtesting," Papers 1801.04112, arXiv.org, revised Sep 2019.
    59. 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.
    60. Wei Kuang, 2022. "Oil tail-risk forecasts: from financial crisis to COVID-19," Risk Management, Palgrave Macmillan, vol. 24(4), pages 420-460, December.
    61. Enilov, Martin & Mensi, Walid & Stankov, Petar, 2023. "Does safe haven exist? Tail risks of commodity markets during COVID-19 pandemic," Journal of Commodity Markets, Elsevier, vol. 29(C).
    62. Kwangmin Jung & Donggyu Kim & Seunghyeon Yu, 2022. "Next generation models for portfolio risk management: An approach using financial big data," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 89(3), pages 765-787, September.
    63. Qiu, Zhiguo & Lazar, Emese & Nakata, Keiichi, 2024. "VaR and ES forecasting via recurrent neural network-based stateful models," International Review of Financial Analysis, Elsevier, vol. 92(C).
    64. Timo Dimitriadis & Julie Schnaitmann, 2019. "Forecast Encompassing Tests for the Expected Shortfall," Papers 1908.04569, arXiv.org, revised Aug 2020.
    65. Giovanni Bonaccolto, 2021. "Quantile– based portfolios: post– model– selection estimation with alternative specifications," Computational Management Science, Springer, vol. 18(3), pages 355-383, July.
    66. 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.
    67. Le-Yu Chen & Yu-Min Yen, 2025. "Estimation of the Local Conditional Tail Average Treatment Effect," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 43(1), pages 241-255, January.
    68. Jian, Zhihong & Li, Xupei & Zhu, Zhican, 2020. "Sequential forecasting of downside extreme risk during overnight and daytime: Evidence from the Chinese Stock Market☆," Pacific-Basin Finance Journal, Elsevier, vol. 64(C).
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    54. Cipollini, Fabrizio & Gallo, Giampiero M. & Palandri, Alessandro, 2021. "A dynamic conditional approach to forecasting portfolio weights," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1111-1126.
    55. Laura Capera Romero & Anne Opschoor, 2024. "Realized Variances vs. Correlations: Unlocking the Gains in Multivariate Volatility Forecasting," Tinbergen Institute Discussion Papers 24-059/III, Tinbergen Institute.
    56. 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.

  8. Dong Hwan Oh & Andrew J. Patton, 2015. "Modelling Dependence in High Dimensions with Factor Copulas," Finance and Economics Discussion Series 2015-51, Board of Governors of the Federal Reserve System (U.S.).

    Cited by:

    1. K. B. Gubbels & J. Y. Ypma & C. W. Oosterlee, 2023. "Principal Component Copulas for Capital Modelling and Systemic Risk," Papers 2312.13195, arXiv.org, revised Jul 2025.
    2. Bartels, Mariana & Ziegelmann, Flavio A., 2016. "Market risk forecasting for high dimensional portfolios via factor copulas with GAS dynamics," Insurance: Mathematics and Economics, Elsevier, vol. 70(C), pages 66-79.
    3. 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.
    4. Mayer, Alexander & Wied, Dominik, 2023. "Estimation and inference in factor copula models with exogenous covariates," Journal of Econometrics, Elsevier, vol. 235(2), pages 1500-1521.
    5. Choe, Geon Ho & Choi, So Eun & Jang, Hyun Jin, 2020. "Assessment of time-varying systemic risk in credit default swap indices: Simultaneity and contagiousness," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    6. Nagler, Thomas & Krüger, Daniel & Min, Aleksey, 2022. "Stationary vine copula models for multivariate time series," Journal of Econometrics, Elsevier, vol. 227(2), pages 305-324.
    7. Hoang Nguyen & Audron.e Virbickait.e & M. Concepci'on Aus'in & Pedro Galeano, 2024. "Structured factor copulas for modeling the systemic risk of European and United States banks," Papers 2401.03443, arXiv.org.
    8. Chen, Yanghan & Lin, Juan, 2024. "Measuring systemic risk in Asian foreign exchange markets," Journal of International Money and Finance, Elsevier, vol. 146(C).
    9. Georgios Bampinas & Theodore Panagiotidis, 2024. "How would the war and the pandemic affect the stock and cryptocurrency cross-market linkages?," Working Paper series 24-01, Rimini Centre for Economic Analysis.
    10. Stanislav Anatolyev & Vladimir Pyrlik, 2021. "Shrinkage for Gaussian and t Copulas in Ultra-High Dimensions," CERGE-EI Working Papers wp699, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    11. Peter Reinhard Hansen & Chen Tong, 2024. "Convolution-t Distributions," Papers 2404.00864, arXiv.org.
    12. Yujian Liu & Dejun Xie & Siyi Yu, 2025. "Copula hidden Markov model with unknown number of states," Computational Statistics, Springer, vol. 40(5), pages 2583-2610, June.
    13. Hyun Jin Jang & Kiseop Lee & Kyungsub Lee, 2020. "Systemic Risk in Market Microstructure of Crude Oil and Gasoline Futures Prices: A Hawkes Flocking Model Approach," Papers 2012.04181, arXiv.org.
    14. Tachibana, Minoru, 2022. "Safe haven assets for international stock markets: A regime-switching factor copula approach," Research in International Business and Finance, Elsevier, vol. 60(C).
    15. David Neděla & Sergio Ortobelli Lozza & Tomáš Tichý, 2025. "Dynamic Return Scenario Generation Approach for Large-Scale Portfolio Optimisation Framework," Computational Economics, Springer;Society for Computational Economics, vol. 65(2), pages 819-843, February.
    16. Francisco (F.) Blasques & Andre (A.) Lucas & Andries van Vlodrop, 2017. "Finite Sample Optimality of Score-Driven Volatility Models," Tinbergen Institute Discussion Papers 17-111/III, Tinbergen Institute.
    17. Georgios Bampinas & Theodore Panagiotidis & Panagiotis N. Politsidis, 2023. "Sovereign bond and CDS market contagion: A story from the Eurozone crisis," Working Paper series 23-09, Rimini Centre for Economic Analysis.
    18. Jiayuan Zhou & Feiyu Jiang & Ke Zhu & Wai Keung Li, 2019. "Time series models for realized covariance matrices based on the matrix-F distribution," Papers 1903.12077, arXiv.org, revised Jul 2020.
    19. Damien Ackerer & Thibault Vatter, 2016. "Dependent Defaults and Losses with Factor Copula Models," Papers 1610.03050, arXiv.org, revised Jan 2018.
    20. Gaete, Michael & Herrera, Rodrigo, 2023. "Diversification benefits of commodities in portfolio allocation: A dynamic factor copula approach," Journal of Commodity Markets, Elsevier, vol. 32(C).
    21. Gregor Kastner, 2016. "Sparse Bayesian time-varying covariance estimation in many dimensions," Papers 1608.08468, arXiv.org, revised Nov 2017.
    22. Shi, Peng & Zhao, Zifeng, 2024. "Enhanced pricing and management of bundled insurance risks with dependence-aware prediction using pair copula construction," Journal of Econometrics, Elsevier, vol. 240(1).
    23. Michael Stanley Smith, 2021. "Implicit Copulas: An Overview," Papers 2109.04718, arXiv.org.
    24. Ostap Okhrin & Anastasija Tetereva, 2017. "The Realized Hierarchical Archimedean Copula in Risk Modelling," Econometrics, MDPI, vol. 5(2), pages 1-31, June.
    25. Anne Opschoor & André Lucas & István Barra & Dick van Dijk, 2021. "Closed-Form Multi-Factor Copula Models With Observation-Driven Dynamic Factor Loadings," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(4), pages 1066-1079, October.
    26. 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).
    27. Ackerer Damien & Vatter Thibault, 2017. "Dependent defaults and losses with factor copula models," Dependence Modeling, De Gruyter, vol. 5(1), pages 375-399, December.
    28. Hua, Lei & Joe, Harry, 2017. "Multivariate dependence modeling based on comonotonic factors," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 317-333.
    29. Kreuzer, Alexander & Czado, Claudia, 2021. "Bayesian inference for a single factor copula stochastic volatility model using Hamiltonian Monte Carlo," Econometrics and Statistics, Elsevier, vol. 19(C), pages 130-150.
    30. Aviral Kumar Tiwari & Sangram Keshari Jena & Satish Kumar & Erik Hille, 2022. "Is oil price risk systemic to sectoral equity markets of an oil importing country? Evidence from a dependence-switching copula delta CoVaR approach," Annals of Operations Research, Springer, vol. 315(1), pages 429-461, August.
    31. Hofert, Marius & Prasad, Avinash & Zhu, Mu, 2022. "Multivariate time-series modeling with generative neural networks," Econometrics and Statistics, Elsevier, vol. 23(C), pages 147-164.
    32. Nguyen, Hoang & Ausín, M. Concepción & Galeano, Pedro, 2020. "Variational inference for high dimensional structured factor copulas," Computational Statistics & Data Analysis, Elsevier, vol. 151(C).
    33. Sirio Aramonte & Mohammad R. Jahan-Parvar & Samuel Rosen & John W. Schindler, 2022. "Firm-Specific Risk-Neutral Distributions with Options and CDS," Management Science, INFORMS, vol. 68(9), pages 7018-7033, September.
    34. Antonia Arsova & Deniz Dilan Karaman Örsal, 2016. "An intersection test for the cointegrating rank in dependent panel data," Working Paper Series in Economics 357, University of Lüneburg, Institute of Economics.
    35. Okhrin, Yarema & Uddin, Gazi Salah & Yahya, Muhammad, 2023. "Nonlinear and asymmetric interconnectedness of crude oil with financial and commodity markets," Energy Economics, Elsevier, vol. 125(C).
    36. Loaiza-Maya, Rubén & Smith, Michael Stanley & Nott, David J. & Danaher, Peter J., 2022. "Fast and accurate variational inference for models with many latent variables," Journal of Econometrics, Elsevier, vol. 230(2), pages 339-362.
    37. Yousaf Ali Khan, 2022. "Modeling Dependent Structure Among Micro-Economics Variables Through COPAR (1)-Model in Pakistan," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(1), pages 257-279, March.
    38. Blasques, Francisco & Lucas, André & van Vlodrop, Andries C., 2021. "Finite Sample Optimality of Score-Driven Volatility Models: Some Monte Carlo Evidence," Econometrics and Statistics, Elsevier, vol. 19(C), pages 47-57.
    39. Smith, Michael Stanley & Maneesoonthorn, Worapree, 2018. "Inversion copulas from nonlinear state space models with an application to inflation forecasting," International Journal of Forecasting, Elsevier, vol. 34(3), pages 389-407.
    40. Andre Lucas & Anne Opschoor & Luca Rossini, 2021. "Tail Heterogeneity for Dynamic Covariance Matrices: the F-Riesz Distribution," Tinbergen Institute Discussion Papers 21-010/III, Tinbergen Institute, revised 11 Jul 2023.
    41. Lin Deng & Michael Stanley Smith & Worapree Maneesoonthorn, 2023. "Large Skew-t Copula Models and Asymmetric Dependence in Intraday Equity Returns," Papers 2308.05564, arXiv.org, revised Jul 2024.
    42. Maximilian Coblenz & Simon Holz & Hans‐Jörg Bauer & Oliver Grothe & Rainer Koch, 2020. "Modelling fuel injector spray characteristics in jet engines by using vine copulas," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 863-886, August.
    43. Chen, Zhenlong & Chang, Jing & Hao, Xiaozhen, 2024. "Portfolio selection via high-dimensional stochastic factor Copula," Finance Research Letters, Elsevier, vol. 67(PA).
    44. Don O’Sullivan & Leon Zolotoy & Madhu Veeraraghavan & Jennifer R. Overbeck, 2025. "Are Employees Safer When the CEO Looks Greedy?," Journal of Business Ethics, Springer, vol. 198(3), pages 655-673, May.
    45. Bhat, Chandra R. & Mondal, Aupal, 2022. "A New Flexible Generalized Heterogeneous Data Model (GHDM) with an Application to Examine the Effect of High Density Neighborhood Living on Bicycling Frequency," Transportation Research Part B: Methodological, Elsevier, vol. 164(C), pages 244-266.
    46. Jin Xisong & Lehnert Thorsten, 2018. "Large portfolio risk management and optimal portfolio allocation with dynamic elliptical copulas," Dependence Modeling, De Gruyter, vol. 6(1), pages 19-46, February.
    47. Tong, Chen & Hansen, Peter Reinhard, 2023. "Characterizing correlation matrices that admit a clustered factor representation," Economics Letters, Elsevier, vol. 233(C).
    48. Ballotta, Laura & Fusai, Gianluca & Marazzina, Daniele, 2019. "Integrated structural approach to Credit Value Adjustment," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1143-1157.
    49. Quanrui Song & Jianxu Liu & Songsak Sriboonchitta, 2019. "Risk Measurement of Stock Markets in BRICS, G7, and G20: Vine Copulas versus Factor Copulas," Mathematics, MDPI, vol. 7(3), pages 1-16, March.
    50. Arnold Polanski & Evarist Stoja & Ching-Wai (Jeremy) Chiu, 2019. "Tail risk interdependence," Bank of England working papers 815, Bank of England.
    51. Hamori, Shigeyuki & Motegi, Kaiji & Zhang, Zheng, 2019. "Calibration estimation of semiparametric copula models with data missing at random," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 85-109.
    52. Müller, Dominik & Czado, Claudia, 2019. "Dependence modelling in ultra high dimensions with vine copulas and the Graphical Lasso," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 211-232.
    53. Manner, Hans & Rodríguez, Gabriel & Stöckler, Florian, 2024. "A changepoint analysis of exchange rate and commodity price risks for Latin American stock markets," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 1385-1403.
    54. Oh, Dong Hwan & Patton, Andrew J., 2023. "Dynamic factor copula models with estimated cluster assignments," Journal of Econometrics, Elsevier, vol. 237(2).
    55. Dellaportas, Petros & Tsionas, Mike G., 2019. "Importance sampling from posterior distributions using copula-like approximations," Journal of Econometrics, Elsevier, vol. 210(1), pages 45-57.
    56. 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).
    57. Han, Yingwei & Li, Jie, 2022. "Should investors include green bonds in their portfolios? Evidence for the USA and Europe," International Review of Financial Analysis, Elsevier, vol. 80(C).
    58. Dong Hwan Oh & Andrew J. Patton, 2021. "Dynamic Factor Copula Models with Estimated Cluster Assignments," Finance and Economics Discussion Series 2021-029r1, Board of Governors of the Federal Reserve System (U.S.), revised 06 May 2022.
    59. Siburg, Karl Friedrich & Strothmann, Christopher & Weiß, Gregor, 2024. "Comparing and quantifying tail dependence," Insurance: Mathematics and Economics, Elsevier, vol. 118(C), pages 95-103.
    60. Li, Degui, 2024. "Estimation of Large Dynamic Covariance Matrices: A Selective Review," Econometrics and Statistics, Elsevier, vol. 29(C), pages 16-30.
    61. Alcock, Jamie & Sinagl, Petra, 2022. "International determinants of asymmetric dependence in investment returns," Journal of International Money and Finance, Elsevier, vol. 122(C).
    62. Siem Jan Koopman & Rutger Lit & André Lucas & Anne Opschoor, 2018. "Dynamic discrete copula models for high‐frequency stock price changes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(7), pages 966-985, November.
    63. Alessandro Barbiero, 2021. "Inducing a desired value of correlation between two point-scale variables: a two-step procedure using copulas," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(2), pages 307-334, June.
    64. Jianxu Liu & Quanrui Song & Yang Qi & Sanzidur Rahman & Songsak Sriboonchitta, 2020. "Measurement of Systemic Risk in Global Financial Markets and Its Application in Forecasting Trading Decisions," Sustainability, MDPI, vol. 12(10), pages 1-15, May.
    65. León, Ángel & Ñíguez, Trino-Manuel, 2020. "Modeling asset returns under time-varying semi-nonparametric distributions," Journal of Banking & Finance, Elsevier, vol. 118(C).
    66. Bassetti, Federico & De Giuli, Maria Elena & Nicolino, Enrica & Tarantola, Claudia, 2018. "Multivariate dependence analysis via tree copula models: An application to one-year forward energy contracts," European Journal of Operational Research, Elsevier, vol. 269(3), pages 1107-1121.
    67. Smith, Michael Stanley, 2023. "Implicit Copulas: An Overview," Econometrics and Statistics, Elsevier, vol. 28(C), pages 81-104.
    68. Xu Wang & Xueyan Wu & Yingying Zhou, 2022. "Conditional Dynamic Dependence and Risk Spillover between Crude Oil Prices and Foreign Exchange Rates: New Evidence from a Dynamic Factor Copula Model," Energies, MDPI, vol. 15(14), pages 1-21, July.
    69. Conlon, Thomas & Cotter, John & Kovalenko, Illia & Post, Thierry, 2023. "A financial modeling approach to industry exchange-traded funds selection," Journal of Empirical Finance, Elsevier, vol. 74(C).
    70. Ouyang, Ruolan & Chen, Xiang & Fang, Yi & Zhao, Yang, 2022. "Systemic risk of commodity markets: A dynamic factor copula approach," International Review of Financial Analysis, Elsevier, vol. 82(C).
    71. Michael A. Goldstein & Joseph McCarthy & Alexei G. Orlov, 2019. "The Core, Periphery, and Beyond: Stock Market Comovements among EU and Non‐EU Countries," The Financial Review, Eastern Finance Association, vol. 54(1), pages 5-56, February.
    72. Anatolyev, Stanislav & Pyrlik, Vladimir, 2022. "Copula shrinkage and portfolio allocation in ultra-high dimensions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    73. Cyril Bénézet & Emmanuel Gobet & Rodrigo Targino, 2023. "Transform MCMC Schemes for Sampling Intractable Factor Copula Models," Methodology and Computing in Applied Probability, Springer, vol. 25(1), pages 1-41, March.
    74. Bormann, Carsten & Schienle, Melanie, 2019. "Detecting structural differences in tail dependence of financial time series," Working Paper Series in Economics 122, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    75. McCausland, William & Miller, Shirley & Pelletier, Denis, 2021. "Multivariate stochastic volatility using the HESSIAN method," Econometrics and Statistics, Elsevier, vol. 17(C), pages 76-94.
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    78. Arnold Polanski & Evarist Stoja & Ching‐Wai (Jeremy) Chiu, 2021. "Tail risk interdependence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5499-5511, October.
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  9. Tim Bollerslev & Andrew J. Patton & Rogier Quaedvlieg, 2015. "Exploiting the Errors: A Simple Approach for Improved Volatility Forecasting," CREATES Research Papers 2015-14, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. 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.
    2. Han, Chulwoo & Park, Frank C., 2022. "A geometric framework for covariance dynamics," Journal of Banking & Finance, Elsevier, vol. 134(C).
    3. Gong, Xu & Lin, Boqiang, 2018. "The incremental information content of investor fear gauge for volatility forecasting in the crude oil futures market," Energy Economics, Elsevier, vol. 74(C), pages 370-386.
    4. Harvey, A. & Palumbo, D., 2019. "Score-Driven Models for Realized Volatility," Cambridge Working Papers in Economics 1950, Faculty of Economics, University of Cambridge.
    5. S. Al Wadi, 2017. "Improving Volatility Risk Forecasting Accuracy in Industry Sector," International Journal of Mathematics and Mathematical Sciences, Hindawi, vol. 2017, pages 1-6, November.
    6. Özbekler, Ali Gencay & Kontonikas, Alexandros & Triantafyllou, Athanasios, 2021. "Volatility forecasting in European government bond markets," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1691-1709.
    7. Ding, Shusheng & Cui, Tianxiang & Zhang, Yongmin, 2022. "Futures volatility forecasting based on big data analytics with incorporating an order imbalance effect," International Review of Financial Analysis, Elsevier, vol. 83(C).
    8. 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.
    9. 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.
    10. Mengxi He & Xianfeng Hao & Yaojie Zhang & Fanyi Meng, 2021. "Forecasting stock return volatility using a robust regression model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1463-1478, December.
    11. Hardik A. Marfatia & Qiang Ji & Jiawen Luo, 2022. "Forecasting the volatility of agricultural commodity futures: The role of co‐volatility and oil volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 383-404, March.
    12. Zargar, Faisal Nazir & Kumar, Dilip, 2020. "Modeling unbiased extreme value volatility estimator in presence of heterogeneity and jumps: A study with economic significance analysis," International Review of Economics & Finance, Elsevier, vol. 67(C), pages 25-41.
    13. Cheng, Mingmian & Liao, Yuan & Yang, Xiye, 2023. "Uniform predictive inference for factor models with instrumental and idiosyncratic betas," Journal of Econometrics, Elsevier, vol. 237(2).
    14. 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.
    15. Sucarrat, Genaro, 2020. "Identification of Volatility Proxies as Expectations of Squared Financial Return," MPRA Paper 101953, University Library of Munich, Germany.
    16. 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.
    17. Toshiaki Ogawa & Masato Ubukata & Toshiaki Watanabe, 2020. "Stock Return Predictability and Variance Risk Premia around the ZLB," IMES Discussion Paper Series 20-E-09, Institute for Monetary and Economic Studies, Bank of Japan.
    18. Lyócsa, Štefan & Todorova, Neda, 2021. "What drives volatility of the U.S. oil and gas firms?," Energy Economics, Elsevier, vol. 100(C).
    19. Ruijun Bu & Rodrigo Hizmeri & Marwan Izzeldin & Anthony Murphy & Mike G. Tsionas, 2019. "The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility," Working Papers 1902, Federal Reserve Bank of Dallas, revised 17 Dec 2022.
    20. 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".
    21. Evan Anderson & Ai-ru (Meg) Cheng, 2022. "Portfolio Choices with Many Big Models," Management Science, INFORMS, vol. 68(1), pages 690-715, January.
    22. Chao Zhang & Xingyue Pu & Mihai Cucuringu & Xiaowen Dong, 2023. "Graph Neural Networks for Forecasting Multivariate Realized Volatility with Spillover Effects," Papers 2308.01419, arXiv.org.
    23. Lai T. Hoang & Dirk G. Baur, 2020. "Forecasting bitcoin volatility: Evidence from the options market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(10), pages 1584-1602, October.
    24. 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.
    25. BAUWENS, Luc & BRAIONE, Manuela & STORTI, Giuseppe, 2016. "Multiplicative Conditional Correlation Models for Realized Covariance Matrices," LIDAM Discussion Papers CORE 2016041, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    26. Zhang, Yaojie & He, Mengxi & Wang, Yudong & Wen, Danyan, 2025. "Model specification for volatility forecasting benchmark," International Review of Financial Analysis, Elsevier, vol. 97(C).
    27. Jian, Zhihong & Li, Xupei & Zhu, Zhican, 2022. "Extreme risk transmission channels between the stock index futures and spot markets: Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    28. Verena Monschang & Bernd Wilfling, 2022. "A procedure for upgrading linear-convex combination forecasts with an application to volatility prediction," CQE Working Papers 9722, Center for Quantitative Economics (CQE), University of Muenster.
    29. Matias Quiroz & Laleh Tafakori & Hans Manner, 2024. "Forecasting realized covariances using HAR-type models," Papers 2412.10791, arXiv.org.
    30. Fabrizio Cipollini & Giampiero M Gallo & Alessandro Palandri, 2020. "Realized Variance Modeling: Decoupling Forecasting from Estimation," Journal of Financial Econometrics, Oxford University Press, vol. 18(3), pages 532-555.
    31. Catania, Leopoldo & Proietti, Tommaso, 2020. "Forecasting volatility with time-varying leverage and volatility of volatility effects," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1301-1317.
    32. Luo, Jiawen & Cepni, Oguzhan & Demirer, Riza & Gupta, Rangan, 2025. "Forecasting multivariate volatilities with exogenous predictors: An application to industry diversification strategies," Journal of Empirical Finance, Elsevier, vol. 81(C).
    33. Worapree Maneesoonthorn & Catherine S. Forbes & Gael M. Martin, 2013. "Inference on Self-Exciting Jumps in Prices and Volatility using High Frequency Measures," Monash Econometrics and Business Statistics Working Papers 28/13, Monash University, Department of Econometrics and Business Statistics.
    34. Bissoondoyal-Bheenick, Emawtee & Brooks, Robert & Do, Hung Xuan & Smyth, Russell, 2020. "Exploiting the heteroskedasticity in measurement error to improve volatility predictions in oil and biofuel feedstock markets," Energy Economics, Elsevier, vol. 86(C).
    35. Peter Reinhard Hansen & Chen Tong, 2024. "Convolution-t Distributions," Papers 2404.00864, arXiv.org.
    36. Proelss, Juliane & Schweizer, Denis & Seiler, Volker, 2020. "The economic importance of rare earth elements volatility forecasts," International Review of Financial Analysis, Elsevier, vol. 71(C).
    37. Ran Xiao, 2019. "Essays on Price Discovery and Volatility Dynamics in Emerging Market Currencies," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 5-2019, January-A.
    38. Chao Liang & Yan Li & Feng Ma & Yaojie Zhang, 2022. "Forecasting international equity market volatility: A new approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1433-1457, November.
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    169. Byounghyun Jeon & Sung Won Seo & Jun Sik Kim, 2020. "Uncertainty and the volatility forecasting power of option‐implied volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(7), pages 1109-1126, July.
    170. A Clements & M Doolan, 2018. "Combining Multivariate Volatility Forecasts using Weighted Losses," NCER Working Paper Series 119, National Centre for Econometric Research.
    171. Qiu, Yue & Wang, Zongrun & Xie, Tian & Zhang, Xinyu, 2021. "Forecasting Bitcoin realized volatility by exploiting measurement error under model uncertainty," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 179-201.
    172. Benschop, Thijs & López Cabrera, Brenda, 2017. "Realized volatility of CO₂ futures," SFB 649 Discussion Papers 2017-025, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    173. Pan, Zhiyuan & Fu, Ziqian & Wang, Yudong & Dong, Qingma, 2024. "Exploiting the sentiments: A simple approach for improving cross hedging effectiveness," Energy Economics, Elsevier, vol. 134(C).
    174. Tianlun Fei & Xiaoquan Liu & Conghua Wen, 2023. "Forecasting stock return volatility: Realized volatility‐type or duration‐based estimators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1594-1621, November.
    175. Ding, Shusheng & Cui, Tianxiang & Zhang, Yongmin, 2020. "Incorporating the RMB internationalization effect into its exchange rate volatility forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    176. Papantonis, Ioannis & Rompolis, Leonidas & Tzavalis, Elias, 2023. "Improving variance forecasts: The role of Realized Variance features," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1221-1237.
    177. Li, Zhenghui & Chen, Liming & Dong, Hao, 2021. "What are bitcoin market reactions to its-related events?," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 1-10.
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    179. Izzeldin, Marwan & Muradoğlu, Yaz Gülnur & Pappas, Vasileios & Petropoulou, Athina & Sivaprasad, Sheeja, 2023. "The impact of the Russian-Ukrainian war on global financial markets," International Review of Financial Analysis, Elsevier, vol. 87(C).
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    181. Simon Tranberg Bodilsen & Asger Lunde, 2025. "Exploiting News Analytics for Volatility Forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 40(1), pages 18-36, January.
    182. Martin Magris, 2019. "A Vine-copula extension for the HAR model," Papers 1907.08522, arXiv.org.
    183. Niu, Zibo & Demirer, Riza & Suleman, Muhammad Tahir & Zhang, Hongwei & Zhu, Xuehong, 2024. "Do industries predict stock market volatility? Evidence from machine learning models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 90(C).
    184. Dumitru, Ana Maria H. & Hizmeri, Rodrigo & Izzeldin, Marwan, 2025. "Forecasting the realized variance in the presence of intraday periodicity," Journal of Banking & Finance, Elsevier, vol. 170(C).
    185. Audrino, Francesco & Sigrist, Fabio & Ballinari, Daniele, 2020. "The impact of sentiment and attention measures on stock market volatility," International Journal of Forecasting, Elsevier, vol. 36(2), pages 334-357.
    186. Lu, Botao & Ma, Feng & Wang, Jiqian & Ding, Hui & Wahab, M.I.M., 2021. "Harnessing the decomposed realized measures for volatility forecasting: Evidence from the US stock market," International Review of Economics & Finance, Elsevier, vol. 72(C), pages 672-689.
    187. Ma, Feng & Wahab, M.I.M. & Huang, Dengshi & Xu, Weiju, 2017. "Forecasting the realized volatility of the oil futures market: A regime switching approach," Energy Economics, Elsevier, vol. 67(C), pages 136-145.
    188. Cheng, Mingmian & Swanson, Norman R. & Yang, Xiye, 2021. "Forecasting volatility using double shrinkage methods," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 46-61.
    189. Sebastiano Michele Zema, 2020. "Directed Acyclic Graph based Information Shares for Price Discovery," LEM Papers Series 2020/28, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    190. Rice, Gregory & Wirjanto, Tony & Zhao, Yuqian, 2021. "Exploring volatility of crude oil intra-day return curves: a functional GARCH-X Model," MPRA Paper 109231, University Library of Munich, Germany.
    191. Sucarrat, Genaro, 2021. "Identification of volatility proxies as expectations of squared financial returns," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1677-1690.
    192. Andersen, Torben G. & Li, Yingying & Todorov, Viktor & Zhou, Bo, 2023. "Volatility measurement with pockets of extreme return persistence," Journal of Econometrics, Elsevier, vol. 237(2).
    193. 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.
    194. Danyan Wen & Mengxi He & Yaojie Zhang & Yudong Wang, 2022. "Forecasting realized volatility of Chinese stock market: A simple but efficient truncated approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 230-251, March.
    195. Plíhal, Tomáš & Lyócsa, Štefan, 2021. "Modeling realized volatility of the EUR/USD exchange rate: Does implied volatility really matter?," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 811-829.
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    197. Ulrich Hounyo & Zhi Liu & Rasmus T. Varneskov, 2023. "Bootstrapping Laplace transforms of volatility," Quantitative Economics, Econometric Society, vol. 14(3), pages 1059-1103, July.
    198. Linyi Yang & Jiazheng Li & Ruihai Dong & Yue Zhang & Barry Smyth, 2022. "NumHTML: Numeric-Oriented Hierarchical Transformer Model for Multi-task Financial Forecasting," Papers 2201.01770, arXiv.org.
    199. Chen, Yixiang & Ma, Feng & Zhang, Yaojie, 2019. "Good, bad cojumps and volatility forecasting: New evidence from crude oil and the U.S. stock markets," Energy Economics, Elsevier, vol. 81(C), pages 52-62.
    200. 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.

  10. 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.).

    Cited by:

    1. Bauwens, Luc & Xu, Yongdeng, 2023. "DCC- and DECO-HEAVY: Multivariate GARCH models based on realized variances and correlations," International Journal of Forecasting, Elsevier, vol. 39(2), pages 938-955.
    2. 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.
    3. Bauwens, Luc & Dzuverovic, Emilija & Hafner, Christian, 2024. "Asymmetric Models for Realized Covariances," LIDAM Discussion Papers ISBA 2024022, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    4. Luo, Jiawen & Cepni, Oguzhan & Demirer, Riza & Gupta, Rangan, 2025. "Forecasting multivariate volatilities with exogenous predictors: An application to industry diversification strategies," Journal of Empirical Finance, Elsevier, vol. 81(C).
    5. Stanislav Anatolyev & Vladimir Pyrlik, 2021. "Shrinkage for Gaussian and t Copulas in Ultra-High Dimensions," CERGE-EI Working Papers wp699, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    6. Kim Christensen & Charlotte Christiansen & Anders M. Posselt, 2019. "The Economic Value of VIX ETPs," CREATES Research Papers 2019-14, Department of Economics and Business Economics, Aarhus University.
    7. 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).
    8. 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.
    9. Qu, Hui & Zhang, Yi, 2022. "Asymmetric multivariate HAR models for realized covariance matrix: A study based on volatility timing strategies," Economic Modelling, Elsevier, vol. 106(C).
    10. Laura Capera Romero & Anne Opschoor, 2025. "Revisiting EWMA in High-Frequency Portfolio Optimization: A Comparative Assessment," Tinbergen Institute Discussion Papers 25-041/III, Tinbergen Institute.
    11. Luc Bauwens & Edoardo Otranto, 2023. "Modeling Realized Covariance Matrices: A Class of Hadamard Exponential Models," Journal of Financial Econometrics, Oxford University Press, vol. 21(4), pages 1376-1401.
    12. Jianqing Fan & Donggyu Kim & Minseok Shin & Yazhen Wang, 2024. "Factor and Idiosyncratic VAR-Ito Volatility Models for Heavy-Tailed High-Frequency Financial Data," Working Papers 202415, University of California at Riverside, Department of Economics.
    13. Bauwens, Luc & Otranto, Edoardo, 2023. "Realized Covariance Models with Time-varying Parameters and Spillover Effects," LIDAM Discussion Papers CORE 2023019, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    14. Dhaene, Geert & Wu, Jianbin, 2020. "Incorporating overnight and intraday returns into multivariate GARCH volatility models," Journal of Econometrics, Elsevier, vol. 217(2), pages 471-495.
    15. Jin Xisong & Lehnert Thorsten, 2018. "Large portfolio risk management and optimal portfolio allocation with dynamic elliptical copulas," Dependence Modeling, De Gruyter, vol. 6(1), pages 19-46, February.
    16. Giuseppe Buccheri & Davide Pirino & Luca Trapin, 2021. "Managing liquidity with portfolio staleness," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(1), pages 215-239, June.
    17. Hamori, Shigeyuki & Motegi, Kaiji & Zhang, Zheng, 2019. "Calibration estimation of semiparametric copula models with data missing at random," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 85-109.
    18. Rong Gan & Shuqian Gu & Xiaoxia Tong & Jinqiang Lu & Hui Tang, 2024. "A nonparametric standardized runoff index for characterizing hydrological drought in the Shaying River Basin, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(3), pages 2233-2253, February.
    19. Bohan Zhao & Hong Yin & Yonghong Long, 2024. "An Advanced Time-Varying Capital Asset Pricing Model via Heterogeneous Autoregressive Framework: Evidence from the Chinese Stock Market," Mathematics, MDPI, vol. 13(1), pages 1-14, December.
    20. BAUWENS Luc, & XU Yongdeng,, 2019. "DCC-HEAVY: A multivariate GARCH model based on realized variances and correlations," LIDAM Discussion Papers CORE 2019025, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    21. Rub'en Loaiza-Maya & Michael S. Smith & Worapree Maneesoonthorn, 2017. "Time Series Copulas for Heteroskedastic Data," Papers 1701.07152, arXiv.org.
    22. Clements, Adam & Vasnev, Andrey L., 2023. "Combining simple multivariate HAR-like models for portfolio construction," Working Papers BAWP-2023-03, University of Sydney Business School, Discipline of Business Analytics.
    23. Jiawen Luo & Shengjie Fu & Oguzhan Cepni & Rangan Gupta, 2025. "The Role of Uncertainty in Forecasting Realized Covariance of US State-Level Stock Returns: A Reverse-MIDAS Approach," Working Papers 202501, University of Pretoria, Department of Economics.
    24. Kaveh Salehzadeh Nobari, 2021. "Pair copula constructions of point-optimal sign-based tests for predictive linear and nonlinear regressions," Papers 2111.04919, arXiv.org.
    25. Sergii Pypko, 2015. "Volatility Forecast in Crises and Expansions," JRFM, MDPI, vol. 8(3), pages 1-26, August.
    26. Anatolyev, Stanislav & Pyrlik, Vladimir, 2022. "Copula shrinkage and portfolio allocation in ultra-high dimensions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    27. Nasri, Bouchra R., 2020. "On non-central squared copulas," Statistics & Probability Letters, Elsevier, vol. 161(C).
    28. Kerem Tuzcuoglu, 2019. "Composite Likelihood Estimation of an Autoregressive Panel Probit Model with Random Effects," Staff Working Papers 19-16, Bank of Canada.
    29. Laura Capera Romero & Anne Opschoor, 2024. "Realized Variances vs. Correlations: Unlocking the Gains in Multivariate Volatility Forecasting," Tinbergen Institute Discussion Papers 24-059/III, Tinbergen Institute.
    30. Rewat Khanthaporn, 2022. "Analysis of Nonlinear Comovement of Benchmark Thai Government Bond Yields," PIER Discussion Papers 183, Puey Ungphakorn Institute for Economic Research.

  11. Tim Bollerslev & Andrew J. Patton & Wenjing Wang, 2015. "Daily House Price Indices: Construction, Modeling, and Longer-Run Predictions," CREATES Research Papers 2015-02, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Mehmet Balcilar & Elie Bouri & Rangan Gupta & Mark E. Wohar, 2020. "Mortgage Default Risks and High-Frequency Predictability of the U.S. Housing Market: A Reconsideration," Journal of Real Estate Portfolio Management, Taylor & Francis Journals, vol. 26(2), pages 111-117, December.
    2. Wendy Nyakabawo & Rangan Gupta & Hardik A. Marfatia, 2018. "High-Frequency Impact of Monetary Policy and Macroeconomic Surprises on US MSAs and Aggregate US Housing Returns and Volatility: A GJR-GARCH Approach," Working Papers 201817, University of Pretoria, Department of Economics.
    3. Mawuli Segnon & Rangan Gupta & Keagile Lesame & Mark E. Wohar, 2019. "High-Frequency Volatility Forecasting of US Housing Markets," Working Papers 201977, University of Pretoria, Department of Economics.
    4. Yang, Jian & Tong, Meng & Yu, Ziliang, 2021. "Housing market spillovers through the lens of transaction volume: A new spillover index approach," Journal of Empirical Finance, Elsevier, vol. 64(C), pages 351-378.
    5. Bob Kaempff & David Kremer, 2025. "Using machine learning to aggregate apartment prices: Comparing the performance of different Luxembourg indices," BCL working papers 194, Central Bank of Luxembourg.
    6. Aviral Kumar Tiwari & Rangan Gupta & Juncal Cunado & Xin Sheng, 2019. "Testing the White Noise Hypothesis in High-Frequency Housing Returns of the United States," Working Papers 201952, University of Pretoria, Department of Economics.
    7. Robert J. Hill & Alicia N. Rambaldi, 2022. "Hedonic Models and House Price Index Numbers," Springer Books, in: Duangkamon Chotikapanich & Alicia N. Rambaldi & Nicholas Rohde (ed.), Advances in Economic Measurement, chapter 0, pages 413-444, Springer.
    8. Enwei Zhu & Jing Wu & Hongyu Liu & Keyang Li, 2023. "A Sentiment Index of the Housing Market in China: Text Mining of Narratives on Social Media," The Journal of Real Estate Finance and Economics, Springer, vol. 66(1), pages 77-118, January.
    9. Wang, Xiaodan & Li, Keyang & Wu, Jing, 2020. "House price index based on online listing information: The case of China," Journal of Housing Economics, Elsevier, vol. 50(C).
    10. Marc Francke & Lyndsey Rolheiser & Alex Minne, 2025. "Estimating Census Tract House Price Indexes: A New Spatial Dynamic Factor Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 70(3), pages 483-514, April.
    11. Bremus, Franziska & Krause, Thomas & Noth, Felix, 2021. "Lender-specific mortgage supply shocks and macroeconomic performance in the United States," IWH Discussion Papers 3/2021, Halle Institute for Economic Research (IWH).
    12. Isaiah Hull & Conny Olovsson & Karl Walentin & Andreas Westermark, 2022. "Manufacturing Decline and House Price Volatility," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 45, pages 264-281, July.
    13. Badarinza, Cristian & Ramadorai, Tarun, 2018. "Home away from home? Foreign demand and London house prices," Journal of Financial Economics, Elsevier, vol. 130(3), pages 532-555.
    14. Robert J. Hill & Alicia N. Rambaldi & Michael Scholz, 2018. "Higher Frequency Hedonic Property Price Indices: A State Space Approach," Graz Economics Papers 2018-04, University of Graz, Department of Economics.
    15. Bouri, Elie & Gupta, Rangan & Kyei, Clement Kweku & Shivambu, Rinsuna, 2021. "Uncertainty and daily predictability of housing returns and volatility of the United States: Evidence from a higher-order nonparametric causality-in-quantiles test," The Quarterly Review of Economics and Finance, Elsevier, vol. 82(C), pages 200-206.
    16. Yongheng Deng & Eric Girardin & Roselyne Joyeux, 2018. "Fundamentals and the volatility of real estate prices in China: A sequential modelling strategy," Post-Print hal-01996210, HAL.
    17. Franziska Bremus & Thomas Krause & Felix Noth, 2021. "Lender-Specific Mortgage Supply Shocks and Macroeconomic Performance in the United States," Discussion Papers of DIW Berlin 1936, DIW Berlin, German Institute for Economic Research.
    18. Yongheng Deng & Eric Girardin & Roselyne Joyeux, 2015. "Fundamentals and the Volatility of Real Estate Prices in China: A Sequential Modelling Strategy," Working Papers 222015, Hong Kong Institute for Monetary Research.
    19. Ryan Greenaway-McGrevy & Kade Sorensen, 2021. "A spatial model averaging approach to measuring house prices," Journal of Spatial Econometrics, Springer, vol. 2(1), pages 1-32, December.
    20. Goodness C. Aye & Christina Christou & Rangan Gupta & Christis Hassapis, 2024. "High-Frequency Contagion between Aggregate and Regional Housing Markets of the United States with Financial Assets: Evidence from Multichannel Tests," The Journal of Real Estate Finance and Economics, Springer, vol. 69(2), pages 253-276, August.
    21. Jian Yang & Meng Tong & Ziliang Yu, 2023. "Can volume be more informative than prices? Evidence from Chinese housing markets," Review of Quantitative Finance and Accounting, Springer, vol. 61(2), pages 633-672, August.
    22. Aviral Kumar Tiwari & Rangan Gupta & Mark E. Wohar, 2019. "Is the Housing Market in the United States Really Weakly-Efficient?," Working Papers 201934, University of Pretoria, Department of Economics.
    23. Franziska Bremus & Thomas Krause & Felix Noth, 2017. "Bank-Specific Shocks and House Price Growth in the U.S," Discussion Papers of DIW Berlin 1636, DIW Berlin, German Institute for Economic Research.
    24. Wendy Nyakabawo & Rangan Gupta & Hardik A. Marfatia, 2018. "High Frequency Impact Of Monetary Policy And Macroeconomic Surprises On Us Msas, Aggregate Us Housing Returns And Asymmetric Volatility," Advances in Decision Sciences, Asia University, Taiwan, vol. 22(1), pages 204-229, December.
    25. Elie Bouri & Rangan Gupta & Hardik A. Marfatia & Jacobus Nel, 2022. "Do Climate Risks Predict US Housing Returns and Volatility? Evidence from a Quantiles-Based Approach," Working Papers 202240, University of Pretoria, Department of Economics.
    26. Lajos Horv'ath & Lorenzo Trapani, 2023. "Real-time monitoring with RCA models," Papers 2312.11710, arXiv.org.
    27. Elliot Anenberg & Steven Laufer, 2014. "Using Data on Seller Behavior to Forecast Short-run House Price Changes," Finance and Economics Discussion Series 2014-16, Board of Governors of the Federal Reserve System (U.S.).

  12. Patton, Andrew & Kruttli, Mathias, 2014. "The Impact of Hedge Funds on Asset Markets," CEPR Discussion Papers 10151, C.E.P.R. Discussion Papers.

    Cited by:

    1. Kruttli, Mathias S. & Monin, Phillip J. & Petrasek, Lubomir & Watugala, Sumudu W., 2025. "LTCM Redux? Hedge fund Treasury trading, funding fragility, and risk constraints," Journal of Financial Economics, Elsevier, vol. 169(C).
    2. Mathias S. Kruttli & Phillip J. Monin & Sumudu W. Watugala, 2017. "Investor Concentration, Flows, and Cash Holdings: Evidence from Hedge Funds," Working Papers 17-07, Office of Financial Research, US Department of the Treasury, revised 02 Jun 2020.
    3. Mathias S. Kruttli, 2016. "From Which Consumption-Based Asset Pricing Models Can Investors Profit? Evidence from Model-Based Priors," Finance and Economics Discussion Series 2016-027, Board of Governors of the Federal Reserve System (U.S.).
    4. Jean-Sébastien Fontaine & René Garcia & Sermin Gungor, 2016. "Funding Liquidity, Market Liquidity and the Cross-Section of Stock Returns," CIRANO Working Papers 2016s-21, CIRANO.
    5. Mathias S. Kruttli & Phillip J. Monin & Lubomir Petrasek & Sumudu W. Watugala, 2021. "Hedge Fund Treasury Trading and Funding Fragility: Evidence from the COVID-19 Crisis," Finance and Economics Discussion Series 2021-038, Board of Governors of the Federal Reserve System (U.S.).
    6. Kolokolova, Olga & Lin, Ming-Tsung & Poon, Ser-Huang, 2020. "Too big to ignore? Hedge fund flows and bond yields," Journal of Banking & Finance, Elsevier, vol. 112(C).
    7. Sandro Lunghi & Daniel Schmidt & Bastian von Beschwitz, 2021. "Fundamental Arbitrage under the Microscope: Evidence from Detailed Hedge Fund Transaction Data," Finance and Economics Discussion Series 2021-022, Board of Governors of the Federal Reserve System (U.S.).
    8. Andrew W. Lo & Mila Getmansky & Peter A. Lee, 2015. "Hedge Funds: A Dynamic Industry in Transition," Annual Review of Financial Economics, Annual Reviews, vol. 7(1), pages 483-577, December.
    9. Mathias S. Kruttli & Phillip J. Monin & Sumudu W. Watugala, 2019. "The Life of the Counterparty: Shock Propagation in Hedge Fund-Prime Broker Credit Networks," Working Papers 19-03, Office of Financial Research, US Department of the Treasury.

  13. Jia Li & Andrew J. Patton, 2013. "Asymptotic Inference about Predictive Accuracy Using High Frequency Data," Working Papers 13-27, Duke University, Department of Economics.

    Cited by:

    1. Lai, Yu-Sheng, 2022. "Improving hedging performance by using high–low range," Finance Research Letters, Elsevier, vol. 48(C).
    2. Laura Coroneo & Fabrizio Iacone, 2020. "Comparing predictive accuracy in small samples using fixed‐smoothing asymptotics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(4), pages 391-409, June.
    3. Niu, Zibo & Ma, Feng & Zhang, Hongwei, 2022. "The role of uncertainty measures in volatility forecasting of the crude oil futures market before and during the COVID-19 pandemic," Energy Economics, Elsevier, vol. 112(C).
    4. Jia Li & Peter C. B. Phillips & Shuping Shi & Jun Yu, 2022. "Weak Identification of Long Memory with Implications for Inference," Cowles Foundation Discussion Papers 2334, Cowles Foundation for Research in Economics, Yale University.
    5. Ilze Kalnina & Kokouvi Tewou, 2024. "Cross-sectional Dependence in Idiosyncratic Volatility," Papers 2408.13437, arXiv.org, revised May 2025.
    6. Lukas Bauer, 2025. "Evaluating financial tail risk forecasts: Testing Equal Predictive Ability," Papers 2505.23333, arXiv.org.
    7. Matei Demetrescu & Christoph Hanck & Robinson Kruse‐Becher, 2022. "Robust inference under time‐varying volatility: A real‐time evaluation of professional forecasters," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 1010-1030, August.
    8. Yu‐Sheng Lai, 2022. "Use of high‐frequency data to evaluate the performance of dynamic hedging strategies," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(1), pages 104-124, January.
    9. Coroneo, Laura & Iacone, Fabrizio & Profumo, Fabio, 2024. "Survey density forecast comparison in small samples," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1486-1504.
    10. Lai, Yu-Sheng, 2023. "Economic evaluation of dynamic hedging strategies using high-frequency data," Finance Research Letters, Elsevier, vol. 57(C).
    11. Michael W. McCracken, 2019. "Tests of Conditional Predictive Ability: Some Simulation Evidence," Working Papers 2019-11, Federal Reserve Bank of St. Louis.
    12. Laura Coroneo & Fabrizio Iacone, 2015. "Comparing predictive accuracy in small samples," Discussion Papers 15/15, Department of Economics, University of York.
    13. Michael W. McCracken, 2020. "Tests of Conditional Predictive Ability: Existence, Size, and Power," Working Papers 2020-050, Federal Reserve Bank of St. Louis.
    14. Yu‐Sheng Lai, 2023. "Optimal futures hedging by using realized semicovariances: The information contained in signed high‐frequency returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(5), pages 677-701, May.

  14. Dong Hwan Oh & Andrew J. Patton, 2013. "Time-Varying Systemic Risk: Evidence from a Dynamic Copula Model of CDS Spreads," Working Papers 13-30, Duke University, Department of Economics.

    Cited by:

    1. Francisco Blasques & Siem Jan Koopman & André Lucas, 2014. "Information Theoretic Optimality of Observation Driven Time Series Models," Tinbergen Institute Discussion Papers 14-046/III, Tinbergen Institute.
    2. Bartels, Mariana & Ziegelmann, Flavio A., 2016. "Market risk forecasting for high dimensional portfolios via factor copulas with GAS dynamics," Insurance: Mathematics and Economics, Elsevier, vol. 70(C), pages 66-79.
    3. Anne Opschoor & André Lucas, 2019. "Time-varying tail behavior for realized kernels," Tinbergen Institute Discussion Papers 19-051/IV, Tinbergen Institute.
    4. 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.
    5. Umlandt, Dennis, 2023. "Score-driven asset pricing: Predicting time-varying risk premia based on cross-sectional model performance," Journal of Econometrics, Elsevier, vol. 237(2).
    6. Mayer, Alexander & Wied, Dominik, 2023. "Estimation and inference in factor copula models with exogenous covariates," Journal of Econometrics, Elsevier, vol. 235(2), pages 1500-1521.
    7. Chen, Juan & Xiao, Zuoping, 2025. "Is the business cycle getting hit by climate policy uncertainty in China?," Finance Research Letters, Elsevier, vol. 71(C).
    8. Choe, Geon Ho & Choi, So Eun & Jang, Hyun Jin, 2020. "Assessment of time-varying systemic risk in credit default swap indices: Simultaneity and contagiousness," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    9. Huaming Du & Xingyan Chen & Yu Zhao & Qing Li & Fuzhen Zhuang & Fuji Ren & Gang Kou, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective," Papers 2211.14997, arXiv.org, revised Mar 2025.
    10. Hoang Nguyen & Audron.e Virbickait.e & M. Concepci'on Aus'in & Pedro Galeano, 2024. "Structured factor copulas for modeling the systemic risk of European and United States banks," Papers 2401.03443, arXiv.org.
    11. Chen, Yanghan & Lin, Juan, 2024. "Measuring systemic risk in Asian foreign exchange markets," Journal of International Money and Finance, Elsevier, vol. 146(C).
    12. Brechmann, Eike C. & Hendrich, Katharina & Czado, Claudia, 2013. "Conditional copula simulation for systemic risk stress testing," Insurance: Mathematics and Economics, Elsevier, vol. 53(3), pages 722-732.
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    Cited by:

    1. Adam Clements & Neda Todorova, 2014. "The impact of information flow and trading activity on gold and oil futures volatility," NCER Working Paper Series 102, National Centre for Econometric Research.
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    6. 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.
    7. Degiannakis, Stavros & Filis, George & Klein, Tony & Walther, Thomas, 2019. "Forecasting Realized Volatility of Agricultural Commodities," MPRA Paper 96267, University Library of Munich, Germany.
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    289. Richard T. Baillie & Dooyeon Cho & Seunghwa Rho, 2023. "Approximating long-memory processes with low-order autoregressions: Implications for modeling realized volatility," Empirical Economics, Springer, vol. 64(6), pages 2911-2937, June.
    290. Liu, Yifan & Popova, Ivilina, 2023. "Threats to central bank independence and exchange rate volatility: High-frequency identification with Trump’s Fed tweets," Finance Research Letters, Elsevier, vol. 53(C).
    291. Qiu, Yue & Wang, Yifan & Xie, Tian, 2021. "Forecasting Bitcoin realized volatility by measuring the spillover effect among cryptocurrencies," Economics Letters, Elsevier, vol. 208(C).
    292. Ma, Feng & Wahab, M.I.M. & Huang, Dengshi & Xu, Weiju, 2017. "Forecasting the realized volatility of the oil futures market: A regime switching approach," Energy Economics, Elsevier, vol. 67(C), pages 136-145.
    293. 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.
    294. Wang, Jiqian & Ma, Feng & Wang, Tianyang & Wu, Lan, 2023. "International stock volatility predictability: New evidence from uncertainties," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 85(C).
    295. Jin, Daxiang & He, Mengxi & Xing, Lu & Zhang, Yaojie, 2022. "Forecasting China's crude oil futures volatility: How to dig out the information of other energy futures volatilities?," Resources Policy, Elsevier, vol. 78(C).
    296. Christian Conrad & Onno Kleen, 2020. "Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(1), pages 19-45, January.
    297. 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).
    298. Wang, Jiqian & Huang, Yisu & Ma, Feng & Chevallier, Julien, 2020. "Does high-frequency crude oil futures data contain useful information for predicting volatility in the US stock market? New evidence," Energy Economics, Elsevier, vol. 91(C).
    299. Song, Yuping & Huang, Jiefei & Zhang, Qichao & Xu, Yang, 2024. "Heterogeneity effect of positive and negative jumps on the realized volatility: Evidence from China," Economic Modelling, Elsevier, vol. 136(C).
    300. Hongjun Zeng & Abdullahi D. Ahmed & Ran Lu, 2024. "The Bitcoin‐agricultural commodities nexus: Fresh insight from COVID‐19 and 2022 Russia–Ukraine war," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 68(3), pages 653-677, July.
    301. Iryna Kaminska & Matt Roberts-Sklar, 2015. "A global factor in variance risk premia and local bond pricing," Bank of England working papers 576, Bank of England.
    302. Zongwu Cai & Chaoqun Ma & Xianhua Mi, 2020. "Realized Volatility Forecasting Based on Dynamic Quantile Model Averaging," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202016, University of Kansas, Department of Economics, revised Sep 2020.
    303. Czado, Claudia & Ivanov, Eugen & Okhrin, Yarema, 2019. "Modelling temporal dependence of realized variances with vines," Econometrics and Statistics, Elsevier, vol. 12(C), pages 198-216.
    304. Zhou, Dong-hai & Liu, Xiao-xing & Tang, Chun & Yang, Guang-yi, 2023. "Time-varying risk spillovers in Chinese stock market – New evidence from high-frequency data," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    305. Petter N. Kolm & Jeremy Turiel & Nicholas Westray, 2023. "Deep order flow imbalance: Extracting alpha at multiple horizons from the limit order book," Mathematical Finance, Wiley Blackwell, vol. 33(4), pages 1044-1081, October.
    306. Danyan Wen & Mengxi He & Yaojie Zhang & Yudong Wang, 2022. "Forecasting realized volatility of Chinese stock market: A simple but efficient truncated approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 230-251, March.
    307. Plíhal, Tomáš & Lyócsa, Štefan, 2021. "Modeling realized volatility of the EUR/USD exchange rate: Does implied volatility really matter?," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 811-829.
    308. Taylor, Nick, 2017. "Timing strategy performance in the crude oil futures market," Energy Economics, Elsevier, vol. 66(C), pages 480-492.
    309. Gould, Martin D. & Hautsch, Nikolaus & Howison, Sam D. & Porter, Mason A., 2017. "Counterparty credit limits: An effective tool for mitigating counterparty risk?," CFS Working Paper Series 581, Center for Financial Studies (CFS).
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    311. 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.
    312. Chen, Yixiang & Ma, Feng & Zhang, Yaojie, 2019. "Good, bad cojumps and volatility forecasting: New evidence from crude oil and the U.S. stock markets," Energy Economics, Elsevier, vol. 81(C), pages 52-62.
    313. 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.
    314. Walid Ben Omrane & Khaled Guesmi & Qi Qianru & Samir Saadi, 2023. "The high-frequency impact of macroeconomic news on jumps and co-jumps in the cryptocurrency markets," Annals of Operations Research, Springer, vol. 330(1), pages 177-209, November.
    315. 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.

  16. 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.

    Cited by:

    1. Francisco Blasques & Siem Jan Koopman & André Lucas, 2014. "Information Theoretic Optimality of Observation Driven Time Series Models," Tinbergen Institute Discussion Papers 14-046/III, Tinbergen Institute.
    2. Bernardi, Mauro & Catania, Leopoldo, 2018. "Portfolio optimisation under flexible dynamic dependence modelling," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 1-18.
    3. Alanya-Beltran Willy, 2023. "Modelling volatility dependence with score copula models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 27(5), pages 649-668, December.
    4. 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.
    5. Xu, Buyun & Wu, Zhimin, 2025. "Real-time GARCH@CARR: A joint model of returns, realized measure of volatility and current intraday information," The North American Journal of Economics and Finance, Elsevier, vol. 76(C).
    6. 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.
    7. Blasques, F. & Gorgi, P. & Koopman, S.J., 2021. "Missing observations in observation-driven time series models," Journal of Econometrics, Elsevier, vol. 221(2), pages 542-568.
    8. Fengler, Matthias R. & Okhrin, Ostap, 2016. "Managing risk with a realized copula parameter," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 131-152.
    9. Francisco (F.) Blasques & Andre (A.) Lucas & Andries van Vlodrop, 2017. "Finite Sample Optimality of Score-Driven Volatility Models," Tinbergen Institute Discussion Papers 17-111/III, Tinbergen Institute.
    10. Chang, Kuang-Liang, 2023. "The low-magnitude and high-magnitude asymmetries in tail dependence structures in international equity markets and the role of bilateral exchange rate," Journal of International Money and Finance, Elsevier, vol. 133(C).
    11. Yang Zhao & Charalampos Stasinakis & Georgios Sermpinis & Filipa Da Silva Fernandes, 2019. "Revisiting Fama–French factors' predictability with Bayesian modelling and copula‐based portfolio optimization," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(4), pages 1443-1463, October.
    12. Mauro Bernardi & Leopoldo Catania, 2015. "Switching-GAS Copula Models With Application to Systemic Risk," Papers 1504.03733, arXiv.org, revised Jan 2016.
    13. Blasques, Francisco & van Brummelen, Janneke & Koopman, Siem Jan & Lucas, André, 2022. "Maximum likelihood estimation for score-driven models," Journal of Econometrics, Elsevier, vol. 227(2), pages 325-346.
    14. Hendriks, Johannes Jurgens & Bonga-Bonga, Lumengo, 2020. "Sectoral dependence and contagion in the BRICS grouping: an application of the R-Vine copulas," MPRA Paper 102473, University Library of Munich, Germany.
    15. Leopoldo Catania & Anna Gloria Billé, 2016. "Dynamic Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances," CEIS Research Paper 375, Tor Vergata University, CEIS, revised 31 Mar 2016.
    16. Aepli, Matthias D. & Füss, Roland & Henriksen, Tom Erik S. & Paraschiv, Florentina, 2017. "Modeling the multivariate dynamic dependence structure of commodity futures portfolios," Journal of Commodity Markets, Elsevier, vol. 6(C), pages 66-87.
    17. Ostap Okhrin & Anastasija Tetereva, 2017. "The Realized Hierarchical Archimedean Copula in Risk Modelling," Econometrics, MDPI, vol. 5(2), pages 1-31, June.
    18. Ouyang, Ruolan & Zhuang, Chengkai & Wang, Tingting & Zhang, Xuan, 2022. "Network analysis of risk transmission among energy futures: An industrial chain perspective," Energy Economics, Elsevier, vol. 107(C).
    19. Siem Jan Koopman & Rutger Lit & André Lucas, 2015. "Intraday Stock Price Dependence using Dynamic Discrete Copula Distributions," Tinbergen Institute Discussion Papers 15-037/III/DSF90, Tinbergen Institute.
    20. P. Gorgi & Siem Jan (S.J.) Koopman & R. Lit, 2018. "The analysis and forecasting of ATP tennis matches using a high-dimensional dynamic model," Tinbergen Institute Discussion Papers 18-009/III, Tinbergen Institute.
    21. Shijia Song & Handong Li, 2023. "A new model for forecasting VaR and ES using intraday returns aggregation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1039-1054, August.
    22. Blasques, Francisco & Lucas, André & van Vlodrop, Andries C., 2021. "Finite Sample Optimality of Score-Driven Volatility Models: Some Monte Carlo Evidence," Econometrics and Statistics, Elsevier, vol. 19(C), pages 47-57.
    23. Smith, Michael Stanley & Maneesoonthorn, Worapree, 2018. "Inversion copulas from nonlinear state space models with an application to inflation forecasting," International Journal of Forecasting, Elsevier, vol. 34(3), pages 389-407.
    24. Francisco (F.) Blasques & Paolo Gorgi & Siem Jan (S.J.) Koopman, 2017. "Accelerating GARCH and Score-Driven Models: Optimality, Estimation and Forecasting," Tinbergen Institute Discussion Papers 17-059/III, Tinbergen Institute.
    25. Ryoko Ito, 2016. "Asymptotic Theory for Beta-t-GARCH," Cambridge Working Papers in Economics 1607, Faculty of Economics, University of Cambridge.
    26. Denisa BANULESCU-RADU & Elena Ivona DUMITRESCU, 2019. "Do High-frequency-based Measures Improve Conditional Covariance Forecasts?," LEO Working Papers / DR LEO 2709, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    27. Dhaene, Geert & Wu, Jianbin, 2020. "Incorporating overnight and intraday returns into multivariate GARCH volatility models," Journal of Econometrics, Elsevier, vol. 217(2), pages 471-495.
    28. Yu‐Sheng Lai, 2021. "Generalized autoregressive score model with high‐frequency data for optimal futures hedging," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(12), pages 2023-2045, December.
    29. Blasques, F. & Gorgi, P. & Koopman, S.J., 2019. "Accelerating score-driven time series models," Journal of Econometrics, Elsevier, vol. 212(2), pages 359-376.
    30. Yu-Sheng Lai, 2018. "Dynamic hedging with futures: a copula-based GARCH model with high-frequency data," Review of Derivatives Research, Springer, vol. 21(3), pages 307-329, October.
    31. Zhimin Wu & Guanghui Cai, 2024. "Can intraday data improve the joint estimation and prediction of risk measures? Evidence from a variety of realized measures," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 1956-1974, September.
    32. Hamori, Shigeyuki & Motegi, Kaiji & Zhang, Zheng, 2019. "Calibration estimation of semiparametric copula models with data missing at random," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 85-109.
    33. Ewald, Christian & Zou, Yihan, 2021. "Stochastic volatility: A tale of co-jumps, non-normality, GMM and high frequency data," Journal of Empirical Finance, Elsevier, vol. 64(C), pages 37-52.
    34. Tobias Fissler & Yannick Hoga, 2021. "Backtesting Systemic Risk Forecasts using Multi-Objective Elicitability," Papers 2104.10673, arXiv.org, revised Feb 2022.
    35. Mario Cerrato & John Crosby & Minjoo Kim & Yang Zhao, 2015. "Correlated Defaults of UK Banks: Dynamics and Asymmetries," Working Papers 2015_24, Business School - Economics, University of Glasgow.
    36. Rub'en Loaiza-Maya & Michael S. Smith & Worapree Maneesoonthorn, 2017. "Time Series Copulas for Heteroskedastic Data," Papers 1701.07152, arXiv.org.
    37. Ito, R., 2016. "Spline-DCS for Forecasting Trade Volume in High-Frequency Finance," Cambridge Working Papers in Economics 1606, Faculty of Economics, University of Cambridge.
    38. Wang, Rui & Liao, Xin & Peng, Zuoxiang, 2017. "Second-order expansions for maxima of dynamic bivariate normal copulas," Statistics & Probability Letters, Elsevier, vol. 129(C), pages 275-283.
    39. Siem Jan Koopman & Rutger Lit & André Lucas & Anne Opschoor, 2018. "Dynamic discrete copula models for high‐frequency stock price changes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(7), pages 966-985, November.
    40. Tobias Eckernkemper, 2018. "Modeling Systemic Risk: Time-Varying Tail Dependence When Forecasting Marginal Expected Shortfall," Journal of Financial Econometrics, Oxford University Press, vol. 16(1), pages 63-117.
    41. BenSaïda, Ahmed, 2018. "The contagion effect in European sovereign debt markets: A regime-switching vine copula approach," International Review of Financial Analysis, Elsevier, vol. 58(C), pages 153-165.
    42. Harvey, A., 2021. "Score-driven time series models," Cambridge Working Papers in Economics 2133, Faculty of Economics, University of Cambridge.
    43. Ouyang, Ruolan & Chen, Xiang & Fang, Yi & Zhao, Yang, 2022. "Systemic risk of commodity markets: A dynamic factor copula approach," International Review of Financial Analysis, Elsevier, vol. 82(C).
    44. Sergii Pypko, 2015. "Volatility Forecast in Crises and Expansions," JRFM, MDPI, vol. 8(3), pages 1-26, August.
    45. Astrid Ayala & Szabolcs Blazsek, 2018. "Equity market neutral hedge funds and the stock market: an application of score-driven copula models," Applied Economics, Taylor & Francis Journals, vol. 50(37), pages 4005-4023, August.
    46. Ouyang, Ruolan & Zhang, Xuan, 2020. "Financialization of agricultural commodities: Evidence from China," Economic Modelling, Elsevier, vol. 85(C), pages 381-389.
    47. Barry K. Goodwin & Matthew T. Holt & Gülcan Önel & Jeffrey P. Prestemon, 2018. "Copula-based nonlinear modeling of the law of one price for lumber products," Empirical Economics, Springer, vol. 54(3), pages 1237-1265, May.
    48. Jiang, Cuixia & Ding, Xiaoyi & Xu, Qifa & Tong, Yongbo, 2020. "A TVM-Copula-MIDAS-GARCH model with applications to VaR-based portfolio selection," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    49. Marco Bazzi & Francisco Blasques & Siem Jan Koopman & Andre Lucas, 2017. "Time-Varying Transition Probabilities for Markov Regime Switching Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(3), pages 458-478, May.
    50. Markus Vogl, 2022. "Quantitative modelling frontiers: a literature review on the evolution in financial and risk modelling after the financial crisis (2008–2019)," SN Business & Economics, Springer, vol. 2(12), pages 1-69, December.
    51. Siyao Wei & Pengfei Luo & Jiashan Song & Kunliang Jiang, 2025. "Portfolio Optimization During the COVID-19 Epidemic: Based on an Improved QBAS Algorithm and a Dynamic Mixed Frequency Model," Computational Economics, Springer;Society for Computational Economics, vol. 65(4), pages 1999-2028, April.
    52. Pircalabu, A. & Hvolby, T. & Jung, J. & Høg, E., 2017. "Joint price and volumetric risk in wind power trading: A copula approach," Energy Economics, Elsevier, vol. 62(C), pages 139-154.
    53. Mauro Bernardi & Leopoldo Catania, 2016. "Portfolio Optimisation Under Flexible Dynamic Dependence Modelling," Papers 1601.05199, arXiv.org.
    54. Rewat Khanthaporn, 2022. "Analysis of Nonlinear Comovement of Benchmark Thai Government Bond Yields," PIER Discussion Papers 183, Puey Ungphakorn Institute for Economic Research.

  17. Patton, Andrew & Streatfield, Michael, 2012. "Change You Can Believe In? Hedge Fund Data Revisions," CEPR Discussion Papers 8898, C.E.P.R. Discussion Papers.

    Cited by:

    1. Jesse Blocher & Marat Molyboga, 2017. "The Revealed Preference of Sophisticated Investors," European Financial Management, European Financial Management Association, vol. 23(5), pages 839-872, October.
    2. Roy Cerqueti & Mario Maggi & Jessica Riccioni, 2024. "Statistical methods for decision support systems in finance: how Benford’s law predicts financial risk," Annals of Operations Research, Springer, vol. 342(3), pages 1445-1469, November.
    3. Almeida, Caio & Ardison, Kym & Garcia, René, 2020. "Nonparametric assessment of hedge fund performance," Journal of Econometrics, Elsevier, vol. 214(2), pages 349-378.
    4. Fan Yang & Tomas Havranek & Zuzana Irsova & Jiri Novak, 2022. "Hedge Fund Performance: A Quantitative Survey," Working Papers IES 2022/15, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Jun 2022.
    5. Guillermo Baquero & Marno Verbeek, 2022. "Hedge Fund Flows and Performance Streaks: How Investors Weigh Information," Management Science, INFORMS, vol. 68(6), pages 4151-4172, June.
    6. Gregoriou, Greg N. & Racicot, François-Éric & Théoret, Raymond, 2021. "The response of hedge fund tail risk to macroeconomic shocks: A nonlinear VAR approach," Economic Modelling, Elsevier, vol. 94(C), pages 843-872.
    7. Racicot, François-Éric & Théoret, Raymond, 2018. "Multi-moment risk, hedging strategies, & the business cycle," International Review of Economics & Finance, Elsevier, vol. 58(C), pages 637-675.
    8. Sinclair, Andrew J., 2023. "Do prime brokers intermediate capital?," Journal of Financial Intermediation, Elsevier, vol. 53(C).
    9. François-Éric Racicot & Raymond Théoret, 2022. "Tracking market and non-traditional sources of risks in procyclical and countercyclical hedge fund strategies under extreme scenarios: a nonlinear VAR approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-56, December.
    10. Aiken, Adam L. & Kilic, Osman & Reid, Sean, 2016. "Can hedge funds time global equity markets? Evidence from emerging markets," Review of Financial Economics, Elsevier, vol. 29(C), pages 2-11.
    11. Arpit Gupta & Kunal Sachdeva, 2019. "Skin or Skim? Inside Investment and Hedge Fund Performance," NBER Working Papers 26113, National Bureau of Economic Research, Inc.
    12. Auer, Benjamin R. & Marohn, Marcel, 2024. "Computational dynamics of information ratios," Economics Letters, Elsevier, vol. 236(C).
    13. Adam L. Aiken & Osman Kilic & Sean Reid, 2016. "Can hedge funds time global equity markets? Evidence from emerging markets," Review of Financial Economics, John Wiley & Sons, vol. 29(1), pages 2-11, April.
    14. Bali, Turan G. & Weigert, Florian, 2021. "Hedge funds and the positive idiosyncratic volatility effect," CFR Working Papers 21-01, University of Cologne, Centre for Financial Research (CFR).
    15. Gordon Cookson & Tim Jenkinson & Howard Jones & Jose Vicente Martinez, 2022. "Virtual Reality? Investment Consultants’ Claims About Their Own Performance," Management Science, INFORMS, vol. 68(11), pages 8301-8318, November.
    16. Qifei Zhu, 2020. "The Missing New Funds," Management Science, INFORMS, vol. 66(3), pages 1193-1204, March.
    17. Hao Liang & Lin Sun & Melvyn Teo, 2022. "Responsible Hedge Funds [Role of managerial incentives and discretion in hedge fund performance]," Review of Finance, European Finance Association, vol. 26(6), pages 1585-1633.
    18. Kosowski, Robert & Joenväärä, Juha & Kaupila, Mikko & Tolonen, Pekka, 2019. "Hedge Fund Performance: Are Stylized Facts Sensitive to Which Database One Uses?," CEPR Discussion Papers 13618, C.E.P.R. Discussion Papers.
    19. Alper Darendeli, 2025. "How do retail investors respond to summary disclosure? Evidence from mutual fund factsheets," Review of Accounting Studies, Springer, vol. 30(2), pages 1222-1266, June.
    20. Yao, Juan & Wu, Bochen & Gao, Yang, 2021. "Death and the life hereafter: A study of the subsequent hedge funds," Finance Research Letters, Elsevier, vol. 40(C).
    21. Vikas Agarwal & Yan Lu & Sugata Ray, 2016. "Under One Roof: A Study of Simultaneously Managed Hedge Funds and Funds of Hedge Funds," Management Science, INFORMS, vol. 62(3), pages 722-740, March.
    22. Fan Yang & Tomas Havranek & Zuzana Irsova & Jiri Novak, 2024. "Is research on hedge fund performance published selectively? A quantitative survey," Journal of Economic Surveys, Wiley Blackwell, vol. 38(4), pages 1085-1131, September.

  18. Timmermann, Allan & Patton, Andrew, 2011. "Forecast Rationality Tests Based on Multi-Horizon Bounds," CEPR Discussion Papers 8194, C.E.P.R. Discussion Papers.

    Cited by:

    1. Wright, Jonathan H., 2019. "Some observations on forecasting and policy," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1186-1192.
    2. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2012. "Time-varying Combinations of Predictive Densities using Nonlinear Filtering," Tinbergen Institute Discussion Papers 12-118/III, Tinbergen Institute.
    3. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
    4. Stan Hurn & Jing Tian & Lina Xu, 2021. "Assessing the Informational Content of Official Australian Bureau of Meteorology Forecasts of Wind Speed," The Economic Record, The Economic Society of Australia, vol. 97(319), pages 525-547, December.
    5. Cepni, Oguzhan & Clements, Michael P., 2024. "How local is the local inflation factor? Evidence from emerging European countries," International Journal of Forecasting, Elsevier, vol. 40(1), pages 160-183.
    6. Katja Heinisch, 2025. "Step by Step—A Quarterly Evaluation of EU Commission's GDP Forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(3), pages 1026-1041, April.
    7. Clark, Todd & McCracken, Michael, 2013. "Advances in Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1107-1201, Elsevier.
    8. Breitung, Jörg & Knüppel, Malte, 2018. "How far can we forecast? Statistical tests of the predictive content," Discussion Papers 07/2018, Deutsche Bundesbank.
    9. Pablo Pincheira Brown & Nicolás Hardy, 2024. "Correlation‐based tests of predictability," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 1835-1858, September.
    10. Amy Y. Guisinger & Michael W. Mccracken & Michael T. Owyang, 2025. "Reconsidering the Fed's Inflation Forecasting Advantage," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 57(1), pages 5-30, February.
    11. Pedersen, Michael, 2019. "Anomalies in macroeconomic prediction errors–evidence from Chilean private forecasters," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1100-1107.
    12. Pablo Pincheira, 2012. "Are Forecast Combinations Efficient?," Working Papers Central Bank of Chile 661, Central Bank of Chile.
    13. Qu, Ritong & Timmermann, Allan & Zhu, Yinchu, 2024. "Comparing forecasting performance with panel data," International Journal of Forecasting, Elsevier, vol. 40(3), pages 918-941.
    14. Daniela Bragoli & Michele Modugno, 2016. "A Nowcasting Model for Canada: Do U.S. Variables Matter?," Finance and Economics Discussion Series 2016-036, Board of Governors of the Federal Reserve System (U.S.).
    15. Hajo Holzmann & Matthias Eulert, 2014. "The role of the information set for forecasting - with applications to risk management," Papers 1404.7653, arXiv.org.
    16. Barbara Rossi & Tatevik Sekhposyan, 2014. "Forecast rationality tests in the presence of instabilities, with applications to Federal Reserve and survey forecasts," Economics Working Papers 1426, Department of Economics and Business, Universitat Pompeu Fabra, revised Nov 2014.
    17. Natsuki Arai, 2016. "Evaluating the Efficiency of the FOMC's New Economic Projections," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(5), pages 1019-1049, August.
    18. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 195-237, Elsevier.
    19. Giorgio Canarella & Stephen M. Miller & Stephen K. Pollard, 2014. "Unemployment Rate Hysteresis and the Great Recession: Exploring the Metropolitan Evidence," Working Papers 1403, University of Nevada, Las Vegas , Department of Economics.
    20. Hari P. Regmi & Todd H. Kuethe, 2024. "An evaluation of Congressional Budget Office's baseline projections of USDA mandatory farm and nutrition programs," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 46(3), pages 1214-1240, September.
    21. Lukas Hoesch & Barbara Rossi & Tatevik Sekhposyan, 2023. "Has the Information Channel of Monetary Policy Disappeared? Revisiting the Empirical Evidence," American Economic Journal: Macroeconomics, American Economic Association, vol. 15(3), pages 355-387, July.
    22. Michael P. Clements, 2022. "Forecaster Efficiency, Accuracy, and Disagreement: Evidence Using Individual‐Level Survey Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(2-3), pages 537-568, March.
    23. Boussios, David & Skorbiansky, Sharon Raszap & MacLachlan, Matthew, 2021. "Evaluating U.S. Department of Agriculture’s Long-Term Forecasts for U.S. Harvested Area," Economic Research Report 327201, United States Department of Agriculture, Economic Research Service.
    24. Jeff Messina & Tara M. Sinclair & Herman O. Stekler, 2014. "What Can We Learn From Revisions To The Greenbook Forecasts?," Working Papers 2014-003, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    25. Thomas Jobert & Lionel Persyn, 2012. "Quelques constats sur les prévisions conjoncturelles de la croissance française," Revue d'économie politique, Dalloz, vol. 122(6), pages 833-849.
    26. Andrew B. Martinez & Neil R. Ericsson, 2025. "Improving empirical models and forecasts with saturation-based machine learning," Annals of Operations Research, Springer, vol. 346(1), pages 447-487, March.
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    32. Goodwin, Thomas & Tian, Jing, 2017. "A state space approach to evaluate multi-horizon forecasts," Working Papers 2017-15, University of Tasmania, Tasmanian School of Business and Economics.
    33. Pincheira, Pablo & Hardy, Nicolas, 2021. "The Mean Squared Prediction Error Paradox," MPRA Paper 107403, University Library of Munich, Germany.
    34. Xavier D'Haultfoeuille & Christophe Gaillac & Arnaud Maurel, 2021. "Rationalizing rational expectations: Characterizations and tests," Quantitative Economics, Econometric Society, vol. 12(3), pages 817-842, July.
    35. Boussios, David & Skoriansky, Sharon Raszap & MacLachlan, Matthew, 2021. "Evaluating U.S. Department of Agriculture’s Long-Term Forecasts for U.S. Harvested Area," USDA Miscellaneous 309619, United States Department of Agriculture.
    36. Wang, Yiyao & Lee, Tae-Hwy, 2014. "Asymmetric loss in the Greenbook and the Survey of Professional Forecasters," International Journal of Forecasting, Elsevier, vol. 30(2), pages 235-245.
    37. Manganelli, Simone, 2016. "Deciding with judgment," Working Paper Series 1947, European Central Bank.
    38. 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.
    39. Faust, Jon & Wright, Jonathan H., 2013. "Forecasting Inflation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 2-56, Elsevier.
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    44. Jing Tian & Firmin Doko Tchatoka & Thomas Goodwin, 2022. "Are internally consistent forecasts rational?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1338-1355, November.
    45. Han Liu & Yongjing Wang & Haiyan Song & Ying Liu, 2023. "Measuring tourism demand nowcasting performance using a monotonicity test," Tourism Economics, , vol. 29(5), pages 1302-1327, August.
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    49. Siddhartha S. Bora & Ani L. Katchova & Todd H. Kuethe, 2023. "The accuracy and informativeness of agricultural baselines," American Journal of Agricultural Economics, John Wiley & Sons, vol. 105(4), pages 1116-1148, August.
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    73. Arai, Natsuki, 2020. "Investigating the inefficiency of the CBO’s budgetary projections," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1290-1300.

  19. Patton, Andrew, 2011. "On the High-Frequency Dynamics of Hedge Fund Risk Exposures," CEPR Discussion Papers 8479, C.E.P.R. Discussion Papers.

    Cited by:

    1. Yamani, Ehab, 2019. "Diversification role of currency momentum for carry trade: Evidence from financial crises," Journal of Multinational Financial Management, Elsevier, vol. 49(C), pages 1-19.
    2. Della Corte, Pasquale & Ramadorai, Tarun & Sarno, Lucio, 2016. "Volatility risk premia and exchange rate predictability," Journal of Financial Economics, Elsevier, vol. 120(1), pages 21-40.
    3. Menkhoff, Lukas & Sarno, Lucio & Schmeling, Maik & Schrimpf, Andreas, 2012. "Currency momentum strategies," Journal of Financial Economics, Elsevier, vol. 106(3), pages 660-684.
    4. Vikas Agarwal & Stefan Ruenzi & Florian Weigert, 2018. "Unobserved Performance of Hedge Funds," Working Papers on Finance 1825, University of St. Gallen, School of Finance.
    5. Mathias S. Kruttli & Phillip J. Monin & Sumudu W. Watugala, 2017. "Investor Concentration, Flows, and Cash Holdings: Evidence from Hedge Funds," Working Papers 17-07, Office of Financial Research, US Department of the Treasury, revised 02 Jun 2020.
    6. Fan Yang & Tomas Havranek & Zuzana Irsova & Jiri Novak, 2022. "Hedge Fund Performance: A Quantitative Survey," Working Papers IES 2022/15, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Jun 2022.
    7. Fan Yang & Tomas Havranek & Zuzana Irsova & Jiri Novak, 2024. "Where Have All the Alphas Gone? A Meta-Analysis of Hedge Fund Performance," Working Papers IES 2024/15, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Apr 2024.
    8. Stafylas, Dimitrios & Andrikopoulos, Athanasios, 2020. "Determinants of hedge fund performance during ‘good’ and ‘bad’ economic periods," Research in International Business and Finance, Elsevier, vol. 52(C).
    9. Ardia, David & Barras, Laurent & Gagliardini, Patrick & Scaillet, Olivier, 2024. "Is it alpha or beta? Decomposing hedge fund returns when models are misspecified," Journal of Financial Economics, Elsevier, vol. 154(C).
    10. Ferson, Wayne E., 2013. "Investment Performance: A Review and Synthesis," 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 969-1010, Elsevier.
    11. Michael S. O’Doherty & N. E. Savin & Ashish Tiwari, 2016. "Evaluating Hedge Funds with Pooled Benchmarks," Management Science, INFORMS, vol. 62(1), pages 69-89, January.
    12. Mark D. Flood & Phillip Monin & Lina Bandyopadhyay, 2015. "Gauging Form PF: Data Tolerances in Regulatory Reporting on Hedge Fund Risk Exposures," Working Papers 15-13, Office of Financial Research, US Department of the Treasury.
    13. Charles Chevalier & Serge Darolles, 2019. "Trends everywhere? The case of hedge fund styles," Post-Print hal-02573075, HAL.
    14. Arısoy, Yakup Eser & Altay-Salih, Aslıhan & Akdeniz, Levent, 2015. "Aggregate volatility expectations and threshold CAPM," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 231-253.
    15. Ma, Tian & Wang, Wanwan & Jiang, Fuwei, 2025. "Machine learning the performance of hedge fund," Journal of International Money and Finance, Elsevier, vol. 155(C).
    16. Roumpis, Efthymios & Syriopoulos, Theodore, 2014. "Dynamics and risk factors in hedge funds returns: Implications for portfolio construction and performance evaluation," The Journal of Economic Asymmetries, Elsevier, vol. 11(C), pages 58-77.
    17. Bandi, Federico M. & Renò, Roberto, 2022. "β in the tails," Journal of Econometrics, Elsevier, vol. 227(1), pages 134-150.
    18. Sermin Gungor & Jesus Sierra, 2014. "Search-for-Yield in Canadian Fixed-Income Mutual Funds and Monetary Policy," Staff Working Papers 14-3, Bank of Canada.
    19. Wang, Jiqian & Lu, Xinjie & He, Feng & Ma, Feng, 2020. "Which popular predictor is more useful to forecast international stock markets during the coronavirus pandemic: VIX vs EPU?," International Review of Financial Analysis, Elsevier, vol. 72(C).
    20. Racicot, François-Éric & Théoret, Raymond, 2018. "Multi-moment risk, hedging strategies, & the business cycle," International Review of Economics & Finance, Elsevier, vol. 58(C), pages 637-675.
    21. Bali, Turan G. & Brown, Stephen J. & Caglayan, Mustafa O., 2019. "Upside potential of hedge funds as a predictor of future performance," Journal of Banking & Finance, Elsevier, vol. 98(C), pages 212-229.
    22. Huang, Ying Sophie & Yao, Juan & Zhu, Yu, 2018. "Thriving in a disrupted market: a study of Chinese hedge fund performance," Pacific-Basin Finance Journal, Elsevier, vol. 48(C), pages 210-223.
    23. Denitsa Stefanova & Arjen Siegmann, 2014. "The Evolving Beta-Liquidity Relationship of Hedge Funds," LSF Research Working Paper Series 14-12, Luxembourg School of Finance, University of Luxembourg.
    24. Lieven Baele & Geert Bekaert & Koen Inghelbrecht & Min Wei, 2012. "Flights to Safety," Working Paper Research 230, National Bank of Belgium.
    25. Noori, Mohammad & Hitaj, Asmerilda, 2023. "Dissecting hedge funds' strategies," International Review of Financial Analysis, Elsevier, vol. 85(C).
    26. Ben-David, Itzhak & Franzoni, Francesco & Landier, Augustin & Moussawi, Rabih, 2011. "Do Hedge Funds Manipulate Stock Prices?," TSE Working Papers 11-221, Toulouse School of Economics (TSE).
    27. Baibing Li & Ji Luo & Kai†Hong Tee, 2017. "The Market Liquidity Timing Skills of Debt†oriented Hedge Funds," European Financial Management, European Financial Management Association, vol. 23(1), pages 32-54, January.
    28. Joenväärä, Juha & Kauppila, Mikko & Kahra, Hannu, 2021. "Hedge fund portfolio selection with fund characteristics," Journal of Banking & Finance, Elsevier, vol. 132(C).
    29. Malakhov, Alexey & Riley, Timothy B. & Yan, Qing, 2024. "Do hedge funds bet against beta?," International Review of Economics & Finance, Elsevier, vol. 93(PA), pages 1507-1525.
    30. Ma, Feng & Wahab, M.I.M. & Zhang, Yaojie, 2019. "Forecasting the U.S. stock volatility: An aligned jump index from G7 stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 54(C), pages 132-146.
    31. Elias Cavalcante Junior & Fernando Moraes & Rodrigo De Losso, 2020. "Unskilled Fund Managers: Replicating Active Fund Performance With Few ETFs," Working Papers, Department of Economics 2020_14, University of São Paulo (FEA-USP), revised 15 Sep 2020.
    32. Julien Hambuckers & Marie Kratz & Antoine Usseglio-Carleve, 2023. "Efficient Estimation In Extreme Value Regression Models Of Hedge Fund Tail Risks," Working Papers hal-04090916, HAL.
    33. Julien Hambuckers & Marie Kratz & Antoine Usseglio-Carleve, 2023. "Efficient Estimation in Extreme Value Regression Models of Hedge Fund Tail Risks," Papers 2304.06950, arXiv.org.
    34. Arpit Gupta & Kunal Sachdeva, 2019. "Skin or Skim? Inside Investment and Hedge Fund Performance," NBER Working Papers 26113, National Bureau of Economic Research, Inc.
    35. Martin, Vance L. & Tang, Chrismin & Yao, Wenying, 2018. "News and expected returns in East Asian equity markets: The RV-GARCHM model," Journal of Asian Economics, Elsevier, vol. 57(C), pages 36-52.
    36. Newton, David & Platanakis, Emmanouil & Stafylas, Dimitrios & Sutcliffe, Charles & Ye, Xiaoxia, 2021. "Hedge fund strategies, performance &diversification: A portfolio theory & stochastic discount factor approach," The British Accounting Review, Elsevier, vol. 53(5).
    37. Patton, Andrew & Kruttli, Mathias, 2014. "The Impact of Hedge Funds on Asset Markets," CEPR Discussion Papers 10151, C.E.P.R. Discussion Papers.
    38. Mark D. Flood & Phillip Monin, 2016. "Form PF and Hedge Funds: Risk-measurement Precision for Option Portfolios," Working Papers 16-02, Office of Financial Research, US Department of the Treasury.
    39. Erkko Etula & Kalle Rinne & Matti Suominen & Lauri Vaittinen, 2020. "Dash for Cash: Monthly Market Impact of Institutional Liquidity Needs," The Review of Financial Studies, Society for Financial Studies, vol. 33(1), pages 75-111.
    40. Bussière, Matthieu & Hoerova, Marie & Klaus, Benjamin, 2015. "Commonality in hedge fund returns: Driving factors and implications," Journal of Banking & Finance, Elsevier, vol. 54(C), pages 266-280.
    41. Stafylas, Dimitrios & Anderson, Keith & Uddin, Moshfique, 2018. "Hedge fund performance attribution under various market conditions," International Review of Financial Analysis, Elsevier, vol. 56(C), pages 221-237.
    42. Bollen, Nicolas P. B., 2013. "Zero-R2Hedge Funds and Market Neutrality," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(2), pages 519-547, April.
    43. Sandro Lunghi & Daniel Schmidt & Bastian von Beschwitz, 2021. "Fundamental Arbitrage under the Microscope: Evidence from Detailed Hedge Fund Transaction Data," Finance and Economics Discussion Series 2021-022, Board of Governors of the Federal Reserve System (U.S.).
    44. Lambert, Marie & Platania, Federico, 2020. "The macroeconomic drivers in hedge fund beta management," Economic Modelling, Elsevier, vol. 91(C), pages 65-80.
    45. Denitsa Stefanova & Arjen Siegmann & Marcin Zamojski, 2014. "Hedge Fund Innovation," LSF Research Working Paper Series 14-13, Luxembourg School of Finance, University of Luxembourg.
    46. Namvar, Ethan & Phillips, Blake & Pukthuanthong, Kuntara & Raghavendra Rau, P., 2016. "Do hedge funds dynamically manage systematic risk?," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 1-15.
    47. Dragomirescu-Gaina, Catalin & Philippas, Dionisis & Tsionas, Mike G., 2021. "Trading off accuracy for speed: Hedge funds' decision-making under uncertainty," International Review of Financial Analysis, Elsevier, vol. 75(C).
    48. Andrew W. Lo & Mila Getmansky & Peter A. Lee, 2015. "Hedge Funds: A Dynamic Industry in Transition," Annual Review of Financial Economics, Annual Reviews, vol. 7(1), pages 483-577, December.
    49. Huang, Huichou & MacDonald, Ronald & Zhao, Yang, 2012. "Global Currency Misalignments, Crash Sensitivity, and Downside Insurance Costs," MPRA Paper 53745, University Library of Munich, Germany, revised 18 Nov 2013.
    50. Fullwood, Jonathan & James, Jessica & Marsh, Ian W., 2021. "Volatility and the cross-section of returns on FX options," Journal of Financial Economics, Elsevier, vol. 141(3), pages 1262-1284.
    51. Osinga, Albert Jakob & Schauten, Marc B.J. & Zwinkels, Remco C.J., 2021. "Timing is money: The factor timing ability of hedge fund managers," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 266-281.
    52. Urbi Garay & Enrique Ter Horst & German Molina & Abel Rodriguez, 2016. "Bayesian Nonparametric Measurement of Factor Betas and Clustering with Application to Hedge Fund Returns," Econometrics, MDPI, vol. 4(1), pages 1-23, March.
    53. Savona, Roberto, 2014. "Hedge fund systemic risk signals," European Journal of Operational Research, Elsevier, vol. 236(1), pages 282-291.
    54. Ozgur (Ozzy) Akay & Zeynep Senyuz & Emre Yoldas, 2013. "Hedge Fund Contagion and Risk-adjusted Returns: A Markov-switching Dynamic Factor Approach," Working Papers 13-03, Office of Financial Research, US Department of the Treasury.
    55. Jean-Marc Le Caillec, 2022. "Hypothesis Testing Fusion for Nonlinearity Detection in Hedge Fund Price Returns," Post-Print hal-03739132, HAL.
    56. Margherita Giuzio & Kay Eichhorn-Schott & Sandra Paterlini & Vincent Weber, 2018. "Tracking hedge funds returns using sparse clones," Annals of Operations Research, Springer, vol. 266(1), pages 349-371, July.
    57. Astrid Ayala & Szabolcs Blazsek, 2018. "Equity market neutral hedge funds and the stock market: an application of score-driven copula models," Applied Economics, Taylor & Francis Journals, vol. 50(37), pages 4005-4023, August.
    58. Stafylas, Dimitrios & Anderson, Keith & Uddin, Moshfique, 2017. "Recent advances in explaining hedge fund returns: Implicit factors and exposures," Global Finance Journal, Elsevier, vol. 33(C), pages 69-87.
    59. Stephan Schwill, 2018. "Entropy Analysis of Financial Time Series," Papers 1807.09423, arXiv.org.
    60. Lukas Mankhoff & Lucio Sarno & Maik Schmeling & Andreas Schrimpf, 2013. "Information flows in foreign exchange markets: dissecting customer currency trades," BIS Working Papers 405, Bank for International Settlements.
    61. Benoît Dewaele, 2013. "Portfolio Optimization for Hedge Funds through Time-Varying Coefficients," Working Papers CEB 13-032, ULB -- Universite Libre de Bruxelles.
    62. Paul Karehnke & Frans de Roon, 2020. "Spanning Tests for Assets with Option-Like Payoffs: The Case of Hedge Funds," Management Science, INFORMS, vol. 66(12), pages 5969-5989, December.
    63. Piccotti, Louis R., 2017. "Financial contagion risk and the stochastic discount factor," Journal of Banking & Finance, Elsevier, vol. 77(C), pages 230-248.
    64. Bali, Turan G. & Brown, Stephen J. & Caglayan, Mustafa O., 2014. "Macroeconomic risk and hedge fund returns," Journal of Financial Economics, Elsevier, vol. 114(1), pages 1-19.

  20. Patton, Andrew, 2010. "On the Dynamics of Hedge Fund Risk Exposures," CEPR Discussion Papers 7780, C.E.P.R. Discussion Papers.

    Cited by:

    1. Arjen Siegmann & Denitsa Stefanova, 2011. "Market Liquidity and Exposure of Hedge Funds," Tinbergen Institute Discussion Papers 11-150/2/DSF27, Tinbergen Institute.
    2. Menkhoff, Lukas & Sarno, Lucio & Schmeling, Maik & Schrimpf, Andreas, 2012. "Currency momentum strategies," Journal of Financial Economics, Elsevier, vol. 106(3), pages 660-684.
    3. Teo, Melvyn, 2011. "The liquidity risk of liquid hedge funds," Journal of Financial Economics, Elsevier, vol. 100(1), pages 24-44, April.
    4. Bali, Turan G. & Brown, Stephen J. & Caglayan, Mustafa Onur, 2012. "Systematic risk and the cross section of hedge fund returns," Journal of Financial Economics, Elsevier, vol. 106(1), pages 114-131.

  21. Patton, Andrew J. & Verardo, Michela, 2009. "Does beta move with news? Systematic risk and firm-specific information flows," LSE Research Online Documents on Economics 24421, London School of Economics and Political Science, LSE Library.

    Cited by:

    1. 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.

  22. Andrew J. Patton, 2008. "Copula-Based Models for Financial Time Series," Economics Series Working Papers 2008fe21, University of Oxford, Department of Economics.

    Cited by:

    1. Pál Rakonczai & László Márkus & András Zempléni, 2012. "Autocopulas: Investigating the Interdependence Structure of Stationary Time Series," Methodology and Computing in Applied Probability, Springer, vol. 14(1), pages 149-167, March.
    2. Brendan K. Beare, 2010. "Copulas and Temporal Dependence," Econometrica, Econometric Society, vol. 78(1), pages 395-410, January.
    3. Vandna Jowaheer & Nafeessah Z. B. Ameerudden, 2012. "Modelling the Dependence Structure of MUR/USD and MUR/INR Exchange Rates using Copula," International Journal of Economics and Financial Issues, Econjournals, vol. 2(1), pages 27-32.
    4. Xiaohong Chen & Wei Biao Wu & Yanping Yi, 2009. "Efficient Estimation of Copula-based Semiparametric Markov Models," Cowles Foundation Discussion Papers 1691, Cowles Foundation for Research in Economics, Yale University, revised Mar 2009.
    5. Dominique Guegan & Pierre-André Maugis, 2010. "An Econometric Study of Vine Copulas," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00492124, HAL.
    6. Diks, Cees & Panchenko, Valentyn & van Dijk, Dick, 2010. "Out-of-sample comparison of copula specifications in multivariate density forecasts," Journal of Economic Dynamics and Control, Elsevier, vol. 34(9), pages 1596-1609, September.
    7. Peter Christoffersen & Hugues Langlois, 2011. "The Joint Dynamics of Equity Market Factors," CREATES Research Papers 2011-45, Department of Economics and Business Economics, Aarhus University.
    8. Yongmin Chen & Michael H. Riordan, 2008. "Price‐increasing competition," RAND Journal of Economics, RAND Corporation, vol. 39(4), pages 1042-1058, December.
    9. Zhang, Dalu, 2014. "Vine copulas and applications to the European Union sovereign debt analysis," International Review of Financial Analysis, Elsevier, vol. 36(C), pages 46-56.
    10. Cathy Ning & Dinghai Xu & Tony Wirjanto, 2009. "Modeling Asymmetric Volatility Clusters Using Copulas and High Frequency Data," Working Papers 006, Toronto Metropolitan University, Department of Economics.
    11. Chollete, Loran & Ning, Cathy, 2009. "The Dependence Structure of Macroeconomic Variables in the US," UiS Working Papers in Economics and Finance 2009/31, University of Stavanger.
    12. Cathy Ning & Dinghai Xu & Tony Wirjanto, 2014. "Is Volatility Clustering of Asset Returns Asymmetric?," Working Papers 050, Toronto Metropolitan University, Department of Economics.
    13. Chollete, Loran & Pena, Victor de la & Lu, Ching-Chih, 2009. "International Diversification: A Copula Approach," UiS Working Papers in Economics and Finance 2009/27, University of Stavanger.
    14. Pierre-André Maugis & Dominique Guegan, 2010. "Note on new prospects on vines," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00471362, HAL.
    15. Romera, Rosario & Molanes, Elisa M., 2008. "Copulas in finance and insurance," DES - Working Papers. Statistics and Econometrics. WS ws086321, Universidad Carlos III de Madrid. Departamento de Estadística.
    16. Leonidas Tsiaras, 2010. "Dynamic Models of Exchange Rate Dependence Using Option Prices and Historical Returns," CREATES Research Papers 2010-35, Department of Economics and Business Economics, Aarhus University.
    17. Shenqiu Zhang & Ivan Paya & David Peel, 2009. "Linkages between Shanghai and Hong Kong stock indices," Applied Financial Economics, Taylor & Francis Journals, vol. 19(23), pages 1847-1857.
    18. Chollete, Loran & de la Pena , Victor & Lu, Ching-Chih, 2009. "International Diversification: An Extreme Value Approach," UiS Working Papers in Economics and Finance 2009/26, University of Stavanger.
    19. Lin, Feng & Peng, Liang & Xie, Jiehua & Yang, Jingping, 2018. "Stochastic distortion and its transformed copula," Insurance: Mathematics and Economics, Elsevier, vol. 79(C), pages 148-166.
    20. Yongmin Chen & Michael H. Riordan, 2013. "Profitability Of Product Bundling," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 54(1), pages 35-57, February.
    21. Bukre Yildirim Kulekci & Gulden Poyraz & Ismail Gur & Ozan Evkaya, 2023. "Dependence Analysis of the ISE100 Banking Sector Using Vine Copula," Istanbul Journal of Economics-Istanbul Iktisat Dergisi, Istanbul University, Faculty of Economics, vol. 73(73-1), pages 55-81, June.

  23. Kevin Sheppard & Andrew J. Patton, 2008. "Evaluating Volatility and Correlation Forecasts," Economics Series Working Papers 2008fe22, University of Oxford, Department of Economics.

    Cited by:

    1. 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.
    2. Leonidas Tsiaras, 2010. "The Forecast Performance of Competing Implied Volatility Measures: The Case of Individual Stocks," CREATES Research Papers 2010-34, Department of Economics and Business Economics, Aarhus University.
    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. Michael McAleer & Massimiliano Caporin, 2012. "Robust Ranking of Multivariate GARCH Models by Problem Dimension," KIER Working Papers 815, Kyoto University, Institute of Economic Research.
    5. Rossi, E. & Spazzini, F., 2010. "Model and distribution uncertainty in multivariate GARCH estimation: A Monte Carlo analysis," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2786-2800, November.
    6. Alvaro Escribano & Genaro Sucarrat, 2011. "Automated model selection in finance: General-to-speci c modelling of the mean and volatility speci cations," Working Papers 2011-09, Instituto Madrileño de Estudios Avanzados (IMDEA) Ciencias Sociales.
    7. Roxana Halbleib & Valeri Voev, 2011. "Forecasting multivariate volatility using the VARFIMA model on realized covariance cholesky factors," ULB Institutional Repository 2013/195065, ULB -- Universite Libre de Bruxelles.
    8. Hautsch, Nikolaus & Kyj, Lada M. & Malec, Peter, 2011. "The merit of high-frequency data in portfolio allocation," CFS Working Paper Series 2011/24, Center for Financial Studies (CFS).

  24. Andrew J. Patton & Allan Timmermann, 2008. "The Resolution of Macroeconomic Uncertainty: Evidence from Survey Forecast," CREATES Research Papers 2008-54, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Pfajfar, D. & Zakelj, B., 2012. "Uncertainty and Disagreement in Forecasting Inflation : Evidence from the Laboratory (Revised version of CentER DP 2011-053)," Discussion Paper 2012-072, Tilburg University, Center for Economic Research.
    2. Pfajfar, D. & Zakelj, B., 2012. "Uncertainty and Disagreement in Forecasting Inflation : Evidence from the Laboratory (Revised version of EBC DP 2011-014)," Other publications TiSEM 2b92a09f-918e-4614-978d-0, Tilburg University, School of Economics and Management.
    3. Jonas Dovern & Ulrich Fritsche & Jiri Slacalek, 2012. "Disagreement Among Forecasters in G7 Countries," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1081-1096, November.
    4. Fernandes, Marcelo & Thiele, Eduardo, 2015. "The Macroeconomic Determinants of the Term Structure of Inflation Expectations in Brazil," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 35(1), October.
    5. Ohnsorge,Franziska Lieselotte & Stocker,Marc & Some,Modeste Y., 2016. "Quantifying uncertainties in global growth forecasts," Policy Research Working Paper Series 7770, The World Bank.

  25. Timmermann, Allan & Patton, Andrew, 2007. "Learning in Real Time: Theory and Empirical Evidence from the Term Structure of Survey Forecasts," CEPR Discussion Papers 6526, C.E.P.R. Discussion Papers.

    Cited by:

    1. Kajal Lahiri & Xuguang Sheng, 2008. "Measuring Forecast Uncertainty by Disagreement: The Missing Link," ifo Working Paper Series 60, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    2. 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.
    3. 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.

  26. Andrew Patton, 2006. "Volatility Forecast Comparison using Imperfect Volatility Proxies," Research Paper Series 175, Quantitative Finance Research Centre, University of Technology, Sydney.

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    1. Szabolcs Blazsek & Marco Villatoro, 2015. "Is Beta- t -EGARCH(1,1) superior to GARCH(1,1)?," Applied Economics, Taylor & Francis Journals, vol. 47(17), pages 1764-1774, April.
    2. 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.
    3. 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.
    4. Andrea Bucci, 2020. "Realized Volatility Forecasting with Neural Networks," Journal of Financial Econometrics, Oxford University Press, vol. 18(3), pages 502-531.
    5. 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.
    6. Harvey, A. & Palumbo, D., 2019. "Score-Driven Models for Realized Volatility," Cambridge Working Papers in Economics 1950, Faculty of Economics, University of Cambridge.
    7. Tae-Hwy Lee & Ekaterina Seregina & Yaojue Xu, 2023. "Elicitability and Encompassing for Volatility Forecasts by Bregman Functions," Working Papers 202311, University of California at Riverside, Department of Economics.
    8. Matteo Barigozzi & Christian T. Brownlees & Giampiero M. Gallo & David Veredas, 2010. "Disentangling Systematic and Idiosyncratic Risk for Large Panels of Assets," Econometrics Working Papers Archive wp2010_06, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    9. Eduardo Rossi & Dean Fantazzini, 2012. "Long memory and Periodicity in Intraday Volatility," DEM Working Papers Series 015, University of Pavia, Department of Economics and Management.
    10. 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.
    11. Özbekler, Ali Gencay & Kontonikas, Alexandros & Triantafyllou, Athanasios, 2021. "Volatility forecasting in European government bond markets," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1691-1709.
    12. Degiannakis, Stavros & Filis, George & Klein, Tony & Walther, Thomas, 2019. "Forecasting Realized Volatility of Agricultural Commodities," MPRA Paper 96267, University Library of Munich, Germany.
    13. Xiaoning Kang & Xinwei Deng & Kam‐Wah Tsui & Mohsen Pourahmadi, 2020. "On variable ordination of modified Cholesky decomposition for estimating time‐varying covariance matrices," International Statistical Review, International Statistical Institute, vol. 88(3), pages 616-641, December.
    14. Georgiana-Denisa Banulescu & Bertrand Candelon & Christophe Hurlin & Sébastien Laurent, 2014. "Do We Need Ultra-High Frequency Data to Forecast Variances?," Working Papers halshs-01078158, HAL.
    15. Bermudez, P. de Zea & Marín, J. Miguel & Rue, Håvard & Veiga, Helena, 2024. "Integrated nested Laplace approximations for threshold stochastic volatility models," Econometrics and Statistics, Elsevier, vol. 30(C), pages 15-35.
    16. Donggyu Kim & Minseok Shin & Yazhen Wang, 2023. "Overnight GARCH-Itô Volatility Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(4), pages 1215-1227, October.
    17. 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.
    18. Cavit Pakel & Neil Shephard & Kevin Sheppard, 2009. "Nuisance parameters, composite likelihoods and a panel of GARCH models," OFRC Working Papers Series 2009fe03, Oxford Financial Research Centre.
    19. 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.
    20. Pawel Janus & Siem Jan Koopman & André Lucas, 2011. "Long Memory Dynamics for Multivariate Dependence under Heavy Tails," Tinbergen Institute Discussion Papers 11-175/2/DSF28, Tinbergen Institute.
    21. Mawuli Segnon & Rangan Gupta & Bernd Wilfling, 2022. "Forecasting Stock Market Volatility with Regime-Switching GARCH-MIDAS: The Role of Geopolitical Risks," Working Papers 202203, University of Pretoria, Department of Economics.
    22. Bauwens, Luc & Xu, Yongdeng, 2023. "DCC- and DECO-HEAVY: Multivariate GARCH models based on realized variances and correlations," International Journal of Forecasting, Elsevier, vol. 39(2), pages 938-955.
    23. Geert Dhaene & Piet Sercu & Jianbin Wu, 2022. "Volatility spillovers: A sparse multivariate GARCH approach with an application to commodity markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(5), pages 868-887, May.
    24. Fantazzini, Dean, 2024. "Adaptive Conformal Inference for computing Market Risk Measures: an Analysis with Four Thousands Crypto-Assets," MPRA Paper 121214, University Library of Munich, Germany.
    25. 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.
    26. Jérémy Leymarie & Christophe Hurlin & Antoine Patin, 2018. "Loss Functions for LGD Models Comparison," Post-Print hal-01923050, HAL.
    27. Demetrio Lacava & Luca Scaffidi Domianello, 2021. "The Incidence of Spillover Effects during the Unconventional Monetary Policies Era," JRFM, MDPI, vol. 14(6), pages 1-18, May.
    28. 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.
    29. Michael McAleer & Marcelo Cunha Medeiros, 2010. "Forecasting Realized Volatility with Linear and Nonlinear Models," Textos para discussão 568, Department of Economics PUC-Rio (Brazil).
    30. Shelton Peiris & Manabu Asai & Michael McAleer, 2016. "Estimating and Forecasting Generalized Fractional Long Memory Stochastic Volatility Models," Tinbergen Institute Discussion Papers 16-044/III, Tinbergen Institute.
    31. Chao Liang & Yin Liao & Feng Ma & Bo Zhu, 2022. "United States Oil Fund volatility prediction: the roles of leverage effect and jumps," Empirical Economics, Springer, vol. 62(5), pages 2239-2262, May.
    32. Cerovecki, Clément & Francq, Christian & Hörmann, Siegfried & Zakoïan, Jean-Michel, 2019. "Functional GARCH models: The quasi-likelihood approach and its applications," Journal of Econometrics, Elsevier, vol. 209(2), pages 353-375.
    33. 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).
    34. 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.
    35. Bauwens, Luc & Braione, Manuela & Storti, Giuseppe, 2017. "A dynamic component model for forecasting high-dimensional realized covariance matrices," Econometrics and Statistics, Elsevier, vol. 1(C), pages 40-61.
    36. 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.
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    40. Asai, Manabu & Gupta, Rangan & McAleer, Michael, 2020. "Forecasting volatility and co-volatility of crude oil and gold futures: Effects of leverage, jumps, spillovers, and geopolitical risks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 933-948.
    41. Byun, Suk Joon & Kim, Jun Sik, 2013. "The information content of risk-neutral skewness for volatility forecasting," Journal of Empirical Finance, Elsevier, vol. 23(C), pages 142-161.
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    44. Leonidas Tsiaras, 2010. "The Forecast Performance of Competing Implied Volatility Measures: The Case of Individual Stocks," CREATES Research Papers 2010-34, Department of Economics and Business Economics, Aarhus University.
    45. Hung, Jui-Cheng & Liu, Hung-Chun & Yang, J. Jimmy, 2020. "Improving the realized GARCH’s volatility forecast for Bitcoin with jump-robust estimators," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
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    1. Aretz, Kevin & Bartram, Söhnke M. & Pope, Peter F., 2011. "Asymmetric Loss Functions and the Rationality of Expected Stock Returns," MPRA Paper 47343, University Library of Munich, Germany.

  28. Patton, Andrew J., 2004. "Are "market neutral" hedge funds really market neutral?," LSE Research Online Documents on Economics 24819, London School of Economics and Political Science, LSE Library.

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    9. Almeida, Caio & Fernandes, Marcelo & Valente, Joao Paulo, 2022. "Tail risk exposures of hedge funds: Evidence from unique Brazilian data," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 41(1), June.
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    30. Chabi-Yo, Fousseni & Ruenzi, Stefan & Weigert, Florian, 2013. "Crash Sensitivity and the Cross-Section of Expected Stock Returns," Working Papers on Finance 1324, University of St. Gallen, School of Finance, revised Feb 2016.
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    50. Agarwal, Vikas & Green, T. Clifton & Ren, Honglin, 2018. "Alpha or beta in the eye of the beholder: What drives hedge fund flows?," Journal of Financial Economics, Elsevier, vol. 127(3), pages 417-434.
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    54. Polanski, Arnold & Stoja, Evarist & Zhang, Ren, 2013. "Multidimensional risk and risk dependence," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 3286-3294.
    55. Bali, Turan G. & Brown, Stephen J. & Caglayan, Mustafa Onur, 2011. "Do hedge funds' exposures to risk factors predict their future returns?," Journal of Financial Economics, Elsevier, vol. 101(1), pages 36-68, July.
    56. Bali, Turan G. & Brown, Stephen J. & Caglayan, Mustafa O., 2014. "Macroeconomic risk and hedge fund returns," Journal of Financial Economics, Elsevier, vol. 114(1), pages 1-19.
    57. El Kalak, Izidin & Azevedo, Alcino & Hudson, Robert, 2016. "Reviewing the hedge funds literature II: Hedge funds' returns and risk management characteristics," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 55-66.
    58. Patton, Andrew J., 2012. "A review of copula models for economic time series," Journal of Multivariate Analysis, Elsevier, vol. 110(C), pages 4-18.
    59. Papageorgiou, Nicolas & Reeves, Jonathan J. & Xie, Xuan, 2016. "Betas and the myth of market neutrality," International Journal of Forecasting, Elsevier, vol. 32(2), pages 548-558.
    60. Patton, Andrew, 2013. "Copula Methods for Forecasting Multivariate Time Series," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 899-960, Elsevier.
    61. Fousseni Chabi-Yo & Markus Huggenberger & Florian Weigert, 2019. "Multivariate Crash Risk," Working Papers on Finance 1901, University of St. Gallen, School of Finance.

  29. Chen, Xiaohong & Fan, Yanqin & Patton, Andrew J., 2004. "Simple tests for models of dependence between multiple financial time series, with applications to U.S. equity returns and exchange rates," LSE Research Online Documents on Economics 24681, London School of Economics and Political Science, LSE Library.

    Cited by:

    1. Carluccio Bianchi & Dean Fantazzini & Maria Elena De Giuli & Mario Maggi, 2009. "Small Sample Properties of Copula-GARCH Modelling: A Monte Carlo Study," Quaderni di Dipartimento 093, University of Pavia, Department of Economics and Quantitative Methods.
    2. Heinen, Andreas & Rengifo, Erick, 2007. "Multivariate autoregressive modeling of time series count data using copulas," Journal of Empirical Finance, Elsevier, vol. 14(4), pages 564-583, September.
    3. Jean-David Fermanian, 2012. "An overview of the goodness-of-fit test problem for copulas," Papers 1211.4416, arXiv.org.
    4. Kole, Erik & Koedijk, Kees & Verbeek, Marno, 2007. "Selecting copulas for risk management," Journal of Banking & Finance, Elsevier, vol. 31(8), pages 2405-2423, August.
    5. Dominique Guegan & Pierre-André Maugis, 2010. "An Econometric Study of Vine Copulas," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00492124, HAL.
    6. Dominique Guegan & Pierre-André Maugis, 2010. "New Prospects on Vines," Post-Print halshs-00348884, HAL.
    7. Carluccio Bianchi & Alessandro Carta & Dean Fantazzini & Maria Elena De Giuli & Mario A. Maggi, 2009. "A Copula-VAR-X Approach for Industrial Production Modelling and Forecasting," Quaderni di Dipartimento 105, University of Pavia, Department of Economics and Quantitative Methods.
    8. Sim, Nicholas, 2016. "Modeling the dependence structures of financial assets through the Copula Quantile-on-Quantile approach," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 31-45.
    9. Li, Lihui & Wen, Tao, 2013. "Estimation of C-MGARCH models based on the MBP method," Statistics & Probability Letters, Elsevier, vol. 83(2), pages 665-673.
    10. René Garcia & Georges Tsafack, 2009. "Dependence Structure and Extreme Comovements in International Equity and Bond Markets," CIRANO Working Papers 2009s-21, CIRANO.
    11. Hong-Ming Huang & Chihwa Kao & Giovanni Urga, 2007. "Copula-Based Tests for Cross-Sectional Independence in Panel Models," Center for Policy Research Working Papers 99, Center for Policy Research, Maxwell School, Syracuse University.
    12. Wolfgang Härdle & Ostap Okhrin, 2010. "De copulis non est disputandum," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 94(1), pages 1-31, March.
    13. Dominique Guegan & Pierre-André Maugis, 2010. "An Econometric Study of Vine Copulas," Post-Print halshs-00492124, HAL.
    14. Pierre-André Maugis & Dominique Guegan, 2010. "Note on new prospects on vines," Post-Print halshs-00471362, HAL.
    15. Andrew J. Patton, 2006. "Estimation of multivariate models for time series of possibly different lengths," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(2), pages 147-173.
    16. Dominique Guegan & Pierre-André Maugis, 2011. "An econometric Study for Vine Copulas," Post-Print halshs-00645799, HAL.
    17. Matthias Fischer & Christian Kock & Stephan Schluter & Florian Weigert, 2009. "An empirical analysis of multivariate copula models," Quantitative Finance, Taylor & Francis Journals, vol. 9(7), pages 839-854.
    18. Panchenko, V., 2004. "Goodness-of-fit test for copulas," CeNDEF Working Papers 04-16, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    19. Daniel Berg, 2009. "Copula goodness-of-fit testing: an overview and power comparison," The European Journal of Finance, Taylor & Francis Journals, vol. 15(7-8), pages 675-701.
    20. Dean Fantazzini, 2011. "Analysis of multidimensional probability distributions with copula functions," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 22(2), pages 98-134.
    21. Gonzalo, Jesús & Olmo, José, 2007. "The impact of heavy tails and comovements in downside-risk diversification," UC3M Working papers. Economics we20070208, Universidad Carlos III de Madrid. Departamento de Economía.
    22. Okhrin, Ostap & Okhrin, Yarema & Schmid, Wolfgang, 2013. "On the structure and estimation of hierarchical Archimedean copulas," Journal of Econometrics, Elsevier, vol. 173(2), pages 189-204.
    23. Pierre-André Maugis & Dominique Guegan, 2010. "Note on new prospects on vines," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00471362, HAL.
    24. Pierre-André Maugis & Dominique Guegan, 2010. "Note on new prospects on vines," PSE-Ecole d'économie de Paris (Postprint) halshs-00471362, HAL.
    25. Matthias Fischer & Christian Köck, 2007. "Multivariate Copula Models at Work: Dependence Structure of Energie Prices," Energy and Environmental Modeling 2007 24000014, EcoMod.
    26. Roch, Oriol & Alegre, Antonio, 2006. "Testing the bivariate distribution of daily equity returns using copulas. An application to the Spanish stock market," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1312-1329, November.
    27. 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.
    28. Söderberg, Jonas, 2008. "Test of the Gaussian Copula on the Swedish Stock Market," CAFO Working Papers 2009:9, Linnaeus University, Centre for Labour Market Policy Research (CAFO), School of Business and Economics.
    29. Fischer, Matthias J. & Köck, Christian & Schlüter, Stephan & Weigert, Florian, 2007. "Multivariate Copula Models at Work: Outperforming the desert island copula?," Discussion Papers 79/2007, Friedrich-Alexander University Erlangen-Nuremberg, Chair of Statistics and Econometrics.
    30. Leonidas Tsiaras, 2010. "Dynamic Models of Exchange Rate Dependence Using Option Prices and Historical Returns," CREATES Research Papers 2010-35, Department of Economics and Business Economics, Aarhus University.
    31. Dean Fantazzini, 2011. "Analysis of multidimensional probability distributions with copula functions. III," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 24(4), pages 100-130.
    32. Christian Gourieroux & Wei Liu, 2006. "Sensitivity Analysis of Distortion Risk Measures," Working Papers 2006-33, Center for Research in Economics and Statistics.
    33. Luciana Dalla Valle, 2009. "Bayesian Copulae Distributions, with Application to Operational Risk Management," Methodology and Computing in Applied Probability, Springer, vol. 11(1), pages 95-115, March.
    34. Mark Trede & Cornelia Savu, 2013. "Do stock returns have an Archimedean copula?," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(8), pages 1764-1778, August.
    35. Nikoloulopoulos, Aristidis K. & Karlis, Dimitris, 2008. "Copula model evaluation based on parametric bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3342-3353, March.
    36. Oriol Roch Casellas & Antonio Alegre Escolano, 2005. "Testing the bivariate distribution of daily equity returns using copulas. An application to the Spanish stock market," Working Papers in Economics 143, Universitat de Barcelona. Espai de Recerca en Economia.
    37. Dominique Guegan & Pierre-André Maugis, 2011. "An econometric Study for Vine Copulas," PSE-Ecole d'économie de Paris (Postprint) halshs-00645799, HAL.
    38. Fantazzini, Dean, 2009. "The effects of misspecified marginals and copulas on computing the value at risk: A Monte Carlo study," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2168-2188, April.
    39. Grundke, Peter & Polle, Simone, 2012. "Crisis and risk dependencies," European Journal of Operational Research, Elsevier, vol. 223(2), pages 518-528.
    40. Okhrin, Ostap, 2010. "Fitting high-dimensional copulae to data," SFB 649 Discussion Papers 2010-022, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

  30. Yanqin Fan & Xiaohong Chen & Andrew Patton, 2004. "(IAM Series No 003) Simple Tests for Models of Dependence Between Multiple Financial Time Series, with Applications to U.S. Equity Returns and Exchange Rates," FMG Discussion Papers dp483, Financial Markets Group.

    Cited by:

    1. Chen, Xiaohong & Fan, Yanqin, 2006. "Estimation of copula-based semiparametric time series models," Journal of Econometrics, Elsevier, vol. 130(2), pages 307-335, February.
    2. Kole, Erik & Koedijk, Kees & Verbeek, Marno, 2007. "Selecting copulas for risk management," Journal of Banking & Finance, Elsevier, vol. 31(8), pages 2405-2423, August.
    3. Sim, Nicholas, 2016. "Modeling the dependence structures of financial assets through the Copula Quantile-on-Quantile approach," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 31-45.
    4. Andrew J. Patton, 2006. "Estimation of multivariate models for time series of possibly different lengths," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(2), pages 147-173.
    5. Scaillet, Olivier, 2007. "Kernel-based goodness-of-fit tests for copulas with fixed smoothing parameters," Journal of Multivariate Analysis, Elsevier, vol. 98(3), pages 533-543, March.
    6. Panchenko, V., 2004. "Goodness-of-fit test for copulas," CeNDEF Working Papers 04-16, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    7. Matthias Fischer & Christian Köck, 2007. "Multivariate Copula Models at Work: Dependence Structure of Energie Prices," Energy and Environmental Modeling 2007 24000014, EcoMod.
    8. 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.
    9. Mark Trede & Cornelia Savu, 2013. "Do stock returns have an Archimedean copula?," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(8), pages 1764-1778, August.
    10. Oriol Roch Casellas & Antonio Alegre Escolano, 2005. "Testing the bivariate distribution of daily equity returns using copulas. An application to the Spanish stock market," Working Papers in Economics 143, Universitat de Barcelona. Espai de Recerca en Economia.

  31. Timmermann, Allan & Patton, Andrew, 2003. "Properties of Optimal Forecasts," CEPR Discussion Papers 4037, C.E.P.R. Discussion Papers.

    Cited by:

    1. 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.
    2. Romulo A. Chumacero, 2004. "Forecasting Chilean Industrial Production with Automated Procedures," Econometric Society 2004 Latin American Meetings 177, Econometric Society.
    3. Capistrán Carlos & López Moctezuma Gabriel, 2008. "Experts' Macroeconomics Expectations: An Evaluation of Mexican Short-Run Forecasts," Working Papers 2008-11, Banco de México.
    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. Romulo A. Chumacero, 2004. "Forecasting Chilean Industrial Production and Sales with Automated Procedures," Computing in Economics and Finance 2004 112, Society for Computational Economics.
    6. Timmermann Allan & Capistrán Carlos, 2006. "Disagreement and Biases in Inflation Expectations," Working Papers 2006-07, Banco de México.
    7. Timmermann, Allan & Elliott, Graham & Komunjer, Ivana, 2003. "Estimating Loss Function Parameters," CEPR Discussion Papers 3821, C.E.P.R. Discussion Papers.
    8. Anatolyev, Stanislav, 2009. "Dynamic modeling under linear-exponential loss," Economic Modelling, Elsevier, vol. 26(1), pages 82-89, January.
    9. Thomas Jobert & Lionel Persyn, 2012. "Quelques constats sur les prévisions conjoncturelles de la croissance française," Revue d'économie politique, Dalloz, vol. 122(6), pages 833-849.
    10. 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).
    11. Aiolfi Marco & Capistrán Carlos & Timmermann Allan, 2010. "Forecast Combinations," Working Papers 2010-04, Banco de México.
    12. 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.
    13. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    14. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
    15. Patton, Andrew J. & Timmermann, Allan, 2005. "Testable implications of forecast optimality," LSE Research Online Documents on Economics 6834, London School of Economics and Political Science, LSE Library.

  32. Granger, Clive W. J. & Terasvirta, Timo & Patton, Andrew J., 2003. "Common factors in conditional distributions for Bivariate time series," LSE Research Online Documents on Economics 24854, London School of Economics and Political Science, LSE Library.

    Cited by:

    1. Tae-Hwy Lee & Weiping Yang, 2014. "Granger-Causality in Quantiles between Financial Markets: Using Copula Approach," Working Papers 201406, University of California at Riverside, Department of Economics.
    2. Dominique Guegan & Jing Zang, 2009. "Pricing bivariate option under GARCH-GH model with dynamic copula: application for Chinese market," The European Journal of Finance, Taylor & Francis Journals, vol. 15(7-8), pages 777-795.
    3. Dean Fantazzini, 2008. "Econometric Analysis of Financial Data in Risk Management (continuation). Section III: Managing Operational Risk," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 11(3), pages 87-122.
    4. Kim, Gunky & Silvapulle, Mervyn J. & Silvapulle, Paramsothy, 2007. "Comparison of semiparametric and parametric methods for estimating copulas," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 2836-2850, March.
    5. Jozef Barun'ik & Tobias Kley, 2015. "Quantile Coherency: A General Measure for Dependence between Cyclical Economic Variables," Papers 1510.06946, arXiv.org, revised Dec 2018.
    6. D. Guegan & J. Zhang, 2010. "Change analysis of a dynamic copula for measuring dependence in multivariate financial data," Quantitative Finance, Taylor & Francis Journals, vol. 10(4), pages 421-430.
    7. Matthias R. Fengler & Helmut Herwartz & Christian Werner, 2012. "A Dynamic Copula Approach to Recovering the Index Implied Volatility Skew," Journal of Financial Econometrics, Oxford University Press, vol. 10(3), pages 457-493, June.
    8. Chen, Xiaohong & Fan, Yanqin, 2006. "Estimation of copula-based semiparametric time series models," Journal of Econometrics, Elsevier, vol. 130(2), pages 307-335, February.
    9. Dominique Guegan & Jing Zhang, 2010. "Change analysis of a dynamic copula for measuring dependence in multivariate financial data," Post-Print halshs-00368334, HAL.
    10. Miguel Carvalho & António Rua, 2014. "Extremal Dependence in International Output Growth: Tales from the Tails," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(4), pages 605-620, August.
    11. Dominique Guegan & Jing Zhang, 2009. "Pricing bivariate option under GARCH-GH model with dynamic copula: application for Chinese market," PSE-Ecole d'économie de Paris (Postprint) halshs-00368336, HAL.
    12. Carluccio Bianchi & Alessandro Carta & Dean Fantazzini & Maria Elena De Giuli & Mario A. Maggi, 2009. "A Copula-VAR-X Approach for Industrial Production Modelling and Forecasting," Quaderni di Dipartimento 105, University of Pavia, Department of Economics and Quantitative Methods.
    13. Power, Gabriel J. & Vedenov, Dmitry V., 2008. "The Shape of the Optimal Hedge Ratio: Modeling Joint Spot-Futures Prices using an Empirical Copula-GARCH Model," 2008 Conference, April 21-22, 2008, St. Louis, Missouri 37609, NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    14. Pérez, Ana & Prieto-Alaiz, Mercedes, 2016. "A note on nonparametric estimation of copula-based multivariate extensions of Spearman’s rho," Statistics & Probability Letters, Elsevier, vol. 112(C), pages 41-50.
    15. Xiaohong Chen & Yanqin Fan & Victor Tsyrennifov, 2004. "Efficient Estimation of Semiparametric Multivariate Copula Models," Vanderbilt University Department of Economics Working Papers 0420, Vanderbilt University Department of Economics.
    16. Gong, Yuting & He, Zhongzhi & Xue, Wenjun, 2022. "EPU spillovers and stock return predictability: A cross-country study," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 78(C).
    17. Dominique Guegan & Jing Zhang, 2010. "Change analysis of a dynamic copula for measuring dependence in multivariate financial data," PSE-Ecole d'économie de Paris (Postprint) halshs-00368334, HAL.
    18. 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.
    19. Malo, Pekka, 2009. "Modeling electricity spot and futures price dependence: A multifrequency approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(22), pages 4763-4779.
    20. Dean Fantazzini, 2009. "Econometric Analysis of Financial Data in Risk Management," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 14(2), pages 100-127.
    21. Manner, H. & Candelon, B., 2007. "Testing for asset market linkages: a new approach based on time-varying copulas," Research Memorandum 052, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    22. Param Silvapulle & Xibin Zhang, 2006. "Assessing Dependence Changes in the Asian Financial Market Returns Using Plots Based on Nonparametric Measures," Monash Econometrics and Business Statistics Working Papers 9/06, Monash University, Department of Econometrics and Business Statistics.
    23. Cyril Caillault & Dominique Guegan, 2009. "Forecasting VaR and Expected Shortfall using Dynamical Systems: A Risk Management Strategy," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00375765, HAL.
    24. Xiaohong Chen & Yanqin Fan, 2004. "Estimation and Model Selection of Semiparametric Copula-Based Multivariate Dynamic Models under Copula Misspecification," Vanderbilt University Department of Economics Working Papers 0419, Vanderbilt University Department of Economics, revised Sep 2004.
    25. Guoxiang Xu & Wangfeng Gao, 2019. "Financial Risk Contagion in Stock Markets: Causality and Measurement Aspects," Sustainability, MDPI, vol. 11(5), pages 1-20, March.
    26. Wanat, Stanisław & Papież, Monika & Śmiech, Sławomir, 2014. "Causality in distribution between European stock markets and commodity prices: Using independence test based on the empirical copula," MPRA Paper 57706, University Library of Munich, Germany.
    27. Leonidas Tsiaras, 2010. "Dynamic Models of Exchange Rate Dependence Using Option Prices and Historical Returns," CREATES Research Papers 2010-35, Department of Economics and Business Economics, Aarhus University.
    28. Dominique Guegan & Jing Zhang, 2006. "Change analysis of dynamic copula for measuring dependence in multivariate financial data," Post-Print halshs-00189141, HAL.
    29. Dominique Guegan & Jing Zhang, 2009. "Pricing bivariate option under GARCH-GH model with dynamic copula: application for Chinese market," Post-Print halshs-00368336, HAL.
    30. Dean Fantazzini, 2008. "Credit Risk Management," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 12(4), pages 84-137.
    31. Campbell, Rachel A.J. & Forbes, Catherine S. & Koedijk, Kees G. & Kofman, Paul, 2008. "Increasing correlations or just fat tails?," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 287-309, March.
    32. Xu Wang & Xueyan Wu & Yingying Zhou, 2022. "Conditional Dynamic Dependence and Risk Spillover between Crude Oil Prices and Foreign Exchange Rates: New Evidence from a Dynamic Factor Copula Model," Energies, MDPI, vol. 15(14), pages 1-21, July.
    33. Dominique Guegan & Jing Zhang, 2007. "Pricing bivariate option under GARCH-GH model with dynamic copula : application for Chinese market," Post-Print halshs-00188248, HAL.
    34. Fantazzini, Dean, 2010. "Three-stage semi-parametric estimation of T-copulas: Asymptotics, finite-sample properties and computational aspects," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2562-2579, November.
    35. Robert B. Durand & Markus Junker & Alex Szimayer, 2010. "The flight‐to‐quality effect: a copula‐based analysis," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 50(2), pages 281-299, June.
    36. Chollete, Loran & Ning, Cathy, 2012. "Asymmetric Dependence in the US Economy: Application to Money and the Phillips Curve," UiS Working Papers in Economics and Finance 2012/1, University of Stavanger.
    37. Michał Majsterek & Emilia Gosińska, 2020. "Structural Change in the Deterministic and Stochastic Part of VECM. I(1) and I(2) Case," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 12(4), pages 317-345, December.
    38. Patton, Andrew J., 2012. "A review of copula models for economic time series," Journal of Multivariate Analysis, Elsevier, vol. 110(C), pages 4-18.
    39. 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).
    40. Patton, Andrew, 2013. "Copula Methods for Forecasting Multivariate Time Series," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 899-960, Elsevier.
    41. Cyril Caillault & Dominique Guegan, 2009. "Forecasting VaR and Expected Shortfall using Dynamical Systems: A Risk Management Strategy," Post-Print halshs-00375765, HAL.
    42. Wang, Kehluh & Chen, Yi-Hsuan & Huang, Szu-Wei, 2011. "The dynamic dependence between the Chinese market and other international stock markets: A time-varying copula approach," International Review of Economics & Finance, Elsevier, vol. 20(4), pages 654-664, October.

  33. Patton, Andrew J., 2002. "On the out-of-sample importance of skewness and asymetric dependence for asset allocation," LSE Research Online Documents on Economics 24951, London School of Economics and Political Science, LSE Library.

    Cited by:

    1. Chollete, Lorán & Heinen, Andreas, 2006. "Frequent Turbulence? A Dynamic Copula Approach," Discussion Papers 2006/10, Norwegian School of Economics, Department of Business and Management Science.
    2. 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.
    3. Guo, Dong & Zhou, Peng, 2021. "Green Bonds as Hedging Assets before and after COVID: A Comparative Study between the US and China," Cardiff Economics Working Papers E2021/28, Cardiff University, Cardiff Business School, Economics Section.
    4. Rand Kwong Yew Low, 2018. "Vine copulas: modelling systemic risk and enhancing higher‐moment portfolio optimisation," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(S1), pages 423-463, November.
    5. Bücher, Axel & Ruppert, Martin, 2013. "Consistent testing for a constant copula under strong mixing based on the tapered block multiplier technique," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 208-229.
    6. Haas, Markus & Mittnik, Stefan & Paolella, Marc S., 2008. "Asymmetric multivariate normal mixture GARCH," CFS Working Paper Series 2008/07, Center for Financial Studies (CFS).
    7. Ito, Kakeru & Yoshiba, Toshinao, 2025. "Dynamic asymmetric tail dependence structure among multi-asset classes for portfolio management: Dynamic skew-t copula approach," International Review of Economics & Finance, Elsevier, vol. 97(C).
    8. Thomas Conlon & John Cotter & Chenglu Jin, 2019. "Co-skewness across Return Horizons," Working Papers 201910, Geary Institute, University College Dublin.
    9. Ruenzi, Stefan & Ungeheuer, Michael & Weigert, Florian, 2020. "Joint extreme events in equity returns and liquidity and their cross-sectional pricing implications," CFR Working Papers 20-01, University of Cologne, Centre for Financial Research (CFR).
    10. Fengler, Matthias R. & Okhrin, Ostap, 2012. "Realized copula," SFB 649 Discussion Papers 2012-034, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    11. Bu, Ruijun & Jawadi, Fredj & Li, Yuyi, 2017. "An empirical comparison of transformed diffusion models for VIX and VIX futures," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 46(C), pages 116-127.
    12. Jia Xu & Longbing Cao, 2023. "Copula Variational LSTM for High-dimensional Cross-market Multivariate Dependence Modeling," Papers 2305.08778, arXiv.org.
    13. Wang, Chou-Wen & Yang, Sharon S. & Huang, Hong-Chih, 2015. "Modeling multi-country mortality dependence and its application in pricing survivor index swaps—A dynamic copula approach," Insurance: Mathematics and Economics, Elsevier, vol. 63(C), pages 30-39.
    14. Grover, Vaibhav, 2015. "Identifying Dependence Structure among Equities in Indian Markets using Copulas," MPRA Paper 66302, University Library of Munich, Germany.
    15. Tobias Fissler & Yannick Hoga, 2024. "How to Compare Copula Forecasts?," Papers 2410.04165, arXiv.org.
    16. Mario Cerrato & Danyang Li & Zhekai Zhang, 2020. "Factor Investing and forex Portfolio Management," Working Papers 2020_01, Business School - Economics, University of Glasgow.
    17. Desislava Chetalova & Marcel Wollschlager & Rudi Schafer, 2015. "Dependence structure of market states," Papers 1503.09004, arXiv.org, revised Jul 2015.
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    Cited by:

    1. Chen, Xiaohong & Fan, Yanqin, 2006. "Estimation of copula-based semiparametric time series models," Journal of Econometrics, Elsevier, vol. 130(2), pages 307-335, February.
    2. Gonzalo, Jesús & Olmo, José, 2005. "Contagion versus flight to quality in financial markets," UC3M Working papers. Economics we051810, Universidad Carlos III de Madrid. Departamento de Economía.

  35. Patton, Andrew J, 2001. "Modelling Time-Varying Exchange Rate Dependence Using the Conditional Copula," University of California at San Diego, Economics Working Paper Series qt01q7j1s2, Department of Economics, UC San Diego.

    Cited by:

    1. Markus Junker & Alexander Szimayer & Niklas Wagner, 2004. "Nonlinear Term Structure Dependence: Copula Functions, Empirics, and Risk Implications," Econometrics 0401007, University Library of Munich, Germany.
    2. Granger, Clive W. J. & Terasvirta, Timo & Patton, Andrew J., 2003. "Common factors in conditional distributions for Bivariate time series," LSE Research Online Documents on Economics 24854, London School of Economics and Political Science, LSE Library.
    3. Granger, Clive W.J. & Teräsvirta, Timo & Patton, Andrew J, 2002. "Common Factors in Conditional Distributions," University of California at San Diego, Economics Working Paper Series qt3bd1n1x5, Department of Economics, UC San Diego.
    4. Mihaela ŞErban & Anthony Brockwell & John Lehoczky & Sanjay Srivastava, 2007. "Modelling the Dynamic Dependence Structure in Multivariate Financial Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(5), pages 763-782, September.
    5. Morettin Pedro A. & Toloi Clelia M.C. & Chiann Chang & de Miranda José C.S., 2011. "Wavelet Estimation of Copulas for Time Series," Journal of Time Series Econometrics, De Gruyter, vol. 3(3), pages 1-31, October.
    6. Beatriz Vaz de Melo Mendes, 2005. "Computing Conditional VaR using Time-varying CopulasComputing Conditional VaR using Time-varying Copulas," Brazilian Review of Finance, Brazilian Society of Finance, vol. 3(2), pages 251-265.
    7. Su, EnDer, 2014. "Measuring Contagion Risk in High Volatility State between Major Banks in Taiwan by Threshold Copula GARCH Model," MPRA Paper 58161, University Library of Munich, Germany.
    8. Dominique Guegan, 2007. "La persistance dans les marchés financiers," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00179269, HAL.
    9. Youssef Elouerkhaoui, 2007. "Pricing And Hedging In A Dynamic Credit Model," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 10(04), pages 703-731.
    10. Marco Sorge, 2004. "Stress-testing financial systems: an overview of current methodologies," BIS Working Papers 165, Bank for International Settlements.
    11. Pircalabu, A. & Benth, F.E., 2017. "A regime-switching copula approach to modeling day-ahead prices in coupled electricity markets," Energy Economics, Elsevier, vol. 68(C), pages 283-302.
    12. Xiaohong Chen & Yanqin Fan, 2002. "Evaluating Density Forecasts via the Copula Approach," Vanderbilt University Department of Economics Working Papers 0225, Vanderbilt University Department of Economics, revised Sep 2003.
    13. Karmakar, Madhusudan, 2017. "Dependence structure and portfolio risk in Indian foreign exchange market: A GARCH-EVT-Copula approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 64(C), pages 275-291.
    14. 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.
    15. Sorge, Marco & Virolainen, Kimmo, 2006. "A comparative analysis of macro stress-testing methodologies with application to Finland," Journal of Financial Stability, Elsevier, vol. 2(2), pages 113-151, June.
    16. Ehouman, Yao Axel, 2021. "Dependence structure between oil price volatility and sovereign credit risk of oil exporters: Evidence using a copula approach," International Economics, Elsevier, vol. 168(C), pages 76-97.
    17. Andrew J. Patton, 2004. "On the Out-of-Sample Importance of Skewness and Asymmetric Dependence for Asset Allocation," Journal of Financial Econometrics, Oxford University Press, vol. 2(1), pages 130-168.
    18. Fermanian, Jean-David & Scaillet, Olivier, 2003. "Nonparametric estimation of copulas for time series," Working Papers unige:41797, University of Geneva, Geneva School of Economics and Management.
    19. Han, Yingying & Gong, Pu & Zhou, Xiang, 2016. "Correlations and risk contagion between mixed assets and mixed-asset portfolio VaR measurements in a dynamic view: An application based on time varying copula models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 940-953.
    20. Jean-David Fermanian, 2003. "Goodness of Fit Tests for Copulas," Working Papers 2003-34, Center for Research in Economics and Statistics.
    21. Araichi, Sawssen & Peretti, Christian de & Belkacem, Lotfi, 2017. "Reserve modelling and the aggregation of risks using time varying copula models," Economic Modelling, Elsevier, vol. 67(C), pages 149-158.
    22. EnDer Su, 2017. "Measuring and Testing Tail Dependence and Contagion Risk Between Major Stock Markets," Computational Economics, Springer;Society for Computational Economics, vol. 50(2), pages 325-351, August.
    23. Wenming Shi & Kevin X. Li & Zhongzhi Yang & Ganggang Wang, 2017. "Time-varying copula models in the shipping derivatives market," Empirical Economics, Springer, vol. 53(3), pages 1039-1058, November.
    24. Zhu, Xiaoqian & Xie, Yongjia & Li, Jianping & Wu, Dengsheng, 2015. "Change point detection for subprime crisis in American banking: From the perspective of risk dependence," International Review of Economics & Finance, Elsevier, vol. 38(C), pages 18-28.
    25. Yali Dou & Haiyan Liu & Georgios Aivaliotis, 2019. "Dynamic Dependence Modeling in financial time series," Papers 1908.05130, arXiv.org.
    26. Jacek Leskow & Justyna Mokrzycka & Krzysztof Krawiec, 2011. "Modeling Stock Market Indexes With Copula Functions," "e-Finanse", University of Information Technology and Management, Institute of Financial Research and Analysis, vol. 7(2), pages 1-16, August.
    27. Alexander Knyazev & Oleg Lepekhin & Arkady Shemyakin, 2016. "Joint distribution of stock indices: Methodological aspects of construction and selection of copula models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 42, pages 30-53.
    28. 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.
    29. Kim, Jong-Min & Jung, Hojin, 2017. "Can asymmetric conditional volatility imply asymmetric tail dependence?," Economic Modelling, Elsevier, vol. 64(C), pages 409-418.
    30. Beatriz V. M. Mendes & Eduardo F. L. De Melo, 2010. "Local Estimation Of Dynamic Copula Models," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 13(02), pages 241-258.
    31. Mirza Nazmul Hasan & Roel Braekers, 2022. "Modelling the association in bivariate survival data by using a Bernstein copula," Computational Statistics, Springer, vol. 37(2), pages 781-815, April.
    32. Charles, Don & McLean, Sheldon & Rajkumar, Antonio, 2021. "Navigating transfer pricing risk in the oil and gas sector: Essential elements of a policy framework for Trinidad and Tobago and Guyana," Studies and Perspectives – ECLAC Subregional Headquarters for The Caribbean 46813, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    33. Hu, Haiqing & Chen, Di & Sui, Bo & Zhang, Lang & Wang, Yinyin, 2020. "Price volatility spillovers between supply chain and innovation of financial pledges in China," Economic Modelling, Elsevier, vol. 89(C), pages 397-413.
    34. Alqahtani, Abdullah & Klein, Tony & Khalid, Ali, 2019. "The impact of oil price uncertainty on GCC stock markets," Resources Policy, Elsevier, vol. 64(C).
    35. Paul Doukhan & Jean-David Fermanian & Gabriel Lang, 2005. "Copulas of a Vector-Valued Stationary Weakly Dependent Process," Working Papers 2005-48, Center for Research in Economics and Statistics.
    36. Fortin, Ines & Kuzmics, Christoph, 2002. "Tail-Dependence in Stock-Return Pairs," Economics Series 126, Institute for Advanced Studies.
    37. Mukhtar A. Kassem & Afiqah R. Radzi & Asankha Pradeep & Mohammed Algahtany & Rahimi A. Rahman, 2023. "Impacts and Response Strategies of the COVID-19 Pandemic on the Construction Industry Using Structural Equation Modeling," Sustainability, MDPI, vol. 15(3), pages 1-24, February.
    38. R. Ballini & A. R. R. Mendonça & F. Gomide, 2009. "Evolving fuzzy modelling in risk analysis," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 16(1‐2), pages 71-86, January.
    39. Bodnar, Taras & Hautsch, Nikolaus, 2013. "Copula-based dynamic conditional correlation multiplicative error processes," CFS Working Paper Series 2013/19, Center for Financial Studies (CFS).
    40. Donald Lien & Chongfeng Wu & Li Yang & Chunyang Zhou, 2013. "Dynamic and Asymmetric Dependences Between Chinese Yuan and Other Asia‐Pacific Currencies," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 33(8), pages 696-723, August.
    41. Han, Liyan & Zhou, Yimin & Yin, Libo, 2015. "Exogenous impacts on the links between energy and agricultural commodity markets," Energy Economics, Elsevier, vol. 49(C), pages 350-358.
    42. Bielecki, Tomasz R. & Cousin, Areski & Crépey, Stéphane & Herbertsson, Alexander, 2011. "Dynamic Hedging of Portfolio Credit Risk in a Markov Copula Model (Previous title: Dynamic Modeling of Portfolio Credit Risk with Common Shocks)," Working Papers in Economics 502, University of Gothenburg, Department of Economics, revised 12 Oct 2012.
    43. Yao Axel Ehouman, 2021. "Dependence structure between oil price volatility and sovereign credit risk of oil exporters : Evidence using a Copula Approach," Post-Print hal-03348410, HAL.
    44. Zhou, Wei & Chen, Yan & Chen, Jin, 2024. "Dynamic volatility spillover and market emergency: Matching and forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 71(C).
    45. Beatriz de la Flor & Javier Ojea-Ferreiro & Eva Ferreira, 2022. "The Hedging Cost of Forgetting the Exchange Rate," Documentos de Trabajo del ICAE 2022-01, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    46. Fermanian, Jean-David, 2005. "Goodness-of-fit tests for copulas," Journal of Multivariate Analysis, Elsevier, vol. 95(1), pages 119-152, July.
    47. Arturo Lorenzo Valdés & Ricardo Massa Roldán, 2013. "Measuring dependence in financial crisis: A copula approach for Mexico and Brazil," Economía Mexicana NUEVA ÉPOCA, CIDE, División de Economía, vol. 0(2), pages 341-355, July-Dece.
    48. Duy Duong & Toan Luu Duc Huynh, 2020. "Tail dependence in emerging ASEAN-6 equity markets: empirical evidence from quantitative approaches," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-26, December.
    49. Yao Axel Ehouman, 2020. "Dependence structure between oil price volatility and sovereign credit risk of oil exporters: Evidence using a Copula Approach," EconomiX Working Papers 2020-31, University of Paris Nanterre, EconomiX.
    50. EnDer Su, 2018. "Measuring contagion risk in high volatility state among Taiwanese major banks," Risk Management, Palgrave Macmillan, vol. 20(3), pages 185-241, August.
    51. Gonzalo, Jesús & Olmo, José, 2005. "Contagion versus flight to quality in financial markets," UC3M Working papers. Economics we051810, Universidad Carlos III de Madrid. Departamento de Economía.
    52. Patton, Andrew J, 2001. "Estimation of Copula Models for Time Series of Possibly Different Length," University of California at San Diego, Economics Working Paper Series qt3fc1c8hw, Department of Economics, UC San Diego.

  36. Patton, Andrew J, 2001. "Estimation of Copula Models for Time Series of Possibly Different Length," University of California at San Diego, Economics Working Paper Series qt3fc1c8hw, Department of Economics, UC San Diego.

    Cited by:

    1. Sulin Pang & Jinwang Xiao & Shuqing Li, 2015. "Pricing method and applications for the farmer's joint liability based on intensity model and Monte Carlo simulation," Journal of Financial Engineering (JFE), World Scientific Publishing Co. Pte. Ltd., vol. 2(01), pages 1-21.
    2. Granger, Clive W. J. & Terasvirta, Timo & Patton, Andrew J., 2003. "Common factors in conditional distributions for Bivariate time series," LSE Research Online Documents on Economics 24854, London School of Economics and Political Science, LSE Library.
    3. Granger, Clive W.J. & Teräsvirta, Timo & Patton, Andrew J, 2002. "Common Factors in Conditional Distributions," University of California at San Diego, Economics Working Paper Series qt3bd1n1x5, Department of Economics, UC San Diego.
    4. Morettin Pedro A. & Toloi Clelia M.C. & Chiann Chang & de Miranda José C.S., 2011. "Wavelet Estimation of Copulas for Time Series," Journal of Time Series Econometrics, De Gruyter, vol. 3(3), pages 1-31, October.
    5. Xiaohui Chen & Lin Zhang & Ze Zhang, 2020. "An integrated model for maintenance policies and production scheduling based on immune–culture algorithm," Journal of Risk and Reliability, , vol. 234(5), pages 651-663, October.
    6. Yannick Malevergne & Didier Sornette, 2003. "Testing the Gaussian copula hypothesis for financial assets dependences," Post-Print hal-00520539, HAL.
    7. Fermanian, Jean-David & Scaillet, Olivier, 2003. "Nonparametric estimation of copulas for time series," Working Papers unige:41797, University of Geneva, Geneva School of Economics and Management.
    8. Eric Jondeau & Michael Rockinger, 2002. "Conditional Dependency of Financial Series: The Copula-GARCH Model," FAME Research Paper Series rp69, International Center for Financial Asset Management and Engineering.
    9. Jean-David Fermanian, 2003. "Goodness of Fit Tests for Copulas," Working Papers 2003-34, Center for Research in Economics and Statistics.
    10. Alqahtani, Abdullah & Klein, Tony & Khalid, Ali, 2019. "The impact of oil price uncertainty on GCC stock markets," Resources Policy, Elsevier, vol. 64(C).
    11. Rémy Chicheportiche & Jean-Philippe Bouchaud, 2012. "The Joint Distribution Of Stock Returns Is Not Elliptical," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 15(03), pages 1-23.
    12. Y. Malevergne & D. Sornette, 2002. "Investigating Extreme Dependences: Concepts and Tools," Papers cond-mat/0203166, arXiv.org.
    13. Söehnke Bartram & Stephen Taylor & Yaw-Huei Wang, 2004. "The Euro and European Financial Market Integration," Money Macro and Finance (MMF) Research Group Conference 2004 49, Money Macro and Finance Research Group, revised 13 Oct 2004.
    14. Fermanian, Jean-David, 2005. "Goodness-of-fit tests for copulas," Journal of Multivariate Analysis, Elsevier, vol. 95(1), pages 119-152, July.
    15. Y. Malevergne & D. Sornette, 2002. "Tail Dependence of Factor Models," Papers cond-mat/0202356, arXiv.org.

  37. Engle, Robert F & Patton, Andrew J, 2000. "Impacts of Trades in an Error-Correction Model of Quote Prices," University of California at San Diego, Economics Working Paper Series qt6dm6093f, Department of Economics, UC San Diego.

    Cited by:

    1. Foucault, Thierry & Kandel, Eugene & Kadan, Ohad, 2001. "Limit Order Book as a Market for Liquidity," CEPR Discussion Papers 2889, C.E.P.R. Discussion Papers.
    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. Luintel, Kul B & Xu, Yongdeng, 2013. "Testing weak exogeneity in multiplicative error models," Cardiff Economics Working Papers E2013/6, Cardiff University, Cardiff Business School, Economics Section.
    4. Sabrina Buti & Barbara Rindi & Ingrid M. Werner, 2011. "Dark Pool Trading Strategies," Working Papers 421, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    5. Takatoshi Ito & Yuko Hashimoto, 2006. "Intra-Day Seasonality in Activities of the Foreign Exchange Markets: Evidence From the Electronic Broking System," NBER Working Papers 12413, National Bureau of Economic Research, Inc.
    6. 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.
    7. Frino, Alex & Jarnecic, Elvis & Lepone, Andrew, 2009. "An event time study of the price reaction to large retail trades," The Quarterly Review of Economics and Finance, Elsevier, vol. 49(2), pages 617-632, May.
    8. Fleming, Michael J. & Mizrach, Bruce & Nguyen, Giang, 2018. "The microstructure of a U.S. Treasury ECN: The BrokerTec platform," Journal of Financial Markets, Elsevier, vol. 40(C), pages 2-22.
    9. Schotman, Peter C & Frijns, Bart, 2004. "Price Discovery in Tick Time," CEPR Discussion Papers 4456, C.E.P.R. Discussion Papers.
    10. 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).
    11. Ferriani, Fabrizio, 2010. "Informed and uninformed traders at work: evidence from the French market," MPRA Paper 24487, University Library of Munich, Germany.
    12. Groß-Klußmann, Axel & Hautsch, Nikolaus, 2011. "Predicting bid-ask spreads using long memory autoregressive conditional poisson models," SFB 649 Discussion Papers 2011-044, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    13. 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.
    14. Hautsch, Nikolaus & Huang, Ruihong, 2009. "The market impact of a limit order," CFS Working Paper Series 2009/23, Center for Financial Studies (CFS).
    15. Nowak, Sylwia & Anderson, Heather M., 2014. "How does public information affect the frequency of trading in airline stocks?," Journal of Banking & Finance, Elsevier, vol. 44(C), pages 26-38.
    16. Yiuman Tse & Brian C. McTier & John K. Wald, 2011. "Do Stock Markets Catch the Flu? We examine the impact of influenza on the U.S. stock market. A higher incidence of flu is associated with decreased trading, decreased volatility, and higher bid-ask sp," Working Papers 0004, College of Business, University of Texas at San Antonio.
    17. Chen, Yu-Lun & Gau, Yin-Feng, 2014. "Asymmetric responses of ask and bid quotes to information in the foreign exchange market," Journal of Banking & Finance, Elsevier, vol. 38(C), pages 194-204.
    18. Frino, Alex & Jarnecic, Elvis & Johnstone, David & Lepone, Andrew, 2005. "Bid-ask bounce and the measurement of price behavior around block trades on the Australian Stock Exchange," Pacific-Basin Finance Journal, Elsevier, vol. 13(3), pages 247-262, June.
    19. Härdle, Wolfgang Karl & Hautsch, Nikolaus & Mihoci, Andrija, 2012. "Modelling and forecasting liquidity supply using semiparametric factor dynamics," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 610-625.
    20. Frijns, Bart & Schotman, Peter C., 2006. "Nonlinear dynamics in Nasdaq dealer quotes," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2246-2266, December.
    21. Menkhoff, Lukas & Schmeling, Maik, 2010. "Whose trades convey information? Evidence from a cross-section of traders," Journal of Financial Markets, Elsevier, vol. 13(1), pages 101-128, February.
    22. Yiuman Tse & Brian C. McTier & John K. Wald, 2011. "Do Stock Markets Catch the Flu? We examine the impact of influenza on the U.S. stock market. A higher incidence of flu is associated with decreased trading, decreased volatility, and higher bid-ask sp," Working Papers 0004, College of Business, University of Texas at San Antonio.
    23. Chor-yiu SIN, 2004. "Estimation and Testing for Partially Nonstationary Vector Autoregressive Models with GARCH: WLS versus QMLE," Econometric Society 2004 North American Summer Meetings 476, Econometric Society.
    24. Chen, Tao & Li, Jie & Cai, Jun, 2008. "Information content of inter-trade time on the Chinese market," Emerging Markets Review, Elsevier, vol. 9(3), pages 174-193, September.
    25. Chakravarty, Sugato & Harris, Fredreck H. deB. & Wood, Roger A., 2001. "Do Bid-Ask Spreads or Bid and Ask Depths Convey New Information First?," Purdue University Economics Working Papers 1149, Purdue University, Department of Economics.
    26. Yang, Joey Wenling, 2011. "Transaction duration and asymmetric price impact of trades--Evidence from Australia," Journal of Empirical Finance, Elsevier, vol. 18(1), pages 91-102, January.
    27. 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.
    28. Andersson, Jonas & Moberg, Jan-Magnus, 2007. "Structural breaks in point processes: With an application to reporting delays for trades on the New York stock exchange," Discussion Papers 2007/28, Norwegian School of Economics, Department of Business and Management Science.
    29. Hagströmer, Björn & Anderson, Richard G. & Binner, Jane & Nilsson, Birger, 2009. "Dynamics in Systematic Liquidity," Working Papers 2009:7, Lund University, Department of Economics.
    30. Cebiroglu, Gökhan & Hautsch, Nikolaus & Horst, Ulrich, 2014. "Order exposure and liquidity coordination: Does hidden liquidity harm price efficiency?," CFS Working Paper Series 468, Center for Financial Studies (CFS).
    31. 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.
    32. Andros Gregoriou, 2007. "The Asymmetry of the Price Impact of Block Trades and the Bid-Ask Spread. Evidence from the London Stock Exchange," Money Macro and Finance (MMF) Research Group Conference 2006 76, Money Macro and Finance Research Group.
    33. Danielsson, Jon & Love, Ryan, 2004. "Feedback trading," LSE Research Online Documents on Economics 24760, London School of Economics and Political Science, LSE Library.
    34. B. Mizrach, 2006. "Does SIZE matter? Liquidity Provision by the Nasdaq Anonymous Trading Facility," Competition and Regulation in Network Industries, Intersentia, vol. 7(4), pages 471-486, December.
    35. 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).
    36. Pascual, Roberto & Pascual-Fuster, Bartolomé, 2014. "The relative contribution of ask and bid quotes to price discovery," Journal of Financial Markets, Elsevier, vol. 20(C), pages 129-150.
    37. Erindi Allaj, 2017. "Implicit Transaction Costs And The Fundamental Theorems Of Asset Pricing," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 20(04), pages 1-39, June.
    38. Jing Nie, 2019. "High‐Frequency Price Discovery and Price Efficiency on Interest Rate Futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(11), pages 1394-1434, November.
    39. Alvaro Escribano & Roberto Pascual, 2008. "Asymmetries in bid and ask responses to innovations in the trading process," Studies in Empirical Economics, in: Luc Bauwens & Winfried Pohlmeier & David Veredas (ed.), High Frequency Financial Econometrics, pages 49-82, Springer.
    40. Jahanshahloo, Hossein & Spokeviciute, Laima, 2021. "Time weighted price contribution," Finance Research Letters, Elsevier, vol. 43(C).
    41. Martin D. Gould & Mason A. Porter & Stacy Williams & Mark McDonald & Daniel J. Fenn & Sam D. Howison, 2013. "Limit order books," Quantitative Finance, Taylor & Francis Journals, vol. 13(11), pages 1709-1742, November.
    42. Engle, Robert F & Patton, Andrew J, 2000. "Impacts of Trades in an Error-Correction Model of Quote Prices," University of California at San Diego, Economics Working Paper Series qt6dm6093f, Department of Economics, UC San Diego.
    43. Mircea BAHNA & Cosmin-Octavian CEPOI & Bogdan Andrei DUMITRESCU & Virgil DAMIAN, 2018. "Estimating the Price Impact of Market Orders on the Bucharest Stock Exchange," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 120-133, December.
    44. Tejas Ramdas & Martin T. Wells, 2024. "Bellwether Trades: Characteristics of Trades influential in Predicting Future Price Movements in Markets," Papers 2409.05192, arXiv.org.
    45. Edson VENGESAI & Adefemi A. OBALADE & Paul-Francois MUZINDUTSI, 2021. "Country Risk Dynamics and Stock Market Volatility: Evidence from the JSE Cross-Sector Analysis," Journal of Economics and Financial Analysis, Tripal Publishing House, vol. 5(2), pages 63-84.
    46. Lo, Ingrid & Sapp, Stephen G., 2010. "Order aggressiveness and quantity: How are they determined in a limit order market?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 20(3), pages 213-237, July.
    47. Chen, Yu-Lun & Gau, Yin-Feng, 2015. "Foreign exchange market intervention and price discovery," Journal of the Japanese and International Economies, Elsevier, vol. 38(C), pages 214-227.
    48. Cebiroglu, Gökhan & Hautsch, Nikolaus & Walsh, Christopher, 2019. "Revisiting the stealth trading hypothesis: Does time-varying liquidity explain the size-effect?," CFS Working Paper Series 625, Center for Financial Studies (CFS).
    49. Martin D. Gould & Mason A. Porter & Stacy Williams & Mark McDonald & Daniel J. Fenn & Sam D. Howison, 2010. "Limit Order Books," Papers 1012.0349, arXiv.org, revised Apr 2013.
    50. Lo, Danny K. & Hall, Anthony D., 2015. "Resiliency of the limit order book," Journal of Economic Dynamics and Control, Elsevier, vol. 61(C), pages 222-244.
    51. Wuyts, Gunther, 2008. "The impact of liquidity shocks through the limit order book," CFS Working Paper Series 2008/53, Center for Financial Studies (CFS).
    52. Bruce Mizrach, 2008. "The next tick on Nasdaq," Quantitative Finance, Taylor & Francis Journals, vol. 8(1), pages 19-40.
    53. Pascual, Roberto & Escribano, Alvaro & Tapia, Mikel, 2004. "Adverse selection costs, trading activity and price discovery in the NYSE: An empirical analysis," Journal of Banking & Finance, Elsevier, vol. 28(1), pages 107-128, January.
    54. PASCUAL, Roberto & VEREDAS, David, 2006. "Does the open limit order book matter in explaining long run volatility ?," LIDAM Discussion Papers CORE 2006110, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    55. Zhang, Sijia & Gregoriou, Andros, 2019. "The price behavior around initial loan announcements: Evidence from zero-leverage firms in the UK," Research in International Business and Finance, Elsevier, vol. 50(C), pages 191-200.
    56. Loderer, Claudio & Roth, Lukas, 2005. "The pricing discount for limited liquidity: evidence from SWX Swiss Exchange and the Nasdaq," Journal of Empirical Finance, Elsevier, vol. 12(2), pages 239-268, March.
    57. Sylwia Nowak, 2008. "How Do Public Announcements Affect The Frequency Of Trading In U.S. Airline Stocks?," CAMA Working Papers 2008-38, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    58. Zhang, Sijia & Gregoriou, Andros & Wu, He, 2024. "Asymmetric post earnings announcement drift and order flow imbalance: The impact on stock market returns," International Review of Financial Analysis, Elsevier, vol. 94(C).
    59. Chiyachantana, Chiraphol & Jain, Pankaj K. & Jiang, Christine & Sharma, Vivek, 2017. "Permanent price impact asymmetry of trades with institutional constraints," Journal of Financial Markets, Elsevier, vol. 36(C), pages 1-16.
    60. Paola Arce & Jonathan Antognini & Werner Kristjanpoller & Luis Salinas, 2019. "Fast and Adaptive Cointegration Based Model for Forecasting High Frequency Financial Time Series," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 99-112, June.
    61. Danny Lo, 2015. "Essays in Market Microstructure and Investor Trading," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 4-2015, January-A.
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    64. Daniel Havran & Kata Varadi, 2015. "Price Impact and the Recovery of the Limit Order Book: Why Should We Care About Informed Liquidity Providers?," CERS-IE WORKING PAPERS 1540, Institute of Economics, Centre for Economic and Regional Studies.
    65. Carlos Jorge Lenczewski Martins, 2019. "Market and limit orders and their role in the price discovery process," Bank i Kredyt, Narodowy Bank Polski, vol. 50(6), pages 551-570.
    66. Pham, Manh Cuong & Anderson, Heather Margot & Duong, Huu Nhan & Lajbcygier, Paul, 2020. "The effects of trade size and market depth on immediate price impact in a limit order book market," Journal of Economic Dynamics and Control, Elsevier, vol. 120(C).
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    3. Aristide Houndetoungan & Abdoul Haki Maoude, 2024. "Inference for Two-Stage Extremum Estimators," THEMA Working Papers 2024-01, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.

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  3. Peter Horvath & Jia Li & Zhipeng Liao & Andrew J. Patton, 2022. "A consistent specification test for dynamic quantile models," Quantitative Economics, Econometric Society, vol. 13(1), pages 125-151, January.

    Cited by:

    1. Sullivan Hu'e & Christophe Hurlin & Yang Lu, 2024. "Backtesting Expected Shortfall: Accounting for both duration and severity with bivariate orthogonal polynomials," Papers 2405.02012, arXiv.org, revised May 2024.
    2. Christis Katsouris, 2023. "Quantile Time Series Regression Models Revisited," Papers 2308.06617, arXiv.org, revised Aug 2023.
    3. Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2024. "Reprint of: Out-of-sample tests for conditional quantile coverage: An application to Growth-at-Risk," Journal of Econometrics, Elsevier, vol. 244(2).
    4. Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2023. "Out-of-sample tests for conditional quantile coverage an application to Growth-at-Risk," Journal of Econometrics, Elsevier, vol. 236(2).

  4. Andrew J Patton & Brian M Weller, 2022. "Risk Price Variation: The Missing Half of Empirical Asset Pricing," The Review of Financial Studies, Society for Financial Studies, vol. 35(11), pages 5127-5184.

    Cited by:

    1. Cong, Lin William & Feng, Guanhao & He, Jingyu & He, Xin, 2025. "Growing the efficient frontier on panel trees," Journal of Financial Economics, Elsevier, vol. 167(C).
    2. Oh, Dong Hwan & Patton, Andrew J., 2023. "Dynamic factor copula models with estimated cluster assignments," Journal of Econometrics, Elsevier, vol. 237(2).
    3. Yuan Liao & Xinjie Ma & Andreas Neuhierl & Linda Schilling, 2025. "The Uncertainty of Machine Learning Predictions in Asset Pricing," Papers 2503.00549, arXiv.org.
    4. Gruenthaler, Thomas & Lorenz, Friedrich & Meyerhof, Paul, 2022. "Option-based intermediary leverage," Journal of Banking & Finance, Elsevier, vol. 145(C).
    5. Lin William Cong & Guanhao Feng & Jingyu He & Xin He, 2022. "Growing the Efficient Frontier on Panel Trees," NBER Working Papers 30805, National Bureau of Economic Research, Inc.

  5. Sander Barendse & Andrew J. Patton, 2022. "Comparing Predictive Accuracy in the Presence of a Loss Function Shape Parameter," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1057-1069, June.
    See citations under working paper version above.
  6. Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2022. "Realized semibetas: Disentangling “good” and “bad” downside risks," Journal of Financial Economics, Elsevier, vol. 144(1), pages 227-246.

    Cited by:

    1. Chen, Yan & Liu, Yakun & Zhang, Feipeng, 2024. "Coskewness and the short-term predictability for Bitcoin return," Technological Forecasting and Social Change, Elsevier, vol. 200(C).

  7. Tim Bollerslev & Jia Li & Andrew J. Patton & Rogier Quaedvlieg, 2020. "Realized Semicovariances," Econometrica, Econometric Society, vol. 88(4), pages 1515-1551, July.

    Cited by:

    1. Boudt, Kris & Dragun, Kirill & Sauri, Orimar & Vanduffel, Steven, 2023. "ETF Basket-Adjusted Covariance estimation," Journal of Econometrics, Elsevier, vol. 235(2), pages 1144-1171.
    2. Christis Katsouris, 2021. "Optimal Portfolio Choice and Stock Centrality for Tail Risk Events," Papers 2112.12031, arXiv.org.
    3. Bo Yu & Dayong Zhang & Qiang Ji, 2025. "Forecasting portfolio variance: a new decomposition approach," Annals of Operations Research, Springer, vol. 348(1), pages 543-578, May.
    4. 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.
    5. Li, Dan & Drovandi, Christopher & Clements, Adam, 2024. "Outlier-robust methods for forecasting realized covariance matrices," International Journal of Forecasting, Elsevier, vol. 40(1), pages 392-408.
    6. Chanatásig-Niza, Evelyn & Ciarreta, Aitor & Zarraga, Ainhoa, 2022. "A volatility spillover analysis with realized semi(co)variances in Australian electricity markets," Energy Economics, Elsevier, vol. 111(C).
    7. Kirill Dragun & Kris Boudt & Orimar Sauri & Steven Vanduffel, 2021. "Beta-Adjusted Covariance Estimation," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 21/1010, Ghent University, Faculty of Economics and Business Administration.
    8. Li, Yuan & Pakkanen, Mikko S. & Veraart, Almut E.D., 2023. "Limit theorems for the realised semicovariances of multivariate Brownian semistationary processes," Stochastic Processes and their Applications, Elsevier, vol. 155(C), pages 202-231.
    9. Bollerslev, Tim & Patton, Andrew J. & Zhang, Haozhe, 2022. "Equity clusters through the lens of realized semicorrelations," Economics Letters, Elsevier, vol. 211(C).
    10. Mahmood, Faisal & Ben Zaied, Younes & Abedin, Mohammad Zoynul, 2024. "Role of green finance instruments in shaping economic cycles," Technological Forecasting and Social Change, Elsevier, vol. 209(C).
    11. Maki, Daiki, 2024. "Evaluation of volatility spillovers for asymmetric realized covariance," The North American Journal of Economics and Finance, Elsevier, vol. 73(C).
    12. Cavaliere, Giuseppe & Mikosch, Thomas & Rahbek, Anders & Vilandt, Frederik, 2024. "Tail behavior of ACD models and consequences for likelihood-based estimation," Journal of Econometrics, Elsevier, vol. 238(2).
    13. G.M. Gallo & O. Okhrin & G. Storti, 2024. "Dynamic tail risk forecasting: what do realized skewness and kurtosis add?," Working Paper CRENoS 202416, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    14. 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).
    15. Asgar Ali & K. N. Badhani, 2023. "Downside risk matters once the lottery effect is controlled: explaining risk–return relationship in the Indian equity market," Journal of Asset Management, Palgrave Macmillan, vol. 24(1), pages 27-43, February.
    16. Andrea Bucci & Giulio Palomba & Eduardo Rossi, 2019. "Does macroeconomics help in predicting stock markets volatility comovements? A nonlinear approach," Working Papers 440, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    17. Zhang, Hanyu & Dufour, Alfonso, 2024. "Managing portfolio risk during crisis times: A dynamic conditional correlation perspective," The Quarterly Review of Economics and Finance, Elsevier, vol. 94(C), pages 241-251.
    18. Nguyen, Minh Nhat & Liu, Ruipeng & Li, Youwei, 2025. "Performance of energy ETFs and climate risks," Energy Economics, Elsevier, vol. 141(C).
    19. Izzeldin, Marwan & Muradoğlu, Yaz Gülnur & Pappas, Vasileios & Petropoulou, Athina & Sivaprasad, Sheeja, 2023. "The impact of the Russian-Ukrainian war on global financial markets," International Review of Financial Analysis, Elsevier, vol. 87(C).
    20. Niu, Zibo & Demirer, Riza & Suleman, Muhammad Tahir & Zhang, Hongwei & Zhu, Xuehong, 2024. "Do industries predict stock market volatility? Evidence from machine learning models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 90(C).
    21. Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2022. "Realized semibetas: Disentangling “good” and “bad” downside risks," Journal of Financial Economics, Elsevier, vol. 144(1), pages 227-246.
    22. Neil Shephard, 2020. "An estimator for predictive regression: reliable inference for financial economics," Papers 2008.06130, arXiv.org.
    23. Yu‐Sheng Lai, 2023. "Optimal futures hedging by using realized semicovariances: The information contained in signed high‐frequency returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(5), pages 677-701, May.
    24. Ma, Feng & Zhang, Yaojie & Huang, Dengshi & Lai, Xiaodong, 2018. "Forecasting oil futures price volatility: New evidence from realized range-based volatility," Energy Economics, Elsevier, vol. 75(C), pages 400-409.
    25. Amaya, Diego & Herrerias, Renata & Perez, Fernando & Vasquez, Aurelio, 2023. "Realized semibetas and international stock return predictability," Finance Research Letters, Elsevier, vol. 58(PC).

  8. 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.

    Cited by:

    1. Asai Manabu & So Mike K. P., 2023. "Realized BEKK-CAW Models," Journal of Time Series Econometrics, De Gruyter, vol. 15(1), pages 49-77, January.
    2. Marius Matei & Xari Rovira & Núria Agell, 2019. "Bivariate Volatility Modeling with High-Frequency Data," Econometrics, MDPI, vol. 7(3), pages 1-15, September.
    3. Jiti Gao & Bin Peng & Wei Biao Wu & Yayi Yan, 2022. "Time-Varying Multivariate Causal Processes," Papers 2206.00409, arXiv.org.
    4. Bauwens, Luc & Dzuverovic, Emilija & Hafner, Christian, 2024. "Asymmetric Models for Realized Covariances," LIDAM Discussion Papers ISBA 2024022, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. 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.
    6. 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.
    7. Qu, Hui & Zhang, Yi, 2022. "Asymmetric multivariate HAR models for realized covariance matrix: A study based on volatility timing strategies," Economic Modelling, Elsevier, vol. 106(C).
    8. Paolo Gorgi & Siem Jan Koopman, 2020. "Beta observation-driven models with exogenous regressors: a joint analysis of realized correlation and leverage effects," Tinbergen Institute Discussion Papers 20-004/III, Tinbergen Institute.
    9. Maki, Daiki, 2024. "Evaluation of volatility spillovers for asymmetric realized covariance," The North American Journal of Economics and Finance, Elsevier, vol. 73(C).
    10. Dey, Asim K. & Hoque, G.M. Toufiqul & Das, Kumer P. & Panovska, Irina, 2022. "Impacts of COVID-19 local spread and Google search trend on the US stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    11. Manabu Asai & Mike K. P. So, 2021. "Quasi‐maximum likelihood estimation of conditional autoregressive Wishart models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(3), pages 271-294, May.
    12. Tim Bollerslev & Jia Li & Andrew J. Patton & Rogier Quaedvlieg, 2020. "Realized Semicovariances," Econometrica, Econometric Society, vol. 88(4), pages 1515-1551, July.
    13. Alessio Brini & Jimmie Lenz, 2024. "A comparison of cryptocurrency volatility-benchmarking new and mature asset classes," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-38, December.
    14. Alessio Brini & Jimmie Lenz, 2024. "A Comparison of Cryptocurrency Volatility-benchmarking New and Mature Asset Classes," Papers 2404.04962, arXiv.org.
    15. Zhang, Hongwei & Wang, Wentao & Niu, Zibo, 2024. "Geopolitical risks and crude oil futures volatility: Evidence from machine learning," Resources Policy, Elsevier, vol. 98(C).
    16. Niu, Zibo & Demirer, Riza & Suleman, Muhammad Tahir & Zhang, Hongwei & Zhu, Xuehong, 2024. "Do industries predict stock market volatility? Evidence from machine learning models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 90(C).
    17. Anke D. Leroux & Vance L. Martin & Kathryn A. St. John, 2022. "Modeling time varying risk of natural resource assets: Implications of climate change," Quantitative Economics, Econometric Society, vol. 13(1), pages 225-257, January.
    18. Yu‐Sheng Lai, 2023. "Optimal futures hedging by using realized semicovariances: The information contained in signed high‐frequency returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(5), pages 677-701, May.

  9. Patton, Andrew J. & Weller, Brian M., 2020. "What you see is not what you get: The costs of trading market anomalies," Journal of Financial Economics, Elsevier, vol. 137(2), pages 515-549.

    Cited by:

    1. Jules Van Binsbergen & Jungsuk Han & Hongxun Ruan & Ran Xing, 2024. "A Horizon‐Based Decomposition of Mutual Fund Value Added Using Transactions," Journal of Finance, American Finance Association, vol. 79(3), pages 1831-1882, June.
    2. Bing Xiao, 2023. "The Size Effect and the Value Effect in the American Stock Market," Post-Print hal-04194510, HAL.
    3. Mauro Bernardi & Daniele Bianchi & Nicolas Bianco, 2022. "Smoothing volatility targeting," Papers 2212.07288, arXiv.org.
    4. Duchin, Ran & Martin, Xiumin & Michaely, Roni & Wang, Hanmeng, 2022. "Concierge treatment from banks: Evidence from the paycheck protection program," Journal of Corporate Finance, Elsevier, vol. 72(C).
    5. Söhnke M. Bartram & Harald Lohre & Peter F. Pope & Ananthalakshmi Ranganathan, 2021. "Navigating the factor zoo around the world: an institutional investor perspective," Journal of Business Economics, Springer, vol. 91(5), pages 655-703, July.
    6. Peress, Joël & Dong, Xi & KANG, NAMHO, 2020. "Fast and Slow Arbitrage: Fund Flows and Mispricing in the Frequency Domain," CEPR Discussion Papers 15235, C.E.P.R. Discussion Papers.
    7. Hoang, Khoa & Huang, Ronghong & Truong, Helen, 2023. "Resurrecting the market factor: A case of data mining across international markets," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).
    8. Kaplanski, Guy, 2023. "The race to exploit anomalies and the cost of slow trading," Journal of Financial Markets, Elsevier, vol. 62(C).
    9. Shynkevich, Andrei, 2025. "Wine market efficiency: Is glass half full or half empty?," International Review of Economics & Finance, Elsevier, vol. 98(C).
    10. Pätäri, Eero & Ahmed, Sheraz & Luukka, Pasi & Yeomans, Julian Scott, 2023. "Can monthly-return rank order reveal a hidden dimension of momentum? The post-cost evidence from the U.S. stock markets," The North American Journal of Economics and Finance, Elsevier, vol. 65(C).
    11. Kan, Raymond & Wang, Xiaolu & Zheng, Xinghua, 2024. "In-sample and out-of-sample Sharpe ratios of multi-factor asset pricing models," Journal of Financial Economics, Elsevier, vol. 155(C).
    12. Ardia, David & Guidotti, Emanuele & Kroencke, Tim A., 2024. "Efficient estimation of bid–ask spreads from open, high, low, and close prices," Journal of Financial Economics, Elsevier, vol. 161(C).
    13. Vandenbruaene, Jonas & De Ceuster, Marc & Annaert, Jan, 2023. "Does time series momentum also exist outside traditional financial markets? Near-laboratory evidence from sports betting," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 104(C).
    14. Barroso, Pedro & Detzel, Andrew, 2021. "Do limits to arbitrage explain the benefits of volatility-managed portfolios?," Journal of Financial Economics, Elsevier, vol. 140(3), pages 744-767.
    15. Yuan Liao & Xinjie Ma & Andreas Neuhierl & Linda Schilling, 2025. "The Uncertainty of Machine Learning Predictions in Asset Pricing," Papers 2503.00549, arXiv.org.
    16. Choi, Jungjun & Yang, Xiye, 2022. "Asymptotic properties of correlation-based principal component analysis," Journal of Econometrics, Elsevier, vol. 229(1), pages 1-18.
    17. Fieberg, Christian & Liedtke, Gerrit & Zaremba, Adam, 2024. "Cryptocurrency anomalies and economic constraints," International Review of Financial Analysis, Elsevier, vol. 94(C).
    18. Fletcher, Jonathan, 2021. "International equity U.S. mutual funds and diversification benefits," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 246-257.

  10. Andrew J. Patton, 2020. "Comparing Possibly Misspecified Forecasts," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(4), pages 796-809, October.

    Cited by:

    1. Cathy W. S. Chen & Takaaki Koike & Wei‐Hsuan Shau, 2024. "Tail risk forecasting with semiparametric regression models by incorporating overnight information," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1492-1512, August.
    2. Oh, Dong Hwan & Patton, Andrew J., 2024. "Better the devil you know: Improved forecasts from imperfect models," Journal of Econometrics, Elsevier, vol. 242(1).
    3. Timo Dimitriadis & Andrew J. Patton & Patrick W. Schmidt, 2019. "Testing Forecast Rationality for Measures of Central Tendency," Papers 1910.12545, arXiv.org, revised Jul 2024.
    4. Tobias Fissler & Yannick Hoga, 2024. "How to Compare Copula Forecasts?," Papers 2410.04165, arXiv.org.
    5. Conrad, Christian & Lahiri, Kajal, 2023. "Heterogeneous expectations among professional forecasters," ZEW Discussion Papers 23-062, ZEW - Leibniz Centre for European Economic Research.
    6. Xenxo Vidal-Llana & Carlos Salort Sánchez & Vincenzo Coia & Montserrat Guillen, 2022. ""Non-Crossing Dual Neural Network: Joint Value at Risk and Conditional Tail Expectation estimations with non-crossing conditions"," IREA Working Papers 202215, University of Barcelona, Research Institute of Applied Economics, revised Oct 2022.
    7. Charles F. Manski, 2019. "Econometrics For Decision Making: Building Foundations Sketched By Haavelmo And Wald," Papers 1912.08726, arXiv.org, revised Feb 2021.
    8. Lazar, Emese & Wang, Shixuan & Xue, Xiaohan, 2023. "Loss function-based change point detection in risk measures," European Journal of Operational Research, Elsevier, vol. 310(1), pages 415-431.
    9. Llorens-Terrazas, Jordi & Brownlees, Christian, 2023. "Projected Dynamic Conditional Correlations," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1761-1776.
    10. Patrick Schmidt & Matthias Katzfuss & Tilmann Gneiting, 2021. "Interpretation of point forecasts with unknown directive," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 728-743, September.
    11. Dong Hwan Oh & Andrew J. Patton, 2021. "Better the Devil You Know: Improved Forecasts from Imperfect Models," Finance and Economics Discussion Series 2021-071, Board of Governors of the Federal Reserve System (U.S.).
    12. Alexander Henzi & Johanna F Ziegel, 2022. "Valid sequential inference on probability forecast performance [A comparison of the ECMWF, MSC, and NCEP global ensemble prediction systems]," Biometrika, Biometrika Trust, vol. 109(3), pages 647-663.
    13. Mucahit Aygun & Fabio Bellini & Roger J. A. Laeven, 2023. "Elicitability of Return Risk Measures," Papers 2302.13070, arXiv.org, revised Mar 2023.
    14. Lukas Bauer, 2025. "Evaluating financial tail risk forecasts: Testing Equal Predictive Ability," Papers 2505.23333, arXiv.org.
    15. Christian Conrad & Robert F. Engle, 2021. "Modelling Volatility Cycles: The (MF)2 GARCH Model," Working Paper series 21-05, Rimini Centre for Economic Analysis.
    16. Tobias Fissler & Yannick Hoga, 2021. "Backtesting Systemic Risk Forecasts using Multi-Objective Elicitability," Papers 2104.10673, arXiv.org, revised Feb 2022.
    17. 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.
    18. Brownlees, Christian & Llorens-Terrazas, Jordi, 2024. "Empirical risk minimization for time series: Nonparametric performance bounds for prediction," Journal of Econometrics, Elsevier, vol. 244(1).
    19. Boskabadi, Elahe, 2022. "Economic policy uncertainty and forecast bias in the survey of professional forecasters," MPRA Paper 115081, University Library of Munich, Germany.
    20. Fissler Tobias & Ziegel Johanna F., 2021. "On the elicitability of range value at risk," Statistics & Risk Modeling, De Gruyter, vol. 38(1-2), pages 25-46, January.
    21. 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.
    22. Tobias Fissler & Jana Hlavinová & Birgit Rudloff, 2021. "Elicitability and identifiability of set-valued measures of systemic risk," Finance and Stochastics, Springer, vol. 25(1), pages 133-165, January.
    23. Denuit, Michel & Trufin, Julien, 2024. "Convex and Lorenz orders under balance correction in nonlife insurance pricing: Review and new developments," Insurance: Mathematics and Economics, Elsevier, vol. 118(C), pages 123-128.
    24. David T. Frazier & Donald S. Poskitt, 2025. "Sequential Scoring Rule Evaluation for Forecast Method Selection," Papers 2505.09090, arXiv.org.
    25. Denuit, Michel & Trufin, Julien, 2022. "Autocalibration by balance correction in nonlife insurance pricing," LIDAM Discussion Papers ISBA 2022041, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    26. Takaaki Koike & Cathy W. S. Chen & Edward M. H. Lin, 2024. "Forecasting and Backtesting Gradient Allocations of Expected Shortfall," Papers 2401.11701, arXiv.org, revised Jun 2024.
    27. 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.
    28. Alexander I. Jordan & Anja Mühlemann & Johanna F. Ziegel, 2022. "Characterizing the optimal solutions to the isotonic regression problem for identifiable functionals," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(3), pages 489-514, June.
    29. Lukas Bauer & Ekaterina Kazak, 2025. "Conditional Method Confidence Set," Papers 2505.21278, arXiv.org.

  11. Patton, Andrew J. & Ziegel, Johanna F. & Chen, Rui, 2019. "Dynamic semiparametric models for expected shortfall (and Value-at-Risk)," Journal of Econometrics, Elsevier, vol. 211(2), pages 388-413.
    See citations under working paper version above.
  12. Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2018. "Modeling and forecasting (un)reliable realized covariances for more reliable financial decisions," Journal of Econometrics, Elsevier, vol. 207(1), pages 71-91.
    See citations under working paper version above.
  13. 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.
    See citations under working paper version above.
  14. Dong Hwan Oh & Andrew J. Patton, 2018. "Time-Varying Systemic Risk: Evidence From a Dynamic Copula Model of CDS Spreads," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(2), pages 181-195, April.
    See citations under working paper version above.
  15. Dong Hwan Oh & Andrew J. Patton, 2017. "Modeling Dependence in High Dimensions With Factor Copulas," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 139-154, January.
    See citations under working paper version above.
  16. Tim Bollerslev & Andrew J. Patton & Wenjing Wang, 2016. "Daily House Price Indices: Construction, Modeling, and Longer‐run Predictions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(6), pages 1005-1025, September.
    See citations under working paper version above.
  17. 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.
    See citations under working paper version above.
  18. Oh, Dong Hwan & Patton, Andrew J., 2016. "High-dimensional copula-based distributions with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 349-366.
    See citations under working paper version above.
  19. Mathias S. Kruttli & Andrew J. Patton & Tarun Ramadorai, 2015. "The Impact of Hedge Funds on Asset Markets," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 5(2), pages 185-226.
    See citations under working paper version above.
  20. Andrew J. Patton & Tarun Ramadorai & Michael Streatfield, 2015. "Change You Can Believe In? Hedge Fund Data Revisions," Journal of Finance, American Finance Association, vol. 70(3), pages 963-999, June.
    See citations under working paper version above.
  21. 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.
    See citations under working paper version above.
  22. Andrew J. Patton & Kevin Sheppard, 2015. "Good Volatility, Bad Volatility: Signed Jumps and The Persistence of Volatility," The Review of Economics and Statistics, MIT Press, vol. 97(3), pages 683-697, July.

    Cited by:

    1. Segal, Gill & Shaliastovich, Ivan & Yaron, Amir, 2015. "Good and bad uncertainty: Macroeconomic and financial market implications," Journal of Financial Economics, Elsevier, vol. 117(2), pages 369-397.
    2. Zhang, Yaojie & He, Jiaxin & He, Mengxi & Li, Shaofang, 2023. "Geopolitical risk and stock market volatility: A global perspective," Finance Research Letters, Elsevier, vol. 53(C).
    3. 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).
    4. Buncic, Daniel & Müller, Oliver, 2017. "Measuring the output gap in Switzerland with linear opinion pools," Economic Modelling, Elsevier, vol. 64(C), pages 153-171.
    5. Gong, Xu & Lin, Boqiang, 2018. "The incremental information content of investor fear gauge for volatility forecasting in the crude oil futures market," Energy Economics, Elsevier, vol. 74(C), pages 370-386.
    6. 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.
    7. 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.
    8. Harvey, A. & Palumbo, D., 2019. "Score-Driven Models for Realized Volatility," Cambridge Working Papers in Economics 1950, Faculty of Economics, University of Cambridge.
    9. Brini, Alessio & Lenz, Jimmie, 2024. "Pricing cryptocurrency options with machine learning regression for handling market volatility," Economic Modelling, Elsevier, vol. 136(C).
    10. Özbekler, Ali Gencay & Kontonikas, Alexandros & Triantafyllou, Athanasios, 2021. "Volatility forecasting in European government bond markets," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1691-1709.
    11. Degiannakis, Stavros & Filis, George & Klein, Tony & Walther, Thomas, 2019. "Forecasting Realized Volatility of Agricultural Commodities," MPRA Paper 96267, University Library of Munich, Germany.
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    3. Mayer, Alexander & Wied, Dominik, 2023. "Estimation and inference in factor copula models with exogenous covariates," Journal of Econometrics, Elsevier, vol. 235(2), pages 1500-1521.
    4. Caballero, Diego & Lucas, André & Schwaab, Bernd & Zhang, Xin, 2019. "Risk endogeneity at the lender/investor-of-last-resort," Working Paper Series 2225, European Central Bank.
    5. Oliver R. Cutbill & Rami V. Tabri, 2022. "The Impossibility of Testing for Dependence Using Kendall’s Ƭ Under Missing Data of Unknown Form," Working Papers 2022-03, University of Sydney, School of Economics.
    6. Jozef Barun'ik & Tobias Kley, 2015. "Quantile Coherency: A General Measure for Dependence between Cyclical Economic Variables," Papers 1510.06946, arXiv.org, revised Dec 2018.
    7. Jie Cheng, 2024. "Evaluating Density Forecasts Using Weighted Multivariate Scores in a Risk Management Context," Computational Economics, Springer;Society for Computational Economics, vol. 64(6), pages 3617-3643, December.
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    9. Fan, Yanqin & Henry, Marc, 2023. "Vector copulas," Journal of Econometrics, Elsevier, vol. 234(1), pages 128-150.
    10. Sentana, Enrique & Amengual, Dante & Bei, Xinyue, 2020. "Hypothesis tests with a repeatedly singular information matrix," CEPR Discussion Papers 14415, C.E.P.R. Discussion Papers.
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    21. Anne Opschoor & André Lucas & István Barra & Dick van Dijk, 2021. "Closed-Form Multi-Factor Copula Models With Observation-Driven Dynamic Factor Loadings," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(4), pages 1066-1079, October.
    22. Salah Uddin, Gazi & Lucey, Brian & Rahman, Md Lutfur & Stenvall, David, 2024. "Quantile coherency across bonds, commodities, currencies, and equities," Journal of Commodity Markets, Elsevier, vol. 33(C).
    23. Woraphon Yamaka & Rangan Gupta & Sukrit Thongkairat & Paravee Maneejuk, 2023. "Structural and predictive analyses with a mixed copula‐based vector autoregression model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 223-239, March.
    24. Elsayed, Ahmed H. & Sohag, Kazi & Sousa, Ricardo M., 2024. "Oil shocks and financial stability in MENA countries," Resources Policy, Elsevier, vol. 89(C).
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    28. Zongwu Cai & Guannan Liu & Wei Long & Xuelong Luo, 2024. "Semiparametric Conditional Mixture Copula Models with Copula Selection," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202401, University of Kansas, Department of Economics, revised Jan 2024.
    29. Antonella D’agostino & Giovanni De Luca & Dominique Guégan, 2023. "Estimating Lower Tail Dependence Between Pairs of Poverty Dimensions in Europe," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 69(2), pages 419-442, June.
    30. Iryna Kyzyma & Alessio Fusco & Philippe Van Kerm, 2022. "Distributional Change: Assessing the Contribution of Household Income Sources," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(1), pages 158-184, February.
    31. Yanqin Fan & Marc Henry, 2020. "Vector copulas," Papers 2009.06558, arXiv.org, revised Apr 2021.
    32. Guannan Liu & Wei Long & Bingduo Yang & Zongwu Cai, 2022. "Semiparametric estimation and model selection for conditional mixture copula models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(1), pages 287-330, March.
    33. Hamori, Shigeyuki & Motegi, Kaiji & Zhang, Zheng, 2019. "Calibration estimation of semiparametric copula models with data missing at random," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 85-109.
    34. Selmi, Refk & Hammoudeh, Shawkat & Kasmaoui, Kamal & Sousa, Ricardo M. & Errami, Youssef, 2022. "The dual shocks of the COVID-19 and the oil price collapse: A spark or a setback for the circular economy?," Energy Economics, Elsevier, vol. 109(C).
    35. Alexander J. McNeil, 2021. "Modelling Volatile Time Series with V-Transforms and Copulas," Risks, MDPI, vol. 9(1), pages 1-26, January.
    36. Zhang, Yi & Gomes, António Topa & Beer, Michael & Neumann, Ingo & Nackenhorst, Udo & Kim, Chul-Woo, 2019. "Reliability analysis with consideration of asymmetrically dependent variables: Discussion and application to geotechnical examples," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 261-277.
    37. Zhi, Bangdong & Wang, Xiaojun & Xu, Fangming, 2020. "Impawn rate optimisation in inventory financing: A canonical vine copula-based approach," International Journal of Production Economics, Elsevier, vol. 227(C).
    38. Kajal Lahiri & Liu Yang, 2023. "Predicting binary outcomes based on the pair-copula construction," Empirical Economics, Springer, vol. 64(6), pages 3089-3119, June.
    39. Maziar Sahamkhadam, 2021. "Dynamic copula-based expectile portfolios," Journal of Asset Management, Palgrave Macmillan, vol. 22(3), pages 209-223, May.
    40. Fan, Yanqin & Han, Fang & Park, Hyeonseok, 2023. "Estimation and inference in a high-dimensional semiparametric Gaussian copula vector autoregressive model," Journal of Econometrics, Elsevier, vol. 237(1).
    41. Dardati, Evangelina & Saygili, Meryem, 2020. "Aggregate impacts of cap-and-trade programs with heterogeneous firms," Energy Economics, Elsevier, vol. 92(C).
    42. Martyna Kobus & Radoslaw Kurek, 2017. "Copula-based measurement of interdependence for discrete distributions," Working Papers 431, ECINEQ, Society for the Study of Economic Inequality.
    43. Barry K. Goodwin & Matthew T. Holt & Gülcan Önel & Jeffrey P. Prestemon, 2018. "Copula-based nonlinear modeling of the law of one price for lumber products," Empirical Economics, Springer, vol. 54(3), pages 1237-1265, May.
    44. Ba Chu & Stephen Satchell, 2016. "Recovering the Most Entropic Copulas from Preliminary Knowledge of Dependence," Econometrics, MDPI, vol. 4(2), pages 1-21, March.
    45. Tsionas, Mike G. & Andrikopoulos, Athanasios, 2020. "On a High-Dimensional Model Representation method based on Copulas," European Journal of Operational Research, Elsevier, vol. 284(3), pages 967-979.
    46. Alexander J. McNeil, 2020. "Modelling volatile time series with v-transforms and copulas," Papers 2002.10135, arXiv.org, revised Jan 2021.
    47. Bladt, Martin & McNeil, Alexander J., 2022. "Time series copula models using d-vines and v-transforms," Econometrics and Statistics, Elsevier, vol. 24(C), pages 27-48.
    48. Manner, Hans & Stark, Florian & Wied, Dominik, 2019. "Testing for structural breaks in factor copula models," Journal of Econometrics, Elsevier, vol. 208(2), pages 324-345.
    49. Rewat Khanthaporn, 2022. "Analysis of Nonlinear Comovement of Benchmark Thai Government Bond Yields," PIER Discussion Papers 183, Puey Ungphakorn Institute for Economic Research.
    50. Fousseni Chabi-Yo & Markus Huggenberger & Florian Weigert, 2019. "Multivariate Crash Risk," Working Papers on Finance 1901, University of St. Gallen, School of Finance.

  25. Andrew J. Patton & Tarun Ramadorai, 2013. "On the High-Frequency Dynamics of Hedge Fund Risk Exposures," Journal of Finance, American Finance Association, vol. 68(2), pages 597-635, April.
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  26. Dong Hwan Oh & Andrew J. Patton, 2013. "Simulated Method of Moments Estimation for Copula-Based Multivariate Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(502), pages 689-700, June.

    Cited by:

    1. Chen, Hua & MacMinn, Richard & Sun, Tao, 2015. "Multi-population mortality models: A factor copula approach," Insurance: Mathematics and Economics, Elsevier, vol. 63(C), pages 135-146.
    2. Mayer, Alexander & Wied, Dominik, 2023. "Estimation and inference in factor copula models with exogenous covariates," Journal of Econometrics, Elsevier, vol. 235(2), pages 1500-1521.
    3. Xiaohong Chen & Zhijie Xiao & Bo Wang, 2020. "Copula-Based Time Series With Filtered Nonstationarity," Cowles Foundation Discussion Papers 2242, Cowles Foundation for Research in Economics, Yale University.
    4. Stanislav Anatolyev & Vladimir Pyrlik, 2021. "Shrinkage for Gaussian and t Copulas in Ultra-High Dimensions," CERGE-EI Working Papers wp699, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    5. Dong Hwan Oh & Andrew J. Patton, 2015. "Modelling Dependence in High Dimensions with Factor Copulas," Finance and Economics Discussion Series 2015-51, Board of Governors of the Federal Reserve System (U.S.).
    6. Fengler, Matthias R. & Okhrin, Ostap, 2016. "Managing risk with a realized copula parameter," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 131-152.
    7. Nevrla, Matěj, 2020. "Systemic risk in European financial and energy sectors: Dynamic factor copula approach," Economic Systems, Elsevier, vol. 44(4).
    8. David T. Frazier & Tatsushi Oka & Dan Zhu, 2017. "Indirect Inference with a Non-Smooth Criterion Function," Papers 1708.02365, arXiv.org, revised Jul 2019.
    9. Stanislav Anatolyev & Renat Khabibullin & Artem Prokhorov, 2013. "Reconstructing high dimensional dynamic distributions from distributions of lower dimension," Working Papers w0167, New Economic School (NES).
    10. Damien Ackerer & Thibault Vatter, 2016. "Dependent Defaults and Losses with Factor Copula Models," Papers 1610.03050, arXiv.org, revised Jan 2018.
    11. Shi, Peng & Zhao, Zifeng, 2024. "Enhanced pricing and management of bundled insurance risks with dependence-aware prediction using pair copula construction," Journal of Econometrics, Elsevier, vol. 240(1).
    12. Ackerer Damien & Vatter Thibault, 2017. "Dependent defaults and losses with factor copula models," Dependence Modeling, De Gruyter, vol. 5(1), pages 375-399, December.
    13. Stolfi, Paola & Bernardi, Mauro & Petrella, Lea, 2025. "Sparse simulation-based estimator built on quantiles," Econometrics and Statistics, Elsevier, vol. 34(C), pages 32-43.
    14. Bruno Solnik & Thaisiri Watewai, 2016. "International Correlation Asymmetries: Frequent-but-Small and Infrequent-but-Large Equity Returns," PIER Discussion Papers 31, Puey Ungphakorn Institute for Economic Research.
    15. Smith, Michael Stanley & Maneesoonthorn, Worapree, 2018. "Inversion copulas from nonlinear state space models with an application to inflation forecasting," International Journal of Forecasting, Elsevier, vol. 34(3), pages 389-407.
    16. Benjamin V. Rosa, 2019. "Resident Bid Preference, Affiliation, and Procurement Competition: Evidence from New Mexico," Journal of Industrial Economics, Wiley Blackwell, vol. 67(2), pages 161-208, June.
    17. Lin Deng & Michael Stanley Smith & Worapree Maneesoonthorn, 2023. "Large Skew-t Copula Models and Asymmetric Dependence in Intraday Equity Returns," Papers 2308.05564, arXiv.org, revised Jul 2024.
    18. Chen, Zhenlong & Chang, Jing & Hao, Xiaozhen, 2024. "Portfolio selection via high-dimensional stochastic factor Copula," Finance Research Letters, Elsevier, vol. 67(PA).
    19. Manner, Hans & Rodríguez, Gabriel & Stöckler, Florian, 2024. "A changepoint analysis of exchange rate and commodity price risks for Latin American stock markets," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 1385-1403.
    20. Pavel Krupskii & Harry Joe, 2022. "Approximate likelihood with proxy variables for parameter estimation in high-dimensional factor copula models," Statistical Papers, Springer, vol. 63(2), pages 543-569, April.
    21. Chen, Xiaohong & Xiao, Zhijie & Wang, Bo, 2022. "Copula-based time series with filtered nonstationarity," Journal of Econometrics, Elsevier, vol. 228(1), pages 127-155.
    22. Mohamed Belalia & Jean-François Quessy, 2024. "Generalized simulated method-of-moments estimators for multivariate copulas," Statistical Papers, Springer, vol. 65(8), pages 4811-4841, October.
    23. Bruno Rémillard, 2017. "Goodness-of-Fit Tests for Copulas of Multivariate Time Series," Econometrics, MDPI, vol. 5(1), pages 1-23, March.
    24. Anatolyev, Stanislav & Khabibullin, Renat & Prokhorov, Artem, 2014. "An algorithm for constructing high dimensional distributions from distributions of lower dimension," Economics Letters, Elsevier, vol. 123(3), pages 257-261.
    25. Anatolyev, Stanislav & Pyrlik, Vladimir, 2022. "Copula shrinkage and portfolio allocation in ultra-high dimensions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    26. Bormann, Carsten & Schienle, Melanie, 2019. "Detecting structural differences in tail dependence of financial time series," Working Paper Series in Economics 122, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    27. Manner, Hans & Stark, Florian & Wied, Dominik, 2019. "Testing for structural breaks in factor copula models," Journal of Econometrics, Elsevier, vol. 208(2), pages 324-345.
    28. Florian Stark & Sven Otto, 2020. "Testing and Dating Structural Changes in Copula-based Dependence Measures," Papers 2011.05036, arXiv.org.
    29. Juwon Seo, 2018. "Randomization Tests for Equality in Dependence Structure," Papers 1811.02105, arXiv.org.
    30. Verhoijsen Alex & Krupskiy Pavel, 2022. "Fast inference methods for high-dimensional factor copulas," Dependence Modeling, De Gruyter, vol. 10(1), pages 270-289, January.

  27. Andrew J. Patton & Michela Verardo, 2012. "Does Beta Move with News? Firm-Specific Information Flows and Learning about Profitability," The Review of Financial Studies, Society for Financial Studies, vol. 25(9), pages 2789-2839.

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    1. 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.
    2. Riccardo Borghi & Eric Hillebrand & Jakob Mikkelsen & Giovanni Urga, 2018. "The dynamics of factor loadings in the cross-section of returns," CREATES Research Papers 2018-38, Department of Economics and Business Economics, Aarhus University.
    3. Wang, Baolian, 2019. "The cash conversion cycle spread," Journal of Financial Economics, Elsevier, vol. 133(2), pages 472-497.
    4. Azi Ben‐Rephael & Bruce I. Carlin & Zhi Da & Ryan D. Israelsen, 2021. "Information Consumption and Asset Pricing," Journal of Finance, American Finance Association, vol. 76(1), pages 357-394, February.
    5. Chen, Dachuan & Mykland, Per A. & Zhang, Lan, 2024. "Realized regression with asynchronous and noisy high frequency and high dimensional data," Journal of Econometrics, Elsevier, vol. 239(2).
    6. Chan, Kam Fong & Marsh, Terry, 2022. "Asset pricing on earnings announcement days," Journal of Financial Economics, Elsevier, vol. 144(3), pages 1022-1042.
    7. Pierre Bajgrowicz & Olivier Scaillet & Adrien Treccani, 2016. "Jumps in High-Frequency Data: Spurious Detections, Dynamics, and News," Management Science, INFORMS, vol. 62(8), pages 2198-2217, August.
    8. Serge Darolles & Christian Francq & Sébastien Laurent, 2017. "Asymptotics of Cholesky GARCH models and time-varying conditional betas," Post-Print hal-04590471, HAL.
    9. Bonato, Matteo, 2019. "Realized correlations, betas and volatility spillover in the agricultural commodity market: What has changed?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 62(C), pages 184-202.
    10. Timmermann, Allan & Pettenuzzo, Davide & Sabbatucci, Riccardo, 2019. "Cash Flow News and Stock Price Dynamics," CEPR Discussion Papers 14117, C.E.P.R. Discussion Papers.
    11. Hollstein, Fabian & Prokopczuk, Marcel & Wese Simen, Chardin, 2020. "Beta uncertainty," Journal of Banking & Finance, Elsevier, vol. 116(C).
    12. F. Blasques & Christian Francq & Sébastien Laurent, 2024. "Autoregressive conditional betas," Post-Print hal-04676069, HAL.
    13. Jahan-Pavar, Mohammad R. & Lang, William J., 2024. "Which daily equity returns improve output forecasts?," Economics Letters, Elsevier, vol. 243(C).
    14. Giacomo Livan & Simone Alfarano & Mishael Milaković & Enrico Scalas, 2015. "A spectral perspective on excess volatility," Applied Economics Letters, Taylor & Francis Journals, vol. 22(9), pages 745-750, June.
    15. Yang, Chih-Yuan & Jhang, Ling-Jhen & Chang, Chia-Chien, 2016. "Do investor sentiment, weather and catastrophe effects improve hedging performance? Evidence from the Taiwan options market," Pacific-Basin Finance Journal, Elsevier, vol. 37(C), pages 35-51.
    16. Ilze Kalnina & Dacheng Xiu, 2017. "Nonparametric Estimation of the Leverage Effect: A Trade-Off Between Robustness and Efficiency," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 384-396, January.
    17. Kapadia, Nishad & Zekhnini, Morad, 2019. "Do idiosyncratic jumps matter?," Journal of Financial Economics, Elsevier, vol. 131(3), pages 666-692.
    18. 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.
    19. Li, Yi & Shen, Dehua & Wang, Pengfei & Zhang, Wei, 2019. "Do analyst recommendations matter for rival companies?," International Review of Financial Analysis, Elsevier, vol. 65(C).
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    21. Shabir A A Saleem & Peter N Smith & Abdullah Yalaman, 2021. "Analysis of systematic risk around firm-specific news in an emerging market using high frequency data," CAMA Working Papers 2021-35, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    22. Bilel Sanhaji & Julien Chevallier, 2023. "Tracking ‘Pure’ Systematic Risk with Realized Betas for Bitcoin and Ethereum," Econometrics, MDPI, vol. 11(3), pages 1-36, August.
    23. Peter R. Hansen & Asger Lunde & Valeri Voev, 2010. "Realized Beta GARCH: A Multivariate GARCH Model with Realized Measures of Volatility and CoVolatility," CREATES Research Papers 2010-74, Department of Economics and Business Economics, Aarhus University.
    24. Kris Boudt & Christopher J. Neely & Piet Sercu & Marjan Wauters, 2017. "The response of multinationals’ foreign exchange rate exposure to macroeconomic news," Working Papers 2017-20, Federal Reserve Bank of St. Louis.
    25. Andersen, Torben G. & Riva, Raul & Thyrsgaard, Martin & Todorov, Viktor, 2023. "Intraday cross-sectional distributions of systematic risk," Journal of Econometrics, Elsevier, vol. 235(2), pages 1394-1418.
    26. Davide Pettenuzzo & Riccardo Sabbatucci & Allan Timmermann, 2018. "High-frequency Cash Flow Dynamics," Working Papers 120, Brandeis University, Department of Economics and International Business School.
    27. Fabian Hollstein & Marcel Prokopczuk & Chardin Wese Simen, 2020. "The Conditional Capital Asset Pricing Model Revisited: Evidence from High-Frequency Betas," Management Science, INFORMS, vol. 66(6), pages 2474-2494, June.
    28. Hae mi Choi, 2014. "When Good News Is Not So Good: Economy-wide Uncertainty and Stock Returns," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 41(9-10), pages 1101-1123, November.
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    30. Yasser Alhenawi & M. Kabir Hassan, 2023. "How do investors price accrual risk during crises?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 4684-4706, October.
    31. Lee, Suzanne S. & Wang, Minho, 2019. "The impact of jumps on carry trade returns," Journal of Financial Economics, Elsevier, vol. 131(2), pages 433-455.
    32. Dmitry Bazhutov & André Betzer & Richard Stehle, 2023. "Beta estimation in the European network regulation context: what matters, what doesn’t, and what is indispensable," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 37(3), pages 239-275, September.
    33. Dinesh Gajurel & Mardi Dungey & Wenying Yao & Nagaratnam Jeyasreedharan, 2020. "Jump Risk in the US Financial Sector," The Economic Record, The Economic Society of Australia, vol. 96(314), pages 331-349, September.
    34. Chapman, Kimball, 2018. "Earnings notifications, investor attention, and the earnings announcement premium," Journal of Accounting and Economics, Elsevier, vol. 66(1), pages 222-243.
    35. Ilze KALNINA, 2015. "Inference for Nonparametric High-Frequency Estimators with an Application to Time Variation in Betas," Cahiers de recherche 13-2015, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    36. Van Ness, Bonnie & Van Ness, Robert & Yildiz, Serhat, 2021. "Private information in trades, R2, and large stock price movements," Journal of Banking & Finance, Elsevier, vol. 131(C).
    37. Chowdhury, Biplob & Jeyasreedharan, Nagaratnam & Dungey, Mardi, 2017. "Quantile relationships between standard, diffusion and jump betas across Japanese banks," Working Papers 2017-10, University of Tasmania, Tasmanian School of Business and Economics.
    38. Huang, Xiaowei & Cheng, Ge & Zhang, Man, 2025. "Climate change risk and real estate prices—Micro evidence from coastal cities in China," Pacific-Basin Finance Journal, Elsevier, vol. 90(C).
    39. Aman, Hiroyuki & Moriyasu, Hiroshi, 2017. "Volatility and public information flows: Evidence from disclosure and media coverage in the Japanese stock market," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 660-676.
    40. Bodilsen, Simon & Eriksen, Jonas N. & Grønborg, Niels S., 2021. "Asset pricing and FOMC press conferences," Journal of Banking & Finance, Elsevier, vol. 128(C).
    41. Alexeev, Vitali & Dungey, Mardi & Yao, Wenying, 2017. "Time-varying continuous and jump betas: The role of firm characteristics and periods of stress," Journal of Empirical Finance, Elsevier, vol. 40(C), pages 1-19.
    42. Tim Bollerslev & Sophia Zhengzi Li & Viktor Todorov, 2014. "Roughing up Beta: Continuous vs. Discontinuous Betas, and the Cross-Section of Expected Stock Returns," CREATES Research Papers 2014-48, Department of Economics and Business Economics, Aarhus University.
    43. Pacicco, Fausto & Vena, Luigi & Venegoni, Andrea, 2020. "Communication and financial supervision: How does disclosure affect market stability?," Journal of Empirical Finance, Elsevier, vol. 57(C), pages 1-15.
    44. Entrop, O. & von la Hausse, L. & Wilkens, M., 2017. "Looking beyond banks’ average interest rate risk: Determinants of high exposures," The Quarterly Review of Economics and Finance, Elsevier, vol. 63(C), pages 204-218.
    45. Shuxing Yin & Khelifa Mazouz & Abdelhafid Benamraoui & Brahim Saadouni, 2018. "Stock price reaction to profit warnings: the role of time-varying betas," Review of Quantitative Finance and Accounting, Springer, vol. 50(1), pages 67-93, January.
    46. Claudiu Botoc, 2014. "How Risky Are Sif'S Securities?," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(1), pages 845-850, July.
    47. Tang, Guohao & Wu, Yiyong & Lou, Guanyu, 2024. "Extrapolation beyond peers: An asset pricing perspective," Journal of International Money and Finance, Elsevier, vol. 148(C).
    48. Dungey, Mardi & Matei, Marius & Treepongkaruna, Sirimon, 2014. "Identifying periods of financial stress in Asian currencies: the role of high frequency financial market data," Working Papers 2014-12, University of Tasmania, Tasmanian School of Business and Economics.
    49. Beetsma, Roel & de Jong, Frank & Giuliodori, Massimo & Widijanto, Daniel, 2014. "The Impact of News and the SMP on Realized (Co)Variances in the Eurozone Sovereign Debt Market," CEPR Discussion Papers 9803, C.E.P.R. Discussion Papers.
    50. Frank S. Zhou & Yuqing Zhou, 2020. "The Dog that Did Not Bark: Limited Price Efficiency and Strategic Nondisclosure," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 58(1), pages 155-197, March.
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    55. Dungey, Mardi & Luciani, Matteo & Matei, Marius & Veredas, David, 2015. "Surfing through the GFC: systemic risk in Australia," Working Papers 2015-01, University of Tasmania, Tasmanian School of Business and Economics.
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    5. 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, September.
    6. Cerrato, Mario & Crosby, John & Kim, Minjoo & Zhao, Yang, 2014. "Modeling Dependence Structure and Forecasting Portfolio Value-at-Risk with Dynamic Copulas," SIRE Discussion Papers 2015-25, Scottish Institute for Research in Economics (SIRE).
    7. Jean‐Paul Chavas & Giorgia Rivieccio & Salvatore Di Falco & Giovanni De Luca & Fabian Capitanio, 2022. "Agricultural diversification, productivity, and food security across time and space," Agricultural Economics, International Association of Agricultural Economists, vol. 53(S1), pages 41-58, November.
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    217. Shahzad, Umer & Ghaemi Asl, Mahdi & Tedeschi, Marco, 2023. "Is there any market state-dependent contribution from Blockchain-enabled solutions to ESG investments? Evidence from conventional and Islamic ESG stocks," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 139-154.
    218. Sundusit Saekow & Phisanu Chiawkhun & Woraphon Yamaka & Nawapon Nakharutai & Parkpoom Phetpradap, 2024. "Estimation of Contagion: Bayesian Model Averaging on Tail Dependence of Mixture Copula," Mathematics, MDPI, vol. 12(21), pages 1-23, October.
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    221. Fousseni Chabi-Yo & Markus Huggenberger & Florian Weigert, 2019. "Multivariate Crash Risk," Working Papers on Finance 1901, University of St. Gallen, School of Finance.
    222. Zheng, Yingfei & Shen, Anran & Li, Ruihai & Yang, Yuhong & Wang, Shengjin & Cheng, Lee-Young, 2023. "Spillover effects between internet financial industry and traditional financial industry: Evidence from the Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 79(C).
    223. Acar, Elif F. & Czado, Claudia & Lysy, Martin, 2019. "Flexible dynamic vine copula models for multivariate time series data," Econometrics and Statistics, Elsevier, vol. 12(C), pages 181-197.
    224. Hanif, Waqas & Mensi, Walid & Alomari, Mohammad & Andraz, Jorge Miguel, 2023. "Downside and upside risk spillovers between precious metals and currency markets: Evidence from before and during the COVID-19 crisis," Resources Policy, Elsevier, vol. 81(C).
    225. Bekkerman, Anton & Weaver, David K., 2018. "Modeling Joint Dependence of Managed Ecosystems Pests: The Case of the Wheat Stem Sawfly," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 43(2), May.
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  29. Andrew J. Patton & Allan Timmermann, 2011. "Forecast Rationality Tests Based on Multi-Horizon Bounds," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 1-17, June.
    See citations under working paper version above.
  30. Patton, Andrew J., 2011. "Data-based ranking of realised volatility estimators," Journal of Econometrics, Elsevier, vol. 161(2), pages 284-303, April.

    Cited by:

    1. Christos Floros & Konstantinos Gkillas & Christoforos Konstantatos & Athanasios Tsagkanos, 2020. "Realized Measures to Explain Volatility Changes over Time," JRFM, MDPI, vol. 13(6), pages 1-19, June.
    2. Cubadda, Gianluca & Guardabascio, Barbara & Hecq, Alain, 2017. "A vector heterogeneous autoregressive index model for realized volatility measures," International Journal of Forecasting, Elsevier, vol. 33(2), pages 337-344.
    3. Aït-Sahalia, Yacine & Xiu, Dacheng, 2019. "A Hausman test for the presence of market microstructure noise in high frequency data," Journal of Econometrics, Elsevier, vol. 211(1), pages 176-205.
    4. 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.
    5. Vortelinos, Dimitrios I., 2017. "Forecasting realized volatility: HAR against Principal Components Combining, neural networks and GARCH," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 824-839.
    6. Fissler, Tobias & Pesenti, Silvana M., 2023. "Sensitivity measures based on scoring functions," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1408-1423.
    7. Liudas Giraitis & Yufei Li & Peter C.B. Phillips, 2023. "Robust Inference on Correlation under General Heterogeneity," Cowles Foundation Discussion Papers 2354, Cowles Foundation for Research in Economics, Yale University.
    8. Michel Ferreira Cardia Haddad & Szabolcs Blazsek & Philip Arestis & Franz Fuerst & Hsia Hua Sheng, 2023. "The two-component Beta-t-QVAR-M-lev: a new forecasting model," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 37(4), pages 379-401, December.
    9. Jianqing Fan & Jingjin Zhang & Ke Yu, 2008. "Asset Allocation and Risk Assessment with Gross Exposure Constraints for Vast Portfolios," Papers 0812.2604, arXiv.org.
    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. 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..
    12. 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.
    13. 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.
    14. Giraitis, Liudas & Li, Yufei & Phillips, Peter C.B., 2024. "Reprint of: Robust inference on correlation under general heterogeneity," Journal of Econometrics, Elsevier, vol. 244(2).
    15. Bekierman, Jeremias & Manner, Hans, 2018. "Forecasting realized variance measures using time-varying coefficient models," International Journal of Forecasting, Elsevier, vol. 34(2), pages 276-287.
    16. Timo Dimitriadis & Roxana Halbleib & Jeannine Polivka & Jasper Rennspies & Sina Streicher & Axel Friedrich Wolter, 2022. "Efficient Sampling for Realized Variance Estimation in Time-Changed Diffusion Models," Papers 2212.11833, arXiv.org, revised May 2025.
    17. Tobias Fissler & Jana Hlavinov'a & Birgit Rudloff, 2019. "Elicitability and Identifiability of Systemic Risk Measures," Papers 1907.01306, arXiv.org, revised Oct 2019.
    18. Sutton, Maxwell & Vasnev, Andrey L. & Gerlach, Richard, 2019. "Mixed interval realized variance: A robust estimator of stock price volatility," Econometrics and Statistics, Elsevier, vol. 11(C), pages 43-62.
    19. 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.
    20. 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.
    21. Dimitriadis, Timo & Schnaitmann, Julie, 2021. "Forecast encompassing tests for the expected shortfall," International Journal of Forecasting, Elsevier, vol. 37(2), pages 604-621.
    22. Brownlees, Christian & Hans, Christina & Nualart, Eulalia, 2021. "Bank credit risk networks: Evidence from the Eurozone," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 585-599.
    23. Pan, Ging-Ginq & Shiu, Yung-Ming & Wu, Tu-Cheng, 2022. "Can risk-neutral skewness and kurtosis subsume the information content of historical jumps?," Journal of Financial Markets, Elsevier, vol. 57(C).
    24. Ping, Yuan & Li, Rui, 2018. "Forecasting realized volatility based on the truncated two-scales realized volatility estimator (TTSRV): Evidence from China's stock market," Finance Research Letters, Elsevier, vol. 25(C), pages 222-229.
    25. Konstantinos Gkillas & Dimitrios Vortelinos & Christos Floros & Alexandros Garefalakis & Nikolaos Sariannidis, 2020. "Greek sovereign crisis and European exchange rates: effects of news releases and their providers," Annals of Operations Research, Springer, vol. 294(1), pages 515-536, November.
    26. 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.
    27. Maria Čuljak & Josip Arnerić & Ante Žigman, 2022. "Is Jump Robust Two Times Scaled Estimator Superior among Realized Volatility Competitors?," Mathematics, MDPI, vol. 10(12), pages 1-11, June.
    28. Huiling Yuan & Guodong Li & Junhui Wang, 2022. "High-Frequency-Based Volatility Model with Network Structure," Papers 2204.12933, arXiv.org.
    29. Umberto Triacca & Fulvia Focker, 2014. "Estimating overnight volatility of asset returns by using the generalized dynamic factor model approach," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 37(2), pages 235-254, October.
    30. Piotr Fiszeder & Grzegorz Perczak, 2013. "A new look at variance estimation based on low, high and closing prices taking into account the drift," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 67(4), pages 456-481, November.
    31. Fissler Tobias & Ziegel Johanna F., 2021. "On the elicitability of range value at risk," Statistics & Risk Modeling, De Gruyter, vol. 38(1-2), pages 25-46, January.
    32. Takayuki Morimoto & Yoshinori Kawasaki, 2017. "Forecasting Financial Market Volatility Using a Dynamic Topic Model," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 24(3), pages 149-167, September.
    33. Seema REHMAN & Saqib SHARIF & Wali ULLAH, 2023. "Relative Signed Jump and Future Stock Returns," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 25-45, March.
    34. Kathleen E. Miao & Silvana M. Pesenti, 2024. "Robust Elicitable Functionals," Papers 2409.04412, arXiv.org, revised Feb 2025.
    35. Werner Kristjanpoller, 2024. "A hybrid econometrics and machine learning based modeling of realized volatility of natural gas," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-32, December.
    36. Ahoniemi, Katja & Lanne, Markku, 2013. "Overnight stock returns and realized volatility," International Journal of Forecasting, Elsevier, vol. 29(4), pages 592-604.
    37. Mao, Xiuping & Ruiz, Esther & Veiga, Helena, 2017. "Threshold stochastic volatility: Properties and forecasting," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1105-1123.

  31. Patton, Andrew J. & Timmermann, Allan, 2011. "Predictability of Output Growth and Inflation: A Multi-Horizon Survey Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(3), pages 397-410.

    Cited by:

    1. 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.
    2. Camba-Méndez, Gonzalo & Werner, Thomas, 2017. "The inflation risk premium in the post-Lehman period," Working Paper Series 2033, European Central Bank.
    3. Hecq, Alain & Jacobs, Jan P.A.M. & Stamatogiannis, Michalis P., 2019. "Testing for news and noise in non-stationary time series subject to multiple historical revisions," Journal of Macroeconomics, Elsevier, vol. 60(C), pages 396-407.
    4. Atalla, Tarek & Joutz, Fred & Pierru, Axel, 2016. "Does disagreement among oil price forecasters reflect volatility? Evidence from the ECB surveys," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1178-1192.
    5. Burban, Valentin & De Backer, Bruno & Vladu, Andreea Liliana, 2024. "Inflation (de-)anchoring in the euro area," Working Paper Series 2964, European Central Bank.
    6. Beckmann, Joscha & Czudaj, Robert L., 2020. "Fundamental determinants of exchange rate expectations," VfS Annual Conference 2020 (Virtual Conference): Gender Economics 224617, Verein für Socialpolitik / German Economic Association.
    7. Marcela De Castro-Valderrama & Santiago Forero-Alvarado & Nicolás Moreno-Arias & Sara Naranjo-Saldarriaga, 2021. "Unraveling the Exogenous Forces Behind Analysts’ Macroeconomic Forecasts," Borradores de Economia 1184, Banco de la Republica de Colombia.
    8. Beckmann, Joscha & Czudaj, Robert L. & Murach, Michael, 2024. "Macroeconomic Effects from Media Coverage of the China-U.S. Trade War on selected EU Countries," MPRA Paper 121751, University Library of Munich, Germany.
    9. Qu, Ritong & Timmermann, Allan & Zhu, Yinchu, 2024. "Comparing forecasting performance with panel data," International Journal of Forecasting, Elsevier, vol. 40(3), pages 918-941.
    10. Czudaj, Robert & Beckmann, Joscha, 2018. "Monetary policy shocks, expectations and information rigidities," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181573, Verein für Socialpolitik / German Economic Association.
    11. Todd E. Clark & Gergely Ganics & Elmar Mertens, 2024. "Constructing fan charts from the ragged edge of SPF forecasts," Working Papers 2429, Banco de España.
    12. Meade, Nigel & Driver, Ciaran, 2023. "Differing behaviours of forecasters of UK GDP growth," International Journal of Forecasting, Elsevier, vol. 39(2), pages 772-790.
    13. Bruno Deschamps & Christos Ioannidis, 2014. "The Efficiency of Multivariate Macroeconomic Forecasts," Manchester School, University of Manchester, vol. 82(5), pages 509-523, September.
    14. Wojciech Charemza & Carlos Diaz Vela & Svetlana Makarova, 2013. "Inflation fan charts, monetary policy and skew normal distribution," Discussion Papers in Economics 13/06, Division of Economics, School of Business, University of Leicester.
    15. William A. Branch, 2014. "Nowcasting and the Taylor Rule," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(5), pages 1035-1055, August.
    16. Robert L. Czudaj, 2019. "Is the negative interest rate policy effective?," Chemnitz Economic Papers 034, Department of Economics, Chemnitz University of Technology, revised Dec 2019.
    17. Andrew J. Patton & Allan Timmermann, 2011. "Forecast Rationality Tests Based on Multi-Horizon Bounds," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 1-17, June.
    18. Bennett Schmanski & Chiara Scotti & Clara Vega, 2023. "Fed Communication, News, Twitter, and Echo Chambers," Finance and Economics Discussion Series 2023-036, Board of Governors of the Federal Reserve System (U.S.).
    19. Clements, Michael P. & Galvão, Ana Beatriz, 2017. "Model and survey estimates of the term structure of US macroeconomic uncertainty," International Journal of Forecasting, Elsevier, vol. 33(3), pages 591-604.
    20. Feunou Bruno & Fontaine Jean-Sébastien & Jin Jianjian, 2021. "What model for the target rate," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 25(1), pages 1-23, February.
    21. 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.
    22. Clements, Michael P., 2012. "Subjective and Ex Post Forecast Uncertainty: US Inflation and Output Growth," Economic Research Papers 270629, University of Warwick - Department of Economics.
    23. Joscha Beckmann & Robert L. Czudaj, 2023. "The role of expectations for currency crisis dynamics—The case of the Turkish lira," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 625-642, April.
    24. Michele Bernardi & Jaqueson K. Galimberti, 2014. "A Note on the Representative Adaptive Learning Algorithm," KOF Working papers 14-356, KOF Swiss Economic Institute, ETH Zurich.
    25. Michael P. Clements, 2020. "Individual Forecaster Perceptions of the Persistence of Shocks to GDP," ICMA Centre Discussion Papers in Finance icma-dp2020-02, Henley Business School, University of Reading.
    26. Timmermann, Allan & Zhu, Yinchu, 2019. "Comparing Forecasting Performance with Panel Data," CEPR Discussion Papers 13746, C.E.P.R. Discussion Papers.
    27. Knüppel, Malte & Vladu, Andreea L., 2016. "Approximating fixed-horizon forecasts using fixed-event forecasts," Discussion Papers 28/2016, Deutsche Bundesbank.
    28. Knüppel, Malte, 2018. "Forecast-error-based estimation of forecast uncertainty when the horizon is increased," International Journal of Forecasting, Elsevier, vol. 34(1), pages 105-116.
    29. 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.
    30. Fritsch, Markus & Haupt, Harry & Schnurbus, Joachim, 2025. "Efficiency of poll-based multi-period forecasting systems for German state elections," International Journal of Forecasting, Elsevier, vol. 41(2), pages 670-688.
    31. Dybowski, T. Philipp & Kempa, Bernd, 2020. "The European Central Bank’s monetary pillar after the financial crisis," Journal of Banking & Finance, Elsevier, vol. 121(C).
    32. Michael P Clements, 2014. "Assessing the Evidence of Macro- Forecaster Herding: Forecasts of Inflation and Output Growth," ICMA Centre Discussion Papers in Finance icma-dp2014-12, Henley Business School, University of Reading.
    33. Filippo Busetto, 2024. "Asymmetric expectations of monetary policy," Bank of England working papers 1058, Bank of England.
    34. Joscha Beckmann & Robert L. Czudaj, 2020. "Professional forecasters' expectations, consistency, and international spillovers," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1001-1024, November.
    35. Michele Berardi & Jaqueson K. Galimberti, 2012. "On the plausibility of adaptive learning in macroeconomics: A puzzling conflict in the choice of the representative algorithm," Centre for Growth and Business Cycle Research Discussion Paper Series 177, Economics, The University of Manchester.
    36. Fabian Kruger & Hendrik Plett, 2022. "Prediction intervals for economic fixed-event forecasts," Papers 2210.13562, arXiv.org, revised Mar 2024.

  32. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    See citations under working paper version above.
  33. Patton, Andrew J. & Timmermann, Allan, 2010. "Why do forecasters disagree? Lessons from the term structure of cross-sectional dispersion," Journal of Monetary Economics, Elsevier, vol. 57(7), pages 803-820, October.

    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. 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.
    3. 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.
    4. Atalla, Tarek & Joutz, Fred & Pierru, Axel, 2016. "Does disagreement among oil price forecasters reflect volatility? Evidence from the ECB surveys," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1178-1192.
    5. Soojin Jo & Rodrigo Sekkel, 2019. "Macroeconomic Uncertainty Through the Lens of Professional Forecasters," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(3), pages 436-446, July.
    6. Michael D. Bauer & Carolin Pflueger & Adi Sunderam, 2023. "Perceptions about Monetary Policy," Working Paper Series 2023-31, Federal Reserve Bank of San Francisco.
    7. Jonathan Benchimol & Makram El-Shagi & Yossi Saadon, 2020. "Do Expert Experience and Characteristics Affect Inflation Forecasts?," Bank of Israel Working Papers 2020.11, Bank of Israel.
    8. 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.
    9. Maiko Koga & Haruko Kato, 2017. "Behavioral Biases in Firms' Growth Expectations," Bank of Japan Working Paper Series 17-E-9, Bank of Japan.
    10. 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.
    11. Osnat Zohar, 2021. "Cyclicality of Uncertainty and Disagreement," Bank of Israel Working Papers 2021.09, Bank of Israel.
    12. Cordeiro, Yara de Almeida Campos & Gaglianone, Wagner Piazza & Issler, João Victor, 2016. "Inattention in individual expectations," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 776, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    13. Andrew C. Chang & Trace J. Levinson, 2023. "Raiders of the lost high‐frequency forecasts: New data and evidence on the efficiency of the Fed's forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(1), pages 88-104, January.
    14. Coibion, Olivier & Gorodnichenko, Yuriy & Kumar, Saten & Ryngaert, Jane, 2021. "Do You Know that I Know that You Know…? Higher-Order Beliefs in Survey Data," Department of Economics, Working Paper Series qt5cd1r3bd, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    15. Kamil Kladívko & Pär Österholm, 2024. "An Analysis of UK Households’ Directional Forecasts of Interest Rates," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 20(3), pages 423-442, November.
    16. Clements, Michael P., 2014. "Probability distributions or point predictions? Survey forecasts of US output growth and inflation," International Journal of Forecasting, Elsevier, vol. 30(1), pages 99-117.
    17. Farmer, Leland E & Nakamura, Emi & Steinsson, Jón, 2024. "Learning about the Long Run," Department of Economics, Working Paper Series qt0tn1s1hp, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    18. James Yetman, 2018. "The perils of approximating fixed-horizon inflation forecasts with fixed-event forecasts," BIS Working Papers 700, Bank for International Settlements.
    19. Robert Rich & Joseph Tracy, 2021. "A Closer Look at the Behavior of Uncertainty and Disagreement: Micro Evidence from the Euro Area," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(1), pages 233-253, February.
    20. Marcela De Castro-Valderrama & Santiago Forero-Alvarado & Nicolás Moreno-Arias & Sara Naranjo-Saldarriaga, 2021. "Unraveling the Exogenous Forces Behind Analysts’ Macroeconomic Forecasts," Borradores de Economia 1184, Banco de la Republica de Colombia.
    21. Dovern, Jonas, 2014. "A Multivariate Analysis of Forecast Disagreement: Confronting Models of Disagreement with SPF Data," Working Papers 0571, University of Heidelberg, Department of Economics.
    22. Alice Hsiaw & Ing-Haw Cheng, 2016. "Distrust in Experts and the Origins of Disagreement," Working Papers 110R2, Brandeis University, Department of Economics and International Business School, revised Jan 2017.
    23. Conrad, Christian & Lahiri, Kajal, 2023. "Heterogeneous expectations among professional forecasters," ZEW Discussion Papers 23-062, ZEW - Leibniz Centre for European Economic Research.
    24. Dick, Christian D. & Schmeling, Maik & Schrimpf, Andreas, 2010. "Macro expectations, aggregate uncertainty, and expected term premia," ZEW Discussion Papers 10-064, ZEW - Leibniz Centre for European Economic Research.
    25. Lena Dräger & Michael J. Lamla, 2015. "Disagreement à la Taylor: Evidence from Survey Microdata," Macroeconomics and Finance Series 201503, University of Hamburg, Department of Socioeconomics.
    26. Mikhail Anufriev & Aleksei Chernulich & Jan Tuinstra, 2020. "Asset Price Volatility and Investment Horizons: An Experimental Investigation," Working Papers 20200053, New York University Abu Dhabi, Department of Social Science, revised Aug 2020.
    27. Alonso, Ricardo & Câmara, Odilon, 2016. "Bayesian persuasion with heterogeneous priors," LSE Research Online Documents on Economics 67950, London School of Economics and Political Science, LSE Library.
    28. James M. Nason & Gregor W. Smith, 2013. "Measuring The Slowly Evolving Trend In Us Inflation With Professional Forecasts," Working Paper 1316, Economics Department, Queen's University.
    29. Giacomini, Raffaella & Skreta, Vasiliki & Turen, Javier, 2016. "Models, inattention and expectation updates," LSE Research Online Documents on Economics 86245, London School of Economics and Political Science, LSE Library.
    30. Oscar Claveria, 2021. "Uncertainty indicators based on expectations of business and consumer surveys," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 48(2), pages 483-505, May.
    31. Charles F. Manski, 2018. "Survey Measurement of Probabilistic Macroeconomic Expectations: Progress and Promise," NBER Macroeconomics Annual, University of Chicago Press, vol. 32(1), pages 411-471.
    32. Qu, Ritong & Timmermann, Allan & Zhu, Yinchu, 2024. "Comparing forecasting performance with panel data," International Journal of Forecasting, Elsevier, vol. 40(3), pages 918-941.
    33. Andrew J. Patton & Allan Timmermann, 2008. "The Resolution of Macroeconomic Uncertainty: Evidence from Survey Forecast," CREATES Research Papers 2008-54, Department of Economics and Business Economics, Aarhus University.
    34. Hasselgren, Anton & Hou, Ai Jun & Suardi, Sandy & Xu, Caihong & Ye, Xiaoxia, 2025. "Do oil price forecast disagreement of survey of professional forecasters predict crude oil return volatility?," International Journal of Forecasting, Elsevier, vol. 41(1), pages 141-152.
    35. Michael Clements, 2017. "Do forecasters target first or later releases of national accounts data?," ICMA Centre Discussion Papers in Finance icma-dp2017-03, Henley Business School, University of Reading.
    36. Robert A. Connolly & Chris Stivers & Licheng Sun, 2022. "Stock returns and inflation shocks in weaker economic times," Financial Management, Financial Management Association International, vol. 51(3), pages 827-867, September.
    37. Paul Hubert, 2014. "FOMC Forecasts as a Focal Point for Private Expectations," Post-Print hal-03399408, HAL.
    38. Andrade, Philippe & Le Bihan, Hervé, 2013. "Inattentive professional forecasters," Journal of Monetary Economics, Elsevier, vol. 60(8), pages 967-982.
    39. Michael P. Clements, 2018. "Do Macroforecasters Herd?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(2-3), pages 265-292, March.
    40. Richard K. Crump & Stefano Eusepi & Emanuel Moench & Bruce Preston, 2021. "The Term Structure of Expectations," Staff Reports 992, Federal Reserve Bank of New York.
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    1. Wan-Ni Lai & Claire Y. T. Chen & Edward W. Sun, 2022. "Risk factor extraction with quantile regression method," Annals of Operations Research, Springer, vol. 316(2), pages 1543-1572, September.
    2. Ruenzi, Stefan & Ungeheuer, Michael & Weigert, Florian, 2020. "Joint extreme events in equity returns and liquidity and their cross-sectional pricing implications," CFR Working Papers 20-01, University of Cologne, Centre for Financial Research (CFR).
    3. Suneel Maheshwari & Deepak Raghava Naik, 2024. "Predicting Mutual Fund Stress Levels Utilizing SEBI’s Stress Test Parameters in MidCap and SmallCap Funds Using Deep Learning Models," Risks, MDPI, vol. 12(11), pages 1-17, November.
    4. Klein, Tobias & Salm, Martin & Upadhyay, Suraj, 2020. "The Response to Dynamic Incentives in Insurance Contracts with a Deductible: Evidence from a Differences-in-Regression-Disconti," CEPR Discussion Papers 14552, C.E.P.R. Discussion Papers.
    5. Sullivan, Michael & Zhang, Andrew (Jianzhong), 2011. "Are investment and financing anomalies two sides of the same coin?," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 616-633, September.
    6. Roh, Tai-Yong & Lee, Changjun & Min, Byoung-Kyu, 2019. "Consumption growth predictability and asset prices," Journal of Empirical Finance, Elsevier, vol. 51(C), pages 95-118.
    7. Zaremba, Adam & Karathanasopoulos, Andreas & Maydybura, Alina & Czapkiewicz, Anna & Bagheri, Noushin, 2020. "Dissecting anomalies in Islamic stocks: Integrated or segmented pricing?," Pacific-Basin Finance Journal, Elsevier, vol. 62(C).
    8. Ruenzi, Stefan & Ungeheuer, Michael & Weigert, Florian, 2013. "Extreme Downside Liquidity Risk," Working Papers on Finance 1326, University of St. Gallen, School of Finance, revised Jul 2015.
    9. Altavilla, Carlo & Gürkaynak, Refet & Quaedvlieg, Rogier, 2024. "Macro and Micro of External Finance Premium and Monetary Policy Transmission," CEPR Discussion Papers 19044, C.E.P.R. Discussion Papers.
    10. Sarno, Lucio & Schmeling, Maik, 2013. "Which Fundamentals Drive Exchange Rates? A Cross-Sectional Perspective," CEPR Discussion Papers 9472, C.E.P.R. Discussion Papers.
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    16. Zaremba, Adam & Bilgin, Mehmet Huseyin & Long, Huaigang & Mercik, Aleksander & Szczygielski, Jan J., 2021. "Up or down? Short-term reversal, momentum, and liquidity effects in cryptocurrency markets," International Review of Financial Analysis, Elsevier, vol. 78(C).
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    1. Axel Bücher & Holger Dette & Florian Heinrichs, 2020. "Detecting deviations from second-order stationarity in locally stationary functional time series," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(4), pages 1055-1094, August.
    2. Ian W. R. Martin & Christian Wagner, 2019. "What Is the Expected Return on a Stock?," Journal of Finance, American Finance Association, vol. 74(4), pages 1887-1929, August.
    3. Fulvio Pegoraro & Siegel, A. F. & Tiozzo Pezzoli, L., 2014. "Specification Analysis of International Treasury Yield Curve Factors," Working papers 490, Banque de France.
    4. Pouliot, Sébastien & Sumner, Daniel A., 2012. "Differential Impacts of Country of Origin Labeling: COOL Econometric Evidence from Cattle Markets," Working Papers 148593, Structure and Performance of Agriculture and Agri-products Industry (SPAA).
    5. Iulia Lupu & Radu Lupu & Adina Criste, 2023. "The Nexus between Green Bonds and European Banks: A Cross-Quantilogram Approach," Energies, MDPI, vol. 16(24), pages 1-19, December.
    6. Monica Billio & Lorenzo Frattarolo & Dominique Guegan, 2017. "Multivariate Reflection Symmetry of Copula Functions," Post-Print halshs-01592147, HAL.
    7. Esfandiar Maasoumi & Jeffrey Racine, 2009. "A Robust Entropy-Based Test of Asymmetry for Discrete and Continuous Processes," Econometric Reviews, Taylor & Francis Journals, vol. 28(1-3), pages 246-261.
    8. Patrick Fève & Alain Guay, 2010. "Identification of Technology Shocks in Structural Vars," Economic Journal, Royal Economic Society, vol. 120(549), pages 1284-1318, December.
    9. Paulo Parente & Richard J. Smith, 2019. "Quasi-maximum likelihood and the kernel block bootstrap for nonlinear dynamic models," CeMMAP working papers CWP60/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. Patrice Bertail & Stéphan Clémençon, 2006. "Approximate Regenerative-block Bootstrap for Markov Chains : Some Simulation Studies," Working Papers 2006-19, Center for Research in Economics and Statistics.
    11. Manuel Lukas & Eric Hillebrand, 2014. "Bagging Weak Predictors," CREATES Research Papers 2014-01, Department of Economics and Business Economics, Aarhus University.
    12. Francis In & Sangbae Kim & Philip I Ji, 2014. "On timing ability in Australian managed funds," Australian Journal of Management, Australian School of Business, vol. 39(1), pages 93-106, February.
    13. Raffaella Giacomini & Dimitris N. Politis & Halbert White, 2012. "A warp-speed method for conducting Monte Carlo experiments involving bootstrap estimators," CeMMAP working papers CWP11/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. Shahzad, Syed Jawad Hussain & Arreola-Hernandez, Jose & Bekiros, Stelios & Rehman, Mobeen Ur, 2018. "Risk transmitters and receivers in global currency markets," Finance Research Letters, Elsevier, vol. 25(C), pages 1-9.
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    34. Sylvain Barde, 2015. "A Practical, Universal, Information Criterion over Nth Order Markov Processes," Studies in Economics 1504, School of Economics, University of Kent.
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    3. Lyócsa, Štefan & Todorova, Neda, 2021. "What drives volatility of the U.S. oil and gas firms?," Energy Economics, Elsevier, vol. 100(C).
    4. Ruijun Bu & Rodrigo Hizmeri & Marwan Izzeldin & Anthony Murphy & Mike G. Tsionas, 2019. "The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility," Working Papers 1902, Federal Reserve Bank of Dallas, revised 17 Dec 2022.
    5. João F. Caldeira & Guilherme V. Moura & Francisco J. Nogales & André A. P. Santos, 2017. "Combining Multivariate Volatility Forecasts: An Economic-Based Approach," Journal of Financial Econometrics, Oxford University Press, vol. 15(2), pages 247-285.
    6. Chaker, Selma, 2019. "The signal and the noise volatilities," Research in International Business and Finance, Elsevier, vol. 50(C), pages 79-105.
    7. Fuertes, Ana-Maria & Olmo, Jose, 2013. "Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction," International Journal of Forecasting, Elsevier, vol. 29(1), pages 28-42.
    8. Cubadda, Gianluca & Guardabascio, Barbara & Hecq, Alain, 2017. "A vector heterogeneous autoregressive index model for realized volatility measures," International Journal of Forecasting, Elsevier, vol. 33(2), pages 337-344.
    9. Zhang, Yaojie & He, Mengxi & Wang, Yudong & Wen, Danyan, 2025. "Model specification for volatility forecasting benchmark," International Review of Financial Analysis, Elsevier, vol. 97(C).
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    11. Yaojie Zhang & Yudong Wang & Feng Ma & Yu Wei, 2022. "To jump or not to jump: momentum of jumps in crude oil price volatility prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-31, December.
    12. Lyócsa, Štefan & Molnár, Peter & Plíhal, Tomáš, 2019. "Central bank announcements and realized volatility of stock markets in G7 countries," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 58(C), pages 117-135.
    13. Lyócsa, Štefan & Molnár, Peter & Plíhal, Tomáš & Širaňová, Mária, 2020. "Impact of macroeconomic news, regulation and hacking exchange markets on the volatility of bitcoin," Journal of Economic Dynamics and Control, Elsevier, vol. 119(C).
    14. Fei Su & Lei Wang, 2020. "Conditional Volatility Persistence and Realized Volatility Asymmetry: Evidence from the Chinese Stock Markets," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 56(14), pages 3252-3269, November.
    15. Bonato, Mateo & Caporin, Massimiliano & Ranaldo, Angelo, 2012. "Risk Spillovers in International Equity Portfolios," Working Papers on Finance 1214, University of St. Gallen, School of Finance.
    16. 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.
    17. Venter, J.H. & de Jongh, P.J., 2014. "Extended stochastic volatility models incorporating realised measures," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 687-707.
    18. CHEN, Cathy W.S. & WENG, Monica M.C. & WATANABE, Toshiaki & 渡部, 渡部, 2015. "Employing Bayesian Forecasting of Value-at-Risk to Determine an Appropriate Model for Risk Management," Discussion paper series HIAS-E-16, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    19. Martina Halouskov'a & Daniel Stav{s}ek & Mat'uv{s} Horv'ath, 2022. "The role of investor attention in global asset price variation during the invasion of Ukraine," Papers 2205.05985, arXiv.org, revised Aug 2022.
    20. Lyu, Zhichong & Ma, Feng & Zhang, Jixiang, 2023. "Oil futures volatility prediction: Bagging or combination?," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 457-467.
    21. 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).
    22. Santos, Douglas G. & Candido, Osvaldo & Tófoli, Paula V., 2022. "Forecasting risk measures using intraday and overnight information," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    23. Wang, Yajing & Liang, Fang & Wang, Tianyi & Huang, Zhuo, 2020. "Does measurement error matter in volatility forecasting? Empirical evidence from the Chinese stock market," Economic Modelling, Elsevier, vol. 87(C), pages 148-157.
    24. Julien Chevallier & Benoît Sévi, 2009. "On the Realized Volatility of the ECX CO2 Emissions 2008 Futures Contract: Distribution, Dynamics and Forecasting," Working Papers 2009.113, Fondazione Eni Enrico Mattei.
    25. Douglas G. Santos & Flavio A. Ziegelmann, 2014. "Volatility Forecasting via MIDAS, HAR and their Combination: An Empirical Comparative Study for IBOVESPA," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(4), pages 284-299, July.
    26. Antoine Mandel & Amir Sani, 2016. "Learning Time-Varying Forecast Combinations," Documents de travail du Centre d'Economie de la Sorbonne 16036, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    27. Antoine Mandel & Amir Sani, 2017. "A Machine Learning Approach to the Forecast Combination Puzzle," Working Papers halshs-01317974, HAL.
    28. Yaojie Zhang & Yudong Wang & Feng Ma, 2021. "Forecasting US stock market volatility: How to use international volatility information," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 733-768, August.
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    30. 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.
    31. 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.
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    33. Halousková, Martina & Stašek, Daniel & Horváth, Matúš, 2022. "The role of investor attention in global asset price variation during the invasion of Ukraine," Finance Research Letters, Elsevier, vol. 50(C).
    34. Moro Matheus Fernando & Weise Andreas Dittmar & Bornia Antonio Cezar, 2020. "Model Hybrid for Sales Forecast for the Housing Market of São Paulo," Real Estate Management and Valuation, Sciendo, vol. 28(3), pages 45-64, September.
    35. Horváth, Roman & Kalistová, Anna & Lyócsa, Štefan & Miškufová, Marta & Moravcová, Michala, 2025. "Do hurricanes cause storm on the stock market? The case of US energy companies," International Review of Financial Analysis, Elsevier, vol. 97(C).
    36. Zhikai Zhang & Yaojie Zhang & Yudong Wang & Qunwei Wang, 2024. "The predictability of carbon futures volatility: New evidence from the spillovers of fossil energy futures returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(4), pages 557-584, April.
    37. Lyócsa, Štefan & Plíhal, Tomáš & Výrost, Tomáš, 2021. "FX market volatility modelling: Can we use low-frequency data?," Finance Research Letters, Elsevier, vol. 40(C).
    38. 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.
    39. Syed jawad hussain Shahzad & Elie Bouri & Román Ferrer, 2023. "Twitter sentiment and stock return volatility of US travel and leisure firms," Economics Bulletin, AccessEcon, vol. 43(2), pages 1133-1142.
    40. Chen Xilong & Ghysels Eric & Wang Fangfang, 2011. "HYBRID GARCH Models and Intra-Daily Return Periodicity," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-28, February.
    41. Pan, Ging-Ginq & Shiu, Yung-Ming & Wu, Tu-Cheng, 2022. "Can risk-neutral skewness and kurtosis subsume the information content of historical jumps?," Journal of Financial Markets, Elsevier, vol. 57(C).
    42. Lyócsa, Štefan & Todorova, Neda & Výrost, Tomáš, 2021. "Predicting risk in energy markets: Low-frequency data still matter," Applied Energy, Elsevier, vol. 282(PA).
    43. 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.
    44. Hamid, Alain & Heiden, Moritz, 2015. "Forecasting volatility with empirical similarity and Google Trends," Journal of Economic Behavior & Organization, Elsevier, vol. 117(C), pages 62-81.
    45. Vitali Alexeev & Mardi Dungey, 2015. "Equity portfolio diversification with high frequency data," Quantitative Finance, Taylor & Francis Journals, vol. 15(7), pages 1205-1215, July.
    46. 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.
    47. Maria Čuljak & Josip Arnerić & Ante Žigman, 2022. "Is Jump Robust Two Times Scaled Estimator Superior among Realized Volatility Competitors?," Mathematics, MDPI, vol. 10(12), pages 1-11, June.
    48. Lyócsa, Štefan & Molnár, Peter, 2017. "The effect of non-trading days on volatility forecasts in equity markets," Finance Research Letters, Elsevier, vol. 23(C), pages 39-49.
    49. Zhang, Yaojie & Ma, Feng & Wei, Yu, 2019. "Out-of-sample prediction of the oil futures market volatility: A comparison of new and traditional combination approaches," Energy Economics, Elsevier, vol. 81(C), pages 1109-1120.
    50. Taylor, Nick, 2017. "Realised variance forecasting under Box-Cox transformations," International Journal of Forecasting, Elsevier, vol. 33(4), pages 770-785.
    51. Zhang, Yaojie & Wei, Yu & Zhang, Yi & Jin, Daxiang, 2019. "Forecasting oil price volatility: Forecast combination versus shrinkage method," Energy Economics, Elsevier, vol. 80(C), pages 423-433.
    52. Panos K. Pouliasis & Ilias D. Visvikis & Nikos C. Papapostolou & Alexander A. Kryukov, 2020. "A novel risk management framework for natural gas markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(3), pages 430-459, March.
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    55. James, Robert & Leung, Henry & Prokhorov, Artem, 2023. "A machine learning attack on illegal trading," Journal of Banking & Finance, Elsevier, vol. 148(C).
    56. Trucíos, Carlos, 2019. "Forecasting Bitcoin risk measures: A robust approach," International Journal of Forecasting, Elsevier, vol. 35(3), pages 836-847.
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    62. Yuqing Feng & Yaojie Zhang & Yudong Wang, 2024. "Out‐of‐sample volatility prediction: Rolling window, expanding window, or both?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 567-582, April.
    63. Lyócsa, Štefan & Molnár, Peter & Výrost, Tomáš, 2021. "Stock market volatility forecasting: Do we need high-frequency data?," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1092-1110.
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    75. 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.
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    Cited by:

    1. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
    2. Stan Hurn & Jing Tian & Lina Xu, 2021. "Assessing the Informational Content of Official Australian Bureau of Meteorology Forecasts of Wind Speed," The Economic Record, The Economic Society of Australia, vol. 97(319), pages 525-547, December.
    3. 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.
    4. Sinclair, Tara M. & Gamber, Edward N. & Stekler, Herman & Reid, Elizabeth, 2012. "Jointly evaluating the Federal Reserve’s forecasts of GDP growth and inflation," International Journal of Forecasting, Elsevier, vol. 28(2), pages 309-314.
    5. Shea, Paul, 2015. "Red herrings and revelations: does learning about a new variable worsen forecasts?," Economic Modelling, Elsevier, vol. 49(C), pages 395-406.
    6. Emilio Zanetti Chini, 2018. "Forecaster’s utility and forecasts coherence," CREATES Research Papers 2018-01, Department of Economics and Business Economics, Aarhus University.
    7. Christian Pierdzioch & Jan-Christoph Rülke & Peter Tillmann, 2013. "Using forecasts to uncover the loss function of FOMC members," MAGKS Papers on Economics 201302, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    8. Mehmet Pinar & Thanasis Stengos & M. Ege Yazgan, 2018. "Quantile forecast combination using stochastic dominance," Empirical Economics, Springer, vol. 55(4), pages 1717-1755, December.
    9. Dr Silvia Lui & Dr Martin Weale & Dr. James Mitchell, 2009. "The utility of expectational data: Firm-level evidence using matched qualitative-quantitative UK surveys," National Institute of Economic and Social Research (NIESR) Discussion Papers 343, National Institute of Economic and Social Research.
    10. Pierdzioch, Christian & Rülke, Jan-Christoph, 2013. "Do inflation targets anchor inflation expectations?," Economic Modelling, Elsevier, vol. 35(C), pages 214-223.
    11. Conrad, Christian & Lahiri, Kajal, 2023. "Heterogeneous expectations among professional forecasters," ZEW Discussion Papers 23-062, ZEW - Leibniz Centre for European Economic Research.
    12. Buse, Rebekka & Schienle, Melanie, 2019. "Measuring connectedness of euro area sovereign risk," International Journal of Forecasting, Elsevier, vol. 35(1), pages 25-44.
    13. Lima, Luiz Renato & Meng, Fanning & Godeiro, Lucas, 2020. "Quantile forecasting with mixed-frequency data," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1149-1162.
    14. Bauer, Christian & Neuenkirch, Matthias, 2017. "Forecast uncertainty and the Taylor rule," Journal of International Money and Finance, Elsevier, vol. 77(C), pages 99-116.
    15. Timmermann Allan & Capistrán Carlos, 2006. "Disagreement and Biases in Inflation Expectations," Working Papers 2006-07, Banco de México.
    16. Pablo Pincheira B. & Nicolás Fernández, 2011. "Jaque Mate a las Proyecciones de Consenso," Working Papers Central Bank of Chile 630, Central Bank of Chile.
    17. Nazaria Solferino & Robert Waldmann, 2010. "Predicting the signs of forecast errors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(5), pages 476-485.
    18. Matei Demetrescu & Sinem Hacioglu Hoke, 2018. "Predictive regressions under asymmetric loss: factor augmentation and model selection," Bank of England working papers 723, Bank of England.
    19. Jens J. Krüger, 2014. "A multivariate evaluation of German output growth and inflation forecasts," Economics Bulletin, AccessEcon, vol. 34(3), pages 1410-1418.
    20. Anatolyev, Stanislav, 2009. "Dynamic modeling under linear-exponential loss," Economic Modelling, Elsevier, vol. 26(1), pages 82-89, January.
    21. Coroneo, Laura & Iacone, Fabrizio & Paccagnini, Alessia & Santos Monteiro, Paulo, 2023. "Testing the predictive accuracy of COVID-19 forecasts," International Journal of Forecasting, Elsevier, vol. 39(2), pages 606-622.
    22. Cui, Qiurong & Xu, Yuqing & Zhang, Zhengjun & Chan, Vincent, 2021. "Max-linear regression models with regularization," Journal of Econometrics, Elsevier, vol. 222(1), pages 579-600.
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    25. Goodwin, Thomas & Tian, Jing, 2017. "A state space approach to evaluate multi-horizon forecasts," Working Papers 2017-15, University of Tasmania, Tasmanian School of Business and Economics.
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    27. Andrew J. Patton & Allan Timmermann, 2011. "Forecast Rationality Tests Based on Multi-Horizon Bounds," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 1-17, June.
    28. Fritsche, Ulrich & Pierdzioch, Christian & Rülke, Jan-Christoph & Stadtmann, Georg, 2015. "Forecasting the Brazilian real and the Mexican peso: Asymmetric loss, forecast rationality, and forecaster herding," International Journal of Forecasting, Elsevier, vol. 31(1), pages 130-139.
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    31. Joanna Bruzda, 2020. "Multistep quantile forecasts for supply chain and logistics operations: bootstrapping, the GARCH model and quantile regression based approaches," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(1), pages 309-336, March.
    32. Wang, Yiyao & Lee, Tae-Hwy, 2014. "Asymmetric loss in the Greenbook and the Survey of Professional Forecasters," International Journal of Forecasting, Elsevier, vol. 30(2), pages 235-245.
    33. Atsushi Inoue & Barbara Rossi, 2011. "Out-of-sample forecast tests robust to the choice of window size," Working Papers 11-31, Federal Reserve Bank of Philadelphia.
    34. Kostas Mouratidis & Dimitris Kenourgios & Aris Samitas, 2010. "Evaluating currency crisis:A multivariate Markov switching approach," Working Papers 2010018, The University of Sheffield, Department of Economics, revised Oct 2010.
    35. 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.
    36. Faust, Jon & Wright, Jonathan H., 2013. "Forecasting Inflation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 2-56, Elsevier.
    37. Chevillon, Guillaume, 2016. "Multistep forecasting in the presence of location shifts," International Journal of Forecasting, Elsevier, vol. 32(1), pages 121-137.
    38. Conrad, Christian, 2017. "When does information on forecast variance improve the performance of a combined forecast?," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168200, Verein für Socialpolitik / German Economic Association.
    39. Gneiting, Tilmann, 2011. "Quantiles as optimal point forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 197-207, April.
    40. Philip Hans Franses & Michael McAleer & Rianne Legerstee, 2009. "Expert opinion versus expertise in forecasting," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 63(3), pages 334-346, August.
    41. Florian Peters & Simas Kucinskas, 2018. "Measuring Biases in Expectation Formation," Tinbergen Institute Discussion Papers 18-058/IV, Tinbergen Institute.
    42. Andrea Betancor & Pablo Pincheira, 2008. "Forecasting Inflation Forecast Errors," Working Papers Central Bank of Chile 477, Central Bank of Chile.
    43. Xinglin Yang & Peng Wang, 2018. "VIX futures pricing with conditional skewness," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(9), pages 1126-1151, September.
    44. Demetrescu, Matei & Golosnoy, Vasyl & Titova, Anna, 2020. "Bias corrections for exponentially transformed forecasts: Are they worth the effort?," International Journal of Forecasting, Elsevier, vol. 36(3), pages 761-780.
    45. G. Kontogeorgos & K. Lambrias, 2022. "Evaluating the Eurosystem/ECB staff macroeconomic projections: The first 20 years," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 213-229, March.
    46. Changro Lee, 2025. "Using an Asymmetric Loss Function to Alleviate the Risk of Loan Collateral Overvaluation," International Real Estate Review, Global Social Science Institute, vol. 28(1), pages 53-69.
    47. Fritsch, Markus & Haupt, Harry & Schnurbus, Joachim, 2025. "Efficiency of poll-based multi-period forecasting systems for German state elections," International Journal of Forecasting, Elsevier, vol. 41(2), pages 670-688.
    48. 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.
    49. João Henrique Gonçalves Mazzeu & Esther Ruiz & Helena Veiga, 2018. "Uncertainty And Density Forecasts Of Arma Models: Comparison Of Asymptotic, Bayesian, And Bootstrap Procedures," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 388-419, April.
    50. Barbara Rossi, 2011. "Comment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 25-29, August.
    51. Bovi, Maurizio, 2019. "A Time-Varying Expectations Formation Mechanism," MPRA Paper 97624, University Library of Munich, Germany.
    52. Bruzda, Joanna, 2019. "Quantile smoothing in supply chain and logistics forecasting," International Journal of Production Economics, Elsevier, vol. 208(C), pages 122-139.
    53. Tian, Jing & Goodwin, Thomas, 2018. "An unobserved component modeling approach to evaluate multi-horizon forecasts," Working Papers 2018-04, University of Tasmania, Tasmanian School of Business and Economics.
    54. Lillestøl, Jostein & Sinding-Larsen, Richard, 2015. "Best estimate reporting with asymmetric loss," Discussion Papers 2015/7, Norwegian School of Economics, Department of Business and Management Science.
    55. 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.
    56. Bourmpoula, Evangelia. & Wieser, Christina., 2014. "An accuracy assessment of the global employment trends' unemployment rate forecasts," ILO Working Papers 994888903402676, International Labour Organization.
    57. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    58. Chia-Lin Chang & Philip Hans Franses & Michael McAleer, 2009. "How Accurate are Government Forecasts of Economic Fundamentals? The Case of Taiwan," CIRJE F-Series CIRJE-F-637, CIRJE, Faculty of Economics, University of Tokyo.
    59. 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).
    60. Rodríguez-Vargas, Adolfo, 2020. "Forecasting Costa Rican inflation with machine learning methods," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    61. Xinyu Zhang & Alan T. K. Wan & Sherry Z. Zhou, 2011. "Focused Information Criteria, Model Selection, and Model Averaging in a Tobit Model With a Nonzero Threshold," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 132-142, June.
    62. Federico Bassetti & Roberto Casarin & Marco Del Negro, 2022. "A Bayesian Approach to Inference on Probabilistic Surveys," Staff Reports 1025, Federal Reserve Bank of New York.
    63. Bourmpoula, Evangelia. & Wieser, Christina., 2014. "An accuracy assessment of the global employment trends' unemployment rate forecasts," Global Employment Trends Reports 994888903402676, International Labour Office, Economic and Labour Market Analysis Department.
    64. Giovannelli, Alessandro & Pericoli, Filippo Maria, 2020. "Are GDP forecasts optimal? Evidence on European countries," International Journal of Forecasting, Elsevier, vol. 36(3), pages 963-973.
    65. Ildeberta Abreu, 2011. "International organisations’ vs. private analysts’ forecasts: an evaluation," Working Papers w201120, Banco de Portugal, Economics and Research Department.
    66. Sizova, Natalia, 2011. "Integrated variance forecasting: Model based vs. reduced form," Journal of Econometrics, Elsevier, vol. 162(2), pages 294-311, June.
    67. Qiu, Yajie & Deschamps, Bruno & Liu, Xiaoquan, 2024. "Uncertainty and macroeconomic forecasts: Evidence from survey data," Journal of Economic Behavior & Organization, Elsevier, vol. 224(C), pages 463-480.
    68. Gonçalves Mazzeu, Joao Henrique & Ruiz Ortega, Esther & Veiga, Helena, 2015. "Model uncertainty and the forecast accuracy of ARMA models: A survey," DES - Working Papers. Statistics and Econometrics. WS ws1508, Universidad Carlos III de Madrid. Departamento de Estadística.
    69. Roman Huptas, 2019. "Point forecasting of intraday volume using Bayesian autoregressive conditional volume models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(4), pages 293-310, July.
    70. Gneiting, Tilmann, 2011. "Quantiles as optimal point forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 197-207.
    71. Luetkepohl Helmut & Xu Fang, 2011. "Forecasting Annual Inflation with Seasonal Monthly Data: Using Levels versus Logs of the Underlying Price Index," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-23, February.
    72. Michael W. McCracken & Giorgio Valente, 2012. "Asymptotic Inference for Performance Fees and the Predictability of Asset Returns," Working Papers 2012-049, Federal Reserve Bank of St. Louis.
    73. Pablo Pincheira, 2010. "A Real Time Evaluation of the Central Bank of Chile GDP Growth Forecasts," Working Papers Central Bank of Chile 556, Central Bank of Chile.
    74. Thiago De Oliveira Souza, 2011. "Forecasting Investment-Grade Credit-Spreads. A Regularized Approach," Working Papers ECARES ECARES 2011-037, ULB -- Universite Libre de Bruxelles.
    75. Kontogeorgos, Georgios & Lambrias, Kyriacos, 2019. "An analysis of the Eurosystem/ECB projections," Working Paper Series 2291, European Central Bank.
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  39. Patton, Andrew J. & Timmermann, Allan, 2007. "Testing Forecast Optimality Under Unknown Loss," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1172-1184, December.

    Cited by:

    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.
    2. D'Agostino, Antonello & McQuinn, Kieran & Whelan, Karl, 2010. "Are Some Forecasters Really Better Than Others?," Research Technical Papers 5/RT/10, Central Bank of Ireland.
    3. Sinclair, Tara M. & Gamber, Edward N. & Stekler, Herman & Reid, Elizabeth, 2012. "Jointly evaluating the Federal Reserve’s forecasts of GDP growth and inflation," International Journal of Forecasting, Elsevier, vol. 28(2), pages 309-314.
    4. Wieland, Volker & Wolters, Maik, 2013. "Forecasting and Policy Making," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 239-325, Elsevier.
    5. Aretz, Kevin & Bartram, Söhnke M. & Pope, Peter F., 2011. "Asymmetric Loss Functions and the Rationality of Expected Stock Returns," MPRA Paper 47343, University Library of Munich, Germany.
    6. Emilio Zanetti Chini, 2018. "Forecaster’s utility and forecasts coherence," CREATES Research Papers 2018-01, Department of Economics and Business Economics, Aarhus University.
    7. Christian Pierdzioch & Jan-Christoph Rülke & Peter Tillmann, 2013. "Using forecasts to uncover the loss function of FOMC members," MAGKS Papers on Economics 201302, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    8. Timo Dimitriadis & Andrew J. Patton & Patrick W. Schmidt, 2019. "Testing Forecast Rationality for Measures of Central Tendency," Papers 1910.12545, arXiv.org, revised Jul 2024.
    9. Dr Silvia Lui & Dr Martin Weale & Dr. James Mitchell, 2009. "The utility of expectational data: Firm-level evidence using matched qualitative-quantitative UK surveys," National Institute of Economic and Social Research (NIESR) Discussion Papers 343, National Institute of Economic and Social Research.
    10. 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.
    11. Chia-Lin Chang & Philip Hans Franses & Michael McAleer, 2010. "Combining Non-Replicable Forecasts," Working Papers in Economics 10/35, University of Canterbury, Department of Economics and Finance.
    12. Foltas, Alexander & Pierdzioch, Christian, 2020. "Business-cycle reports and the efficiency of macroeconomic forecasts for Germany," Working Papers 22, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    13. Wagner Piazza Gaglianone & Luiz Renato Lima, 2014. "Constructing Optimal Density Forecasts From Point Forecast Combinations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(5), pages 736-757, August.
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    3. Marimoutou, Vêlayoudom & Soury, Manel, 2015. "Energy markets and CO2 emissions: Analysis by stochastic copula autoregressive model," Energy, Elsevier, vol. 88(C), pages 417-429.
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    5. 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.
    6. Chukiat Chaiboonsri & Prasert Chaitip, 2012. "A Comparative Analysis of ASEAN Currencies Using a Copula Approach and a Dynamic Copula Approach," Annals of the University of Petrosani, Economics, University of Petrosani, Romania, vol. 12(4), pages 39-52.
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    21. Cerrato, Mario & Crosby, John & Kim, Minjoo & Zhao, Yang, 2014. "Modeling Dependence Structure and Forecasting Portfolio Value-at-Risk with Dynamic Copulas," SIRE Discussion Papers 2015-25, Scottish Institute for Research in Economics (SIRE).
    22. Pawel Janus & Siem Jan Koopman & André Lucas, 2011. "Long Memory Dynamics for Multivariate Dependence under Heavy Tails," Tinbergen Institute Discussion Papers 11-175/2/DSF28, Tinbergen Institute.
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    7. Malik, Farooq & Ewing, Bradley T., 2009. "Volatility transmission between oil prices and equity sector returns," International Review of Financial Analysis, Elsevier, vol. 18(3), pages 95-100, June.
    8. 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.
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    37. Lee, Mingchih & Chen, Chun-Da, 2005. "The intraday behaviors and relationships with its underlying assets: evidence on option market in Taiwan," International Review of Financial Analysis, Elsevier, vol. 14(5), pages 587-603.
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    39. Belke, Ansgar & Gokus, Christian, 2011. "Volatility Patterns of CDS, Bond and Stock Markets Before and During the Financial Crisis – Evidence from Major Financial Institutions," Ruhr Economic Papers 243, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    40. Helena Chulia & Francisco Climent & Pilar Soriano & Hipolit Torro, 2009. "Volatility transmission patterns and terrorist attacks," Quantitative Finance, Taylor & Francis Journals, vol. 9(5), pages 607-619.
    41. Chulwoo Han & Frank C. Park & Jangkoo Kang, 2017. "A geometric treatment of time-varying volatilities," Review of Quantitative Finance and Accounting, Springer, vol. 49(4), pages 1121-1141, November.
    42. Deqing Diane Li & YingChou Lin & John Jin, 2012. "International Volatility Transmission Of Reit Returns," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 6(3), pages 41-51.
    43. Helen Higgs & Andrew Worthington, 2004. "Transmission of returns and volatility in art markets: a multivariate GARCH analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 11(4), pages 217-222.
    44. Nguyen, Trang & Chaiechi, Taha & Eagle, Lynne & Low, David, 2020. "Dynamic transmissions between main stock markets and SME stock markets: Evidence from tropical economies," The Quarterly Review of Economics and Finance, Elsevier, vol. 75(C), pages 308-324.
    45. Ngo Thai Hung, 2021. "Volatility Behaviour of the Foreign Exchange Rate and Transmission Among Central and Eastern European Countries: Evidence from the EGARCH Model," Global Business Review, International Management Institute, vol. 22(1), pages 36-56, February.
    46. Furió, Dolores & Chuliá, Helena, 2012. "Price and volatility dynamics between electricity and fuel costs: Some evidence for Spain," Energy Economics, Elsevier, vol. 34(6), pages 2058-2065.
    47. Higgs, Helen, 2009. "Modelling price and volatility inter-relationships in the Australian wholesale spot electricity markets," Energy Economics, Elsevier, vol. 31(5), pages 748-756, September.
    48. Nikkinen, Jussi & Sahlstrom, Petri & Vahamaa, Sami, 2006. "Implied volatility linkages among major European currencies," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 16(2), pages 87-103, April.
    49. B. Balaji & S. Raja Sethu Durai & M. Ramachandran, 2018. "Spillover Effects of Real and Nominal Uncertainties in India," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 16(1), pages 143-162, December.
    50. 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.
    51. Acatrinei, Marius & Gorun, Adrian & Marcu, Nicu, 2013. "A DCC-GARCH Model To Estimate the Risk to the Capital Market in Romania," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 136-148, March.
    52. Malik, Farooq, 2003. "Sudden changes in variance and volatility persistence in foreign exchange markets," Journal of Multinational Financial Management, Elsevier, vol. 13(3), pages 217-230, July.
    53. Xiarchos, Irene M. & Fletcher, Jerald J., 2009. "Price and volatility transmission between primary and scrap metal markets," Resources, Conservation & Recycling, Elsevier, vol. 53(12), pages 664-673.
    54. Hong Miao & Sanjay Ramchander & Marc W. Simpson, 2011. "Return and Volatility Transmission in U.S. Housing Markets," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 39(4), pages 701-741, December.
    55. B. Anand & Sunil Paul & M. Ramachandran, 2014. "Volatility Spillover between Oil and Stock Market Returns," Working Papers 2014-095, Madras School of Economics,Chennai,India.
    56. Tanattrin Bunnag, 2015. "Hedging Petroleum Futures with Multivariate GARCH Models," International Journal of Energy Economics and Policy, Econjournals, vol. 5(1), pages 105-120.
    57. Syed Jawad Hussain Shahzad & Jose Arreola‐Hernandez & Md Lutfur Rahman & Gazi Salah Uddin & Muhammad Yahya, 2021. "Asymmetric interdependence between currency markets' volatilities across frequencies and time scales," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2436-2457, April.
    58. Ozer-Imer, Itir & Ozkan, Ibrahim, 2014. "An empirical analysis of currency volatilities during the recent global financial crisis," Economic Modelling, Elsevier, vol. 43(C), pages 394-406.
    59. Mohamed Osman, 2015. "Dynamic Asymmetries in the Electric Consumption of the GCC Countries," International Journal of Energy Economics and Policy, Econjournals, vol. 5(2), pages 461-467.
    60. Jorge Pérez-Rodríguez, 2006. "The Euro and Other Major Currencies Floating Against the U.S. Dollar," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 34(4), pages 367-384, December.
    61. Krenar Avdulaj & Jozef Barunik, 2013. "Are benefits from oil - stocks diversification gone? New evidence from a dynamic copula and high frequency data," Papers 1307.5981, arXiv.org, revised Feb 2015.
    62. 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.
    63. Dimitrios Kartsonakis‐Mademlis & Nikolaos Dritsakis, 2021. "Asymmetric volatility spillovers between world oil prices and stock markets of the G7 countries in the presence of structural breaks," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 3930-3944, July.
    64. Do, Hung Xuan & Brooks, Robert & Treepongkaruna, Sirimon & Wu, Eliza, 2016. "Stock and currency market linkages: New evidence from realized spillovers in higher moments," International Review of Economics & Finance, Elsevier, vol. 42(C), pages 167-185.
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    67. Zouheir Mighri & Faysal Mansouri, 2013. "Dynamic Conditional Correlation Analysis of Stock Market Contagion: Evidence from the 2007-2010 Financial Crises," International Journal of Economics and Financial Issues, Econjournals, vol. 3(3), pages 637-661.
    68. Fujen Daniel Hsiao & Yan Hu, 2014. "International Evidence of Spillover Effects of Deposit Rates: A Multivariate Garch Model," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 8(1), pages 31-44.
    69. Park, Yang-Ho, 2020. "Variance disparity and market frictions," Journal of Econometrics, Elsevier, vol. 214(2), pages 326-348.
    70. Linkon Mondal, 2014. "Volatility spillover between the RBI’s intervention and exchange rate," International Economics and Economic Policy, Springer, vol. 11(4), pages 549-560, December.
    71. Nàtalia Valls & Helena Chulià, 2014. "“Volatility Transmission between the stock and Currency Markets in Emerging Asia: the Impact of the Global Financial Crisis”," IREA Working Papers 201431, University of Barcelona, Research Institute of Applied Economics, revised Dec 2014.
    72. Das, Suman & Roy, Saikat Sinha, 2023. "Following the leaders? A study of co-movement and volatility spillover in BRICS currencies," Economic Systems, Elsevier, vol. 47(2).
    73. Luca Vincenzo Ballestra & Riccardo De Blasis & Graziella Pacelli, 2025. "Multivariate GARCH models with spherical parameterizations: an oil price application," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-20, December.
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Chapters

  1. Patton, Andrew, 2013. "Copula Methods for Forecasting Multivariate Time Series," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 899-960, Elsevier.

    Cited by:

    1. Haffar, Adlane & Le Fur, Éric, 2022. "Time-varying dependence of Bitcoin," The Quarterly Review of Economics and Finance, Elsevier, vol. 86(C), pages 211-220.
    2. Yongqiang Zhu & Xinyi Li & Xizhen Mu & Yue Zhao, 2024. "Analysis of the Relationships between Variables and Their Applications in the Energy Saving Field," Energies, MDPI, vol. 17(15), pages 1-16, July.
    3. Alexander, Carol & Han, Yang & Meng, Xiaochun, 2023. "Static and dynamic models for multivariate distribution forecasts: Proper scoring rule tests of factor-quantile versus multivariate GARCH models," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1078-1096.
    4. Ozan Evkaya & İsmail Gür & Bükre Yıldırım Külekci & Gülden Poyraz, 2024. "Vine Copula Approach to Understand the Financial Dependence of the Istanbul Stock Exchange Index," Computational Economics, Springer;Society for Computational Economics, vol. 64(5), pages 2935-2980, November.
    5. Chen, Yanghan & Lin, Juan, 2024. "Measuring systemic risk in Asian foreign exchange markets," Journal of International Money and Finance, Elsevier, vol. 146(C).
    6. Zhou, You & Lin, Lichao & Huang, Ziling, 2024. "Diversification value of green Bonds: Fresh evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 74(C).
    7. Garcia-Jorcano, Laura & Benito, Sonia, 2020. "Studying the properties of the Bitcoin as a diversifying and hedging asset through a copula analysis: Constant and time-varying," Research in International Business and Finance, Elsevier, vol. 54(C).
    8. 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.
    9. Małgorzata Doman & Ryszard Doman, 2014. "Dynamic Linkages in the Pairs (GBP/EUR, USD/EUR) and (GBP/USD, EUR/USD): How Do They Change During a Day?," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 6(1), pages 33-56, March.
    10. Fousekis, Panos & Grigoriadis, Vasilis, 2019. "Integration and Hierarchy of Pork Markets in the EU: An Analysis from the Vantage of Graph Theory," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 68(2), June.
    11. Stanislav Anatolyev & Vladimir Pyrlik, 2021. "Shrinkage for Gaussian and t Copulas in Ultra-High Dimensions," CERGE-EI Working Papers wp699, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    12. Christensen, Troels Sønderby & Pircalabu, Anca & Høg, Esben, 2019. "A seasonal copula mixture for hedging the clean spark spread with wind power futures," Energy Economics, Elsevier, vol. 78(C), pages 64-80.
    13. Krenar AVDULAJ & Jozef BARUNIK, 2013. "Can We Still Benefit from International Diversification? The Case of the Czech and German Stock Markets," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 63(5), pages 425-442, November.
    14. Bai, Xiwen, 2021. "Tanker freight rates and economic policy uncertainty: A wavelet-based copula approach," Energy, Elsevier, vol. 235(C).
    15. Guilherme Armando Almeida Pereira & Álvaro Veiga, 2019. "Periodic Copula Autoregressive Model Designed to Multivariate Streamflow Time Series Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(10), pages 3417-3431, August.
    16. Pircalabu, A. & Benth, F.E., 2017. "A regime-switching copula approach to modeling day-ahead prices in coupled electricity markets," Energy Economics, Elsevier, vol. 68(C), pages 283-302.
    17. Fei, Fei & Fuertes, Ana-Maria & Kalotychou, Elena, 2017. "Dependence in credit default swap and equity markets: Dynamic copula with Markov-switching," International Journal of Forecasting, Elsevier, vol. 33(3), pages 662-678.
    18. J. A. Carrillo & M. Nieto & J. F. Velez & D. Velez, 2021. "A New Machine Learning Forecasting Algorithm Based on Bivariate Copula Functions," Forecasting, MDPI, vol. 3(2), pages 1-22, May.
    19. Fenech, Jean-Pierre & Vosgha, Hamed, 2019. "Oil price and Gulf Corporation Council stock indices: New evidence from time-varying copula models," Economic Modelling, Elsevier, vol. 77(C), pages 81-91.
    20. 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.
    21. Fousekis, Panos & Grigoriadis, Vasilis, 2022. "Conditional tail price risk spillovers in coffee markets across quality, physical space, and time: Empirical analysis with penalized quantile regressions," Economic Modelling, Elsevier, vol. 106(C).
    22. GRIGORIADIS, Vasilis & EMMANOUILIDES, Christos & FOUSEKIS, Panos, 2016. "The Integration Of Pigmeat Markets In The Eu. Evidence From A Regular Mixed Vine Copula," Review of Agricultural and Applied Economics (RAAE), Faculty of Economics and Management, Slovak Agricultural University in Nitra, vol. 19(01), pages 1-10, March.
    23. Yang Zhao & Charalampos Stasinakis & Georgios Sermpinis & Filipa Da Silva Fernandes, 2019. "Revisiting Fama–French factors' predictability with Bayesian modelling and copula‐based portfolio optimization," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(4), pages 1443-1463, October.
    24. Tranberg, Bo & Hansen, Rasmus Thrane & Catania, Leopoldo, 2020. "Managing volumetric risk of long-term power purchase agreements," Energy Economics, Elsevier, vol. 85(C).
    25. Timmermann, Allan, 2018. "Forecasting Methods in Finance," CEPR Discussion Papers 12692, C.E.P.R. Discussion Papers.
    26. Qiang Ji & Bing-Yue Liu & Juncal Cunado & Rangan Gupta, 2017. "Risk Spillover between the US and the Remaining G7 Stock Markets Using Time-Varying Copulas with Markov Switching: Evidence from Over a Century of Data," Working Papers 201759, University of Pretoria, Department of Economics.
    27. Fabrizio Durante & Roberta Pappadà & Nicola Torelli, 2015. "Clustering of time series via non-parametric tail dependence estimation," Statistical Papers, Springer, vol. 56(3), pages 701-721, August.
    28. Dimitrios Panagiotou & Athanassios Stavrakoudis, 2023. "Price dependence among the major EU extra virgin olive oil markets: a time scale analysis," Review of Agricultural, Food and Environmental Studies, Springer, vol. 104(1), pages 1-26, March.
    29. Nguyen, Hoang & Virbickaitė, Audronė, 2023. "Modeling stock-oil co-dependence with Dynamic Stochastic MIDAS Copula models," Energy Economics, Elsevier, vol. 124(C).
    30. Igor Kheifets, 2014. "Specification Tests for Nonlinear Dynamic Models," Cowles Foundation Discussion Papers 1937, Cowles Foundation for Research in Economics, Yale University, revised Oct 2014.
    31. Yang, Lu & Yang, Lei & Ho, Kung-Cheng & Hamori, Shigeyuki, 2020. "Dependence structures and risk spillover in China’s credit bond market: A copula and CoVaR approach," Journal of Asian Economics, Elsevier, vol. 68(C).
    32. Wu, Weiou & Lau, Marco Chi Keung & Vigne, Samuel A., 2017. "Modelling asymmetric conditional dependence between Shanghai and Hong Kong stock markets," Research in International Business and Finance, Elsevier, vol. 42(C), pages 1137-1149.
    33. Sheng Fang & Paul Egan, 2021. "Tail dependence between oil prices and China's A‐shares: Evidence from firm‐level data," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 1469-1487, January.
    34. 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.
    35. 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.
    36. Fousekis, Panos & Grigoriadis, Vasilis, 2017. "Price co-movement and the crack spread in the US futures markets," Journal of Commodity Markets, Elsevier, vol. 7(C), pages 57-71.
    37. Apergis, Nicholas & Gozgor, Giray & Lau, Chi Keung Marco & Wang, Shixuan, 2020. "Dependence structure in the Australian electricity markets: New evidence from regular vine copulae," Energy Economics, Elsevier, vol. 90(C).
    38. Anne Opschoor & André Lucas & István Barra & Dick van Dijk, 2021. "Closed-Form Multi-Factor Copula Models With Observation-Driven Dynamic Factor Loadings," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(4), pages 1066-1079, October.
    39. Dean Fantazzini & Stephan Zimin, 2020. "A multivariate approach for the simultaneous modelling of market risk and credit risk for cryptocurrencies," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 47(1), pages 19-69, March.
    40. Ouyang, Ruolan & Zhuang, Chengkai & Wang, Tingting & Zhang, Xuan, 2022. "Network analysis of risk transmission among energy futures: An industrial chain perspective," Energy Economics, Elsevier, vol. 107(C).
    41. Ngo Thai HUNG, 2020. "Conditional dependence between oil prices and CEE stock markets: a copula-GARCH approach Abstract: This study investigates both the constant and time-varying conditional dependency between crude oil a," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 11, pages 62-86, June.
    42. Müller, Alfred & Reuber, Matthias, 2023. "A copula-based time series model for global horizontal irradiation," International Journal of Forecasting, Elsevier, vol. 39(2), pages 869-883.
    43. Ahmed, Osama & Serra, Teresa, 2015. "Vertical Price Transmission in the Egyptian Tomato Sector After the Arab Spring," 2015 Conference, August 9-14, 2015, Milan, Italy 212523, International Association of Agricultural Economists.
    44. Johannes Kaufmann & Philipp Artur Kienscherf & Wolfgang Ketter, 2020. "Modeling and Managing Joint Price and Volumetric Risk for Volatile Electricity Portfolios," Energies, MDPI, vol. 13(14), pages 1-19, July.
    45. David E. Allen, 2022. "Cryptocurrencies, Diversification and the COVID-19 Pandemic," JRFM, MDPI, vol. 15(3), pages 1-25, February.
    46. Negi, Digvijay S., 2018. "Tail-dependent Rainfall Risk and Demand for Index based Crop Insurance," 2018 Annual Meeting, August 5-7, Washington, D.C. 274481, Agricultural and Applied Economics Association.
    47. Liu, Jianing & Man, Yuanyuan & Dong, Xiuliang, 2023. "Tail dependence and risk spillover effects between China's carbon market and energy markets," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 553-567.
    48. Fousekis, Panos, 2024. "Connectedness between conventional and organic milk prices in the USA," The Journal of Economic Asymmetries, Elsevier, vol. 30(C).
    49. Guizhou Liu & Xiao-Jing Cai & Shigeyuki Hamori, 2018. "Modeling the Dependence Structure of Share Prices among Three Chinese City Banks," JRFM, MDPI, vol. 11(4), pages 1-18, September.
    50. Denisa BANULESCU-RADU & Elena Ivona DUMITRESCU, 2019. "Do High-frequency-based Measures Improve Conditional Covariance Forecasts?," LEO Working Papers / DR LEO 2709, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    51. Fabrizio Durante & Roberta Pappadà & Nicola Torelli, 2014. "Clustering of financial time series in risky scenarios," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(4), pages 359-376, December.
    52. Wafa Miled & Zied Ftiti & Jean-Michel Sahut, 2022. "Spatial contagion between financial markets: new evidence of asymmetric measures," Annals of Operations Research, Springer, vol. 313(2), pages 1183-1220, June.
    53. Fousekis, Panos, 2020. "Sign and size asymmetry in the stock returns-implied volatility relationship," The Journal of Economic Asymmetries, Elsevier, vol. 21(C).
    54. Wadud, Sania & Gronwald, Marc & Durand, Robert B. & Lee, Seungho, 2023. "Co-movement between commodity and equity markets revisited—An application of the Thick Pen method," International Review of Financial Analysis, Elsevier, vol. 87(C).
    55. Pedro Antonio Martín Cervantes & Salvador Cruz Rambaud & María del Carmen Valls Martínez, 2020. "An Application of the SRA Copulas Approach to Price-Volume Research," Mathematics, MDPI, vol. 8(11), pages 1-28, October.
    56. Li, Danyang & Shi, Yukun & Xu, Liao & Xu, Yahua & Zhao, Yang, 2022. "Dynamic asymmetric dependence and portfolio management in cryptocurrency markets," Finance Research Letters, Elsevier, vol. 48(C).
    57. Xiaoqian Wen & Duc Khuong Nguyen, 2017. "Can Investors of Chinese Energy Stocks Benefit from Diversification into Commodity Futures?," Working Papers 2017-004, Department of Research, Ipag Business School.
    58. Ahmed, Osama & Serra, Teresa, 2015. "Evaluate the economic consequences of revenue insurance programs in Spain using copula models. The case of orange and apple," 2015 Conference, August 9-14, 2015, Milan, Italy 212522, International Association of Agricultural Economists.
    59. Guo, Peng & Chen, Si & Chu, Jingchun & Infield, David, 2020. "Wind direction fluctuation analysis for wind turbines," Renewable Energy, Elsevier, vol. 162(C), pages 1026-1035.
    60. Wang, Qin & Li, Xianhua, 2024. "Copula-MIDAS-TRV model for risk spillover analysis − Evidence from the Chinese stock market," The North American Journal of Economics and Finance, Elsevier, vol. 74(C).
    61. Fousekis, Panos, 2022. "Price risk connectedness in the principal olive oil markets of the EU," The Journal of Economic Asymmetries, Elsevier, vol. 26(C).
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