IDEAS home Printed from https://ideas.repec.org/e/c/phi41.html
   My authors  Follow this author

Eric Hillebrand

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. Eric Hillebrand & Tae-Hwy Lee, 2012. "Stein-Rule Estimation and Generalized Shrinkage Methods for Forecasting Using Many Predictors," CREATES Research Papers 2012-18, Department of Economics and Business Economics, Aarhus University.

    Mentioned in:

    1. What I Learned Last Week
      by Dave Giles in Econometrics Beat: Dave Giles' Blog on 2012-10-13 09:19:00

Working papers

  1. Mikkel Bennedsen & Eric Hillebrand & Siem Jan Koopman, 2020. "A statistical model of the global carbon budget," CREATES Research Papers 2020-18, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Chen, Liang & Dolado, Juan José & Gonzalo, Jesús & Ramos Ramírez, Andrey David, 2023. "Heterogeneous Predictive Association of CO2 with Global Warming," UC3M Working papers. Economics 36451, Universidad Carlos III de Madrid. Departamento de Economía.
    2. Marina Friedrich & Luca Margaritella & Stephan Smeekes, 2023. "High-Dimensional Granger Causality for Climatic Attribution," Papers 2302.03996, arXiv.org, revised Jun 2024.

  2. Eric Hillebrand & Jakob Mikkelsen & Lars Spreng & Giovanni Urga, 2020. "Exchange Rates and Macroeconomic Fundamentals: Evidence of Instabilities from Time-Varying Factor Loadings," CREATES Research Papers 2020-19, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Eric Hillebrand & Jakob Guldbæk Mikkelsen & Lars Spreng & Giovanni Urga, 2023. "Exchange rates and macroeconomic fundamentals: Evidence of instabilities from time‐varying factor loadings," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(6), pages 857-877, September.

  3. Mikkel Bennedsen & Eric Hillebrand & Siem Jan Koopman, 2019. "Modeling, Forecasting, and Nowcasting U.S. CO2 Emissions Using Many Macroeconomic Predictors," CREATES Research Papers 2019-21, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Morten Ørregaard Nielsen & Antoine L. Noël, 2020. "To infinity and beyond: Efficient computation of ARCH(1) models," CREATES Research Papers 2020-13, Department of Economics and Business Economics, Aarhus University.
    2. Ar'anzazu de Juan & Pilar Poncela & Vladimir Rodr'iguez-Caballero & Esther Ruiz, 2022. "Economic activity and climate change," Papers 2206.03187, arXiv.org, revised Jun 2022.
    3. Krishnamurthy Baskar Keerthana & Shih-Wei Wu & Mu-En Wu & Thangavelu Kokulnathan, 2023. "The United States Energy Consumption and Carbon Dioxide Emissions: A Comprehensive Forecast Using a Regression Model," Sustainability, MDPI, vol. 15(10), pages 1-20, May.
    4. Ignacio Garr'on & Andrey Ramos, 2025. "High-frequency Density Nowcasts of U.S. State-Level Carbon Dioxide Emissions," Papers 2501.03380, arXiv.org.
    5. Karen Miranda & Pilar Poncela & Esther Ruiz, 2022. "Dynamic factor models: Does the specification matter?," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 13(1), pages 397-428, May.
    6. Min Thura Mon & Roengchai Tansuchat & Woraphon Yamaka, 2024. "CCUS Technology and Carbon Emissions: Evidence from the United States," Energies, MDPI, vol. 17(7), pages 1-18, April.
    7. Alina Stundziene & Vaida Pilinkiene & Jurgita Bruneckiene & Andrius Grybauskas & Mantas Lukauskas & Irena Pekarskiene, 2024. "Future directions in nowcasting economic activity: A systematic literature review," Journal of Economic Surveys, Wiley Blackwell, vol. 38(4), pages 1199-1233, September.
    8. Hamed Khosravi & Ahmed Shoyeb Raihan & Farzana Islam & Ashish Nimbarte & Imtiaz Ahmed, 2025. "A Comprehensive Approach to CO 2 Emissions Analysis in High-Human-Development-Index Countries Using Statistical and Time Series Approaches," Sustainability, MDPI, vol. 17(2), pages 1-35, January.
    9. Ding, Song & Hu, Jiaqi & Lin, Qianqian, 2023. "Accurate forecasts and comparative analysis of Chinese CO2 emissions using a superior time-delay grey model," Energy Economics, Elsevier, vol. 126(C).
    10. Fosten, Jack & Nandi, Shaoni, 2025. "Nowcasting U.S. state-level CO2 emissions and energy consumption," International Journal of Forecasting, Elsevier, vol. 41(1), pages 20-30.
    11. Zeng, Qingshun & Shi, Changfeng & Zhu, Wenjun & Zhi, Jiaqi & Na, Xiaohong, 2023. "Sequential data-driven carbon peaking path simulation research of the Yangtze River Delta urban agglomeration based on semantic mining and heuristic algorithm optimization," Energy, Elsevier, vol. 285(C).
    12. Hong, Cencen & Liu, Nan & Zhang, Kai, 2024. "What are the best alternatives for sustainability? A rationalization theme for natural resource depletion and technical innovation," Resources Policy, Elsevier, vol. 95(C).
    13. Mikkel Bennedsen & Eric Hillebrand & Siem Jan Koopman, 2020. "A statistical model of the global carbon budget," CREATES Research Papers 2020-18, Department of Economics and Business Economics, Aarhus University.
    14. Xu, Guangyue & Huang, Zili & Jiang, Mingqi & Rehman, Hafiz Ur, 2024. "“Gray” Prediction of Carbon Neutral Pathways in the G7 Economies by 2050," Applied Energy, Elsevier, vol. 373(C).
    15. Robinson Kruse‐Becher, 2025. "Adaptive Now‐ and Forecasting of Global Temperatures Under Smooth Structural Changes," Environmetrics, John Wiley & Sons, Ltd., vol. 36(6), September.
    16. Mikkel Bennedsen & Eric Hillebrand & Morten {O}rregaard Nielsen, 2024. "The Global Carbon Budget as a cointegrated system," Papers 2412.09226, arXiv.org, revised Feb 2025.
    17. Iania, Leonardo & Algieri, Bernardina & Leccadito, Arturo, 2022. "Forecasting total energy’s CO2 emissions," LIDAM Discussion Papers LFIN 2022003, Université catholique de Louvain, Louvain Finance (LFIN).
    18. Anine E. Bolko & Kim Christensen & Mikko S. Pakkanen & Bezirgen Veliyev, 2020. "Roughness in spot variance? A GMM approach for estimation of fractional log-normal stochastic volatility models using realized measures," CREATES Research Papers 2020-12, Department of Economics and Business Economics, Aarhus University.
    19. Duras, Toni & Javed, Farrukh & Månsson, Kristofer & Sjölander, Pär & Söderberg, Magnus, 2023. "Using machine learning to select variables in data envelopment analysis: Simulations and application using electricity distribution data," Energy Economics, Elsevier, vol. 120(C).

  4. Tae-Hwy Lee & Eric Hillebrand & Huiyu Huang & Canlin Li, 2018. "Using the Entire Yield Curve in Forecasting Output and Inflation," Working Papers 201903, University of California at Riverside, Department of Economics.

    Cited by:

    1. Rangan Gupta & Syed Jawad Hussain Shahzad & Xin Sheng & Sowmya Subramaniam, 2023. "The role of oil and risk shocks in the high‐frequency movements of the term structure of interest rates: Evidence from the U.S. Treasury market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1845-1857, April.
    2. Elie Bouri & Rangan Gupta & Anandamayee Majumdar & Sowmya Subramaniam, 2020. "Time-Varying Risk Aversion and Forecastability of the US Term Structure of Interest Rates," Working Papers 202098, University of Pretoria, Department of Economics.
    3. Rangan Gupta & Sowmya Subramaniam & Elie Bouri & Qiang Ji, 2020. "Infectious Disease-Related Uncertainty and the Safe-Haven Characteristic of US Treasury Securities," Working Papers 202078, University of Pretoria, Department of Economics.
    4. Oguzhan Cepni & Rangan Gupta & Cenk C. Karahan & Brian M. Lucey, 2020. "Oil Price Shocks and Yield Curve Dynamics in Emerging Markets," Working Papers 202036, University of Pretoria, Department of Economics.
    5. Rangan Gupta & Syed Jawad Hussain Shahzad & Xin Sheng & Sowmya Subramaniam, 2020. "The Role of Oil and Risk Shocks in the High-Frequency Movements of the Term Structure of Interest Rates of the United States," Working Papers 202063, University of Pretoria, Department of Economics.
    6. Han, Yang & Jiao, Anqi & Ma, Jun, 2021. "The predictive power of Nelson–Siegel factor loadings for the real economy," Journal of Empirical Finance, Elsevier, vol. 64(C), pages 95-127.
    7. Duc Do, Nguyen, 2024. "Money/asset ratio as a predictor of inflation," The Quarterly Review of Economics and Finance, Elsevier, vol. 97(C).
    8. Andrew B. Martinez, 2020. "Extracting Information from Different Expectations," Working Papers 2020-008, The George Washington University, The Center for Economic Research.
    9. Elie Bouri & Rangan Gupta & Clement Kweku Kyei & Sowmya Subramaniam, 2020. "High-Frequency Movements of the Term Structure of Interest Rates of the United States: The Role of Oil Market Uncertainty," Working Papers 202085, University of Pretoria, Department of Economics.

  5. Tommaso Proietti & Eric Hillebrand, 2015. "Seasonal Changes in Central England Temperatures," CREATES Research Papers 2015-28, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. C. Vladimir Rodr'iguez-Caballero & Esther Ruiz, 2024. "Temperature in the Iberian Peninsula: Trend, seasonality, and heterogeneity," Papers 2406.14145, arXiv.org.
    2. Gadea Rivas, María Dolores & Gonzalo, Jesús, 2017. "Trends in distributional characteristics : Existence of global warming," UC3M Working papers. Economics 24121, Universidad Carlos III de Madrid. Departamento de Economía.
    3. He, Changli & Kang, Jian & Silvennoinen, Annastiina & Teräsvirta, Timo, 2024. "Long monthly temperature series and the Vector Seasonal Shifting Mean and Covariance Autoregressive model," Journal of Econometrics, Elsevier, vol. 239(1).
    4. Andrew Harvey & Ryoko Ito, 2017. "Modeling time series with zero observations," Economics Papers 2017-W01, Economics Group, Nuffield College, University of Oxford.
    5. Meng, Xiaochun & Taylor, James W., 2022. "Comparing probabilistic forecasts of the daily minimum and maximum temperature," International Journal of Forecasting, Elsevier, vol. 38(1), pages 267-281.
    6. Liudas Giraitis & Fulvia Marotta & Peter C B Phillips, 2024. "Cyclical Time Series: An Empirical Analysis of Temperatures in Central England Over Three Centuries," Cowles Foundation Discussion Papers 2409, Cowles Foundation for Research in Economics, Yale University.
    7. He, Changli & Kang, Jian & Teräsvirta, Timo & Zhang, Shuhua, 2019. "The shifting seasonal mean autoregressive model and seasonality in the Central England monthly temperature series, 1772–2016," Econometrics and Statistics, Elsevier, vol. 12(C), pages 1-24.
    8. González-Rivera, Gloria & Rodríguez Caballero, Carlos Vladimir & Ruiz Ortega, Esther, 2023. "Modelling intervals of minimum/maximum temperatures in the Iberian Peninsula," DES - Working Papers. Statistics and Econometrics. WS 37968, Universidad Carlos III de Madrid. Departamento de Estadística.
    9. Federico Maddanu & Tommaso Proietti, 2023. "Trends in atmospheric ethane," Climatic Change, Springer, vol. 176(5), pages 1-23, May.
    10. Caterina Schiavoni & Siem Jan Koopman & Franz Palm & Stephan Smeekes & Jan van den Brakel, 2021. "Time-varying state correlations in state space models and their estimation via indirect inference," Tinbergen Institute Discussion Papers 21-020/III, Tinbergen Institute.
    11. Bent Jesper Christensen & Nabanita Datta Gupta & Paolo Santucci de Magistris, 2021. "Measuring the impact of clean energy production on CO2 abatement in Denmark: Upper bound estimation and forecasting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 118-149, January.
    12. Harvey, Andrew & Ito, Ryoko, 2020. "Modeling time series when some observations are zero," Journal of Econometrics, Elsevier, vol. 214(1), pages 33-45.
    13. Francesco Battaglia & Domenico Cucina & Manuel Rizzo, 2020. "Detection and estimation of additive outliers in seasonal time series," Computational Statistics, Springer, vol. 35(3), pages 1393-1409, September.

  6. Jakob Guldbæk Mikkelsen & Eric Hillebrand & Giovanni Urga, 2015. "Maximum Likelihood Estimation of Time-Varying Loadings in High-Dimensional Factor Models," CREATES Research Papers 2015-61, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. 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.
    2. Kutateladze, Varlam, 2022. "The kernel trick for nonlinear factor modeling," International Journal of Forecasting, Elsevier, vol. 38(1), pages 165-177.
    3. Varlam Kutateladze, 2021. "The Kernel Trick for Nonlinear Factor Modeling," Papers 2103.01266, arXiv.org.
    4. Pierre Guérin & Danilo Leiva-Leon & Massimiliano Marcellino, 2017. "Markov-switching three-pass regression filter," Working Papers 1748, Banco de España.
    5. Fu, Zhonghao & Hong, Yongmiao & Su, Liangjun & Wang, Xia, 2023. "Specification tests for time-varying coefficient models," Journal of Econometrics, Elsevier, vol. 235(2), pages 720-744.

  7. Lorenzo Boldrini & Eric Hillebrand, 2015. "Supervision in Factor Models Using a Large Number of Predictors," CREATES Research Papers 2015-38, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Lorenzo Boldrini & Eric Hillebrand, 2015. "The Forecasting Power of the Yield Curve, a Supervised Factor Model Approach," CREATES Research Papers 2015-39, Department of Economics and Business Economics, Aarhus University.

  8. Manuel Lukas & Eric Hillebrand, 2014. "Bagging Weak Predictors," CREATES Research Papers 2014-01, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Özen, Kadir & Yıldırım, Dilem, 2021. "Application of bagging in day-ahead electricity price forecasting and factor augmentation," Energy Economics, Elsevier, vol. 103(C).
    2. Philippe Goulet Coulombe, 2020. "The Macroeconomy as a Random Forest," Papers 2006.12724, arXiv.org, revised Mar 2021.
    3. Peng Qin & Manying Bai, 2022. "Does oil price uncertainty matter in stock market volatility forecasting?," PLOS ONE, Public Library of Science, vol. 17(12), pages 1-21, December.
    4. Philippe Goulet Coulombe, 2020. "To Bag is to Prune," Papers 2008.07063, arXiv.org, revised Sep 2024.
      • Philippe Goulet Coulombe, 2021. "To Bag is to Prune," Working Papers 21-03, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Jun 2021.
    5. Luo, Qin & Bu, Jinfeng & Xu, Weiju & Huang, Dengshi, 2023. "Stock market volatility prediction: Evidence from a new bagging model," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 445-456.

  9. Tae-Hwy Lee & Eric Hillebrand & Marcelo Medeiros, 2014. "Bagging Constrained Equity Premium Predictors," Working Papers 201421, University of California at Riverside, Department of Economics, revised Feb 2013.

    Cited by:

    1. Manuel Lukas & Eric Hillebrand, 2014. "Bagging Weak Predictors," CREATES Research Papers 2014-01, Department of Economics and Business Economics, Aarhus University.
    2. Jordan, Steven J. & Vivian, Andrew & Wohar, Mark E., 2017. "Forecasting market returns: bagging or combining?," International Journal of Forecasting, Elsevier, vol. 33(1), pages 102-120.

  10. Eric Hillebrand & Tae-Hwy Lee, 2012. "Stein-Rule Estimation and Generalized Shrinkage Methods for Forecasting Using Many Predictors," CREATES Research Papers 2012-18, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Marine Carrasco & Barbara Rossi, 2016. "In-sample inference and forecasting in misspecified factor models," Economics Working Papers 1530, Department of Economics and Business, Universitat Pompeu Fabra.
    2. Bent Jesper Christensen & Morten Ørregaard Nielsen & Jie Zhu, 2012. "The impact of financial crises on the risk-return tradeoff and the leverage effect," CREATES Research Papers 2012-19, Department of Economics and Business Economics, Aarhus University.
    3. Hendrik Kaufmann & Robinson Kruse & Philipp Sibbertsen, 2012. "On tests for linearity against STAR models with deterministic trends," CREATES Research Papers 2012-20, Department of Economics and Business Economics, Aarhus University.
    4. Lee C. Adkins & Melissa S. Waters & R. Carter Hill, 2015. "Collinearity Diagnostics in gretl," Economics Working Paper Series 1506, Oklahoma State University, Department of Economics and Legal Studies in Business.

  11. Eric Hillebrand & Marcelo C. Medeiros & Junyue Xu, 2012. "Asymptotic Theory for Regressions with Smoothly Changing Parameters," CREATES Research Papers 2012-31, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Gilles Dufrénot & Guillaume A. Khayat, 2017. "Monetary Policy Switching in the Euro Area and Multiple Steady States: An Empirical Investigation," Post-Print hal-01590000, HAL.
    2. Silvennoinen, Annastiina & Teräsvirta, Timo, 2024. "Consistency and asymptotic normality of maximum likelihood estimators of a multiplicative time-varying smooth transition correlation GARCH model," Econometrics and Statistics, Elsevier, vol. 32(C), pages 57-72.
    3. Andrea Bucci, 2025. "A Smooth Transition Autoregressive Model for Matrix-Variate Time Series," Computational Economics, Springer;Society for Computational Economics, vol. 65(1), pages 429-458, January.
    4. Gilles Dufrénot & Anwar Khayat, 2014. "Monetary Policy Switching in the Euro Area and Multiple Equilibria: An Empirical Investigation," AMSE Working Papers 1408, Aix-Marseille School of Economics, France, revised Jan 2014.
    5. Jorge M. Uribe, 2018. "“Scaling Down Downside Risk with Inter-Quantile Semivariances”," IREA Working Papers 201826, University of Barcelona, Research Institute of Applied Economics, revised Oct 2018.
    6. A.S. Hurn & Annastiina Silvennoinen & Timo Teräsvirta, 2014. "A Smooth Transition Logit Model of the Effects of Deregulation in the Electricity Market," CREATES Research Papers 2014-09, Department of Economics and Business Economics, Aarhus University.
    7. Janak Raj & Joice John, 0. "Steering interest rates amidst large structural surplus liquidity: a tale of three central banks," Indian Economic Review, Springer, vol. 0, pages 1-24.
    8. Cho, Dooyeon, 2018. "On the persistence of the forward premium in the joint presence of nonlinearity, asymmetry, and structural changes," Economic Modelling, Elsevier, vol. 70(C), pages 310-319.
    9. Matthew T. Holt & Timo Teräsvirta, 2017. "Global Hemispheric Temperatures and Co–Shifting: A Vector Shifting–Mean Autoregressive Analysis," CREATES Research Papers 2017-05, Department of Economics and Business Economics, Aarhus University.
    10. Line Elvstrøm Ekner & Emil Nejstgaard, 2013. "Parameter Identification in the Logistic STAR Model," Discussion Papers 13-07, University of Copenhagen. Department of Economics.
    11. Janak Raj & Joice John, 2020. "Steering interest rates amidst large structural surplus liquidity: a tale of three central banks," Indian Economic Review, Springer, vol. 55(1), pages 93-116, June.
    12. Dimitris Christopoulos & Peter McAdam & Elias Tzavalis, 2023. "Exploring Okun's law asymmetry: An endogenous threshold logistic smooth transition regression approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(1), pages 123-158, February.

  12. Eric Hillebrand & Marcelo C. Medeiros, 2012. "Nonlinearity, Breaks, and Long-Range Dependence in Time-Series Models," CREATES Research Papers 2012-30, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Jorge M. Uribe, 2018. "“Scaling Down Downside Risk with Inter-Quantile Semivariances”," IREA Working Papers 201826, University of Barcelona, Research Institute of Applied Economics, revised Oct 2018.
    2. Mawuli Segnon & Chi Keung Lau & Bernd Wilfling & Rangan Gupta, 2017. "Are multifractal processes suited to forecasting electricity price volatility? Evidence from Australian intraday data," CQE Working Papers 6117, Center for Quantitative Economics (CQE), University of Muenster.
    3. Mawuli Segnon & Stelios Bekiros & Bernd Wilfling, 2018. "Forecasting Inflation Uncertainty in the G7 Countries," CQE Working Papers 7118, Center for Quantitative Economics (CQE), University of Muenster.
    4. Pedro Chaim & Márcio Poletti Laurini, 2024. "Bayesian Inference for Long Memory Stochastic Volatility Models," Econometrics, MDPI, vol. 12(4), pages 1-28, November.
    5. 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.

  13. Mihaela Craioveanu & Eric Hillebrand, 2012. "Why It Is Ok To Use The Har-Rv(1,5,21) Model," Working Papers 1201, University of Central Missouri, Department of Economics & Finance, revised Aug 2012.

    Cited by:

    1. Fengler, Matthias R. & Mammen, Enno & Vogt, Michael, 2013. "Additive modeling of realized variance: tests for parametric specifications and structural breaks," Economics Working Paper Series 1332, University of St. Gallen, School of Economics and Political Science.
    2. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Post-Print halshs-01442618, HAL.
    3. Artur Nagapetyan, 2019. "Precondition stock and stock indices volatility modeling based on market diversification potential: Evidence from Russian market," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 56, pages 45-61.
    4. Audrino, Francesco & Camponovo, Lorenzo & Roth, Constantin, 2015. "Testing the lag structure of assets’ realized volatility dynamics," Economics Working Paper Series 1501, University of St. Gallen, School of Economics and Political Science.
    5. Lehrer, Steven & Xie, Tian & Zhang, Xinyu, 2021. "Social media sentiment, model uncertainty, and volatility forecasting," Economic Modelling, Elsevier, vol. 102(C).
    6. Tim Bollerslev & Benjamin Hood & John Huss & Lasse Heje Pedersen, 2018. "Risk Everywhere: Modeling and Managing Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2729-2773.
    7. Neda Todorova & Michael Soucek & Eduardo Roca, 2015. "Volatility spillovers from international commodity markets to the Australian equity market," Discussion Papers in Finance finance:201505, Griffith University, Department of Accounting, Finance and Economics.
    8. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01442618, HAL.
    9. Audrino, Francesco & Knaus, Simon, 2012. "Lassoing the HAR model: A Model Selection Perspective on Realized Volatility Dynamics," Economics Working Paper Series 1224, University of St. Gallen, School of Economics and Political Science.
    10. Lahaye, Jerome & Shaw, Philip, 2014. "Can we reject linearity in an HAR-RV model for the S&P 500? Insights from a nonparametric HAR-RV," Economics Letters, Elsevier, vol. 125(1), pages 43-46.
    11. Hui Qu & Ping Ji, 2016. "Modeling Realized Volatility Dynamics with a Genetic Algorithm," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(5), pages 434-444, August.
    12. Artem Aganin, 2017. "Forecast comparison of volatility models on Russian stock market," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 48, pages 63-84.
    13. Alexandr Shcherba, 2014. "Comparing «Realized volatility» models in the VaR calculation for the Russian equity market," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 34(2), pages 120-136.
    14. Ding, Yi & Kambouroudis, Dimos & McMillan, David G., 2021. "Forecasting realised volatility: Does the LASSO approach outperform HAR?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    15. Tian Xie, 2019. "Forecast Bitcoin Volatility with Least Squares Model Averaging," Econometrics, MDPI, vol. 7(3), pages 1-20, September.

  14. Eric Hillebrand & Tae-Hwy Lee & Marcelo C. Medeiros, 2012. "Let's Do It Again: Bagging Equity Premium Predictors," CREATES Research Papers 2012-41, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Marcelo C. Medeiros & Eduardo F. Mendes, 2012. "Estimating High-Dimensional Time Series Models," CREATES Research Papers 2012-37, Department of Economics and Business Economics, Aarhus University.

  15. Eric Hillebrand & Huiyu Huang & Tae-Hwy Lee & Canlin Li, 2011. "Using the Yield Curve in Forecasting Output Growth and In?flation," CREATES Research Papers 2012-17, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Daniela Osterrieder, 2013. "Interest Rates with Long Memory: A Generalized Affine Term-Structure Model," CREATES Research Papers 2013-17, Department of Economics and Business Economics, Aarhus University.
    2. Eric Hillebrand & Tae-Hwy Lee, 2012. "Stein-Rule Estimation and Generalized Shrinkage Methods for Forecasting Using Many Predictors," CREATES Research Papers 2012-18, Department of Economics and Business Economics, Aarhus University.
    3. Tae-Hwy Lee & Zhou Xi & Ru Zhang, 2013. "Testing for Neglected Nonlinearity Using Regularized Artificial Neural Networks," Working Papers 201422, University of California at Riverside, Department of Economics, revised Apr 2012.
    4. Abdymomunov, Azamat, 2013. "Predicting output using the entire yield curve," Journal of Macroeconomics, Elsevier, vol. 37(C), pages 333-344.

  16. Eric Hillebrand & Marcelo Cunha Medeiros, 2010. "Asymmetries, breaks, and long-range dependence: An estimation framework for daily realized volatility," Textos para discussão 578, Department of Economics PUC-Rio (Brazil).

    Cited by:

    1. Manabu Asai & Michael McAleer & Marcelo C. Medeiros, 2011. "Modelling and Forecasting Noisy Realized Volatility," KIER Working Papers 758, Kyoto University, Institute of Economic Research.
    2. S. Bordignon & D. Raggi, 2010. "Long memory and nonlinearities in realized volatility: a Markov switching approach," Working Papers 694, Dipartimento Scienze Economiche, Universita' di Bologna.
    3. De Santis, Roberto A. & Stein, Michael, 2016. "Correlation changes between the risk-free rate and sovereign yields of euro area countries," Working Paper Series 1979, European Central Bank.
    4. Michael McAleer & Marcelo Cunha Medeiros, 2006. "Realized volatility: a review," Textos para discussão 531 Publication status: F, Department of Economics PUC-Rio (Brazil).
    5. Nima Nonejad, 2013. "Long Memory and Structural Breaks in Realized Volatility: An Irreversible Markov Switching Approach," CREATES Research Papers 2013-26, Department of Economics and Business Economics, Aarhus University.
    6. De Santis, Roberto A. & Stein, Michael, 2015. "Financial indicators signaling correlation changes in sovereign bond markets," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 86-102.

  17. Eric Hillebrand & Marcelo Cunha Medeiros, 2007. "Forecasting realized volatility models:the benefits of bagging and nonlinear specifications," Textos para discussão 547, Department of Economics PUC-Rio (Brazil).

    Cited by:

    1. Härdle, Wolfgang Karl & Hautsch, Nikolaus & Pigorsch, Uta, 2008. "Measuring and modeling risk using high-frequency data," SFB 649 Discussion Papers 2008-045, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    2. David E. Allen & Michael McAleer & Marcel Scharth, 2010. "Realized Volatility Risk," KIER Working Papers 753, Kyoto University, Institute of Economic Research.
    3. Allen, David E. & McAleer, Michael & Scharth, Marcel, 2011. "Monte Carlo option pricing with asymmetric realized volatility dynamics," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(7), pages 1247-1256.
    4. Erik Hillebrand & Tae-Hwy Lee & Marcelo Cunha Medeiros, 2012. "Let´s do it again: bagging equity premium predictors," Textos para discussão 604, Department of Economics PUC-Rio (Brazil).
    5. Francesco Audrino & Marcelo C. Medeiros, 2008. "Smooth Regimes, Macroeconomic Variables, and Bagging for the Short-Term Interest Rate Process," University of St. Gallen Department of Economics working paper series 2008 2008-16, Department of Economics, University of St. Gallen.
    6. Francesco Audrino & Marcelo C. Medeiros, 2011. "Modeling and forecasting short‐term interest rates: The benefits of smooth regimes, macroeconomic variables, and bagging," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 999-1022, September.

  18. Eric Hillebrand & Gunther Schnabl & Yasemin Ulu, 2006. "Japanese Foreign Exchange Intervention and the Yen/Dollar Exchange Rate: A Simultaneous Equations Approach Using Realized Volatility," CESifo Working Paper Series 1766, CESifo.

    Cited by:

    1. Heijdra, B.J. & Ligthart, J.E., 2005. "Fiscal Policy, Monopolistic Competition and Finite Lives," Other publications TiSEM 305239e1-d4e2-4d0e-b950-b, Tilburg University, School of Economics and Management.
    2. Reitz, Stefan & Stadtmann, Georg & Taylor, Mark P., 2010. "The effects of Japanese interventions on FX-forecast heterogeneity," Economics Letters, Elsevier, vol. 108(1), pages 62-64, July.
    3. Schnabl, Gunther & Hillebrand, Eric, 2006. "A structural break in the effects of Japanese foreign exchange intervention on yen/dollar exchange rate volatility," Working Paper Series 650, European Central Bank.
    4. Ai-ru (Meg) Cheng & Kuntal Das & Takeshi Shimatani, 2013. "Central Bank Intervention and Exchange Rate Volatility: Evidence from Japan Using Realized Volatility," Working Papers in Economics 13/19, University of Canterbury, Department of Economics and Finance.
    5. Vithessonthi, Chaiporn & Tongurai, Jittima, 2014. "The spillover effects of unremunerated reserve requirements: Evidence from Thailand," Journal of Banking & Finance, Elsevier, vol. 45(C), pages 338-351.
    6. Kitamura, Yoshihiro, 2020. "A lesson from the four recent large public Japanese FX interventions," Journal of the Japanese and International Economies, Elsevier, vol. 57(C).
    7. Vithessonthi, Chaiporn & Tongurai, Jittima, 2013. "The perils of a central bank's capital control: How substantial is the effect on firm value?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 23(C), pages 111-135.
    8. Marsh, Ian W., 2011. "Order flow and central bank intervention: An empirical analysis of recent Bank of Japan actions in the foreign exchange market," Journal of International Money and Finance, Elsevier, vol. 30(2), pages 377-392, March.
    9. João Barata Ribeiro Blanco Barroso, 2018. "Realized Volatility as an Instrument to Official Intervention," Investigación Conjunta-Joint Research, in: Alberto Ortiz-Bolaños (ed.), Monetary Policy and Financial Stability in Latin America and the Caribbean, edition 1, volume 1, chapter 8, pages 259-281, Centro de Estudios Monetarios Latinoamericanos, CEMLA.
    10. Nikkinen, Jussi & Vähämaa, Sami, 2009. "Central bank interventions and implied exchange rate correlations," Journal of Empirical Finance, Elsevier, vol. 16(5), pages 862-873, December.
    11. Grossmann, Axel & Orlov, Alexei G., 2012. "Exchange rate misalignments in frequency domain," International Review of Economics & Finance, Elsevier, vol. 24(C), pages 185-199.

  19. Schnabl, Gunther & Hillebrand, Eric, 2006. "A structural break in the effects of Japanese foreign exchange intervention on yen/dollar exchange rate volatility," Working Paper Series 650, European Central Bank.

    Cited by:

    1. Eric Hillebrand & Gunther Schnabl & Yasemin Ulu, 2006. "Japanese Foreign Exchange Intervention and the Yen/Dollar Exchange Rate: A Simultaneous Equations Approach Using Realized Volatility," CESifo Working Paper Series 1766, CESifo.
    2. Broto, Carmen, 2013. "The effectiveness of forex interventions in four Latin American countries," Emerging Markets Review, Elsevier, vol. 17(C), pages 224-240.
    3. Andreas Hoffmann & Gunther Schnabl, 2007. "Monetary Policy, Vagabonding Liquidity and Bursting Bubbles in New and Emerging Markets – An Overinvestment View," CESifo Working Paper Series 2100, CESifo.
    4. Gunther Schnabl & Christian Danne, 2007. "A Role Model for China? Exchange Rate Flexibility and Monetary Policy in Japan," CESifo Working Paper Series 2051, CESifo.
    5. Hui, Eddie C.M. & Yu, Carisa K.W. & Ip, Wai-Cheung, 2010. "Jump point detection for real estate investment success," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(5), pages 1055-1064.
    6. Calvo-Gonzalez, Oscar & Shankar, Rashmi & Trezzi, Riccardo, 2010. "Are commodity prices more volatile now ? a long-run perspective," Policy Research Working Paper Series 5460, The World Bank.
    7. Ronald McDonald & Xuxin Mao, 2016. "Japan's Currency Intervention Regimes: A Microstructural Analysis with Speculation and Sentiment," Working Papers 2016_06, Business School - Economics, University of Glasgow.
    8. Chen, Ho-Chyuan & Chang, Kuang-Liang & Yu, Shih-Ti, 2012. "Application of the Tobit model with autoregressive conditional heteroscedasticity for foreign exchange market interventions," Japan and the World Economy, Elsevier, vol. 24(4), pages 274-282.
    9. Beckmann, Joscha & Belke, Ansgar & Kühl, Michael, 2013. "Foreign Exchange Market Interventions and the $-¥ Exchange Rate in the Long-Run," Ruhr Economic Papers 428, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    10. Huang, Alex YiHou & Peng, Sheng-Pen & Li, Fangjhy & Ke, Ching-Jie, 2011. "Volatility forecasting of exchange rate by quantile regression," International Review of Economics & Finance, Elsevier, vol. 20(4), pages 591-606, October.
    11. Konstantinos N. Konstantakis & Ioannis G. Melissaropoulos & Theodoros Daglis & Panayotis G. Michaelides, 2023. "The euro to dollar exchange rate in the Covid‐19 era: Evidence from spectral causality and Markov‐switching estimation," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 2037-2055, April.
    12. Nikkinen, Jussi & Vähämaa, Sami, 2009. "Central bank interventions and implied exchange rate correlations," Journal of Empirical Finance, Elsevier, vol. 16(5), pages 862-873, December.
    13. Brause, Alexander, 2008. "Foreign exchange interventions in emerging market countries: New lessons from Argentina," W.E.P. - Würzburg Economic Papers 79, University of Würzburg, Department of Economics.
    14. Grossmann, Axel & Orlov, Alexei G., 2012. "Exchange rate misalignments in frequency domain," International Review of Economics & Finance, Elsevier, vol. 24(C), pages 185-199.

  20. Eric Hillebrand, 2005. "Overlaying Time Scales in Financial Volatility Data," Econometrics 0501015, University Library of Munich, Germany.

    Cited by:

    1. Matthew Lorig, 2010. "Time-Changed Fast Mean-Reverting Stochastic Volatility Models," Papers 1010.5203, arXiv.org, revised Apr 2012.
    2. Vyacheslav Abramov & Fima Klebaner, 2007. "Estimation and Prediction of a Non-Constant Volatility," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 14(1), pages 1-23, March.
    3. Jean-Pierre Fouque & Sebastian Jaimungal & Matthew Lorig, 2010. "Spectral Decomposition of Option Prices in Fast Mean-Reverting Stochastic Volatility Models," Papers 1007.4361, arXiv.org, revised Apr 2012.

  21. Eric Hillebrand & Gunther Schnabl, 2004. "The Effects of Japanese Foreign Exchange Intervention: GARCH Estimation and Change Point Detection," International Finance 0410008, University Library of Munich, Germany.

    Cited by:

    1. Alberto Humala & Gabriel Rodriguez, 2010. "Foreign exchange intervention and exchange rate volatility in Peru," Applied Economics Letters, Taylor & Francis Journals, vol. 17(15), pages 1485-1491.
    2. Michael Frenkel & Christian Pierdzioch & Georg Stadtmann, 2004. "On the determinants of “small” and “large” foreign exchange market interventions: The case of the Japanese interventions in the 1990s," Review of Financial Economics, John Wiley & Sons, vol. 13(3), pages 231-243.
    3. Christopher J. Neely, 2005. "An analysis of recent studies of the effect of foreign exchange intervention," Review, Federal Reserve Bank of St. Louis, vol. 87(Nov), pages 685-718.
    4. Gunther Schnabl, 2004. "International Capital Markets, Macroeconomic Stability, and Exchange Rate Stabilization in the CIS and East Asia," International Finance 0410009, University Library of Munich, Germany, revised 01 Mar 2005.
    5. Ronald McKinnon & Gunther Schnabl, 2006. "The East Asian Dollar Standard, Fear of Floating, and Original Sin," Chapters, in: Volbert Alexander & Hans-Helmut Kotz (ed.), Global Divergence in Trade, Money and Policy, chapter 3, pages 45-71, Edward Elgar Publishing.
    6. Reza Habibi, 2010. "Distribution Approximations for Cusum and Cusumsq Statistics," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 11(3), pages 585-596, December.
    7. Christopher J. Neely, 2007. "Central bank authorities’ beliefs about foreign exchange intervention," Working Papers 2006-045, Federal Reserve Bank of St. Louis.
    8. Morel, Christophe & Teïletche, Jérôme, 2008. "Do interventions in foreign exchange markets modify investors' expectations? The experience of Japan between 1992 and 2004," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 211-231, March.
    9. Jer-Yuh Wan & Chung-Wei Kao, 2010. "Effects of Japanese intervention on yen/dollar exchange rate volatility: a conditional jump dynamics approach," Applied Economics Letters, Taylor & Francis Journals, vol. 17(4), pages 367-373.
    10. Schnabl, Gunther & Danne, Christian, 2005. "The Changing Role of the Yen/Dollar Exchange Rate for Japanese Monetary Policy," Tübinger Diskussionsbeiträge 290, University of Tübingen, School of Business and Economics.
    11. Jaqueline Terra Moura Marins & Gustavo Silva Araujo & José Valentim Machado Vicente, 2015. "As Atuações Cambiais do Banco Central Afetam as Expectativas de Mercado?," Working Papers Series 393, Central Bank of Brazil, Research Department.
    12. Kim, Suk-Joong, 2007. "Intraday evidence of efficacy of 1991-2004 Yen intervention by the Bank of Japan," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 17(4), pages 341-360, October.
    13. Ronald McKinnon & Gunther Schnabl, 2004. "The Return to Soft Dollar Pegging in East Asia. Mitigating Conflicted Virtue," International Finance 0406007, University Library of Munich, Germany, revised 07 Jul 2004.
    14. Smita Roy Trivedi & P. G. Apte, 2016. "Central Bank Intervention in USD/INR Market: Estimating Its Reaction Function and Impact on Volatility," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 23(3), pages 263-279, September.
    15. Schnabl, Gunther, 2005. "International Capital Markets and Informal Dollar Standards in the CIS and East Asia," HWWA Discussion Papers 326, Hamburg Institute of International Economics (HWWA).
    16. Frenkel, Michael & Pierdzioch, Christian & Stadtmann, Georg, 2003. "The Effects of Japanese Foreign Exchange Market Interventions on the Yen/U.S. Dollar Exchange Rate Volatility," Kiel Working Papers 1165, Kiel Institute for the World Economy (IfW Kiel).
    17. Watanabe, Toshiaki & Harada, Kimie, 2006. "Effects of the Bank of Japan's intervention on yen/dollar exchange rate volatility," Journal of the Japanese and International Economies, Elsevier, vol. 20(1), pages 99-111, March.
    18. Ito, Takatoshi & Yabu, Tomoyoshi, 2007. "What prompts Japan to intervene in the Forex market? A new approach to a reaction function," Journal of International Money and Finance, Elsevier, vol. 26(2), pages 193-212, March.
    19. Jaqueline Terra Moura Marins & Gustavo Silva Araujo & José Valentim Machado Vicente, 2017. "Do central bank foreign exchange interventions affect market expectations?," Applied Economics, Taylor & Francis Journals, vol. 49(31), pages 3017-3031, July.
    20. Takatoshi Ito, 2005. "The Exchange Rate In The Japanese Economy: The Past, Puzzles, And Prospects," The Japanese Economic Review, Japanese Economic Association, vol. 56(1), pages 1-38, March.
    21. Beckmann, Joscha & Belke, Ansgar & Kühl, Michael, 2013. "Foreign Exchange Market Interventions and the $-¥ Exchange Rate in the Long-Run," Ruhr Economic Papers 428, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    22. Seojin Lee & Young Min Kim, 2020. "Effect of foreign exchange intervention: The case of Korea," Pacific Economic Review, Wiley Blackwell, vol. 25(5), pages 641-659, December.
    23. Pontines, Victor, 2018. "Self-selection and treatment effects: Revisiting the effectiveness of foreign exchange intervention," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 299-316.
    24. Habibi Reza, 2011. "A note on approximating distribution functions of cusum and cusumsq tests," Monte Carlo Methods and Applications, De Gruyter, vol. 17(1), pages 1-10, January.
    25. Suardi, Sandy, 2008. "Central bank intervention, threshold effects and asymmetric volatility: Evidence from the Japanese yen-US dollar foreign exchange market," Economic Modelling, Elsevier, vol. 25(4), pages 628-642, July.

  22. Eric Hillebrand, 2004. "Neglecting Parameter Changes in Autoregressive Models," Departmental Working Papers 2004-04, Department of Economics, Louisiana State University.

    Cited by:

    1. Eric Hillebrand, 2005. "Mean Reversion Expectations and the 1987 Stock Market Crash: An Empirical Investigation," Finance 0501015, University Library of Munich, Germany.

  23. Eric Hillebrand, 2003. "Overlaying Time Scales and Persistence Estimation in GARCH(1,1) Models," Econometrics 0301003, University Library of Munich, Germany.

    Cited by:

    1. Matthew Lorig, 2010. "Time-Changed Fast Mean-Reverting Stochastic Volatility Models," Papers 1010.5203, arXiv.org, revised Apr 2012.
    2. Jean-Pierre Fouque & Matthew Lorig, 2010. "A Fast Mean-Reverting Correction to Heston's Stochastic Volatility Model," Papers 1007.4366, arXiv.org, revised Apr 2012.

Articles

  1. Hillebrand, Eric & Lukas, Manuel & Wei, Wei, 2021. "Bagging weak predictors," International Journal of Forecasting, Elsevier, vol. 37(1), pages 237-254.
    See citations under working paper version above.
  2. Bennedsen, Mikkel & Hillebrand, Eric & Koopman, Siem Jan, 2021. "Modeling, forecasting, and nowcasting U.S. CO2 emissions using many macroeconomic predictors," Energy Economics, Elsevier, vol. 96(C).
    See citations under working paper version above.
  3. Eric Hillebrand & Søren Johansen & Torben Schmith, 2020. "Data Revisions and the Statistical Relation of Global Mean Sea Level and Surface Temperature," Econometrics, MDPI, vol. 8(4), pages 1-19, November.

    Cited by:

    1. Rocco Mosconi & Paolo Paruolo, 2022. "A Conversation with Søren Johansen," Econometrics, MDPI, vol. 10(2), pages 1-16, April.

  4. Mikkelsen, Jakob Guldbæk & Hillebrand, Eric & Urga, Giovanni, 2019. "Consistent estimation of time-varying loadings in high-dimensional factor models," Journal of Econometrics, Elsevier, vol. 208(2), pages 535-562.

    Cited by:

    1. Tingting Cheng & Chaohua Dong & Jiti Gao & Oliver Linton, 2022. "GMM Estimation for High-Dimensional Panel Data Models," Monash Econometrics and Business Statistics Working Papers 11/22, Monash University, Department of Econometrics and Business Statistics.
    2. Bai, Jushan & Duan, Jiangtao & Han, Xu, 2024. "The likelihood ratio test for structural changes in factor models," Journal of Econometrics, Elsevier, vol. 238(2).
    3. Fu, Zhonghao & Hong, Yongmiao & Wang, Xia, 2023. "Testing for structural changes in large dimensional factor models via discrete Fourier transform," Journal of Econometrics, Elsevier, vol. 233(1), pages 302-331.
    4. Ergemen, Yunus Emre & Rodríguez-Caballero, C. Vladimir, 2023. "Estimation of a dynamic multi-level factor model with possible long-range dependence," International Journal of Forecasting, Elsevier, vol. 39(1), pages 405-430.
    5. Ying Lun Cheung, 2024. "Identification of Time-Varying Factor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(1), pages 76-94, January.
    6. Urga, Giovanni & Wang, Fa, 2022. "Estimation and Inference for High Dimensional Factor Model with Regime Switching," MPRA Paper 117012, University Library of Munich, Germany, revised 10 Apr 2023.
    7. Li, Degui, 2024. "Estimation of Large Dynamic Covariance Matrices: A Selective Review," Econometrics and Statistics, Elsevier, vol. 29(C), pages 16-30.
    8. Urga, Giovanni & Wang, Fa, 2022. "Estimation and inference for high dimensional factor model with regime switching," MPRA Paper 113172, University Library of Munich, Germany.
    9. Duo Zhang & Jiayu Li & Junyi Mo & Elynn Chen, 2025. "Time-Varying Factor-Augmented Models for Volatility Forecasting," Papers 2508.01880, arXiv.org, revised Oct 2025.
    10. Ziyan Zhao & Qingfeng Liu, 2024. "Time-Varying Structural Approximate Dynamic Factor Model," Economic Growth Centre Working Paper Series 2401, Nanyang Technological University, School of Social Sciences, Economic Growth Centre.
    11. Eric Hillebrand & Jakob Mikkelsen & Lars Spreng & Giovanni Urga, 2020. "Exchange Rates and Macroeconomic Fundamentals: Evidence of Instabilities from Time-Varying Factor Loadings," CREATES Research Papers 2020-19, Department of Economics and Business Economics, Aarhus University.
    12. Bai, Jushan & Duan, Jiangtao & Han, Xu, 2024. "Reprint of: The likelihood ratio test for structural changes in factor models," Journal of Econometrics, Elsevier, vol. 244(2).
    13. Urga, Giovanni & Wang, Fa, 2024. "Estimation and inference for high dimensional factor model with regime switching," Journal of Econometrics, Elsevier, vol. 241(2).
    14. Giuseppe Buccheri & Fulvio Corsi & Emilija Dzuverovic, 2024. "From rotational to scalar invariance: Enhancing identifiability in score-driven factor models," Papers 2412.01367, arXiv.org.

  5. Eric Hillebrand & Huiyu Huang & Tae-Hwy Lee & Canlin Li, 2018. "Using the Entire Yield Curve in Forecasting Output and Inflation," Econometrics, MDPI, vol. 6(3), pages 1-27, August.
    See citations under working paper version above.
  6. Tommaso Proietti & Eric Hillebrand, 2017. "Seasonal changes in central England temperatures," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(3), pages 769-791, June.
    See citations under working paper version above.
  7. Eric Hillebrand & Marcelo C. Medeiros, 2016. "Nonlinearity, Breaks, and Long-Range Dependence in Time-Series Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 23-41, January.
    See citations under working paper version above.
  8. Hillebrand Eric & Medeiros Marcelo C. & Xu Junyue, 2013. "Asymptotic Theory for Regressions with Smoothly Changing Parameters," Journal of Time Series Econometrics, De Gruyter, vol. 5(2), pages 133-162, April.
    See citations under working paper version above.
  9. Mihaela Craioveanu & Eric Hillebrand, 2012. "Level changes in volatility models," Annals of Finance, Springer, vol. 8(2), pages 277-308, May.

    Cited by:

    1. Eric Hillebrand & Marcelo C. Medeiros, 2012. "Nonlinearity, Breaks, and Long-Range Dependence in Time-Series Models," CREATES Research Papers 2012-30, Department of Economics and Business Economics, Aarhus University.
    2. Han, Heejoon & Park, Joon Y., 2014. "GARCH with omitted persistent covariate," Economics Letters, Elsevier, vol. 124(2), pages 248-254.

  10. Eric Hillebrand & Marcelo Medeiros, 2010. "The Benefits of Bagging for Forecast Models of Realized Volatility," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 571-593.

    Cited by:

    1. Michael McAleer & Marcelo Cunha Medeiros, 2010. "Forecasting Realized Volatility with Linear and Nonlinear Models," Textos para discussão 568, Department of Economics PUC-Rio (Brazil).
    2. Manuel Lukas & Eric Hillebrand, 2014. "Bagging Weak Predictors," CREATES Research Papers 2014-01, Department of Economics and Business Economics, Aarhus University.
    3. 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.
    4. Heejoon Han & Myung D. Park & Shen Zhang, 2015. "A Multiplicative Error Model with Heterogeneous Components for Forecasting Realized Volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(3), pages 209-219, April.
    5. Marcelo Fernandes & Marcelo Cunha Medeiros & MArcelo Scharth, 2007. "Modeling and predicting the CBOE market volatility index," Textos para discussão 548, Department of Economics PUC-Rio (Brazil).
    6. Philippe Goulet Coulombe, 2020. "The Macroeconomy as a Random Forest," Papers 2006.12724, arXiv.org, revised Mar 2021.
    7. 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.
    8. Dantas, Tiago Mendes & Cyrino Oliveira, Fernando Luiz & Varela Repolho, Hugo Miguel, 2017. "Air transportation demand forecast through Bagging Holt Winters methods," Journal of Air Transport Management, Elsevier, vol. 59(C), pages 116-123.
    9. 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.
    10. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023. "Machine learning advances for time series forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
    11. Tae-Hwy Lee & Huiyu Huang, 2014. "Forecasting Value-at-Risk Using High Frequency Information," Working Papers 201409, University of California at Riverside, Department of Economics.
    12. Muhammad Nasir Amin & Hassan Ali Alkadhim & Waqas Ahmad & Kaffayatullah Khan & Hisham Alabduljabbar & Abdullah Mohamed, 2023. "Experimental and machine learning approaches to investigate the effect of waste glass powder on the flexural strength of cement mortar," PLOS ONE, Public Library of Science, vol. 18(1), pages 1-20, January.
    13. Hwang, Eunju & Shin, Dong Wan, 2014. "Infinite-order, long-memory heterogeneous autoregressive models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 339-358.
    14. Dantas, Tiago Mendes & Cyrino Oliveira, Fernando Luiz, 2018. "Improving time series forecasting: An approach combining bootstrap aggregation, clusters and exponential smoothing," International Journal of Forecasting, Elsevier, vol. 34(4), pages 748-761.
    15. Yang, Ke & Tian, Fengping & Chen, Langnan & Li, Steven, 2017. "Realized volatility forecast of agricultural futures using the HAR models with bagging and combination approaches," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 276-291.
    16. Audrino, Francesco & Corsi, Fulvio, 2010. "Modeling tick-by-tick realized correlations," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2372-2382, November.
    17. Rafael Branco & Alexandre Rubesam & Mauricio Zevallos, 2024. "Forecasting realized volatility: Does anything beat linear models?," Post-Print hal-04835657, HAL.
    18. Philippe Goulet Coulombe, 2020. "To Bag is to Prune," Papers 2008.07063, arXiv.org, revised Sep 2024.
      • Philippe Goulet Coulombe, 2021. "To Bag is to Prune," Working Papers 21-03, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Jun 2021.
    19. Stoupos, Nikolaos & Kiohos, Apostolos, 2021. "Energy commodities and advanced stock markets: A post-crisis approach," Resources Policy, Elsevier, vol. 70(C).
    20. de Oliveira, Erick Meira & Cyrino Oliveira, Fernando Luiz, 2018. "Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods," Energy, Elsevier, vol. 144(C), pages 776-788.
    21. Ranjit Kumar Paul & Md Yeasin & Pramod Kumar & Prabhakar Kumar & M Balasubramanian & H S Roy & A K Paul & Ajit Gupta, 2022. "Machine learning techniques for forecasting agricultural prices: A case of brinjal in Odisha, India," PLOS ONE, Public Library of Science, vol. 17(7), pages 1-17, July.
    22. Siem Jan Koopman & Marcel Scharth, 2011. "The Analysis of Stochastic Volatility in the Presence of Daily Realised Measures," Tinbergen Institute Discussion Papers 11-132/4, Tinbergen Institute.
    23. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.
    24. Robinson Kruse‐Becher, 2025. "Adaptive Now‐ and Forecasting of Global Temperatures Under Smooth Structural Changes," Environmetrics, John Wiley & Sons, Ltd., vol. 36(6), September.
    25. Barrow, Devon K. & Crone, Sven F., 2016. "A comparison of AdaBoost algorithms for time series forecast combination," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1103-1119.
    26. Majdi Ameen Alfaiad & Kaffayatullah Khan & Waqas Ahmad & Muhammad Nasir Amin & Ahmed Farouk Deifalla & Nivin A. Ghamry, 2023. "Evaluating the compressive strength of glass powder-based cement mortar subjected to the acidic environment using testing and modeling approaches," PLOS ONE, Public Library of Science, vol. 18(4), pages 1-26, April.
    27. Yuetong Zhang & Ying Peng & Yuping Song, 2025. "Realized Volatility Forecasting for Stocks and Futures Indices with Rolling CEEMDAN and Machine Learning Models," Computational Economics, Springer;Society for Computational Economics, vol. 66(2), pages 1215-1268, August.
    28. Ding, Yi & Kambouroudis, Dimos & McMillan, David G., 2021. "Forecasting realised volatility: Does the LASSO approach outperform HAR?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    29. Luo, Qin & Bu, Jinfeng & Xu, Weiju & Huang, Dengshi, 2023. "Stock market volatility prediction: Evidence from a new bagging model," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 445-456.
    30. Barrow, Devon K. & Crone, Sven F., 2016. "Cross-validation aggregation for combining autoregressive neural network forecasts," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1120-1137.

  11. Hillebrand, Eric & Schnabl, Gunther & Ulu, Yasemin, 2009. "Japanese foreign exchange intervention and the yen-to-dollar exchange rate: A simultaneous equations approach using realized volatility," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 19(3), pages 490-505, July.
    See citations under working paper version above.
  12. Don M. Chance & Eric Hillebrand & Jimmy E. Hilliard, 2008. "Pricing an Option on Revenue from an Innovation: An Application to Movie Box Office Revenue," Management Science, INFORMS, vol. 54(5), pages 1015-1028, May.

    Cited by:

    1. Youseok Lee & Sang-Hoon Kim & Kyoung Cheon Cha, 2023. "The diffusion pattern of new products: evidence from the Korean movie industry," Asian Business & Management, Palgrave Macmillan, vol. 22(5), pages 1830-1847, November.
    2. Trigeorgis, Lenos & Tsekrekos, Andrianos E., 2018. "Real Options in Operations Research: A Review," European Journal of Operational Research, Elsevier, vol. 270(1), pages 1-24.
    3. Lee, Youseok & Kim, Sang-Hoon & Cha, Kyoung Cheon, 2021. "Impact of online information on the diffusion of movies: Focusing on cultural differences," Journal of Business Research, Elsevier, vol. 130(C), pages 603-609.

  13. Eric Hillebrand & Gunther Schnabl, 2008. "A structural break in the effects of Japanese foreign exchange intervention on yen/dollar exchange rate volatility," International Economics and Economic Policy, Springer, vol. 5(4), pages 389-401, December.
    See citations under working paper version above.
  14. Eric Hillebrand & Faik Koray, 2008. "Interest rate volatility and home mortgage loans," Applied Economics, Taylor & Francis Journals, vol. 40(18), pages 2381-2385.

    Cited by:

    1. İbrahim Ekmen & Yalçın Karatepe, 2024. "Examining the Nexus of Financial Inclusion, Household Consumption, and Economic Growth: A 3SLS Approach," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, vol. 9(1), pages 1-25.
    2. Kuo‐Shing Chen & J. Jimmy Yang, 2020. "Housing Price Dynamics, Mortgage Credit and Reverse Mortgage Demand: Theory and Empirical Evidence," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 48(2), pages 599-632, June.
    3. Shapran WITDIYANTO & Ariodillah HIDAYAT & Mukhlis MUKHLIS & Sri ANDAIYANI, 2022. "Economic Development through Mortgage Loan Distribution in Indonesia," Management and Economics Review, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 7(1), pages 4-13, February.

  15. Hillebrand, Eric, 2005. "Neglecting parameter changes in GARCH models," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 121-138.

    Cited by:

    1. Eric Hillebrand & Gunther Schnabl & Yasemin Ulu, 2006. "Japanese Foreign Exchange Intervention and the Yen/Dollar Exchange Rate: A Simultaneous Equations Approach Using Realized Volatility," CESifo Working Paper Series 1766, CESifo.
    2. David McMillan & Mark Wohar, 2011. "Structural breaks in volatility: the case of UK sector returns," Applied Financial Economics, Taylor & Francis Journals, vol. 21(15), pages 1079-1093.
    3. Amado Peir, 2016. "Changes in the Unconditional Variance and Autoregressive Conditional Heteroscedasticity," International Journal of Economics and Financial Issues, Econjournals, vol. 6(4), pages 1338-1343.
    4. Wang, Bo & Xiao, Yang, 2023. "Risk spillovers from China's and the US stock markets during high-volatility periods: Evidence from East Asianstock markets," International Review of Financial Analysis, Elsevier, vol. 86(C).
    5. Dinghai Xu, 2019. "A Study on Volatility Spurious Almost Integration Effect: A Threshold Realized GARCH Approach," Working Papers 1903, University of Waterloo, Department of Economics, revised Dec 2019.
    6. Hemei Li & Zhenya Liu & Shixuan Wang, 2022. "Vines climbing higher: Risk management for commodity futures markets using a regular vine copula approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2438-2457, April.
    7. Mensi, Walid & Hammoudeh, Shawkat & Yoon, Seong-Min, 2015. "Structural breaks, dynamic correlations, asymmetric volatility transmission, and hedging strategies for petroleum prices and USD exchange rate," Energy Economics, Elsevier, vol. 48(C), pages 46-60.
    8. Amélie Charles & Olivier Darné, 2012. "Volatility Persistence in Crude Oil Markets," Working Papers hal-00719387, HAL.
    9. Fang, WenShwo & Miller, Stephen M., 2009. "Modeling the volatility of real GDP growth: The case of Japan revisited," Japan and the World Economy, Elsevier, vol. 21(3), pages 312-324, August.
    10. 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.
    11. Giorgio Canarella & WenShwo Fang & Stephen M. Miller & Stephen K. Pollard, 2008. "Is the Great Moderation Ending? UK and US Evidence," Working papers 2008-24, University of Connecticut, Department of Economics.
    12. Mstislav Elagin, 2008. "Locally adaptive estimation methods with application to univariate time series," Papers 0812.0449, arXiv.org.
    13. Mihaela Craioveanu & Eric Hillebrand, 2012. "Why It Is Ok To Use The Har-Rv(1,5,21) Model," Working Papers 1201, University of Central Missouri, Department of Economics & Finance, revised Aug 2012.
    14. Steven Trypsteen, 2014. "Cross-Country Interactions, the Great Moderation and the Role of Output Volatility in Growth," Discussion Papers 2014/10, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
    15. Liu, Hung-Chun & Chiang, Shu-Mei & Cheng, Nick Ying-Pin, 2012. "Forecasting the volatility of S&P depositary receipts using GARCH-type models under intraday range-based and return-based proxy measures," International Review of Economics & Finance, Elsevier, vol. 22(1), pages 78-91.
    16. Ruiwen Yang & Pathairat Pastpipatkul & Chaiwat Nimanussornkul, 2020. "Dynamic Volatility Spillover Among Chinese Black Series Futures Under Structural Breaks," International Journal of Business and Administrative Studies, Professor Dr. Bahaudin G. Mujtaba, vol. 6(5), pages 236-246.
    17. Bruno Spilak & Wolfgang Karl Hardle, 2020. "Tail-risk protection: Machine Learning meets modern Econometrics," Papers 2010.03315, arXiv.org, revised Aug 2021.
    18. Jorge Miguel Lopo Gonçalves Andraz & Nélia Maria Afonso Norte, 2013. "Output volatility in the OECD: Are the member states becoming less vulnerable to exogenous shocks?," CEFAGE-UE Working Papers 2013_17, University of Evora, CEFAGE-UE (Portugal).
    19. Xu, Ke-Li, 2013. "Power monotonicity in detecting volatility levels change," Economics Letters, Elsevier, vol. 121(1), pages 64-69.
    20. WenShwo Fang & Stephen M. Miller & ChunShen Lee, 2007. "Cross-Country Evidence on Output Growth Volatility: Nonstationary Variance and GARCH Models," Working papers 2007-20, University of Connecticut, Department of Economics, revised Mar 2008.
    21. Jean-Pierre Fouque & Matthew Lorig & Ronnie Sircar, 2016. "Second order multiscale stochastic volatility asymptotics: stochastic terminal layer analysis and calibration," Finance and Stochastics, Springer, vol. 20(3), pages 543-588, July.
    22. Berens, Tobias & Weiß, Gregor N.F. & Wied, Dominik, 2015. "Testing for structural breaks in correlations: Does it improve Value-at-Risk forecasting?," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 135-152.
    23. David E. Rapach & Jack K. Strauss, 2008. "Structural breaks and GARCH models of exchange rate volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(1), pages 65-90.
    24. Casini, Alessandro, 2023. "Theory of evolutionary spectra for heteroskedasticity and autocorrelation robust inference in possibly misspecified and nonstationary models," Journal of Econometrics, Elsevier, vol. 235(2), pages 372-392.
    25. Vasiliki Chatzikonstanti & Michail Karoglou, 2022. "Can black swans be tamed with a flexible mean‐variance specification?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3202-3227, July.
    26. Hamadi, Hassan & Bassil, Charbel & Nehme, Tamara, 2017. "News surprises and volatility spillover among agricultural commodities: The case of corn, wheat, soybean and soybean oil," Research in International Business and Finance, Elsevier, vol. 41(C), pages 148-157.
    27. WenSho Fang & Stephen M. Miller, 2007. "The Great Moderation and the Relationship between Output Growth and Its Volatility," Working papers 2007-04, University of Connecticut, Department of Economics.
    28. Jing Li & Henry Thompson, 2010. "A Note on the Oil Price Trend and GARCH Shocks," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 159-166.
    29. Demetrescu, Matei & Kruse-Becher, Robinson, 2025. "Is U.S. real output growth non-normal? A tale of time-varying location and scale," Journal of Economic Dynamics and Control, Elsevier, vol. 171(C).
    30. Harry J. Turtle & Chengping Zhang, 2015. "Structural breaks and portfolio performance in global equity markets," Quantitative Finance, Taylor & Francis Journals, vol. 15(6), pages 909-922, June.
    31. Conrad, Christian & Schienle, Melanie, 2019. "Testing for an omitted multiplicative long-term component in GARCH models," Working Paper Series in Economics 121, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    32. Boudt, Kris & Croux, Christophe, 2010. "Robust M-estimation of multivariate GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2459-2469, November.
    33. WenShwo Fang & Stephen M. Miller, 2012. "Output Growth and Its Volatility: The Gold Standard through the Great Moderation," Working papers 2012-11, University of Connecticut, Department of Economics.
    34. Hematizadeh, Roksana & Tajaddini, Reza, 2024. "A state-dependent international CAPM for partially integrated markets: Using local and US risk factors," Global Finance Journal, Elsevier, vol. 62(C).
    35. Amélie Charles & Olivier Darné & Laurent Ferrara, 2014. "Does the Great Recession imply the end of the Great Moderation? International evidence," Working Papers hal-04141344, HAL.
    36. Aneta Bełdycka-Bórawska & Piotr Bórawski & Michał Borychowski & Rafał Wyszomierski & Marek Bartłomiej Bórawski & Tomasz Rokicki & Luiza Ochnio & Krzysztof Jankowski & Bartosz Mickiewicz & James W. Dun, 2021. "Development of Solid Biomass Production in Poland, Especially Pellet, in the Context of the World’s and the European Union’s Climate and Energy Policies," Energies, MDPI, vol. 14(12), pages 1-22, June.
    37. WenShwo Fang & Stephen M. Miller & ChunShen Lee, 2009. "The Great Moderation Flattens Fat Tails: Disappearing Leptokurtosis," Working Papers 0903, University of Nevada, Las Vegas , Department of Economics.
    38. Silvennoinen Annastiina & Teräsvirta Timo, 2016. "Testing constancy of unconditional variance in volatility models by misspecification and specification tests," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(4), pages 347-364, September.
    39. Stefan Richter & Weining Wang & Wei Biao Wu, 2023. "Testing for parameter change epochs in GARCH time series," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 467-491.
    40. Han, Heejoon & Park, Joon Y., 2006. "Time series properties of ARCH processes with persistent covariates," MPRA Paper 5199, University Library of Munich, Germany.
    41. Richard T. Baillie & Claudio Morana, 2007. "Modeling Long Memory and Structural Breaks in Conditional Variances: an Adaptive FIGARCH Approach," ICER Working Papers - Applied Mathematics Series 11-2007, ICER - International Centre for Economic Research.
    42. Niklas Ahlgren & Alexander Back & Timo Terasvirta, 2024. "A new GARCH model with a deterministic time-varying intercept," Papers 2410.03239, arXiv.org, revised Oct 2024.
    43. Andreï Kostyrka & Dmitry Malakhov, 2021. "Was there ever a shift: Empirical analysis of structural-shift tests for return volatility," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 61, pages 110-139.
    44. Anjum, Hassan & Malik, Farooq, 2020. "Forecasting risk in the US Dollar exchange rate under volatility shifts," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    45. Härdle, Wolfgang Karl & Mungo, Julius, 2007. "Long memory persistence in the factor of Implied volatility dynamics," SFB 649 Discussion Papers 2007-027, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    46. Stéphane Goutte & Amine Ismail & Huyên Pham, 2017. "Regime-switching Stochastic Volatility Model : Estimation and Calibration to VIX options," Post-Print hal-01212018, HAL.
    47. Enders, Walter & Li, Jing, 2015. "Trend-cycle decomposition allowing for multiple smooth structural changes in the trend of US real GDP," Journal of Macroeconomics, Elsevier, vol. 44(C), pages 71-81.
    48. He, Zhongfang & Maheu, John M., 2010. "Real time detection of structural breaks in GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2628-2640, November.
    49. Ewing, Bradley T. & Malik, Farooq, 2013. "Volatility transmission between gold and oil futures under structural breaks," International Review of Economics & Finance, Elsevier, vol. 25(C), pages 113-121.
    50. Zhang, Yue-Jun & Yao, Ting & He, Ling-Yun & Ripple, Ronald, 2019. "Volatility forecasting of crude oil market: Can the regime switching GARCH model beat the single-regime GARCH models?," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 302-317.
    51. Jorge M. Andraz & Nélia M. Norte, 2017. "Gross domestic product growth, volatility and regime changes nexus: the case of Portugal," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 16(1), pages 1-16, April.
    52. Batten, Jonathan A. & Kinateder, Harald & Szilagyi, Peter G. & Wagner, Niklas F., 2019. "Liquidity, surprise volume and return premia in the oil market," Energy Economics, Elsevier, vol. 77(C), pages 93-104.
    53. Ewing, Bradley T. & Malik, Farooq, 2017. "Modelling asymmetric volatility in oil prices under structural breaks," Energy Economics, Elsevier, vol. 63(C), pages 227-233.
    54. Leonardo Chaves Borges Cardoso & Maurício Vaz Lobo Bittencourt, 2016. "Price Volatility Transmission From Oil To Energy And Non-Energy Agricultural Commodities," Anais do XLII Encontro Nacional de Economia [Proceedings of the 42nd Brazilian Economics Meeting] 181, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    55. Korkmaz, Turhan & Çevik, Emrah İ. & Atukeren, Erdal, 2012. "Return and volatility spillovers among CIVETS stock markets," Emerging Markets Review, Elsevier, vol. 13(2), pages 230-252.
    56. Karmakar, Sayar & Richter, Stefan & Wu, Wei Biao, 2022. "Simultaneous inference for time-varying models," Journal of Econometrics, Elsevier, vol. 227(2), pages 408-428.
    57. Taewook Lee & Moosup Kim & Changryong Baek, 2015. "Tests for Volatility Shifts in Garch Against Long-Range Dependence," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(2), pages 127-153, March.
    58. Chun Liu & John M. Maheu, 2008. "Are There Structural Breaks in Realized Volatility?," Journal of Financial Econometrics, Oxford University Press, vol. 6(3), pages 326-360, Summer.
    59. Dimitris N. Politis & Dimitrios D. Thomakos, 2007. "NoVaS Transformations: Flexible Inference for Volatility Forecasting," Working Paper series 44_07, Rimini Centre for Economic Analysis.
    60. Yanlin Shi & Yang Yang, 2018. "Modeling High Frequency Data with Long Memory and Structural Change: A-HYEGARCH Model," Risks, MDPI, vol. 6(2), pages 1-28, March.
    61. Dimitrios Kartsonakis-Mademlis & Nikolaos Dritsakis, 2022. "Asymmetric volatility transmission in Japanese stock market in the presence of structural breaks," The Japanese Economic Review, Springer, vol. 73(4), pages 647-677, October.
    62. Tsuji, Chikashi, 2020. "Correlation and spillover effects between the US and international banking sectors: New evidence and implications for risk management," International Review of Financial Analysis, Elsevier, vol. 70(C).
    63. Yıldırım, Durmuş Çağrı & Cevik, Emrah Ismail & Esen, Ömer, 2020. "Time-varying volatility spillovers between oil prices and precious metal prices," Resources Policy, Elsevier, vol. 68(C).
    64. Steven Trypsteen, 2014. "The Importance of a Time-Varying Variance and Cross-Country Interactions in Forecast Models," Discussion Papers 2014/15, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
    65. Arouri, Mohamed El Hédi & Lahiani, Amine & Lévy, Aldo & Nguyen, Duc Khuong, 2012. "Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models," Energy Economics, Elsevier, vol. 34(1), pages 283-293.
    66. Cizek, P., 2010. "Modelling Conditional Heteroscedasticity in Nonstationary Series," Other publications TiSEM a5a7b05f-5f1f-46ed-8ce8-5, Tilburg University, School of Economics and Management.
    67. Fakhfekh, Mohamed & Hachicha, Nejib & Jawadi, Fredj & Selmi, Nadhem & Idi Cheffou, Abdoulkarim, 2016. "Measuring volatility persistence for conventional and Islamic banks: An FI-EGARCH approach," Emerging Markets Review, Elsevier, vol. 27(C), pages 84-99.
    68. Dinghai Xu, 2020. "Canadian Stock Market Volatility under COVID-19," Working Papers 2001, University of Waterloo, Department of Economics, revised May 2020.
    69. Scharth, Marcel & Medeiros, Marcelo C., 2009. "Asymmetric effects and long memory in the volatility of Dow Jones stocks," International Journal of Forecasting, Elsevier, vol. 25(2), pages 304-327.
    70. Schnabl, Gunther & Hillebrand, Eric, 2006. "A structural break in the effects of Japanese foreign exchange intervention on yen/dollar exchange rate volatility," Working Paper Series 650, European Central Bank.
    71. Ke-Li Xu & Jui-Chung Yang, 2015. "Towards Uniformly Efficient Trend Estimation Under Weak/Strong Correlation and Non-stationary Volatility," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(1), pages 63-86, March.
    72. Krämer, Walter & Tameze, Baudouin & Christou, Konstantinos, 2012. "On the origin of high persistence in GARCH-models," Economics Letters, Elsevier, vol. 114(1), pages 72-75.
    73. Ahmed, Walid M.A., 2018. "On the interdependence of natural gas and stock markets under structural breaks," The Quarterly Review of Economics and Finance, Elsevier, vol. 67(C), pages 149-161.
    74. Krämer, Walter & Messow, Philip, 2012. "Structural Change and Spurious Persistence in Stochastic Volatility," Ruhr Economic Papers 310, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    75. BAUWENS, Luc & DUFAYS, Arnaud & DE BACKER, Bruno, 2011. "Estimating and forecasting structural breaks in financial time series," LIDAM Discussion Papers CORE 2011055, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    76. Azamo, Baudouin Tameze & Krämer, Walter, 2006. "Structural Change and long memory in the GARCH(1,1)-model," Technical Reports 2006,33, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    77. Emrah Ismail Cevik & Sel Dibooglu & Atif Awad Abdallah & Eisa Abdulrahman Al-Eisa, 2021. "Oil prices, stock market returns, and volatility spillovers: evidence from Saudi Arabia," International Economics and Economic Policy, Springer, vol. 18(1), pages 157-175, February.
    78. Teterin, Pavel & Brooks, Robert & Enders, Walter, 2016. "Smooth volatility shifts and spillovers in U.S. crude oil and corn futures markets," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 22-36.
    79. Mazur Błażej & Pipień Mateusz, 2018. "Time-varying asymmetry and tail thickness in long series of daily financial returns," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(5), pages 1-21, December.
    80. Michael McAleer & Marcelo Cunha Medeiros, 2006. "Realized volatility: a review," Textos para discussão 531 Publication status: F, Department of Economics PUC-Rio (Brazil).
    81. Sang Hoon Kang & Seong-Min Yoon, 2010. "Sudden Changes and Persistence in Volatility of Korean Equity Sector Returns," Korean Economic Review, Korean Economic Association, vol. 26, pages 431-451.
    82. Bauwens, L. & Hafner, C. & Laurent, S., 2012. "Volatility Models," LIDAM Reprints ISBA 2012028, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    83. Bergsli, Lykke Øverland & Lind, Andrea Falk & Molnár, Peter & Polasik, Michał, 2022. "Forecasting volatility of Bitcoin," Research in International Business and Finance, Elsevier, vol. 59(C).
    84. Mihaela Craioveanu & Eric Hillebrand, 2012. "Level changes in volatility models," Annals of Finance, Springer, vol. 8(2), pages 277-308, May.
    85. Jensen Anders Tolver & Lange Theis, 2010. "On Convergence of the QMLE for Misspecified GARCH Models," Journal of Time Series Econometrics, De Gruyter, vol. 2(1), pages 1-31, June.
    86. Julia S. Mehlitz & Benjamin R. Auer, 2021. "Time‐varying dynamics of expected shortfall in commodity futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(6), pages 895-925, June.
    87. Mohamed El Hedi Arouri & Shawkat Hammoudeh & Amine Lahiani & Duc Khuong Nguyen, 2013. "Long memory and structural breaks in modeling the return and volatility dynamics of precious metals," Working Papers hal-00798033, HAL.
    88. Hassan Anjum, 2019. "Estimating volatility transmission between oil prices and the US Dollar exchange rate under structural breaks," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 43(4), pages 750-763, October.
    89. Ahmed, Walid M.A., 2019. "Islamic and conventional equity markets: Two sides of the same coin, or not?," The Quarterly Review of Economics and Finance, Elsevier, vol. 72(C), pages 191-205.
    90. Farooq Malik, 2022. "Volatility spillover among sector equity returns under structural breaks," Review of Quantitative Finance and Accounting, Springer, vol. 58(3), pages 1063-1080, April.
    91. Naeem, Muhammad & Tiwari, Aviral Kumar & Mubashra, Sana & Shahbaz, Muhammad, 2019. "Modeling volatility of precious metals markets by using regime-switching GARCH models," Resources Policy, Elsevier, vol. 64(C).
    92. Ewing, Bradley T. & Malik, Farooq, 2016. "Volatility spillovers between oil prices and the stock market under structural breaks," Global Finance Journal, Elsevier, vol. 29(C), pages 12-23.
    93. Ali Babikir & Rangan Gupta & Chance Mwabutwa & Emmanuel Owusu-Sekyere, 2010. "Structural Breaks and GARCH Models of Stock Return Volatility: The Case of South Africa," Working Papers 201030, University of Pretoria, Department of Economics.
    94. Grote, Claudia & Bertram, Philip, 2015. "A comparative Study of Volatility Breaks," Hannover Economic Papers (HEP) dp-558, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    95. Jean-Pierre Fouque & Matthew Lorig & Ronnie Sircar, 2012. "Second Order Multiscale Stochastic Volatility Asymptotics: Stochastic Terminal Layer Analysis & Calibration," Papers 1208.5802, arXiv.org, revised Sep 2015.
    96. Bildirici, Melike & Ersin, Özgür, 2012. "Nonlinear volatility models in economics: smooth transition and neural network augmented GARCH, APGARCH, FIGARCH and FIAPGARCH models," MPRA Paper 40330, University Library of Munich, Germany, revised May 2012.
    97. Amélie Charles & Olivier Darné, 0. "Econometric history of the growth–volatility relationship in the USA: 1919–2017," Cliometrica, Springer;Cliometric Society (Association Francaise de Cliométrie), vol. 0, pages 1-24.
    98. Anthony Msafiri Nyangarika & Alexey Yurievich Mikhaylov & Bao-jun Tang, 2018. "Correlation of Oil Prices and Gross Domestic Product in Oil Producing Countries," International Journal of Energy Economics and Policy, Econjournals, vol. 8(5), pages 42-48.
    99. Cristina Chinazzo & Vahidin Jeleskovic, 2024. "Forecasting Bitcoin Volatility: A Comparative Analysis of Volatility Approaches," Papers 2401.02049, arXiv.org.
    100. Dannemann, Tebbe & Prehn, Soren & Brümmer, Bernhard, 2014. "Optionshandel Und Maispreisvolatilitat: Does the Tail Wag the Dog?," 54th Annual Conference, Goettingen, Germany, September 17-19, 2014 187371, German Association of Agricultural Economists (GEWISOLA).
    101. Davide De Gaetano, 2017. "Forecasting With Garch Models Under Structural Breaks: An Approach Based On Combinations Across Estimation Windows," Departmental Working Papers of Economics - University 'Roma Tre' 0219, Department of Economics - University Roma Tre.
    102. Song, Junmo & Baek, Changryong, 2019. "Detecting structural breaks in realized volatility," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 58-75.
    103. Ciarreta, Aitor & Pizarro-Irizar, Cristina & Zarraga, Ainhoa, 2020. "Renewable energy regulation and structural breaks: An empirical analysis of Spanish electricity price volatility," Energy Economics, Elsevier, vol. 88(C).
    104. Igor LEBRUN & Ludovic DOBBELAERE, 2010. "A Macro-econometric Model for the Economy of Lesotho," EcoMod2010 259600102, EcoMod.
    105. Amélie Charles & Chew Lian Chua & Olivier Darné & Sandy Suardi, 2020. "On the Pernicious Effects of Oil Price Uncertainty on U.S. Real Economic Activities," Post-Print hal-03040689, HAL.
    106. Spilak, Bruno & Härdle, Wolfgang Karl, 2020. "Tail-risk protection: Machine Learning meets modern Econometrics," IRTG 1792 Discussion Papers 2020-015, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    107. Hood, Matthew & Malik, Farooq, 2018. "Estimating downside risk in stock returns under structural breaks," International Review of Economics & Finance, Elsevier, vol. 58(C), pages 102-112.
    108. Alexey Yurievich Mikhaylov, 2018. "Volatility Spillover Effect between Stock and Exchange Rate in Oil Exporting Countries," International Journal of Energy Economics and Policy, Econjournals, vol. 8(3), pages 321-326.
    109. Olofsson, Petter & Råholm, Anna & Uddin, Gazi Salah & Troster, Victor & Kang, Sang Hoon, 2021. "Ethical and unethical investments under extreme market conditions," International Review of Financial Analysis, Elsevier, vol. 78(C).
    110. Eric Hillebrand, 2005. "Mean Reversion Expectations and the 1987 Stock Market Crash: An Empirical Investigation," Finance 0501015, University Library of Munich, Germany.
    111. Conrad, Christian & Kleen, Onno, 2016. "On the statistical properties of multiplicative GARCH models," Working Papers 0613, University of Heidelberg, Department of Economics.
    112. Visser, Marcel P., 2008. "Garch Parameter Estimation Using High-Frequency Data," MPRA Paper 9076, University Library of Munich, Germany.
    113. Moosup Kim & Sangyeol Lee, 2016. "On the tail index inference for heavy-tailed GARCH-type innovations," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 68(2), pages 237-267, April.
    114. Kumar, Pawan & Singh, Vipul Kumar & Rao, Sandeep, 2023. "Does the substitution effect lead to feedback effect linkage between ethanol, crude oil, and soft agricultural commodities?," Energy Economics, Elsevier, vol. 119(C).
    115. Mofleh Alshogeathri & Jamel Jouini, 2017. "Linkages Between Equity and Global Food Markets: New Evidence from Including Structural Changes," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 67(3), pages 166-198, June.
    116. Vivian, Andrew & Wohar, Mark E., 2012. "Commodity volatility breaks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(2), pages 395-422.
    117. Carlos Trucíos & James W. Taylor, 2023. "A comparison of methods for forecasting value at risk and expected shortfall of cryptocurrencies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 989-1007, July.
    118. Malik, Farooq, 2021. "Volatility spillover between exchange rate and stock returns under volatility shifts," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 605-613.
    119. Alicia Adsera, Ana Ferrer and Virginia Herranz, 2020. "Descriptive labour market outcomes of immigrant women across Europe," Working Papers 2004, University of Waterloo, Department of Economics, revised 2020.
    120. Walter Kraemer, 2008. "Long Memory with Markov-Switching GARCH," CESifo Working Paper Series 2225, CESifo.
    121. McAleer, Michael & Medeiros, Marcelo C., 2008. "A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries," Journal of Econometrics, Elsevier, vol. 147(1), pages 104-119, November.
    122. Aloui, Chaker & Hamida, Hela ben, 2014. "Modelling and forecasting value at risk and expected shortfall for GCC stock markets: Do long memory, structural breaks, asymmetry, and fat-tails matter?," The North American Journal of Economics and Finance, Elsevier, vol. 29(C), pages 349-380.
    123. DUFAYS, Arnaud, 2012. "Infinite-state Markov-switching for dynamic volatility and correlation models," LIDAM Discussion Papers CORE 2012043, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    124. 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.
    125. VAN BELLEGEM, Sébastien, 2011. "Locally stationary volatility modelling," LIDAM Discussion Papers CORE 2011041, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    126. Alessandro Casini, 2021. "Theory of Evolutionary Spectra for Heteroskedasticity and Autocorrelation Robust Inference in Possibly Misspecified and Nonstationary Models," Papers 2103.02981, arXiv.org, revised Aug 2024.
    127. Halkos, George E. & Tsirivis, Apostolos S., 2019. "Effective energy commodity risk management: Econometric modeling of price volatility," Economic Analysis and Policy, Elsevier, vol. 63(C), pages 234-250.
    128. Walid Mensi & Shawkat Hammoude & Seong-Min Yoon, 2014. "Structural Breaks, Dynamic Correlations, Volatility Transmission, and Hedging Strategies for International Petroleum Prices and U.S. Dollar Exchange Rate," Working Papers 884, Economic Research Forum, revised Dec 2014.
    129. Kim, Moosup & Lee, Taewook & Noh, Jungsik & Baek, Changryong, 2014. "Quasi-maximum likelihood estimation for multiple volatility shifts," Statistics & Probability Letters, Elsevier, vol. 86(C), pages 50-60.
    130. Halkos, George & Tzirivis, Apostolos, 2018. "Effective energy commodities’ risk management: Econometric modeling of price volatility," MPRA Paper 90781, University Library of Munich, Germany.
    131. Jin, Xin & Maheu, John M., 2016. "Modeling covariance breakdowns in multivariate GARCH," Journal of Econometrics, Elsevier, vol. 194(1), pages 1-23.
    132. Lajos Horvath & Lorenzo Trapani & Shixuan Wang, 2024. "Sequential monitoring for explosive volatility regimes," Papers 2404.17885, arXiv.org.
    133. Yanlin Shi, 2023. "Long memory and regime switching in the stochastic volatility modelling," Annals of Operations Research, Springer, vol. 320(2), pages 999-1020, January.
    134. Aharon, David Y. & Butt, Hassan Anjum & Jaffri, Ali & Nichols, Brian, 2023. "Asymmetric volatility in the cryptocurrency market: New evidence from models with structural breaks," International Review of Financial Analysis, Elsevier, vol. 87(C).
    135. Ahmed, Walid M.A., 2017. "On the dynamic interactions between energy and stock markets under structural shifts: Evidence from Egypt," Research in International Business and Finance, Elsevier, vol. 42(C), pages 61-74.
    136. Xu, Ke-Li, 2013. "Powerful tests for structural changes in volatility," Journal of Econometrics, Elsevier, vol. 173(1), pages 126-142.
    137. Alessandro Casini & Taosong Deng & Pierre Perron, 2021. "Theory of Low Frequency Contamination from Nonstationarity and Misspecification: Consequences for HAR Inference," Papers 2103.01604, arXiv.org, revised Sep 2024.
    138. Trypsteen, Steven, 2017. "The growth-volatility nexus: New evidence from an augmented GARCH-M model," Economic Modelling, Elsevier, vol. 63(C), pages 15-25.
    139. Han, Heejoon & Park, Joon Y., 2014. "GARCH with omitted persistent covariate," Economics Letters, Elsevier, vol. 124(2), pages 248-254.
    140. Hotta, Luiz Koodi & Trucíos Maza, Carlos César & Pereira, Pedro L. Valls & Zevallos Herencia, Mauricio Henrique, 2024. "Forecasting VaR and ES through Markov-switching GARCH models: does the specication matter?," Textos para discussão 567, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    141. Mustafa Okur & Emrah Cevik, 2013. "Testing Intraday Volatility Spillovers in Turkish Capital Markets: Evidence from Ise," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 26(3), pages 99-116, January.
    142. Matei Demetrescu & Robinson Kruse-Becher, 2021. "Is U.S. real output growth really non-normal? Testing distributional assumptions in time-varying location-scale models," CREATES Research Papers 2021-07, Department of Economics and Business Economics, Aarhus University.
    143. Adams, Zeno & Füss, Roland & Glück, Thorsten, 2017. "Are correlations constant? Empirical and theoretical results on popular correlation models in finance," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 9-24.
    144. Marvasti, Akbar & Lamberte, Antonio, 2016. "Commodity price volatility under regulatory changes and disaster," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 355-361.
    145. Cevik, Nuket Kirci & Cevik, Emrah I. & Dibooglu, Sel, 2020. "Oil prices, stock market returns and volatility spillovers: Evidence from Turkey," Journal of Policy Modeling, Elsevier, vol. 42(3), pages 597-614.
    146. Bradley T. Ewing & Farooq Malik & Hassan Anjum, 2019. "Forecasting value‐at‐risk in oil prices in the presence of volatility shifts," Review of Financial Economics, John Wiley & Sons, vol. 37(3), pages 341-350, July.
    147. Vyacheslav Abramov & Fima Klebaner, 2007. "Estimation and Prediction of a Non-Constant Volatility," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 14(1), pages 1-23, March.
    148. Broto, Carmen, 2011. "Inflation targeting in Latin America: Empirical analysis using GARCH models," Economic Modelling, Elsevier, vol. 28(3), pages 1424-1434, May.
    149. Cizek, P. & Haerdle, W. & Spokoiny, V., 2007. "Adaptive Pointwise Estimation in Time-Inhomogeneous Time-Series Models," Discussion Paper 2007-35, Tilburg University, Center for Economic Research.
    150. Matei, Marius, 2011. "Non-Linear Volatility Modeling of Economic and Financial Time Series Using High Frequency Data," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 116-141, June.
    151. Anders Tolver Jensen & Theis Lange, 2009. "On IGARCH and convergence of the QMLE for misspecified GARCH models," CREATES Research Papers 2009-06, Department of Economics and Business Economics, Aarhus University.
    152. Ewing, Bradley T. & Malik, Farooq & Payne, James E., 2024. "Volatility transmission between upstream and midstream energy sectors," International Review of Economics & Finance, Elsevier, vol. 92(C), pages 1191-1199.
    153. Haipeng Xing & Hongsong Yuan & Sichen Zhou, 2017. "A Mixtured Localized Likelihood Method for GARCH Models with Multiple Change-points," Review of Economics & Finance, Better Advances Press, Canada, vol. 8, pages 44-60, May.
    154. Messow, Philip & Krämer, Walter, 2013. "Spurious persistence in stochastic volatility," Economics Letters, Elsevier, vol. 121(2), pages 221-223.
    155. Luis A. Gil-Alana & Goodness C. Aye & Rangan Gupta, 2012. "Testing for Persistence with Breaks and Outliers in South African House Prices," Faculty Working Papers 20/12, School of Economics and Business Administration, University of Navarra.
    156. Andrew Papanicolaou & Ronnie Sircar, 2014. "A regime-switching Heston model for VIX and S&P 500 implied volatilities," Quantitative Finance, Taylor & Francis Journals, vol. 14(10), pages 1811-1827, October.
    157. Farooq Malik, 2015. "Revisiting the relationship between risk and return," Review of Quantitative Finance and Accounting, Springer, vol. 44(1), pages 25-40, January.
    158. BAUWENS, Luc & DE BACKER, Bruno & DUFAYS, Arnaud, 2014. "A Bayesian method of change-point estimation with recurrent regimes: application to GARCH models," LIDAM Reprints CORE 2641, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    159. Sinem Derindere KOSEOGLU & Emrah Ismail CEVIK, 2013. "Testing for Causality in Mean and Variance between the Stock Market and the Foreign Exchange Market: An Application to the Major Central and Eastern European Countries," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 63(1), pages 65-86, March.
    160. Emrah İ. Çevik & Erdal Atukeren & Turhan Korkmaz, 2018. "Oil Prices and Global Stock Markets: A Time-Varying Causality-In-Mean and Causality-in-Variance Analysis," Energies, MDPI, vol. 11(10), pages 1-22, October.
    161. Vasyl Golosnoy, 2007. "Sequential monitoring of minimum variance portfolio," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 91(1), pages 39-55, March.
    162. Banerjee, Anindya & Urga, Giovanni, 2005. "Modelling structural breaks, long memory and stock market volatility: an overview," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 1-34.
    163. Eric Hillebrand & Marcelo Cunha Medeiros, 2010. "Asymmetries, breaks, and long-range dependence: An estimation framework for daily realized volatility," Textos para discussão 578, Department of Economics PUC-Rio (Brazil).
    164. 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.
    165. Francq, Christian & ZakoI¨an, Jean-Michel, 2005. "The L2-structures of standard and switching-regime GARCH models," Stochastic Processes and their Applications, Elsevier, vol. 115(9), pages 1557-1582, September.
    166. Güloğlu, Bülent & Kaya, Pınar & Aydemir, Resul, 2016. "Volatility transmission among Latin American stock markets under structural breaks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 330-340.
    167. BenSaïda, Ahmed, 2015. "The frequency of regime switching in financial market volatility," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 63-79.
    168. Hu Liang & Shin Yongcheol, 2008. "Optimal Test for Markov Switching GARCH Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 12(3), pages 1-27, September.
    169. Jovan Njegić & Dejan Živkov & Irena Janković, 2018. "Interrelationship and Spillover Effect between Stock and Exchange Rate Markets in the Major Emerging Economies," Prague Economic Papers, Prague University of Economics and Business, vol. 2018(3), pages 270-292.
    170. Ane, Thierry, 2006. "An analysis of the flexibility of Asymmetric Power GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1293-1311, November.

Books

    Sorry, no citations of books recorded.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.