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On the forecasting accuracy of multivariate GARCH models
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Cited by:
- Marchese, Malvina & Kyriakou, Ioannis & Tamvakis, Michael & Di Iorio, Francesca, 2020. "Forecasting crude oil and refined products volatilities and correlations: New evidence from fractionally integrated multivariate GARCH models," Energy Economics, Elsevier, vol. 88(C).
- Nikolaus Hautsch & Lada M. Kyj & Peter Malec, 2015.
"Do High‐Frequency Data Improve High‐Dimensional Portfolio Allocations?,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(2), pages 263-290, March.
- Hautsch, Nikolaus & Kyj, Lada. M. & Malec, Peter, 2013. "Do high-frequency data improve high-dimensional portfolio allocations?," SFB 649 Discussion Papers 2013-014, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
- Stanislav Anatolyev & Nikita Kobotaev, 2018.
"Modeling and forecasting realized covariance matrices with accounting for leverage,"
Econometric Reviews, Taylor & Francis Journals, vol. 37(2), pages 114-139, February.
- Stanislav Anatolyev & Nikita Kobotaev, 2015. "Modeling and Forecasting Realized Covariance Matrices with Accounting for Leverage," Working Papers w0213, Center for Economic and Financial Research (CEFIR).
- Stanislav Anatolyev & Nikita Kobotaev, 2015. "Modeling and Forecasting Realized Covariance Matrices with Accounting for Leverage," Working Papers w0213, New Economic School (NES).
- Carlos Trucíos & Mauricio Zevallos & Luiz K. Hotta & André A. P. Santos, 2019. "Covariance Prediction in Large Portfolio Allocation," Econometrics, MDPI, vol. 7(2), pages 1-24, May.
- Han, Chulwoo & Park, Frank C., 2022. "A geometric framework for covariance dynamics," Journal of Banking & Finance, Elsevier, vol. 134(C).
- Inoue, Atsushi & Jin, Lu & Rossi, Barbara, 2017.
"Rolling window selection for out-of-sample forecasting with time-varying parameters,"
Journal of Econometrics, Elsevier, vol. 196(1), pages 55-67.
- Atsushi Inoue & Lu Jin & Barbara Rossi, 2014. "Rolling Window Selection for Out-of-Sample Forecasting with Time-Varying Parameters," Working Papers 768, Barcelona School of Economics.
- Atsushi Inoue & Lu Jin & Barbara Rossi, 2014. "Rolling window selection for out-of-sample forecasting with time-varying parameters," Economics Working Papers 1435, Department of Economics and Business, Universitat Pompeu Fabra, revised Apr 2016.
- Massimiliano Caporin & Michael McAleer, 2011.
"Ranking Multivariate GARCH Models by Problem Dimension: An Empirical Evaluation,"
Documentos de Trabajo del ICAE
2011-20, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
- Caporin, M. & McAleer, M.J., 2011. "Ranking Multivariate GARCH Models by Problem Dimension: An Empirical Evaluation," Econometric Institute Research Papers EI 2011-18, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
- Michael McAleer & Massimiliano Caporin, 2011. "Ranking Multivariate GARCH Models by Problem Dimension:An Empirical Evaluation," KIER Working Papers 778, Kyoto University, Institute of Economic Research.
- Massimiliano Caporin & Michael McAleer, 2011. "Ranking Multivariate GARCH Models by Problem Dimension: An Empirical Evaluation," Working Papers in Economics 11/23, University of Canterbury, Department of Economics and Finance.
- Tobias Hartl & Roland Weigand, 2018.
"Multivariate Fractional Components Analysis,"
Papers
1812.09149, arXiv.org, revised Jan 2019.
- Hartl, Tobias & Weigand, Roland, 2019. "Multivariate Fractional Components Analysis," University of Regensburg Working Papers in Business, Economics and Management Information Systems 38283, University of Regensburg, Department of Economics.
- Szymon Lis & Marcin Chlebus, 2021. "Comparison of the accuracy in VaR forecasting for commodities using different methods of combining forecasts," Working Papers 2021-11, Faculty of Economic Sciences, University of Warsaw.
- Rasmus S. Pedersen & Anders Rahbek, 2014.
"Multivariate variance targeting in the BEKK–GARCH model,"
Econometrics Journal, Royal Economic Society, vol. 17(1), pages 24-55, February.
- Rasmus Søndergaard Pedersen & Anders Rahbek, 2012. "Multivariate Variance Targeting in the BEKK-GARCH Model," CREATES Research Papers 2012-53, Department of Economics and Business Economics, Aarhus University.
- Rasmus Søndergaard Pedersen & Anders Rahbek, 2012. "Multivariate Variance Targeting in the BEKK-GARCH Model," Discussion Papers 12-23, University of Copenhagen. Department of Economics.
- Karl Oton Rudolf & Samer Ajour El Zein & Nicola Jackman Lansdowne, 2021. "Bitcoin as an Investment and Hedge Alternative. A DCC MGARCH Model Analysis," Risks, MDPI, vol. 9(9), pages 1-22, August.
- Laurent, Sébastien & Lecourt, Christelle & Palm, Franz C., 2016.
"Testing for jumps in conditionally Gaussian ARMA–GARCH models, a robust approach,"
Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 383-400.
- Sébastien Laurent & Christelle Lecourt & Franz C. Palm, 2016. "Testing for jumps in conditionally Gaussian ARMA-GARCH models, a robust approach," Post-Print hal-01447861, HAL.
- Hallin, Marc & Trucíos, Carlos, 2023. "Forecasting value-at-risk and expected shortfall in large portfolios: A general dynamic factor model approach," Econometrics and Statistics, Elsevier, vol. 27(C), pages 1-15.
- Caporin, Massimiliano & McAleer, Michael, 2014.
"Robust ranking of multivariate GARCH models by problem dimension,"
Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 172-185.
- Caporin, M. & McAleer, M.J., 2012. "Robust Ranking of Multivariate GARCH Models by Problem Dimension," Econometric Institute Research Papers EI2012-13, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
- Michael McAleer & Massimiliano Caporin, 2012. "Robust Ranking of Multivariate GARCH Models by Problem Dimension," KIER Working Papers 815, Kyoto University, Institute of Economic Research.
- Massimiliano Caporin & Michael McAleer, 2012. "Robust Ranking of Multivariate GARCH Models by Problem Dimension," Working Papers in Economics 12/06, University of Canterbury, Department of Economics and Finance.
- Massimiliano Caporin & Michael McAleer, 2012. "Robust Ranking of Multivariate GARCH Models by Problem Dimension," Documentos de Trabajo del ICAE 2012-06, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico, revised Apr 2012.
- Chao Liang & Yu Wei & Likun Lei & Feng Ma, 2022. "Global equity market volatility forecasting: New evidence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 594-609, January.
- Ye, Wuyi & Xia, Wenjing & Wu, Bin & Chen, Pengzhan, 2022. "Using implied volatility jumps for realized volatility forecasting: Evidence from the Chinese market," International Review of Financial Analysis, Elsevier, vol. 83(C).
- Triki, Mohamed Bilel & Ben Maatoug, Abderrazek, 2021. "The GOLD market as a safe haven against the stock market uncertainty: Evidence from geopolitical risk," Resources Policy, Elsevier, vol. 70(C).
- Radovan Parrák, 2013. "The Economic Valuation of Variance Forecasts: An Artificial Option Market Approach," Working Papers IES 2013/09, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Aug 2013.
- Darolles, Serge & Francq, Christian & Laurent, Sébastien, 2018.
"Asymptotics of Cholesky GARCH models and time-varying conditional betas,"
Journal of Econometrics, Elsevier, vol. 204(2), pages 223-247.
- Serge Darolles & Christian Francq & Sébastien Laurent, 2016. "Asymptotics of Cholesky GARCH models and time-varying conditional betas," Post-Print hal-04590533, HAL.
- Serge Darolles & Christian Francq & Sebastien Laurent, 2018. "Asymptotics of Cholesky GARCH models and time-varying conditional betas," Post-Print hal-04590180, HAL.
- Serge Darolles & Christian Francq & Sébastien Laurent, 2018. "Asymptotics of Cholesky GARCH models and time-varying conditional betas," Post-Print hal-04590251, HAL.
- Serge Darolles & Christian Francq & Sébastien Laurent, 2018. "Asymptotics of Cholesky GARCH Models and Time-Varying Conditional Betas," AMSE Working Papers 1845, Aix-Marseille School of Economics, France.
- Serge Darolles & Christian Francq & Sébastien Laurent, 2018. "Asymptotics of Cholesky GARCH models and time-varying conditional betas," Post-Print hal-04590232, HAL.
- Serge Darolles & Christian Francq & Sébastien Laurent, 2018. "Asymptotics of Cholesky GARCH Models and Time-Varying Conditional Betas," Working Papers halshs-01944656, HAL.
- Serge Darolles & Christian Francq & Sébastien Laurent, 2017. "Asymptotics of Cholesky GARCH models and time-varying conditional betas," Post-Print hal-04590471, HAL.
- Serge Darolles & Christian Francq & Sébastien Laurent, 2018. "Asymptotics of Cholesky GARCH models and time-varying conditional betas," Post-Print hal-01980815, HAL.
- Serge Darolles & Christian Francq & Sébastien Laurent, 2017. "Asymptotics of Cholesky GARCH models and time-varying conditional betas," Post-Print hal-04590522, HAL.
- Darolles, Serges & Francq, Christian & Laurent, Sébastien, 2018. "Asymptotics of Cholesky GARCH models and time-varying conditional betas," MPRA Paper 83988, University Library of Munich, Germany.
- Asai, Manabu & McAleer, Michael, 2015.
"Forecasting co-volatilities via factor models with asymmetry and long memory in realized covariance,"
Journal of Econometrics, Elsevier, vol. 189(2), pages 251-262.
- Manabu Asai & Michael McAleer, 2014. "Forecasting Co-Volatilities via Factor Models with Asymmetry and Long Memory in Realized Covariance," Documentos de Trabajo del ICAE 2014-05, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
- Manabu Asai & Michael McAleer, 2014. "Forecasting Co-Volatilities via Factor Models with Asymmetry and Long Memory in Realized Covariance," Tinbergen Institute Discussion Papers 14-037/III, Tinbergen Institute.
- Manabu Asai & Michael McAleer, 2014. "Forecasting Co-Volatilities via Factor Models with Asymmetry and Long Memory in Realized Covariance," Working Papers in Economics 14/10, University of Canterbury, Department of Economics and Finance.
- Aslanidis, Nektarios & Casas, Isabel, 2011. "Modelling asset correlations: A nonparametric approach," Working Papers 2011-01, University of Sydney, School of Economics.
- repec:hum:wpaper:sfb649dp2013-014 is not listed on IDEAS
- Mei, Dexiang & Zeng, Qing & Cao, Xiang & Diao, Xiaohua, 2019. "Uncertainty and oil volatility: New evidence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 155-163.
- Carlos Trucíos & João H. G. Mazzeu & Marc Hallin & Luiz K. Hotta & Pedro L. Valls Pereira & Mauricio Zevallos, 2022.
"Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: A General Dynamic Factor Approach,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(1), pages 40-52, December.
- Marc Hallin & Luis K. Hotta & João H. G Mazzeu & Carlos Cesar Trucios-Maza & Pedro L. Valls Pereira & Mauricio Zevallos, 2019. "Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: a General Dynamic Factor Approach," Working Papers ECARES 2019-14, ULB -- Universite Libre de Bruxelles.
- Trucíos Maza, Carlos César & Mazzeu, João H. G. & Hallin, Marc & Hotta, Luiz Koodi & Pereira, Pedro L. Valls & Zevallos, Mauricio, 2019. "Forecasting conditional covariance matrices in high-dimensional time series: a general dynamic factor approach," Textos para discussão 505, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
- Xiaoning Kang & Xinwei Deng & Kam‐Wah Tsui & Mohsen Pourahmadi, 2020. "On variable ordination of modified Cholesky decomposition for estimating time‐varying covariance matrices," International Statistical Review, International Statistical Institute, vol. 88(3), pages 616-641, December.
- Bauwens, Luc & Braione, Manuela & Storti, Giuseppe, 2017.
"A dynamic component model for forecasting high-dimensional realized covariance matrices,"
Econometrics and Statistics, Elsevier, vol. 1(C), pages 40-61.
- BAUWENS, Luc & BRAIONE, Manuela & STORTI, Giuseppe, 2016. "A dynamic component model for forecasting high-dimensional realized covariance matrices," LIDAM Discussion Papers CORE 2016001, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Luc Bauwens & Manuela Braione & Giuseppe Storti, 2020. "A Dynamic Component Model for Forecasting High-Dimensional Realized Covariances Matrices," Working Papers 3_234, Dipartimento di Scienze Economiche e Statistiche, Università degli Studi di Salerno, revised Jul 2020.
- Luc BAUWENS, Manuela BRAIONE and Giuseppe STORTI & Luc BAUWENS, Manuela BRAIONE and Giuseppe STORTI & Luc BAUWENS, Manuela BRAIONE and Giuseppe STORTI, 2017. "A dynamic component model for forecasting high-dimensional realized covariance matrices," LIDAM Reprints CORE 2812, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Erik Kole & Thijs Markwat & Anne Opschoor & Dick van Dijk, 2017.
"Forecasting Value-at-Risk under Temporal and Portfolio Aggregation,"
Journal of Financial Econometrics, Oxford University Press, vol. 15(4), pages 649-677.
- Erik Kole & Thijs Markwat & Anne Opschoor & Dick van Dijk, 2015. "Forecasting Value-at-Risk under Temporal and Portfolio Aggregation," Tinbergen Institute Discussion Papers 15-140/III, Tinbergen Institute, revised 19 Apr 2017.
- Varneskov, Rasmus & Voev, Valeri, 2013.
"The role of realized ex-post covariance measures and dynamic model choice on the quality of covariance forecasts,"
Journal of Empirical Finance, Elsevier, vol. 20(C), pages 83-95.
- Rasmus Tangsgaard Varneskov & Valeri Voev, 2010. "The Role of Realized Ex-post Covariance Measures and Dynamic Model Choice on the Quality of Covariance Forecasts," CREATES Research Papers 2010-45, Department of Economics and Business Economics, Aarhus University.
- Moura, Guilherme V. & Santos, André A. P., 2019. "Comparing Forecasts of Extremely Large Conditional Covariance Matrices," DES - Working Papers. Statistics and Econometrics. WS 29291, Universidad Carlos III de Madrid. Departamento de EstadÃstica.
- Aielli, Gian Piero & Caporin, Massimiliano, 2014.
"Variance clustering improved dynamic conditional correlation MGARCH estimators,"
Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 556-576.
- Gian Piero Aielli & Massimiliano Caporin, 2011. "Variance Clustering Improved Dynamic Conditional Correlation MGARCH Estimators," "Marco Fanno" Working Papers 0133, Dipartimento di Scienze Economiche "Marco Fanno".
- Chao Liang & Yin Liao & Feng Ma & Bo Zhu, 2022. "United States Oil Fund volatility prediction: the roles of leverage effect and jumps," Empirical Economics, Springer, vol. 62(5), pages 2239-2262, May.
- repec:uts:finphd:39 is not listed on IDEAS
- Wang, Yizhi & Lucey, Brian M. & Vigne, Samuel A. & Yarovaya, Larisa, 2022. "The Effects of Central Bank Digital Currencies News on Financial Markets," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
- Pedersen, Rasmus Søndergaard, 2016.
"Targeting Estimation Of Ccc-Garch Models With Infinite Fourth Moments,"
Econometric Theory, Cambridge University Press, vol. 32(2), pages 498-531, April.
- Rasmus Søndergaard Pedersen, 2014. "Targeting estimation of CCC-Garch models with infinite fourth moments," Discussion Papers 14-04, University of Copenhagen. Department of Economics.
- Jacobs, Michael & Karagozoglu, Ahmet K., 2014. "On the characteristics of dynamic correlations between asset pairs," Research in International Business and Finance, Elsevier, vol. 32(C), pages 60-82.
- Lv, Wendai, 2018. "Does the OVX matter for volatility forecasting? Evidence from the crude oil market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 916-922.
- de Almeida, Daniel & Hotta, Luiz K. & Ruiz, Esther, 2018.
"MGARCH models: Trade-off between feasibility and flexibility,"
International Journal of Forecasting, Elsevier, vol. 34(1), pages 45-63.
- Almeida, Daniel de & Hotta, Luiz, 2015. "MGARCH models: tradeoff between feasibility and flexibility," DES - Working Papers. Statistics and Econometrics. WS ws1516, Universidad Carlos III de Madrid. Departamento de EstadÃstica.
- Stavroula P. Fameliti & Vasiliki D. Skintzi, 2020. "Predictive ability and economic gains from volatility forecast combinations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 200-219, March.
- Mohammad Alomari & David. M. Power & Nongnuch Tantisantiwong, 2018. "Determinants of equity return correlations: a case study of the Amman Stock Exchange," Review of Quantitative Finance and Accounting, Springer, vol. 50(1), pages 33-66, January.
- Tran, Thuy Nhung, 2022. "The Volatility of the Stock Market and Financial Cycle: GARCH Family Models," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 56(1), pages 151-168.
- Yu‐Sheng Lai, 2019. "Flexible covariance dynamics, high‐frequency data, and optimal futures hedging," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(12), pages 1529-1548, December.
- Feng Ma & Yu Wei & Wang Chen & Feng He, 2018. "Forecasting the volatility of crude oil futures using high-frequency data: further evidence," Empirical Economics, Springer, vol. 55(2), pages 653-678, September.
- Tobias Fissler & Yannick Hoga, 2024. "How to Compare Copula Forecasts?," Papers 2410.04165, arXiv.org.
- Alexander, Carol & Han, Yang & Meng, Xiaochun, 2023. "Static and dynamic models for multivariate distribution forecasts: Proper scoring rule tests of factor-quantile versus multivariate GARCH models," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1078-1096.
- Manabu Asai & Michael McAleer, 2022.
"Bayesian Analysis of Realized Matrix-Exponential GARCH Models,"
Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 103-123, January.
- Asai, M. & McAleer, M.J., 2018. "Bayesian Analysis of Realized Matrix-Exponential GARCH Models," Econometric Institute Research Papers 2018-005/III, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
- Manabu Asai & Michael McAleer, 2018. "Bayesian Analysis of Realized Matrix-Exponential GARCH Models," Tinbergen Institute Discussion Papers 18-005/III, Tinbergen Institute.
- Manabu Asai & Michael McAleer, 2018. "Bayesian analysis of realized matrix-exponential GARCH models," Documentos de Trabajo del ICAE 2018-04, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
- Rombouts, Jeroen & Stentoft, Lars & Violante, Franceso, 2014.
"The value of multivariate model sophistication: An application to pricing Dow Jones Industrial Average options,"
International Journal of Forecasting, Elsevier, vol. 30(1), pages 78-98.
- ROMBOUTS, Jeroen V. K. & STENTOFT, Lars & VIOLANTE, Francesco, 2012. "The value of multivariate model sophistication: an application to pricing Dow Jones Industrial Average options," LIDAM Discussion Papers CORE 2012003, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Jeroen Rombouts & Lars Stentoft & Francesco Violente, 2012. "The Value of Multivariate Model Sophistication: An Application to pricing Dow Jones Industrial Average Options," CIRANO Working Papers 2012s-05, CIRANO.
- Jeroen V.K. Rombouts & Lars Stentoft & Francesco Violante, 2012. "The Value of Multivariate Model Sophistication: An Application to pricing Dow Jones Industrial Average options," CREATES Research Papers 2012-04, Department of Economics and Business Economics, Aarhus University.
- Lu, Xinjie & Ma, Feng & Li, Haibo & Wang, Jianqiong, 2023. "INE oil futures volatility prediction: Exchange rates or international oil futures volatility?," Energy Economics, Elsevier, vol. 126(C).
- Dahiru A. Balaa & Taro Takimotob, 2017. "Stock markets volatility spillovers during financial crises: A DCC-MGARCH with skewed-t density approach," Borsa Istanbul Review, Research and Business Development Department, Borsa Istanbul, vol. 17(1), pages 25-48, March.
- Noori, Mohammad & Hitaj, Asmerilda, 2023. "Dissecting hedge funds' strategies," International Review of Financial Analysis, Elsevier, vol. 85(C).
- Fu, Yang & Zheng, Zeyu, 2020. "Volatility modeling and the asymmetric effect for China’s carbon trading pilot market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
- Peng, Huan & Chen, Ruoxun & Mei, Dexiang & Diao, Xiaohua, 2018. "Forecasting the realized volatility of the Chinese stock market: Do the G7 stock markets help?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 78-85.
- Dark, Jonathan, 2015. "Futures hedging with Markov switching vector error correction FIEGARCH and FIAPARCH," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 269-285.
- Adam E Clements & Mark Doolan & Stan Hurn & Ralf Becker, 2012. "Selecting forecasting models for portfolio allocation," NCER Working Paper Series 85, National Centre for Econometric Research.
- Fresoli, Diego E. & Ruiz, Esther, 2016.
"The uncertainty of conditional returns, volatilities and correlations in DCC models,"
Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 170-185.
- Fresoli, Diego Eduardo, 2014. "The uncertainty of conditional returns, volatilities and correlations in DCC models," DES - Working Papers. Statistics and Econometrics. WS ws140202, Universidad Carlos III de Madrid. Departamento de EstadÃstica.
- Adam Clements & Ayesha Scott & Annastiina Silvennoinen, 2019. "Volatility-dependent correlations: further evidence of when, where and how," Empirical Economics, Springer, vol. 57(2), pages 505-540, August.
- Timo Dimitriadis & Yannick Hoga, 2022. "Dynamic CoVaR Modeling," Papers 2206.14275, arXiv.org, revised Feb 2024.
- Alain Hecq & Sébastien Laurent & Franz C. Palm, 2011.
"Common Intraday Periodicity,"
Journal of Financial Econometrics, Oxford University Press, vol. 10(2), pages 325-353, 2012 20 1.
- Hecq, A.W. & Palm, F.C. & Laurent, S.F.J.A., 2011. "Common intraday periodicity," Research Memorandum 010, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
- João F. Caldeira & Guilherme V. Moura & Francisco J. Nogales & André A. P. Santos, 2017. "Combining Multivariate Volatility Forecasts: An Economic-Based Approach," Journal of Financial Econometrics, Oxford University Press, vol. 15(2), pages 247-285.
- Ralf Becker & Adam Clements & Robert O'Neill, 2018. "A Multivariate Kernel Approach to Forecasting the Variance Covariance of Stock Market Returns," Econometrics, MDPI, vol. 6(1), pages 1-27, February.
- Adam E Clements & Ayesha Scott & Annastiina Silvennoinen, 2012. "Forecasting multivariate volatility in larger dimensions: some practical issues," NCER Working Paper Series 80, National Centre for Econometric Research.
- Asai, M. & Chang, C-L. & McAleer, M.J. & Pauwels, L., 2018.
"Asymptotic Theory for Rotated Multivariate GARCH Models,"
Econometric Institute Research Papers
EI2018-38, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
- Asai, Manabu & Chang, Chia-Lin & McAleer, Michael & Pauwels, Laurent, 2019. "Asymptotic Theory for Rotated Multivariate GARCH Models," Working Papers BAWP-2019-03, University of Sydney Business School, Discipline of Business Analytics.
- Manabu Asai & Chia-Lin Chang & Michael McAleer & Laurent Pauwels, 2018. "Asymptotic Theory for Rotated Multivariate GARCH Models," Documentos de Trabajo del ICAE 2018-27, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
- Duan, Yinying & Chen, Wang & Zeng, Qing & Liu, Zhicao, 2018. "Leverage effect, economic policy uncertainty and realized volatility with regime switching," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 493(C), pages 148-154.
- Ralf Becker & Adam Clements & Robert O'Neill, 2010.
"A Kernel Technique for Forecasting the Variance-Covariance Matrix,"
Centre for Growth and Business Cycle Research Discussion Paper Series
151, Economics, The University of Manchester.
- Ralf Becker & Adam Clements & Robert O'Neill, 2010. "A Kernel Technique for Forecasting the Variance-Covariance Matrix," NCER Working Paper Series 66, National Centre for Econometric Research.
- Billio, Monica & Caporin, Massimiliano & Frattarolo, Lorenzo & Pelizzon, Loriana, 2023.
"Networks in risk spillovers: A multivariate GARCH perspective,"
Econometrics and Statistics, Elsevier, vol. 28(C), pages 1-29.
- Monica Billio & Massimiliano Caporin & Lorenzo Frattarolo & Loriana Pelizzon, 2016. "Networks in risk spillovers: a multivariate GARCH perspective," Working Papers 2016:03, Department of Economics, University of Venice "Ca' Foscari".
- Billio, Monica & Caporin, Massimiliano & Frattarolo, Lorenzo & Pelizzon, Loriana, 2018. "Networks in risk spillovers: A multivariate GARCH perspective," SAFE Working Paper Series 225, Leibniz Institute for Financial Research SAFE.
- Monica Billio & Massimiliano Caporin & Lorenzo Frattarolo & Loriana Pelizzon, 2020. "Networks in risk spillovers: A multivariate GARCH perspective," Working Papers 2020:16, Department of Economics, University of Venice "Ca' Foscari".
- Fiszeder, Piotr & Fałdziński, Marcin, 2019. "Improving forecasts with the co-range dynamic conditional correlation model," Journal of Economic Dynamics and Control, Elsevier, vol. 108(C).
- Köchling, Gerrit & Schmidtke, Philipp & Posch, Peter N., 2020. "Volatility forecasting accuracy for Bitcoin," Economics Letters, Elsevier, vol. 191(C).
- Lakshina, Valeriya, 2014. "Is it possible to break the «curse of dimensionality»? Spatial specifications of multivariate volatility models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 36(4), pages 61-78.
- Bouteska, Ahmed & Sharif, Taimur & Abedin, Mohammad Zoynul, 2023. "COVID-19 and stock returns: Evidence from the Markov switching dependence approach," Research in International Business and Finance, Elsevier, vol. 64(C).
- R. Khalfaoui & M. Boutahar, 2012.
"Portfolio Risk Evaluation: An Approach Based on Dynamic Conditional Correlations Models and Wavelet Multi-Resolution Analysis,"
Working Papers
halshs-00793068, HAL.
- Khalfaoui, R & Boutahar, M, 2012. "Portfolio risk evaluation: An approach based on dynamic conditional correlations models and wavelet multiresolution analysis," MPRA Paper 41624, University Library of Munich, Germany.
- Rabeh Khalfaoui & Mohammed Boutahar, 2012. "Portfolio Risk Evaluation An Approach Based on Dynamic Conditional Correlations Models and Wavelet Multi-Resolution Analysis," AMSE Working Papers 1208, Aix-Marseille School of Economics, France.
- Conrad, Christian & Loch, Karin & Rittler, Daniel, 2014. "On the macroeconomic determinants of long-term volatilities and correlations in U.S. stock and crude oil markets," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 26-40.
- Weigand, Roland, 2014.
"Matrix Box-Cox Models for Multivariate Realized Volatility,"
University of Regensburg Working Papers in Business, Economics and Management Information Systems
478, University of Regensburg, Department of Economics.
- Roland Weigand, 2014. "Matrix Box-Cox Models for Multivariate Realized Volatility," Working Papers 144, Bavarian Graduate Program in Economics (BGPE).
- Marc Hallin & Carlos Trucíos, 2020. "Forecasting Value-at-Risk and Expected Shortfall in Large Portfolios: a General Dynamic Factor Approach," Working Papers ECARES 2020-50, ULB -- Universite Libre de Bruxelles.
- Lütkepohl, Helmut & Schlaak, Thore, 2018.
"Choosing Between Different Time-Varying Volatility Models for Structural Vector Autoregressive Analysis,"
EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, issue 4, pages 715-735.
- Helmut Lütkepohl & Thore Schlaak, 2018. "Choosing Between Different Time‐Varying Volatility Models for Structural Vector Autoregressive Analysis," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 80(4), pages 715-735, August.
- Helmut Lütkepohl & Thore Schlaak, 2017. "Choosing between Different Time-Varying Volatility Models for Structural Vector Autoregressive Analysis," Discussion Papers of DIW Berlin 1672, DIW Berlin, German Institute for Economic Research.
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