Dynamic conditional correlation models for realized covariance matrices
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Cited by:
- Roxana Halbleib & Valerie Voev, 2011.
"Forecasting Covariance Matrices: A Mixed Frequency Approach,"
Working Papers ECARES
ECARES 2011-002, ULB -- Universite Libre de Bruxelles.
- Roxana Halbleib & Valeri Voev, 2012. "Forecasting Covariance Matrices: A Mixed Frequency Approach," Working Paper Series of the Department of Economics, University of Konstanz 2012-30, Department of Economics, University of Konstanz.
- Roxana Halbleib & Valeri Voev, 2011. "Forecasting Covariance Matrices: A Mixed Frequency Approach," CREATES Research Papers 2011-03, Department of Economics and Business Economics, Aarhus University.
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"Multivariate Fractional Components Analysis,"
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1812.09149, arXiv.org, revised Jan 2019.
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- 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).
- Bauwens, Luc & Xu, Yongdeng, 2023.
"DCC- and DECO-HEAVY: Multivariate GARCH models based on realized variances and correlations,"
International Journal of Forecasting, Elsevier, vol. 39(2), pages 938-955.
- Bauwens, Luc & Xu, Yongdeng, 2019. "DCC and DECO-HEAVY: a multivariate GARCH model based on realized variances and correlations," Cardiff Economics Working Papers E2019/5, Cardiff University, Cardiff Business School, Economics Section, revised Aug 2021.
- BAUWENS Luc, & XU Yongdeng,, 2019. "DCC-HEAVY: A multivariate GARCH model based on realized variances and correlations," LIDAM Discussion Papers CORE 2019025, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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- BAUWENS, Luc & STORTI, Giuseppe, 2012.
"Computationally efficient inference procedures for vast dimensional realized covariance models,"
LIDAM Discussion Papers CORE
2012028, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- BAUWENS, Luc & STORTI, Giuseppe, 2013. "Computationally efficient inference procedures for vast dimensional realized covariance models," LIDAM Reprints CORE 2469, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Luc Bauwens & Edoardo Otranto, 2023.
"Modeling Realized Covariance Matrices: A Class of Hadamard Exponential Models,"
Journal of Financial Econometrics, Oxford University Press, vol. 21(4), pages 1376-1401.
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- Bauwens, Luc & Otranto, Edoardo, 2022. "Modeling Realized Covariance Matrices: A Class of Hadamard Exponential Models," LIDAM Reprints CORE 3202, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- L. Bauwens & E. Otranto, 2020. "Modelling Realized Covariance Matrices: a Class of Hadamard Exponential Models," Working Paper CRENoS 202007, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
- Boudt, Kris & Laurent, Sébastien & Lunde, Asger & Quaedvlieg, Rogier & Sauri, Orimar, 2017.
"Positive semidefinite integrated covariance estimation, factorizations and asynchronicity,"
Journal of Econometrics, Elsevier, vol. 196(2), pages 347-367.
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- Roxana Halbleib & Valeri Voev, 2016. "Forecasting Covariance Matrices: A Mixed Approach," Journal of Financial Econometrics, Oxford University Press, vol. 14(2), pages 383-417.
- Luc Bauwens & Manuela Braione & Giuseppe Storti, 2016.
"Forecasting Comparison of Long Term Component Dynamic Models for Realized Covariance Matrices,"
Annals of Economics and Statistics, GENES, issue 123-124, pages 103-134.
- BAUWENS, Luc & BRAIONE, Manuela & STORTI, Giuseppe, 2014. "Forecasting comparison of long term component dynamic models for realized covariance matrices," LIDAM Discussion Papers CORE 2014053, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Luc Bauwens & Manuela Braione & Giuseppe Storti, 2016. "Forecasting comparison of long term component dynamic models for realized covariance matrices," LIDAM Reprints CORE 2923, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Vassallo, Danilo & Buccheri, Giuseppe & Corsi, Fulvio, 2021. "A DCC-type approach for realized covariance modeling with score-driven dynamics," International Journal of Forecasting, Elsevier, vol. 37(2), pages 569-586.
- Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2020. "Multivariate leverage effects and realized semicovariance GARCH models," Journal of Econometrics, Elsevier, vol. 217(2), pages 411-430.
- Bastian Gribisch, 2018. "A latent dynamic factor approach to forecasting multivariate stock market volatility," Empirical Economics, Springer, vol. 55(2), pages 621-651, September.
- Harry Vander Elst & David Veredas, 2017. "Smoothing it Out: Empirical and Simulation Results for Disentangled Realized Covariances," Journal of Financial Econometrics, Oxford University Press, vol. 15(1), pages 106-138.
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More about this item
Keywords
realized covariance; dynamic conditional correlations; covariance targeting; Wishart distribution; composite likelihood;All these keywords.
JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
- C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
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