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Forecasting comparison of long term component dynamic models for realized covariance matrices

Author

Listed:
  • Luc Bauwens
  • Manuela Braione
  • Giuseppe Storti

Abstract

Novel model specifications that include a time-varying long-run component in the dynamics of realized covariance matrices are proposed. The modelling framework allows the secular component to enter the model either additively or as a multiplicative factor, and to be specified parametrically, using a MIDAS filter, or non-parametrically. Estimation is performed by maximizing a Wishart quasi-likelihood function. The one-step ahead forecasting performance is assessed by means of three approaches: model confidence sets, minimum variance portfolios and Value-at-Risk. The results show that the proposed models outperform benchmarks incorporating a constant long-run component both in and out-of-sample.
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Suggested Citation

  • 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).
    • Handle: RePEc:cor:louvrp:2923
    Note: In : Annals of Economics and Statistics, 123/124, 103-154, 2016
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    Cited by:

    1. Bauwens, Luc & Xu, Yongdeng, 2023. "DCC- and DECO-HEAVY: Multivariate GARCH models based on realized variances and correlations," International Journal of Forecasting, Elsevier, vol. 39(2), pages 938-955.
    2. 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.
    3. Matias Quiroz & Laleh Tafakori & Hans Manner, 2024. "Forecasting realized covariances using HAR-type models," Papers 2412.10791, arXiv.org.
    4. Naimoli, Antonio & Storti, Giuseppe, 2019. "Heterogeneous component multiplicative error models for forecasting trading volumes," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1332-1355.
    5. Jin, Xin & Maheu, John M., 2016. "Bayesian semiparametric modeling of realized covariance matrices," Journal of Econometrics, Elsevier, vol. 192(1), pages 19-39.
    6. Matias Quiroz & Laleh Tafakori & Hans Manner, 2024. "Forecasting Realized Covariances Using HAR-Type Models," Graz Economics Papers 2024-20, University of Graz, Department of Economics.
    7. 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.
    8. 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.
    9. Conrad, Christian & Stuermer, Karin, 2017. "On the economic determinants of optimal stock-bond portfolios: international evidence," Working Papers 0636, University of Heidelberg, Department of Economics.
    10. 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).
    11. Gribisch, Bastian & Hartkopf, Jan Patrick, 2023. "Modeling realized covariance measures with heterogeneous liquidity: A generalized matrix-variate Wishart state-space model," Journal of Econometrics, Elsevier, vol. 235(1), pages 43-64.
    12. 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).
    13. Gribisch, Bastian & Hartkopf, Jan Patrick & Liesenfeld, Roman, 2020. "Factor state–space models for high-dimensional realized covariance matrices of asset returns," Journal of Empirical Finance, Elsevier, vol. 55(C), pages 1-20.
    14. Xin Jin & John M. Maheu & Qiao Yang, 2019. "Bayesian parametric and semiparametric factor models for large realized covariance matrices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(5), pages 641-660, August.
    15. Hartkopf, Jan Patrick & Reh, Laura, 2023. "Challenging golden standards in EWMA smoothing parameter calibration based on realized covariance measures," Finance Research Letters, Elsevier, vol. 56(C).
    16. Braione, Manuela, 2016. "A time-varying long run HEAVY model," Statistics & Probability Letters, Elsevier, vol. 119(C), pages 36-44.
    17. 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.

    More about this item

    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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