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A dynamic component model for forecasting high-dimensional realized covariance matrices

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  • Bauwens, Luc
  • Braione, Manuela
  • Storti, Giuseppe

Abstract

The Multiplicative MIDAS Realized DCC (MMReDCC) model simultaneously accounts for short and long term dynamics in the conditional (co)volatilities of asset returns, in line with the empirical evidence suggesting that their level is changing over time as a function of economic conditions. Herein the applicability of the model is improved along two directions. First, by proposing an algorithm that relies on the maximization of an iteratively re-computed moment-based profile likelihood function and keeps estimation feasible in large dimensions by mitigating the incidental parameter problem. Second, by illustrating a conditional bootstrap procedure to generate multi-step ahead predictions from the model. In an empirical application on a dataset of forty-six equities, the MMReDCC model is found to statistically outperform the selected benchmarks in terms of in-sample fit as well as in terms of out-of-sample covariance predictions. The latter are mostly significant in periods of high market volatility.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ecosta:v:1:y:2017:i:c:p:40-61
    DOI: 10.1016/j.ecosta.2016.09.003
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    Cited by:

    1. 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.
    2. 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.
    3. Braione, Manuela, 2016. "A time-varying long run HEAVY model," Statistics & Probability Letters, Elsevier, vol. 119(C), pages 36-44.
    4. Naimoli, Antonio & Storti, Giuseppe, 2019. "Heterogeneous component multiplicative error models for forecasting trading volumes," MPRA Paper 93802, University Library of Munich, Germany.
    5. repec:eee:ecmode:v:75:y:2018:i:c:p:422-431 is not listed on IDEAS
    6. repec:eee:ecmode:v:76:y:2019:i:c:p:135-152 is not listed on IDEAS

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