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Forecasting Comparison of Long Term Component Dynamic Models for Realized Covariance Matrices

Listed author(s):
  • Luc Bauwens
  • Manuela Braione
  • Giuseppe Storti

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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File URL: http://www.jstor.org/stable/10.15609/annaeconstat2009.123-124.0103
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Article provided by GENES in its journal Annals Of Economics and Statistics.

Volume (Year): (2016)
Issue (Month): 123-124 ()
Pages: 103-134

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Handle: RePEc:adr:anecst:y:2016:i:123-124:p:103-134
DOI: 10.15609/annaeconstat2009.123-124.0103
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  1. Luc Bauwens & Christian M. Hafner & Diane Pierret, 2013. "Multivariate Volatility Modeling Of Electricity Futures," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(5), pages 743-761, 08.
  2. Laurent, Sébastien & Rombouts, Jeroen V.K. & Violante, Francesco, 2013. "On loss functions and ranking forecasting performances of multivariate volatility models," Journal of Econometrics, Elsevier, vol. 173(1), pages 1-10.
  3. Golosnoy, Vasyl & Gribisch, Bastian & Liesenfeld, Roman, 2012. "The conditional autoregressive Wishart model for multivariate stock market volatility," Journal of Econometrics, Elsevier, vol. 167(1), pages 211-223.
  4. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
  5. Colacito, Riccardo & Engle, Robert F. & Ghysels, Eric, 2011. "A component model for dynamic correlations," Journal of Econometrics, Elsevier, vol. 164(1), pages 45-59, September.
  6. BAUWENS, Luc & STORTI, Giuseppe & VIOLANTE, Francesco, 2012. "Dynamic conditional correlation models for realized covariance matrices," CORE Discussion Papers 2012060, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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