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

Bauwens, Luc;Braione, Manuela;Giuseppe Storti
(2016) Annals of Economics and Statistics — Vol. 123/124, p. 103-134 (2016)

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  • Bauwens, Lucorcid-logoUCLouvain
    Author
  • Braione, ManuelaUCLouvain
    Author
  • Giuseppe StortiUniversità di Salerno
    Author
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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Bauwens, L., Braione, M., & Giuseppe Storti. (2016). Forecasting Comparison of Long Term Component Dynamic Models for Realized Covariance Matrices. Annals of Economics and Statistics, 123/124, 103-134. https://doi.org/10.15609/annaeconstat2009.123-124.0103 (Original work published 2016)