A DCC-type approach for realized covariance modeling with score-driven dynamics

Danilo Vassallo, Giuseppe Buccheri and Fulvio Corsi

International Journal of Forecasting, 2021, vol. 37, issue 2, 569-586

Abstract: We propose a class of score-driven realized covariance models where volatilities and correlations are separately estimated. We can thus combine univariate realized volatility models with a recently introduced class of score-driven realized covariance models based on Wishart and matrix-F distributions. Compared to the latter, the proposed models remain computationally simple at high dimensions and allow for higher flexibility in parameter estimation. Through a Monte-Carlo study, we show that the two-step maximum likelihood procedure provides accurate parameter estimates in small samples. Empirically, we find that the proposed models outperform those based on joint estimation, with forecasting gains that become more significant as the cross-section dimension increases.

Keywords: Realized covariance; Dynamic dependencies; Covariance forecasting; Score-driven models; Estimation errors (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:37:y:2021:i:2:p:569-586

DOI: 10.1016/j.ijforecast.2020.07.006

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