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

Vassallo, D., Buccheri, G. & Corsi, F. ORCID: 0000-0003-2683-4479 (2020). A DCC-type approach for realized covariance modeling with score-driven dynamics. International Journal of Forecasting, doi: 10.1016/j.ijforecast.2020.07.006

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.

Publication Type: Article
Additional Information: © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Publisher Keywords: Realized covariance, Dynamic dependencies, Covariance forecasting, Score-driven models, Estimation errors
Subjects: H Social Sciences > HB Economic Theory
Departments: School of Policy & Global Affairs > Economics
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