A DCC-Type Approach for Realized Covariance Modelling With Score-Driven Dynamics
38 Pages Posted: 8 Jan 2019 Last revised: 3 Mar 2020
Date Written: March 3, 2020
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. The proposed models are computationally simple to estimate in high dimensions and allow complete flexibility in the choice of the univariate specification. 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 joint estimations, with forecasting gains that become more significant as dimension increases.
Keywords: Realized Covariance, Dynamic Dependencies, Covariance forecasting, Score-driven models, Portfolio construction
JEL Classification: C58, D53, D81
Suggested Citation: Suggested Citation