Title: A DCC-type approach for realized covariance modelling with score-driven dynamics
Authors: Giuseppe Buccheri - Scuola Normale Superiore (Italy)
Danilo Vassallo - Scuola Normale Superiore of Pisa (Italy) [presenting]
Fulvio Corsi - University of Pisa and City University London (Italy)
Abstract: A class of dynamic models for realized covariances is introduced 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 F-matrix 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 existing benchmarks, with forecasting gains that increase with the dimension.