Hierarchical DCC-HEAVY Model for High-Dimensional Covariance Matrices

Abstract

We introduce a HD DCC-HEAVY class of hierarchical-type factor models for high-dimensional covariance matrices, employing the realized measures built from higher-frequency data. The modelling approach features straightforward estimation and forecasting schemes, independent of the cross-sectional dimension of the assets under consideration, and accounts for sophisticated asymmetric dynamics in the covariances. Empirical analyses suggest that the HD DCC-HEAVY models have a better in-sample fit and deliver statistically and economically significant out-of-sample gains relative to the existing hierarchical factor model and standard benchmarks. The results are robust under different frequencies and market conditions.