Hierarchical DCC-HEAVY Model for High-Dimensional Covariance Matrices

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