Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: A General Dynamic Factor Approach

Based on a General Dynamic Factor Model with infinite-dimensional factor space and MGARCH common shocks, we develop new estimation and forecasting procedures for conditional covariance matrices in high-dimensional time series. The finite-sample performance of our approach is evaluated via Monte Carlo experiments, outperforming most alternative methods. The new procedure is used to construct one-step-ahead minimum variance portfolios for a high-dimensional panel of assets. The results are shown to achieve better out-of-sample portfolio performance than alternative existing procedures.