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 volatility models, we develop new estimation forecasting procedures for conditional covariance matrices in high-dimensional time series. The finite-sample performance of our approach is evaluated via Monte Carlo experiments outperforms the most alternative methods. This also used to construct minimum one-step-ahead variance portfolios panel assets. results are shown match recent proposals by Engle, Ledoit, Wolf achieve better out-of-sample portfolio than proposed literature.