Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: A General Dynamic Factor Approach
高维时间序列条件协方差矩阵预测：广义动态因子方法
Based on a General Dynamic Factor Model with infinite-dimensional factor space and MGARCH volatility models, 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 and outperforms the most alternative methods. This new approach is also used to construct minimum one-step-ahead variance portfolios for a high-dimensional panel…