Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: A General Dynamic Factor Approach
基于无限维因子空间的通用动态因子模型和MGARCH波动率模型，提出了高维时间序列条件协方差矩阵的估计和预测方法，在蒙特卡洛实验和资产组合应用中优于多数替代方法。
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 of assets. The results are shown to match the results of recent proposals by Engle, Ledoit, and Wolf and achieve better out-of-sample portfolio performance than alternative procedures proposed in the literature.