A Test of Covariance-Matrix Forecasting Methods
通过样本外测试，比较了多种协方差矩阵预测方法在预测准确性、跟踪最小方差组合波动率以及维持目标波动率方面的表现，发现多元GARCH预测显著优于样本协方差矩阵，而指数加权法略逊于GARCH。
Providing a more accurate covariance matrix forecast can substantially improve the performance of optimized portfolios. Using out-of-sample tests, in this article the author evaluates alternative covariance matrix-forecasting methods by looking at: (1) their forecast accuracy, (2) their ability to track the volatility of a minimum-variance portfolio, and (3) their ability to keep the volatility of a minimum-variance portfolio at a target level. The author finds large differences between the methods. The results suggest that shrinking the sample covariance matrix improves neither the forecast accuracy nor the performance of minimum-variance portfolios. In contrast, switching from the sample covariance matrix forecast to a multivariate generalized autoregressive conditional heteroskedasticity (GARCH) forecast reduces the forecasting error and portfolio tracking error by at least half. The findings also reveal that the exponentially weighted covariance matrix forecast performs only slightly worse than the multivariate GARCH forecast. <b>TOPICS:</b>Portfolio management/multi-asset allocation, statistical methods