On Portfolio Optimization: Forecasting Covariances and Choosing the Risk Model
评估了不同股票收益协方差模型在投资组合选择中的表现，发现少数因子能捕捉协方差结构，但增加因子不提升预测力；不同模型优化组合的波动率相似，但基于基准匹配的启发式方法在跟踪误差上表现更优。
We evaluate the performance of different models for the covariance structure of stock returns, focusing on their use for optimal portfolio selection. Comparisons are based on forecasts of future covariances as well as the out-of-sample volatility of optimized portfolios from each model. A few factors capture the general covariance structure but adding more factors does not improve forecast power. Portfolio optimization helps for risk control, but the different covariance models yield similar results. Using a tracking error volatility criterion, larger differences appear, with particularly favorable results for a heuristic approach based on matching the benchmark's attributes.