Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps
解释了为什么在估计最优投资组合时，即使无卖空约束是错误的，也能降低风险，并发现样本协方差矩阵在约束下表现不亚于复杂估计方法。
ABSTRACT Green and Hollifield (1992) argue that the presence of a dominant factor would result in extreme negative weights in mean‐variance efficient portfolios even in the absence of estimation errors. In that case, imposing no‐short‐sale constraints should hurt, whereas empirical evidence is often to the contrary. We reconcile this apparent contradiction. We explain why constraining portfolio weights to be nonnegative can reduce the risk in estimated optimal portfolios even when the constraints are wrong. Surprisingly, with no‐short‐sale constraints in place, the sample covariance matrix performs as well as covariance matrix estimates based on factor models, shrinkage estimators, and daily data.