The Mean and Variance of the Mean‐Variance Decision Rule
研究基于估计参数的均值-方差决策的可靠性，发现决策向量存在偏差且方差很大，导致最优决策与真实最优可能差异显著。
Abstract The widely used mean‐variance approach to decisions under uncertainty requires estimates of the parameters of the joint distribution of returns. When optimal behavior is determined using estimates, rather than the true values, the decision is a random variable. We consider the reliability of mean‐variance analysis by examining the bias and variance‐covariance matrix for the decision vector. The latter shows that decisions based on estimated parameters can have a large variance around the true optimum. The results show that optimal decisions can differ substantially from those based on mean‐variance analysis.