Estimation Risk when Theory Meets Reality
指出参数不确定时存在估计风险，贝叶斯准则是符合期望效用最大化的方法，并分析了均值方差框架下的最优决策、确定性等价回报计算及样本信息估值三个问题。
Estimation risk occurs in the almost universal situation where parameters of importance for decision making are not known with certainty. Bayes' criterion is the procedure consistent with expected utility maximization in the presence of estimation risk. Three interrelated problems in the presence of estimation risk are analyzed: (i) the choice of the utility-maximizing decision rule in a mean-variance framework, (ii) the calculation of certainty equivalent returns, and (iii) the valuation of additional sample information.