Portfolio selection with higher moments
提出一种贝叶斯决策框架下的投资组合选择方法，能处理高阶矩和参数不确定性，使用偏正态分布建模多变量收益，相比重抽样等方法能获得更高期望效用。
We propose a method for optimal portfolio selection using a Bayesian decision theoretic framework that addresses two major shortcomings of the traditional Markowitz approach: the ability to handle higher moments and parameter uncertainty. We employ the skew normal distribution which has many attractive features for modeling multivariate returns. Our results suggest that it is important to incorporate higher order moments in portfolio selection. Further, our comparison to other methods where parameter uncertainty is either ignored or accommodated in an ad hoc way, shows that our approach leads to higher expected utility than competing methods, such as the resampling methods that are common in the practice of finance.