50 Pages Posted: 29 Dec 2004 Last revised: 16 Mar 2010
Date Written: December 13, 2004
We propose a method for optimal portfolio selection using a Bayesian decision theoretic framework that addresses two major shortcomings of the Markowitz approach: the ability to handle higher moments and estimation error. 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 the resampling methods that are common in the practice of finance.
Keywords: Bayesian decision problem, multivariate skewness, parameter uncertainty, optimal portfolios, utility function maximization, resampling, resampled portfolios, estimation error, mean-variance portfolios, expected returns, Markowitz optimization
JEL Classification: G11, G12, G10, C11
Suggested Citation: Suggested Citation
Harvey, Campbell R. and Liechty, John and Liechty, Merrill W. and Mueller, Peter, Portfolio Selection with Higher Moments (December 13, 2004). Available at SSRN: https://ssrn.com/abstract=634141 or http://dx.doi.org/10.2139/ssrn.634141
By Dušan Isakov