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Portfolio selection with higher moments

Campbell Harvey (), John Liechty, Merrill Liechty and Peter Muller

Quantitative Finance, 2010, vol. 10, issue 5, 469-485

Abstract: 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.

Keywords: Bayesian decision problem; Multivariate skewness; Parameter uncertainty; Optimal portfolios; Utility function maximization (search for similar items in EconPapers)
Date: 2010
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