Portfolio selection with higher moments

Campbell R. Harvey, John C. Liechty, Merrill W. Liechty, Müller Peter

Research output: Contribution to journalArticlepeer-review

183 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)469-485
Number of pages17
JournalQuantitative Finance
Issue number5
StatePublished - May 2010

All Science Journal Classification (ASJC) codes

  • Finance
  • Economics, Econometrics and Finance(all)


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