Portfolio selection with higher moments


Journal Article

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. © 2010 Taylor & Francis.

Full Text

Cited Authors

  • Harvey, CR; Liechty, JC; Liechty, MW; Peter, M

Published Date

  • 2010-05-01

Published In

Volume / Issue

  • 10 / 5

Start / End Page

  • 469 - 485

Electronic International Standard Serial Number (EISSN)

  • 1469-7696

International Standard Serial Number (ISSN)

  • 1469-7688

Digital Object Identifier (DOI)

  • 10.1080/14697681003756877

Citation Source

  • Scopus