Portfolio selection with higher moments

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

Research output: Contribution to journalArticle

111 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)469-485
Number of pages17
JournalQuantitative Finance
Volume10
Issue number5
DOIs
StatePublished - May 1 2010

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Portfolio selection
Parameter uncertainty
Modeling
Ad hoc
Higher order moments
Resampling methods
Optimal portfolio selection
Normal distribution
Finance
Expected utility

All Science Journal Classification (ASJC) codes

  • Finance
  • Economics, Econometrics and Finance(all)

Cite this

Harvey, Campbell R. ; Liechty, John C. ; Liechty, Merrill W. ; Peter, Müller. / Portfolio selection with higher moments. In: Quantitative Finance. 2010 ; Vol. 10, No. 5. pp. 469-485.
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Harvey, CR, Liechty, JC, Liechty, MW & Peter, M 2010, 'Portfolio selection with higher moments', Quantitative Finance, vol. 10, no. 5, pp. 469-485. https://doi.org/10.1080/14697681003756877

Portfolio selection with higher moments. / Harvey, Campbell R.; Liechty, John C.; Liechty, Merrill W.; Peter, Müller.

In: Quantitative Finance, Vol. 10, No. 5, 01.05.2010, p. 469-485.

Research output: Contribution to journalArticle

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