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Portfolio selection with higher moments

Author

Listed:
  • Campbell Harvey
  • John Liechty
  • Merrill Liechty
  • Peter Muller

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.

Suggested Citation

  • Campbell Harvey & John Liechty & Merrill Liechty & Peter Muller, 2010. "Portfolio selection with higher moments," Quantitative Finance, Taylor & Francis Journals, vol. 10(5), pages 469-485.
    • Handle: RePEc:taf:quantf:v:10:y:2010:i:5:p:469-485
    DOI: 10.1080/14697681003756877
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    References listed on IDEAS

    as
    1. Nicholas Barberis & Ming Huang, 2008. "Stocks as Lotteries: The Implications of Probability Weighting for Security Prices," American Economic Review, American Economic Association, vol. 98(5), pages 2066-2100, December.
    2. Eric Jondeau & Michael Rockinger, 2006. "The Economic Value of Distributional Timing," Swiss Finance Institute Research Paper Series 06-35, Swiss Finance Institute.
    3. DeMiguel, Victor & Plyakha, Yuliya & Uppal, Raman & Vilkov, Grigory, 2013. "Improving Portfolio Selection Using Option-Implied Volatility and Skewness," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(6), pages 1813-1845, December.
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