## Portfolio Selection with Higher Moments,” Working Paper (2003)

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Citations: | 43 - 4 self |

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@MISC{Harvey03portfolioselection,

author = {Campbell R. Harvey and John C. Liechty and Peter Müller},

title = {Portfolio Selection with Higher Moments,” Working Paper},

year = {2003}

}

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### Abstract

We propose a method for optimal portfolio selection using a Bayesian framework that addresses two major shortcomings of the Markowitz approach: the ability to handle higher moments and estimation error. 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 the resampling methods that are common in the practice of finance.

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Citation Context ...Model Choice The Bayes Factor (BF) is a well developed and frequently used tool for model selection which naturally accounts for both the explanatory power and the complexity of competing models (see =-=Berger 1985-=- and O’Hagan 1994 for further discussion of Bayes Factors). For two competing models (M1 and M2), the Bayes factor is: BF = posterior odds/prior odds = p(x|M 1 )/p(x|M 2 ). We use a sampling based est... |

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