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Journal article
Portfolio selection with higher moments
Quantitative finance, v 10(5), pp 469-485
01 May 2010
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
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Details
- Title
- Portfolio selection with higher moments
- Creators
- Campbell R. Harvey - National Bureau of Economic ResearchJohn C. Liechty - Pennsylvania State UniversityMerrill W. Liechty - Drexel UniversityPeter Müller - The University of Texas MD Anderson Cancer Center
- Publication Details
- Quantitative finance, v 10(5), pp 469-485
- Publisher
- Routledge
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Decision Sciences (and Management Information Systems)
- Web of Science ID
- WOS:000277674600003
- Scopus ID
- 2-s2.0-77951925762
- Other Identifier
- 991019169644004721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Business, Finance
- Economics
- Mathematics, Interdisciplinary Applications
- Social Sciences, Mathematical Methods