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A new rank dependent utility approach to model risk averse preferences in portfolio optimization

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  • Leili Javanmardi
  • Yuri Lawryshyn

Abstract

In this paper we introduce a new rank dependent utility approach, which unlike existing models, provides an SSD efficient portfolio as a function of the investors’ quantified risk aversion degrees. A parametric family of distortion functions is considered to model various levels of risk aversion. Under assumptions of equally probable scenarios, for any distortion function the corresponding optimization models can be expressed as linear program and easily solved. An empirical study is performed to compare the performance of our proposed model to the previously proposed portfolio selection models in the literature. Copyright Springer Science+Business Media New York 2016

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  • Leili Javanmardi & Yuri Lawryshyn, 2016. "A new rank dependent utility approach to model risk averse preferences in portfolio optimization," Annals of Operations Research, Springer, vol. 237(1), pages 161-176, February.
    • Handle: RePEc:spr:annopr:v:237:y:2016:i:1:p:161-176:10.1007/s10479-014-1761-9
    DOI: 10.1007/s10479-014-1761-9
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    1. repec:bla:jfinan:v:58:y:2003:i:5:p:1905-1932 is not listed on IDEAS
    2. G. Hanoch & H. Levy, 1969. "The Efficiency Analysis of Choices Involving Risk," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 36(3), pages 335-346.
    3. James P. Quirk & Rubin Saposnik, 1962. "Admissibility and Measurable Utility Functions," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 29(2), pages 140-146.
    4. Andrey Lizyayev, 2012. "Stochastic dominance efficiency analysis of diversified portfolios: classification, comparison and refinements," Annals of Operations Research, Springer, vol. 196(1), pages 391-410, July.
    5. Timo Kuosmanen, 2004. "Efficient Diversification According to Stochastic Dominance Criteria," Management Science, INFORMS, vol. 50(10), pages 1390-1406, October.
    6. Renata Mansini & Włodzimierz Ogryczak & M. Speranza, 2007. "Conditional value at risk and related linear programming models for portfolio optimization," Annals of Operations Research, Springer, vol. 152(1), pages 227-256, July.
    7. Post, Thierry, 2008. "On the dual test for SSD efficiency: With an application to momentum investment strategies," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1564-1573, March.
    8. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
    9. Yaari, Menahem E, 1987. "The Dual Theory of Choice under Risk," Econometrica, Econometric Society, vol. 55(1), pages 95-115, January.
    10. Wang, Shaun, 1996. "Premium Calculation by Transforming the Layer Premium Density," ASTIN Bulletin, Cambridge University Press, vol. 26(1), pages 71-92, May.
    11. Thierry Post, 2003. "Empirical Tests for Stochastic Dominance Efficiency," Journal of Finance, American Finance Association, vol. 58(5), pages 1905-1931, October.
    12. Quiggin, John, 1982. "A theory of anticipated utility," Journal of Economic Behavior & Organization, Elsevier, vol. 3(4), pages 323-343, December.
    13. Acerbi, Carlo, 2002. "Spectral measures of risk: A coherent representation of subjective risk aversion," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1505-1518, July.
    14. Andrey Lizyayev, 2012. "Stochastic Dominance: Convexity And Some Efficiency Tests," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 15(05), pages 1-19.
    15. Oliver Linton & Esfandiar Maasoumi & Yoon-Jae Whang, 2005. "Consistent Testing for Stochastic Dominance under General Sampling Schemes," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 735-765.
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    Cited by:

    1. Cristiano Arbex Valle & Diana Roman & Gautam Mitra, 2017. "Novel approaches for portfolio construction using second order stochastic dominance," Computational Management Science, Springer, vol. 14(2), pages 257-280, April.

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