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Risk Preferences and Loss Aversion in Portfolio Optimization

In: Computational Methods in Financial Engineering

Author

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  • Dietmar Maringer

    (University of Essex)

Abstract

Traditionally, portfolio optimization is associated with finding the ideal trade-off between return and risk by maximizing the expected utility. Investors’ preferences are commonly assumed to follow a quadratic or power utility function, and asset returns are often assumed to follow a Gaussian distribution. Investment analysis has therefore long been focusing on the first two moments of the distribution, mean and variance. However, empirical asset returns are not normally distributed, and neither utility function captures investors’ true attitudes towards losses. The impact of these specification errors under realistic assumptions is investigated. As traditional optimization techniques cannot deal reliably with the extended problem, the use of a heuristic optimization approach is suggested. It is found that loss aversion has a substantial impact on what investors consider to be an efficient portfolio and that mean-variance analysis alone can be utterly misguiding.

Suggested Citation

  • Dietmar Maringer, 2008. "Risk Preferences and Loss Aversion in Portfolio Optimization," Springer Books, in: Erricos J. Kontoghiorghes & Berç Rustem & Peter Winker (ed.), Computational Methods in Financial Engineering, pages 27-45, Springer.
  • Handle: RePEc:spr:sprchp:978-3-540-77958-2_2
    DOI: 10.1007/978-3-540-77958-2_2
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    Cited by:

    1. Blueschke, D. & Blueschke-Nikolaeva, V. & Savin, I., 2013. "New insights into optimal control of nonlinear dynamic econometric models: Application of a heuristic approach," Journal of Economic Dynamics and Control, Elsevier, vol. 37(4), pages 821-837.
    2. Manfred Gilli & Enrico Schumann, 2012. "Heuristic optimisation in financial modelling," Annals of Operations Research, Springer, vol. 193(1), pages 129-158, March.
    3. Fulga, Cristinca, 2016. "Portfolio optimization with disutility-based risk measure," European Journal of Operational Research, Elsevier, vol. 251(2), pages 541-553.

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