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An empirical comparison of different risk measures in portfolio optimization

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  • Hoe, Lam Weng
  • Saiful Hafizah, Jaaman
  • Zaidi, Isa

Abstract

Risk is one of the important parameters in portfolio optimization problem. Since the introduction of the mean-variance model, variance has become the most common risk measure used by practitioners and researchers in portfolio optimization. However, the mean-variance model relies strictly on the assumptions that assets returns are multivariate normally distributed or investors have a quadratic utility function. Many studies have proposed different risk measures to overcome the drawbacks of variance. The purpose of this paper is to discuss and compare the portfolio compositions and performances of four different portfolio optimization models employing different risk measures, specifically the variance, absolute deviation, minimax and semi-variance. Results of this study show that the minimax model outperforms the other models. The minimax model is appropriate for investors who have a strong downside risk aversion.

Suggested Citation

  • Hoe, Lam Weng & Saiful Hafizah, Jaaman & Zaidi, Isa, 2010. "An empirical comparison of different risk measures in portfolio optimization," Business and Economic Horizons (BEH), Prague Development Center (PRADEC), vol. 1(1), pages 1-7, April.
    • Handle: RePEc:ags:pdcbeh:95934
    DOI: 10.22004/ag.econ.95934
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    References listed on IDEAS

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    1. Bawa, Vijay S., 1975. "Optimal rules for ordering uncertain prospects," Journal of Financial Economics, Elsevier, vol. 2(1), pages 95-121, March.
    2. Fishburn, Peter C, 1977. "Mean-Risk Analysis with Risk Associated with Below-Target Returns," American Economic Review, American Economic Association, vol. 67(2), pages 116-126, March.
    3. Hiroshi Konno & Hiroaki Yamazaki, 1991. "Mean-Absolute Deviation Portfolio Optimization Model and Its Applications to Tokyo Stock Market," Management Science, INFORMS, vol. 37(5), pages 519-531, May.
    4. Peter Byrne & Stephen Lee, 2004. "Different Risk Measures: Different Portfolio Compositions?," ERES eres2004_516, European Real Estate Society (ERES).
    5. Grootveld, Henk & Hallerbach, Winfried, 1999. "Variance vs downside risk: Is there really that much difference?," European Journal of Operational Research, Elsevier, vol. 114(2), pages 304-319, April.
    6. Peter Byrne & Stephen Lee, 2004. "Different Risk Measures: Different Portfolio Compositions?," Real Estate & Planning Working Papers rep-wp2004-03, Henley Business School, University of Reading.
    7. Yusif Simaan, 1997. "Estimation Risk in Portfolio Selection: The Mean Variance Model Versus the Mean Absolute Deviation Model," Management Science, INFORMS, vol. 43(10), pages 1437-1446, October.
    8. Martin R. Young, 1998. "A Minimax Portfolio Selection Rule with Linear Programming Solution," Management Science, INFORMS, vol. 44(5), pages 673-683, May.
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    Cited by:

    1. Marah-Lisanne Thormann & Phan Tu Vuong & Alain B. Zemkoho, 2024. "The Boosted Difference of Convex Functions Algorithm for Value-at-Risk Constrained Portfolio Optimization," Papers 2402.09194, arXiv.org.
    2. Pokharel, Krisha Prasad & Featherstone, Allen M. & Archer, David W., 2019. "Estimating Economic Efficiency Under Risk For Agricultural Cooperatives," International Journal of Food and Agricultural Economics (IJFAEC), Alanya Alaaddin Keykubat University, Department of Economics and Finance, vol. 7(2), April.
    3. Todor Stoilov & Krasimira Stoilova & Miroslav Vladimirov, 2021. "Explicit Value at Risk Goal Function in Bi-Level Portfolio Problem for Financial Sustainability," Sustainability, MDPI, vol. 13(4), pages 1-14, February.

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