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Portfolios Dominating Indices: Optimization with Second-Order Stochastic Dominance Constraints vs. Minimum and Mean Variance Portfolios

Author

Listed:
  • Neslihan Fidan Keçeci

    (Istanbul University, School of Business, Avcılar 34850, Istanbul, Turkey)

  • Viktor Kuzmenko

    (Glushkov Institute of Cybernetics, Kyiv 03115, Ukraine)

  • Stan Uryasev

    (Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL 32611, USA)

Abstract

The paper compares portfolio optimization with the Second-Order Stochastic Dominance (SSD) constraints with mean-variance and minimum variance portfolio optimization. As a distribution-free decision rule, stochastic dominance takes into account the entire distribution of return rather than some specific characteristic, such as variance. The paper is focused on practical applications of the portfolio optimization and uses the Portfolio Safeguard (PSG) package, which has precoded modules for optimization with SSD constraints, mean-variance and minimum variance portfolio optimization. We have done in-sample and out-of-sample simulations for portfolios of stocks from the Dow Jones, S&P 100 and DAX indices. The considered portfolios’ SSD dominate the Dow Jones, S&P 100 and DAX indices. Simulation demonstrated a superior performance of portfolios with SD constraints, versus mean-variance and minimum variance portfolios.

Suggested Citation

  • Neslihan Fidan Keçeci & Viktor Kuzmenko & Stan Uryasev, 2016. "Portfolios Dominating Indices: Optimization with Second-Order Stochastic Dominance Constraints vs. Minimum and Mean Variance Portfolios," JRFM, MDPI, vol. 9(4), pages 1-14, October.
    • Handle: RePEc:gam:jjrfmx:v:9:y:2016:i:4:p:11-:d:79820
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    References listed on IDEAS

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    Cited by:

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    2. Vrinda Dhingra & Amita Sharma & Shiv K. Gupta, 2021. "Sectoral portfolio optimization by judicious selection of financial ratios via PCA," Papers 2106.11484, arXiv.org, revised Jan 2023.
    3. Liwei Zhang & Yule Zhang & Jia Wu & Xiantao Xiao, 2022. "Solving Stochastic Optimization with Expectation Constraints Efficiently by a Stochastic Augmented Lagrangian-Type Algorithm," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 2989-3006, November.
    4. Runsheng Gu & Lioudmila Vostrikova & Bruno Séjourné, 2020. "Portfolio optimization of euro-denominated funds in French life insurance," Working Papers hal-03025191, HAL.
    5. Neslihan Fidan Keçeci & Yonca Erdem Demirtaş, 2018. "Risk-Based DEA Efficiency and SSD Efficiency of OECD Members Stock Indices," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 6(1), pages 25-36, March.

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