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Robustness to Dependency in Portfolio Optimization Using Overlapping Marginals

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  • Xuan Vinh Doan

    (DIMAP and ORMS Group, Warwick Business School, University of Warwick, Coventry, CV4 7AL, United Kingdom)

  • Xiaobo Li

    (Department of Industrial and Systems Engineering, University of Minnesota, Minneapolis, Minnesota 55455)

  • Karthik Natarajan

    (Engineering Systems and Design, Singapore University of Technology and Design, Singapore 487372)

Abstract

In this paper, we develop a distributionally robust portfolio optimization model where the robustness is across different dependency structures among the random losses. For a Fréchet class of discrete distributions with overlapping marginals, we show that the distributionally robust portfolio optimization problem is efficiently solvable with linear programming. To guarantee the existence of a joint multivariate distribution consistent with the overlapping marginal information, we make use of a graph theoretic property known as the running intersection property. Building on this property, we develop a tight linear programming formulation to find the optimal portfolio that minimizes the worst-case conditional value-at-risk measure. Lastly, we use a data-driven approach with financial return data to identify the Fréchet class of distributions satisfying the running intersection property and then optimize the portfolio over this class of distributions. Numerical results in two different data sets show that the distributionally robust portfolio optimization model improves on the sample-based approach.

Suggested Citation

  • Xuan Vinh Doan & Xiaobo Li & Karthik Natarajan, 2015. "Robustness to Dependency in Portfolio Optimization Using Overlapping Marginals," Operations Research, INFORMS, vol. 63(6), pages 1468-1488, December.
    • Handle: RePEc:inm:oropre:v:63:y:2015:i:6:p:1468-1488
    DOI: 10.1287/opre.2015.1424
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    References listed on IDEAS

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    Cited by:

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    2. Chang, Zhiqi & Ding, Jian-Ya & Song, Shiji, 2019. "Distributionally robust scheduling on parallel machines under moment uncertainty," European Journal of Operational Research, Elsevier, vol. 272(3), pages 832-846.
    3. Panos Xidonas & Ralph Steuer & Christis Hassapis, 2020. "Robust portfolio optimization: a categorized bibliographic review," Annals of Operations Research, Springer, vol. 292(1), pages 533-552, September.
    4. Doan, Xuan Vinh, 2022. "Distributionally robust optimization under endogenous uncertainty with an application in retrofitting planning," European Journal of Operational Research, Elsevier, vol. 300(1), pages 73-84.
    5. Alireza Ghahtarani & Ahmed Saif & Alireza Ghasemi, 2022. "Robust portfolio selection problems: a comprehensive review," Operational Research, Springer, vol. 22(4), pages 3203-3264, September.
    6. Viet Anh Nguyen & Soroosh Shafiee & Damir Filipovi'c & Daniel Kuhn, 2021. "Mean-Covariance Robust Risk Measurement," Papers 2112.09959, arXiv.org, revised Nov 2023.
    7. Anulekha Dhara & Bikramjit Das & Karthik Natarajan, 2021. "Worst-Case Expected Shortfall with Univariate and Bivariate Marginals," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 370-389, January.
    8. David Bergman & Andre A. Cire, 2018. "Discrete Nonlinear Optimization by State-Space Decompositions," Management Science, INFORMS, vol. 64(10), pages 4700-4720, October.
    9. Napat Rujeerapaiboon & Daniel Kuhn & Wolfram Wiesemann, 2016. "Robust Growth-Optimal Portfolios," Management Science, INFORMS, vol. 62(7), pages 2090-2109, July.
    10. Zhifeng Dai & Jie Kang, 2022. "Some new efficient mean–variance portfolio selection models," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4784-4796, October.
    11. Alireza Ghahtarani & Ahmed Saif & Alireza Ghasemi, 2021. "Robust Portfolio Selection Problems: A Comprehensive Review," Papers 2103.13806, arXiv.org, revised Jan 2022.
    12. Amir Ahmadi-Javid & Malihe Fallah-Tafti, 2017. "Portfolio Optimization with Entropic Value-at-Risk," Papers 1708.05713, arXiv.org.
    13. Xuan Vinh Doan & Tri-Dung Nguyen, 2019. "Technical Note—Robust Newsvendor Games with Ambiguity in Demand Distributions," Operations Research, INFORMS, vol. 68(4), pages 1047-1062, July.
    14. Anulekha Dhara & Bikramjit Das & Karthik Natarajan, 2017. "Worst-Case Expected Shortfall with Univariate and Bivariate Marginals," Papers 1701.04167, arXiv.org.
    15. Chang, Zhiqi & Song, Shiji & Zhang, Yuli & Ding, Jian-Ya & Zhang, Rui & Chiong, Raymond, 2017. "Distributionally robust single machine scheduling with risk aversion," European Journal of Operational Research, Elsevier, vol. 256(1), pages 261-274.

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