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Multi-objective probabilistically constrained programs with variable risk: Models for multi-portfolio financial optimization

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  • Lejeune, Miguel A.
  • Shen, Siqian

Abstract

We consider a class of multi-objective probabilistically constrained programs (MOPCP) with a joint probabilistic constraint and a variable risk level. We consider two cases with only a random right-hand side vector or a multi-row random technology matrix, and propose a Boolean modeling framework to derive new mixed-integer linear programs (MILP) that are either equivalent reformulations or inner approximations of MOPCP, respectively. Via testing randomly generated MOPCP instances, we demonstrate modeling insights pertaining to the most suitable MILP, to the trade-offs between conflicting objectives of cost/revenue and reliability, and to the parameter scalarization determining relative importance of each objective. We then focus on several MOPCP variants of a multi-portfolio financial optimization problem to implement a downside risk measure, which can be used in a centralized or decentralized investment context. We study the impact of modeling parameters on the portfolios, show, via a cross-validation study, robustness of MOPCP, and perform a comparative analysis of the optimal investment decisions.

Suggested Citation

  • Lejeune, Miguel A. & Shen, Siqian, 2016. "Multi-objective probabilistically constrained programs with variable risk: Models for multi-portfolio financial optimization," European Journal of Operational Research, Elsevier, vol. 252(2), pages 522-539.
    • Handle: RePEc:eee:ejores:v:252:y:2016:i:2:p:522-539
    DOI: 10.1016/j.ejor.2016.01.039
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    References listed on IDEAS

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    Cited by:

    1. Lejeune, Miguel & Lozin, Vadim & Lozina, Irina & Ragab, Ahmed & Yacout, Soumaya, 2019. "Recent advances in the theory and practice of Logical Analysis of Data," European Journal of Operational Research, Elsevier, vol. 275(1), pages 1-15.
    2. Horng, Shih-Cheng & Lin, Shieh-Shing, 2019. "Bat algorithm assisted by ordinal optimization for solving discrete probabilistic bicriteria optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 166(C), pages 346-364.
    3. Ki Taek Park & Hyejeong Yang & So Young Sohn, 2022. "Recommendation of investment portfolio for peer-to-peer lending with additional consideration of bidding period," Annals of Operations Research, Springer, vol. 315(2), pages 1083-1105, August.
    4. Marla, Lavanya & Rikun, Alexander & Stauffer, Gautier & Pratsini, Eleni, 2020. "Robust modeling and planning: Insights from three industrial applications," Operations Research Perspectives, Elsevier, vol. 7(C).
    5. Nilay Noyan & Gábor Rudolf & Miguel Lejeune, 2022. "Distributionally Robust Optimization Under a Decision-Dependent Ambiguity Set with Applications to Machine Scheduling and Humanitarian Logistics," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 729-751, March.
    6. Miguel A. Lejeune & Janne Kettunen, 2018. "A fractional stochastic integer programming problem for reliability-to-stability ratio in forest harvesting," Computational Management Science, Springer, vol. 15(3), pages 583-597, October.
    7. Yiling Zhang & Jin Dong, 2022. "Building Load Control Using Distributionally Robust Chance-Constrained Programs with Right-Hand Side Uncertainty and the Risk-Adjustable Variants," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1531-1547, May.
    8. repec:cte:idrepe:24017 is not listed on IDEAS
    9. Zheng, Xiaojin & Wu, Baiyi & Cui, Xueting, 2017. "Cell-and-bound algorithm for chance constrained programs with discrete distributions," European Journal of Operational Research, Elsevier, vol. 260(2), pages 421-431.

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