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Multi-objective portfolio optimization considering the dependence structure of asset returns

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  • Babaei, Sadra
  • Sepehri, Mohammad Mehdi
  • Babaei, Edris

Abstract

Portfolio optimization context has shed only a little light on the dependence structure among the financial returns along with the fat-tailed distribution associated with them. This study tries to find a remedy for this shortcoming by exploiting stable distributions as the marginal distributions together with the dependence structure based on copula function. We formulate the portfolio optimization problem as a multi-objective mixed integer programming. Value-at-Risk (VaR) is specified as the risk measure due to its intuitive appeal and importance in financial regulations. In order to enhance the model's applicability, we take into account cardinality and quantity constraints in the model. Imposing such practical constraints has resulted in a non-continuous feasible region. Hence, we propose two variants of multi-objective particle swarm optimization (MOPSO) algorithms to tackle this issue. Finally, a comparative study among the proposed MOPSOs, NSGAII and SPEA2 algorithms is made to demonstrate which algorithm is outperformed. The empirical results reveal that one of the proposed MOPSOs is superior over the other salient algorithms in terms of performance metrics.

Suggested Citation

  • Babaei, Sadra & Sepehri, Mohammad Mehdi & Babaei, Edris, 2015. "Multi-objective portfolio optimization considering the dependence structure of asset returns," European Journal of Operational Research, Elsevier, vol. 244(2), pages 525-539.
    • Handle: RePEc:eee:ejores:v:244:y:2015:i:2:p:525-539
    DOI: 10.1016/j.ejor.2015.01.025
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    3. Francisco Luna & David Quintana & Sandra García & Pedro Isasi, 2016. "Enhancing Financial Portfolio Robustness with an Objective Based on ϵ-Neighborhoods," Post-Print cea-01849801, HAL.
    4. Doering, Jana & Kizys, Renatas & Juan, Angel A. & Fitó, Àngels & Polat, Onur, 2019. "Metaheuristics for rich portfolio optimisation and risk management: Current state and future trends," Operations Research Perspectives, Elsevier, vol. 6(C).
    5. Gong, Xiao-Li & Xiong, Xiong, 2021. "Multi-objective portfolio optimization under tempered stable Lévy distribution with Copula dependence," Finance Research Letters, Elsevier, vol. 38(C).
    6. David Quintana & Roman Denysiuk & Sandra García-Rodríguez & Antonio Gaspar-Cunha, 2017. "Portfolio implementation risk management using evolutionary multiobjective optimization," Post-Print hal-01881379, HAL.
    7. BenSaïda, Ahmed, 2018. "The contagion effect in European sovereign debt markets: A regime-switching vine copula approach," International Review of Financial Analysis, Elsevier, vol. 58(C), pages 153-165.
    8. JeongHoe Lee & Navid Sabbaghi, 2020. "Multi-objective optimization case study for algorithmic trading strategies in foreign exchange markets," Digital Finance, Springer, vol. 2(1), pages 15-37, September.
    9. Forouli, Aikaterini & Gkonis, Nikolaos & Nikas, Alexandros & Siskos, Eleftherios & Doukas, Haris & Tourkolias, Christos, 2019. "Energy efficiency promotion in Greece in light of risk: Evaluating policies as portfolio assets," Energy, Elsevier, vol. 170(C), pages 818-831.
    10. Rewat Khanthaporn, 2022. "Analysis of Nonlinear Comovement of Benchmark Thai Government Bond Yields," PIER Discussion Papers 183, Puey Ungphakorn Institute for Economic Research.
    11. Cui, Xiangyu & Gao, Jianjun & Shi, Yun & Zhu, Shushang, 2019. "Time-consistent and self-coordination strategies for multi-period mean-Conditional Value-at-Risk portfolio selection," European Journal of Operational Research, Elsevier, vol. 276(2), pages 781-789.
    12. Lourme, Alexandre & Maurer, Frantz, 2017. "Testing the Gaussian and Student's t copulas in a risk management framework," Economic Modelling, Elsevier, vol. 67(C), pages 203-214.
    13. Francesco Bianchi & Lorenzo Mercuri & Edit Rroji, 2022. "Portfolio Selection with Irregular Time Grids: an example using an ICA-COGARCH(1, 1) approach," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 36(1), pages 57-85, March.
    14. Chen, Lin & Han, Qian & Qiao, Zhilin & Stanley, H. Eugene, 2020. "Correlation analysis and systemic risk measurement of regional, financial and global stock indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    15. Luo, Ming & Wu, Shaomin, 2018. "A value-at-risk approach to optimisation of warranty policy," European Journal of Operational Research, Elsevier, vol. 267(2), pages 513-522.
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