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Portfolio selection problem with liquidity constraints under non-extensive statistical mechanics

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  • Zhao, Pan
  • Xiao, Qingxian

Abstract

In this study, we consider the optimal portfolio selection problem with liquidity limits. A portfolio selection model is proposed in which the risky asset price is driven by the process based on non-extensive statistical mechanics instead of the classic Wiener process. Using dynamic programming and Lagrange multiplier methods, we obtain the optimal policy and value function. Moreover, the numerical results indicate that this model is considerably different from the model based on the classic Wiener process, the optimal strategy is affected by the non-extensive parameter q, the increase in the investment in the risky asset is faster at a larger parameter q and the increase in wealth is similar.

Suggested Citation

  • Zhao, Pan & Xiao, Qingxian, 2016. "Portfolio selection problem with liquidity constraints under non-extensive statistical mechanics," Chaos, Solitons & Fractals, Elsevier, vol. 82(C), pages 5-10.
    • Handle: RePEc:eee:chsofr:v:82:y:2016:i:c:p:5-10
    DOI: 10.1016/j.chaos.2015.10.026
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    Cited by:

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    2. Zhang, Qingye & Gao, Yan, 2016. "Optimal consumption—portfolio problem with CVaR constraints," Chaos, Solitons & Fractals, Elsevier, vol. 91(C), pages 516-521.
    3. Masoud Rahiminezhad Galankashi & Farimah Mokhatab Rafiei & Maryam Ghezelbash, 2020. "Portfolio selection: a fuzzy-ANP approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-34, December.
    4. repec:cte:idrepe:27672 is not listed on IDEAS
    5. Ruili Sun & Tiefeng Ma & Shuangzhe Liu & Milind Sathye, 2019. "Improved Covariance Matrix Estimation for Portfolio Risk Measurement: A Review," JRFM, MDPI, vol. 12(1), pages 1-34, March.
    6. repec:cte:idrepe:24017 is not listed on IDEAS
    7. repec:cte:idrepe:22932 is not listed on IDEAS
    8. repec:cte:idrepe:23546 is not listed on IDEAS
    9. repec:cte:idrepe:id-16-01 is not listed on IDEAS

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