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Mean–variance portfolio selection under partial information with drift uncertainty

Author

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  • Jie Xiong
  • Zuo Quan Xu
  • Jiayu Zheng

Abstract

In this paper, we study the mean–variance portfolio selection problem under partial information with drift uncertainty. First we show that the market model is complete even in this case while the information is not complete and the drift is uncertain. Then, the optimal strategy based on partial information is derived, which reduces to solving a related backward stochastic differential equation (BSDE). Finally, we propose an efficient numerical scheme to approximate the optimal portfolio that is the solution of the BSDE mentioned above. Malliavin calculus and the particle representation play important roles in this scheme.

Suggested Citation

  • Jie Xiong & Zuo Quan Xu & Jiayu Zheng, 2021. "Mean–variance portfolio selection under partial information with drift uncertainty," Quantitative Finance, Taylor & Francis Journals, vol. 21(9), pages 1461-1473, September.
  • Handle: RePEc:taf:quantf:v:21:y:2021:i:9:p:1461-1473
    DOI: 10.1080/14697688.2021.1889650
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    Cited by:

    1. Manli Ban & Hua He & Xiaoqing Liang, 2022. "Optimal Investment Strategy for DC Pension Schemes under Partial Information," Risks, MDPI, vol. 10(11), pages 1-20, November.
    2. Xing, Jie & Ma, Jingtang & Yang, Wensheng, 2023. "Optimal entry decision of unemployment insurance under partial information," Insurance: Mathematics and Economics, Elsevier, vol. 110(C), pages 31-52.
    3. Chonghu Guan & Xiaomin Shi & Zuo Quan Xu, 2022. "Continuous-time Markowitz's mean-variance model under different borrowing and saving rates," Papers 2201.00914, arXiv.org, revised May 2023.
    4. Chonghu Guan & Xiaomin Shi & Zuo Quan Xu, 2023. "Continuous-Time Markowitz’s Mean-Variance Model Under Different Borrowing and Saving Rates," Journal of Optimization Theory and Applications, Springer, vol. 199(1), pages 167-208, October.

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