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Optimal Investment for Defined-Contribution Pension Plans with the Return of Premium Clause under Partial Information

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  • Zilan Liu

    (School of Business, Hunan Normal University, Changsha 410081, China
    Faculty of Economics and Management, Hengyang Normal University, Hengyang 421002, China)

  • Huanying Zhang

    (Key Laboratory of Computing and Stochastic Mathematics (Ministry of Education), School of Mathematics and Statistics, Hunan Normal University, Changsha 410081, China)

  • Yijun Wang

    (School of Finance, Henan University of Economics and Law, Zhengzhou 450016, China)

  • Ya Huang

    (School of Business, Hunan Normal University, Changsha 410081, China)

Abstract

The optimal investment problem for defined contribution (DC) pension plans with partial information is the subject of this paper. The purpose of the return of premium clauses is to safeguard the rights of DC pension plan participants who pass away during accumulation. We assume that the market price of risk consists of observable and unobservable factors that follow the Ornstein-Uhlenbeck processes, and the pension fund managers estimate the unobservable component from known information through Bayesian learning. Considering maximizing the expected utility of fund wealth at the terminal time, optimal investment strategies and the corresponding value function are determined using the dynamical programming principle approach and the filtering technique. Additionally, fund managers forsake learning, which results in the presentation of suboptimal strategies and utility losses for comparative analysis. Lastly, numerical analyses are included to demonstrate the impact of model parameters on the optimal strategy.

Suggested Citation

  • Zilan Liu & Huanying Zhang & Yijun Wang & Ya Huang, 2024. "Optimal Investment for Defined-Contribution Pension Plans with the Return of Premium Clause under Partial Information," Mathematics, MDPI, vol. 12(13), pages 1-22, July.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:13:p:2130-:d:1430410
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    References listed on IDEAS

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