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Optimal Investment Strategy for DC Pension Schemes under Partial Information

Author

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  • Manli Ban

    (School of Sciences, Hebei University of Technology, Tianjin 300401, China)

  • Hua He

    (School of Sciences, Hebei University of Technology, Tianjin 300401, China)

  • Xiaoqing Liang

    (School of Sciences, Hebei University of Technology, Tianjin 300401, China)

Abstract

We consider a defined-contribution (DC)-pension-fund-management problem under partial information. The fund manager is allowed to invest the wealth from the fund account into a financial market consisting of a risk-free account, a stock and a rolling bond. The aim of the fund manager is to maximize the expected utility of the terminal wealth. In contrast to the traditional literature, we assume that the fund manager can only observe the stock-price process and the interest-rate process, but the expected return rate of the stock is unobservable, following a mean-reverting stochastic process. We apply a martingale approach and Clark’s formula to solve this problem and the closed-form representations for the optimal terminal wealth and trading strategy are derived. We further present the results for the constant relative risk aversion (CRRA) function as a special case.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jrisks:v:10:y:2022:i:11:p:211-:d:966050
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    References listed on IDEAS

    as
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    3. Baltas, I. & Dopierala, L. & Kolodziejczyk, K. & Szczepański, M. & Weber, G.-W. & Yannacopoulos, A.N., 2022. "Optimal management of defined contribution pension funds under the effect of inflation, mortality and uncertainty," European Journal of Operational Research, Elsevier, vol. 298(3), pages 1162-1174.
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    5. Tomas Björk & Mark Davis & Camilla Landén, 2010. "Optimal investment under partial information," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 71(2), pages 371-399, April.
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