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Optimal reinsurance and investment via stochastic projected gradient method based on Malliavin calculus

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  • Yuta Otsuki
  • Shotaro Yagishita

Abstract

This paper proposes a new approach using the stochastic projected gradient method and Malliavin calculus for optimal reinsurance and investment strategies. Unlike traditional methodologies, we aim to optimize static investment and reinsurance strategies by directly minimizing the ruin probability. Furthermore, we provide a convergence analysis of the stochastic projected gradient method for general constrained optimization problems whose objective function has H\"older continuous gradient. Numerical experiments show the effectiveness of our proposed method.

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  • Yuta Otsuki & Shotaro Yagishita, 2024. "Optimal reinsurance and investment via stochastic projected gradient method based on Malliavin calculus," Papers 2411.05417, arXiv.org.
    • Handle: RePEc:arx:papers:2411.05417
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    References listed on IDEAS

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