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

Fuente: arXiv
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Hauptverfasser: Otsuki, Yuta, Yagishita, Shotaro
Format: Preprint
Veröffentlicht: 2024
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author Otsuki, Yuta
Yagishita, Shotaro
author_facet Otsuki, Yuta
Yagishita, Shotaro
contents 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 Hölder continuous gradient. Numerical experiments show the effectiveness of our proposed method.
format Preprint
id arxiv_https___arxiv_org_abs_2411_05417
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Optimal reinsurance and investment via stochastic projected gradient method based on Malliavin calculus
Otsuki, Yuta
Yagishita, Shotaro
Mathematical Finance
Optimization and Control
Computational Finance
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 Hölder continuous gradient. Numerical experiments show the effectiveness of our proposed method.
title Optimal reinsurance and investment via stochastic projected gradient method based on Malliavin calculus
topic Mathematical Finance
Optimization and Control
Computational Finance
url https://arxiv.org/abs/2411.05417