Issue |
RAIRO-Oper. Res.
Volume 58, Number 6, November-December 2024
|
|
---|---|---|
Page(s) | 5211 - 5236 | |
DOI | https://doi.org/10.1051/ro/2024205 | |
Published online | 06 December 2024 |
Optimal reinsurance strategy with mean-variance premium principle and relative performance concern
1
School of Management, Guangdong University of Technology, Guangzhou 510006, P.R. China
2
School of Finance, Guangdong University of Foreign Studies, Guangzhou 510006, P.R. China
* Corresponding author: chenshumin@gdut.edu.cn
Received:
19
November
2023
Accepted:
23
October
2024
This paper investigates the optimal reinsurance strategies for n insurers who compete with each other within the non-zero-sum game framework, as well as the optimal reinsurance premium loadings under the Stackelberg framework. The reinsurance premium is determined in accordance with the mean-variance principle. The insurers’ objectives are to maximize their utility of relative wealth over a finite decision horizon. Firstly, utilizing the dynamic programming technique, we derive a system of coupled Hamilton–Jacobi–Bellman (HJB) equations and characterize the equilibrium reinsurance strategies. We also obtain explicit solutions in the special case where the insurers possess exponential utility functions and present numerical examples to illustrate our theoretical findings. Secondly, leveraging the outcomes from the first section, we derive the optimal premium loadings for the reinsurer. We formulate the HJB equation and, for the special case of exponential utility, we numerically and explicitly obtain optimal decisions. Furthermore, we provide numerical examples to illustrate the impact of model parameters on the optimal reinsurance premium loadings.
Mathematics Subject Classification: 91A06 / 91A15 / 91A25
Key words: Reinsurance / mean-variance principle / robust optimal control / non-zero-sum game
© The authors. Published by EDP Sciences, ROADEF, SMAI 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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