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Research Article | Open Access

Robust equilibrium reinsurance and investment strategy for the insurer and reinsurer under weighted mean-variance criterion

Yiming Su1Haiyan Liu1Mi Chen1,2( )
School of Mathematics and Statistics & Fujian Key Laboratory of Analytical Mathematics and Applications, Fujian Normal University, Fuzhou 350117, China
Key Laboratory of Analytical Mathematics and Applications (Ministry of Education) & Fujian Provincial Key Laboratory of Statistics and Artificial Intelligence, Fujian Normal University, Fuzhou 350117, China
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Abstract

This paper investigates the time-consistent robust optimal reinsurance problem for the insurer and reinsurer under weighted objective criteria. The joint objective criterion is obtained by weighting the mean-variance objectives of both the insurer and reinsurer. Specifically, we assume that the net claim process is approximated by a diffusion model, and the insurer can purchase proportional reinsurance from the reinsurer. The insurer adopts the loss-dependent premium principle considering historical claims, while the reinsurance contract still uses the expected premium principle due to information asymmetry. Both the insurer and reinsurer can invest in risk-free assets and risky assets, where the risky asset price is described by the constant elasticity of variance model. Additionally, the ambiguity-averse insurer and ambiguity-averse reinsurer worry about the uncertainty of parameter estimation in the model, therefore, we obtain a robust optimization objective through the robust control method. By solving the corresponding extended Hamilton-Jacobi-Bellman equation, we derive the time-consistent robust equilibrium reinsurance and investment strategy and corresponding value function. Finally, we examined the impact of various parameters on the robust equilibrium strategy through numerical examples.

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Electronic Research Archive
Pages 6384-6411

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Cite this article:
Su Y, Liu H, Chen M. Robust equilibrium reinsurance and investment strategy for the insurer and reinsurer under weighted mean-variance criterion. Electronic Research Archive, 2023, 31(10): 6384-6411. https://doi.org/10.3934/era.2023323

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Received: 05 August 2023
Revised: 10 September 2023
Accepted: 20 September 2023
Published: 15 October 2023
©2023 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)