A Stackelberg reinsurance-investment game with asymmetric information and delay

This paper investigates a Stackelberg stochastic differential reinsurance-investment game problem, in which the reinsurer is the leader and the insurer is the follower. The unequal status of the reinsurer and the insurer in the financial market is characterized by the asymmetric information model. W...

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Bibliographic Details
Published inOptimization Vol. 70; no. 10; pp. 2131 - 2168
Main Authors Bai, Yanfei, Zhou, Zhongbao, Xiao, Helu, Gao, Rui
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 03.10.2021
Taylor & Francis LLC
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Summary:This paper investigates a Stackelberg stochastic differential reinsurance-investment game problem, in which the reinsurer is the leader and the insurer is the follower. The unequal status of the reinsurer and the insurer in the financial market is characterized by the asymmetric information model. We consider their wealth processes with delay to characterize bounded memory. The objective of the reinsurer is to find the optimal premium pricing strategy and investment strategy such that its constant absolute risk aversion (CARA) utility of the combination of terminal wealth and average performance wealth is maximized. The objective of the insurer is to find the optimal reinsurance strategy and investment strategy such that its CARA utility of the relative performance is maximized. We derive the equilibrium strategy explicitly for the game by solving corresponding Hamilton-Jacobi-Bellman equations sequentially. Then, we establish the verification theorem. The equilibrium investment strategy indicates that the insurer with less information completely imitates the investment strategy of the reinsurer who has more information on the financial market. Further, we find that the effect of the delay weight on the equilibrium strategy is related to the length of delay time. Finally, we present some numerical examples to demonstrate the findings.
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ISSN:0233-1934
1029-4945
DOI:10.1080/02331934.2020.1777125