Robust mean-variance stochastic differential reinsurance and investment games under volatility risk and model uncertainty
Guohui Guan, Zongxia Liang, Yi Xia

TL;DR
This paper develops a robust mean-variance stochastic differential game model for insurers under model uncertainty and stochastic volatility, deriving equilibrium strategies and analyzing the impact of competition.
Contribution
It introduces a novel robust time-consistent mean-field game framework with explicit equilibrium solutions under complex stochastic volatility models.
Findings
Explicit semi-closed form equilibrium strategies derived
Convergence of n-insurer game to mean-field game as n increases
Numerical analysis illustrates herd effects and competitive behaviors
Abstract
This paper investigates robust stochastic differential games among insurers under model uncertainty and stochastic volatility. The surplus processes of ambiguity-averse insurers (AAIs) are characterized by drifted Brownian motion with both common and idiosyncratic insurance risks. To mitigate these risks, AAIs can purchase proportional reinsurance. Besides, AAIs allocate their wealth in a financial market consisting of cash, and a stock characterized by the 4/2 stochastic volatility model. AAIs compete with each other based on relative performance with the mean-variance criterion under the worst-case scenario. This paper formulates a robust time-consistent mean-field game in a non-linear system. The AAIs seek robust, time-consistent response strategies to achieve Nash equilibrium strategies in the game. We introduce -dimensional extended Hamilton-Jacobi-Bellman-Isaacs (HJBI)…
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Taxonomy
TopicsStochastic processes and financial applications · Capital Investment and Risk Analysis · Risk and Portfolio Optimization
