Paper: arXiv 2305.00509

Authors: Liyuan Lin, Fangda Liu, Jingzhen Liu abd Luyang Yu

Abstract

We study optimal reinsurance in the framework of stochastic game theory, in which there is an insurer and two reinsurers. A Stackelberg model is established to analyze the non-cooperative relationship between the insurer and reinsurers, where the insurer is considered as the follower and the reinsurers are considered as the leaders. The insurer is a price taker who determines reinsurance demand in the reinsurance market, while the reinsurers can price the reinsurance treaties. Our contribution is to use a Nash game to describe the price-competition between two reinsurers. We assume that one of the reinsurers adopts the variance premium principle and the other adopts the expected value premium principle. The insurer and the reinsurers aim to maximize their respective mean-variance cost functions which lead to a time-inconsistency control problem. To overcome the time-inconsistency issue in the game, we formulate the optimization problem of each player as an embedded game and solve it via a corresponding extended Hamilton-Jacobi-Bellman equation. We find that the insurer will sign propositional and excess loss reinsurance strategies with reinsurer 1 and reinsurer 2, respectively. When the claim size follows exponential distribution, there exists a unique equilibrium reinsurance premium strategy. Our numerical analysis verifies the impact of claim size, risk aversion and interest rates of the insurer and reinsurers on equilibrium reinsurance strategy and premium strategy, which can help to understand competition in the reinsurance market

Complexity vs Empirical Score

  • Math Complexity: 8.0/10
  • Empirical Rigor: 2.5/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper presents a rigorous theoretical framework using stochastic Stackelberg-Nash games and extended Hamilton-Jacobi-Bellman equations to address time-inconsistency in reinsurance pricing. However, it lacks empirical validation, relying solely on theoretical derivations and basic numerical simulations.

Research Flowchart

  flowchart TD
    A[Research Goal: Model optimal reinsurance strategy with price-competition between two reinsurers] --> B[Game Framework Setup]
    
    subgraph B [Game Framework Setup]
        B1[Stackelberg Game: Insurer Follower vs Reinsurers Leaders]
        B2[Nash Game: Price-competition between Reinsurer 1 Variance & Reinsurer 2 Expected Value]
    end
    
    B --> C[Optimization & Time-Inconsistency Control]
    
    subgraph C [Optimization & Time-Inconsistency Control]
        C1[Objective: Maximize Mean-Variance Cost Functions]
        C2[Method: Formulate Embedded Games & Solve Extended HJB Equations]
    end

    C --> D[Numerical Analysis Inputs: Claim Sizes Exp Distribution, Risk Aversion, Interest Rates]
    
    D --> E[Key Outcomes & Findings]
    
    subgraph E [Key Outcomes & Findings]
        E1[Insurer buys Proportional from Reinsurer 1 & Excess-of-Loss from Reinsurer 2]
        E2[Proved existence of a unique equilibrium premium strategy]
    end