Robust Investment-Driven Insurance Pricing under Correlation Ambiguity

Battle Count: 85

The paper is primarily focused on insurance pricing and underwriting decisions rather than trading strategies. However, the robust control framework under correlation ambiguity and the G-expectation methodology have indirect relevance to quantitative trading, particularly in portfolio construction under model uncertainty and correlation estimation. The finding that ambiguity does not necessarily reduce utility could inform risk management approaches in trading. The Sharpe ratio analysis and investment position characterization have some applicability to asset allocation decisions.

The paper involves advanced mathematical finance concepts including G-expectation framework, HJBI equations, viscosity solutions, Kuhn-Tucker conditions, and stochastic differential equations. Implementing the numerical simulations requires solving the equilibrium conditions across multiple regimes with regime-switching detection. The G-expectation framework is non-standard and requires specialized knowledge of nonlinear expectations and sublinear stochastic calculus. However, the final equilibrium formulas are explicit and can be evaluated numerically given parameter inputs.

The paper provides complete analytical solutions with explicit formulas for all equilibrium regimes. Benchmark parameter values are given in Table 1 (following Luciano and Rochet, 2022). The ambiguity radius calibration procedure via Fisher transformation is described. However, no code or numerical implementation is provided, and the G-expectation framework requires specialized mathematical knowledge. The theoretical derivations are self-contained but complex.