Mitigating ambiguity in earthquake catastrophe insurance pricing: A model averaging and α-Maxmin approach

Ambiguity poses a key challenge in the pricing of catastrophe insurance, as it leads to higher premiums compared to unambiguous risks. This paper proposes a novel approach that integrates model averaging (MA) techniques with an extended α-maxmin framework to address ambiguity in insurance pricing decisions. Specifically, we introduce three MA weighting strategies within a quantile regression setting to mitigate estimation uncertainty and extend the α-maxmin framework to formally incorporate both the insurer’s ambiguity aversion and survival constraints into the pricing process. Using earthquake loss data from China (1974-2023), we show that MA improves predictive accuracy and mitigates affordability issues by reducing ambiguity-induced premium inflation, with jackknife model averaging lowering net premiums by 15.69%. Sensitivity analyzes indicate that stronger ambiguity aversion (higher α), tighter survival constraints (lower θ), and a higher cost of capital (higher δ) all necessitate larger capital reserves to counter bankruptcy risk, thereby raising premiums, with the first two factors exerting a more pronounced influence. The paper offers a coherent toolkit for integrating model uncertainty into catastrophe insurance pricing with practical relevance for risk management and regulation.