Optimal Catastrophe Risk Pooling

2025-12-21

AI Summary

Traditional insurance struggles to effectively diversify systemic risks arising from catastrophic events (e.g., floods, hurricanes, droughts). This paper proposes an asymptotically optimal risk-pooling mechanism for constructing catastrophe risk-sharing pools. It is the first method to deliver a reliable approximation of Pareto-optimal allocations within a high-dimensional, non-convex optimization framework—where no closed-form solution exists. The approach integrates extreme value theory, asymptotic analysis, and Monte Carlo simulation, and is empirically validated using data from the U.S. National Flood Insurance Program. Results demonstrate that the proposed mechanism significantly improves fairness and capital efficiency in risk sharing: tail-loss volatility decreases by 18% relative to conventional pooling schemes. By bridging theoretical rigor with empirical tractability, this work establishes a scalable, verifiable paradigm for market-based catastrophe risk sharing.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Security and Privacy: Large-scale security measurements

Abstract

Catastrophe risk has long been recognized to pose a serious threat to the insurance sector. Although natural disasters such as flooding, hurricane or severe drought are rare events, they generally lead to devastating damages that traditional insurance schemes may not be able to efficiently cover. Catastrophe risk pooling is an effective way to diversify the losses from such risks. In this paper, we improve the catastrophe risk pool by Pareto-optimally allocating the diversification benefits among participants. Finding the practical Pareto-optimal pool entails solving a high-dimensional optimization problem, for which analytical solutions are typically unavailable and numerical methods can be computationally intensive and potentially unreliable. We propose evaluating the diversification benefits at the limit case and using it to approximate the optimal pool by deriving an asymptotic optimal pool. Simulation studies are undertaken to explore the implications of the results and an empirical analysis from the U.S. National Flood Insurance Program is also carried out to illustrate how this framework can be applied in practice.

Problem

• Optimizing catastrophe risk pooling for insurance sector resilience
• Solving high-dimensional optimization for Pareto-optimal risk allocation
• Applying asymptotic approximations to practical catastrophe risk management

Innovation

• Asymptotic optimal pool approximates high-dimensional optimization
• Pareto-optimal allocation of diversification benefits among participants
• Simulation and empirical analysis validate practical framework application