Mitigating wildfire losses via insurance‐linked securities: Modeling and risk management perspectives
研究了巨灾债券作为野火风险管理工具，通过贝叶斯动态模型分析定价和对冲效果，发现指数型巨灾债券能有效减轻保险公司尾部风险，是对传统再保险的补充。
Abstract This paper investigates the use of catastrophe (CAT) bonds as a risk management tool for wildfires. We introduce a set of Bayesian dynamic models designed to accurately represent wildfire losses, allowing a thorough examination of wildfire CAT bond pricing and hedge effectiveness. Our model captures crucial attributes of wildfire data, such as zero inflation, overdispersion, temporal fluctuations, and spatial dependence. Employing extensive quantitative analyses of US wildfire data, we highlight that CAT bonds can substantially mitigate tail risk associated with insurers' liability. Importantly, index‐based CAT bonds, drawing their payouts from aggregate wildfire losses over a larger geographical scope than an insurer's operational area, also provide effective hedges. Our research underscores the potential of wildfire CAT bonds as an enhancement to traditional reinsurance strategies, offering insurers an improved means to manage and mitigate wildfire exposures amidst inherent uncertainties.