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A Probabilistic Paradigm for the Parametric Insurance of Natural Hazards

机译：自然灾害参数保险的概率范式

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摘要

There is a pressing need for simple and reliable risk transfer mechanisms that can pay out quickly after natural disasters without delays caused by loss estimation, and the need for long historical claims records. One such approach, known as parametric insurance, pays out when a key hazard variable exceeds a predetermined threshold. However, this approach to catastrophe risk, based on making deterministic binary predictions of loss occurrence, is susceptible to basis risk (mismatch between payouts and realized losses). A more defensible approach is to issue probabilistic predictions of loss occurrence, which then allows uncertainty to be properly quantified, communicated, and evaluated. This study proposes a generic probabilistic framework for parametric trigger modeling based on logistic regression, and idealized modeling of potential damage given knowledge of a hazard variable. We also propose various novel methods for evaluating the quality and utility of such predictions as well as more traditional trigger indices. The methodology is demonstrated by application to flood-related disasters in Jamaica from 1998 to 2016 using gridded precipitation data as the hazard variable. A hydrologically motivated transformation is proposed for calculating potential damage from daily rainfall data. Despite the simplicity of the approach, the model has substantial skill at predicting the probability of occurrence of loss days as demonstrated by traditional goodness-of-fit measures (i.e., pseudo-R-2 of 0.55) as well as probabilistic verification diagnostics such as receiver operating characteristics. Using conceptual models of decisionmaker expenses, we also demonstrate that the system can provide considerable utility to involved parties, e.g., insured parties, insurers, and risk managers.
机译：迫切需要一种简单而可靠的风险转移机制，该机制可以在自然灾害发生后迅速付款，而不会因损失估计而造成延误，并且需要长期的历史索赔记录。当关键危害变量超过预定阈值时，一种称为参数保险的方法会赔付。但是，这种基于巨灾风险确定性二进制预测的巨灾风险方法易受基础风险（支出与已实现损失之间的不匹配）的影响。更具辩护性的方法是发布损失发生的概率预测，然后允许对不确定性进行适当的量化，传达和评估。这项研究为基于逻辑回归的参数触发建模提出了一个通用的概率框架，并在已知危险变量的情况下对潜在损害进行了理想化建模。我们还提出了各种新颖的方法来评估此类预测以及更传统的触发指标的质量和效用。该方法通过将栅格化降水数据作为灾害变量应用于1998年至2016年牙买加与洪水有关的灾害中得到证明。提出了一种水文驱动的变换，用于根据每日降雨数据计算潜在破坏。尽管该方法很简单，但该模型在预测损失天数的可能性方面具有强大的技能，如传统的拟合优度度量（即，伪R-2为0.55）以及概率验证诊断（例如，接收器的工作特性。使用决策者费用的概念模型，我们还证明了该系统可以为相关方（例如，被保险方，保险公司和风险管理者）提供可观的效用。

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