This article contributes to insurance risk management by modeling extreme climate risk and extreme mortality risk in an integrated manner via extreme value theory (EVT). We conduct an empirical study using monthly temperature and death data in the United States and find that the joint extremes in cold weather and old-age death counts exhibit the strongest level of dependence. Based on the estimated bivariate generalized Pareto distribution, we quantify the extremal dependence between death counts and temperature indexes. Methodologically, we employ the cutting-edge multivariate peaks over threshold (POT) approach, which is readily applicable to a wide range of topics in extreme risk management.

本文通过极值理论 (EVT) 以综合方式对极端气候风险和极端死亡风险进行建模，从而为保险风险管理做出贡献。我们使用美国的月度温度和死亡数据进行了一项实证研究，发现寒冷天气和老年死亡人数的联合极端表现出最强的依赖性。基于估计的二元广义帕累托分布，我们量化了死亡人数和温度指数之间的极值依赖性。在方法论上，我们采用了前沿的多元峰值超过阈值 (POT) 方法，该方法很容易适用于极端风险管理中的广泛主题。