In the aftermath of the COVID-19 pandemic, empirical data have revealed that large-scale health crises not only cause immediate disruptions in mortality dynamics but also have persistent effects that may last for several years. Existing mortality models largely assume that mortality shocks are transitory and overlook how their effects can be long-lasting and heterogeneous across age groups and causes of death. In response to this limitation, we propose a novel stochastic mortality model that captures age- and cause-specific long-lasting effects of mortality jumps through a gamma-density-like decay function, estimated via a customized conditional maximum likelihood algorithm. Applying the model to recent U.S. mortality data, we reveal divergent persistence patterns across demographic groups and provide key insights into the tail risk profiles of life insurance and annuity products. Our scenario-based analyses further show that neglecting persistent shock effects can lead to suboptimal hedging, while the proposed model enables what-if testing to analyze such effects under potential future health crises.

翻译：在新冠疫情之后，实证数据揭示大规模健康危机不仅会立即扰乱死亡率动态，其影响还可能持续数年之久。现有死亡率模型大多假设死亡率冲击是暂时性的，忽视了其影响可能具有长期性且在不同年龄组和死因之间存在异质性。针对这一局限性，我们提出了一种新颖的随机死亡率模型，该模型通过伽马密度型衰减函数捕捉年龄和死因特异性的死亡率跳跃长期影响，并采用定制化的条件极大似然估计算法进行参数估计。将所提出的模型应用于美国最新死亡率数据后，我们揭示了不同人口群体间存在差异化的持续性模式，并获得了关于寿险和年金产品尾部风险特征的关键见解。基于情景的分析进一步表明，忽视冲击的持续性效应可能导致套期保值策略次优，而所提出的模型能够通过"假设分析"来评估未来潜在健康危机下的此类影响。