The Long Shadow of Pandemic: Understanding the lingering effects of cause-specific mortality shocks

TL;DR

This paper introduces a new stochastic mortality model that captures the long-lasting, cause-specific effects of mortality shocks like pandemics, revealing heterogeneous persistence across demographics and improving risk assessment for insurance products.

Contribution

The paper develops a novel model that accounts for persistent, cause-specific mortality shocks, addressing limitations of existing models and enabling better risk management and scenario analysis.

Findings

Divergent persistence patterns across demographic groups.

Significant impact of ignoring long-lasting effects on hedging strategies.

Model effectively captures long-term effects of mortality shocks.

Abstract

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.