Modeling cause-of-death mortality with jump effects: Implications on risk management to life insurers
Abstract: Modeling cause-specific mortality dynamics has gained increasing interest in both academia and industry, especially since the beginning of the current pandemic period. Recent industry reports have found that the excess deaths and claims caused by the COVID-19 pandemic are asymmetric among causes of death. Understanding the effect of jump effects on cause-specific mortality rates can help life insurers to better evaluate the risk underlying their insurance products. In this paper, we develop a cause-of-death mortality model with jump effects to analyze the impact of mortality shocks on different age- and cause-specific mortality rates. We estimate our model using the conditional maximum likelihood method. Fitting our model to the most recent mortality data in the US reveals that the effect of a mortality shock not only varies among different causes but also among different age groups. We further examine the impact of mortality shocks on life insurance products with various product mixes to explore any potential diversification effect that could be utilized by a life insurer to better manage catastrophic mortality risk.
Join via Zoom: https://illinois.zoom.us/j/89759624078?pwd=bmkrQk1KTzJRMHIxNUdmRU5vYjFoZz09
