Accounting for COVID-19-type shocks in mortality modeling: a comparative study

Abstract

Mortality shocks such as the one induced by the COVID-19 pandemic have substantial impact on mortality models. We describe how to deal with them in the period effect of the Lee–Carter model. The main idea is to not rely on the usual normal distribution assumption as it is not always justified. We consider a mixture distribution model based on the peaks-over-threshold method, a jump model, and a regime switching model and introduce a modified calibration procedure to account for the fact that varying amounts of data are necessary for calibrating different parts of these models. We perform an extensive empirical study for nine European countries, comparing the models with respect to their parameters, quality of fit, and forecasting performance. Moreover, we define five exemplary scenarios regarding the future development of pandemic-related mortality. As a result of our evaluations, we recommend the peaks-over-threshold approach for applications with a possibility of extreme mortality events.

A Quantitative Comparison of Mortality Models with Jumps: Pre- and Post-COVID Insights on Insurance Pricing

“…On the other hand, the parameters of the models, which are assumed to be constant over time and given in Table 3, are estimated based on a time invariance assumption. However, this assumption has been proven to be incorrect for many populations over long calibration periods in the literature (see Schnürch et al 2023 for more details). This distortion in the time variance assumption, which encompasses different mortality regimes over time, might have been reflected in the forecast performance of the jump models in pandemic years.…”