Assessing the impact of the COVID-19 shock on a stochastic multi-population mortality model

TL;DR

This paper evaluates how the COVID-19 pandemic data point influences the calibration and projections of a stochastic multi-population mortality model, specifically the Li & Lee model, using weekly age-bucket data transformed into annual age-specific data.

Contribution

It introduces a protocol to convert weekly age-bucket mortality data into annual age-specific data and assesses the impact of pandemic data points on mortality projections.

Findings

Quantifies the impact of COVID-19 data on mortality projections.

Provides a framework for scenario analysis of pandemic effects.

Demonstrates the influence of data weighting on future mortality estimates.

Abstract

We aim to assess the impact of a pandemic data point on the calibration of a stochastic multi-population mortality projection model and its resulting projections for future mortality rates. Throughout the paper we put focus on the Li & Lee mortality model, which has become a standard for projecting mortality in Belgium and the Netherlands. We calibrate this mortality model on annual deaths and exposures at the level of individual ages. This type of mortality data is typically collected, produced and reported with a significant delay of -- for some countries -- several years on a platform such as the Human Mortality Database. To enable a timely evaluation of the impact of a pandemic data point we have to rely on other data sources (e.g. the Short-Term Mortality Fluctuations Data series) that swiftly publish weekly mortality data collected in age buckets. To be compliant with the design and calibration strategy of the Li & Lee model, we have to transform the weekly mortality data collected in age buckets to yearly, age-specific observations. Therefore, our paper constructs a protocol to ungroup the deaths and exposures registered in age buckets to individual ages. To evaluate the impact of a pandemic shock, like COVID-19 in the year 2020, we weigh this data point in either the calibration or projection step. Obviously, the more weight we place on this data point, the more impact we observe on future estimated mortality rates and life expectancies. Our paper allows to quantify this impact and provides actuaries and actuarial associations with a framework to generate scenarios of future mortality under various assessments of the pandemic data point.