Life Table Forecasting in COVID-19 Times: An Ensemble Learning Approach

Stochastic mortality modeling play a critical role in public pension design, population and public health projections and in the design, pricing and risk management of life insurance contracts and longevity-linked securities. There is no general method to forecast mortality rate applicable to all situations especially for unusual years such as the COVID-19 pandemic. In this paper, we investigate the feasibility of using an ensemble of traditional and machine learning time series methods to empower forecasts of age-specific mortality rates for groups of countries that share common longevity trends. We use Generalized Age-Period-Cohort stochastic mortality models to capture age and period effects, apply K-means clustering to time series to group countries following common longevity trends and use ensemble learning to forecast future longevity and annuity price markers. To calibrate models, we use data for 14 European countries from 1960 to 2018. The results show that the ensemble method presents the best robust results overall with minimum RMSE in the presence of structural changes in the shape of time series at the time of COVID-19.