Abstract
Estimation of future mortality rates still plays a central role among life insurers in pricing their products and managing longevity risk. In the literature on mortality modeling, a wide number of stochastic models have been proposed, most of them forecasting future mortality rates by extrapolating one or more latent factors. The abundance of proposed models shows that forecasting future mortality from historical trends is non-trivial. Following the idea proposed in Deprez et al. (2017), we use machine learning algorithms, able to catch patterns that are not commonly identifiable, to calibrate a parameter (the machine learning estimator), improving the goodness of fit of standard stochastic mortality models. The machine learning estimator is then forecasted according to the Lee-Carter framework, allowing one to obtain a higher forecasting quality of the standard stochastic models. Out-of sample forecasts are provided to verify the model accuracy.
Highlights
During the 20th Century, mortality has declined at all ages, producing a steep increase in life expectancy
We investigate the ability of machine learning to improve the accuracy of some standard stochastic mortality models, both in the estimation and forecasting of mortality rates
We extend the work of Deprez et al (2017), which applied a regression tree boosting machine to improve the fitting of the LC and the RH model
Summary
During the 20th Century, mortality has declined at all ages, producing a steep increase in life expectancy. The work in Deprez et al (2017) showed that machine learning algorithms are useful to assess the goodness of fit of the mortality estimates provided by standard stochastic mortality models (they considered Lee-Carter and Renshaw-Haberman models). They applied a regression tree boosting machine to “analyze how the modeling should be improved based on feature components of an individual, such as its age or its birth cohort. We show that the implementation of these machine learning techniques, based on features components such as age, sex, calendar year, and birth cohort, leads to a better fit of the historical data, with respect to the estimates given by the Lee-Carter, Renshaw-Haberman, and Plat models.
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