Gaussian Process Models for Mortality Rates and Improvement Factors

TL;DR

This paper introduces a Gaussian process framework for modeling mortality rates and improvements, providing a flexible, data-driven approach that quantifies uncertainty and adapts to new data, with applications to US mortality data.

Contribution

The paper presents a novel Gaussian process modeling approach for mortality data, including methods for uncertainty quantification and dynamic updating, improving on existing models.

Findings

Decline in mortality improvement factors over recent years

Strong age-dependency observed in mortality improvements

GP model outperforms traditional models in forecasting accuracy

Abstract

We develop a Gaussian process ("GP") framework for modeling mortality rates and mortality improvement factors. GP regression is a nonparametric, data-driven approach for determining the spatial dependence in mortality rates and jointly smoothing raw rates across dimensions, such as calendar year and age. The GP model quantifies uncertainty associated with smoothed historical experience and generates full stochastic trajectories for out-of-sample forecasts. Our framework is well suited for updating projections when newly available data arrives, and for dealing with "edge" issues where credibility is lower. We present a detailed analysis of Gaussian process model performance for US mortality experience based on the CDC datasets. We investigate the interaction between mean and residual modeling, Bayesian and non-Bayesian GP methodologies, accuracy of in-sample and out-of-sample forecasting, and stability of model parameters. We also document the general decline, along with strong age-dependency, in mortality improvement factors over the past few years, contrasting our findings with the Society of Actuaries ("SOA") MP-2014 and -2015 models that do not fully reflect these recent trends.