Mean reversion in stochastic mortality: why and how?

Life insurance companies use stochastic models to forecast mortality. According to the literature, non-mean reversion models are more suitable for mortality modelling than mean reversion models with a fixed long-term target. In this paper, we adopt stochastic affine processes for the force of mortal...

Full description

Saved in:
Bibliographic Details
Published inEuropean actuarial journal Vol. 10; no. 2; pp. 499 - 525
Main Authors Zeddouk, Fadoua, Devolder, Pierre
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2020
Springer Nature B.V
Subjects
Actuarial science
Applications of Mathematics
Brownian motion
Economics
Financial Services
Game Theory
Interest rates
Life expectancy
Life insurance
Mathematics
Mathematics and Statistics
Mortality
Ordinary differential equations
Original Research Paper
Quantitative Finance
Random variables
Social and Behav. Sciences
Solvency
Stochastic models
Trends
United States > US
Stochastic mortality
Mean reversion
Affine process
Online AccessGet full text

Cover

More Information
Summary:Life insurance companies use stochastic models to forecast mortality. According to the literature, non-mean reversion models are more suitable for mortality modelling than mean reversion models with a fixed long-term target. In this paper, we adopt stochastic affine processes for the force of mortality and study the impact of adding a time-dependent long-term mean reversion level to two non-mean-reverting processes. We calibrate the models to different generations of the Belgian population and assess these models’ abilities to predict mortality using different statistical methodologies. The backtest shows that the survival curves provided by the mean-reverting processes are closer to reality. Thus, we conclude that incorporating a time-dependent target into these considered models improves their performance significantly.
ISSN:2190-9733
2190-9741
DOI:10.1007/s13385-020-00237-y