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GAUSSIAN PROCESS MODELS FOR MORTALITY RATES AND IMPROVEMENT FACTORS

Published online by Cambridge University Press:  22 August 2018

Mike Ludkovski ,
Jimmy Risk  and
Howard Zail
Mike Ludkovski
Affiliation:
Department of Statistics & Applied Probability, University of California, Santa Barbara CA 93106-3110, USA E-Mail: [email protected]
Jimmy Risk*
Affiliation:
Department of Mathematical Sciences, Cal Poly Pomona, Pomona, CA 91768, USA
Howard Zail
Affiliation:
Actuary and Founder of Elucidor, LLC, NY 10016, USA E-Mail: [email protected]
*
E-Mail: [email protected]
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Abstract

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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 GP model performance for US mortality experience based on the CDC (Center for Disease Control) 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.

Keywords

mortalitymortality improvementmortality modelingGaussian processes
Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 48 , Issue 3 , September 2018 , pp. 1307 - 1347
Copyright
Copyright © Astin Bulletin 2018 

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