Gaussian Process Models for Mortality Rates and Improvement Factors

27 Pages Posted: 31 Aug 2016

See all articles by Michael Ludkovski

Michael Ludkovski

University of California, Santa Barbara

James Risk

University of California, Santa Barbara (UCSB) - Statistics & Applied Probablity

Howard Zail

Elucidor, LLC

Date Written: August 17, 2016

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.

Keywords: Gaussian Processes, Mortality Modeling, Kriging, Actuarial Science

JEL Classification: I13, C53

Suggested Citation

Ludkovski, Mike and Risk, James and Zail, Howard, Gaussian Process Models for Mortality Rates and Improvement Factors (August 17, 2016). Available at SSRN: https://ssrn.com/abstract=2831831 or http://dx.doi.org/10.2139/ssrn.2831831

Mike Ludkovski

University of California, Santa Barbara ( email )

Santa Barbara, CA 93106
United States

HOME PAGE: http://www.pstat.ucsb.edu/faculty/ludkovski

James Risk (Contact Author)

University of California, Santa Barbara (UCSB) - Statistics & Applied Probablity ( email )

United States

Howard Zail

Elucidor, LLC ( email )

140 Broadway
46th Floor
New York, NY 10005
United States

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