Constrained Kriging for Smoothing and Forecasting Mortality Rates

13 Pages Posted: 3 Feb 2022

See all articles by Zied Chaeib

Zied Chaeib

Quantlabs (Quanteam Group)

Djibril Gueye

Quantlabs (Quanteam Group)

Date Written: November 30, 2021

Abstract

Mortality surface is a function of age and year with the main characteristic of being increasing in age direction from a given age. One of the major challenges of its construction is to take this last specificity into account. In this paper, we propose to use constrained Kriging for such construction. Our approach is based on the finite-dimensional approximation of the Gaussian process. We first show the ability of the constrained Kriging to construct mortality surfaces and then compare its performance against classical Kriging models with trend functions such as those used in [LRZ18]. Our empirical study based on mortality data from three countries (France, Italy, and Germany) showed the need to add a constraint of convexity in age direction and illustrated through an RMSE criterion that the constrained Kriging provided better results in terms of out-of-sample forecasting.

Keywords: Mortality, Constrained Kriging, Smoothing, Forecasting

Suggested Citation

Chaeib, Zied and Gueye, Djibril, Constrained Kriging for Smoothing and Forecasting Mortality Rates (November 30, 2021). Available at SSRN: https://ssrn.com/abstract=3980885 or http://dx.doi.org/10.2139/ssrn.3980885

Zied Chaeib

Quantlabs (Quanteam Group)

8 rue Euler
Paris, 75008
France

Djibril Gueye (Contact Author)

Quantlabs (Quanteam Group) ( email )

8 rue Euler
Paris, 75008
France

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