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NEIGHBOURING PREDICTION FOR MORTALITY

Published online by Cambridge University Press:  12 May 2021

Chou-Wen Wang ,
Jinggong Zhang  and
Wenjun Zhu Open the ORCID record for Wenjun Zhu [Opens in a new window]
Chou-Wen Wang
Affiliation:
Department of Finance National Sun Yat-Sen UniversityKaohsiung, TaiwanRisk and Insurance Research Center College of Commerce, National Chengchi University Taipei, Taiwan E-Mail: chouwenwang@mail.nsysu.edu.tw
Jinggong Zhang
Affiliation:
Nanyang Business School Nanyang Technological UniversitySingapore E-Mail: jgzhang@ntu.edu.sg
Wenjun Zhu*
Affiliation:
Nanyang Business School Nanyang Technological UniversitySingapore E-Mail: wjzhu@ntu.edu.sg
*
E-Mail: wjzhu@ntu.edu.sg
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Abstract

We propose a new neighbouring prediction model for mortality forecasting. For each mortality rate at age x in year t, mx,t, we construct an image of neighbourhood mortality data around mx,t, that is, Ꜫmx,t (x1, x2, s), which includes mortality information for ages in [x-x1, x+x2], lagging k years (1 ≤ k ≤ s). Combined with the deep learning model – convolutional neural network, this framework is able to capture the intricate nonlinear structure in the mortality data: the neighbourhood effect, which can go beyond the directions of period, age, and cohort as in classic mortality models. By performing an extensive empirical analysis on all the 41 countries and regions in the Human Mortality Database, we find that the proposed models achieve superior forecasting performance. This framework can be further enhanced to capture the patterns and interactions between multiple populations.

Keywords

Mortality forecastingneighbourhood effectartificial intelligencedeep learningconvolutional neural networkslongevity riskC45C51C52C53G22J11
Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 51 , Issue 3 , September 2021 , pp. 689 - 718
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of The International Actuarial Association

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Footnotes

*

We thank Mario V. Wüthrich (editor) and three anonymous referees for helpful comments. Wang acknowledges the support of MOST (107-2410-H-110-010-MY3). Zhang thanks the research funding support from the Nanyang Technological University Start-up Grant (04INS000509C300) and the Ministry of Education Academic Research Fund Tier 1 Grant (RG55/20). Zhu also thanks the research funding support from the Nanyang Technological University Start-Up Grant (04INS000384C300), Singapore Ministry of Education Academic Research Fund Tier 1 (RG143/19), and the Society of Actuaries Education Institution Grant.

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