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MULTIVARIATE LONG-MEMORY COHORT MORTALITY MODELS

Published online by Cambridge University Press:  23 December 2019

Hongxuan Yan ,
Gareth W. Peters Open the ORCID record for Gareth W. Peters [Opens in a new window]  and
Jennifer S.K. Chan
Hongxuan Yan
Affiliation:
School of Mathematics and StatisticsThe University of Sydney E-Mail: yhx19901122@gmail.com
Gareth W. Peters*
Affiliation:
Department of Actuarial Mathematics and Statistics Heriot-Watt University E-Mail: garethpeters78@gmail.com
Jennifer S.K. Chan
Affiliation:
School of Mathematics and StatisticsThe University of Sydney E-Mail: jchan@maths.usyd.edu.au
*
E-Mail: garethpeters78@gmail.com
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Abstract

The existence of long memory in mortality data improves the understandings of features of mortality data and provides a new approach for establishing mortality models. The findings of long-memory phenomena in mortality data motivate us to develop new mortality models by extending the Lee–Carter (LC) model to death counts and incorporating long-memory model structure. Furthermore, there are no identification issues arising in the proposed model class. Hence, the constraints which cause many computational issues in LC models are removed. The models are applied to analyse mortality death count data sets from three different countries divided according to genders. Bayesian inference with various selection criteria is applied to perform the model parameter estimation and mortality rate forecasting. Results show that multivariate long-memory mortality model with long-memory cohort effect model outperforms multivariate extended LC cohort model in both in-sample fitting and out-sample forecast. Increasing the accuracy of forecasting of mortality rates and improving the projection of life expectancy is an important consideration for insurance companies and governments since misleading predictions may result in insufficient funds for retirement and pension plans.

Keywords

Life tableGegenbauer polynomialLee–Carter cohort modellong memoryBayesian inference

Information

Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 50 , Issue 1 , January 2020 , pp. 223 - 263
Copyright
© Astin Bulletin 2019 

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