Multivariate Long Memory Cohort Mortality Models

25 Pages Posted: 23 Apr 2018

See all articles by Hongxuan Yan

Hongxuan Yan

The University of Sydney - School of Mathematics and Statistics

Gareth Peters

Department of Actuarial Mathematics and Statistics, Heriot-Watt University; University College London - Department of Statistical Science; University of Oxford - Oxford-Man Institute of Quantitative Finance; London School of Economics & Political Science (LSE) - Systemic Risk Centre; University of New South Wales (UNSW) - Faculty of Science

Jennifer Chan

The University of Sydney - School of Mathematics and Statistics

Date Written: April 22, 2018

Abstract

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. 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 is 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.

Keywords: mortality modelling, life expectancy, multivariate, cohort, long memory, count time series, pension

Suggested Citation

Yan, Hongxuan and Peters, Gareth and Chan, Jennifer, Multivariate Long Memory Cohort Mortality Models (April 22, 2018). Available at SSRN: https://ssrn.com/abstract=3166884 or http://dx.doi.org/10.2139/ssrn.3166884

Hongxuan Yan

The University of Sydney - School of Mathematics and Statistics ( email )

Sydney, New South Wales 2006
Australia

Gareth Peters (Contact Author)

Department of Actuarial Mathematics and Statistics, Heriot-Watt University ( email )

Edinburgh Campus
Edinburgh, EH14 4AS
United Kingdom

HOME PAGE: http://garethpeters78.wixsite.com/garethwpeters

University College London - Department of Statistical Science ( email )

1-19 Torrington Place
London, WC1 7HB
United Kingdom

University of Oxford - Oxford-Man Institute of Quantitative Finance ( email )

University of Oxford Eagle House
Walton Well Road
Oxford, OX2 6ED
United Kingdom

London School of Economics & Political Science (LSE) - Systemic Risk Centre ( email )

Houghton St
London
United Kingdom

University of New South Wales (UNSW) - Faculty of Science ( email )

Australia

Jennifer Chan

The University of Sydney - School of Mathematics and Statistics ( email )

Sydney, New South Wales 2006
Australia

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