Explaining young mortality

Colin O'Hare, Youwei Li

Research output: Contribution to journalArticle

24 Citations (Scopus)

Abstract

Stochastic modeling of mortality rates focuses on fitting linear models to logarithmically adjusted mortality data from the middle or late ages. Whilst this modeling enables insurers to project mortality rates and hence price mortality products it does not provide good fit for younger aged mortality. Mortality rates below the early 20's are important to model as they give an insight into estimates of the cohort effect for more recent years of birth. It is also important given the cumulative nature of life expectancy to be able to forecast mortality improvements at all ages. When we attempt to fit existing models to a wider age range, 5-89, rather than 20-89 or 50-89, their weaknesses are revealed as the results are not satisfactory. The linear innovations in existing models are not flexible enough to capture the non-linear profile of mortality rates that we see at the lower ages. In this paper we modify an existing 4 factor model of mortality to enable better fitting to a wider age range, and using data from seven developed countries our empirical results show that the proposed model has a better fit to the actual data, is robust, and has good forecasting ability.
Original languageEnglish
Pages (from-to)12-25
JournalInsurance: Mathematics and Economics
Volume50
Issue number1
Early online date10 Oct 2011
DOIs
Publication statusPublished - Jan 2012

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Mortality
Mortality Rate
Life Expectancy
Stochastic Modeling
Factor Models
Model
Range of data
Forecast
Forecasting
Linear Model
Mortality rate
Modeling
Estimate

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O'Hare, Colin ; Li, Youwei. / Explaining young mortality. In: Insurance: Mathematics and Economics. 2012 ; Vol. 50, No. 1. pp. 12-25.
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Explaining young mortality. / O'Hare, Colin; Li, Youwei.

In: Insurance: Mathematics and Economics, Vol. 50, No. 1, 01.2012, p. 12-25.

Research output: Contribution to journalArticle

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