Modeling Period Effects in Multi-Population Mortality Models: Applications to Solvency II

Abstract We compare results for 12 multi-population mortality models fitted to 10 distinct socio-economic groups in England, subdivided using the Index of Multiple Deprivation. Using the Bayes Information Criterion to compare models, we find that a special case of the common age effect (CAE) model fits best in a variety of situations, achieving the best balance between goodness of fit and parsimony. We provide a detailed discussion of key models to highlight which features are important. Group-specific period effects are found to be more important than group-specific age effects, and non-parametric age effects deliver significantly better results than parametric (e.g. linear) age effects. We also find that the addition of cohort effects is beneficial in some cases but not all. The preferred CAE model has the additional benefit of being coherent in the sense of Hyndman et al. ((2013) Demography50(1), 261–283); some of the other models considered are not.

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Multi-population mortality models: fitting, forecasting and comparisons

Scandinavian Actuarial Journal ◽

10.1080/03461238.2015.1133450 ◽

2016 ◽

Vol 2017 (4) ◽

pp. 319-342 ◽

Cited By ~ 23

Author(s):

Vasil Enchev ◽

Torsten Kleinow ◽

Andrew J. G. Cairns

Keyword(s):

Mortality Models ◽

Population Mortality

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Multi-population mortality models: A factor copula approach

Insurance Mathematics and Economics ◽

10.1016/j.insmatheco.2015.03.022 ◽

2015 ◽

Vol 63 ◽

pp. 135-146 ◽

Cited By ~ 15

Author(s):

Hua Chen ◽

Richard MacMinn ◽

Tao Sun

Keyword(s):

Mortality Models ◽

Copula Approach ◽

Population Mortality

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Forecasting U.K. Population Mortality Allowing for Age, Period and Cohort Effects

British Actuarial Journal ◽

10.1017/s1357321700005523 ◽

2009 ◽

Vol 15 (S1) ◽

pp. 73-89 ◽

Cited By ~ 2

Author(s):

D. O. Forfar

Keyword(s):

Cohort Effect ◽

Cohort Effects ◽

Mortality Data ◽

Period Effect ◽

Female Mortality ◽

Mortality Experience ◽

National Statistics ◽

Period Effects ◽

Males And Females ◽

Population Mortality

ABSTRACTThe mortality data (registered deaths and population size) over the years 1961–2007 for the population of England and Wales and for Scotland were obtained from the Office for National Statistics (ONS) and from the Scottish Registrar General. This paper addresses the following questions:(i) Is there statistical evidence for a cohort effect (i.e. a generation effect separate from the period effect) being present in the data?(ii) Do both males and females exhibit similar cohort (generation) effects?(iii) Are period effects (i.e. the improvement in mortality with time) more significant than cohort effects?(iv) How should one allow, in forecasts of population mortality, for age, period and cohort effects?(v) Is it sensible to combine male and female mortality experience to determine the period effect and the cohort effect?(vi) How do the forecasts for the expectation of life at birth, using the Extended-Lee–Carter-Combined (ELCC) model (described in the paper) differ from the (2008 based) Office of National Statistics (ONS) forecasts of the expectation of life at birth?

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Multi-Population Mortality Models: A Factor Copula Approach

SSRN Electronic Journal ◽

10.2139/ssrn.2544194 ◽

2014 ◽

Cited By ~ 3

Author(s):

Hua Chen ◽

Richard D. MacMinn ◽

Tao Sun

Keyword(s):

Mortality Models ◽

Copula Approach ◽

Population Mortality

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Common Factor Cause-Specific Mortality Model

Risks ◽

10.3390/risks9120221 ◽

2021 ◽

Vol 9 (12) ◽

pp. 221

Author(s):

Geert Zittersteyn ◽

Jennifer Alonso-García

Keyword(s):

Life Expectancy ◽

Common Factor ◽

Trivial Extension ◽

Mortality Data ◽

Mortality Model ◽

Mortality Models ◽

Specific Mortality ◽

Accurate Forecast ◽

Mortality Forecasts ◽

Population Mortality

Recent pension reforms in Europe have implemented a link between retirement age and life expectancy. The accurate forecast of life tables and life expectancy is hence paramount for governmental policy and financial institutions. We developed a multi-population mortality model which includes a cause-specific environment using Archimedean copulae to model dependence between various groups of causes of death. For this, Dutch data on cause-of-death mortality and cause-specific mortality data from 14 comparable European countries were used. We find that the inclusion of a common factor to a cause-specific mortality context increases the robustness of the forecast and we underline that cause-specific mortality forecasts foresee a more pessimistic mortality future than general mortality models. Overall, we find that this non-trivial extension is robust to the copula specification for commonly chosen dependence parameters.

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