Abstract
We introduce a model for the mortality rates of multiple populations. To build
the proposed model we investigate to what extent a common age effect can be found among the mortality experiences of several countries and use a common principal component analysis to estimate a common age effect in an ageperiod model for multiple populations. The fit of the proposed model is then compared to ageperiod models fitted to each country individually, and to the fit of the model proposed by Li & Lee (2005).
Although we do not consider stochastic mortality projections in this paper, we
argue that the proposed common age effect model can be extended to a stochastic mortality model for multiple populations, which allows to generate mortality scenarios simultaneously for all considered populations. This is particularly relevant when mortality derivatives are used to hedge the longevity risk in an annuity portfolio as this often means that the underlying population for the derivatives is not the same as the population in the annuity portfolio.
the proposed model we investigate to what extent a common age effect can be found among the mortality experiences of several countries and use a common principal component analysis to estimate a common age effect in an ageperiod model for multiple populations. The fit of the proposed model is then compared to ageperiod models fitted to each country individually, and to the fit of the model proposed by Li & Lee (2005).
Although we do not consider stochastic mortality projections in this paper, we
argue that the proposed common age effect model can be extended to a stochastic mortality model for multiple populations, which allows to generate mortality scenarios simultaneously for all considered populations. This is particularly relevant when mortality derivatives are used to hedge the longevity risk in an annuity portfolio as this often means that the underlying population for the derivatives is not the same as the population in the annuity portfolio.
Original language  English 

Pages (fromto)  147–152 
Journal  Insurance: Mathematics and Economics 
Volume  63 
DOIs  
Publication status  Published  Jul 2015 
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Profiles

Torsten Kleinow
 School of Mathematical & Computer Sciences  Associate Professor
 School of Mathematical & Computer Sciences, Actuarial Mathematics & Statistics  Associate Professor
 Research Centres and Themes, Centre for Finance & Investment  Associate Professor
Person: Academic (Research & Teaching)