The incorporation of non-linear pattern of early ages has led to new research
directions on improving the existing stochastic mortalitymodel structure. Several authors
have outlined the importance of encompassing the full age range in dealing with longevity
risk exposure, by not ignoring the dependence between young and old ages. In this study,
we consider the two extensions of the Cairns, Blake and Dowd model that incorporate the
irregularity profile seen at the mortality of lower ages, which are the Plat, and the O’Hare
and Li models respectively. The models’ performances in terms of in-sample fitting and
out-sample forecasts were examined and compared. The results indicated that the O’Hare
and Li model performs better as compared to the Plat model.
Abstract Demographers and actuaries are very much conscious of the trend of mortality in their own country or in the world in general. This is because mortality is the basis for longevity risk evaluation. Mortality is showing a declining trend and it is expected to further decline in the future. This will lead to continuous increase in life expectancy. Several stochastic models have been developed throughout the years to capture mortality and its variability. This includes Lee Carter (LC) model which has been extended by various researchers. This paper will be focusing on comparing LC model and another mortality model proposed by Cairns, Blake and Dowd (CBD). The LC uses the log of central rate of mortality and CBD uses logit of the mortality odds as dependent variable. Analysis of comparison is done using a few techniques including Akaike information criteria (AIC) and Bayesian information criterion (BIC). From the overall results, there is no model better than the other in every aspect tested. We illustrate this via visual inspection and in sample and outof sample analysis using Malaysian mortality data from 1980 to 2017.
The growing number of multi-population mortality models in the recent years signifies the mortality improvement in
developed countries. In this case, there exists a narrowing gap of sex-differential in life expectancy between populations;
hence multi-population mortality models are designed to assimilate the correlation between populations. The present
study considers two extensions of the single-population Lee-Carter model, namely the independent model and augmented
common factor model. The independent model incorporates the information between male and female separately
whereas the augmented common factor model incorporates the information between male and female simultaneously.
The methods are demonstrated in two perspectives: First is by applying them to Malaysian mortality data and second
is by comparing the significance of the methods to the annuity pricing. The performances of the two methods are then
compared in which has been found that the augmented common factor model is more superior in terms of historical fit,
forecast performance, and annuity pricing.