  There have been significant efforts devoted to solving the longevity risk
given that a continuous growth in population ageing has become a severe issue
for many developed countries over the past few decades. The Cairns-Blake-Dowd
(CBD) model, which incorporates cohort effects parameters in its parsimonious
design, is one of the most well-known approaches for mortality modelling at
higher ages and longevity risk. This article proposes a novel mixed-effects
time-series approach for mortality modelling and forecasting with
considerations of age groups dependence and random cohort effects parameters.
The proposed model can disclose more mortality data information and provide a
natural quantification of the model parameters uncertainties with no
pre-specified constraint required for estimating the cohort effects parameters.
The abilities of the proposed approach are demonstrated through two
applications with empirical male and female mortality data. The proposed
approach shows remarkable improvements in terms of forecast accuracy compared
to the CBD model in the short-, mid-and long-term forecasting using mortality
data of several developed countries in the numerical examples.
