  An essential input of annuity pricing is the future retiree mortality. From
observed age-specific mortality data, modeling and forecasting can be taken
place in two routes. On the one hand, we can first truncate the available data
to retiree ages and then produce mortality forecasts based on a partial
age-range model. On the other hand, with all available data, we can first apply
a full age-range model to produce forecasts and then truncate the mortality
forecasts to retiree ages. We investigate the difference in modeling the
logarithmic transformation of the central mortality rates between a partial
age-range and a full age-range model, using data from mainly developed
countries in the Human Mortality Database (2020). By evaluating and comparing
the short-term point and interval forecast accuracies, we recommend the first
strategy by truncating all available data to retiree ages and then produce
mortality forecasts. However, when considering the long-term forecasts, it is
unclear which strategy is better since it is more difficult to find a model and
parameters that are optimal. This is a disadvantage of using methods based on
time series extrapolation for long-term forecasting. Instead, an expectation
approach, in which experts set a future target, could be considered, noting
that this method has also had limited success in the past.
