Method for Forecasting Mortality Based on Key Rates

We develop a model to construct dynamic life tables based on the idea that the behavior of whole life table can be explained by a reduced number of factors. These factors are identified with some mortality rates at specific ages. These key mortality rates and model parameters estimates are obtained by applying a maximum likelihood criteria under the hypothesis of a binomial distribution of the number of deaths. We develop the single factor version of the model, which is implemented to the male USA population. The model is compared with a set of alternative well-known life tables models. To test the forecasting ability of the model we apply a battery of tests using out of sample data. Despite its simplicity, the outcomes indicate that this model is not outperformed by other more complex mortality models. Other important advantage of this model is that it can be easily implemented to address some longevity risk linked problems in the context of Solvency II.