  Predicting the evolution of mortality rates plays a central role for life
insurance and pension funds. Various stochastic frameworks have been developed
to model mortality patterns taking into account the main stylized facts driving
these patterns. However, relying on the prediction of one specific model can be
too restrictive and lead to some well documented drawbacks including model
misspecification, parameter uncertainty and overfitting. To address these
issues we first consider mortality modelling in a Bayesian Negative-Binomial
framework to account for overdispersion and the uncertainty about the parameter
estimates in a natural and coherent way. Model averaging techniques, which
consists in combining the predictions of several models, are then considered as
a response to model misspecifications. In this paper, we propose two methods
based on leave-future-out validation which are compared to the standard
Bayesian model averaging (BMA) based on marginal likelihood. Using
out-of-sample errors is a well-known workaround for overfitting issues. We show
that it also produces better forecasts. An intensive numerical study is carried
out over a large range of simulation setups to compare the performances of the
proposed methodologies. An illustration is then proposed on real-life mortality
datasets which includes a sensitivity analysis to a Covid-type scenario.
Overall, we found that both methods based on out-of-sample criterion outperform
the standard BMA approach in terms of prediction performance and robustness.
