  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 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. 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.
