Seminar by Jackie Wong

Please Note: This seminar will be given in person.

Bayesian model comparison for mortality forecasting  (Coherent Quantification of Longevity Risk) 

Stochastic models are appealing for mortality forecasting in their ability to generate intervals that represent uncertainties underlying the forecasts. This is particularly crucial in the quantification of longevity risks which translates into monetary terms in actuarial computations. In this talk, we present a fully Bayesian implementation of the age-period-cohort-improvement (APCI) model with overdispersion, which is compared with the well-known Lee–Carter model with cohorts. We show that naive prior specification can yield misleading inferences, where we propose Laplace prior as an elegant solution. We also perform model averaging to incorporate model uncertainty. Overall, our approach allows coherent inclusion of multiple sources of uncertainty, producing well-calibrated probabilistic intervals. Our findings indicate that the APCI model offers better fit and forecast for England and Wales data spanning 1961–2002.