  We propose the use of statistical emulators for the purpose of valuing
mortality-linked contracts in stochastic mortality models. Such models
typically require (nested) evaluation of expected values of nonlinear
functionals of multi-dimensional stochastic processes. Except in the simplest
cases, no closed-form expressions are available, necessitating numerical
approximation. Rather than building ad hoc analytic approximations, we advocate
the use of modern statistical tools from machine learning to generate a
flexible, non-parametric surrogate for the true mappings. This method allows
performance guarantees regarding approximation accuracy and removes the need
for nested simulation. We illustrate our approach with case studies involving
(i) a Lee-Carter model with mortality shocks, (ii) index-based static hedging
with longevity basis risk; (iii) a Cairns-Blake-Dowd stochastic survival
probability model.
