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We propose the use of statistical emulators for the purpose of valuingmortality-linked contracts in stochastic mortality models. Such modelstypically require (nested) evaluation of expected values of nonlinearfunctionals of multi-dimensional stochastic processes. Except in the simplestcases, no closed-form expressions are available, necessitating numericalapproximation. Rather than building ad hoc analytic approximations, we advocatethe use of modern statistical tools from machine learning to generate aflexible, non-parametric surrogate for the true mappings. This method allowsperformance guarantees regarding approximation accuracy and removes the needfor nested simulation. We illustrate our approach with case studies involving(i) a Lee-Carter model with mortality shocks, (ii) index-based static hedgingwith longevity basis risk; (iii) a Cairns-Blake-Dowd stochastic survivalprobability model.

Statistical Emulators for Pricing and Hedging Longevity Risk Products