Speaker:

Michael Ludkovski & Jimmy Risk (PSTAT-UCSB)

Title:

Gaussian Process Models in Actuarial Science

Abstract: Gaussian processes (GP) offer a flexible framework for nonparametric regression. Originating in the machine learning context, they are quickly becoming the tool of choice for a variety of response surface modeling problems. This introductory talk will consist of two parts and two speakers. In the first part, Mike will give an introduction to GP regression and discuss its advantages compared to traditional tools. In particular, we will discuss smoothing of noisy observations, and trend/residuals decomposition. A motivating example will be presented for mortality rate estimation and forecasting. In the second part, Jimmy will focus on the problem of efficient valuation of deferred annuities under stochastic mortality. This is an example of a nested simulation problem, where GP surrogates offer an effective, data-driven approximation strategy.