Selection of an appropriate longevity model and detection of abrupt changes in longevity dynamics is critical for the insurance industry. In this project, we investigate the model choice issue by the use of regularization and bayesian statistics. Regularization is a great tool to reduce overfitting and bayesian statistics allows to account for parameter and model uncertainty.

Published paper on Smoothing and forecasting mortality using regularization and cross-validation

Working paper on Bayesian model averaging for mortality forecasting using leave-future-out validation

Collaborators: Yahia Salhi, Stephane Loisel and Pierre-Olivier Goffard

Pricing life insurance contracts with financial death and survival guarantees boils down to solving a high-dimensional pricing problem. In this project, we show that the price is the solution of a multi-dimensional partial differential equation in continuous time. By using the connection between PDE and BSDE, we solve the problem numerically by deep neural networks.

Working paper

Collaborator: Lukasz Delong

Hedging insurance and financial products is typically a dynamic problem where hedging positions and insurance valuations have to be determined at different points in time. In this context, it is important that the different valuations are consistent with each other, what is called time-consistency. Moreover, to appropriately account for the risk in the tails, we make use of quantile hedging.

Working paper

Collaborators: Valeria Bignozzi and Andreas Tsanakas