Date: Thursday, 17 April 2025, at 12.30pm (CET)

Dual format: ESSEC Paris La Défense (CNIT), Room TBA
and via Zoom, please click here

This work aims to investigate the use of generative methods based on neural networks to simulate extreme events. Although they are very popular, these methods are mainly invoked in empirical works. Therefore, providing theoretical guidelines for using such models in an extreme-value context is of utmost importance. To this end, we propose an overview of some generative methods dedicated to extremes, giving theoretical tips on their tail behaviour thanks to extreme-value theory. More specifically, we focus on a new parametrization for the generator of a Generative Adversarial Network (GAN) adapted to the heavy tail framework. An analysis of the uniform error between an extreme quantile and its GAN approximation is provided: We establish that the rate of convergence of the error is mainly driven by the second-order parameter of the data distribution. The above results are illustrated on simulated data and real financial data. This is joint work with Emmanuel Gobet (CMAP, Ecole Polytechnique) and Michaël Allouche (Kaiko, Paris).

Kind regards,
Jeremy Heng, Olga Klopp, Roberto Reno, Marie Kratz and Riada Djebbar (Singapore Actuarial Society - ERM)