On the approximation of extreme quantiles with neural networks.

Summary In this study, we propose a new parametrization for the generator of a Generative adversarial network (GAN) adapted to data from heavy-tailed distributions. We provide an analysis of the uniform error between an extreme quantile and its GAN approximation. Numerical experiments are conducted both on real and simulated data.