Generative neural networks for characteristic functions
Florian Brück
In this work, we provide a simulation algorithm to simulate from a
(multivariate) characteristic function, which is only accessible in a black-box
format. We construct a generative neural network, whose loss function exploits
a specific representation of the Maximum-Mean-Discrepancy metric to directly
incorporate the targeted characteristic function. The construction is universal
in the sense that it is independent of the dimension and that it does not
require any assumptions on the given characteristic function. Furthermore,
finite sample guarantees on the approximation quality in terms of the
Maximum-Mean Discrepancy metric are derived. The method is illustrated in a
short simulation study.