  Generative moment matching networks (GMMNs) are suggested for modeling the
cross-sectional dependence between stochastic processes. The stochastic
processes considered are geometric Brownian motions and ARMA-GARCH models.
Geometric Brownian motions lead to an application of pricing American basket
call options under dependence and ARMA-GARCH models lead to an application of
simulating predictive distributions. In both types of applications the benefit
of using GMMNs in comparison to parametric dependence models is highlighted and
the fact that GMMNs can produce dependent quasi-random samples with no
additional effort is exploited to obtain variance reduction.
