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.

翻译：为模拟不同随机学过程之间的跨部门依赖性,建议建立生成时匹配网络(GMMNs),考虑的随机学过程是几何布朗运动和ARMA-GARCH模型,几何布朗运动导致采用依赖性美国篮子呼叫选项定价,ARMA-GARCH模型导致采用模拟预测分布。在这两类应用中,都强调了使用GMMNs与参数依赖性模型相比的好处,以及GMMNs可以产生依赖性准随机样本,而没有作出额外努力来减少差异。