论文标题
使用神经网络的多元准随机抽样应用
Applications of multivariate quasi-random sampling with neural networks
论文作者
论文摘要
建议使用生成力矩匹配网络（GMMN）来建模随机过程之间的横截面依赖性。所考虑的随机过程是几何布朗尼运动和Arma-Garch模型。几何布朗尼动作导致了定价美国篮子呼叫选项的应用，而Arma-Garch模型则导致了模拟预测分布的应用。在两种类型的应用程序中，与参数依赖模型相比，使用GMMN的好处是强调的，并且GMMN可以生产依赖性的准随机样品而没有额外努力来利用差异来减少方差。
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.
