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2022 | OriginalPaper | Chapter

Applications of Multivariate Quasi-Random Sampling with Neural Networks

Authors : Marius Hofert, Avinash Prasad, Mu Zhu

Published in: Monte Carlo and Quasi-Monte Carlo Methods

Publisher: Springer International Publishing

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Abstract

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.

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previous chapter A Note on Transformed Fourier Systems for the Approximation of Non-periodic Signals

next chapter Artificial Neural Networks Generated by Low Discrepancy Sequences

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Title: Applications of Multivariate Quasi-Random Sampling with Neural Networks
Authors: Marius Hofert
Avinash Prasad
Mu Zhu
Publisher: Springer International Publishing
Book: Monte Carlo and Quasi-Monte Carlo Methods
Print ISBN: 978-3-030-98318-5

Electronic ISBN: 978-3-030-98319-2

Copyright Year: 2022
DOI: https://doi.org/10.1007/978-3-030-98319-2_14

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