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Conference Paper: Applications of Multivariate Quasi-Random Sampling with Neural Networks

TitleApplications of Multivariate Quasi-Random Sampling with Neural Networks
Authors
KeywordsAmerican basket option pricing
ARMA–GARCH
Copulas
Generative moment matching networks
Predictive distributions
Quasi-random sampling
Issue Date2022
PublisherSpringer
Citation
14th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing (MCQMC 2020), virtual, online, 10-14 August 2020. In Keller, A (Ed.), Monte Carlo and Quasi-Monte Carlo Methods: MCQMC 2020, Oxford, United Kingdom, August 10-14, p. 273-289. Cham: Springer, 2022 How to Cite?
AbstractGenerative 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.
Persistent Identifierhttp://hdl.handle.net/10722/325561
ISBN
ISSN
2023 SCImago Journal Rankings: 0.168
ISI Accession Number ID
Series/Report no.Springer Proceedings in Mathematics & Statistics ; 387

 

DC FieldValueLanguage
dc.contributor.authorHofert, Marius-
dc.contributor.authorPrasad, Avinash-
dc.contributor.authorZhu, Mu-
dc.date.accessioned2023-02-27T07:34:18Z-
dc.date.available2023-02-27T07:34:18Z-
dc.date.issued2022-
dc.identifier.citation14th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing (MCQMC 2020), virtual, online, 10-14 August 2020. In Keller, A (Ed.), Monte Carlo and Quasi-Monte Carlo Methods: MCQMC 2020, Oxford, United Kingdom, August 10-14, p. 273-289. Cham: Springer, 2022-
dc.identifier.isbn9783030983185-
dc.identifier.issn2194-1009-
dc.identifier.urihttp://hdl.handle.net/10722/325561-
dc.description.abstractGenerative 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.-
dc.languageeng-
dc.publisherSpringer-
dc.relation.ispartofMonte Carlo and Quasi-Monte Carlo Methods: MCQMC 2020, Oxford, United Kingdom, August 10-14-
dc.relation.ispartofseriesSpringer Proceedings in Mathematics & Statistics ; 387-
dc.subjectAmerican basket option pricing-
dc.subjectARMA–GARCH-
dc.subjectCopulas-
dc.subjectGenerative moment matching networks-
dc.subjectPredictive distributions-
dc.subjectQuasi-random sampling-
dc.titleApplications of Multivariate Quasi-Random Sampling with Neural Networks-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-030-98319-2_14-
dc.identifier.scopuseid_2-s2.0-85131127715-
dc.identifier.spage273-
dc.identifier.epage289-
dc.identifier.eissn2194-1017-
dc.identifier.isiWOS:000871749800014-
dc.publisher.placeCham-

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