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Article: Quasi-random numbers for copula models

TitleQuasi-random numbers for copula models
Authors
KeywordsConditional distribution method
Copulas
Marshall–Olkin algorithm
Quasi-random numbers
Risk measures
Tail events
Issue Date2017
Citation
Statistics and Computing, 2017, v. 27, n. 5, p. 1307-1329 How to Cite?
AbstractThe present work addresses the question how sampling algorithms for commonly applied copula models can be adapted to account for quasi-random numbers. Besides sampling methods such as the conditional distribution method (based on a one-to-one transformation), it is also shown that typically faster sampling methods (based on stochastic representations) can be used to improve upon classical Monte Carlo methods when pseudo-random number generators are replaced by quasi-random number generators. This opens the door to quasi-random numbers for models well beyond independent margins or the multivariate normal distribution. Detailed examples (in the context of finance and insurance), illustrations and simulations are given and software has been developed and provided in the R packages copula and qrng.
Persistent Identifierhttp://hdl.handle.net/10722/325324
ISSN
2023 Impact Factor: 1.6
2023 SCImago Journal Rankings: 0.923
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorCambou, Mathieu-
dc.contributor.authorHofert, Marius-
dc.contributor.authorLemieux, Christiane-
dc.date.accessioned2023-02-27T07:31:33Z-
dc.date.available2023-02-27T07:31:33Z-
dc.date.issued2017-
dc.identifier.citationStatistics and Computing, 2017, v. 27, n. 5, p. 1307-1329-
dc.identifier.issn0960-3174-
dc.identifier.urihttp://hdl.handle.net/10722/325324-
dc.description.abstractThe present work addresses the question how sampling algorithms for commonly applied copula models can be adapted to account for quasi-random numbers. Besides sampling methods such as the conditional distribution method (based on a one-to-one transformation), it is also shown that typically faster sampling methods (based on stochastic representations) can be used to improve upon classical Monte Carlo methods when pseudo-random number generators are replaced by quasi-random number generators. This opens the door to quasi-random numbers for models well beyond independent margins or the multivariate normal distribution. Detailed examples (in the context of finance and insurance), illustrations and simulations are given and software has been developed and provided in the R packages copula and qrng.-
dc.languageeng-
dc.relation.ispartofStatistics and Computing-
dc.subjectConditional distribution method-
dc.subjectCopulas-
dc.subjectMarshall–Olkin algorithm-
dc.subjectQuasi-random numbers-
dc.subjectRisk measures-
dc.subjectTail events-
dc.titleQuasi-random numbers for copula models-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/s11222-016-9688-4-
dc.identifier.scopuseid_2-s2.0-84982975997-
dc.identifier.volume27-
dc.identifier.issue5-
dc.identifier.spage1307-
dc.identifier.epage1329-
dc.identifier.eissn1573-1375-
dc.identifier.isiWOS:000400831700011-

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