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dc.contributor.authorGrobys, Klausen
dc.date.accessioned2012-03-21-
dc.date.accessioned2012-05-22T16:44:33Z-
dc.date.available2012-05-22T16:44:33Z-
dc.date.issued2012-
dc.identifier.citation|aJournal of Finance and Investment Analysis|c2241-0996|v1|h1|nInternational Scientific Press|y2012|p55-67en
dc.identifier.urihttp://hdl.handle.net/10419/58007-
dc.description.abstractThis contribution studies the application of heteroskedasticity robust estimation of Vector-Autoregressive (VAR) models. VAR models have become one of the most applied models for the analysis of multivariate time series. Econometric standard software usually provides parameter estimators that are not robust against unknown forms of heteroskedasticity. Different bootstrap methodologies are available which are able to generate heteroskedasticity robust parameter estimates. However, common literature is mostly focused on univariate time series models. This study applies a natural extension of the non-parametric pairs bootstrap methodology to different VAR models, taking into account empirical stock market data of the FTSE 100, DAX 30 and S&P 500. A comparison shows that the t-values of the bootstrap models' parameters are considerably lower than the ordinary ones and that the determinants of the covariance matrices are clearly smaller.en
dc.language.isoengen
dc.publisher|aInternational Scientific Pressen
dc.subject.jelC13en
dc.subject.jelC32en
dc.subject.jelC51en
dc.subject.jelG10en
dc.subject.ddc330en
dc.subject.keywordVAR modelsen
dc.subject.keywordpairs bootstrappingen
dc.subject.keywordheteroskedasticity robust estimationen
dc.subject.keywordnon-parametric approachen
dc.subject.keywordstock market dataen
dc.titleA non-parametric approach of heteroskedasticity robust estimation of Vector-Autoregressive (VAR) models-
dc.typeArticleen
dc.identifier.ppn688928919en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
econstor.citation.journaltitleJournal of Finance and Investment Analysisen
econstor.citation.issn2241-0996en
econstor.citation.volume1en
econstor.citation.issue1en
econstor.citation.publisherInternational Scientific Pressen
econstor.citation.year2012en
econstor.citation.startpage55en
econstor.citation.endpage67en

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