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Multivariate Stochastic Variance Models

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorHarvey, Andrew
dc.contributor.authorRuiz Ortega, Esther
dc.contributor.authorShephard, Neil
dc.date.accessioned2009-07-15T08:56:37Z
dc.date.available2009-07-15T08:56:37Z
dc.date.issued1995
dc.descriptionPublicado además en: The Review of Economic Studies, 1994, vol. 61, n. 2, p. 247-264
dc.descriptionPublicado además en: Recent developments in Time Series, 2003, vol. 2, pp. 134-152
dc.descriptionPublicado además en: Selected Readings for Stochastic Volatility, 2005, p. 156-176, ISBN10: 0199257191, ISBN13: 9780199257195
dc.description.abstractChanges in variance, or volatility, over time can be modeled using the approach based on autoregressive conditional heteroscedasticity. Another approach is to model variance as an unobserved stochastic process. Although it is not easy to obtain the exact likelihood function for such stochastic variance models, they tie in closely with developments in finance theory and have certain statistical attractions. This article sets up a multivariate model, discusses its statistical treatment, and shows how it can be modified to capture common movements in volatility in a very natural way. The model is then fitted to daily observations on exchange rates.
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationARCH: Selected readings, 1995, p. 256-276
dc.identifier.isbn9780198774327
dc.identifier.isbn019877432X
dc.identifier.urihttps://hdl.handle.net/10016/4783
dc.language.isoeng
dc.publisherOxford University Press
dc.rights.accessRightsopen access
dc.subject.ecienciaEstadística
dc.subject.otherModel Construction and Estimation (C510)
dc.subject.otherMultiple or Simultaneous Equation Models
dc.subject.otherTime-Series Models
dc.subject.otherDynamic Quantile Regressions (C320)
dc.subject.otherForeign Exchange (F310)
dc.titleMultivariate Stochastic Variance Models
dc.typebook part*
dc.type.reviewPeerReviewed
dspace.entity.typePublication
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