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dc.contributor.author Gallant, AR
dc.contributor.author Hsiehb, D
dc.contributor.author Tauchen, G
dc.date.accessioned 2010-03-09T15:42:56Z
dc.date.issued 1997-11-01
dc.identifier.citation Journal of Econometrics, 1997, 81 (1), pp. 159 - 192
dc.identifier.issn 0304-4076
dc.identifier.uri http://hdl.handle.net/10161/2057
dc.description.abstract Efficient method of moments (EMM) is used to fit the standard stochastic volatility model and various extensions to several daily financial time series. EMM matches to the score of a model determined by data analysis called the score generator. Discrepancies reveal characteristics of data that stochastic volatility models cannot approximate. The two score generators employed here are 'semiparametric ARCH' and 'nonlinear nonparametric'. With the first, the standard model is rejected, although some extensions are accepted. With the second, all versions are rejected. The extensions required for an adequate fit are so elaborate that nonparametric specifications are probably more convenient. © 1997 Elsevier Science S.A.
dc.format.extent 159 - 192
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.relation.ispartof Journal of Econometrics
dc.title Estimation of stochastic volatility models with diagnostics
dc.type Journal Article
dc.department Economics
pubs.issue 1
pubs.organisational-group /Duke
pubs.organisational-group /Duke/Faculty
pubs.organisational-group /Duke/Fuqua School of Business
pubs.organisational-group /Duke/Trinity College of Arts & Sciences
pubs.organisational-group /Duke/Trinity College of Arts & Sciences/Economics
pubs.publication-status Published
pubs.volume 81

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