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dc.contributor.authorAltay-Salih, A.en_US
dc.contributor.authorPınar, M. Ç.en_US
dc.contributor.authorLeyffer, S.en_US
dc.date.accessioned2016-02-08T10:29:22Z
dc.date.available2016-02-08T10:29:22Z
dc.date.issued2003en_US
dc.identifier.issn0036-1445
dc.identifier.urihttp://hdl.handle.net/11693/24434
dc.description.abstractThis paper proposes a constrained nonlinear programming view of generalized autoregressive conditional heteroskedasticity (GARCH) volatility estimation models in financial econometrics. These models are usually presented to the reader as unconstrained optimization models with recursive terms in the literature, whereas they actually fall into the domain of nonconvex nonlinear programming. Our results demonstrate that constrained nonlinear programming is a worthwhile exercise for GARCH models, especially for the bivariate and trivariate cases, as they offer a significant improvement in the quality of the solution of the optimization problem over the diagonal VECH and the BEKK representations of the multivariate GARCH model.en_US
dc.language.isoEnglishen_US
dc.source.titleSIAM Reviewen_US
dc.subjectTime series econometricsen_US
dc.subjectConstrained nonlinear programmingen_US
dc.subjectMultivariate GARCHen_US
dc.subjectVolatility estimationen_US
dc.subjectMaximum likelihood estimationen_US
dc.titleConstrained nonlinear programming for volatility estimation with GARCH modelsen_US
dc.typeArticleen_US
dc.departmentDepartment of Industrial Engineeringen_US
dc.citation.spage485en_US
dc.citation.epage503en_US
dc.citation.volumeNumber45en_US
dc.citation.issueNumber3en_US
dc.identifier.eissn1095-7200


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