Estimation and inference in univariate and multivariate log-GARCH-X models when the conditional density is unknown

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dc.contributor.author Sucarrat, Genaro Daniel
dc.contributor.author Gronneberg, Steffen
dc.contributor.author Escribano, Álvaro
dc.date.accessioned 2017-09-04T14:57:09Z
dc.date.available 2018-08-01T22:00:05Z
dc.date.issued 2016-08-01
dc.identifier.bibliographicCitation Sucarrat, G., Grønneberg, S. and Escribano, A. (2016). Estimation and inference in univariate and multivariate log- GARCH-X models when the conditional density is unknown. Computational Statistics & Data Analysis, v. 100, pp. 582-594.
dc.identifier.issn 0167-9473
dc.identifier.uri http://hdl.handle.net/10016/25169
dc.description.abstract A general framework for the estimation and inference in univariate and multivariate Generalised log-ARCH-X (i.e. log-GARCH-X) models when the conditional density is unknown is proposed. The framework employs (V)ARMA-X representations and relies on a bias-adjustment in the log-volatility intercept. The bias is induced by (V)ARMA estimators, but the remaining parameters can be estimated in a consistent and asymptotically normal manner by usual (V)ARMA methods. An estimator of the bias and a closed-form expression for the asymptotic variance is derived. Adding covariates and/or increasing the dimension of the model does not change the structure of the problem, so the univariate bias adjustment procedure is applicable not only in univariate log-GARCH-X models estimated by the ARMA-X representation, but also in multivariate log-GARCH-X models estimated by VARMA-X representations. Extensive simulations verify the properties of the log-moment estimator, and an empirical application illustrates the usefulness of the methods. (C) 2015 Elsevier B.V. All rights reserved.
dc.description.sponsorship Support from the Ministerio Economia y Competitividad (Spain), grant MDM 2014-0431, funding from the 6th European Community Framework Programme, MICIN ECO2009-08308, and funding from The Bank of Spain Excellence Program are gratefully acknowledged.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Elsevier
dc.rights © Elsevier
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Log-garch-x
dc.subject.other Arma-x
dc.subject.other Multivariate log-garch-x
dc.subject.other Varma-x
dc.subject.other Maximum-likelihood-estimation
dc.subject.other Asymptotic theory
dc.subject.other Volatility
dc.subject.other Heteroskedasticity
dc.subject.other Prices
dc.title Estimation and inference in univariate and multivariate log-GARCH-X models when the conditional density is unknown
dc.type article
dc.relation.publisherversion https://doi.org/10.1016/j.csda.2015.12.005
dc.subject.eciencia Economía
dc.identifier.doi https://doi.org/10.1016/j.csda.2015.12.005
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. MDM 2014-0431
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 582
dc.identifier.publicationlastpage 594
dc.identifier.publicationtitle Computational statistics and data analysis
dc.identifier.publicationvolume 100
dc.identifier.uxxi AR/0000018032
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