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Article: On the estimation and diagnostic checking of the ARFIMA-HYGARCH model

TitleOn the estimation and diagnostic checking of the ARFIMA-HYGARCH model
Authors
KeywordsAsymptotic distributions
Asymptotic normality
Asymptotic properties
Auto-regressive integrated moving average
Conditional means
Issue Date2012
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csda
Citation
Computational Statistics & Data Analysis, 2012, v. 56 n. 11, p. 3632-3644 How to Cite?
AbstractThe estimation and diagnostic checking of the fractional autoregressive integrated moving average with hyperbolic generalized autoregressive conditional heteroscedasticity (ARFIMA-HYGARCH) model is considered. The ARFIMA-HYGARCH model is a long-memory model for the conditional mean that also allows for long memory in the conditional variance, the latter given by an HYGARCH model that nests both the GARCH and integrated GARCH models. It is therefore important to provide a thorough treatment of its statistical inference. Asymptotic properties of the maximum likelihood estimators under the Student's t distribution are established, and the asymptotic normality of the Gaussian quasi-maximum likelihood estimation is also derived. Two portmanteau test statistics based on the residual autocorrelations and squared residual autocorrelations are defined and their asymptotic distributions are derived. These tests will be useful in model diagnostic checking. Simulation results show that the tests have reasonable empirical size and power. © 2010 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/172499
ISSN
2015 Impact Factor: 1.179
2015 SCImago Journal Rankings: 1.283
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorKwan, Wen_US
dc.contributor.authorLi, WKen_US
dc.contributor.authorLi, Gen_US
dc.date.accessioned2012-10-30T06:22:49Z-
dc.date.available2012-10-30T06:22:49Z-
dc.date.issued2012en_US
dc.identifier.citationComputational Statistics & Data Analysis, 2012, v. 56 n. 11, p. 3632-3644en_US
dc.identifier.issn0167-9473en_US
dc.identifier.urihttp://hdl.handle.net/10722/172499-
dc.description.abstractThe estimation and diagnostic checking of the fractional autoregressive integrated moving average with hyperbolic generalized autoregressive conditional heteroscedasticity (ARFIMA-HYGARCH) model is considered. The ARFIMA-HYGARCH model is a long-memory model for the conditional mean that also allows for long memory in the conditional variance, the latter given by an HYGARCH model that nests both the GARCH and integrated GARCH models. It is therefore important to provide a thorough treatment of its statistical inference. Asymptotic properties of the maximum likelihood estimators under the Student's t distribution are established, and the asymptotic normality of the Gaussian quasi-maximum likelihood estimation is also derived. Two portmanteau test statistics based on the residual autocorrelations and squared residual autocorrelations are defined and their asymptotic distributions are derived. These tests will be useful in model diagnostic checking. Simulation results show that the tests have reasonable empirical size and power. © 2010 Elsevier B.V. All rights reserved.en_US
dc.languageengen_US
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csdaen_US
dc.relation.ispartofComputational Statistics & Data Analysisen_US
dc.subjectAsymptotic distributionsen_US
dc.subjectAsymptotic normalityen_US
dc.subjectAsymptotic propertiesen_US
dc.subjectAuto-regressive integrated moving average-
dc.subjectConditional means-
dc.titleOn the estimation and diagnostic checking of the ARFIMA-HYGARCH modelen_US
dc.typeArticleen_US
dc.identifier.emailKwan, W: ccwilson@hkcc-polyu.edu.hken_US
dc.identifier.emailLi, WK: hrntlwk@hkucc.hku.hken_US
dc.identifier.emailLi, G: gdli@hku.hk-
dc.identifier.authorityLi, WK=rp00741en_US
dc.identifier.authorityLi, G=rp00738en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1016/j.csda.2010.07.010en_US
dc.identifier.scopuseid_2-s2.0-84862012070en_US
dc.identifier.hkuros204205-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-84862012070&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume56en_US
dc.identifier.issue11en_US
dc.identifier.spage3632en_US
dc.identifier.epage3644en_US
dc.identifier.eissn1872-7352-
dc.identifier.isiWOS:000309785500043-
dc.publisher.placeNetherlandsen_US
dc.identifier.scopusauthoridLi, G=52563850500en_US
dc.identifier.scopusauthoridLi, WK=14015971200en_US
dc.identifier.scopusauthoridKwan, W=35797453900en_US
dc.identifier.citeulike7612901-

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