File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: A New Pearson-Type QMLE for Conditionally Heteroscedastic Models

TitleA New Pearson-Type QMLE for Conditionally Heteroscedastic Models
Authors
Issue Date2015
PublisherTaylor & Francis Inc. The Journal's web site is located at http://www.tandfonline.com/loi/ubes20
Citation
Journal of Business and Economic Statistics, 2015, v. 33 n. 4, p. 552-565 How to Cite?
AbstractThis article proposes a novel Pearson-type quasi-maximum likelihood estimator (QMLE) of GARCH(p, q) models. Unlike the existing Gaussian QMLE, Laplacian QMLE, generalized non-Gaussian QMLE, or LAD estimator, our Pearsonian QMLE (PQMLE) captures not just the heavy-tailed but also the skewed innovations. Under strict stationarity and some weak moment conditions, the strong consistency and asymptotic normality of the PQMLE are obtained. With no further efforts, the PQMLE can be applied to other conditionally heteroscedastic models. A simulation study is carried out to assess the performance of the PQMLE. Two applications to four major stock indexes and two exchange rates further highlight the importance of our new method. Heavy-tailed and skewed innovations are often observed together in practice, and the PQMLE now gives us a systematic way to capture these two coexisting features. © 2015 American Statistical Association.
Persistent Identifierhttp://hdl.handle.net/10722/222907
ISSN
2015 Impact Factor: 1.648
2015 SCImago Journal Rankings: 2.566
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhu, K-
dc.contributor.authorLi, WK-
dc.date.accessioned2016-02-12T06:35:32Z-
dc.date.available2016-02-12T06:35:32Z-
dc.date.issued2015-
dc.identifier.citationJournal of Business and Economic Statistics, 2015, v. 33 n. 4, p. 552-565-
dc.identifier.issn0735-0015-
dc.identifier.urihttp://hdl.handle.net/10722/222907-
dc.description.abstractThis article proposes a novel Pearson-type quasi-maximum likelihood estimator (QMLE) of GARCH(p, q) models. Unlike the existing Gaussian QMLE, Laplacian QMLE, generalized non-Gaussian QMLE, or LAD estimator, our Pearsonian QMLE (PQMLE) captures not just the heavy-tailed but also the skewed innovations. Under strict stationarity and some weak moment conditions, the strong consistency and asymptotic normality of the PQMLE are obtained. With no further efforts, the PQMLE can be applied to other conditionally heteroscedastic models. A simulation study is carried out to assess the performance of the PQMLE. Two applications to four major stock indexes and two exchange rates further highlight the importance of our new method. Heavy-tailed and skewed innovations are often observed together in practice, and the PQMLE now gives us a systematic way to capture these two coexisting features. © 2015 American Statistical Association.-
dc.languageeng-
dc.publisherTaylor & Francis Inc. The Journal's web site is located at http://www.tandfonline.com/loi/ubes20-
dc.relation.ispartofJournal of Business and Economic Statistics-
dc.rightsThis is an electronic version of an article published in Journal of Business and Economic Statistics, 2015, v. 33 n. 4, p. 552-565. The article is available online at: http://dx.doi.org/10.1080/07350015.2014.977446-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleA New Pearson-Type QMLE for Conditionally Heteroscedastic Models-
dc.typeArticle-
dc.identifier.emailZhu, K: mazhuke@hku.hk-
dc.identifier.emailLi, WK: hrntlwk@hkucc.hku.hk-
dc.identifier.authorityZhu, K=rp02199-
dc.identifier.authorityLi, WK=rp00741-
dc.description.naturepostprint-
dc.identifier.doi10.1080/07350015.2014.977446-
dc.identifier.scopuseid_2-s2.0-84945269825-
dc.identifier.hkuros256875-
dc.identifier.volume33-
dc.identifier.issue4-
dc.identifier.spage552-
dc.identifier.epage565-
dc.identifier.isiWOS:000363663200007-
dc.publisher.placeUnited States-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats