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Article: Modeling threshold conditional heteroscedasticity with regime-dependent skewness and kurtosis

TitleModeling threshold conditional heteroscedasticity with regime-dependent skewness and kurtosis
Authors
KeywordsGramCharlier density
Kurtosis
Lagrange multiplier test
Skewness
TGARCH-GC model
Threshold GARCH model
Issue Date2011
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csda
Citation
Computational Statistics And Data Analysis, 2011, v. 55 n. 9, p. 2590-2604 How to Cite?
AbstractConstruction of nonlinear time series models with a flexible probabilistic structure is an important challenge for statisticians. Applications of such a time series model include ecology, economics and finance. In this paper we consider a threshold model for all the first four conditional moments of a time series. The nonlinear structure in the conditional mean is specified by a threshold autoregression and that of the conditional variance by a threshold generalized autoregressive conditional heteroscedastic (GARCH) model. There are many options for the conditional innovation density in the modeling of the skewness and kurtosis such as the GramCharlier (GC) density and the skewed-t density. The GramCharlier (GC) density allows explicit modeling of the skewness and kurtosis parameters and therefore is the main focus of this paper. However, its performance is compared with that of Hansen's skewed-t distribution in the data analysis section of the paper. The regime-dependent feature for the first four conditional moments allows more flexibility in modeling and provides better insights into the structure of a time series. A Lagrange multiplier (LM) test is developed for testing for the presence of threshold structure. The test statistic is similar to the classical tests for the presence of a threshold structure but allowing for a more general regime-dependent structure. The new model and the LM test are illustrated using the Dow Jones Industrial Average, the Hong Kong Hang Seng Index and the Yen/US exchange rate. © 2011 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/134473
ISSN
2015 Impact Factor: 1.179
2015 SCImago Journal Rankings: 1.283
ISI Accession Number ID
Funding AgencyGrant Number
Hong Kong Research Grants CouncilHKU7036/06P
University of Hong Kong
Hong Kong University Grid PointSEG HKU09
Funding Information:

W.K. Li's research is partially supported by Hong Kong Research Grants Council GRF grant HKU7036/06P and Philip L.H. Yu's research is supported by a small project funding from the University of Hong Kong. This project is also supported in part by the Hong Kong University Grid Point [UGC Special Equipment Grant (SEG HKU09)]. We thank an associate editor and two anonymous referees for comments that led to the improvement of the paper.

References

 

DC FieldValueLanguage
dc.contributor.authorCheng, Xen_HK
dc.contributor.authorLi, WKen_HK
dc.contributor.authorYu, PLHen_HK
dc.contributor.authorZhou, Xen_HK
dc.contributor.authorWang, Cen_HK
dc.contributor.authorLo, PHen_HK
dc.date.accessioned2011-06-17T09:21:30Z-
dc.date.available2011-06-17T09:21:30Z-
dc.date.issued2011en_HK
dc.identifier.citationComputational Statistics And Data Analysis, 2011, v. 55 n. 9, p. 2590-2604en_HK
dc.identifier.issn0167-9473en_HK
dc.identifier.urihttp://hdl.handle.net/10722/134473-
dc.description.abstractConstruction of nonlinear time series models with a flexible probabilistic structure is an important challenge for statisticians. Applications of such a time series model include ecology, economics and finance. In this paper we consider a threshold model for all the first four conditional moments of a time series. The nonlinear structure in the conditional mean is specified by a threshold autoregression and that of the conditional variance by a threshold generalized autoregressive conditional heteroscedastic (GARCH) model. There are many options for the conditional innovation density in the modeling of the skewness and kurtosis such as the GramCharlier (GC) density and the skewed-t density. The GramCharlier (GC) density allows explicit modeling of the skewness and kurtosis parameters and therefore is the main focus of this paper. However, its performance is compared with that of Hansen's skewed-t distribution in the data analysis section of the paper. The regime-dependent feature for the first four conditional moments allows more flexibility in modeling and provides better insights into the structure of a time series. A Lagrange multiplier (LM) test is developed for testing for the presence of threshold structure. The test statistic is similar to the classical tests for the presence of a threshold structure but allowing for a more general regime-dependent structure. The new model and the LM test are illustrated using the Dow Jones Industrial Average, the Hong Kong Hang Seng Index and the Yen/US exchange rate. © 2011 Elsevier B.V. All rights reserved.en_HK
dc.languageengen_US
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csdaen_HK
dc.relation.ispartofComputational Statistics and Data Analysisen_HK
dc.subjectGramCharlier densityen_HK
dc.subjectKurtosisen_HK
dc.subjectLagrange multiplier testen_HK
dc.subjectSkewnessen_HK
dc.subjectTGARCH-GC modelen_HK
dc.subjectThreshold GARCH modelen_HK
dc.titleModeling threshold conditional heteroscedasticity with regime-dependent skewness and kurtosisen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0167-9473&volume=55&issue=9&spage=2590&epage=2604&date=2011&atitle=Modeling+threshold+conditional+heteroscedasticity+with+regime-dependent+skewness+and+kurtosis-
dc.identifier.emailLi, WK: hrntlwk@hku.hken_HK
dc.identifier.emailYu, PLH: plhyu@hkucc.hku.hken_HK
dc.identifier.authorityLi, WK=rp00741en_HK
dc.identifier.authorityYu, PLH=rp00835en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.csda.2011.03.008en_HK
dc.identifier.scopuseid_2-s2.0-79956151592en_HK
dc.identifier.hkuros185844en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79956151592&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume55en_HK
dc.identifier.issue9en_HK
dc.identifier.spage2590en_HK
dc.identifier.epage2604en_HK
dc.identifier.isiWOS:000291916100005-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridCheng, X=26429080500en_HK
dc.identifier.scopusauthoridLi, WK=14015971200en_HK
dc.identifier.scopusauthoridYu, PLH=7403599794en_HK
dc.identifier.scopusauthoridZhou, X=37076083800en_HK
dc.identifier.scopusauthoridWang, C=37065253600en_HK
dc.identifier.scopusauthoridLo, PH=37075056300en_HK
dc.identifier.citeulike9164237-

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