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Article: On some models for value-at-risk

TitleOn some models for value-at-risk
Authors
KeywordsGARCH model
Mixtures
Threshold models
Value-at-risk
Issue Date2010
PublisherTaylor & Francis Inc. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/07474938.asp
Citation
Econometric Reviews, 2010, v. 29 n. 5, p. 622-641 How to Cite?
AbstractThe idea of statistical learning can be applied in financial risk management. In recent years, value-at-risk (VaR) has become the standard tool for market risk measurement and management. For better VaR estimation, Engle and Manganelli (2004) introduced the conditional autoregressive value-at-risk (CAViaR) model to estimate the VaR directly by quantile regression. To entertain the nonlinearity and structural change in the VaR, we extend the CAViaR idea using two approaches: the threshold GARCH (TGARCH) and the mixture-GARCH models. The estimation method of these models are proposed. Our models should possess all the advantages of the CAViaR model and enhance the nonlinear structure. The methods are applied to the S&P500, Hang Seng, Nikkei and Nasdaq indices to illustrate our models. © Taylor & Francis Group, LLC.
Persistent Identifierhttp://hdl.handle.net/10722/125401
ISSN
2015 Impact Factor: 1.817
2015 SCImago Journal Rankings: 1.836
ISI Accession Number ID
Funding AgencyGrant Number
University of Hong Kong
Croucher Foundation
Hong Kong Research Grant CouncilHKU7036/06P
Funding Information:

Philip L. H. Yu would like to thank a small project fund from the University of Hong Kong for partial support. W. K. Li would like to thank the Croucher Foundation for awarding a Senior Research Fellowship (2003-2004) and the Hong Kong Research Grant Council grant HKU7036/06P for partial support. The authors would also like to thank two referees for their valuable advices.

References

 

DC FieldValueLanguage
dc.contributor.authorYu, PLHen_HK
dc.contributor.authorLi, WKen_HK
dc.contributor.authorJin, Sen_HK
dc.date.accessioned2010-10-31T11:29:19Z-
dc.date.available2010-10-31T11:29:19Z-
dc.date.issued2010en_HK
dc.identifier.citationEconometric Reviews, 2010, v. 29 n. 5, p. 622-641en_HK
dc.identifier.issn0747-4938en_HK
dc.identifier.urihttp://hdl.handle.net/10722/125401-
dc.description.abstractThe idea of statistical learning can be applied in financial risk management. In recent years, value-at-risk (VaR) has become the standard tool for market risk measurement and management. For better VaR estimation, Engle and Manganelli (2004) introduced the conditional autoregressive value-at-risk (CAViaR) model to estimate the VaR directly by quantile regression. To entertain the nonlinearity and structural change in the VaR, we extend the CAViaR idea using two approaches: the threshold GARCH (TGARCH) and the mixture-GARCH models. The estimation method of these models are proposed. Our models should possess all the advantages of the CAViaR model and enhance the nonlinear structure. The methods are applied to the S&P500, Hang Seng, Nikkei and Nasdaq indices to illustrate our models. © Taylor & Francis Group, LLC.en_HK
dc.languageengen_HK
dc.publisherTaylor & Francis Inc. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/07474938.aspen_HK
dc.relation.ispartofEconometric Reviewsen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectGARCH modelen_HK
dc.subjectMixturesen_HK
dc.subjectThreshold modelsen_HK
dc.subjectValue-at-risken_HK
dc.titleOn some models for value-at-risken_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0747-4938&volume=29&spage=622&epage=641&date=2010&atitle=On+some+models+for+value-at-risken_HK
dc.identifier.emailYu, PLH: plhyu@hkucc.hku.hken_HK
dc.identifier.emailLi, WK: hrntlwk@hku.hken_HK
dc.identifier.authorityYu, PLH=rp00835en_HK
dc.identifier.authorityLi, WK=rp00741en_HK
dc.description.naturepostprint-
dc.identifier.doi10.1080/07474938.2010.481972en_HK
dc.identifier.scopuseid_2-s2.0-77956758299en_HK
dc.identifier.hkuros181200en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77956758299&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume29en_HK
dc.identifier.issue5en_HK
dc.identifier.spage622en_HK
dc.identifier.epage641en_HK
dc.identifier.isiWOS:000281853600007-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridYu, PLH=7403599794en_HK
dc.identifier.scopusauthoridLi, WK=14015971200en_HK
dc.identifier.scopusauthoridJin, S=35757710200en_HK

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