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dc.contributor.authorCai, Y
dc.contributor.authorStander, Julian
dc.date.accessioned2019-04-08T10:21:42Z
dc.date.issued2019-05-03
dc.identifier.issn1479-8417
dc.identifier.issn1479-8417
dc.identifier.urihttp://hdl.handle.net/10026.1/13682
dc.description.abstract

<jats:title>Abstract</jats:title><jats:p>We consider multiple threshold value-at-risk (VaRt) estimation and density forecasting for financial data following a threshold GARCH model. We develop an α-quantile quasi-maximum likelihood estimation (QMLE) method for VaRt by showing that the associated density function is an α-quantile density and belongs to the tick-exponential family. This establishes that our estimator is consistent for the parameters of VaRt. We propose a density forecasting method for quantile models based on VaRt at a single nonextreme level, which overcomes some limitations of existing forecasting methods with quantile models. We find that for heavy-tailed financial data our α-quantile QMLE method for VaRt outperforms the Gaussian QMLE method for volatility. We also find that density forecasts based on VaRt outperform those based on the volatility of financial data. Empirical work on market returns shows that our approach also outperforms some benchmark models for density forecasting of financial returns.</jats:p>

dc.format.extent395-424
dc.languageen
dc.language.isoen
dc.publisherOxford University Press (OUP)
dc.subjectalpha-quantile density
dc.subjectdensity forecasting
dc.subjectQMLE
dc.subjectthreshold
dc.subjectvalue-at-risk (VaR)
dc.titleThe threshold GARCH model: estimation and density forecasting for financial returns
dc.typejournal-article
dc.typeJournal Article
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000556573700008&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.issue2
plymouth.volume18
plymouth.publication-statusPublished
plymouth.journalJournal of Financial Econometrics
dc.identifier.doi10.1093/jjfinec/nbz014
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Admin Group - REF
plymouth.organisational-group/Plymouth/Admin Group - REF/REF Admin Group - FoSE
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/EXTENDED UoA 10 - Mathematical Sciences
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA10 Mathematical Sciences
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dcterms.dateAccepted2019-03-25
dc.rights.embargodate2021-5-2
dc.identifier.eissn1479-8417
dc.rights.embargoperiodNot known
rioxxterms.versionAccepted Manuscript
rioxxterms.versionofrecord10.1093/jjfinec/nbz014
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2019-05-03
rioxxterms.typeJournal Article/Review


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