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dc.contributor.authorHuang, Alex YiHouen_US
dc.date.accessioned2015-12-02T02:59:22Z-
dc.date.available2015-12-02T02:59:22Z-
dc.date.issued2015-01-01en_US
dc.identifier.issn0003-6846en_US
dc.identifier.urihttp://dx.doi.org/10.1080/00036846.2015.1037439en_US
dc.identifier.urihttp://hdl.handle.net/11536/128115-
dc.description.abstractThis article proposes a threshold stochastic volatility model that generates volatility forecasts specifically designed for value at risk (VaR) estimation. The method incorporates extreme downside shocks by modelling left-tail returns separately from other returns. Left-tail returns are generated with a t-distributional process based on the historically observed conditional excess kurtosis. This specification allows VaR estimates to be generated with extreme downside impacts, yet remains empirically widely applicable. This article applies the model to daily returns of seven major stock indices over a 22-year period and compares its forecasts to those of several other forecasting methods. Based on back-testing outcomes and likelihood ratio tests, the new model provides reliable estimates and outperforms others.en_US
dc.language.isoen_USen_US
dc.subjectvalue at risken_US
dc.subjectstochastic volatilityen_US
dc.subjectthreshold modelen_US
dc.titleValue at risk estimation by threshold stochastic volatility modelen_US
dc.typeArticleen_US
dc.identifier.doi10.1080/00036846.2015.1037439en_US
dc.identifier.journalAPPLIED ECONOMICSen_US
dc.citation.volume47en_US
dc.citation.issue45en_US
dc.citation.spage4884en_US
dc.citation.epage4900en_US
dc.contributor.department資訊管理與財務金融系 註:原資管所+財金所zh_TW
dc.contributor.departmentDepartment of Information Management and Financeen_US
dc.identifier.wosnumberWOS:000357832800007en_US
dc.citation.woscount0en_US
Appears in Collections:Articles