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Article: Model risk in VaR estimation: An empirical study

TitleModel risk in VaR estimation: An empirical study
Authors
KeywordsGARCH
Model risk
Statistical tests
Value-at-Risk
Issue Date2006
PublisherWorld Scientific Publishing Co Pte Ltd. The Journal's web site is located at http://www.worldscinet.com/ijitdm/ijitdm.shtml
Citation
International Journal Of Information Technology And Decision Making, 2006, v. 5 n. 3, p. 503-512 How to Cite?
AbstractThis paper studies the model risk; the risk of selecting a model for estimating the Value-at-Risk (VaR). By considering four GARCH-type volatility processes exponentially weighted moving average (EWMA), generalized autoregressive conditional heteroskedasticity (GARCH), exponential GARCH (EGARCH), and fractionally integrated GARCH (FIGARCH), we evaluate the performance of the estimated VaRs using statistical tests including the Kupiec's likelihood ratio (LR) test, the Christoffersen's LR test, the CHI (Christoffersen, Hahn, and Inoue) specification test, and the CHI nonnested test. The empirical study based on Shanghai Stock Exchange A Share Index indicates that both EGARCH and FIGARCH models perform much better than the other two in VaR computation and that the two CHI tests are more suitable for analyzing model risk. © World Scientific Publishing Company.
Persistent Identifierhttp://hdl.handle.net/10722/82739
ISSN
2015 Impact Factor: 1.183
2015 SCImago Journal Rankings: 0.675
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorYao, Jen_HK
dc.contributor.authorLi, ZFen_HK
dc.contributor.authorNg, KWen_HK
dc.date.accessioned2010-09-06T08:32:51Z-
dc.date.available2010-09-06T08:32:51Z-
dc.date.issued2006en_HK
dc.identifier.citationInternational Journal Of Information Technology And Decision Making, 2006, v. 5 n. 3, p. 503-512en_HK
dc.identifier.issn0219-6220en_HK
dc.identifier.urihttp://hdl.handle.net/10722/82739-
dc.description.abstractThis paper studies the model risk; the risk of selecting a model for estimating the Value-at-Risk (VaR). By considering four GARCH-type volatility processes exponentially weighted moving average (EWMA), generalized autoregressive conditional heteroskedasticity (GARCH), exponential GARCH (EGARCH), and fractionally integrated GARCH (FIGARCH), we evaluate the performance of the estimated VaRs using statistical tests including the Kupiec's likelihood ratio (LR) test, the Christoffersen's LR test, the CHI (Christoffersen, Hahn, and Inoue) specification test, and the CHI nonnested test. The empirical study based on Shanghai Stock Exchange A Share Index indicates that both EGARCH and FIGARCH models perform much better than the other two in VaR computation and that the two CHI tests are more suitable for analyzing model risk. © World Scientific Publishing Company.en_HK
dc.languageengen_HK
dc.publisherWorld Scientific Publishing Co Pte Ltd. The Journal's web site is located at http://www.worldscinet.com/ijitdm/ijitdm.shtmlen_HK
dc.relation.ispartofInternational Journal of Information Technology and Decision Makingen_HK
dc.subjectGARCHen_HK
dc.subjectModel risken_HK
dc.subjectStatistical testsen_HK
dc.subjectValue-at-Risken_HK
dc.titleModel risk in VaR estimation: An empirical studyen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0219-6220&volume=5&issue=3&spage=503&epage=512&date=2006&atitle=Model+risk+in+VaR+estimation:+An+empirical+studyen_HK
dc.identifier.emailNg, KW: kaing@hkucc.hku.hken_HK
dc.identifier.authorityNg, KW=rp00765en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1142/S021962200600209Xen_HK
dc.identifier.scopuseid_2-s2.0-33749419645en_HK
dc.identifier.hkuros148981en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33749419645&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume5en_HK
dc.identifier.issue3en_HK
dc.identifier.spage503en_HK
dc.identifier.epage512en_HK
dc.identifier.isiWOS:000241441600008-
dc.publisher.placeSingaporeen_HK
dc.identifier.scopusauthoridYao, J=7403503934en_HK
dc.identifier.scopusauthoridLi, ZF=17434361900en_HK
dc.identifier.scopusauthoridNg, KW=7403178774en_HK

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