File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Empirical analysis of GARCH models in value at risk estimation

TitleEmpirical analysis of GARCH models in value at risk estimation
Authors
KeywordsGARCH model
Long memory
Market risk
Issue Date2006
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/intfin
Citation
Journal Of International Financial Markets, Institutions And Money, 2006, v. 16 n. 2, p. 180-197 How to Cite?
AbstractThis paper studies seven GARCH models, including RiskMetrics and two long memory GARCH models, in Value at Risk (VaR) estimation. Both long and short positions of investment were considered. The seven models were applied to 12 market indices and four foreign exchange rates to assess each model in estimating VaR at various confidence levels. The results indicate that both stationary and fractionally integrated GARCH models outperform RiskMetrics in estimating 1% VaR. Although most return series show fat-tailed distribution and satisfy the long memory property, it is more important to consider a model with fat-tailed error in estimating VaR. Asymmetric behavior is also discovered in the stock market data that t-error models give better 1% VaR estimates than normal-error models in long position, but not in short position. No such asymmetry is observed in the exchange rate data. © 2005 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/82869
ISSN
2015 Impact Factor: 1.051
2015 SCImago Journal Rankings: 0.872
References

 

DC FieldValueLanguage
dc.contributor.authorSo, MKPen_HK
dc.contributor.authorYu, PLHen_HK
dc.date.accessioned2010-09-06T08:34:20Z-
dc.date.available2010-09-06T08:34:20Z-
dc.date.issued2006en_HK
dc.identifier.citationJournal Of International Financial Markets, Institutions And Money, 2006, v. 16 n. 2, p. 180-197en_HK
dc.identifier.issn1042-4431en_HK
dc.identifier.urihttp://hdl.handle.net/10722/82869-
dc.description.abstractThis paper studies seven GARCH models, including RiskMetrics and two long memory GARCH models, in Value at Risk (VaR) estimation. Both long and short positions of investment were considered. The seven models were applied to 12 market indices and four foreign exchange rates to assess each model in estimating VaR at various confidence levels. The results indicate that both stationary and fractionally integrated GARCH models outperform RiskMetrics in estimating 1% VaR. Although most return series show fat-tailed distribution and satisfy the long memory property, it is more important to consider a model with fat-tailed error in estimating VaR. Asymmetric behavior is also discovered in the stock market data that t-error models give better 1% VaR estimates than normal-error models in long position, but not in short position. No such asymmetry is observed in the exchange rate data. © 2005 Elsevier B.V. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/intfinen_HK
dc.relation.ispartofJournal of International Financial Markets, Institutions and Moneyen_HK
dc.rightsJournal of International Financial Markets, Institutions & Money. Copyright © Elsevier BV.en_HK
dc.subjectGARCH modelen_HK
dc.subjectLong memoryen_HK
dc.subjectMarket risken_HK
dc.titleEmpirical analysis of GARCH models in value at risk estimationen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1042-4431&volume=16&issue=2&spage=180&epage=197&date=2006&atitle=Empirical+analysis+of+GARCH+models+in+Value+at+Risk+estimationen_HK
dc.identifier.emailYu, PLH: plhyu@hkucc.hku.hken_HK
dc.identifier.authorityYu, PLH=rp00835en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.intfin.2005.02.001en_HK
dc.identifier.scopuseid_2-s2.0-33644795306en_HK
dc.identifier.hkuros133498en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33644795306&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume16en_HK
dc.identifier.issue2en_HK
dc.identifier.spage180en_HK
dc.identifier.epage197en_HK
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridSo, MKP=7004473851en_HK
dc.identifier.scopusauthoridYu, PLH=7403599794en_HK
dc.identifier.citeulike9605963-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats