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Article: On a dynamic mixture GARCH model

TitleOn a dynamic mixture GARCH model
Authors
KeywordsGARCH
Mixture time series
Statistical arbitrage
Issue Date2009
PublisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://www3.interscience.wiley.com/cgi-bin/jhome/2966
Citation
Journal Of Forecasting, 2009, v. 28 n. 3, p. 247-265 How to Cite?
AbstractThis paper proposes a new mixture GARCH model with a dynamic mixture proportion. The mixture Gaussian distribution of the error can vary from time to time. The Bayesian Information Criterion and the EM algorithm are used to estimate the number of parameters as well as the model parameters and their standard errors. The new model is applied to the S&P500 Index and Hang Seng Index and compared with GARCH models with Gaussian error and Student's t error. The result shows that the IGARCH effect in these index returns could be the result of the mixture of one stationary volatility component with another non-stationary volatility component. The VaR based on the new model performs better than traditional GARCH-based VaRs, especially in unstable stock markets. Copyright © 2008 John Wiley & Sons, Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/59866
ISSN
2015 Impact Factor: 0.818
2015 SCImago Journal Rankings: 0.706
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorCheng, Xen_HK
dc.contributor.authorYu, PLHen_HK
dc.contributor.authorLi, WKen_HK
dc.date.accessioned2010-05-31T03:59:03Z-
dc.date.available2010-05-31T03:59:03Z-
dc.date.issued2009en_HK
dc.identifier.citationJournal Of Forecasting, 2009, v. 28 n. 3, p. 247-265en_HK
dc.identifier.issn0277-6693en_HK
dc.identifier.urihttp://hdl.handle.net/10722/59866-
dc.description.abstractThis paper proposes a new mixture GARCH model with a dynamic mixture proportion. The mixture Gaussian distribution of the error can vary from time to time. The Bayesian Information Criterion and the EM algorithm are used to estimate the number of parameters as well as the model parameters and their standard errors. The new model is applied to the S&P500 Index and Hang Seng Index and compared with GARCH models with Gaussian error and Student's t error. The result shows that the IGARCH effect in these index returns could be the result of the mixture of one stationary volatility component with another non-stationary volatility component. The VaR based on the new model performs better than traditional GARCH-based VaRs, especially in unstable stock markets. Copyright © 2008 John Wiley & Sons, Ltd.en_HK
dc.languageengen_HK
dc.publisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://www3.interscience.wiley.com/cgi-bin/jhome/2966en_HK
dc.relation.ispartofJournal of Forecastingen_HK
dc.rightsJournal of Forecasting. Copyright © John Wiley & Sons Ltd.en_HK
dc.subjectGARCHen_HK
dc.subjectMixture time seriesen_HK
dc.subjectStatistical arbitrageen_HK
dc.titleOn a dynamic mixture GARCH modelen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0277-6693&volume=28&spage=247&epage=265&date=2009&atitle=On+a+Dynamic+Mixture+GARCH+Modelen_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.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1002/for.1093en_HK
dc.identifier.scopuseid_2-s2.0-63849095791en_HK
dc.identifier.hkuros154703en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-63849095791&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume28en_HK
dc.identifier.issue3en_HK
dc.identifier.spage247en_HK
dc.identifier.epage265en_HK
dc.identifier.isiWOS:000264805300005-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridCheng, X=26429080500en_HK
dc.identifier.scopusauthoridYu, PLH=7403599794en_HK
dc.identifier.scopusauthoridLi, WK=14015971200en_HK

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