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Article: Value at risk estimation using independent component analysis-generalized autoregressive conditional heteroscedasticity (ICA-garch) models

TitleValue at risk estimation using independent component analysis-generalized autoregressive conditional heteroscedasticity (ICA-garch) models
Authors
Issue Date2006
PublisherWorld Scientific Publishing Co Pte Ltd. The Journal's web site is located at http://www.worldscinet.com/ijns/ijns.shtml
Citation
International Journal Of Neural Systems, 2006, v. 16 n. 5, p. 371-382 How to Cite?
AbstractWe suggest using independent component analysis (ICA) to decompose multivariate time series into statistically independent time series. Then, we propose to use ICA-GARCH models which are computationally efficient to estimate the multivariate volatilities. The experimental results show that the ICA-GARCH models are more effective than existing methods, including DCC, PCA-GARCH, and EWMA. We also apply the proposed models to compute value at risk (VaR) for risk management applications. The backtesting and the out-of-sample tests validate the performance of ICA-GARCH models for value at risk estimation. © World Scientific Publishing Company.
Persistent Identifierhttp://hdl.handle.net/10722/82886
ISSN
2015 Impact Factor: 6.085
2015 SCImago Journal Rankings: 0.909
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorWu, EHCen_HK
dc.contributor.authorYu, PLHen_HK
dc.contributor.authorLi, WKen_HK
dc.date.accessioned2010-09-06T08:34:31Z-
dc.date.available2010-09-06T08:34:31Z-
dc.date.issued2006en_HK
dc.identifier.citationInternational Journal Of Neural Systems, 2006, v. 16 n. 5, p. 371-382en_HK
dc.identifier.issn0129-0657en_HK
dc.identifier.urihttp://hdl.handle.net/10722/82886-
dc.description.abstractWe suggest using independent component analysis (ICA) to decompose multivariate time series into statistically independent time series. Then, we propose to use ICA-GARCH models which are computationally efficient to estimate the multivariate volatilities. The experimental results show that the ICA-GARCH models are more effective than existing methods, including DCC, PCA-GARCH, and EWMA. We also apply the proposed models to compute value at risk (VaR) for risk management applications. The backtesting and the out-of-sample tests validate the performance of ICA-GARCH models for value at risk estimation. © 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/ijns/ijns.shtmlen_HK
dc.relation.ispartofInternational Journal of Neural Systemsen_HK
dc.titleValue at risk estimation using independent component analysis-generalized autoregressive conditional heteroscedasticity (ICA-garch) modelsen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0129-0657&volume=16&issue=5&spage=371&epage=382&date=2006&atitle=Value+at+risk+estimation+using+independent+component+analysis-generalized+autoregressive+conditional+heteroscedasticity+(ICA-GARCH)+modelsen_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.doi10.1142/S0129065706000779en_HK
dc.identifier.pmid17117498-
dc.identifier.scopuseid_2-s2.0-33751171101en_HK
dc.identifier.hkuros125606en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33751171101&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume16en_HK
dc.identifier.issue5en_HK
dc.identifier.spage371en_HK
dc.identifier.epage382en_HK
dc.identifier.isiWOS:000242032400007-
dc.publisher.placeSingaporeen_HK
dc.identifier.scopusauthoridWu, EHC=7202128063en_HK
dc.identifier.scopusauthoridYu, PLH=7403599794en_HK
dc.identifier.scopusauthoridLi, WK=14015971200en_HK

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