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Article: On the threshold hyperbolic GARCH models

TitleOn the threshold hyperbolic GARCH models
Authors
KeywordsHyperbolic garch model
Long memory
Threshold model
Volatility
Issue Date2011
PublisherInternational Press. The Journal's web site is located at http://www.intlpress.com/SII
Citation
Statistics And Its Interface, 2011, v. 4 n. 2, p. 159-166 How to Cite?
AbstractIn the financial market, the volatility of financial assets plays a key role in the problem of measuring market risk in many investment decisions. Insights into economic forces that may contribute to or amplify volatility are thus important. The financial market is characterized by regime switching between phases of low volatility and phases of high volatility. Nonlinearity and long memory are two salient features of volatility. To jointly capture the features of long memory and nonlinearity, a new threshold time series model with hyperbolic generalized autoregressive conditional heteroscedasticity is considered in this article. A goodness of fit test is derived to check the adequacy of the fitted model. Simulation and empirical results provide further support to the proposed model. AMS 2000 subject classifications: Primary 91B84; secondary 62M10.
Persistent Identifierhttp://hdl.handle.net/10722/135494
ISSN
2015 Impact Factor: 1.546
2015 SCImago Journal Rankings: 0.481
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorKwan, Wen_HK
dc.contributor.authorLi, WKen_HK
dc.contributor.authorLi, Gen_HK
dc.date.accessioned2011-07-27T01:36:05Z-
dc.date.available2011-07-27T01:36:05Z-
dc.date.issued2011en_HK
dc.identifier.citationStatistics And Its Interface, 2011, v. 4 n. 2, p. 159-166en_HK
dc.identifier.issn1938-7989en_HK
dc.identifier.urihttp://hdl.handle.net/10722/135494-
dc.description.abstractIn the financial market, the volatility of financial assets plays a key role in the problem of measuring market risk in many investment decisions. Insights into economic forces that may contribute to or amplify volatility are thus important. The financial market is characterized by regime switching between phases of low volatility and phases of high volatility. Nonlinearity and long memory are two salient features of volatility. To jointly capture the features of long memory and nonlinearity, a new threshold time series model with hyperbolic generalized autoregressive conditional heteroscedasticity is considered in this article. A goodness of fit test is derived to check the adequacy of the fitted model. Simulation and empirical results provide further support to the proposed model. AMS 2000 subject classifications: Primary 91B84; secondary 62M10.en_HK
dc.languageengen_US
dc.publisherInternational Press. The Journal's web site is located at http://www.intlpress.com/SIIen_HK
dc.relation.ispartofStatistics and its Interfaceen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsStatistics and Its Interface. Copyright © International Press.-
dc.subjectHyperbolic garch modelen_HK
dc.subjectLong memoryen_HK
dc.subjectThreshold modelen_HK
dc.subjectVolatilityen_HK
dc.titleOn the threshold hyperbolic GARCH modelsen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1938-7989&volume=4&issue=1&spage=159&epage=166&date=2011&atitle=On+The+threshold+Hyperbolic+GARCH+models-
dc.identifier.emailLi, WK: hrntlwk@hku.hken_HK
dc.identifier.emailLi, G: gdli@hku.hken_HK
dc.identifier.authorityLi, WK=rp00741en_HK
dc.identifier.authorityLi, G=rp00738en_HK
dc.description.naturepostprint-
dc.identifier.scopuseid_2-s2.0-84864410862en_HK
dc.identifier.hkuros186947en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-84864410862&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume4en_HK
dc.identifier.issue2en_HK
dc.identifier.spage159en_HK
dc.identifier.epage166en_HK
dc.identifier.isiWOS:000293847000012-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridKwan, W=35797453900en_HK
dc.identifier.scopusauthoridLi, WK=14015971200en_HK
dc.identifier.scopusauthoridLi, G=52563850500en_HK

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