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Article: On the least squares estimation of threshold autoregressive moving-average models

TitleOn the least squares estimation of threshold autoregressive moving-average 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. 1, p. 183-196 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.
Persistent Identifierhttp://hdl.handle.net/10722/135498
ISSN
2014 Impact Factor: 2.933
2014 SCImago Journal Rankings: 0.416

 

DC FieldValueLanguage
dc.contributor.authorLi, Den_US
dc.contributor.authorLi, WKen_US
dc.contributor.authorLing, Sen_US
dc.date.accessioned2011-07-27T01:36:06Z-
dc.date.available2011-07-27T01:36:06Z-
dc.date.issued2011en_US
dc.identifier.citationStatistics and Its Interface, 2011, v. 4 n. 1, p. 183-196en_US
dc.identifier.issn1938-7989-
dc.identifier.urihttp://hdl.handle.net/10722/135498-
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.-
dc.languageengen_US
dc.publisherInternational Press. The Journal's web site is located at http://www.intlpress.com/SIIen_US
dc.relation.ispartofStatistics and Its Interfaceen_US
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsStatistics and Its Interface. Copyright © International Press.-
dc.subjectHyperbolic GARCH model-
dc.subjectLong memory-
dc.subjectThreshold model-
dc.subjectVolatility-
dc.titleOn the least squares estimation of threshold autoregressive moving-average modelsen_US
dc.typeArticleen_US
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1938-7989&volume=4&issue=1&spage=183&epage=196&date=2011&atitle=On+the+least+square+estimation+of+threshold+autoregressive+moving-average+models-
dc.identifier.emailLi, WK: hrntlwk@hkucc.hku.hken_US
dc.identifier.authorityLi, WK=rp00741en_US
dc.description.naturepostprint-
dc.identifier.scopuseid_2-s2.0-84864416563-
dc.identifier.hkuros187177en_US
dc.identifier.volume4en_US
dc.identifier.issue1-
dc.identifier.spage183en_US
dc.identifier.epage196en_US

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