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Article: On the least squares estimation of threshold autoregressive moving-average models
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TitleOn the least squares estimation of threshold autoregressive moving-average models
 
AuthorsLi, D2
Li, WK1
Ling, S2
 
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
 
CitationStatistics 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.
 
ISSN1938-7989
2012 Impact Factor: 0.396
 
DC FieldValue
dc.contributor.authorLi, D
 
dc.contributor.authorLi, WK
 
dc.contributor.authorLing, S
 
dc.date.accessioned2011-07-27T01:36:06Z
 
dc.date.available2011-07-27T01:36:06Z
 
dc.date.issued2011
 
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.description.naturepostprint
 
dc.identifier.citationStatistics and Its Interface, 2011, v. 4 n. 1, p. 183-196 [How to Cite?]
 
dc.identifier.epage196
 
dc.identifier.hkuros187177
 
dc.identifier.issn1938-7989
2012 Impact Factor: 0.396
 
dc.identifier.issue1
 
dc.identifier.openurl
 
dc.identifier.scopuseid_2-s2.0-84864416563
 
dc.identifier.spage183
 
dc.identifier.urihttp://hdl.handle.net/10722/135498
 
dc.identifier.volume4
 
dc.languageeng
 
dc.publisherInternational Press. The Journal's web site is located at http://www.intlpress.com/SII
 
dc.relation.ispartofStatistics and Its Interface
 
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 models
 
dc.typeArticle
 
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Author Affiliations
  1. The University of Hong Kong
  2. Hong Kong University of Science and Technology