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
2011 Impact Factor: 0.702
DC Field
Value
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
2011 Impact Factor: 0.702
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
Author Affiliations
  1. The University of Hong Kong
  2. Hong Kong University of Science and Technology