Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/108956
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dc.contributorDepartment of Applied Mathematics-
dc.contributorUniversity Research Facility in Big Data Analytics-
dc.creatorWang, Qen_US
dc.creatorYiu, KFCen_US
dc.creatorWong, Hen_US
dc.date.accessioned2024-09-11T08:33:54Z-
dc.date.available2024-09-11T08:33:54Z-
dc.identifier.issn1524-1904en_US
dc.identifier.urihttp://hdl.handle.net/10397/108956-
dc.language.isoenen_US
dc.publisherJohn Wiley & Sons Ltd.en_US
dc.rights© 2022 John Wiley & Sons, Ltd.en_US
dc.rightsThis is the peer reviewed version of the following article: Wang Q, Yiu K-FC, Wong H. On a buffered threshold autoregressive stochastic volatility model. Appl Stochastic Models Bus Ind. 2022; 38(6): 974–996, which has been published in final form at https://doi.org/10.1002/asmb.2689. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.en_US
dc.subjectBayesian inferenceen_US
dc.subjectBuffer zoneen_US
dc.subjectKalman filteren_US
dc.subjectStochastic volatilityen_US
dc.subjectThreshold estimationen_US
dc.titleOn a buffered threshold autoregressive stochastic volatility modelen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage974en_US
dc.identifier.epage996en_US
dc.identifier.volume38en_US
dc.identifier.issue6en_US
dc.identifier.doi10.1002/asmb.2689en_US
dcterms.abstractThis article introduces a new autoregressive stochastic volatility (SV) model with a new piecewise linear structure such that the regime-switching mechanism has a buffer zone where regime-switching is delayed. The proposed model allows us to model the hysteretic phenomenon of the regime-switching existing on both the mean equation and the volatility equation. A full description of the proposed Markov chain Monte Carlo method is given. In the empirical study, we consider the daily closing prices of NIKKEI stock average, the exchange rate for US Dollar to Japanese Yen and Hang Seng Index. Deviance information criterion measure shows that our proposed model outperforms the classical threshold SV models.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationApplied stochastic models in business and industry, Nov.-Dec. 2022, v. 38, no. 6, p. 974-996en_US
dcterms.isPartOfApplied stochastic models in business and industryen_US
dcterms.issued2022-11-
dc.identifier.scopus2-s2.0-85130298621-
dc.identifier.eissn1526-4025en_US
dc.description.validate202409 bcch-
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera3186b-
dc.identifier.SubFormID49753-
dc.description.fundingSourceRGCen_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryGreen (AAM)en_US
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