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Article: A Bayesian conditional autoregressive geometric process model for range data

TitleA Bayesian conditional autoregressive geometric process model for range data
Authors
KeywordsBayesian Analysis
Carr Model
Geometric Process
Range Data
Winbugs
Issue Date2012
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csda
Citation
Computational Statistics And Data Analysis, 2012, v. 56 n. 11, p. 3006-3019 How to Cite?
AbstractExtreme value theories indicate that the range is an efficient estimator of local volatility in financial time series. A geometric process (GP) framework that incorporates the conditional autoregressive range (CARR)-type mean function is presented for range data. The proposed model, called the conditional autoregressive geometric process range (CARGPR) model, allows for flexible trend patterns, threshold effects, leverage effects, and long-memory dynamics in financial time series. For robustness considerations, a log-t distribution is adopted. Model implementation can be easily done using the WinBUGS package. A simulation study shows that model parameters are estimated with high accuracy. In the empirical study on the range data of an Australian stock market index, the CARGPR model outperforms the CARR model in both in-sample estimation and out-of-sample forecast. © 2010 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/172498
ISSN
2015 Impact Factor: 1.179
2015 SCImago Journal Rankings: 1.283
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChan, JSKen_US
dc.contributor.authorLam, CPYen_US
dc.contributor.authorYu, PLHen_US
dc.contributor.authorChoy, STBen_US
dc.contributor.authorChen, CWSen_US
dc.date.accessioned2012-10-30T06:22:48Z-
dc.date.available2012-10-30T06:22:48Z-
dc.date.issued2012en_US
dc.identifier.citationComputational Statistics And Data Analysis, 2012, v. 56 n. 11, p. 3006-3019en_US
dc.identifier.issn0167-9473en_US
dc.identifier.urihttp://hdl.handle.net/10722/172498-
dc.description.abstractExtreme value theories indicate that the range is an efficient estimator of local volatility in financial time series. A geometric process (GP) framework that incorporates the conditional autoregressive range (CARR)-type mean function is presented for range data. The proposed model, called the conditional autoregressive geometric process range (CARGPR) model, allows for flexible trend patterns, threshold effects, leverage effects, and long-memory dynamics in financial time series. For robustness considerations, a log-t distribution is adopted. Model implementation can be easily done using the WinBUGS package. A simulation study shows that model parameters are estimated with high accuracy. In the empirical study on the range data of an Australian stock market index, the CARGPR model outperforms the CARR model in both in-sample estimation and out-of-sample forecast. © 2010 Elsevier B.V. All rights reserved.en_US
dc.languageengen_US
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csdaen_US
dc.relation.ispartofComputational Statistics and Data Analysisen_US
dc.subjectBayesian Analysisen_US
dc.subjectCarr Modelen_US
dc.subjectGeometric Processen_US
dc.subjectRange Dataen_US
dc.subjectWinbugsen_US
dc.titleA Bayesian conditional autoregressive geometric process model for range dataen_US
dc.typeArticleen_US
dc.identifier.emailYu, PLH: plhyu@hkucc.hku.hken_US
dc.identifier.authorityYu, PLH=rp00835en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1016/j.csda.2011.01.006en_US
dc.identifier.scopuseid_2-s2.0-84862008026en_US
dc.identifier.hkuros210598-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-84862008026&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume56en_US
dc.identifier.issue11en_US
dc.identifier.spage3006en_US
dc.identifier.epage3019en_US
dc.identifier.isiWOS:000309785500003-
dc.publisher.placeNetherlandsen_US
dc.identifier.scopusauthoridChan, JSK=24467617500en_US
dc.identifier.scopusauthoridLam, CPY=36970929300en_US
dc.identifier.scopusauthoridYu, PLH=7403599794en_US
dc.identifier.scopusauthoridChoy, STB=7004160354en_US
dc.identifier.scopusauthoridChen, CWS=36067962500en_US
dc.identifier.citeulike8766358-

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