File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: On a multivariate conditional heteroscedastic model

TitleOn a multivariate conditional heteroscedastic model
Authors
Wong, HLi, WK
KeywordsCausality in volatility
Conditional heteroscedastic arma model
Random coefficient model
Volatility
Issue Date1997
PublisherOxford University Press. The Journal's web site is located at http://biomet.oxfordjournals.org/
Citation
Biometrika, 1997, v. 84 n. 1, p. 111-123 How to Cite?
DOI: http://dx.doi.org/10.1093/biomet/84.1.111
AbstractTsay (1987) developed the conditional heteroscedastic autoregressive moving-average model, which includes the conditional heteroscedastic autoregressive and random coefficient autoregressive models as special cases. This paper establishes the multivariate conditional heteroscedastic autoregressive moving-average model, and considers its theoretical properties and applications. Maximum likelihood estimation of the model is discussed in detail. A representation of the information matrix is obtained using the star product. This enhances estimation and statistical inferences procedures. Some simulation results and an application to the volatility of the Standard & Poor's 500 and Sydney's All Ordinaries indices are also considered.
Persistent Identifierhttp://hdl.handle.net/10722/82978
ISSN
0006-3444
2021 Impact Factor: 3.028
2020 SCImago Journal Rankings: 3.307
ISI Accession Number ID
WOS:A1997WT08200010
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorWong, Hen_HK
dc.contributor.authorLi, WKen_HK
dc.date.accessioned2010-09-06T08:35:33Z-
dc.date.available2010-09-06T08:35:33Z-
dc.date.issued1997en_HK
dc.identifier.citationBiometrika, 1997, v. 84 n. 1, p. 111-123en_HK
dc.identifier.issn0006-3444en_HK
dc.identifier.urihttp://hdl.handle.net/10722/82978-
dc.description.abstractTsay (1987) developed the conditional heteroscedastic autoregressive moving-average model, which includes the conditional heteroscedastic autoregressive and random coefficient autoregressive models as special cases. This paper establishes the multivariate conditional heteroscedastic autoregressive moving-average model, and considers its theoretical properties and applications. Maximum likelihood estimation of the model is discussed in detail. A representation of the information matrix is obtained using the star product. This enhances estimation and statistical inferences procedures. Some simulation results and an application to the volatility of the Standard & Poor's 500 and Sydney's All Ordinaries indices are also considered.en_HK
dc.languageengen_HK
dc.publisherOxford University Press. The Journal's web site is located at http://biomet.oxfordjournals.org/en_HK
dc.relation.ispartofBiometrikaen_HK
dc.rightsBiometrika. Copyright © Oxford University Press.en_HK
dc.subjectCausality in volatilityen_HK
dc.subjectConditional heteroscedastic arma modelen_HK
dc.subjectRandom coefficient modelen_HK
dc.subjectVolatilityen_HK
dc.titleOn a multivariate conditional heteroscedastic modelen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0006-3444&volume=84&issue=1&spage=111&epage=123&date=1997&atitle=On+a+multivariate+conditional+heteroscedastic+modelen_HK
dc.identifier.emailLi, WK: hrntlwk@hku.hken_HK
dc.identifier.authorityLi, WK=rp00741en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1093/biomet/84.1.111-
dc.identifier.scopuseid_2-s2.0-0000140190en_HK
dc.identifier.hkuros22004en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0000140190&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume84en_HK
dc.identifier.issue1en_HK
dc.identifier.spage111en_HK
dc.identifier.epage123en_HK
dc.identifier.isiWOS:A1997WT08200010-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridWong, H=7402864953en_HK
dc.identifier.scopusauthoridLi, WK=14015971200en_HK
dc.identifier.issnl0006-3444-

Export via OAI-PMH Interface in XML Formats

OR

Export to Other Non-XML Formats