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Article: On fractionally integrated autoregressive moving-average time series models with conditional heteroscedasticity

TitleOn fractionally integrated autoregressive moving-average time series models with conditional heteroscedasticity
Authors
KeywordsFractional differencing
Maximum likelihood estimation
Portmanteau tests: Stationarity and ergodicity
Issue Date1997
PublisherAmerican Statistical Association. The Journal's web site is located at http://www.amstat.org/publications/jasa/index.cfm?fuseaction=main
Citation
Journal Of The American Statistical Association, 1997, v. 92 n. 439, p. 1184-1194 How to Cite?
AbstractThis article considers fractionally integrated autoregressive moving-average time series models with conditional heteroscedasticity, which combines the popular generalized autoregressive conditional heteroscedastic (GARCH) and the fractional (ARMA) models. The fractional differencing parameter d can be greater than 1/2, thus incorporating the important unit root case. Some sufficient conditions for stationarity, ergodicity, and existence of higher-order moments are derived. An algorithm for approximate maximum likelihood (ML) estimation is presented. The asymptotic properties of ML estimators, which include consistency and asymptotic normality, are discussed. The large-sample distributions of the residual autocorrelations and the square-residual autocorrelations are obtained, and two portmanteau test statistics are established for checking model adequacy. In particular, nonstationary FARIMA(p, d, q)-GARCH(r, s) models are also considered. Some simulation results are reported. As an illustration, the proposed model is also applied to the daily returns of the Hong Kong Hang Seng index (1983-1984).
Persistent Identifierhttp://hdl.handle.net/10722/82806
ISSN
2015 Impact Factor: 1.725
2015 SCImago Journal Rankings: 3.447
References

 

DC FieldValueLanguage
dc.contributor.authorLing, Sen_HK
dc.contributor.authorLi, WKen_HK
dc.date.accessioned2010-09-06T08:33:38Z-
dc.date.available2010-09-06T08:33:38Z-
dc.date.issued1997en_HK
dc.identifier.citationJournal Of The American Statistical Association, 1997, v. 92 n. 439, p. 1184-1194en_HK
dc.identifier.issn0162-1459en_HK
dc.identifier.urihttp://hdl.handle.net/10722/82806-
dc.description.abstractThis article considers fractionally integrated autoregressive moving-average time series models with conditional heteroscedasticity, which combines the popular generalized autoregressive conditional heteroscedastic (GARCH) and the fractional (ARMA) models. The fractional differencing parameter d can be greater than 1/2, thus incorporating the important unit root case. Some sufficient conditions for stationarity, ergodicity, and existence of higher-order moments are derived. An algorithm for approximate maximum likelihood (ML) estimation is presented. The asymptotic properties of ML estimators, which include consistency and asymptotic normality, are discussed. The large-sample distributions of the residual autocorrelations and the square-residual autocorrelations are obtained, and two portmanteau test statistics are established for checking model adequacy. In particular, nonstationary FARIMA(p, d, q)-GARCH(r, s) models are also considered. Some simulation results are reported. As an illustration, the proposed model is also applied to the daily returns of the Hong Kong Hang Seng index (1983-1984).en_HK
dc.languageengen_HK
dc.publisherAmerican Statistical Association. The Journal's web site is located at http://www.amstat.org/publications/jasa/index.cfm?fuseaction=mainen_HK
dc.relation.ispartofJournal of the American Statistical Associationen_HK
dc.subjectFractional differencingen_HK
dc.subjectMaximum likelihood estimationen_HK
dc.subjectPortmanteau tests: Stationarity and ergodicityen_HK
dc.titleOn fractionally integrated autoregressive moving-average time series models with conditional heteroscedasticityen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0162-1459&volume=92&issue=439&spage=1184&epage=1194&date=1997&atitle=On+fractionally+integrated+autoregressive+moving-average+time+series+models+with+conditional+heteroscedasticityen_HK
dc.identifier.emailLi, WK: hrntlwk@hku.hken_HK
dc.identifier.authorityLi, WK=rp00741en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-21744436141en_HK
dc.identifier.hkuros28867en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-21744436141&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume92en_HK
dc.identifier.issue439en_HK
dc.identifier.spage1184en_HK
dc.identifier.epage1194en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridLing, S=7102701223en_HK
dc.identifier.scopusauthoridLi, WK=14015971200en_HK

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