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Article: Least absolute deviation estimation for fractionally integrated autoregressive moving average time series models with conditional heteroscedasticity

TitleLeast absolute deviation estimation for fractionally integrated autoregressive moving average time series models with conditional heteroscedasticity
Authors
KeywordsArfima
Conditional Heteroscedasticity
Garch
Heavy Tail
Least Absolute Deviation
Long Memory
Issue Date2008
PublisherOxford University Press. The Journal's web site is located at http://biomet.oxfordjournals.org/
Citation
Biometrika, 2008, v. 95 n. 2, p. 399-414 How to Cite?
AbstractWe consider a unified least absolute deviation estimator for stationary and nonstationary fractionally integrated autoregressive moving average models with conditional heteroscedasticity. Its asymptotic normality is established when the second moments of errors and innovations are finite. Several other alternative estimators are also discussed and are shown to be less efficient and less robust than the proposed approach. A diagnostic tool, consisting of two portmanteau tests, is designed to check whether or not the estimated models are adequate. The simulation experiments give further support to our model and the results for the absolute returns of the Dow Jones Industrial Average Index daily closing price demonstrate their usefulness in modelling time series exhibiting the features of long memory, conditional heteroscedasticity and heavy tails. © 2008 Biometrika Trust.
Persistent Identifierhttp://hdl.handle.net/10722/172449
ISSN
2015 Impact Factor: 1.13
2015 SCImago Journal Rankings: 2.801
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLi, Gen_US
dc.contributor.authorLi, WKen_US
dc.date.accessioned2012-10-30T06:22:34Z-
dc.date.available2012-10-30T06:22:34Z-
dc.date.issued2008en_US
dc.identifier.citationBiometrika, 2008, v. 95 n. 2, p. 399-414en_US
dc.identifier.issn0006-3444en_US
dc.identifier.urihttp://hdl.handle.net/10722/172449-
dc.description.abstractWe consider a unified least absolute deviation estimator for stationary and nonstationary fractionally integrated autoregressive moving average models with conditional heteroscedasticity. Its asymptotic normality is established when the second moments of errors and innovations are finite. Several other alternative estimators are also discussed and are shown to be less efficient and less robust than the proposed approach. A diagnostic tool, consisting of two portmanteau tests, is designed to check whether or not the estimated models are adequate. The simulation experiments give further support to our model and the results for the absolute returns of the Dow Jones Industrial Average Index daily closing price demonstrate their usefulness in modelling time series exhibiting the features of long memory, conditional heteroscedasticity and heavy tails. © 2008 Biometrika Trust.en_US
dc.languageengen_US
dc.publisherOxford University Press. The Journal's web site is located at http://biomet.oxfordjournals.org/en_US
dc.relation.ispartofBiometrikaen_US
dc.rightsBiometrika. Copyright © Oxford University Press.-
dc.subjectArfimaen_US
dc.subjectConditional Heteroscedasticityen_US
dc.subjectGarchen_US
dc.subjectHeavy Tailen_US
dc.subjectLeast Absolute Deviationen_US
dc.subjectLong Memoryen_US
dc.titleLeast absolute deviation estimation for fractionally integrated autoregressive moving average time series models with conditional heteroscedasticityen_US
dc.typeArticleen_US
dc.identifier.emailLi, G: gdli@hku.hken_US
dc.identifier.emailLi, WK: hrntlwk@hku.hken_US
dc.identifier.authorityLi, G=rp00738en_US
dc.identifier.authorityLi, WK=rp00741en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1093/biomet/asn014en_US
dc.identifier.scopuseid_2-s2.0-44849132041en_US
dc.identifier.hkuros148279-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-44849132041&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume95en_US
dc.identifier.issue2en_US
dc.identifier.spage399en_US
dc.identifier.epage414en_US
dc.identifier.eissn1464-3510-
dc.identifier.isiWOS:000256269100010-
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridLi, G=52563850500en_US
dc.identifier.scopusauthoridLi, WK=14015971200en_US
dc.identifier.citeulike6496157-

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