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Article: Least absolute deviation estimation for fractionally integrated autoregressive moving average time series models with conditional heteroscedasticity
Title | Least absolute deviation estimation for fractionally integrated autoregressive moving average time series models with conditional heteroscedasticity |
---|---|
Authors | |
Keywords | Arfima Conditional Heteroscedasticity Garch Heavy Tail Least Absolute Deviation Long Memory |
Issue Date | 2008 |
Publisher | Oxford 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? |
Abstract | We 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 Identifier | http://hdl.handle.net/10722/172449 |
ISSN | 2023 Impact Factor: 2.4 2023 SCImago Journal Rankings: 3.358 |
ISI Accession Number ID | |
References |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Li, G | en_US |
dc.contributor.author | Li, WK | en_US |
dc.date.accessioned | 2012-10-30T06:22:34Z | - |
dc.date.available | 2012-10-30T06:22:34Z | - |
dc.date.issued | 2008 | en_US |
dc.identifier.citation | Biometrika, 2008, v. 95 n. 2, p. 399-414 | en_US |
dc.identifier.issn | 0006-3444 | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/172449 | - |
dc.description.abstract | We 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.language | eng | en_US |
dc.publisher | Oxford University Press. The Journal's web site is located at http://biomet.oxfordjournals.org/ | en_US |
dc.relation.ispartof | Biometrika | en_US |
dc.rights | Biometrika. Copyright © Oxford University Press. | - |
dc.subject | Arfima | en_US |
dc.subject | Conditional Heteroscedasticity | en_US |
dc.subject | Garch | en_US |
dc.subject | Heavy Tail | en_US |
dc.subject | Least Absolute Deviation | en_US |
dc.subject | Long Memory | en_US |
dc.title | Least absolute deviation estimation for fractionally integrated autoregressive moving average time series models with conditional heteroscedasticity | en_US |
dc.type | Article | en_US |
dc.identifier.email | Li, G: gdli@hku.hk | en_US |
dc.identifier.email | Li, WK: hrntlwk@hku.hk | en_US |
dc.identifier.authority | Li, G=rp00738 | en_US |
dc.identifier.authority | Li, WK=rp00741 | en_US |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.doi | 10.1093/biomet/asn014 | en_US |
dc.identifier.scopus | eid_2-s2.0-44849132041 | en_US |
dc.identifier.hkuros | 148279 | - |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-44849132041&selection=ref&src=s&origin=recordpage | en_US |
dc.identifier.volume | 95 | en_US |
dc.identifier.issue | 2 | en_US |
dc.identifier.spage | 399 | en_US |
dc.identifier.epage | 414 | en_US |
dc.identifier.eissn | 1464-3510 | - |
dc.identifier.isi | WOS:000256269100010 | - |
dc.publisher.place | United Kingdom | en_US |
dc.identifier.scopusauthorid | Li, G=52563850500 | en_US |
dc.identifier.scopusauthorid | Li, WK=14015971200 | en_US |
dc.identifier.citeulike | 6496157 | - |
dc.identifier.issnl | 0006-3444 | - |