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Inference in ARCH and GARCH models with heavy-tailed errors

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dc.contributor.authorHall, Peter
dc.contributor.authorYao, Qiwei
dc.date.accessioned2015-12-13T22:29:17Z
dc.date.available2015-12-13T22:29:17Z
dc.date.issued2003
dc.date.updated2015-12-11T08:47:47Z
dc.description.abstractARCH and GARCH models directly address the dependency of conditional second moments, and have proved particularly valuable in modelling processes where a relatively large degree of fluctuation is present. These include financial time series, which can be
dc.identifier.issn0012-9682
dc.identifier.urihttp://hdl.handle.net/1885/74623
dc.publisherBlackwell Publishing Ltd
dc.sourceEconometrica
dc.subjectKeywords: Approximation theory; Finance; Mathematical models; Maximum likelihood estimation; Parameter estimation; Garch models; Inference; Time series analysis Autoregression; Bootstrap; Dependent data; Domain at attraction; Financial data; Limit theory; Percentile-t bootstrap; Quasi-maximum likelihood; Semiparametric inference; Stable law; Studentize; Subsample bootstrap; Time series
dc.titleInference in ARCH and GARCH models with heavy-tailed errors
dc.typeJournal article
local.bibliographicCitation.issue1
local.bibliographicCitation.lastpage317
local.bibliographicCitation.startpage285
local.contributor.affiliationHall, Peter, College of Physical and Mathematical Sciences, ANU
local.contributor.affiliationYao, Qiwei, London School of Economics, University of London
local.contributor.authoruidHall, Peter, u7801145
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor010405 - Statistical Theory
local.identifier.ariespublicationMigratedxPub4212
local.identifier.citationvolume71
local.identifier.scopusID2-s2.0-0037273761
local.type.statusPublished Version

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