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dc.contributor.authorArdelean, Vladen_US
dc.date.accessioned2009-11-09en_US
dc.date.accessioned2010-01-13T14:02:05Z-
dc.date.available2010-01-13T14:02:05Z-
dc.date.issued2009en_US
dc.identifier.urihttp://hdl.handle.net/10419/29561-
dc.description.abstractThe Maximum likelihood estimation (MLE) is the most widely used method to estimate the parameters of a GARCH(p,q) process. This is owed to the fact that the MLE, among other properties, is asymptotically efficient. Even though the MLE is sensitive to outliers, which can occur in time series. In order to abate the influence of outliers, robust estimators are introduced. Afterwards an Monte Carlo study compares the introduced estimators.en_US
dc.language.isoengen_US
dc.publisherUniv., Inst. für Wirtschaftspolitik und Quantitative Wirtschaftsforschung Erlangenen_US
dc.relation.ispartofseriesIWQW discussion paper series 06/2009en_US
dc.subject.ddc330en_US
dc.subject.keywordGARCHen_US
dc.subject.keywordRobust-Estimatesen_US
dc.subject.keywordM-Estimatesen_US
dc.subject.stwARCH-Modellen_US
dc.subject.stwMaximum-Likelihood-Methodeen_US
dc.subject.stwRobustes Verfahrenen_US
dc.subject.stwTheorieen_US
dc.titleThe impacts of outliers on different estimators for GARCH processes: an empirical studyen_US
dc.typeWorking Paperen_US
dc.identifier.ppn612504670en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
dc.identifier.repecRePEc:zbw:iwqwdp:062009-
Appears in Collections:IWQW Discussion Paper Series, FAU Erlangen-Nürnberg

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