Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/25256
Full metadata record
DC FieldValueLanguage
dc.contributor.authorChen, Shiyien_US
dc.contributor.authorJeong, Kihoen_US
dc.contributor.authorHärdle, Wolfgang Karlen_US
dc.date.accessioned2008-02-21en_US
dc.date.accessioned2009-07-23T15:03:36Z-
dc.date.available2009-07-23T15:03:36Z-
dc.date.issued2008en_US
dc.identifier.urihttp://hdl.handle.net/10419/25256-
dc.description.abstractIn recent years support vector regression (SVR), a novel neural network (NN) technique, has been successfully used for financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH method is proposed and is compared with a moving average (MA), a recurrent NN and a parametric GACH in terms of their ability to forecast financial markets volatility. The real data in this study uses British Pound-US Dollar (GBP) daily exchange rates from July 2, 2003 to June 30, 2005 and New York Stock Exchange (NYSE) daily composite index from July 3, 2003 to June 30, 2005. The experiment shows that, under both varying and fixed forecasting schemes, the SVR-based GARCH outperforms the MA, the recurrent NN and the parametric GARCH based on the criteria of mean absolute error (MAE) and directional accuracy (DA). No structured way being available to choose the free parameters of SVR, the sensitivity of performance is also examined to the free parameters. Keywords: recurrent support vector regression ; GARCH model ; volatility forecastingen_US
dc.language.isoengen_US
dc.publisher|aSFB 649, Economic Risk|cBerlinen_US
dc.relation.ispartofseries|aSFB 649 discussion paper|x2008,014en_US
dc.subject.jelC45en_US
dc.subject.jelC53en_US
dc.subject.jelG32en_US
dc.subject.ddc330en_US
dc.subject.stwFinanzmarkten_US
dc.subject.stwVolatilitäten_US
dc.subject.stwPrognoseverfahrenen_US
dc.subject.stwSupport Vector Machineen_US
dc.subject.stwARCH-Modellen_US
dc.subject.stwNeuronale Netzeen_US
dc.subject.stwTheorieen_US
dc.subject.stwSchätzungen_US
dc.subject.stwWechselkursen_US
dc.subject.stwBörsenkursen_US
dc.subject.stwUSAen_US
dc.titleSupport vector regression based GARCH model with application to forecasting volatility of financial returnsen_US
dc.type|aWorking Paperen_US
dc.identifier.ppn558752896en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-

Files in This Item:
File
Size
695.75 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.