Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/25256 
Kompletter Metadatensatz
DublinCore-FeldWertSprache
dc.contributor.authorChen, Shiyien
dc.contributor.authorJeong, Kihoen
dc.contributor.authorHärdle, Wolfgang Karlen
dc.date.accessioned2008-02-21-
dc.date.accessioned2009-07-23T15:03:36Z-
dc.date.available2009-07-23T15:03:36Z-
dc.date.issued2008-
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
dc.language.isoengen
dc.publisher|aHumboldt University of Berlin, Collaborative Research Center 649 - Economic Risk |cBerlinen
dc.relation.ispartofseries|aSFB 649 Discussion Paper |x2008,014en
dc.subject.jelC45en
dc.subject.jelC53en
dc.subject.jelG32en
dc.subject.ddc330en
dc.subject.stwFinanzmarkten
dc.subject.stwVolatilitäten
dc.subject.stwPrognoseverfahrenen
dc.subject.stwSupport Vector Machineen
dc.subject.stwARCH-Modellen
dc.subject.stwNeuronale Netzeen
dc.subject.stwTheorieen
dc.subject.stwSchätzungen
dc.subject.stwWechselkursen
dc.subject.stwBörsenkursen
dc.subject.stwUSAen
dc.titleSupport vector regression based GARCH model with application to forecasting volatility of financial returns-
dc.type|aWorking Paperen
dc.identifier.ppn558752896en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

Datei(en):
Datei
Größe
695.75 kB





Publikationen in EconStor sind urheberrechtlich geschützt.