Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/25293 
Full metadata record
DC FieldValueLanguage
dc.contributor.authorChen, Shiyien
dc.contributor.authorJeong, Kihoen
dc.contributor.authorHärdle, Wolfgang Karlen
dc.date.accessioned2008-11-07-
dc.date.accessioned2009-07-23T15:04:03Z-
dc.date.available2009-07-23T15:04:03Z-
dc.date.issued2008-
dc.identifier.urihttp://hdl.handle.net/10419/25293-
dc.description.abstractRecurrent Support Vector Regression for a Nonlinear ARMA Model with Applications to Forecasting Financial Returns Abstract: Motivated by the recurrent Neural Networks, this paper proposes a recurrent Support Vector Regression (SVR) procedure to forecast nonlinear ARMA model based simulated data and real data of financial returns. The forecasting ability of the recurrent SVR is compared with three competing methods, MLE, recurrent MLP and feedforward SVR. Theoretically, MLE and MLP only focus on fit in-sample, but SVR considers both fit and forecast out-of-sample which endows SVR with an excellent forecasting ability. This is confirmed by the evidence from the simulated and real data based on two forecasting accuracy evaluation metrics (NSME and sign). That is, for one-step-ahead forecasting, the recurrent SVR is consistently better than the MLE and the recurrent MLP in forecasting both the magnitude and turning points, and really improves the forecasting performance as opposed to the usual feedforward SVR.en
dc.language.isoengen
dc.publisher|aHumboldt University of Berlin, Collaborative Research Center 649 - Economic Risk |cBerlinen
dc.relation.ispartofseries|aSFB 649 Discussion Paper |x2008,051en
dc.subject.jelC45en
dc.subject.jelF37en
dc.subject.jelF47en
dc.subject.ddc330en
dc.subject.keywordRecurrent support vector regressionen
dc.subject.keywordMLEen
dc.subject.keywordrecurrent MLPen
dc.subject.keywordnonlinear ARMAen
dc.subject.keywordfinancial forecastingen
dc.subject.stwKapitalertragen
dc.subject.stwPrognoseverfahrenen
dc.subject.stwSupport Vector Machineen
dc.subject.stwARMA-Modellen
dc.subject.stwNichtlineares Verfahrenen
dc.subject.stwRegressionen
dc.subject.stwNeuronale Netzeen
dc.subject.stwTheorieen
dc.titleRecurrent support vector regression for a nonlinear ARMA model with applications to forecasting financial returns-
dc.type|aWorking Paperen
dc.identifier.ppn584571534en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

Files in This Item:
File
Size
522.59 kB





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