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2008-01-31Buch DOI: 10.18452/4106
Support Vector Regression Based GARCH Model withApplication to Forecasting Volatility of Financial Returns
dc.contributor.authorChen, Shiyi
dc.contributor.authorJeong, Kiho
dc.contributor.authorHärdle, Wolfgang Karl
dc.date.accessioned2017-06-15T23:38:21Z
dc.date.available2017-06-15T23:38:21Z
dc.date.created2008-02-08
dc.date.issued2008-01-31
dc.identifier.issn1860-5664
dc.identifier.urihttp://edoc.hu-berlin.de/18452/4758
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.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät
dc.subjectrecurrent support vector regressioneng
dc.subjectGARCH modeleng
dc.subjectvolatility forecastingeng
dc.subject.ddc330 Wirtschaft
dc.titleSupport Vector Regression Based GARCH Model withApplication to Forecasting Volatility of Financial Returns
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-10085134
dc.identifier.doihttp://dx.doi.org/10.18452/4106
local.edoc.container-titleSonderforschungsbereich 649: Ökonomisches Risiko
local.edoc.pages27
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2008
local.edoc.container-issue14
local.edoc.container-year2008
local.edoc.container-erstkatid2195055-6

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