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Support Vector Regression Based GARCH Model with Application to Forecasting Volatility of Financial Returns

Shiyi Chen, Kiho Jeong and Wolfgang Härdle ()

SFB 649 Discussion Papers from Humboldt University, Collaborative Research Center 649

Abstract: In 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 forecasting (search for similar items in EconPapers)
JEL-codes: C45 C53 G32 (search for similar items in EconPapers)
Pages: 27 pages
Date: 2008-01
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-fmk, nep-for and nep-rmg
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