Forecasting volatility with support vector machine-based GARCH model
Wolfgang Härdle () and
Additional contact information
Shiyi Chen: China Center for Economic Studies, School of Economics, Fudan University, Shanghai, China, Postal: China Center for Economic Studies, School of Economics, Fudan University, Shanghai, China
Kiho Jeong: School of Economics and Trade, Kyungpook National University, Daegu, Republic of Korea, Postal: School of Economics and Trade, Kyungpook National University, Daegu, Republic of Korea
Journal of Forecasting, 2010, vol. 29, issue 4, 406-433
Recently, support vector machine (SVM), a novel artificial neural network (ANN), has been successfully used for financial forecasting. This paper deals with the application of SVM in volatility forecasting under the GARCH framework, the performance of which is compared with simple moving average, standard GARCH, nonlinear EGARCH and traditional ANN-GARCH models by using two evaluation measures and robust Diebold-Mariano tests. The real data used in this study are daily GBP exchange rates and NYSE composite index. Empirical results from both simulation and real data reveal that, under a recursive forecasting scheme, SVM-GARCH models significantly outperform the competing models in most situations of one-period-ahead volatility forecasting, which confirms the theoretical advantage of SVM. The standard GARCH model also performs well in the case of normality and large sample size, while EGARCH model is good at forecasting volatility under the high skewed distribution. The sensitivity analysis to choose SVM parameters and cross-validation to determine the stopping point of the recurrent SVM procedure are also examined in this study. Copyright © 2009 John Wiley & Sons, Ltd.
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6) Track citations by RSS feed
Downloads: (external link)
http://hdl.handle.net/10.1002/for.1134 Link to full text; subscription required (text/html)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:jof:jforec:v:29:y:2010:i:4:p:406-433
Access Statistics for this article
Journal of Forecasting is currently edited by Derek W. Bunn
More articles in Journal of Forecasting from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley-Blackwell Digital Licensing ().