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Forecasting volatility with support vector machine-based GARCH model

Author

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  • Shiyi Chen

    (China Center for Economic Studies, School of Economics, Fudan University, Shanghai, China)

  • Wolfgang K. Härdle

    (Center for Applied Statistics and Economics, Humboldt University, Berlin, Germany)

  • Kiho Jeong

    (School of Economics and Trade, Kyungpook National University, Daegu, Republic of Korea)

Abstract

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.

Suggested Citation

  • Shiyi Chen & Wolfgang K. Härdle & Kiho Jeong, 2010. "Forecasting volatility with support vector machine-based GARCH model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(4), pages 406-433.
  • Handle: RePEc:jof:jforec:v:29:y:2010:i:4:p:406-433
    DOI: 10.1002/for.1134
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    2. Li, Ziran & Sun, Jiajing & Wang, Shouyang, 2013. "An information diffusion-based model of oil futures price," Energy Economics, Elsevier, vol. 36(C), pages 518-525.
    3. Marcin Fałdziński & Piotr Fiszeder & Witold Orzeszko, 2020. "Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression," Energies, MDPI, Open Access Journal, vol. 14(1), pages 1-18, December.
    4. Pedro Correia S. Bezerra & Pedro Henrique M. Albuquerque, 2017. "Volatility forecasting via SVR–GARCH with mixture of Gaussian kernels," Computational Management Science, Springer, vol. 14(2), pages 179-196, April.
    5. Hao Sun & Bo Yu, 2020. "Forecasting Financial Returns Volatility: A GARCH-SVR Model," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 451-471, February.
    6. Chen, Shiyi, 2013. "What is the potential impact of a taxation system reform on carbon abatement and industrial growth in China?," Economic Systems, Elsevier, vol. 37(3), pages 369-386.
    7. Yaxiong Zeng & Diego Klabjan, 2017. "Online Adaptive Machine Learning Based Algorithm for Implied Volatility Surface Modeling," Papers 1706.01833, arXiv.org, revised Jun 2018.
    8. Marius Lux & Wolfgang Karl Härdle & Stefan Lessmann, 2020. "Data driven value-at-risk forecasting using a SVR-GARCH-KDE hybrid," Computational Statistics, Springer, vol. 35(3), pages 947-981, September.
    9. Maciej Zieba & Wolfgang K. Härdle, 2016. "Beta-boosted ensemble for big credit scoring data," SFB 649 Discussion Papers SFB649DP2016-052, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

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