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Financial risk forecasting with nonlinear dynamics and support vector regression

Author

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  • H K K Tung

    (City University of Hong Kong)

  • M C S Wong

    (City University of Hong Kong)

Abstract

We propose a dynamical description of financial time series capable of making short-term prediction utilizing support vector regression on neighbourhood points. We include in our analysis estimation on the uncertainty by capturing the exogenous from historical prediction errors and adopting a probabilistic description of the prediction. Evidences from a series of backtesting using financial time series indicate that our model provides accurate description of real market data comparable with GARCH(1,1).

Suggested Citation

  • H K K Tung & M C S Wong, 2009. "Financial risk forecasting with nonlinear dynamics and support vector regression," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(5), pages 685-695, May.
    • Handle: RePEc:pal:jorsoc:v:60:y:2009:i:5:d:10.1057_palgrave.jors.2602594
    DOI: 10.1057/palgrave.jors.2602594
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    References listed on IDEAS

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    4. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    5. Hsieh, David A, 1989. "Testing for Nonlinear Dependence in Daily Foreign Exchange Rates," The Journal of Business, University of Chicago Press, vol. 62(3), pages 339-368, July.
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