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Recurrent Support Vector Regression for a Nonlinear ARMA Model with Applications to Forecasting Financial Returns

Author

Listed:
  • Shiyi Chen
  • Kiho Jeong
  • Wolfgang K. Härdle

Abstract

Motivated by the recurrent Neural Networks, this paper proposes a recurrent Support Vector Regression (SVR) procedure to forecast nonlinear ARMA model based simulated data and real data of financial returns. The forecasting ability of the recurrent SVR is compared with three competing methods, MLE, recurrent MLP and feedforward SVR. Theoretically, MLE and MLP only focus on fit in-sample, but SVR considers both fit and forecast out-of-sample which endows SVR with an excellent forecasting ability. This is confirmed by the evidence from the simulated and real data based on two forecasting accuracy evaluation metrics (NSME and sign). That is, for one-step-ahead forecasting, the recurrent SVR is consistently better than the MLE and the recurrent MLP in forecasting both the magnitude and turning points, and really improves the forecasting performance as opposed to the usual feedforward SVR.

Suggested Citation

  • Shiyi Chen & Kiho Jeong & Wolfgang K. Härdle, 2008. "Recurrent Support Vector Regression for a Nonlinear ARMA Model with Applications to Forecasting Financial Returns," SFB 649 Discussion Papers SFB649DP2008-051, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    • Handle: RePEc:hum:wpaper:sfb649dp2008-051
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    References listed on IDEAS

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    More about this item

    Keywords

    Recurrent Support Vector Regression; MLE; recurrent MLP; nonlinear ARMA; financial forecasting;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications

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