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

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  • 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.

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Bibliographic Info

Paper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2008-051.

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Length: 29 pages
Date of creation: Jul 2008
Date of revision:
Handle: RePEc:hum:wpaper:sfb649dp2008-051

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Keywords: Recurrent Support Vector Regression; MLE; recurrent MLP; nonlinear ARMA; financial forecasting;

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  1. Pesaran, M.H. & Timmermann, A.G., 1990. "The Statistical And Economic Significance Of The Predictability Of Excess Returns On Common Stocks," Papers 26, California Los Angeles - Applied Econometrics.
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  9. Wolfgang Härdle & Rouslan A. Moro & Dorothea Schäfer, 2005. "Predicting Bankruptcy with Support Vector Machines," SFB 649 Discussion Papers SFB649DP2005-009, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  10. Nikolaus Hautsch & Vahidin Jeleskovic, 2008. "Modelling High-Frequency Volatility and Liquidity Using Multiplicative Error Models," SFB 649 Discussion Papers SFB649DP2008-047, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
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  12. Wolfgang Härdle & Rouslan Moro & Dorothea Schäfer, 2006. "Graphical Data Representation in Bankruptcy Analysis," SFB 649 Discussion Papers SFB649DP2006-015, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
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