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Recurrent support vector regression for a non-linear ARMA model with applications to forecasting financial returns

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  • Shiyi Chen
  • Kiho Jeong
  • Wolfgang Härdle

Abstract

Motivated by 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 based ARMA model is compared with five competing models (random walk, threshold ARMA model, MLE based ARMA model, recurrent artificial neural network based ARMA model and feed-forward SVR based ARMA model) by using two forecasting accuracy evaluation metrics (NSME and sign) and robust Diebold–Mariano test. The results reveal that for one-step-ahead forecasting, the recurrent SVR model is consistently better than the benchmark models in forecasting both the magnitude and turning points, and statistically improves the forecasting performance as opposed to the usual feed-forward SVR. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Shiyi Chen & Kiho Jeong & Wolfgang Härdle, 2015. "Recurrent support vector regression for a non-linear ARMA model with applications to forecasting financial returns," Computational Statistics, Springer, vol. 30(3), pages 821-843, September.
    • Handle: RePEc:spr:compst:v:30:y:2015:i:3:p:821-843
    DOI: 10.1007/s00180-014-0543-9
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    1. repec:hum:wpaper:sfb649dp2005-009 is not listed on IDEAS
    2. Lisi, Francesco & Schiavo, Rosa A., 1999. "A comparison between neural networks and chaotic models for exchange rate prediction," Computational Statistics & Data Analysis, Elsevier, vol. 30(1), pages 87-102, March.
    3. Angelos Kanas, 2003. "Non-linear forecasts of stock returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(4), pages 299-315.
    4. Lee, Tian-Shyug & Chiu, Chih-Chou & Chou, Yu-Chao & Lu, Chi-Jie, 2006. "Mining the customer credit using classification and regression tree and multivariate adaptive regression splines," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 1113-1130, February.
    5. Hong, Wei-Chiang, 2011. "Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm," Energy, Elsevier, vol. 36(9), pages 5568-5578.
    6. Tian, Jilei & Juhola, Martti & Gronfors, Tapio, 1997. "AR parameter estimation by a feedback neural network," Computational Statistics & Data Analysis, Elsevier, vol. 25(1), pages 17-24, July.
    7. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    8. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    9. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    10. Evgeniou, Theodoros & Poggio, Tomaso & Pontil, Massimiliano & Verri, Alessandro, 2002. "Regularization and statistical learning theory for data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 421-432, February.
    11. Kuan, Chung-Ming & Liu, Tung, 1995. "Forecasting Exchange Rates Using Feedforward and Recurrent Neural Networks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(4), pages 347-364, Oct.-Dec..
    12. Wu, Berlin, 1995. "Model-free forecasting for nonlinear time series (with application to exchange rates)," Computational Statistics & Data Analysis, Elsevier, vol. 19(4), pages 433-459, April.
    13. repec:hum:wpaper:sfb649dp2006-015 is not listed on IDEAS
    14. Gaudart, Jean & Giusiano, Bernard & Huiart, Laetitia, 2004. "Comparison of the performance of multi-layer perceptron and linear regression for epidemiological data," Computational Statistics & Data Analysis, Elsevier, vol. 44(4), pages 547-570, January.
    15. Härdle, Wolfgang Karl & Moro, Rouslan A. & Schäfer, Dorothea, 2005. "Predicting bankruptcy with support vector machines," SFB 649 Discussion Papers 2005-009, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    16. Härdle, Wolfgang Karl & Moro, Rouslan A. & Schäfer, Dorothea, 2006. "Graphical data representation in bankruptcy analysis," SFB 649 Discussion Papers 2006-015, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
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    More about this item

    Keywords

    Recurrent support vector regression; Non-linear ARMA ; Financial forecasting; C45; C53; F37; F47; G17;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • 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
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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