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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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    References listed on IDEAS

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    1. 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.
    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. 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.
    4. 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.
    5. 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.
    6. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    7. 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.
    8. 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.
    9. Pesaran, M.H. & Timmermann, G., 1990. "The Statistical And Economic Significance Of The Predictability Of Exess Returns On Common Stocks," Cambridge Working Papers in Economics 9022, Faculty of Economics, University of Cambridge.
    10. 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.
    11. Nag, Ashok K & Mitra, Amit, 2002. "Forecasting Daily Foreign Exchange Rates Using Genetically Optimized Neural Networks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(7), pages 501-511, November.
    12. Angelos Kanas, 2003. "Non-linear forecasts of stock returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(4), pages 299-315.
    13. 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.
    14. 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.
    15. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
    16. 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..
    17. 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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    1. repec:gam:jsusta:v:10:y:2018:i:2:p:506-:d:131779 is not listed on IDEAS
    2. Ostap Okhrin & Stefan Trück, 2015. "Editorial to the special issue on Applicable semiparametrics of computational statistics," Computational Statistics, Springer, vol. 30(3), pages 641-646, September.

    More about this item

    Keywords

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

    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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