IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v30y2015i3p821-843.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00180-014-0543-9
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s00180-014-0543-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Angelos Kanas, 2003. "Non-linear forecasts of stock returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(4), pages 299-315.
    6. 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.
    7. 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.
    8. 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.
    9. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    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. 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.
    12. 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.
    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. 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..
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shuyu Li & Xue Yang & Rongrong Li, 2018. "Forecasting China’s Coal Power Installed Capacity: A Comparison of MGM, ARIMA, GM-ARIMA, and NMGM Models," Sustainability, MDPI, vol. 10(2), pages 1-15, February.
    2. Xinyu Han & Rongrong Li, 2019. "Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model," Energies, MDPI, vol. 12(17), pages 1-24, August.
    3. 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.
    4. Wang, Qiang & Jiang, Feng, 2019. "Integrating linear and nonlinear forecasting techniques based on grey theory and artificial intelligence to forecast shale gas monthly production in Pennsylvania and Texas of the United States," Energy, Elsevier, vol. 178(C), pages 781-803.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Shiyi & Jeong, Kiho & Härdle, Wolfgang Karl, 2008. "Recurrent support vector regression for a nonlinear ARMA model with applications to forecasting financial returns," SFB 649 Discussion Papers 2008-051, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    2. repec:hum:wpaper:sfb649dp2008-051 is not listed on IDEAS
    3. Preminger, Arie & Franck, Raphael, 2007. "Forecasting exchange rates: A robust regression approach," International Journal of Forecasting, Elsevier, vol. 23(1), pages 71-84.
    4. Qi, Min & Wu, Yangru, 2003. "Nonlinear prediction of exchange rates with monetary fundamentals," Journal of Empirical Finance, Elsevier, vol. 10(5), pages 623-640, December.
    5. Cavit Pakel & Neil Shephard & Kevin Sheppard, 2009. "Nuisance parameters, composite likelihoods and a panel of GARCH models," Economics Papers 2009-W12, Economics Group, Nuffield College, University of Oxford.
    6. Richard H. Clarida & Lucio Sarno & Mark P. Taylor & Giorgio Valente, 2006. "The Role of Asymmetries and Regime Shifts in the Term Structure of Interest Rates," The Journal of Business, University of Chicago Press, vol. 79(3), pages 1193-1224, May.
    7. Clarida, Richard H. & Sarno, Lucio & Taylor, Mark P. & Valente, Giorgio, 2003. "The out-of-sample success of term structure models as exchange rate predictors: a step beyond," Journal of International Economics, Elsevier, vol. 60(1), pages 61-83, May.
    8. Matei Demetrescu & Christoph Hanck & Robinson Kruse‐Becher, 2022. "Robust inference under time‐varying volatility: A real‐time evaluation of professional forecasters," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 1010-1030, August.
    9. Rossi, Barbara & Sekhposyan, Tatevik, 2011. "Understanding models' forecasting performance," Journal of Econometrics, Elsevier, vol. 164(1), pages 158-172, September.
    10. Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2024. "Predictive ability tests with possibly overlapping models," Journal of Econometrics, Elsevier, vol. 241(1).
    11. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    12. Takaaki Koike & Cathy W. S. Chen & Edward M. H. Lin, 2024. "Forecasting and Backtesting Gradient Allocations of Expected Shortfall," Papers 2401.11701, arXiv.org, revised Jun 2024.
    13. Timo Dimitriadis & Xiaochun Liu & Julie Schnaitmann, 2020. "Encompassing Tests for Value at Risk and Expected Shortfall Multi-Step Forecasts based on Inference on the Boundary," Papers 2009.07341, arXiv.org.
    14. Sarno, Lucio & Schneider, Paul & Wagner, Christian, 2012. "Properties of foreign exchange risk premiums," Journal of Financial Economics, Elsevier, vol. 105(2), pages 279-310.
    15. Malte Knüppel & Fabian Krüger, 2022. "Forecast uncertainty, disagreement, and the linear pool," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 23-41, January.
    16. West, Kenneth D., 2006. "Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 3, pages 99-134, Elsevier.
    17. Stefan Reitz & Jan-Christoph Rülke & Georg Stadtmann, 2009. "Are oil-price-forecasters finally right? – Regressive expectations towards more fundamental values of the oil price," WHU Working Paper Series - Economics Group 09-04, WHU - Otto Beisheim School of Management.
    18. Firmin Doko Tchatoka & Qazi Haque, 2023. "On bootstrapping tests of equal forecast accuracy for nested models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1844-1864, November.
    19. Adrien Verdelhan, 2018. "The Share of Systematic Variation in Bilateral Exchange Rates," Journal of Finance, American Finance Association, vol. 73(1), pages 375-418, February.
    20. , & Stein, Tobias, 2021. "Equity premium predictability over the business cycle," CEPR Discussion Papers 16357, C.E.P.R. Discussion Papers.
    21. Wu, Jyh-Lin & Hu, Yu-Hau, 2009. "New evidence on nominal exchange rate predictability," Journal of International Money and Finance, Elsevier, vol. 28(6), pages 1045-1063, October.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:compst:v:30:y:2015:i:3:p:821-843. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.