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Nonlinearity in Forecasting of High-Frequency Stock Returns

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  • Juan Reboredo

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  • José Matías
  • Raquel Garcia-Rubio

Abstract

Using high-frequency S&P 500 data, we examined intraday efficiency by comparing the ability of several nonlinear models to forecast returns for horizons of 5, 10, 30 and 60 min. Taking into account fat tails and volatility dynamics, we compared the forecasting performance of simple random walk and autoregressive models with Markov switching, artificial neural network and support vector machine regression models in terms of both statistical and economic criteria. Our empirical results for out-of-sample forecasts for high and low volatility samples at different time periods provide weak evidence of intraday predictability in terms of statistical criteria, but corroborate the superiority of nonlinear model predictability using economic criteria such as trading rule profitability and value-at-risk calculations. Copyright Springer Science+Business Media, LLC. 2012

Suggested Citation

  • Juan Reboredo & José Matías & Raquel Garcia-Rubio, 2012. "Nonlinearity in Forecasting of High-Frequency Stock Returns," Computational Economics, Springer;Society for Computational Economics, vol. 40(3), pages 245-264, October.
  • Handle: RePEc:kap:compec:v:40:y:2012:i:3:p:245-264
    DOI: 10.1007/s10614-011-9288-5
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. David E. Allen & Michael McAleer & Shelton Peiris & Abhay K. Singh, 2016. "Nonlinear Time Series and Neural-Network Models of Exchange Rates between the US Dollar and Major Currencies," Risks, MDPI, Open Access Journal, vol. 4(1), pages 1-14, March.
    2. Reboredo, Juan C., 2013. "Is gold a safe haven or a hedge for the US dollar? Implications for risk management," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 2665-2676.
    3. Rodriguez, E. & Aguilar-Cornejo, M. & Femat, R. & Alvarez-Ramirez, J., 2014. "US stock market efficiency over weekly, monthly, quarterly and yearly time scales," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 554-564.
    4. Reboredo, Juan C. & Ugando, Mikel, 2015. "Downside risks in EU carbon and fossil fuel markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 111(C), pages 17-35.
    5. Reboredo, Juan C. & Rivera-Castro, Miguel A., 2014. "Gold and exchange rates: Downside risk and hedging at different investment horizons," International Review of Economics & Finance, Elsevier, vol. 34(C), pages 267-279.
    6. Reboredo, Juan C., 2013. "Modeling EU allowances and oil market interdependence. Implications for portfolio management," Energy Economics, Elsevier, vol. 36(C), pages 471-480.
    7. Charalampos Stasinakis & Georgios Sermpinis & Konstantinos Theofilatos & Andreas Karathanasopoulos, 2016. "Forecasting US Unemployment with Radial Basis Neural Networks, Kalman Filters and Support Vector Regressions," Computational Economics, Springer;Society for Computational Economics, vol. 47(4), pages 569-587, April.
    8. repec:gam:jrisks:v:4:y:2016:i:1:p:7:d:65863 is not listed on IDEAS
    9. Kuo, Chen-Yin, 2016. "Does the vector error correction model perform better than others in forecasting stock price? An application of residual income valuation theory," Economic Modelling, Elsevier, vol. 52(PB), pages 772-789.
    10. Reboredo, Juan C. & Wen, Xiaoqian, 2015. "Are China’s new energy stock prices driven by new energy policies?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 624-636.
    11. Rechenthin, Michael & Street, W. Nick, 2013. "Using conditional probability to identify trends in intra-day high-frequency equity pricing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6169-6188.
    12. repec:kap:rqfnac:v:49:y:2017:i:1:d:10.1007_s11156-016-0587-8 is not listed on IDEAS
    13. Rua-Haun Tsaih & Hsiou-Wei Lin & Wen-Chyan Ke, 2014. "An Abductive-Reasoning Guide for Finance Practitioners," Computational Economics, Springer;Society for Computational Economics, vol. 43(4), pages 411-431, April.

    More about this item

    Keywords

    Nonlinear models; Intraday returns; Markov switching; Artificial neural networks; Support vector machine regression; C22; C45; C52; C53; G17;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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