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Nonlinearity in Forecasting of High-Frequency Stock Returns

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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.

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Authors and Affiliations

  1. Department of Economics, University of Santiago de Compostela, Avda. Xoán XXII, s/n, 15782, Santiago de Compostela, Spain

    Juan C. Reboredo

  2. Department of Statistics, University of Vigo, Vigo, Spain

    José M. Matías

  3. Department of Finance, University of Salamanca, Salamanca, Spain

    Raquel Garcia-Rubio

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  1. Juan C. Reboredo
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  2. José M. Matías
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  3. Raquel Garcia-Rubio
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Correspondence to Juan C. Reboredo.

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Reboredo, J.C., Matías, J.M. & Garcia-Rubio, R. Nonlinearity in Forecasting of High-Frequency Stock Returns. Comput Econ 40, 245–264 (2012). https://doi.org/10.1007/s10614-011-9288-5

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  • DOI: https://doi.org/10.1007/s10614-011-9288-5

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