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Nonlinearity in Forecasting of High-Frequency Stock Returns

Listed author(s):
  • Juan Reboredo

    ()

  • José Matías
  • Raquel Garcia-Rubio

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

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File URL: http://hdl.handle.net/10.1007/s10614-011-9288-5
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Article provided by Springer & Society for Computational Economics in its journal Computational Economics.

Volume (Year): 40 (2012)
Issue (Month): 3 (October)
Pages: 245-264

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Handle: RePEc:kap:compec:v:40:y:2012:i:3:p:245-264
DOI: 10.1007/s10614-011-9288-5
Contact details of provider: Web page: http://www.springer.com

Web page: http://www.comp-econ.org/

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