Nonlinearity in Forecasting of High-Frequency Stock Returns

Authors:
Juan C. Reboredo;José M. Matías;Raquel Garcia-Rubio
Affiliations:
Department of Economics, University of Santiago de Compostela, Santiago de Compostela, Spain 15782;Department of Statistics, University of Vigo, Vigo, Spain;Department of Finance, University of Salamanca, Salamanca, Spain
Venue:
Computational Economics
Year:
2012

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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.