In this paper, we consider a nonlinear model based on neural networks as well as linear models to forecast the daily volatility of the S&P 500 and FTSE 100 futures. As a proxy for daily volatility, we consider a consistent and unbiased estimator of the integrated volatility that is computed from high-frequency intraday returns. We also consider a simple algorithm based on bagging (bootstrap aggregation) in order to specify the models analysed in this paper.

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Keywords Bagging, Financial econometrics, Neural networks, Nonlinear models, Realized volatility, Volatility forecasting
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Journal Journal of Economic Surveys
McAleer, M.J, & Medeiros, M. (2011). Forecasting realized volatility with linear and nonlinear univariate models. Journal of Economic Surveys, 25(1), 6–18. doi:10.1111/j.1467-6419.2010.00640.x