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.

Additional Metadata
Keywords Bagging, Financial econometrics, Neural networks, Nonlinear models, Realized volatility, Volatility forecasting
Persistent URL dx.doi.org/10.1111/j.1467-6419.2010.00640.x, hdl.handle.net/1765/31749
Citation
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