Forecasting Realized Volatility with Linear and Nonlinear Models
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.
Journal of Economic Surveys V 25, N 1, P 6-18, 2011
Michael McAller, Marcelo Cunha Medeiros,