CIRJE-F-686 "Forecasting Realized Volatility with Linear and Nonlinear Models"
Author Name McAleer, Michael and Marcelo C. Medeiros
Date October 2009
Full Paper PDF file
Remarks Subsequently published in Journal of Economic Surveys.

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 indexes. As a proxy for daily volatility, we consider a consistent and unbiased estimator of the integrated volatility that is computed from high frequency intra-day returns. We also consider a simple algorithm based on bagging (bootstrap aggregation) in order to specify the models analyzed in the paper.