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.

Additional Metadata
Keywords bagging, financial econometrics, neural networks, nonlinear models, realized volatility, volatility forecasting
JEL Time-Series Models; Dynamic Quantile Regressions (jel C22), Forecasting and Other Model Applications (jel C53), Asset Pricing (jel G12), Financial Forecasting (jel G17)
Publisher Erasmus School of Economics
Persistent URL
Series Econometric Institute Research Papers
Journal Report / Econometric Institute, Erasmus University Rotterdam
McAleer, M.J, & Medeiros, M.C. (2009). Forecasting Realized Volatility with Linear and Nonlinear Models (No. EI 2009-37). Report / Econometric Institute, Erasmus University Rotterdam (pp. 1–26). Erasmus School of Economics. Retrieved from