Forecasting realized volatility with linear and nonlinear univariate 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.
|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|
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