Forecasting Realized Volatility with Linear and Nonlinear Univariate Models

McAleer, Michael; Medeiros, Marcelo

doi:10.1111/j.1467-6419.2010.00640.x

Forecasting Realized Volatility with Linear and Nonlinear Univariate Models

Journal of Economic Surveys, Vol. 25, Issue 1, pp. 6-18, 2011

13 Pages Posted: 24 Jan 2011

See all articles by Michael McAleer

Michael McAleer

Erasmus University Rotterdam - Erasmus School of Economics, Econometric Institute; Tinbergen Institute; University of Tokyo - Centre for International Research on the Japanese Economy (CIRJE), Faculty of Economics

Marcelo Medeiros

IPEA - Institute for Applied Economic Research; UnB - University of Brasilia

Abstract

Abstract 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

Suggested Citation: Suggested Citation

McAleer, Michael and Medeiros, Marcelo, Forecasting Realized Volatility with Linear and Nonlinear Univariate Models. Journal of Economic Surveys, Vol. 25, Issue 1, pp. 6-18, 2011, Available at SSRN: https://ssrn.com/abstract=1745758 or http://dx.doi.org/10.1111/j.1467-6419.2010.00640.x