Forecasting Realized Volatility with Linear and Nonlinear Univariate Models
13 Pages Posted: 24 Jan 2011
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
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Forecasting Realized Volatility with Linear and Nonlinear Univariate Models
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