Forecasting Realized Volatility with Linear and Nonlinear 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 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

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Year of publication:	2009
Authors:	McAleer, Michael
Other Persons:	Medeiros, Marcelo C. (contributor)
Publisher:	[2009]: [S.l.] : SSRN
Subject:	Volatilität \| Volatility \| Nichtlineare Regression \| Nonlinear regression \| Prognoseverfahren \| Forecasting model \| Neuronale Netze \| Neural networks \| Algorithmus \| Algorithm

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