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McAleer, Michael
Medeiros, Marcelo C.
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Texto para discussão 568
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 this paper.
Financial econometrics
volatility forecasting
neural networks
nonlinear models
realized volatility
Document Type: 
Working Paper
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