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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 intra-day returns. We also consider a simple algorithm based on bagging (bootstrap aggregation) in order to specify the models analyzed.

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Paper provided by University of Canterbury, Department of Economics and Finance in its series Working Papers in Economics with number 10/28.

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Length: 26 pages
Date of creation: 01 May 2010
Date of revision:
Handle: RePEc:cbt:econwp:10/28
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