Forecasting Realized Volatility with Linear and Nonlinear Models

Michael McAleer and Marcelo Medeiros ()

No 568, Textos para discussão from Department of Economics PUC-Rio (Brazil)

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 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.

Keywords: Financial econometrics; volatility forecasting; neural networks; nonlinear models; realized volatility; bagging. (search for similar items in EconPapers)
Pages: 25p
Date: 2010-03
New Economics Papers: this item is included in nep-cmp, nep-ecm, nep-ets, nep-for and nep-mst
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Citations: View citations in EconPapers (1)

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Related works:
Working Paper: Forecasting Realized Volatility with Linear and Nonlinear Models (2009)
Working Paper: Forecasting Realized Volatility with Linear and Nonlinear Models (2009)
Working Paper: Forecasting Realized Volatility with Linear and Nonlinear Models (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:rio:texdis:568

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