Forecasting Realized Volatility with Linear and Nonlinear Models

Michael McAleer and Marcelo Medeiros ()

No CIRJE-F-686, CIRJE F-Series from CIRJE, Faculty of Economics, University of Tokyo

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 the paper.

Pages: 27pages
Date: 2009-10
New Economics Papers: this item is included in nep-cmp, nep-ecm, nep-ets, nep-for and nep-mst
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http://www.cirje.e.u-tokyo.ac.jp/research/dp/2009/2009cf686.pdf (application/pdf)

Related works:
Working Paper: Forecasting Realized Volatility with Linear and Nonlinear Models (2010)
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:tky:fseres:2009cf686

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