Forecasting Realized Volatility with Linear and Nonlinear Models

Michael McAleer and Marcelo Medeiros ()

No CARF-F-189, CARF F-Series from Center for Advanced Research in Finance, Faculty of Economics, The 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: 27 pages
Date: 2009-10
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https://www.carf.e.u-tokyo.ac.jp/old/pdf/workingpaper/fseries/195.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:cfi:fseres:cf189

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