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Forecasting Realized Volatility with Linear and Nonlinear Univariate Models

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Author Info

  • Michael McAleer

    ()
    (University of Canterbury)

  • Marcelo C. Medeiros

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 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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File URL: http://www.econ.canterbury.ac.nz/RePEc/cbt/econwp/1028.pdf
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Bibliographic Info

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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Related research

Keywords: Financial econometrics; volatility forecasting; neural networks; nonlinear models; realized volatility; bagging;

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Cited by:
  1. Stefano Grassi & Paolo Santucci de Magistris, 2013. "It’s all about volatility (of volatility): evidence from a two-factor stochastic volatility model," CREATES Research Papers 2013-03, School of Economics and Management, University of Aarhus.
  2. Stefano Grassi & Paolo Santucci de Magistris, 2013. "It's all about volatility of volatility: evidence from a two-factor stochastic volatility model," Studies in Economics 1404, Department of Economics, University of Kent.

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