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Forecasting Realized Volatility with Linear and Nonlinear Models

Author Info

  • Michael McAleer

    (Econometric Institute, Erasmus University Rotterdam)

  • Marcelo Cunha Medeiros

    ()

    (Department of Economics PUC-Rio)

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.

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Paper provided by Department of Economics PUC-Rio (Brazil) in its series Textos para discussão with number 568.

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Length: 25p
Date of creation: Mar 2010
Date of revision:
Handle: RePEc:rio:texdis:568
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Keywords: Financial econometrics; volatility forecasting; neural networks; nonlinear models; realized volatility; bagging.;

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Find related papers by JEL classification:
  • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
  • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
  • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
  • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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  6. McAleer, Michael & Medeiros, Marcelo C., 2008. "A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries," Journal of Econometrics, Elsevier, vol. 147(1), pages 104-119, November.
  7. van Dijk, D.J.C. & Terasvirta, T. & Franses, Ph.H.B.F., 2000. "Smooth transition autoregressive models - A survey of recent developments," Econometric Institute Research Papers EI 2000-23/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
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