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Forecasting Realized Volatility with Linear and Nonlinear Models

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  • 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.

Suggested Citation

  • Michael McAleer & Marcelo Cunha Medeiros, 2010. "Forecasting Realized Volatility with Linear and Nonlinear Models," Textos para discussão 568, Department of Economics PUC-Rio (Brazil).
    • Handle: RePEc:rio:texdis:568
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    File URL: http://www.econ.puc-rio.br/pdf/td568.pdf
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    References listed on IDEAS

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    Cited by:

    1. Villalba-Padilla, Fátima Irina & Flores-Ortega, Miguel, 2012. "Capacidad de predicción de los modelos GARCH simétricos aplicados a variables financieras de México 2001-2011," eseconomía, Escuela Superior de Economía, Instituto Politécnico Nacional, vol. 0(34), pages 81-124, segundo t.

    More about this item

    Keywords

    Financial econometrics; volatility forecasting; neural networks; nonlinear models; realized volatility; bagging.;

    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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