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Forecasting Realized Volatility with Linear and Nonlinear Univariate Models

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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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  • Michael McAleer & Marcelo C. Medeiros, 2010. "Forecasting Realized Volatility with Linear and Nonlinear Univariate Models," Working Papers in Economics 10/28, University of Canterbury, Department of Economics and Finance.
  • Handle: RePEc:cbt:econwp:10/28
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    File URL: http://www.econ.canterbury.ac.nz/RePEc/cbt/econwp/1028.pdf
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    Cited by:

    1. Grassi, Stefano & Santucci de Magistris, Paolo, 2015. "It's all about volatility of volatility: Evidence from a two-factor stochastic volatility model," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 62-78.
    2. repec:prg:jnlefa:v:2017:y:2017:i:3:id:193:p:145-156 is not listed on IDEAS
    3. Manabu Asai & Michael McAleer, 2017. "Forecasting the volatility of Nikkei 225 futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 37(11), pages 1141-1152, November.
    4. 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.

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    Keywords

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

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