Forecasting Realized Volatility with Linear and Nonlinear Univariate Models
AbstractIn 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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Bibliographic InfoPaper provided by University of Canterbury, Department of Economics and Finance in its series Working Papers in Economics with number 10/28.
Length: 26 pages
Date of creation: 01 May 2010
Date of revision:
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Financial econometrics; volatility forecasting; neural networks; nonlinear models; realized volatility; bagging;
Other versions of this item:
- Michael McAleer & Marcelo C. Medeiros, 2011. "Forecasting Realized Volatility With Linear And Nonlinear Univariate Models," Journal of Economic Surveys, Wiley Blackwell, vol. 25(1), pages 6-18, 02.
- NEP-ALL-2010-05-29 (All new papers)
- NEP-CMP-2010-05-29 (Computational Economics)
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- NEP-ETS-2010-05-29 (Econometric Time Series)
- NEP-FOR-2010-05-29 (Forecasting)
- NEP-MST-2010-05-29 (Market Microstructure)
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- 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.
- 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.
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