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 InfoArticle provided by Wiley Blackwell in its journal Journal of Economic Surveys.
Volume (Year): 25 (2011)
Issue (Month): 1 (02)
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Web page: http://www.blackwellpublishing.com/journal.asp?ref=0950-0804
Other versions of this item:
- 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.
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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," CREATES Research Papers 2013-03, School of Economics and Management, University of Aarhus.
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