Forecasting Realized Volatility with Linear and Nonlinear 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 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 the paper.
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Bibliographic InfoPaper provided by Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo in its series CARF F-Series with number CARF-F-189.
Length: 27 pages
Date of creation: Oct 2009
Date of revision:
Other versions of this item:
- Michael McAleer & Marcelo Cunha Medeiros, 2010. "Forecasting Realized Volatility with Linear and Nonlinear Models," Textos para discussÃ£o 568, Department of Economics PUC-Rio (Brazil).
- McAleer, M.J. & Medeiros, M.C., 2009. "Forecasting Realized Volatility with Linear and Nonlinear Models," Econometric Institute Report EI 2009-37, Erasmus University Rotterdam, Econometric Institute.
- Michael McAleer & Marcelo C. Medeiros, 2009. "Forecasting Realized Volatility with Linear and Nonlinear Models," CIRJE F-Series CIRJE-F-686, CIRJE, Faculty of Economics, University of Tokyo.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Muller, Ulrich A. & Dacorogna, Michel M. & Dave, Rakhal D. & Olsen, Richard B. & Pictet, Olivier V. & von Weizsacker, Jacob E., 1997. "Volatilities of different time resolutions -- Analyzing the dynamics of market components," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 213-239, June.
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