Forecasting Realized Volatility with Linear and Nonlinear Models
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 the paper.
Year of publication: |
2009-10
|
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Authors: | McAleer, Michael ; Medeiros, Marcelo C. |
Institutions: | Center for Advanced Research in Finance, Faculty of Economics |
Saved in:
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