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Forecasting Realized Volatility with Linear and Nonlinear Models

Michael McAleer and Marcelo Medeiros ()

No EI 2009-37, Econometric Institute Research Papers from Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute

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 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.

Keywords: bagging; financial econometrics; neural networks; nonlinear models; realized volatility; volatility forecasting (search for similar items in EconPapers)
JEL-codes: C22 C53 G12 G17 (search for similar items in EconPapers)
Date: 2009-11-24
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https://repub.eur.nl/pub/17303/EI2009-37.pdf (application/pdf)

Related works:
Working Paper: Forecasting Realized Volatility with Linear and Nonlinear Models (2010) Downloads
Working Paper: Forecasting Realized Volatility with Linear and Nonlinear Models (2009) Downloads
Working Paper: Forecasting Realized Volatility with Linear and Nonlinear Models (2009) Downloads
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