Forecasting Realized Volatility with Linear and Nonlinear Univariate Models

Michael McAleer and Marcelo Medeiros ()

Working Papers in Economics from University of Canterbury, Department of Economics and Finance

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 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.

Keywords: Financial econometrics; volatility forecasting; neural networks; nonlinear models; realized volatility; bagging (search for similar items in EconPapers)
Pages: 26 pages
Date: 2010-05-01
New Economics Papers: this item is included in nep-cmp, nep-ecm, nep-ets, nep-for and nep-mst
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Citations: View citations in EconPapers (1)

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https://repec.canterbury.ac.nz/cbt/econwp/1028.pdf (application/pdf)

Related works:
Journal Article: FORECASTING REALIZED VOLATILITY WITH LINEAR AND NONLINEAR UNIVARIATE MODELS (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:cbt:econwp:10/28

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