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Forecasting Realized Volatility With Linear And Nonlinear Univariate Models

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Author Info

  • Michael McAleer
  • Marcelo C. Medeiros

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.

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Bibliographic Info

Article provided by Wiley Blackwell in its journal Journal of Economic Surveys.

Volume (Year): 25 (2011)
Issue (Month): 1 (02)
Pages: 6-18

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Handle: RePEc:bla:jecsur:v:25:y:2011:i:1:p:6-18

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Web page: http://www.blackwellpublishing.com/journal.asp?ref=0950-0804

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Cited by:
  1. 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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