Forecasting realized volatility with linear and nonlinear univariate models

Michael McAleer, Marcelo C. Medeiros

Research output: Contribution to journalArticlepeer-review


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 intraday returns. We also consider a simple algorithm based on bagging (bootstrap aggregation) in order to specify the models analysed in this paper.

Original languageEnglish (US)
Pages (from-to)6-18
Number of pages13
JournalJournal of Economic Surveys
Issue number1
StatePublished - Feb 2011
Externally publishedYes


  • Bagging
  • Financial econometrics
  • Neural networks
  • Nonlinear models
  • Realized volatility
  • Volatility forecasting

ASJC Scopus subject areas

  • Economics and Econometrics


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