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Fourier volatility forecasting with high-frequency data and microstructure noise

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  • Emilio Barucci
  • Davide Magno
  • Maria Elvira Mancino

Abstract

We study the forecasting performance of the Fourier volatility estimator in the presence of microstructure noise. Analytical comparison and simulation studies indicate that the Fourier estimator significantly outperforms realized volatility-type estimators, particularly for high-frequency data and when the noise component is relevant. We show that the Fourier estimator generally exhibits better performance, even compared with methods specifically designed to handle market microstructure contamination.

Suggested Citation

  • Emilio Barucci & Davide Magno & Maria Elvira Mancino, 2012. "Fourier volatility forecasting with high-frequency data and microstructure noise," Quantitative Finance, Taylor & Francis Journals, vol. 12(2), pages 281-293, September.
    • Handle: RePEc:taf:quantf:v:12:y:2012:i:2:p:281-293
    DOI: 10.1080/14697680903413589
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    References listed on IDEAS

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    1. Barndorff-Nielsen, Ole E. & Hansen, Peter Reinhard & Lunde, Asger & Shephard, Neil, 2011. "Subsampling realised kernels," Journal of Econometrics, Elsevier, vol. 160(1), pages 204-219, January.
    2. Meddahi, N., 2001. "An Eigenfunction Approach for Volatility Modeling," Cahiers de recherche 2001-29, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    3. S. Sanfelici & M. E. Mancino, 2008. "Covariance estimation via Fourier method in the presence of asynchronous trading and microstructure noise," Economics Department Working Papers 2008-ME01, Department of Economics, Parma University (Italy).
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