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Cross-correlation measures in the high-frequency domain

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  • Precup, O. V.
  • Iori, G.

Abstract

On a high-frequency scale the time series are not homogeneous, therefore standard correlation measures cannot be directly applied to the raw data. To deal with this problem the time series have to be either homogenized through interpolation, or methods that can handle raw non-synchronous time series need to be employed. This paper compares two traditional methods that use interpolation with an alternative method applied directly to the actual time series. The three methods are tested on simulated data and actual trades time series.
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Suggested Citation

  • Precup, O. V. & Iori, G., 2005. "Cross-correlation measures in the high-frequency domain," Working Papers 05/04, Department of Economics, City University London.
    • Handle: RePEc:cty:dpaper:05/04
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Mattiussi, V. & Iori, G., 2006. "Currency futures volatility during the 1997 East Asian crisis: an application of Fourier analysis," Working Papers 06/09, Department of Economics, City University London.
    2. Ole E. Barndorff-Nielsen & Neil Shephard, 2005. "Variation, jumps, market frictions and high frequency data in financial econometrics," Economics Papers 2005-W16, Economics Group, Nuffield College, University of Oxford.
    3. Patrick Chang & Etienne Pienaar & Tim Gebbie, 2020. "Using the Epps effect to detect discrete processes," Papers 2005.10568, arXiv.org, revised Oct 2021.
    4. Andreea B. Dragut, 2012. "Stock Data Clustering and Multiscale Trend Detection," Methodology and Computing in Applied Probability, Springer, vol. 14(1), pages 87-105, March.
    5. 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).
    6. Iori, G. & Precup, O. V., 2006. "Weighted network analysis of high frequency cross-correlation measures," Working Papers 06/10, Department of Economics, City University London.
    7. Nicolas Huth & Frédéric Abergel, 2010. "High frequency correlation modelling," Post-Print hal-00621244, HAL.
    8. Patrick Chang & Roger Bukuru & Tim Gebbie, 2019. "Revisiting the Epps effect using volume time averaging: An exercise in R," Papers 1912.02416, arXiv.org, revised Feb 2020.
    9. Patrick Chang & Etienne Pienaar & Tim Gebbie, 2020. "Malliavin-Mancino estimators implemented with non-uniform fast Fourier transforms," Papers 2003.02842, arXiv.org, revised Nov 2020.
    10. Xiufeng Yan & Qi Tang, 2021. "Network analysis regarding international trade network," Papers 2111.02633, arXiv.org.
    11. Chang, Patrick & Pienaar, Etienne & Gebbie, Tim, 2021. "The Epps effect under alternative sampling schemes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).

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