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A comparison of high-frequency cross-correlation measures

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  • Precup, Ovidiu V.
  • Iori, Giulia

Abstract

On a high-frequency scale the time series are not homogeneous, therefore standard correlation measures cannot be directly applied to the raw data. There are two ways to deal with this problem. The time series can be homogenised through an interpolation method (An Introduction to High-Frequency Finance, Academic Press, NY, 2001) (linear or previous tick) and then the Pearson correlation statistic computed. Recently, methods that can handle raw non-synchronous time series have been developed (Int. J. Theor. Appl. Finance 6(1) (2003) 87; J. Empirical Finance 4 (1997) 259). This paper compares two traditional methods that use interpolation with an alternative method applied directly to the actual time series.

Suggested Citation

  • Precup, Ovidiu V. & Iori, Giulia, 2004. "A comparison of high-frequency cross-correlation measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 252-256.
    • Handle: RePEc:eee:phsmap:v:344:y:2004:i:1:p:252-256
    DOI: 10.1016/j.physa.2004.06.127
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    References listed on IDEAS

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    Cited by:

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    3. 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.
    4. Vamvakaris, Michail D. & Pantelous, Athanasios A. & Zuev, Konstantin M., 2018. "Time series analysis of S&P 500 index: A horizontal visibility graph approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 497(C), pages 41-51.

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