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Compensating asynchrony effects in the calculation of financial correlations

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  • Michael C. Munnix
  • Rudi Schafer
  • Thomas Guhr

Abstract

We present a method to compensate statistical errors in the calculation of correlations on asynchronous time series. The method is based on the assumption of an underlying time series. We set up a model and apply it to financial data to examine the decrease of calculated correlations towards smaller return intervals (Epps effect). We show that this statistical effect is a major cause of the Epps effect. Hence, we are able to quantify and to compensate it using only trading prices and trading times.

Suggested Citation

  • Michael C. Munnix & Rudi Schafer & Thomas Guhr, 2009. "Compensating asynchrony effects in the calculation of financial correlations," Papers 0910.2909, arXiv.org, revised Jul 2010.
    • Handle: RePEc:arx:papers:0910.2909
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    References listed on IDEAS

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    1. G. Bonanno & F. Lillo & R. N. Mantegna, 2001. "High-frequency cross-correlation in a set of stocks," Quantitative Finance, Taylor & Francis Journals, vol. 1(1), pages 96-104.
    2. Ole E. Barndorff-Nielsen & Neil Shephard, 2001. "Econometric Analysis of Realised Covariation: High Frequency Covariance, Regression and Correlation in Financial Economics," Economics Papers 2002-W13, Economics Group, Nuffield College, University of Oxford, revised 18 Mar 2002.
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    Cited by:

    1. Henryk Gurgul & Artur Machno, 2017. "The impact of asynchronous trading on Epps effect on Warsaw Stock Exchange," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 25(2), pages 287-301, June.
    2. Michael C. Munnix & Rudi Schafer, 2011. "A Copula Approach on the Dynamics of Statistical Dependencies in the US Stock Market," Papers 1102.1099, arXiv.org, revised Mar 2011.
    3. Juan C. Henao-Londono & Sebastian M. Krause & Thomas Guhr, 2021. "Price response functions and spread impact in correlated financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 94(4), pages 1-20, April.
    4. Sandoval, Leonidas & Franca, Italo De Paula, 2012. "Correlation of financial markets in times of crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 187-208.
    5. Münnix, Michael C. & Schäfer, Rudi, 2011. "A copula approach on the dynamics of statistical dependencies in the US stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4251-4259.
    6. Leonidas Sandoval Junior & Italo De Paula Franca, 2011. "Correlation of financial markets in times of crisis," Papers 1102.1339, arXiv.org, revised Mar 2011.
    7. Henao-Londono, Juan C. & Guhr, Thomas, 2022. "Foreign exchange markets: Price response and spread impact," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    8. Sandoval, Leonidas, 2014. "To lag or not to lag? How to compare indices of stock markets that operate on different times," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 403(C), pages 227-243.
    9. Ruijin Du & Gaogao Dong & Lixin Tian & Minggang Wang & Guochang Fang & Shuai Shao, 2016. "Spatiotemporal Dynamics and Fitness Analysis of Global Oil Market: Based on Complex Network," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-17, October.

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