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Impact of the tick-size on financial returns and correlations

Author

Listed:
  • Michael C. Munnix
  • Rudi Schafer
  • Thomas Guhr

Abstract

We demonstrate that the lowest possible price change (tick-size) has a large impact on the structure of financial return distributions. It induces a microstructure as well as it can alter the tail behavior. On small return intervals, the tick-size can distort the calculation of correlations. This especially occurs on small return intervals and thus contributes to the decay of the correlation coefficient towards smaller return intervals (Epps effect). We study this behavior within a model and identify the effect in market data. Furthermore, we present a method to compensate this purely statistical error.

Suggested Citation

  • Michael C. Munnix & Rudi Schafer & Thomas Guhr, 2010. "Impact of the tick-size on financial returns and correlations," Papers 1001.5124, arXiv.org, revised Jul 2010.
    • Handle: RePEc:arx:papers:1001.5124
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    Cited by:

    1. Gabriele La Spada & J. Doyne Farmer & Fabrizio Lillo, 2010. "Tick size and price diffusion," Papers 1009.2329, arXiv.org, revised Oct 2010.
    2. 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).
    3. Thanos Verousis & Pietro Perotti & Georgios Sermpinis, 2018. "One size fits all? High frequency trading, tick size changes and the implications for exchanges: market quality and market structure considerations," Review of Quantitative Finance and Accounting, Springer, vol. 50(2), pages 353-392, February.
    4. 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).
    5. Koike, Yuta, 2014. "Limit theorems for the pre-averaged Hayashi–Yoshida estimator with random sampling," Stochastic Processes and their Applications, Elsevier, vol. 124(8), pages 2699-2753.
    6. Siem Jan Koopman & Rutger Lit & André Lucas, 2015. "Intraday Stock Price Dependence using Dynamic Discrete Copula Distributions," Tinbergen Institute Discussion Papers 15-037/III/DSF90, Tinbergen Institute.
    7. Yang, Honglin & Wan, Hong & Zha, Yong, 2013. "Autocorrelation type, timescale and statistical property in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(7), pages 1681-1693.
    8. Tao You & Paweł Fiedor & Artur Hołda, 2015. "Network Analysis of the Shanghai Stock Exchange Based on Partial Mutual Information," JRFM, MDPI, vol. 8(2), pages 1-19, June.
    9. Fiedor, Paweł, 2014. "Sector strength and efficiency on developed and emerging financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 180-188.
    10. Dimitrakopoulos, Stefanos & Tsionas, Mike, 2019. "Ordinal-response GARCH models for transaction data: A forecasting exercise," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1273-1287.
    11. 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.
    12. 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.
    13. 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.
    14. Shen, Chen-hua & Li, Cao-ling, 2016. "An analysis of the intrinsic cross-correlations between API and meteorological elements using DPCCA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 446(C), pages 100-109.

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