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Revisiting the Epps effect using volume time averaging: An exercise in R

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  • Patrick Chang
  • Roger Bukuru
  • Tim Gebbie

Abstract

We revisit and demonstrate the Epps effect using two well-known non-parametric covariance estimators; the Malliavin and Mancino (MM), and Hayashi and Yoshida (HY) estimators. We show the existence of the Epps effect in the top 10 stocks from the Johannesburg Stock Exchange (JSE) by various methods of aggregating Trade and Quote (TAQ) data. Concretely, we compare calendar time sampling with two volume time sampling methods: asset intrinsic volume time averaging, and volume time averaging synchronised in volume time across assets relative to the least and most liquid asset clocks. We reaffirm the argument made in much of the literature that the MM estimator is more representative of trade time reality because it does not over-estimate short-term correlations in an asynchronous event driven world. We confirm well known market phenomenology with the aim of providing some standardised R based simulation tools.

Suggested Citation

  • 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.
    • Handle: RePEc:arx:papers:1912.02416
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    1. Emmanuel Bacry & Iacopo Mastromatteo & Jean-Franc{c}ois Muzy, 2015. "Hawkes processes in finance," Papers 1502.04592, arXiv.org, revised May 2015.
    2. I. Mastromatteo & M. Marsili & P. Zoi, 2011. "Financial correlations at ultra-high frequency: theoretical models and empirical estimation," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 80(2), pages 243-253, March.
    3. Emanuel Derman, 2002. "The perception of time, risk and return during periods of speculation," Quantitative Finance, Taylor & Francis Journals, vol. 2(4), pages 282-296.
    4. Ovidiu V. Precup & Giulia Iori, 2007. "Cross-correlation Measures in the High-frequency Domain," The European Journal of Finance, Taylor & Francis Journals, vol. 13(4), pages 319-331.
    5. Iacopo Mastromatteo & Matteo Marsili & Patrick Zoi, 2010. "Financial correlations at ultra-high frequency: theoretical models and empirical estimation," Papers 1011.1011, arXiv.org, revised Feb 2011.
    6. D. Hendricks & T. Gebbie & D. Wilcox, 2016. "Detecting intraday financial market states using temporal clustering," Quantitative Finance, Taylor & Francis Journals, vol. 16(11), pages 1657-1678, November.
    7. David Easley & Marcos M. López de Prado & Maureen O'Hara, 2012. "Flow Toxicity and Liquidity in a High-frequency World," The Review of Financial Studies, Society for Financial Studies, vol. 25(5), pages 1457-1493.
    8. Barucci, Emilio & Reno, Roberto, 2002. "On measuring volatility and the GARCH forecasting performance," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 12(3), pages 183-200, July.
    9. Emanuel Derman, 2002. "The Perception of Time, Risk and Return During Periods of Speculation," Papers cond-mat/0201345, arXiv.org.
    10. Bacry, E. & Delattre, S. & Hoffmann, M. & Muzy, J.F., 2013. "Some limit theorems for Hawkes processes and application to financial statistics," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2475-2499.
    11. A. Saichev & D. Sornette, 2014. "A simple microstructure return model explaining microstructure noise and Epps effects," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 25(06), pages 1-36.
    12. Diane Wilcox & Tim Gebbie, 2014. "Hierarchical causality in financial economics," Papers 1408.5585, arXiv.org, revised Sep 2014.
    13. Wilcox, Diane & Gebbie, Tim, 2007. "An analysis of cross-correlations in an emerging market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 375(2), pages 584-598.
    14. Toshiya Hoshikawa & Keiji Nagai & Taro Kanatani & Yoshihiko Nishiyama, 2008. "Nonparametric Estimation Methods of Integrated Multivariate Volatilities," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 112-138.
    15. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    16. Large, Jeremy, 2007. "Measuring the resiliency of an electronic limit order book," Journal of Financial Markets, Elsevier, vol. 10(1), pages 1-25, February.
    17. Gençay, Ramazan & Dacorogna, Michel & Muller, Ulrich A. & Pictet, Olivier & Olsen, Richard, 2001. "An Introduction to High-Frequency Finance," Elsevier Monographs, Elsevier, edition 1, number 9780122796715.
    18. Emmanuel Bacry & Sylvain Delattre & Marc Hoffmann & Jean-François Muzy, 2013. "Some limit theorems for Hawkes processes and application to financial statistics," Post-Print hal-01313994, HAL.
    19. Mancino, M.E. & Sanfelici, S., 2008. "Robustness of Fourier estimator of integrated volatility in the presence of microstructure noise," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 2966-2989, February.
    20. John Ioannidis & Chris Doucouliagos, 2013. "What'S To Know About The Credibility Of Empirical Economics?," Journal of Economic Surveys, Wiley Blackwell, vol. 27(5), pages 997-1004, December.
    21. Griffin, Jim E. & Oomen, Roel C.A., 2011. "Covariance measurement in the presence of non-synchronous trading and market microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 58-68, January.
    22. Dieter Hendricks, 2016. "Using real-time cluster configurations of streaming asynchronous features as online state descriptors in financial markets," Papers 1603.06805, arXiv.org, revised May 2017.
    23. Aït-Sahalia, Yacine & Fan, Jianqing & Xiu, Dacheng, 2010. "High-Frequency Covariance Estimates With Noisy and Asynchronous Financial Data," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1504-1517.
    24. Precup, O. V. & Iori, G., 2005. "Cross-correlation measures in the high-frequency domain," Working Papers 05/04, Department of Economics, City University London.
    25. Maria Elvira Mancino & Paul Malliavin, 2002. "Fourier series method for measurement of multivariate volatilities," Finance and Stochastics, Springer, vol. 6(1), pages 49-61.
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