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Reflecting on the VPIN Dispute

Author

Listed:
  • Torben G. Andersen

    (Northwestern University and CREATES)

  • Oleg Bondarenko

    (University of Illinois at Chicago)

Abstract
In Andersen and Bondarenko (2014), using tick data for S&P 500 futures, we establish that the VPIN metric of Easley, Lopez de Prado, and O'Hara (ELO), by construction, will be correlated with trading volume and return volatility (innovations). Whether VPIN is more strongly correlated with volume or volatility depends on the exact implementation. Hence, it is crucial for the interpretation of VPIN as a harbinger of market turbulence or as a predictor of short-term volatility to control for current volume and volatility. Doing so, we find no evidence of incremental predictive power of VPIN for future volatility. Likewise, VPIN does not attain unusual extremes prior to the flash crash. Moreover, the properties of VPIN are strongly dependent on the underlying trade classification. In particular, using more standard classification techniques, VPIN behaves in the exact opposite manner of what is portrayed in ELO (2011a, 2012a). At a minimum, ELO should rationalize this systematic reversal as the classification becomes more closely aligned with individual transactions. ELO (2014) dispute our findings. This note reviews the econometric methodology and the market microstructure arguments behind our conclusions and responds to a number of inaccurate assertions. In addition, we summarize fresh empirical evidence that corroborates the hypothesis that VPIN is largely driven, and significantly distorted, by the volume and volatility innovations. Furthermore, we note there is compelling new evidence that transaction-based classification schemes are more accurate than the bulk volume strategies advocated by ELO for constructing VPIN. In fact, using perfect classification leads to diametrically opposite results relative to ELO (2011a, 2012a).

Suggested Citation

  • Torben G. Andersen & Oleg Bondarenko, 2013. "Reflecting on the VPIN Dispute," CREATES Research Papers 2013-42, Department of Economics and Business Economics, Aarhus University.
    • Handle: RePEc:aah:create:2013-42
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    References listed on IDEAS

    as
    1. Wu, Kesheng & Bethel, E. Wes & Gu, Ming & Leinweber, David & Rübe, Oliver, 2013. "A big data approach to analyzing market volatility," Algorithmic Finance, IOS Press, vol. 2(3-4), pages 241-267.
    2. Torben G. Andersen & Oleg Bondarenko, 2013. "Assessing Measures of Order Flow Toxicity via Perfect Trade Classification," CREATES Research Papers 2013-43, Department of Economics and Business Economics, Aarhus University.
    3. 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.
    4. Easley, David & López de Prado, Marcos M. & O'Hara, Maureen, 2014. "VPIN and the Flash Crash: A rejoinder," Journal of Financial Markets, Elsevier, vol. 17(C), pages 47-52.
    5. Steffen Bohn, 2011. "The slippage paradox," Papers 1103.2214, arXiv.org.
    6. 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.
    7. Steffen Bohn, 2011. "The slippage paradox," Working Papers hal-00574268, HAL.
    8. Andersen, Torben G, 1996. "Return Volatility and Trading Volume: An Information Flow Interpretation of Stochastic Volatility," Journal of Finance, American Finance Association, vol. 51(1), pages 169-204, March.
    9. Andersen, Torben G. & Bondarenko, Oleg, 2014. "VPIN and the flash crash," Journal of Financial Markets, Elsevier, vol. 17(C), pages 1-46.
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    Cited by:

    1. Usman Arief & Zaäfri Ananto Husodo, 2021. "Private Information from Extreme Price Movements (Empirical Evidences from Southeast Asia Countries)," International Symposia in Economic Theory and Econometrics, in: Recent Developments in Asian Economics International Symposia in Economic Theory and Econometrics, volume 28, pages 221-242, Emerald Group Publishing Limited.
    2. Ulze, Markus & Stadler, Johannes & Rathgeber, Andreas W., 2021. "No country for old distributions? On the comparison of implied option parameters between the Brownian motion and variance gamma process," The Quarterly Review of Economics and Finance, Elsevier, vol. 82(C), pages 163-184.
    3. Paparizos, Panagiotis & Dimitriou, Dimitrios & Kenourgios, Dimitris & Simos, Theodore, 2016. "On high frequency dynamics between information asymmetry and volatility for securities," The Journal of Economic Asymmetries, Elsevier, vol. 13(C), pages 21-34.
    4. Chang, Sanders S. & Wang, F. Albert, 2015. "Adverse selection and the presence of informed trading," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 19-33.
    5. Mila Getmansky & Ravi Jagannathan & Loriana Pelizzon & Ernst Schaumburg & Darya Yuferova, 2017. "Stock Price Crashes: Role of Slow-Moving Capital," NBER Working Papers 24098, National Bureau of Economic Research, Inc.
    6. Quan Gan & Wang Chun Wei & David Johnstone, 2015. "A faster estimation method for the probability of informed trading using hierarchical agglomerative clustering," Quantitative Finance, Taylor & Francis Journals, vol. 15(11), pages 1805-1821, November.
    7. Chang, Sanders S. & Albert Wang, F., 2019. "Informed contrarian trades and stock returns," Journal of Financial Markets, Elsevier, vol. 42(C), pages 75-93.
    8. Steffen, Viktoria, 2023. "A literature review on extreme price movements with reversal," Journal of Behavioral and Experimental Finance, Elsevier, vol. 38(C).

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    More about this item

    Keywords

    VPIN; PIN; High-Frequency Trading; Order Flow Toxicity; Order Imbalance; Flash Crash; VIX; Volatility Forecasting;
    All these keywords.

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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