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VPIN and the flash crash

Author

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  • Andersen, Torben G.
  • Bondarenko, Oleg

Abstract

The Volume-Synchronized Probability of Informed trading (VPIN) metric is introduced by Easley, López de Prado, and O'Hara (2011a) as a real-time indicator of order flow toxicity. They find the measure useful in monitoring order flow imbalances and conclude it may help signal impending market turmoil, exemplified by historical high readings of the metric prior to the flash crash. More generally, they show that VPIN is significantly correlated with future short-term return volatility. In contrast, our empirical investigation of VPIN documents that it is a poor predictor of short run volatility, that it did not reach an all-time high prior, but rather after, the flash crash, and that its predictive content is due primarily to a mechanical relation with the underlying trading intensity. We also investigate a later incarnation of VPIN, stemming from Easley, López de Prado, and O'Hara (2012a), and reach similar conclusions. In general, we stress that adoption of any specific metric for order flow toxicity should be contingent on satisfactory performance relative to suitable benchmarks, exemplified by the analysis we undertake here.

Suggested Citation

  • Andersen, Torben G. & Bondarenko, Oleg, 2014. "VPIN and the flash crash," Journal of Financial Markets, Elsevier, vol. 17(C), pages 1-46.
  • Handle: RePEc:eee:finmar:v:17:y:2014:i:c:p:1-46
    DOI: 10.1016/j.finmar.2013.05.005
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    References listed on IDEAS

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    1. Torben G. Andersen & Oleg Bondarenko & Maria T. Gonzalez-Perez, 2011. "Coherent Model-Free Implied Volatility: A Corridor Fix for High-Frequency VIX," CREATES Research Papers 2011-49, Department of Economics and Business Economics, Aarhus University.
    2. 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.
    3. Torben G. Andersen & Oleg Bondarenko, 2007. "Construction and Interpretation of Model-Free Implied Volatility," CREATES Research Papers 2007-24, Department of Economics and Business Economics, Aarhus University.
    4. Lee, Charles M C & Ready, Mark J, 1991. "Inferring Trade Direction from Intraday Data," Journal of Finance, American Finance Association, vol. 46(2), pages 733-746, June.
    5. Tauchen, George E & Pitts, Mark, 1983. "The Price Variability-Volume Relationship on Speculative Markets," Econometrica, Econometric Society, vol. 51(2), pages 485-505, March.
    6. Epps, Thomas W & Epps, Mary Lee, 1976. "The Stochastic Dependence of Security Price Changes and Transaction Volumes: Implications for the Mixture-of-Distributions Hypothesis," Econometrica, Econometric Society, vol. 44(2), pages 305-321, March.
    7. 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.
    8. 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.
    9. 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.
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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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