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VPIN and the Flash Crash

Author

Listed:
  • Torben G. Andersen

    () (Kellogg School of Management; Northwestern University and CREATES)

  • Oleg Bondarenko

    () (Department of Finance (MC 168), University of Illinois at Chicago)

Abstract

Easley, Lopez de Prado and O'Hara introduce VPIN as a real-time indicator of order flow toxicity. They find it useful for monitoring order fl ow imbalances and signaling impending market turmoil, exemplified by the ash crash. They also deem VPIN a good forecaster of short-term volatility. In contrast, we find that VPIN is a poor volatility predictor, that it only reached an all-time high following the ash crash, and that its predictive content stems from a mechanical relation with trading intensity. Generally, we caution against adoption of any specific market stress metric until it is compared thoroughly to suitable benchmarks.

Suggested Citation

  • Torben G. Andersen & Oleg Bondarenko, 2011. "VPIN and the Flash Crash," CREATES Research Papers 2011-50, Department of Economics and Business Economics, Aarhus University.
  • Handle: RePEc:aah:create:2011-50
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    File URL: ftp://ftp.econ.au.dk/creates/rp/11/rp11_50.pdf
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    References listed on IDEAS

    as
    1. Tauchen, George E & Pitts, Mark, 1983. "The Price Variability-Volume Relationship on Speculative Markets," Econometrica, Econometric Society, vol. 51(2), pages 485-505, March.
    2. David Easley & Marcos M. López de Prado & Maureen O'Hara, 2012. "Flow Toxicity and Liquidity in a High-frequency World," Review of Financial Studies, Society for Financial Studies, vol. 25(5), pages 1457-1493.
    3. Torben G. Andersen & Oleg Bondarenko, 2007. "Construction and Interpretation of Model-Free Implied Volatility," NBER Working Papers 13449, National Bureau of Economic Research, Inc.
    4. 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.
    5. 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.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Mark Paddrik & Roy Hayes & William Scherer & Peter Beling, 2014. "Effects of Limit Order Book Information Level on Market Stability Metrics," Working Papers 14-09, Office of Financial Research, US Department of the Treasury.
    2. Torben G. Andersen & Oleg Bondarenko, 2015. "Assessing Measures of Order Flow Toxicity and Early Warning Signals for Market Turbulence," Review of Finance, European Finance Association, vol. 19(1), pages 1-54.
    3. repec:spr:jeicoo:v:12:y:2017:i:2:d:10.1007_s11403-015-0164-6 is not listed on IDEAS
    4. Chakrabarty, Bidisha & Pascual, Roberto & Shkilko, Andriy, 2015. "Evaluating trade classification algorithms: Bulk volume classification versus the tick rule and the Lee-Ready algorithm," Journal of Financial Markets, Elsevier, vol. 25(C), pages 52-79.
    5. Alexandru Stan, 2015. "A Price Crash Alerting Strategy for Agent-based Artificial Financial Markets," MIC 2015: Managing Sustainable Growth; Proceedings of the Joint International Conference, Portorož, Slovenia, 28–30 May 2015, University of Primorska, Faculty of Management Koper.
    6. repec:spr:rvmgts:v:12:y:2018:i:1:d:10.1007_s11846-016-0219-7 is not listed on IDEAS
    7. Thomas Pöppe & Michael Aitken & Dirk Schiereck & Ingo Wiegand, 2016. "A PIN per day shows what news convey: the intraday probability of informed trading," Review of Quantitative Finance and Accounting, Springer, vol. 47(4), pages 1187-1220, November.
    8. Andersen, Torben G. & Bondarenko, Oleg, 2014. "Reflecting on the VPIN dispute," Journal of Financial Markets, Elsevier, vol. 17(C), pages 53-64.
    9. Xiaohong Chen & Oliver Linton & Stefan Schneeberger & Yanping Yi, 2016. "Simple Nonparametric Estimators for the Bid-Ask Spread in the Roll Model," CeMMAP working papers CWP12/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. Herrmann, Klaus & Teis, Stefan & Yu, Weijun, 2014. "Components of intraday volatility and their prediction at different sampling frequencies with application to DAX and BUND futures," FAU Discussion Papers in Economics 15/2014, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    11. repec:eee:joecas:v:13:y:2016:i:c:p:21-34 is not listed on IDEAS
    12. 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.
    13. repec:eee:jbfina:v:86:y:2018:i:c:p:21-36 is not listed on IDEAS
    14. Cakici, Nusret & Goswami, Gautam & Tan, Sinan, 2014. "Options resilience during extreme volatility: Evidence from the market events of May 2010," Journal of Banking & Finance, Elsevier, vol. 49(C), pages 262-274.

    More about this item

    Keywords

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

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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