Reflecting on the VPIN Dispute
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).
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- 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.
- Andersen, Torben G. & Bondarenko, Oleg, 2014.
"VPIN and the flash crash,"
Journal of Financial Markets,
Elsevier, vol. 17(C), pages 1-46.
- 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.
- 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.
- Steffen Bohn, 2011. "The slippage paradox," Papers 1103.2214, arXiv.org.
- Steffen Bohn, 2011. "The slippage paradox," Working Papers hal-00574268, HAL.
- 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.
- Torben G. Andersen & Oleg Bondarenko, 2013. "Assessing Measures of Order Flow Toxicity via Perfect Trade Classification," CREATES Research Papers 2013-43, School of Economics and Management, University of Aarhus.
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