Testing Vpin on Big Data Response to Reflecting on the Vpin Dispute - eScholarship (original) (raw)

An Assessment of the Prediction Quality of VPIN

Advanced Analytics and Artificial Intelligence Applications [Working Title], 2019

VPIN is a tool designed to predict extreme events like flash crashes. Some concerns have been raised about its reliability. In this chapter we assess VPIN prediction quality (precision and recall rates) of extreme volatility events including its sensitivity to the starting point of computation in a given data set. We benchmark the results with the ones of a "naive classifier." The test data used in this study contains 5.6 year's worth of trading data of the five most liquid futures contracts of this time period. We found that VPIN has poor "flash crash" prediction power with the traditional 0.99 decision threshold. Increasing the decision threshold does not significantly improve overall prediction quality. Nevertheless we found VPIN has a more interesting predictive power for flash events of lower amplitude. Finally, we found that, for practice, the last bar price structure is the least sensitive to the starting point of computation.

Reflecting on the VPIN dispute

Journal of Financial Markets, 2014

In Andersen and Bondarenko (2014), using tick data for S&P 500 futures, we establish that the VPIN metric of Easley, López 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 www.elsevier.com/locate/finmar 1386-4181/$ -see front matter &

Assessing VPIN Measurement of Order Flow Toxicity via Perfect Trade Classification

SSRN Electronic Journal, 2013

Following the much publicized "flash crash" in the U.S. financial markets on May 6, 2010, much work has been done in terms of developing reliable warning signals for impending market stress. However, this has met with limited success, except for one measure. The VPIN, or Volume-synchronized Probability of INformed trading, metric is introduced by Easley, López de Prado and O'Hara (ELO) as a real-time indicator of order flow toxicity. They find the measure useful in predicting return volatility and conclude it, indeed, may help signal impending market turmoil. The VPIN metric involves decomposing volume into active buys and sells. We use the best-bid-offer (BBO) files from the CME Group to construct highly accurate trade classification measures for the E-mini S&P 500 futures contract. Against this benchmark, the ELO Bulk Volume Classification (BVC) scheme is inferior to a standard tick rule based on individual transactions. Moreover, when VPIN is constructed from an accurate classification, it behaves in a diametrically opposite way to BVC-VPIN. We also find the latter to have forecast power for volatility solely because it generates systematic classification errors that are correlated with trading volume and return volatility. Controlling for trading intensity and volatility, the BVC-VPIN measure has no incremental predictive power for future volatility. We conclude that VPIN is not suitable for capturing order flow toxicity or signaling ensuing market turbulence.

VPIN and the flash crash

Journal of Financial Markets, 2014

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.

Parameter Analysis of the VPIN (Volume synchronized Probability of Informed Trading) Metric

VPIN (Volume synchronized Probability of Informed trading) is a leading indicator of liquidity-induced volatility. It is best known for having produced a signal more than hours before the Flash Crash of 2010. On that day, the market saw the biggest one-day point decline in the Dow Jones Industrial Average, which culminated to the market value of $1 trillion disappearing, but only to recover those losses twenty minutes later (Lauricella 2010).

From PIN to VPIN: An introduction to order flow toxicity

The Spanish Review of Financial Economics, 2012

As an update of the well-known PIN measure, Easley et al. (2012a) have developed a new measure of order flow toxicity called Volume-Synchronized Probability of Informed Trading or VPIN. Order flow toxicity makes reference to adverse selection risk but applied to the world of high frequency trading (HFT). We provide a detailed description of the VPIN estimation procedure paying special attention to the main innovations introduced and the key variables of this novel tool. By using a sample of stocks listed on the Spanish market, we compare VPIN to PIN. Although VPIN metric is conceived for the HFT environment, our results suggest that certain VPIN specifications provide proxies for adverse selection risk similar to those obtained by the PIN model. Thus, we consider that the key variable in the VPIN procedure is the number of buckets used and that VPIN can be a helpful device which is not exclusively applicable to the HFT world.

Evaluating VPIN as a Trigger for Single-Stock Circuit Breakers

SSRN Electronic Journal, 2015

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights  VPIN rarely signals abnormal illiquidity.  VPIN only occasionally anticipates price changes leading to actual trading halts.  The capacity of VPIN to anticipate truly toxic events is limited.  VPIN limits cannot substitute traditional price limits.  VPIN-based circuit breakers can be costly in terms of unnecessary trading cessations.

VPIN, Jump Dynamics and Inventory Announcements in Energy Futures Markets

Journal of Futures Markets, 2017

The Volume-Synchronized Probability of Informed Trading (VPIN) metric is proposed by Easley et al. (2011, 2012) as a real-time measure of order flow toxicity in an electronic trading market. This paper examines the performance of VPIN around inventory announcements and price jumps in crude oil and natural gas futures markets with a sample period from January 2009 to May 2015. We have obtained several interesting results: (1) VPIN increased significantly around the inventory announcements with price jumps (scheduled events) and at jumps not associated with any scheduled announcements (unscheduled events). (2) VPIN did not peak prior to the events but shortly after. (3) A minor variation of VPIN based on exponential smoothing significantly improved the early warning signal property of VPIN. Moreover, this estimate of toxicity returns faster to the pre-event level. (4) In general, the VPIN estimate of the toxicity level is higher in natural gas futures than in crude oil futures during our sample period.