Published in «Global Markets and Financial Engineering»2 / 2016
DOI: 10.18334/grfi.3.2.36541

VPIN as Measure of Liquidity, Volatility and Information in Stock-Exchange Price Behavior

Chupriyanov Maksim , National Research University - Higher School of Economics, Moscow, Russian Federation, National Research University - Higher School of Economics, Russia

VPIN как мера ликвидности, волатильности и информации в биржевой динамике цен - View in Russian

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Highlights:
► the information asymmetry is not markedly observed, its presence becomes evident only from the data of market trading
► the first indicator (PIN) describing the information asymmetry was introduced in 1996
► in the conditions of high-frequency trading VPIN is the main indicator of claim flow toxicity
► there are two opposite viewpoints among scholars; one of them postulates that VPIN is a useful and unbiased measuring instrument of the information asymmetry, while the other one asserts that this is just another measure of volatility
► VPIN indicator has a negative correlation with trade intensity and positive correlation with volatility of volumes in time bars
► VPIN indicator has weak forecast power in relation to the future short-term volatility
► VPIN indicator has a positive correlation with the volatility index VIX and daily volume
► on the additional (nonbasic) stock-exchange the appearance of the information asymmetry is a result of information asymmetry on the main instrument trade platform
► the appraisals of VPIN indicator on the additional platform are high and, given that the actors of this platform are likely only to copy the acts on the main platform and there can be no independent information asymmetry, the validity of this indicator raises concerns

Abstract:
In microstructure of financial markets there are two consecutive trade models based on the claim flow imbalance that are used for measuring the informational asymmetry. This research contains the analysis of an indicator of the claim flow toxicity. This indicator has been adjusted for the conditions of VPIN high-frequency trading and is based on the model for determining the probability of informed trading (PIN) that was developed by Easley, Kiefer, O'Hara, and Paperman (1996). This work gives the answer to the following question: is VPIN (Volume-Synchronized Probability of Informed Trading) metrics an appropriate proxy for finding the information asymmetry. In the course of the research the author has tested the forecast capability of VPIN indicator, index unbiasedness based on correlation with trade intensity, volatility of the volumes and volatility index VIX, causal dependence of the indicator on the main (CME) and additional (BM&FBOVESPA) trading platform. The results have demonstrated that VPIN has negative correlation with trade intensity and positive correlation with volatility index VIX, daily volume and inhomogeneity of volumes. VPIN indicator has weak forecast power for finding the future short-term volatility. Information asymmetry cannot be generated on the additional stock-exchange by itself; its appearance is a consequence of the information asymmetry on the main stock-exchange; therefore, VPIN reflects not the information asymmetry, but the volatility of volumes.

Keywords:

high-frequency exchange trading, information asymmetry, liquidity, toxicity, volatility, VPIN

JEL-Classification: D82, G12, G14

Citation:
Maksim Chupriyanov (2016). VPIN as Measure of Liquidity, Volatility and Information in Stock-Exchange Price Behavior [VPIN kak mera likvidnosti, volatilnosti i informatsii v birzhevoy dinamike tsen]. Global Markets and Financial Engineering, 3(2). (in Russian). – doi: 10.18334/grfi.3.2.36541.


References (transliterated):
1. Bukhovtsev, A.G., Moskalev, P.V., Bogatova, V.P., i dr. (2010). Statisticheskiy analiz dannyh v sisteme R. Voronezh: VGAU.
2. Zaryadov, I.S. (2010). Statisticheskiy paket R: teoriya veroyatnostey i matematicheskaya statistika. M.: Izdatelstvo RUDN.
3. Abad, D., Yague, J. (2012). From PIN to VPIN: An introduction to order flow toxicity. The Spanish Review of Financial Economics, 10(2), 74–83.
4. Andersen, T. G., Bondarenko, O. (2014). VPIN and the Flash Crash. Journal of Financial Markets, 17, 1–46.
5. Andersen, T. G., Bondarenko, O. (2014). Reflecting on VPIN dispute. Journal of Financial Markets.
6. Andersen, T. G., Bondarenko, O. (2015). Assessing measures of order flow toxicity and early warning signals for market turbulence. Review of Finance.
7. Duarte, J., Young, L. (2009). Why is PIN priced?. Journal of Financial Economics, 91(2), 119–138.
8. Easley, D., López de Prado, M., O’Hara, M. (2011b). Flow toxicity and liquidity in a high frequency world. Review of Financial Studies, Forthcoming.
9. Easley, D., Engle, R. F., O’Hara, M., et al. (2008). Time-varying arrival rates of informed and uninformed traders. Journal of Financial Econometrics, 6(2), 171–207.
10. Easley, D., Hvidkjaer, S., O’Hara, M. (2010). Factoring information into returns. Journal of Financial and Quantitative Analysis, 45(2), 293–309.
11. Easley, D., López de Prado, M., O’Hara, M. (2011a). The microstructure of the Flash Crash. Journal of Portfolio Management, Winter.
12. Easley, D., López de Prado, M., O’Hara, M. (2011s). Flow toxicity and volatility in a high frequency world.
13. Easley, D., López de Prado, M., O’Hara, M. (2012b). Bulk classification of trading activity.
14. Easley, D., Hvidkjaer, S., O’Hara, M. (2002). Is information risk a determinant of asset returns?. The Journal of Finance, 57(5), 2185–2221.
15. Easley, D., Kiefer, N., O’Hara, M. (1997). One day in the life of a very common stock. The Review of Financial Studies, 10, 805–835.
16. Easley, D., Kiefer, N. M., O’Hara, M., et al. (1996). Liquidity, information, and infrequently traded stocks. The Journal of Finance, 51(4), 1405–1436.
17. Glosten, L., Migrom, P. (1985). Bid, ask and transaction prices in a specialist market with heterogeneously informed traders. Journal of Financial Economics, 14, 71–100.
18. Jiang, J. (2015). Volume-synchronized probability of informed trading (VPIN), market volatility, and high-frequency liquidity. School of Business, Brock University.
19. Hasbrouck, J. (2004). Empirical Market Microstructure.
20. Yildiz, S., Van Ness, R. A., Van Ness, B. F. (2013). Analysis determinants of VPIN, HFTs’ order flow toxicity and impact on stock price variance [Unpublished working paper]. Oxford: University of Mississippi.