Volume-Synchronized Probability of Informed Trading (VPIN) is a tool designed to predict extreme events like flash crashes in high-frequency trading. Its aim is to estimate the Probability of Informed Trading (PIN), which was built from a probabilistic framework. Some concerns have been raised about its theoretical foundations and its reliability. More precisely, it has been shown that theoretically the VPIN does not approximate the PIN as the PIN has been built with a time-clock framework and the VPIN with a volume clock one. On a practical point of view, the VPIN has been found to be sensitive to the starting point of computation of a data set and to different parameters, such as the classification rule. In this paper, in order to improve the PIN theoretical framework, we firstly analyze the theoretical foundations of the PIN and the VPIN models to have a better view of all its different assumption subtleties. It secondly makes it possible to point out some approximation flaws in the formula used to approximate the PIN and to propose another exact way to compute the PIN. All different results are illustrated with simulations.

Les données sont centrales pour les élections 2.0 : elles permettraient les victoires électorales et bénéficient d’une très forte médiatisation. Cela est particulièrement valable pour la technique de micro-ciblage géographique des électeurs potentiels : le micro-ciblage électoral. Cette technique correspond à la caractéristique essentielle du tournant technologique des campagnes électorales mêlant professionnalisation des acteurs, croyance dans les big data et les algorithmes. Pourtant, le micro-ciblage électoral reste un objet encore largement méconnu et mal connu. À partir d’une revue de littérature critique, nous montrons que les dimensions techniques de la technologie politique sont fortement minorées dans les recherches de science politique relatives aux campagnes électorales qui se révèlent très hétérogènes et comportent des manques importants. Ceux-ci empêchent d’approfondir la connaissance du micro-ciblage, en particulier sur ses conséquences électorales. Nous identifions quatre enjeux pour appréhender les enjeux des innovations technologiques dans les campagnes électorales et, plus largement, la technologie politique.

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.