Non-normal identification for price discovery in high-frequency financial markets

The possibility to measure the relative contribution of agents and exchanges to the price formation process in high-frequency financial markets acquired increasingly importance in the financial econometric literature. In this paper I propose to adopt fully data-driven approaches to identify structural vector error correction models (SVECM) typically used for price discovery. Exploiting the non-Normal distributions of the variables under consideration, I propose two novel variants of the widespread Information Share (IS) measure which are able to identify the leaders and the followers in the price formation process. The approaches will be illustrated both from a semiparametric and parametric standpoints, solving the identification problem with no need of increasing the computational complexity which usually arises when working at incredibly short time scales. Finally, an empirical application on IBM intraday data will be provided.