2020/28 | LEM Working Paper Series | ||||||||||||||||
![]() |
|||||||||||||||||
Non-Normal Identification for Price Discovery in High-Frequency Financial Markets |
|||||||||||||||||
Sebastiano Michele Zema |
|||||||||||||||||
Keywords | |||||||||||||||||
![]() |
![]() |
||||||||||||||||
Information Shares; Structural VECM; Microstructure noise; Independent Component Analysis; Directed acyclic graphs.
|
|||||||||||||||||
JEL Classifications | |||||||||||||||||
![]() |
![]() |
||||||||||||||||
C32, C58, G14
|
|||||||||||||||||
Abstract | |||||||||||||||||
![]() |
![]() |
||||||||||||||||
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.
|
Downloads
|
![]() ![]() |
|
![]()
|
![]() |