  We introduce a new non parametric method that allows for a direct, fast and
efficient estimation of the matrix of kernel norms of a multivariate Hawkes
process, also called branching ratio matrix. We demonstrate the capabilities of
this method by applying it to high-frequency order book data from the EUREX
exchange. We show that it is able to uncover (or recover) various relationships
between all the first level order book events associated with some asset when
mapped to a 12-dimensional process. We then scale up the model so as to account
for events on two assets simultaneously and we discuss the joint high-frequency
dynamics.
