Analysis of order book flows using a non-parametric estimation of the branching ratio matrix
提出一种非参数方法直接估计多变量霍克斯过程的分支比矩阵，应用于EUREX交易所高频订单簿数据，揭示资产事件间关系并分析双资产联合高频动态。
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.