Exponential Kernels with Latency in Hawkes Processes: Applications in Finance

TL;DR

This paper introduces a method for incorporating latency into Hawkes process models using exponential kernels shifted by latency, improving modeling of high-frequency market microstructure data.

Contribution

The authors derive the likelihood function for Hawkes processes with latency-adjusted exponential kernels and validate it with simulated and real market data.

Findings

Latency significantly influences decay patterns in Hawkes processes.

The proposed model captures the causality structure in high-frequency trading data.

Most decay behaviors are determined by latency effects.

Abstract

The Tick library allows researchers in market microstructure to simulate and learn Hawkes process in high-frequency data, with optimized parametric and non-parametric learners. But one challenge is to take into account the correct causality of order book events considering latency: the only way one order book event can influence another is if the time difference between them (by the central order book timestamps) is greater than the minimum amount of time for an event to be (i) published in the order book, (ii) reach the trader responsible for the second event, (iii) influence the decision (processing time at the trader) and (iv) the 2nd event reach the order book and be processed. For this we can use exponential kernels shifted to the right by the latency amount. We derive the expression for the log-likelihood to be minimized for the 1-D and the multidimensional cases, and test this method with simulated data and real data. On real data we find that, although not all decays are the same, the latency itself will determine most of the decays. We also show how the decays are related to the latency. Code is available on GitHub at https://github.com/MarcosCarreira/Hawkes-With-Latency.