Transform Analysis for Hawkes Processes with Applications in Dark Pool Trading

Xuefeng Gao, Xiang Zhou and Lingjiong Zhu

Papers from arXiv.org

Abstract: Hawkes processes are a class of simple point processes that are self-exciting and have clustering effect, with wide applications in finance, social networks and many other fields. This paper considers a self-exciting Hawkes process where the baseline intensity is time-dependent, the exciting function is a general function and the jump sizes of the intensity process are independent and identically distributed non-negative random variables. This Hawkes model is non-Markovian in general. We obtain closed-form formulas for the Laplace transform, moments and the distribution of the Hawkes process. To illustrate the applications of our results, we use the Hawkes process to model the clustered arrival of trades in a dark pool and analyze various performance metrics including time-to-first-fill, time-to-complete-fill and the expected fill rate of a resting dark order.

Date: 2017-10
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Published in Quantitative Finance, 2018 Vol. 18, No. 2, 265-282

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