Non-average price impact in order-driven markets

TL;DR

This paper introduces a novel method to measure the price impact of individual sell metaorders in order-driven markets using Hawkes process modeling, revealing that order clustering influences prices more than order size.

Contribution

It develops a new approach to quantify price impact without averaging, utilizing Hawkes processes to analyze the effect of order clustering on prices.

Findings

Order clustering has a greater impact on price than order size.

The method accurately measures price impact from single execution data.

Application to NASDAQ data validates the approach.

Abstract

We present a measurement of price impact in order-driven markets that does not require averages across executions or scenarios. Given the order book data associated with one single execution of a sell metaorder, we measure its contribution to price decrease during the trade. We do so by modelling the limit order book using state-dependent Hawkes processes, and by defining the price impact profile of the execution as a function of the compensator of a stochastic process in our model. We apply our measurement to a data set from NASDAQ, and we conclude that the clustering of sell child orders has a bigger impact on price than their sizes.