Market Liquidity Estimation in a High-Frequency Setup

Abstract

This article deals with the identification of a superior forecasting method for market liquidity using a calibrated Heston model for the bid/ask price path simulation instead of a standard Brownian motion, as well as a compound Poisson process and inverse transform sampling for the generation of the bid/ask volume distribution. We show that the simulated trading volumes converge to one single value, which can be used as a liquidity estimator, and find that the calibrated Heston model as well as the inverse transform sampling are superior to both the use of standard Brownian motion and compound Poisson process.