Stochastic simulation framework for the limit order book using liquidity-motivated agents

In this paper, we develop a new form of simulation model for limit order books based on heterogeneous trading agents, whose motivations are liquidity driven. These agents are abstractions of real market participants, expressed in a stochastic model framework. We develop an efficient way to perform statistical calibration of the model parameters on Level 2 limit order book data from Chi-X, based on a combination of indirect inference and multi-objective optimization. We then demonstrate how such a modeling framework can be of use in testing exchange regulations, as well as informing brokerage decisions and other trading based scenarios.