Can Agent-Based Models Probe Market Microstructure?

TL;DR

This paper investigates the limitations of agent-based models in capturing market microstructure, highlighting calibration challenges and suggesting alternative modeling approaches that do not rely heavily on detailed agent behaviors.

Contribution

It demonstrates the difficulties in calibrating agent-based models rooted in market microstructure and proposes considering alternative approaches that focus less on agent behavior details.

Findings

Calibration of model parameters related to agent incentives remains problematic.

Realistic order-matching processes may cause parameter degeneracies.

Alternative modeling approaches may be necessary for better data-phenomenology linkage.

Abstract

We extend prior evidence that naively using intraday agent-based models that involve realistic order-matching processes for modeling continuous-time double auction markets seems to fail to be able to provide a robust link between data and many model parameters, even when these models are able to reproduce a number of well-known stylized facts of return time series. We demonstrate that while the parameters of intraday agent-based models rooted in market microstructure can be meaningfully calibrated, those exclusively related to agent behaviors and incentives remain problematic. This could simply be a failure of the calibration techniques used but we argue that the observed parameter degeneracies are most likely a consequence of the realistic matching processes employed in these models. This suggests that alternative approaches to linking data, phenomenology and market structure may be necessary and that it is conceivable that one could construct a useful model that does not directly depend on the nuances of agent behaviors, even when it is known that the real agents engage in complex behaviors.