(Submitted on 5 Jun 2016)
Agent-based models, particularly those applied to financial markets, demonstrate the ability to produce realistic, simulated system dynamics, comparable to those observed in empirical investigations. Despite this, they remain fairly difficult to calibrate due to their tendency to be computationally expensive, even with recent advances in technology. For this reason, financial agent-based models are frequently validated by demonstrating an ability to reproduce well-known log return time series and central limit order book stylized facts, as opposed to being rigorously calibrated to transaction data. We thus apply an established financial agent-based model calibration framework to a simple model of high- and low-frequency trader interaction and demonstrate possible inadequacies of a stylized fact-centric approach to model validation. We further argue for the centrality of calibration to the validation of financial agent-based models and possible pitfalls of current approaches to financial agent-based modeling.