  This paper proposes a methodology to empirically validate an agent-based
model (ABM) that generates artificial financial time series data comparable
with real-world financial data. The approach is based on comparing the results
of the ABM against the stylised facts -- the statistical properties of the
empirical time-series of financial data. The stylised facts appear to be
universal and are observed across different markets, financial instruments and
time periods, hence they can serve to validate models of financial markets. If
a given model does not consistently replicate these stylised facts, then we can
reject it as being empirically inadequate. We discuss each stylised fact, the
empirical evidence for it, and introduce appropriate metrics for testing the
presence of these in model generated data. Moreover we investigate the ability
of our model to correctly reproduce these stylised facts. We validate our model
against a comprehensive list of empirical phenomena that qualify as a stylised
fact, of both low and high frequency financial data that can be addressed by
means of a relatively simple ABM of financial markets. This procedure is able
to show whether the model, as an abstraction of reality, has a meaningful
empirical counterpart and the significance of this analysis for the purposes of
ABM validation and their empirical reliability.
