Agent-Based Models in Finance: Foundations; Explanatory Power and Applications

The talk provides an introduction to the rich literature on agent-based modelling (ABM) in finance. ABM uses stochastic simulation models of the interaction of a diverse ensemble of heterogeneous investors to mimic real-world patterns of financial trading within an artificial market. The main goal of the early literature in this area has been the explanation of the salient stylized facts (e.g.; fat tails and heteroscedasticity). There has emerged a literature that provided a generic explanation of these features via the market process; in which an innocuous ‘news arrival process’ for fundamental factors (modelled as white noise) is magnified and transformed into a more volatile and fat tailed distribution of market returns by the interactions of the agents. Over the last decade; such agent-based models have reached a state of maturity that brought the tasks of statistical inference and goodness-of-fit of such models on the agenda of the research community. Using mostly relatively simple models of financial markets; a variety of statistical tools have meanwhile been developed to this end. We illustrate various such approaches and demonstrate how the empirical validation of agent-based models can also be used to extract information on ‘hidden’ variables such as sentiment which constitutes a salient building block of such models