Interbank network and regulation policies: an analysis through agent-based simulations with adaptive learning

In this paper, we develop an agent-based model to study the impact of a broad range of regulation policies on the banking system. The model builds on an iterated version of the Diamond and Dybvig framework and resorts to the experience-weighted attraction learning scheme of Camerer and Ho to demonstrate agents' adaptive learning. Thereby, we can capture not only the direct impact of regulation policies but also the effect of shifting agents' adaptive strategies. Our results show that an interbank clearing house is a good instrument with which to face the risk of contagion; the regulatory guidelines of the Basel Accord are effective in reducing the probability of bank failure; and the adoption of deposit insurance can be adequate to avoid bank runs. However, we also show that these policies have drawbacks and can either reduce bank activity or stimulate moral hazard.