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

We develop an agent-based model to study the impacts of a broad range of regulation policies over the banking system. It builds on an iterated version of the \citet{DiamondDybvig1983} framework and resorts to the experience-weighted attraction learning scheme of \citet{CamererHo1999} to model agents' adaptive learning. Thereby, we can capture not only the direct impacts of regulation policies, but also the ones that take part through shifting agents' adaptive strategies. Our results show that the introduction of an interbank clearinghouse is a good instrument 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 a 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.