This study sets a bank-run equilibrium analysis in a dynamic and incomplete information environment where agents can reconsider attempts to run on the bank over time. The typical static bank-run model is extended in this paper to capture the learning dynamics of agents through time, giving bank-run analysis a more realistic feature. Apart from employing a self-fullling framework in this model, where agents' actions are strategic complements, we allow agents to update over time their beliefs on the strength of the fundamentals that is not commonly known. In particular, we extend the bank-run model analyzed by Goldstein and Pauzner (Journal of Finance 2005) and build it on a dynamic global games framework studied by Angeletos et.al. (Econometrica 2007). We present here how a simple recursive setup can generate a unique monotone perfect Bayesian Nash equilibrium and show how the probability of bank-run is affected through time by the inow of information and the knowledge of previous state outcome. Finally, it is also shown that when an unobservable shock is introduced, multiplicity of equilibria can result in this dynamic learning process.
threshold bank-run monotone perfect Bayesian Nash equilibrium dynamic global games