(DP 2016-07) Threshold Bank-run Equilibrium in Dynamic Games

Romeo Matthew Balanquit


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-fulfilling 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 a¤ected through time by the inflow 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.

JEL Classification: C73, D82, G10


threshold bank-run; monotone perfect Bayesian Nash equilibrium; dynamic global games

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