Systemic banking crisis early warning systems using dynamic Bayesian networks

•Dynamic Bayesian networks are applied as early warning systems in Banking crises.•A comparison to the traditional logit and signal extraction methods is provided.•A unique approach is used to measure the ability of a method to predict a crisis.•Results indicate that dynamic Bayesian networks are superior at predicting crises.

For decades, the literature on banking crisis early-warning systems has been dominated by two methods, namely, the signal extraction and the logit model methods. However, these methods, do not model the dynamics of the systemic banking system. In this study, dynamic Bayesian networks are applied as systemic banking crisis early-warning systems. In particular, the hidden Markov model, the switching linear dynamic system and the naïve Bayes switching linear dynamic system models are considered. These dynamic Bayesian networks provide the means to model system dynamics using the Markovian framework. Given the dynamics, the probability of an impending crisis can be calculated. A unique approach to measuring the ability of a model to predict a crisis is utilised. The results indicate that the dynamic Bayesian network models can provide precise early-warnings compared with the signal extraction and the logit methods.