Systemic banking crisis early warning systems using dynamic Bayesian networks |
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| Affiliation: | 1. Department of Electrical, Electronic and Computer Engineering, University of Pretoria, cnr Lynnwood Road and Roper Street, Pretoria, South Africa;2. Department of Insurance and Actuarial Science, University of Pretoria, cnr Lynnwood Road and Roper Street, Pretoria, South Africa;3. Council for Scientific and Industrial Research, Meiring Naudé Rd, Lynnwood, Pretoria, South Africa;1. UTM Big Data Centre, Ibnu Sina Institute for Scientific and Industrial Research, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia;2. Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia;1. Department of Statistics, National Cheng Kung University, Tainan 70101, Taiwan, ROC;1. Department of Computer Science, Federal University of São Carlos – UFSCar, Sorocaba, 18052-780, Brazil;2. Department of Computer Sciences, University of Wisconsin-Madison – Madison, WI 53703 USA |
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| Abstract: | 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. |
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