Dabrowski, JJBeyers, CDe Villiers, Johan P2017-05-162017-05-162016-11Dabrowski, J.J., Beyers, C. and De Villiers, J.P. 2016. Systemic banking crisis early warning systems using dynamic bayesian networks. Expert Systems with Applications, vol. 62: 225-2420957-4174http://www.sciencedirect.com/science/article/pii/S0957417416303062https://doi.org/10.1016/j.eswa.2016.06.024http://hdl.handle.net/10204/9050© 2016 Elsevier Ltd. This is a pre-print version of the article. The definitive published version can be obtained from http://www.sciencedirect.com/science/article/pii/S0957417416303062For 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.enCC0 1.0 UniversalHidden Markov modelSwitching linear dynamic systemNaive bayes switching linear dynamic systemTime seriesRegimeArticleDabrowski, J., Beyers, C., & De Villiers, J. P. (2016). Systemic banking crisis early warning systems using dynamic bayesian networks. http://hdl.handle.net/10204/9050Dabrowski, JJ, C Beyers, and Johan P De Villiers "Systemic banking crisis early warning systems using dynamic bayesian networks." (2016) http://hdl.handle.net/10204/9050Dabrowski J, Beyers C, De Villiers JP. Systemic banking crisis early warning systems using dynamic bayesian networks. 2016; http://hdl.handle.net/10204/9050.TY - Article AU - Dabrowski, JJ AU - Beyers, C AU - De Villiers, Johan P AB - 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. DA - 2016-11 DB - ResearchSpace DP - CSIR KW - Hidden Markov model KW - Switching linear dynamic system KW - Naive bayes switching linear dynamic system KW - Time series KW - Regime LK - https://researchspace.csir.co.za PY - 2016 SM - 0957-4174 T1 - Systemic banking crisis early warning systems using dynamic bayesian networks TI - Systemic banking crisis early warning systems using dynamic bayesian networks UR - http://hdl.handle.net/10204/9050 ER -