Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/50493
Title: Predicting Systemic Banking Crises using Extreme Gradient Boosting
Authors: Alaminos, D
Fernández-Gámez, M A
Santos, José António C
Campos-Soria, J A
Keywords: Systemic banking crises;Early warning system;Extreme gradient boosting;XGBoost;Global model;Macroeconomic analysis
Issue Date: Sep-2019
Publisher: NISCAIR-CSIR, India
Abstract: Considering the great ability of decision trees techniques to extract useful information from large databases and to handle heterogeneous variables, this paper applies Extreme Gradient Boosting for the prediction of systemic banking crises. To this end, prediction models have been constructed for different regions and the whole world. The results obtained show that Extreme Gradient Boosting overcomes the predictive power of existing models in the previous literature and provides more explanatory information on the causes that produce systemic banking crises, being the demand for deposits, the level of domestic credit and banking assets some of the most significant variables.
Page(s): 571-575
URI: http://nopr.niscair.res.in/handle/123456789/50493
ISSN: 0975-1084 (Online); 0022-4456 (Print)
Appears in Collections:JSIR Vol.78(09) [September 2019]

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