Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/50493
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dc.contributor.authorAlaminos, D-
dc.contributor.authorFernández-Gámez, M A-
dc.contributor.authorSantos, José António C-
dc.contributor.authorCampos-Soria, J A-
dc.date.accessioned2019-09-09T11:53:05Z-
dc.date.available2019-09-09T11:53:05Z-
dc.date.issued2019-09-
dc.identifier.issn0975-1084 (Online); 0022-4456 (Print)-
dc.identifier.urihttp://nopr.niscair.res.in/handle/123456789/50493-
dc.description571-575en_US
dc.description.abstractConsidering 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.en_US
dc.language.isoen_USen_US
dc.publisherNISCAIR-CSIR, Indiaen_US
dc.rights CC Attribution-Noncommercial-No Derivative Works 2.5 Indiaen_US
dc.sourceJSIR Vol.78(09) [September 2019]en_US
dc.subjectSystemic banking crisesen_US
dc.subjectEarly warning systemen_US
dc.subjectExtreme gradient boostingen_US
dc.subjectXGBoosten_US
dc.subjectGlobal modelen_US
dc.subjectMacroeconomic analysisen_US
dc.titlePredicting Systemic Banking Crises using Extreme Gradient Boostingen_US
dc.typeArticleen_US
Appears in Collections:JSIR Vol.78(09) [September 2019]

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