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dc.contributor.author
Castagnolo, Fernando
dc.contributor.author
Ferro, Gustavo Adolfo
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dc.date.available
2018-01-22T21:32:16Z
dc.date.issued
2014-01
dc.identifier.citation
Castagnolo, Fernando; Ferro, Gustavo Adolfo; Models for predicting default: towards efficient forecasts; Emerald; Journal of Risk Finance; 15; 1; 1-2014; 52-70
dc.identifier.issn
1526-5943
dc.identifier.uri
http://hdl.handle.net/11336/34206
dc.description.abstract
PURPOSE: The purpose of this paper is to assess and compare the forecast ability of existing credit risk models, answering three questions: Can these methods adequately predict default events? Are there dominant methods? Is it safer to rely on a mix of methodologies?
DESIGN/METHODOLOGY/APPROACH: The authors examine four existing models: O-score, Z-score, Campbell, and Merton distance to default model (MDDM). The authors compare their ability to forecast defaults using three techniques: intra-cohort analysis, power curves and discrete hazard rate models.
FINDINGS: The authors conclude that better predictions demand a mix of models containing accounting and market information. The authors found evidence of the O-score’s outperformance relative to the other models. The MDDM alone in the sample is not a sufficient default predictor. But discrete hazard rate models suggest that combining both should enhance default prediction models.
RESEARCH LIMITATIONS/IMPLICATIONS: The analysed methods alone cannot adequately predict defaults. The authors found no dominant methods. Instead, it would be advisable to rely on a mix of methodologies, which use complementary information. Practical implications – Better forecasts demand a mix of models containing both accounting and market information.
ORIGINALITY/VALUE: The findings suggest that more precise default prediction models can be built by combining information from different sources in reduced-form models and combining default prediction models that can analyze said information.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Emerald
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Efficiency
dc.subject
Default Models
dc.subject
Forecasting
dc.subject
Financial Crisis
dc.subject
Credit Risk
dc.subject
Empirical Analysis
dc.subject.classification
Economía, Econometría
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dc.subject.classification
Economía y Negocios
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dc.subject.classification
CIENCIAS SOCIALES
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dc.title
Models for predicting default: towards efficient forecasts
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2018-01-22T14:47:25Z
dc.journal.volume
15
dc.journal.number
1
dc.journal.pagination
52-70
dc.journal.pais
Reino Unido
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dc.journal.ciudad
Londres
dc.description.fil
Fil: Castagnolo, Fernando. Citigroup; Reino Unido
dc.description.fil
Fil: Ferro, Gustavo Adolfo. Universidad Argentina de la Empresa. Facultad de Ciencias Económicas. Instituto de Economía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.journal.title
Journal of Risk Finance
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://www.emeraldinsight.com/doi/abs/10.1108/JRF-08-2013-0057
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1108/JRF-08-2013-0057
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