Modeling Credit Risk: An Application of the Rough Set Methodology
The Basel Accords encourages credit entities to implement their own models for measuring financial risk. In this paper, we focus on the use of internal ratings-based (IRB) models for the assessment of credit risk and, specifically, on one component that models the probability of default (PD). The tr...
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Universiti Utara Malaysia
2013-02-01
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doaj-a36927a310224429948ecd841bcb1b002021-06-15T13:16:40ZengUniversiti Utara MalaysiaInternational Journal of Banking and Finance1675-722X2013-02-0110.32890/ijbf2013.10.1.8466Modeling Credit Risk: An Application of the Rough Set MethodologyReyes Samaniego MedinaMaria Jose Vazquez CuetoThe Basel Accords encourages credit entities to implement their own models for measuring financial risk. In this paper, we focus on the use of internal ratings-based (IRB) models for the assessment of credit risk and, specifically, on one component that models the probability of default (PD). The traditional methods used for modelling credit risk, such as discriminant analysis and logit and probit models, start with several statistical restrictions. The rough set methodology avoids these limitations and as such is an alternative to the classic statistical methods. We apply the rough set methodology to a database of 106 companies that are applicants for credit. We obtain ratios that can best discriminate between financially sound and bankrupt companies, along with a series of decision rules that will help detect operations that are potentially in default. Finally, we compare the results obtained using the rough set methodology to those obtained using classic discriminant analysis and logit models. We conclude that the rough set methodology presents better risk classification results. https://www.scienceopen.com/document?vid=3f8d6416-65bd-43f4-b6e8-febddbda5bcb |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Reyes Samaniego Medina Maria Jose Vazquez Cueto |
spellingShingle |
Reyes Samaniego Medina Maria Jose Vazquez Cueto Modeling Credit Risk: An Application of the Rough Set Methodology International Journal of Banking and Finance |
author_facet |
Reyes Samaniego Medina Maria Jose Vazquez Cueto |
author_sort |
Reyes Samaniego Medina |
title |
Modeling Credit Risk: An Application of the Rough Set Methodology |
title_short |
Modeling Credit Risk: An Application of the Rough Set Methodology |
title_full |
Modeling Credit Risk: An Application of the Rough Set Methodology |
title_fullStr |
Modeling Credit Risk: An Application of the Rough Set Methodology |
title_full_unstemmed |
Modeling Credit Risk: An Application of the Rough Set Methodology |
title_sort |
modeling credit risk: an application of the rough set methodology |
publisher |
Universiti Utara Malaysia |
series |
International Journal of Banking and Finance |
issn |
1675-722X |
publishDate |
2013-02-01 |
description |
The Basel Accords encourages credit entities to implement their own models for measuring financial risk. In this paper, we focus on the use of internal ratings-based (IRB) models for the assessment of credit risk and, specifically, on one component that models the probability of default (PD). The traditional methods used for modelling credit risk, such as discriminant analysis and logit and probit models, start with several statistical restrictions. The rough set methodology avoids these limitations and as such is an alternative to the classic statistical methods. We apply the rough set methodology to a database of 106 companies that are applicants for credit. We obtain ratios that can best discriminate between financially sound and bankrupt companies, along with a series of decision rules that will help detect operations that are potentially in default. Finally, we compare the results obtained using the rough set methodology to those obtained using classic discriminant analysis and logit models. We conclude that the rough set methodology presents better risk classification results. |
url |
https://www.scienceopen.com/document?vid=3f8d6416-65bd-43f4-b6e8-febddbda5bcb |
work_keys_str_mv |
AT reyessamaniegomedina modelingcreditriskanapplicationoftheroughsetmethodology AT mariajosevazquezcueto modelingcreditriskanapplicationoftheroughsetmethodology |
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1721376177374887936 |