Documents disponibles écrits par cet auteur

An overview and framework for PD backtesting and benchmarking / G. Castermans in Journal of the operational research society (JORS), Vol. 61 N° 3 (Mars 2010) 

Public ISBD [article]  in Journal of the operational research society (JORS) > Vol. 61 N° 3 (Mars 2010) . - pp. 359–373 Titre : An overview and framework for PD backtesting and benchmarking Type de document : texte imprimé Auteurs : G. Castermans, Auteur ; D. Martens, Auteur ; Van Gestel, T., Auteur Année de publication : 2011 Article en page(s) : pp. 359–373 Note générale : Recherche opérationnelle Langues : Anglais (eng) Mots-clés : Quantitative  validation  Basel  II  Credit  scoring  Traffic  light Index. décimale : 001.424 Résumé : In order to manage model risk, financial institutions need to set up validation processes so as to monitor the quality of the models on an ongoing basis. Validation can be considered from both a quantitative and qualitative point of view. Backtesting and benchmarking are key quantitative validation tools, and the focus of this paper. In backtesting, the predicted risk measurements (PD, LGD, EAD) will be contrasted with observed measurements using a workbench of available test statistics to evaluate the calibration, discrimination and stability of the model. A timely detection of reduced performance is crucial since it directly impacts profitability and risk management strategies. The aim of benchmarking is to compare internal risk measurements with external risk measurements so as to better gauge the quality of the internal rating system. This paper will focus on the quantitative PD validation process within a Basel II context. We will set forth a traffic light indicator approach that employs all relevant statistical tests to quantitatively validate the used PD model, and document this approach with a real-life case study. The set forth methodology and tests are the summary of the authors’ statistical expertise and experience of world-wide observed business practices. DEWEY : 001.424 ISSN : 0160-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v61/n3/abs/jors200969a.html [article] An overview and framework for PD backtesting and benchmarking [texte imprimé] / G. Castermans, Auteur ; D. Martens, Auteur ; Van Gestel, T., Auteur . - 2011 . - pp. 359–373. Recherche opérationnelle Langues : Anglais (eng) in Journal of the operational research society (JORS) > Vol. 61 N° 3 (Mars 2010) . - pp. 359–373 Mots-clés : Quantitative  validation  Basel  II  Credit  scoring  Traffic  light Index. décimale : 001.424 Résumé : In order to manage model risk, financial institutions need to set up validation processes so as to monitor the quality of the models on an ongoing basis. Validation can be considered from both a quantitative and qualitative point of view. Backtesting and benchmarking are key quantitative validation tools, and the focus of this paper. In backtesting, the predicted risk measurements (PD, LGD, EAD) will be contrasted with observed measurements using a workbench of available test statistics to evaluate the calibration, discrimination and stability of the model. A timely detection of reduced performance is crucial since it directly impacts profitability and risk management strategies. The aim of benchmarking is to compare internal risk measurements with external risk measurements so as to better gauge the quality of the internal rating system. This paper will focus on the quantitative PD validation process within a Basel II context. We will set forth a traffic light indicator approach that employs all relevant statistical tests to quantitatively validate the used PD model, and document this approach with a real-life case study. The set forth methodology and tests are the summary of the authors’ statistical expertise and experience of world-wide observed business practices. DEWEY : 001.424 ISSN : 0160-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v61/n3/abs/jors200969a.html

Credit rating prediction using ant colony optimization / D. Martens in Journal of the operational research society (JORS), Vol. 61 N° 4 (Avril 2010) 

Public ISBD [article]  in Journal of the operational research society (JORS) > Vol. 61 N° 4 (Avril 2010) . - pp. 561–573 Titre : Credit rating prediction using ant colony optimization Type de document : texte imprimé Auteurs : D. Martens, Auteur ; Van Gestel, T., Auteur ; De Backer, M., Auteur Année de publication : 2010 Article en page(s) : pp. 561–573 Note générale : Recherche opérationnelle Langues : Anglais (eng) Mots-clés : Ant  colony  optimization  Classification  Credit  scoring  Bankruptcy  prediction  Basel  II Index. décimale : 001.424 Résumé : The introduction of the Basel II Capital Accord has encouraged financial institutions to build internal rating systems assessing the credit risk of their various credit portfolios. One of the key outputs of an internal rating system is the probability of default (PD), which reflects the likelihood that a counterparty will default on his/her financial obligation. Since the PD modelling problem basically boils down to a discrimination problem (defaulter or not), one may rely on the myriad of classification techniques that have been suggested in the literature. However, since the credit risk models will be subject to supervisory review and evaluation, they must be easy to understand and transparent. Hence, techniques such as neural networks or support vector machines are less suitable due to their black box nature. Building upon previous research, we will use AntMiner+ to build internal rating systems for credit risk. AntMiner+ allows to infer a propositional rule set from a given data set, hereby using the principles from Ant Colony Optimization. Experiments will be conducted using various types of credit data sets (retail, small- and medium-sized enterprises and banks). It will be shown that the extracted rule sets are both powerful in terms of discriminatory power and comprehensibility. Furthermore, a framework will be presented describing how AntMiner+ fits into a global Basel II credit risk management system. DEWEY : 001.424 ISSN : 0160-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v61/n4/abs/jors2008164a.html [article] Credit rating prediction using ant colony optimization [texte imprimé] / D. Martens, Auteur ; Van Gestel, T., Auteur ; De Backer, M., Auteur . - 2010 . - pp. 561–573. Recherche opérationnelle Langues : Anglais (eng) in Journal of the operational research society (JORS) > Vol. 61 N° 4 (Avril 2010) . - pp. 561–573 Mots-clés : Ant  colony  optimization  Classification  Credit  scoring  Bankruptcy  prediction  Basel  II Index. décimale : 001.424 Résumé : The introduction of the Basel II Capital Accord has encouraged financial institutions to build internal rating systems assessing the credit risk of their various credit portfolios. One of the key outputs of an internal rating system is the probability of default (PD), which reflects the likelihood that a counterparty will default on his/her financial obligation. Since the PD modelling problem basically boils down to a discrimination problem (defaulter or not), one may rely on the myriad of classification techniques that have been suggested in the literature. However, since the credit risk models will be subject to supervisory review and evaluation, they must be easy to understand and transparent. Hence, techniques such as neural networks or support vector machines are less suitable due to their black box nature. Building upon previous research, we will use AntMiner+ to build internal rating systems for credit risk. AntMiner+ allows to infer a propositional rule set from a given data set, hereby using the principles from Ant Colony Optimization. Experiments will be conducted using various types of credit data sets (retail, small- and medium-sized enterprises and banks). It will be shown that the extracted rule sets are both powerful in terms of discriminatory power and comprehensibility. Furthermore, a framework will be presented describing how AntMiner+ fits into a global Basel II credit risk management system. DEWEY : 001.424 ISSN : 0160-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v61/n4/abs/jors2008164a.html