{"title":"A survey of machine learning in credit risk","authors":"J. Breeden","doi":"10.21314/jcr.2021.008","DOIUrl":"https://doi.org/10.21314/jcr.2021.008","url":null,"abstract":"","PeriodicalId":44244,"journal":{"name":"Journal of Credit Risk","volume":"1 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67703765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Bankcard performance during the Great Recession","authors":"P. Calem, Julapa Jagtiani, Loretta J. Mester","doi":"10.21314/jcr.2020.271","DOIUrl":"https://doi.org/10.21314/jcr.2020.271","url":null,"abstract":"","PeriodicalId":44244,"journal":{"name":"Journal of Credit Risk","volume":"274 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75071481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The economics of debt collection","authors":"Erik Durbin, Charles J. Romeo","doi":"10.21314/jcr.2020.274","DOIUrl":"https://doi.org/10.21314/jcr.2020.274","url":null,"abstract":"","PeriodicalId":44244,"journal":{"name":"Journal of Credit Risk","volume":"5 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73179405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"From incurred loss to current expected credit loss: a forensic analysis of the allowance for loan losses in unconditionally cancelable credit card portfolios","authors":"Jose Canals-Cerda","doi":"10.21314/jcr.2020.273","DOIUrl":"https://doi.org/10.21314/jcr.2020.273","url":null,"abstract":"","PeriodicalId":44244,"journal":{"name":"Journal of Credit Risk","volume":"30 4","pages":""},"PeriodicalIF":0.3,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138518629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Stress testing models have been developed at various levels of data aggregation with or without risk attributes, but there is limited research on the joint impact of these modeling choices. In this paper, we investigate how data aggregation and risk attributes affect the development and performance of stress testing models by studying residential mortgage loan defaults. We develop mortgage default models at various data aggregation levels including loan-level, segment-level, and top-down. We also compare the models with and without risk attributes as control variables. We assess model performance for goodness-of-fit, prediction accuracy, and projection sensitivity for stress testing purposes. We find that the loan-level models do not always win among models with various data aggregation levels, and including risk attributes greatly improves goodness-of-fit and projection accuracy for models of all data aggregation levels. The findings suggest that it is important to consider data aggregation and risk attributes in developing stress testing models.
{"title":"The impact of data aggregation and risk attributes on stress testing models of mortgage default","authors":"Feng Li,Yan Zhang","doi":"10.21314/jcr.2020.269","DOIUrl":"https://doi.org/10.21314/jcr.2020.269","url":null,"abstract":"Stress testing models have been developed at various levels of data aggregation with or without risk attributes, but there is limited research on the joint impact of these modeling choices. In this paper, we investigate how data aggregation and risk attributes affect the development and performance of stress testing models by studying residential mortgage loan defaults. We develop mortgage default models at various data aggregation levels including loan-level, segment-level, and top-down. We also compare the models with and without risk attributes as control variables. We assess model performance for goodness-of-fit, prediction accuracy, and projection sensitivity for stress testing purposes. We find that the loan-level models do not always win among models with various data aggregation levels, and including risk attributes greatly improves goodness-of-fit and projection accuracy for models of all data aggregation levels. The findings suggest that it is important to consider data aggregation and risk attributes in developing stress testing models.","PeriodicalId":44244,"journal":{"name":"Journal of Credit Risk","volume":"104 4","pages":""},"PeriodicalIF":0.3,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138518631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A theoretical method is empirically illustrated in finding the best time to forsake a loan such that the overall credit loss is minimised. This is predicated by forecasting the future cash flows of a loan portfolio up to the contractual term, as a remedy to the inherent right-censoring of real-world `incomplete' portfolios. Two techniques, a simple probabilistic model as well as an eight-state Markov chain, are used to forecast these cash flows independently. We train both techniques from different segments within residential mortgage data, provided by a large South African bank, as part of a comparative experimental framework. As a result, the recovery decision's implied timing is empirically illustrated as a multi-period optimisation problem across uncertain cash flows and competing costs. Using a delinquency measure as a central criterion, our procedure helps to find a loss-optimal threshold at which loan recovery should ideally occur for a given portfolio. Furthermore, both the portfolio's historical risk profile and forecasting thereof are shown to influence the timing of the recovery decision. This work can therefore facilitate the revision of relevant bank policies or strategies towards optimising the loan collections process, especially that of secured lending.
{"title":"The loss optimization of loan recovery decision times using forecast cashflows","authors":"A. Botha, Conrad Beyers, P. D. Villiers","doi":"10.21314/JCR.2020.275","DOIUrl":"https://doi.org/10.21314/JCR.2020.275","url":null,"abstract":"A theoretical method is empirically illustrated in finding the best time to forsake a loan such that the overall credit loss is minimised. This is predicated by forecasting the future cash flows of a loan portfolio up to the contractual term, as a remedy to the inherent right-censoring of real-world `incomplete' portfolios. Two techniques, a simple probabilistic model as well as an eight-state Markov chain, are used to forecast these cash flows independently. We train both techniques from different segments within residential mortgage data, provided by a large South African bank, as part of a comparative experimental framework. As a result, the recovery decision's implied timing is empirically illustrated as a multi-period optimisation problem across uncertain cash flows and competing costs. Using a delinquency measure as a central criterion, our procedure helps to find a loss-optimal threshold at which loan recovery should ideally occur for a given portfolio. Furthermore, both the portfolio's historical risk profile and forecasting thereof are shown to influence the timing of the recovery decision. This work can therefore facilitate the revision of relevant bank policies or strategies towards optimising the loan collections process, especially that of secured lending.","PeriodicalId":44244,"journal":{"name":"Journal of Credit Risk","volume":"1 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2020-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46796112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper analyses the effect of soaring demand in the lending market shortly before a fi nancial crisis (hereinafter "credit run"). A credit run affects the asset correlation, which is one of the main parameters in the Internal Ratings-Based Approach (IRBA) of the Basel III framework. In the framework, these coefficients are predetermined and have not been recalibrated since their introduction in the Basel II Accord. This paper not only questions the assumption of a constant asset correlation, which is a fundamental part of the theoretical foundation of the IRBA, but also shows that a credit run increases the asset correlation value through a new approach. Thereby, this paper offers evidence that the asset correlations given in the IRBA are underestimated. In contrast to other asset correlation studies, this paper provides a new approach which is compatible with the foundation of the IRBA. Assuming asset correlations are calibrated correctly in the IRBA, a 2% downturn add-on may be adequate.
{"title":"How a credit run affects asset correlation","authors":"Christopher Paulus Imanto","doi":"10.2139/SSRN.3582995","DOIUrl":"https://doi.org/10.2139/SSRN.3582995","url":null,"abstract":"This paper analyses the effect of soaring demand in the lending market shortly before a fi nancial crisis (hereinafter \"credit run\"). A credit run affects the asset correlation, which is one of the main parameters in the Internal Ratings-Based Approach (IRBA) of the Basel III framework. In the framework, these coefficients are predetermined and have not been recalibrated since their introduction in the Basel II Accord. This paper not only questions the assumption of a constant asset correlation, which is a fundamental part of the theoretical foundation of the IRBA, but also shows that a credit run increases the asset correlation value through a new approach. Thereby, this paper offers evidence that the asset correlations given in the IRBA are underestimated. In contrast to other asset correlation studies, this paper provides a new approach which is compatible with the foundation of the IRBA. Assuming asset correlations are calibrated correctly in the IRBA, a 2% downturn add-on may be adequate.","PeriodicalId":44244,"journal":{"name":"Journal of Credit Risk","volume":"33 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2020-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73497861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ioannis Anagnostou,Javier Sanchez Rivero,Sumit Sourabh,Drona Kandhai
The robustness of credit portfolio models is of great interest for financial institutions and regulators, since misspecified models translate into insufficient capital buffers and a crisis-prone financial system. In this paper, the authors propose a method to enhance credit portfolio models based on the model of Merton by incorporating contagion effects. While, in most models, the risks related to financial interconnectedness are neglected, the authors use Bayesian network methods to uncover the direct and indirect relationships between credits while maintaining the convenient representation of factor models. A range of techniques to learn the structure and parameters of financial networks from real credit default swaps data are studied and evaluated. Their approach is demonstrated in detail in a stylized portfolio, and the impact on standard risk metrics is estimated.
{"title":"Contagious defaults in a credit portfolio: a Bayesian network approach","authors":"Ioannis Anagnostou,Javier Sanchez Rivero,Sumit Sourabh,Drona Kandhai","doi":"10.21314/jcr.2020.257","DOIUrl":"https://doi.org/10.21314/jcr.2020.257","url":null,"abstract":"The robustness of credit portfolio models is of great interest for financial institutions and regulators, since misspecified models translate into insufficient capital buffers and a crisis-prone financial system. In this paper, the authors propose a method to enhance credit portfolio models based on the model of Merton by incorporating contagion effects. While, in most models, the risks related to financial interconnectedness are neglected, the authors use Bayesian network methods to uncover the direct and indirect relationships between credits while maintaining the convenient representation of factor models. A range of techniques to learn the structure and parameters of financial networks from real credit default swaps data are studied and evaluated. Their approach is demonstrated in detail in a stylized portfolio, and the impact on standard risk metrics is estimated.","PeriodicalId":44244,"journal":{"name":"Journal of Credit Risk","volume":"215 ","pages":""},"PeriodicalIF":0.3,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138518634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper explores the impact of elliptical and Archimedean copula models on the valuation of basket default swaps. We employ Monte Carlo simulation, in connection with the copula models, to estimate the default times and to calculate the swap payment legs and the cumulative swap premium. The numerical experiments reveal some sensitivity analysis on the impact of swap parameters on the fair prices of the 𝑛th-to-default swaps. Finally, using the results presented, an appropriate choice of copula model can be made based on the computation time of the valuation process, and such a choice hugely affects the quantitative risk analysis of the portfolio.
{"title":"Elliptical and Archimedean Copula Models: An Application to the Price Estimation of Portfolio Credit Derivatives","authors":"Matthias Ehrhardt, Nneka Umeorah, Phillip Mashele","doi":"10.21314/jcr.2020.263","DOIUrl":"https://doi.org/10.21314/jcr.2020.263","url":null,"abstract":"This paper explores the impact of elliptical and Archimedean copula models on the valuation of basket default swaps. We employ Monte Carlo simulation, in connection with the copula models, to estimate the default times and to calculate the swap payment legs and the cumulative swap premium. The numerical experiments reveal some sensitivity analysis on the impact of swap parameters on the fair prices of the 𝑛th-to-default swaps. Finally, using the results presented, an appropriate choice of copula model can be made based on the computation time of the valuation process, and such a choice hugely affects the quantitative risk analysis of the portfolio.","PeriodicalId":44244,"journal":{"name":"Journal of Credit Risk","volume":"6 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2019-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82397881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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