{"title":"违约造成的信用损失和系统性损失","authors":"Jon Frye, Michael Jacobs","doi":"10.21314/JCR.2012.138","DOIUrl":null,"url":null,"abstract":"Credit loss varies from period to period, both because the default rate varies and because the loss given default (LGD) rate varies. The default rate has been tied to a firm’s probability of default (PD) and to factors that cause default. The LGD rate has proved more difficult to model because continuous LGD is more subtle than binary default and because LGD data is scarcer and lower in quality. Studies show that the two rates vary together systematically (see Altman and Karlin (2010) and Frye (2000)). Systematic variation works against the lender, who finds that an increase in the number of defaults coincides with an increase in the fraction “percentage”? that is lost in a default. Lenders should therefore anticipate systematic LGD within their credit portfolio loss models, which are required to account for all material risks. This paper presents a model of systematic LGD that is simple and effective. It is simple in that it uses only parameters that are already part of standard models. It is effective in that it survives statistical testing against more complicated models. It may, therefore, serve for comparison in tests of other models of credit risk as well as for the","PeriodicalId":44244,"journal":{"name":"Journal of Credit Risk","volume":"21 1","pages":"109-140"},"PeriodicalIF":0.3000,"publicationDate":"2012-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"25","resultStr":"{\"title\":\"Credit loss and systematic loss given default\",\"authors\":\"Jon Frye, Michael Jacobs\",\"doi\":\"10.21314/JCR.2012.138\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Credit loss varies from period to period, both because the default rate varies and because the loss given default (LGD) rate varies. The default rate has been tied to a firm’s probability of default (PD) and to factors that cause default. The LGD rate has proved more difficult to model because continuous LGD is more subtle than binary default and because LGD data is scarcer and lower in quality. Studies show that the two rates vary together systematically (see Altman and Karlin (2010) and Frye (2000)). Systematic variation works against the lender, who finds that an increase in the number of defaults coincides with an increase in the fraction “percentage”? that is lost in a default. Lenders should therefore anticipate systematic LGD within their credit portfolio loss models, which are required to account for all material risks. This paper presents a model of systematic LGD that is simple and effective. It is simple in that it uses only parameters that are already part of standard models. It is effective in that it survives statistical testing against more complicated models. It may, therefore, serve for comparison in tests of other models of credit risk as well as for the\",\"PeriodicalId\":44244,\"journal\":{\"name\":\"Journal of Credit Risk\",\"volume\":\"21 1\",\"pages\":\"109-140\"},\"PeriodicalIF\":0.3000,\"publicationDate\":\"2012-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"25\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Credit Risk\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://doi.org/10.21314/JCR.2012.138\",\"RegionNum\":4,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Economics, Econometrics and Finance\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Credit Risk","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.21314/JCR.2012.138","RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Economics, Econometrics and Finance","Score":null,"Total":0}
Credit loss varies from period to period, both because the default rate varies and because the loss given default (LGD) rate varies. The default rate has been tied to a firm’s probability of default (PD) and to factors that cause default. The LGD rate has proved more difficult to model because continuous LGD is more subtle than binary default and because LGD data is scarcer and lower in quality. Studies show that the two rates vary together systematically (see Altman and Karlin (2010) and Frye (2000)). Systematic variation works against the lender, who finds that an increase in the number of defaults coincides with an increase in the fraction “percentage”? that is lost in a default. Lenders should therefore anticipate systematic LGD within their credit portfolio loss models, which are required to account for all material risks. This paper presents a model of systematic LGD that is simple and effective. It is simple in that it uses only parameters that are already part of standard models. It is effective in that it survives statistical testing against more complicated models. It may, therefore, serve for comparison in tests of other models of credit risk as well as for the
期刊介绍:
With the re-writing of the Basel accords in international banking and their ensuing application, interest in credit risk has never been greater. The Journal of Credit Risk focuses on the measurement and management of credit risk, the valuation and hedging of credit products, and aims to promote a greater understanding in the area of credit risk theory and practice. The Journal of Credit Risk considers submissions in the form of research papers and technical papers, on topics including, but not limited to: Modelling and management of portfolio credit risk Recent advances in parameterizing credit risk models: default probability estimation, copulas and credit risk correlation, recoveries and loss given default, collateral valuation, loss distributions and extreme events Pricing and hedging of credit derivatives Structured credit products and securitizations e.g. collateralized debt obligations, synthetic securitizations, credit baskets, etc. Measuring managing and hedging counterparty credit risk Credit risk transfer techniques Liquidity risk and extreme credit events Regulatory issues, such as Basel II, internal ratings systems, credit-scoring techniques and credit risk capital adequacy.