{"title":"2018年LGD报告-大型企业借款人","authors":"Nina Brumma, Philip Winckle","doi":"10.2139/ssrn.3289128","DOIUrl":null,"url":null,"abstract":"Recovery rate and its inverse, Loss Given Default (LGD), is a key metric in credit risk modelling, whether for regulatory capital, pricing models, stress testing or expected loss provisioning models. The data is however much more scarce than data for probability of default (PD) because the only cases which can be used come from defaulted loans, which represent around 1% of the total loan book of any bank. GCD member banks have been steadily collecting this data since 2004. \n \nThis report is the first time GCD publishes such extensive analytics on its broad data set. The aim is to present the numerical evidence of recoveries and losses experienced by banks when providing credit facilities to large corporate counterparties. The data set in the report covers Large Corporate (>€50m turnover) borrowers who are recorded as defaulted in bank loan books, using the Basel default definition. \n \nThe long term average LGD levels in this report can be compared to regulatory minima and standardised levels, allowing an industry wide discussion of prudent forward looking LGDs vs historical evidence. Note that the LGDs in this report are cash flow discounted observations of historical outcomes, not forward looking estimates. \n \nIn December 2017 the BCBS made their final decision on what they call the “Finalisation of Basel III”. Regulators have allowed for continued internal modelling of PD, LGD and EAD when calculating regulatory capital, albeit with floors based on standardised levels. The need for internal modelling for pricing, Economic Capital and Credit Loss Provisioning (IFRS9 and CECL) models remains strong. The trend continues with more banks pooling data to better understand their credit risk portfolios and benchmark their models. \n \nThe results in this study offer an overall insight into the data on a global level. The main findings are: \n \nSeniority and collateral are confirmed as LGD drivers (27% senior unsecured vs 40% subordinated unsecured at obligor level. The Total Secured LGD is 23%). \n \nLGD varies over time, indicating that there is a relationship between the economic conditions and recoveries. \n \nBecause GCD data comprises privately held loans, the data set differs from most other studies. Hence the outcome can be compared to, but should not be expected to be the same as, studies which focus on publicly recorded bond defaults, single country data or liquidation only data.","PeriodicalId":11689,"journal":{"name":"ERN: Commercial Banks (Topic)","volume":"26 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2018-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"LGD Report 2018 - Large Corporate Borrowers\",\"authors\":\"Nina Brumma, Philip Winckle\",\"doi\":\"10.2139/ssrn.3289128\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recovery rate and its inverse, Loss Given Default (LGD), is a key metric in credit risk modelling, whether for regulatory capital, pricing models, stress testing or expected loss provisioning models. The data is however much more scarce than data for probability of default (PD) because the only cases which can be used come from defaulted loans, which represent around 1% of the total loan book of any bank. GCD member banks have been steadily collecting this data since 2004. \\n \\nThis report is the first time GCD publishes such extensive analytics on its broad data set. The aim is to present the numerical evidence of recoveries and losses experienced by banks when providing credit facilities to large corporate counterparties. The data set in the report covers Large Corporate (>€50m turnover) borrowers who are recorded as defaulted in bank loan books, using the Basel default definition. \\n \\nThe long term average LGD levels in this report can be compared to regulatory minima and standardised levels, allowing an industry wide discussion of prudent forward looking LGDs vs historical evidence. Note that the LGDs in this report are cash flow discounted observations of historical outcomes, not forward looking estimates. \\n \\nIn December 2017 the BCBS made their final decision on what they call the “Finalisation of Basel III”. Regulators have allowed for continued internal modelling of PD, LGD and EAD when calculating regulatory capital, albeit with floors based on standardised levels. The need for internal modelling for pricing, Economic Capital and Credit Loss Provisioning (IFRS9 and CECL) models remains strong. The trend continues with more banks pooling data to better understand their credit risk portfolios and benchmark their models. \\n \\nThe results in this study offer an overall insight into the data on a global level. The main findings are: \\n \\nSeniority and collateral are confirmed as LGD drivers (27% senior unsecured vs 40% subordinated unsecured at obligor level. The Total Secured LGD is 23%). \\n \\nLGD varies over time, indicating that there is a relationship between the economic conditions and recoveries. \\n \\nBecause GCD data comprises privately held loans, the data set differs from most other studies. Hence the outcome can be compared to, but should not be expected to be the same as, studies which focus on publicly recorded bond defaults, single country data or liquidation only data.\",\"PeriodicalId\":11689,\"journal\":{\"name\":\"ERN: Commercial Banks (Topic)\",\"volume\":\"26 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-04-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ERN: Commercial Banks (Topic)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.3289128\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ERN: Commercial Banks (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3289128","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Recovery rate and its inverse, Loss Given Default (LGD), is a key metric in credit risk modelling, whether for regulatory capital, pricing models, stress testing or expected loss provisioning models. The data is however much more scarce than data for probability of default (PD) because the only cases which can be used come from defaulted loans, which represent around 1% of the total loan book of any bank. GCD member banks have been steadily collecting this data since 2004.
This report is the first time GCD publishes such extensive analytics on its broad data set. The aim is to present the numerical evidence of recoveries and losses experienced by banks when providing credit facilities to large corporate counterparties. The data set in the report covers Large Corporate (>€50m turnover) borrowers who are recorded as defaulted in bank loan books, using the Basel default definition.
The long term average LGD levels in this report can be compared to regulatory minima and standardised levels, allowing an industry wide discussion of prudent forward looking LGDs vs historical evidence. Note that the LGDs in this report are cash flow discounted observations of historical outcomes, not forward looking estimates.
In December 2017 the BCBS made their final decision on what they call the “Finalisation of Basel III”. Regulators have allowed for continued internal modelling of PD, LGD and EAD when calculating regulatory capital, albeit with floors based on standardised levels. The need for internal modelling for pricing, Economic Capital and Credit Loss Provisioning (IFRS9 and CECL) models remains strong. The trend continues with more banks pooling data to better understand their credit risk portfolios and benchmark their models.
The results in this study offer an overall insight into the data on a global level. The main findings are:
Seniority and collateral are confirmed as LGD drivers (27% senior unsecured vs 40% subordinated unsecured at obligor level. The Total Secured LGD is 23%).
LGD varies over time, indicating that there is a relationship between the economic conditions and recoveries.
Because GCD data comprises privately held loans, the data set differs from most other studies. Hence the outcome can be compared to, but should not be expected to be the same as, studies which focus on publicly recorded bond defaults, single country data or liquidation only data.