2018年LGD报告-大型企业借款人

Nina Brumma, Philip Winckle
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引用次数: 4

摘要

无论是对监管资本、定价模型、压力测试还是预期损失准备模型来说,回收率及其逆值——违约损失(LGD),都是信用风险模型中的一个关键指标。然而,这些数据比违约概率(PD)的数据要稀缺得多,因为可以使用的唯一案例来自违约贷款,违约贷款约占任何银行贷款总额的1%。自2004年以来,GCD成员银行一直在稳步收集这些数据。这份报告是GCD首次对其广泛的数据集发布如此广泛的分析。其目的是提供银行在向大型公司对手方提供信贷工具时所经历的收回和损失的数字证据。根据巴塞尔协议的违约定义,报告中的数据涵盖了在银行贷款账簿中被记录为违约的大型企业(营业额超过5000万欧元)借款人。本报告中的长期平均LGD水平可以与监管最低水平和标准化水平进行比较,从而允许业界广泛讨论谨慎的前瞻性LGD与历史证据。请注意,本报告中的lgd是对历史结果的现金流贴现观察,而不是前瞻性估计。2017年12月,BCBS对他们所谓的“巴塞尔协议III的最终确定”做出了最终决定。在计算监管资本时,监管机构允许继续对PD、LGD和EAD进行内部建模,尽管下限是基于标准化水平的。对定价、经济资本和信贷损失准备(IFRS9和CECL)模型的内部建模的需求仍然很强。这一趋势仍在继续,越来越多的银行汇集数据,以更好地了解它们的信贷风险组合,并对它们的模型进行基准测试。本研究的结果提供了对全球数据的总体见解。主要发现是:资历和抵押品被证实是LGD的驱动因素(27%的高级无担保债务对40%的次级无担保债务)。总担保LGD为23%)。LGD随时间变化,表明经济状况与复苏之间存在关系。由于GCD数据包括私人持有的贷款,因此该数据集与大多数其他研究不同。因此,结果可以与侧重于公开记录的债券违约、单一国家数据或仅清算数据的研究进行比较,但不应期望与之相同。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
LGD Report 2018 - Large Corporate Borrowers
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.
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