违约损失率问题初探

巴塞尔新资本协议强调内部评级法在风险管理和资本监管中的重要作用。内部评级法的关键在于对违约率及其相关因素的测量,其中违约概率(PD)和违约损失率(LGD)是内部评级法的核心变量。对国际上关于LGD的表现及影响因素的讨论进行了总结与分析,并重点对LGD与PD之间的关系进行了介绍。     

内部评级法在我国商业银行的应用

内部评级法是新巴塞尔协议的核心,它为商业银行的风险识别及管理提供了依据。虽然目前我国还不具备实施内部评级法的条件,但认真借鉴新巴塞尔协议、积极准备向内部评级法过渡是很有必要的。本文对我国商业银行引入内部评级法的重要意义及实施内部评级法的支持力量及存在的差距进行了探讨,结合我国实际情况,提出了内部评级法在我国银行业实施的几点建议。     

商业银行信贷风险管理分析

本文分析国外在信贷风险评估方法上的新发展趋势,结合内部评级的国际经验,分析了我国商业银行在信用风险管理方式、控制手段和管理框架上的不足,提出了建立内部评级体系的一系列措施,以求为我国金融机构信贷风险管理提供一些有益的参考。     

我国商业银行信贷风险管理分析——基于国外信用评级方法的演进

本文在分析国外在信贷风险评估方法上创新、应用及其发展趋势的基础上,结合内部评级的国际经验,分析了我国商业银行在信用风险管理方式、控制手段和管理框架上的不足,提出了建立内部评级体系的一系列措施,以期为我国金融机构信贷风险管理提供一些有益的参考。     

巴塞尔新资本协议与商业银行信用风险评级体系的构建

本文根据巴塞尔新资本协议关于银行信用风险内部评级体系设计的要求,分析国内商业银行现有信用评级体系的主要差距,并结合实际对国内商业银行构建信用风险内部评级体系的方法步骤提出建议。     

中美商业银行信用风险管理比较分析及我国的对策

文章首先解释银行信用风险并列出其表现形式,进而从银行法制建设、商业银行信用风险管理组织体系、商业银行信用风险评级方法体系及信用风险监管等方面对中美商业银行信用风险管理进行对比,最后提出改善我国商业银行信用风险管理的对策。     

内部评级法在我国商业银行的运用浅探

内部评级法是巴塞尔新资本协议的核心。本文主要对内部评级法的思想、风险要素以及信用风险衡量步骤进行全面剖析,并且提出我国商业银行实施内部评级法应该分三阶段逐步推行的建议。     

银行不良贷款违约损失率结构特征研究

本文对中国银行业面临的信用风险违约损失率(LGD)展开研究,以温州某商业银行不良贷款数据为样本,通过描述性统计,对LGD的结构特征:信用风险暴露规模特征、期限特征、地域特征以及担保特征等进行了详细分析。结果表明LGD与风险暴露规模呈负相关,LGD与贷款期限呈正相关,不同地域、不同担保方式的违约贷款其LGD差异性显著。以上这些结论可为商业银行信用风险管理、信贷投放导向以及信用风险监管提供现实帮助。     

ESG评级对我国商业银行系统性风险的影响研究

基于中国36家上市银行2009～2022年的季度面板数据，利用DCC-SVR-GARCH-CoVaR模型测算系统性风险溢出率，实证分析ESG评级对我国商业银行系统性风险的影响及作用机制。研究发现：ESG评级提高能够显著削弱我国商业银行的系统性风险水平，且ESG评级提高对银行系统性风险水平的削弱作用主要由公司治理（G维度）驱动；地方性银行的ESG评级提高对系统性风险水平的削弱作用更强；ESG评级提高会通过降低银行违约风险发生的概率、减小银行与客户之间的信息不对称程度两种渠道削弱商业银行的系统性风险；经济政策不确定性提升会强化ESG评级提高对银行系统性风险的抑制作用。鉴于此，应充分完善ESG评级体系，将ESG评级纳入商业银行业务战略、内部治理与风险管理体系中，以提高商业银行的风险防控能力。     

内部评级法在我国商业银行的运用浅探

内部评级法是巴塞尔新资本协议的核心。本文主要对内部评级法的思想、风险要素以及信用风险衡量步骤进行全面剖析．并且提出我国商业银行实施内部评级法应该分三阶段逐步推行的建议。     

Benchmarking regression algorithms for loss given default modeling

The introduction of the Basel II Accord has had a huge impact on financial institutions, allowing them to build credit risk models for three key risk parameters: PD (probability of default), LGD (loss given default) and EAD (exposure at default). Until recently, credit risk research has focused largely on the estimation and validation of the PD parameter, and much less on LGD modeling. In this first large-scale LGD benchmarking study, various regression techniques for modeling and predicting LGD are investigated. These include one-stage models, such as those built by ordinary least squares regression, beta regression, robust regression, ridge regression, regression splines, neural networks, support vector machines and regression trees, as well as two-stage models which combine multiple techniques. A total of 24 techniques are compared using six real-life loss datasets from major international banks. It is found that much of the variance in LGD remains unexplained, as the average prediction performance of the models in terms of R² ranges from 4% to 43%. Nonetheless, there is a clear trend that non-linear techniques, and in particular support vector machines and neural networks, perform significantly better than more traditional linear techniques. Also, two-stage models built by a combination of linear and non-linear techniques are shown to have a similarly good predictive power, with the added advantage of having a comprehensible linear model component.     

基于省域视角的信用风险模型适应性分析

巴塞尔新资本协议在鼓励银行采用内部评级法评估信用风险以提取资本准备的同时也强化了各国监管机构对内部评级模型绩效检验与审查的要求.CreditMetrics和CreditRisk+是银行业信用风险评估的基准模型.从建模的数学方法看,CreditRisk+是基于违约的判断,而CreditMetrics则是根据等级变化评价.利用江苏省银监局的相关统计数据对信用风险评估模型进行参数特性审查与绩效检验,结果显示这两类常用模型都可以在江苏的商业银行经营实践中稳定地实现根据信贷组合的实际风险状况进行内部资本配置这一目标.     

Bayesian loss given default estimation for European sovereign bonds

We develop and apply a Bayesian model for the loss rates given defaults (LGDs) of European Sovereigns. Financial institutions are in need of LGD forecasts under Pillar II of the regulatory Basel Accord and the downturn in LGD forecasts under Pillar I. Both are challenging for portfolios with a small number of observations such as sovereigns. Our approach comprises parameter risk and generates LGD forecasts under both regular and downturn conditions. With sovereign-specific rating information, we found that average LGD estimates vary between 0.46 and 0.64, while downturn estimates lay between 0.50 and 0.86.     

Market perception of sovereign credit risk in the euro area during the financial crisis

We study market perception of sovereign credit risk in the euro area during the financial crisis. In our analysis we use a parsimonious CDS pricing model to estimate the probability of default (PD) and the loss given default (LGD) as perceived by financial markets. In our empirical results the estimated LGDs perceived by financial markets stay comfortably below 40% in most of the samples. Global financial indicators are positively and strongly correlated with the market perception of sovereign credit risk; whilst macroeconomic and institutional developments were at best only weakly correlated with the market perception of sovereign credit risk.     

Predicting loss given default (LGD) for residential mortgage loans: A two-stage model and empirical evidence for UK bank data

With the implementation of the Basel II regulatory framework, it became increasingly important for financial institutions to develop accurate loss models. This work investigates the loss given default (LGD) of mortgage loans using a large set of recovery data of residential mortgage defaults from a major UK bank. A Probability of Repossession Model and a Haircut Model are developed and then combined to give an expected loss percentage. We find that the Probability of Repossession Model should consist of more than just the commonly used loan-to-value ratio, and that the estimation of LGD benefits from the Haircut Model, which predicts the discount which the sale price of a repossessed property may undergo. This two-stage LGD model is shown to perform better than a single-stage LGD model (which models LGD directly from loan and collateral characteristics), as it achieves a better R² value and matches the distribution of the observed LGD more accurately.     

Predicting loss given default in leasing: A closer look at models and variable selection

Since the introduction of the Basel II Accord, and given its huge implications for credit risk management, the modeling and prediction of the loss given default (LGD) have become increasingly important tasks. Institutions which use their own LGD estimates can build either simpler or more complex methods. Simpler methods are easier to implement and more interpretable, but more complex methods promise higher prediction accuracies. Using a proprietary data set of 1,184 defaulted corporate leases in Germany, this study explores different parametric, semi-parametric and non-parametric approaches that attempt to predict the LGD. By conducting the analyses for different information sets, we study how the prediction accuracy changes depending on the set of information that is available. Furthermore, we use a variable importance measure to identify the input variables that have the greatest effects on the LGD prediction accuracy for each method. In this regard, we provide new insights on the characteristics of leasing LGDs. We find that (1) more sophisticated methods, especially the random forest, lead to remarkable increases in the prediction accuracy; (2) updating information improves the prediction accuracy considerably; and (3) the outstanding exposure at default, an internal rating, asset types and lessor industries turn out to be important drivers of accurate LGD predictions.     

Predicting LGD distributions with mixed continuous and discrete ordinal outcomes

The loss given default (LGD) distribution is known to have a complex structure. Consequently, the parametric approach for its prediction by fitting a density function may suffer a loss of predictive power. To overcome this potential drawback, we use the cumulative probability model (CPM) to predict the LGD distribution. The CPM applies a transformed variable to model the LGD distribution. This transformed variable has a semiparametric structure. It models the predictor effects parametrically. The functional form of the transformation is unspecified. Thus, CPM provides more flexibility and simplicity in modeling the LGD distribution. To implement CPM, we collect a sample of defaulted debts from Moody’s Default and Recovery Database. Given this sample, we use an expanding rolling window approach to investigate the out-of-time performance of CPM and its alternatives. Our results confirm that CPM is better than its alternatives, in the sense of yielding more accurate LGD distribution predictions.     

Green financing for sustainable development: Insights from multiple cases of Vietnamese commercial banks

Although the public sector is seen as the main party responsible for taking action on climate change and sustainable development, private commercial banks are in a unique position to support or shift the funding focus on green investment. By employing a qualitative research approach based on six commercial banks, this paper aims to investigate the current practices of how commercial banks are contributing to advance green business initiatives. Accordingly, this research examines and identifies the facilitators and challenges in domestic and foreign commercial banks in Vietnam which support green business initiatives. In addition to addressing the recent calls for the investigation of the role of commercial banks in facilitating green finance, our study expands the emerging literature by demonstrating the current efforts of Vietnam's commercial banks in fostering green finance during the Covid-19 pandemic.