信贷违约中期预警模型的构建与分析——以医药制造企业为实证

根据新巴塞尔资本协议,基于内部评级法要求的银行客户信用等级评定可以作为测算客户信用风险违约概率的依据,但其有效期不得超过1年;构建预警模型进行中期(2-4年)预警是深化信贷风险管理的客观要求。在对当前国内外财务预警文献梳理的基础上,选择线性判别模型和logit回归模型作为信贷违约预警的基本分析工具,并利用医药制造行业样本公司实际指标数据,对各个模型的中期预测效果进行分析比较,并提出了政策建议。     

深入开展违约损失率研究 健全银行二维内部评级体系

在巴塞尔新资本协议中,违约损失率是信用风险内部评级法中的重要参数,一般通过二维评级中的债项评级进行量化。目前欧洲、澳洲、新加坡等主要银行系统已经基本完成新资本协议实施进程,在信用风险领域,主要的大型领先银行普遍采用高级内部评级法,即通过开发违约概率和违约损失率模型,建立二维内部评级体系,对银行提高风险管理水平、防范信用风险发挥了重要作用。     

非线性多重约束的内部评级违约概率模型的设计研究

2014年4月,经银监会核准,国内6家银行实施资本计量高级方法,其显著特色是过渡期后利用内部评级初级法下违约概率结果计量风险加权资产和监管资本。目前关于违约概率估计的研究大都存在未能严格遵循监管要求建立识别违约样本的标准,未能建立包括校准环节等在内的完整内部评级模型开发框架,以及未能将专家的业务经验纳入模型设计等方面的不足。鉴此,本文建立了一个满足监管合规要求的模型开发框架,设计了一个非线性多重约束的内部评级违约概率模型,并引入分类器原理评估模型的可靠性。基于某银行业务数据的实证研究表明,该模型效果良好。     

基于违约模型内部评级法对信用风险管理的有效性研究

从《巴塞尔新资本协议》开始,对信用风险的管理越来越受到世界的关注,目前,对内部评级法中信用风险核算变量违约率（PD）的核算的研究更为深入,模型发展更为成熟,基于违约模型的内部评级法日趋成熟,对信用风险管理的有效性日益凸显。基于内部评级法的主要测算变量违约率（PD）构建的Logistic回归模型分析,从违约模型的角度实证研究了内部评级法在信用风险管理中的重要作用。     

浅析违约概率的应用

新巴塞尔资本协议的出台鼓励银行采用内部评级法评估所面临的信用风险,采用内部评级法,可以使银行资本和所承担风险的联系更加紧密、更加直接,可以大幅度提高资本对风险的敏感性。内部评级法计算资本充足率需要四个变量,违约概率是其中之一,同时只要采用内部评级法,不管应用高级法还是初级法,违约概率都需要由银行自己估算,国内某些商业银行已开始着手违约概率的计量。本文主要对违约概率的定义、作用及在现实中的应用作一些简要的介绍。     

银监会发布《商业银行资本计量高级方法验证指引》

《指引》共分为7章153条,对信用风险内部评级体系、市场风险内部模型、操作风险高级计量体系等指标的验证．以及验证监督检查等各方面进行了详细的规定。明确了信用风险内部评级体系验证不同阶段的关注重点,详细规定了对数据、评级模型、违约概率、     

违约概率测度：商业银行信用风险管理的关键

在商业银行信用风险管理中，违约概率是指借款人在未来一定时期内不能按合同要求偿还银行贷款本启、或履行相关义务的可能性。对借款人进行违约概率的测度，已经被列为巴塞尔新资本协议内部评级法的关键内容，是现代商业银行信用风险管理的重要环节。巴塞尔新资本协议要求，采用内部评级法的银行必须对处于风险暴露中的每一借款人进行评级，并估计其违约概率。     

基于内部评级法的县域农村商业银行信用风险度量研究

针对县域农村商业银行信用风险管理粗放的现状,本文通过对巴塞尔协议Ⅲ中内部评级法的研究,在充分考虑县域农村商业银行实际情况基础上,建立基于CreditMetrics的信用风险度量模型,计算出组合贷款和单笔贷款违约概率、违约损失率,从而建立符合该行实际的信用管理体系。     

基于KMV模型与外部资信评级体系的上市公司信用风险度量研究

本文引入2007年新华远东对中国上市公司资信评级结果作为研究样本,并且依据其评级方法引入2007年证监会发出处罚公告的上市公司作为违约部分的拓展样本,采用KMV模型对这些样本公司的违约距离进行度量。结果显示:KMV模型能够很好的区分新华远东资信评级体系中信用等级平均水平以上、平均水平以下、违约级的样本公司,并据此划分出了违约距离等级区间。这对投资者提前识别上市公司潜在的信用风险以及正确度量信用风险,以使投资者能够及时采取措施规避风险具有重要的现实意义。     

Logistic模型和KMV模型在中国上市公司信用风险度量中的比较研究

本文从违约概率衡量上市公司信用风险的角度来看,基于因子分析的Logistic回归模型和KMV模型都能反映上市公司的信用风险状况,但基于因子分析的Logistic回归模型的评级结果比KMV模型较准确。     

Promoting Entrepreneurship at the Base of the Social Pyramid via Pricing Systems: A case Study

Historically, microfinance institutions (MFIs) have played a significant social role by helping people at the base of the socio‐economic pyramid escape from social exclusion through the creation of microenterprises. However, international banks have recently started competing in the microfinance sector. In this adverse environment, MFI management tools should be more innovative and technologically advanced to increase efficiency, solvency and profitability and to compete with commercial banks on equal terms. This study therefore strives to develop a credit‐risk management tool based on a multilayer perceptron (MLP) credit‐scoring model for a Peruvian MFI, and to calculate the capital requirements and microcredit pricing on both internal ratings‐based (IRB) and standardized approaches, analysing the impact of these models on the management of the MFI. Our findings show that the implementation of an IRB approach with default probabilities obtained from an MLP credit‐scoring model produces the best benefit by the MFIs in terms of higher accuracy (reduction of misclassification costs by 13.78%), lower capital requirements (in the range of 8.5–78%) and the best risk‐adjusted interest rates. Furthermore, with the establishment of interest rates adjusted to the real risk of each client, MFIs are fairer and more socially engaged by preventing economically viable low‐risk projects from becoming unviable due to excessive interest rates. This leads to the creation of more small businesses by people from the base of the socio‐economic pyramid and greater economic development and social cohesion. The IRB model should therefore be implemented to improve MFI solvency, profitability, efficiency, survival, management and social performance.     

Optimal Dynamic Capital Requirements

We characterize welfare maximizing capital requirement policies in a quantitative macrobanking model with household, firm, and bank defaults calibrated to Euro Area data. We optimize on the level of the capital requirements applied to each loan class and their sensitivity to changes in default risk. We find that getting the level right (so that bank failure risk remains contained) is of foremost importance, while the optimal sensitivity to default risk is positive but typically smaller than under Basel internal ratings based (IRB) formulas. Starting from low levels, savers and borrowers benefit from higher capital requirements. At higher levels, only savers prefer tighter requirements.     

中小企业集合债券总体信用风险度量研究

中小企业集合债券总体信用风险既包括系统风险产生的周期性违约风险,又包括相互关联关系导致的传染性违约风险。首先通过对因素模型的改进构建模型Ⅰ,研究集合债券的周期性违约风险;在此基础上引入违约传染建立模型Ⅱ,分析违约传染对违约概率及违约相关性的影响,研究集合债券的总体信用风险。最后基于模型Ⅱ进行算例研究,得出结论：企业间的相互关联关系降低了其1次违约概率,增加了其多次违约概率即违约相关性。     

IRB法下公司敞口组合的风险驱动因子计量研究

巴塞尔银行监管委员会针对防范信贷组合信用风险所需要的资本制定的内部评级法,通过风险驱动因子的变化来反映组合回报的变化,并根据风险权重函数,通过风险加权资产转化为与每一项信用风险敞口更准确匹配的资本要求.本文对违约概率、违约损失率、违约敞口、期限因素以及违约相关性等信贷组合信用风险的风险驱动因子的度量进行了综合研究.     

An analysis of simultaneous company defaults using a shot noise process

During the subprime mortgage crisis, it became apparent that practical models, such as the one-factor Gaussian copula, had underestimated company default correlations. Complex models that attempt to incorporate default dependency are difficult to implement in practice. In this study, we develop a model for a company asset process, based on which we calculate simultaneous default probabilities using an option-theoretic approach. In our model, a shot noise process serves as the key element for controlling correlations among companies’ assets. The risk factor driving the shot noise process is common to all companies in an industry but the shot noise parameters are assumed company-specific; therefore, every company responds differently to this common risk factor. Our model gives earlier warning of financial distress and predicts higher simultaneous default probabilities than commonly used geometric Brownian motion asset model. It is also computationally simple and can be extended to analyze any finite number of companies.     

Can machine learning models save capital for banks? Evidence from a Spanish credit portfolio

We study the impact of machine learning (ML) models for credit default prediction in the calculation of regulatory capital by financial institutions. We do so by using a unique and anonymized database from a major Spanish bank. We first compare the statistical performance of five models based on supervised learning like Logistic Lasso, Trees (CART), Random Forest, XGBoost and Deep Learning, with a well-known model like Logit. We measure the statistical performance through different metrics, and for different sample sizes and features available. We find that ML models outperform, even when relatively low amount of data is used. We then translate this statistical performance into economic impact by estimating the savings in capital when using an advanced ML model instead of a simpler one to compute the risk-weighted assets following the Internal Ratings Based (IRB) approach. Our benchmark results show that implementing XGBoost instead of Logistic Lasso could yield savings from 12.4% to 17% in terms of regulatory capital requirements.     

Using extreme value theory to estimate the likelihood of banking sector failure

Abstract

The growing interest in management of credit risk and estimation of default probabilities has given rise to a range of more or less elaborate credit risk models. While these models work well for non-financial firms they are usually not very successful in capturing the financial strength of banks. As an answer to this, Hall and Miles suggest a simple approach of estimating bank failure probabilities based solely on their stock prices. This paper suggests an extension to the Hall and Miles model using extreme value theory and applies the extended model to the Swedish banking sector around the banking crisis of the early 1990s. The extended model captures very well the increased likelihood of a systemic banking sector failure around the peak of the crisis and it produces default probabilities that are more stable, more realistic and more consistent with Moody’s and Fitch rating implied default rates than probabilities from the original Hall and Miles model.     

Default Risk in Corporate Yield Spreads

An important research question examined in the credit risk literature focuses on the proportion of corporate yield spreads attributed to default risk. This topic is reexamined in light of the different issues associated with the computation of default probabilities obtained from historical default data. We find that the estimated default risk proportion in corporate yield spreads is sensitive to the ex ante estimated term structure of default probabilities used as inputs. This proportion can become a large fraction of the spread when sensitivity analyses are made with respect to the period over which the probabilities are estimated and the recovery rates.     

Taxes,Default Risk,and Yield Spreads

This paper develops a model of bond prices and yield spreads that incorporates the effect of both taxes and differences in default probabilities. The tax loss consequences of default are recognized. Traditionally, tax-free (municipal) bond yields have been viewed as linearly related to taxable yields with a slope coefficient equal to one minus the tax rate and the intercept representing differences in default risk. While our model supports the linearity assumption, it implies that the slope and intercept are both functions of both the break-even tax rate and the default probability(ies). Clientele effects among both municipal and taxable bonds are demonstrated. Finally, the implied marginal tax rates and the implied default probabilities are estimated for different categories of municipal bonds.     

An Empirical Investigation of CDS Spreads Using a Regime-Switching Default Risk Model

Default risk in equity returns can be measured by structural models of default. In this article we propose a credit warning signal (CWS) based on the Merton Default Risk (MDR) model and a Regime-Switching Default Risk (RSDR) model. The RSDR model is a generalization of the MDR model, comprises regime-switching asset distribution dynamics, and thus produces more realistic default probability estimates in cases of deteriorating credit quality. Alternatively, it reduces to the MDR model. Using a dataset of U.S. credit default swap (CDS) contracts around the 2007-8 crisis we construct rating-based indices to investigate the MDR and RSDR implied probabilities of default in relation to the market-observed CDS spreads. The proposed CWS measure indicates an increase in implied default probabilities several months ahead of notable increases in CDS spreads.