商业银行信用风险测度的死亡率模型

商业银行传统的信用风险分析方法主要是以信贷专家分析和主观判断为主,存在许多缺陷,难以量化商业银行信用风险.文章尝试运用寿险精算中的死亡率模型测度商业银行客户违约率,分析客户信用等级与违约率之间的数量关系特征,并以此为依据测算贷款的预期违约损失,揭示商业银行信用风险发生的数量规律.根据某商业银行的样本数据对商业银行信用风险死亡率模型进行了实证分析.     

商业银行信用风险量化新方法:死亡率模型

传统的商业银行信用风险分析方法主要是以信贷专家分析和判断为主,存在许多缺陷,难以量化银行信用风险。文章试图创新性地运用寿险精算中的死亡率模型测度商业银行客户违约率,分析客户违约率的数量特征,并以此为依据测算贷款的违约损失,揭示商业银行信用风险发生的数量规律。文章最后模拟了商业银行信用风险死亡率模型的应用。     

基于行业分类的商业银行信用风险的度量

商业银行面临的信用风险指的是由于贷款企业违约而带来的风险。如何及时准确度量商业银行面临的信用风险,一直以来都是国内外理论界和银行实务界的热点问题。在信用风险度量方法中,专家制度是一种最古老的信用风险定性分析方法,其中最著名的是5C、5W和5P信用分析模型。以前对信     

贷款违约概率测算方法:违约比例模型

文章依据生存分析统计方法中的比例危险法,提出了违约比例模型来测算商业银行公司类贷款的违约概率,并采用我国某商业银行数据进行了实证分析。分析结果表明,该模型能够准确测算商业银行公司类贷款的违约概率,以便商业银行提取普通准备金和配置经济资本。     

信用风险管理中损失分布法与价值分布法的模拟比较

文章通过实际模型比较了信用风险的两大类模型:违约模型和盯市模型。信用风险具有广义与侠义之分,狭义的信用风险就指违约风险,即相关金融资产(贷款)发生违约的可能性及违约的可能损失;广义的信用风险在违约风险的基础上应该包括信用变化所带来风险,也就是信用迁移风险。文章通过构建具有蒙特卡洛模拟的信用风险模型,计算这两种风险值,通过比较得出如下结论:并不是广义的信用风险就大,而是与资产组合的信用状况相关,高信用品质的资产信用迁移增加风险,而对低信用品质的资产信用迁移减少风险。     

两种房屋抵押贷款违约概率模型的比较

美国次级危机引发了全球性的金融危机,使得银行必须加强风险控制。现阶段银行面对的主要风险是贷款的信用风险,房屋抵押贷款是银行贷款业务的主要部分。文章对房屋贷款违约模型分别给出了Logistic违约模型和Cox比例危险违约模型,利用美国某州的次级贷款违约数据进行了分析和比较,指出了Cox比例危险违约模型可以给出借款人每个时点的违约风险度量,相对于Logistic回归违约模型具有较高的准确性和稳健性。     

Markov链在预测助学贷款违约率中的运用

贷后风险难以监控是国家助学贷款发放难的重要原因之一。为此,在发放助学贷款的同时,预测其违约率成为了关键。根据我国商业银行的规定,助学贷款的类别变化符合Markov链的状态转移规律。在搜集了足够的数据之后,可以利用Markov链进行助学贷款违约率的预测,以期帮助商业银行加强贷后风险管理。     

Markov链在预测助学贷款违约率中的作用

贷后风险难以监控是国家助学贷款发放难的重要原因之一。为此,在发放助学贷款的同时,预测其违约率成为了关键。根据我国商业银行的规定,助学贷款的类别变化符合Markov链的状态转移规律。在搜集了足够的数据之后,可以利用Markov链进行助学贷款违约率的预测,以期帮助商业银行加强贷后风险管理。     

我国商业银行贷款定价影响因素的实证检验

文章运用2002~2010年我国13家全国性商业银行9年的面板数据,用平均贷款利率作为因变量,用资金成本、违约风险水平、银行相对规模、资本充足率、贷款集中度、银企信息不对称程度作为自变量,分别运用混合数据OLS模型、固定效应模型和随机效应模型实证检验了我国商业银行的贷款定价影响因素,发现除资金成本、违约风险水平与我国商业银行的贷款定价水平具有显著正相关关系,而其它自变量对贷款定价的影响都不显著。     

基于KMV模型的我国农业银行信用风险管理实证研究

文章在充分考虑我国农业银行贷款对象的基础上,选取三十家沪、深上市公司作为研究样本,运用KMV模型度量了上市公司的预期违约率。研究表明,KMV模型总体上能够较好地评估上市公司的信用风险水平,因而适用于我国农业银行的信用风险管理。     

Establishing decision tree-based short-term default credit risk assessment models

ABSTRACT

Traditional credit risk assessment models do not consider the time factor; they only think of whether a customer will default, but not the when to default. The result cannot provide a manager to make the profit-maximum decision. Actually, even if a customer defaults, the financial institution still can gain profit in some conditions. Nowadays, most research applied the Cox proportional hazards model into their credit scoring models, predicting the time when a customer is most likely to default, to solve the credit risk assessment problem. However, in order to fully utilize the fully dynamic capability of the Cox proportional hazards model, time-varying macroeconomic variables are required which involve more advanced data collection. Since short-term default cases are the ones that bring a great loss for a financial institution, instead of predicting when a loan will default, a loan manager is more interested in identifying those applications which may default within a short period of time when approving loan applications. This paper proposes a decision tree-based short-term default credit risk assessment model to assess the credit risk. The goal is to use the decision tree to filter the short-term default to produce a highly accurate model that could distinguish default lending. This paper integrates bootstrap aggregating (Bagging) with a synthetic minority over-sampling technique (SMOTE) into the credit risk model to improve the decision tree stability and its performance on unbalanced data. Finally, a real case of small and medium enterprise loan data that has been drawn from a local financial institution located in Taiwan is presented to further illustrate the proposed approach. After comparing the result that was obtained from the proposed approach with the logistic regression and Cox proportional hazards models, it was found that the classifying recall rate and precision rate of the proposed model was obviously superior to the logistic regression and Cox proportional hazards models.     

Macro-Economic Factors in Credit Risk Calculations: Including Time-Varying Covariates in Mixture Cure Models

The prediction of the time of default in a credit risk setting via survival analysis needs to take a high censoring rate into account. This rate is because default does not occur for the majority of debtors. Mixture cure models allow the part of the loan population that is unsusceptible to default to be modeled, distinct from time of default for the susceptible population. In this article, we extend the mixture cure model to include time-varying covariates. We illustrate the method via simulations and by incorporating macro-economic factors as predictors for an actual bank dataset.     

Credit risk modeling based on survival analysis with immunes

Statistical modeling of credit risk for retail clients is considered. Due to the lack of detailed updated information about the counterparty, traditional approaches such as Merton’s firm-value model, are not applicable. Moreover, the credit default data for retail clients typically exhibit a very small percentage of default rates. This motivates a statistical model based on survival analysis under extreme censoring for the time-to-default variable. The model incorporates the stochastic nature of default and is based on incomplete information. Consistency and asymptotic normality of maximum likelihood estimates of the parameters characterizing the time-to-default distribution are derived. A criterion for constructing confidence ellipsoids for the parameters is obtained from the asymptotic results. An extended model with explanatory variables is also discussed. The results are illustrated by a data example with 670 mortgages.     

中国省域信贷风险判别分析

现阶段，商业银行信贷仍是我国社会资金配置的主要方式。出于盈利和风险考虑，商业银行信贷行为天然具有顺周期特征。为实现稳增长目标，政府更倾向于逆周期调节。受到地方财政收支状况影响，省级地方政府会采取不同方式、不同程度地干预省域资金配置。十九大报告明确指出，要健全货币政策和宏观审慎政策双支柱调控框架。因此，省域信贷风险判别是一个动态过程，需在经济周期与宏观审慎政策框架下整体考虑。在此背景下，本文基于新古典经济学分析框架，建立了2008年以来省域信贷风险识别模型，研究发现，第一，地方财政支出收入比与不良贷款率存在正向影响关系，资本回报率与不良贷款率存在负向影响关系，且地方财政支出收入比对不良贷款率的影响程度更大；第二，依据分类准则，属于信贷高风险的省域分别是：河南，海南，重庆，四川，贵州，云南，陕西，甘肃，青海，宁夏，新疆，西藏；第三，在地方财政支出收入比、资本回报率的显著作用影响下，我国各省域不良贷款率呈现U型变化，不良贷款率阈值为1.49%，即当不良贷款率大于1.49%时，省域贷款风险较高；第四，当我国资本回报率处于企稳阶段，不良贷款率处于低于阈值的谷底阶段，且省域间风险差异性较小。当我国资本回报率处于下行阶段时，不良贷款率上升至阈值线以上，且省域间风险差异性较大。     

Sovereign rescheduling probabilities in emerging markets: a comparison with credit rating agencies’ ratings

This study estimates default probabilities of 124 emerging countries from 1981 to 2002 as a function of a set of macroeconomic and political variables. The estimated probabilities are then compared with the default rates implied by sovereign credit ratings of three major international credit rating agencies (CRAs) – Moody's Investor's Service, Standard & Poor's and Fitch Ratings. Sovereign debt default probabilities are used by investors in pricing sovereign bonds and loans as well as in determining country risk exposure. The study finds that CRAs usually underestimate the risk of sovereign debt as the sovereign credit ratings from rating agencies are usually too optimistic.     

违约风险、房地产贷款市场博弈与房地产价格

本文从事后激励的角度，构建了一个关于房地产个人贷款违约与银行反应策略的博弈模型，对中国房地产价格下跌的诱发机制以及家庭和银行的最优决策进行了理论分析。在此基础上选择35个大中城市作为研究样本，利用面板数据回归模型对相关理论推论进行了实证检验。理论推演和实证研究表明，家庭收入下降和房地产贷款违约是诱发房地产价格下跌的关键因素。提高购房首付比，降低房地产贷款价值比以及保持房地产贷款市场结构的适度集中，既可以抑制房地产价格过快上涨，也可以预防房地产价格发生暴跌风险。当房地产贷款出现违约时，为了避免房地产价格进入下降螺旋，银行的最优策略不是取消房地产抵押品的赎回权，而是采取积极的信贷刺激措施以稳住房地产价格。贷款市场份额占比越高的银行越有激励这样做。     

基于贝叶斯方法的信用评级模型构建与违约概率估计

以贝叶斯方法为基础构建了信用评级和违约概率模型,指出金融机构利用已有评级信息提高债务人信用风险评估准确性的途径,并以单个债务人违约概率度量方法和Merton理论为基础,考虑异质性导致的宏观经济冲击对债务人的不同影响,度量资产组合违约风险。利用相关数据对贝叶斯模型应用给出例证,结果表明贝叶斯方法具有更为灵活的框架和较好的预测能力。     

经济地位对信用卡逾期的影响

我国信用卡业务的迅猛发展助推了消费经济的快速发展，但信用卡的逾期行为不容忽视。收入代表了一个人的经济地位，是信用卡按时还款的保障。本文基于某商业银行信用卡客户的逾期数据，以持卡人的经济地位为视角，分析了经济地位对信用卡逾期行为的影响。研究结果表明，我国商业银行信用卡持卡人的逾期行为具有显著的经济特征，收入对信用卡逾期的影响呈“U”型的非线性特征，即收入较低和收入较高的持卡人逾期的可能性较高，收入处于中间的持卡人逾期的可能性较低。进一步的研究发现，中年群体、工作单位稳定者、有房者会降低经济地位对信用卡逾期行为的非线性影响，而账龄较长的持卡人提升了这种影响。本文的研究为全社会建立良好的信用卡用卡环境，商业银行高效处理信用卡逾期，改进和完善商业银行信用卡风险管理提供了关键证据。     

信用风险、融资担保和动态保费估值

基于市场化信息和风险中性的概念,度量了有担保贷款的边际违约概率和累积违约概率,确定了贷款担保风险的精算现值,根据市场信用价差的变化给出了动态保费每期的调整幅度,并利用数值模拟进行了担保费率的比较静态分析,最后根据实际的担保数据给出了动态保费的实证检验.结果显示,实际的违约支付非常接近于动态保费的估值,证明动态保费估值模型是一个简单、可行和实用的定价模型.     

Quantification of model risk that is caused by model misspecification

In this paper, we suggest a technique to quantify model risk, particularly model misspecification for binary response regression problems found in financial risk management, such as in credit risk modelling. We choose the probability of default model as one instance of many other credit risk models that may be misspecified in a financial institution. By way of illustrating the model misspecification for probability of default, we carry out quantification of two specific statistical predictive response techniques, namely the binary logistic regression and complementary log–log. The maximum likelihood estimation technique is employed for parameter estimation. The statistical inference, precisely the goodness of fit and model performance measurements, are assessed. Using the simulation dataset and Taiwan credit card default dataset, our finding reveals that with the same sample size and very small simulation iterations, the two techniques produce similar goodness-of-fit results but completely different performance measures. However, when the iterations increase, the binary logistic regression technique for balanced dataset reveals prominent goodness of fit and performance measures as opposed to the complementary log–log technique for both simulated and real datasets.