Empirical Study on Credit Risk of Our Listed Company Based on KMV Model

KMV model is one of the most important credit risk evaluation models in the world. It uses B-S option pricing and Morton formula based on the market value and volatility of the company’s equity, debt maturities, risk-free interest rates and the book value of liabilities to estimate the market value of the company’s assets and the volatility of the asset value. In this paper, based on the theory of KMV model, we can derive the listed company’s default rate, and assess credit risk. And the result is reasonable.

The Credit Risk Assessment Model of Internet Supply Chain Finance: Multi-Criteria Decision-Making Model with the Principle of Variable Weight

The characteristics of the financing model are firstly analyzed when the e-commerce enterprises participate in the supply chain finance. Internet supply chain finance models are divided into three categories with the standard of whether the electronic commerce enterprises provide funds for small and medium enterprises instead of banks. And then we further study the financing process and the functions of the e-commerce platform with specific examples. Finally, combined with the characteristics of the supply chain finance model, we set up a small and medium enterprises credit evaluation model based on the principle of variable weight with its dynamic data. At the same time, a multi-time points and multi-indicators decision-making method based on the principle of variable weight is proposed and a specific example is presented. In this paper, the multi-criteria decision-making model with the principle of variable weight has been used two times. At last, a typical case has been analyzed based on this model with a higher accuracy rate of credit risk assessment.

Credit Risk Model Taking Account of Inflation and Its Contribution to Macroeconomic Discussion on Effect of Inflation on Output Growth

We use Extended Merton model(EMM)for estimating the firm’s credit risks in the presence of inflation.We show quantitatively that inflation is an influential factor making either a benign or adverse effect on the firm’s survival,supporting at the microeconomic level New Keynesian findings of the nonlinear inflation effect on output growth.Lower inflation increasing the firm’s expected rate of return can raise its mean year returns and decrease its default probability.Higher inflation,decreasing the expected rate return,makes the opposite effect.The magnitude of the adverse effect depends on the firm strength:for a steady firm,this effect is small,whereas for a weaker firm,it can be fatal.EMM is the only model taking account of inflation.It can be useful for banks or insurance companies estimating credit risks of commercial borrowers over the debt maturity,and for the firm’s management planning long-term business operations.

The Credit Risk Assessment Model of Internet Supply Chain Finance: Multi-Criteria Decision-Making Model with the Principle of Variable Weight

The characteristics of the financing model are firstly analyzed when the e-commerce enterprises participate in the supply chain finance. Internet supply chain finance models are divided into three categories with the standard of whether the Electronic commerce enterprises provide funds for small and medium enterprises instead of banks. And then we further study the financing process and the functions of the e-commerce platform with specific examples. Finally, combined with the characteristics of the supply chain finance model, we set up a small and medium enterprises credit evaluation model based on the principle of variable weight with its dynamic data. At the same time, a multi time points and multi indicators decision-making method based on the principle of variable weight is proposed and a specific example is presented. In this paper, the Multi-criteria decision-making model with the principle of variable weight has been used two times. At last, a typical case has been analyzed based on this model with a higher accuracy rate of credit risk assessment.

Understanding Credit Risk in Internet Consumer Finance:An Empirical Analysis with a Focus on the Young Generation

In recent years,internet finance has garnered increasing attention from the public.Online lending,emerging within the framework of Internet finance as a pivotal component,has witnessed substantial growth.While online credit,within the realm of Internet finance,presents numerous advantages over traditional lending,it concurrently exposes a plethora of credit risk issues.This study aims to facilitate the effective utilization of online credit tools by the young generation within the context of Internet finance.Additionally,it seeks to ensure the overall stability of the Internet finance environment and mitigate risks for the youth.Given the significance of understanding credit risk management for college students in the age of internet finance,this paper adopts the logistic model to evaluate credit risk in internet consumer finance and provides pertinent recommendations from the perspective of the young generation.

Measuring the model risk-adjusted performance of machine learning algorithms in credit default prediction

Implementing new machine learning(ML)algorithms for credit default prediction is associated with better predictive performance;however,it also generates new model risks,particularly concerning the supervisory validation process.Recent industry surveys often mention that uncertainty about how supervisors might assess these risks could be a barrier to innovation.In this study,we propose a new framework to quantify model risk-adjustments to compare the performance of several ML methods.To address this challenge,we first harness the internal ratings-based approach to identify up to 13 risk components that we classify into 3 main categories—statistics,technology,and market conduct.Second,to evaluate the importance of each risk category,we collect a series of regulatory documents related to three potential use cases—regulatory capital,credit scoring,or provisioning—and we compute the weight of each category according to the intensity of their mentions,using natural language processing and a risk terminology based on expert knowledge.Finally,we test our framework using popular ML models in credit risk,and a publicly available database,to quantify some proxies of a subset of risk factors that we deem representative.We measure the statistical risk according to the number of hyperparameters and the stability of the predictions.The technological risk is assessed through the transparency of the algorithm and the latency of the ML training method,while the market conduct risk is quantified by the time it takes to run a post hoc technique(SHapley Additive exPlanations)to interpret the output.

Research on Credit Risk Measurement Based on Uncertain KMV Model

Regarding KMV model identification credit risk profile of small and medium-sized listed companies, at present, domestic scholars has made some achievements in the process of the KMV model combined with China’s national conditions. In this paper, we will amend the model by using uncertain interest rate instead of fixed rate on the basis of existing research. Comparing the uncertain KMV model to traditional KMV model with ST-listed companies and non-ST-listed companies in Shanghai and Shenzhen stock exchange, we find that it performs slightly better as a predictor in uncertain KMV model and in out of sample forecasts.

Fuzzy-Neuro Model for Intelligent Credit Risk Management

This paper presents hybrid fuzzy logic and neural network algorithm to solve credit risk management problem. Credit risk is the risk of loss due to a debtor’s non-payment of a loan or other line of credit. A method of evaluating the credit worthiness of a customer is complex and non-linear due to the diverse combinations of risk involve. To address this problem a credit scoring method is proposed in this paper using hybrid fuzzy logic-neural network (HFNN) model. The model will be implemented, tested, and validated for individual auto loans using real life bank data. The neural network is used as the learner and the fuzzy logic is used as the implementer. The neural network will fine tune the fuzzy sets, remove redundant input variables, and extract fuzzy rules. The extracted fuzzy rules are evaluated to retain the best k number of rules that will give final and intelligent decisions. The experiment results show that the perform-ance of the proposed HFNN model is very accurate, robust, and reliable. Comparison of these results to other previous published works is also presented in this paper.

Study on Credit Card Risk of Commercial Banks in Prefectural-Level Banks Based on Logistic Model

Substantial income has been brought to the bank since credit card business entering the Chinese market,giving rise to a growing number of credit card issuers and more frequent transactions,which also improves convenience of cardholders.However,the booming market of credit card caused a series of credit risk.Credit risk in commercial banks and medium-sized banks in prefectural-level city is an operational risk that cannot be ignored,if not properly handled;it will exacerbate risk control pressure.Credit card risk mainly concerns default risk,and factors causing breach varied.Credit card risk can exist in the whole process,including the customers?application for credit card,card insuring,and transaction settlement.Finding an effective way to identify a variety of credit card risk,and developing a complete and efficient monitoring system to reduce the risk of credit loss is essential for large commercial banks.In terms of smaller-scale banks in prefecture-level city,a credit scoring system to evaluate the customer's credit ability is particularly important.Dataset in this paper mainly comes from a prefecture-level city bank,and the information is anonymous and authentic.This paper starts with the more than 700 customer data of a prefecture-level city bank and comprehensively considers the status quo of credit card development in China's commercial banks and successful domestic and foreign credit risk management experiences,followed with the causes and characteristics of credit card risks,solutions,and proposals,systematically expounding credit card business risk management.This article adopts the Logistic model and the credit scoring model.Through the screening and analysis of dozens of customer's characteristic variables and the use of various commands of statistical software,a prediction model of customer default probability will be constructed.At the same time,a scoring model was introduced to set the threshold for issuing cards in a quantifiable manner to help banks predict the possibility of customer default before issuing credit cards.Finally,through the combination of multiple sets of model comparison and selection,a high level of issuance volume can be ensured,and the risk rate is minimized,which can provide a reference for banks in the practical application of credit risk control.

农地经营权抵押贷款信用风险影响因素及其衡量研究——基于CreditRisk+模型的估计

基于1 173个贷款样本数据,运用Logistic回归分析农地经营权抵押贷款信用风险影响因素并预测违约概率,依据CreditRisk+模型,对农地经营权抵押贷款信用风险衡量进行研究,并进行了压力测试。研究表明,农地经营权抵押贷款信用风险主要受到抵押土地因素、保险与政策因素的影响;影响因素的风险程度具有次序性;贷款期限和农业生产周期不匹配是农地经营权抵押贷款面临的突出矛盾;土地经营权来源不同的贷款风险程度存在明显差异;农地经营权抵押贷款预期损失和非预期损失占VaR比例结构合理,极端情景出现时预期损失会有明显波动。提出应瞄准贷款对象、精确贷款条款和强化风险处置,促进农地经营权抵押贷款顺利开展。

基于CreditRisk+模型的零售贷款经济资本计量方法

针对零售贷款的信用风险特征,在CreditRisk+框架下,采用非线性时变比例模型分类测算零售贷款违约概率,并引入FFT-Panjer算法简化零售贷款组合的损失分布计算,提出了零售贷款信用风险的经济资本计量方法。同时通过算例分析论证了该方法在我国商业银行运用的科学性和可行性。

CreditRisk^+模型在商业银行信贷风险管理中的应用

使用CreditRisk^+(信贷风险附加)模型对选自某商业银行的贷款的资产组合进行风险测度。得到了资产组合中各债务人的预期违约损失和风险贡献,资产组合的预期违约损失分布,以及各置信度损失水平下的临界值等信息,从而完成了资产组合风险的测量。根据测量结果,对资产组合进行了预期违约损失、风险贡献、经济资本和信用准备金等分析,在此基础上提出目前我国商业银行信贷风险管理的现实选择是CreditRisk^+这种违约模式的模型。

基于商业银行贷款风险度量的Credit Risk+模型

文章运用Credit Risk+模型对我国商业银行贷款风险度量进行实证分析.原Credit Risk+模型在计算违约损失率时使用历史平均值的方法有很大的缺陷,现运用清算LGD方法计算贷款的违约损失率.同时,采用更有效的CVaR方法度量风险,提高违约损失的计算精度.

Application of the Credit Metrics in the Credit Risk Management of Commercial Banks

Credit risk is one of the main risks the commercial banks faces all over the world,especially in the risk structure of the banks of China.In order to control credit risk more scientifically,we shall connect the qualitative analysis and the quantitative analysis.Put forward by J.P.Morgan Credit Metrics model is the application of the VaR in the field of credit risk,showing great advantage in quantitative bonds and credit risk of loan.This paper studies the Credit Metrics model and analyzes the hypothesis and framework of this model,attempting to explore the application of the model in China in order to promote the realization of the risk quantification of the commercial banks of China.

Panel Random Analysis of Credit Risk in Business

Market economy is a kind of credit economy. The survival and development of an individual in the society are closely related with his credit. Without credit, market economy can not continue, the society can hardly run in good order and good health. This paper defines the basic concept of trade credit risk with its manifestation and brings forward the basic mode quantitatively analyzing the credit risk. The data structure of information is analyzed, the decomposition model of credit risk is structured and with the aid of statistical analysis, including regression analysis, analysis of variance, test of hypothesized, the description, classification, certification and confirmation of credit risk model are completed, then, we can describe and control the credit risk with the model to provide basis when building credit support system in today＇s society.

基于Credit Risk+模型的我国西部地区农村商业银行信用风险评价及防范研究——以四川某农村商业银行为例

近几年西部地区农村商业银行发展迅速,但其现有的信用风险评价方法较为落后,不能很好地度量其信用风险,本文选用Credit Risk+模型作为研究我国西部地区农村商业银行的信用风险评价模型,以西部地区四川省某农村商业银行为例,对其信用风险进行评价分析,得出相关结论,并提出利于西部地区农村商业银行发展的对策建议。

Research on the Effectiveness of KMV Model in China's Bond Credit Rating Market

In recent years,China's bond market has experienced rapid development,but the pace of credit risk supervision has not kept up.Since 2014,the number of domestic credit bond defaults has increased.In 2016,there were 79 domestic default bonds,with a default amount of up to 40.3 billion Yuan.From the perspective of domestic bond market credit risk supervision and early warning mechanism,rating is not objective,and tracking is not timely also rating methods are backward.Therefore,with the development of big data and other technologies,it is urgent to study credit risk supervision methods suitable for the domestic bond market.On the basis of combing the development of domestic bond market and analyzing the current situation of domestic credit rating,this paper combines the results of theoretical research at home and abroad,the information available in the domestic market,big data mining and automation technology,based on the financial and stock exchange information of listed companies,combined with BS option pricing theory,constructs KMV model.

The credit risk of the borrower＇s credit in P2P network

P2P network lending refers to the individual through the oJfline trading platform provided by the Business Company provides small loans to other individuals. According to the sea tree Lingyi data stalistics, as of November 2014. China has 1371 P2P network lending company, than by the end of2013 638 over more than doubled. Since 2014,The cumulative volume of the whole network lending industry is up to 431.2 billion yuan. With the increasing social awareness in the industry, the future of the number and amount of P2P network lending companies in China will continue to grow rapidly. However, at present our P2P network credit risk management issues is serious, lacking of professional risk management personnel,who audit on the borrower＇s credit mostly limited to the upload information of borrowers. Credit rating is largely dependent on the subjective judgment of the risk control personnel and audit staff, which can not meet the requirements of the transaction participants in the loan security measures.This paper to pat the loan company were empirical research, through the logistic model to find the breach the key influence factors, to construct the borrower＇s credit risk evaluation methods, and to provide the P2P network lending risk control with specific policy recommendations.

我国公司债违约的影响因素研究:基于产权和行业的视角

自从2014年我国第一家公司债违约,我国公司债打破刚性兑付,公司债违约事件逐步增加。债券违约风险的管理与预测已成为亟待解决的重要问题。本文以我国上市公司2008—2020年发行的公司债为样本,从产权属性和所在行业的角度出发,控制公司财务与债券特征因素,研究我国公司债违约风险的影响因素并进行预测检验。Logit回归与平均边际效应检验结果表明,民营企业与制造业上市公司具有较高的违约概率,上市公司净资产增长率和所在地区经济增长水平均与违约概率呈显著负相关,而信用评级和到期期限与违约无显著关联。ROC曲线检验结果表明,本文模型能够有效预测违约风险。

社会信用治理模型选择:由风险预测制迈向行为积分制

模型是对实践的抽象化表达,内含实践的认知理念和价值判断。不同信用模型内含不同的信用治理理念并会产生不同的社会效果。风险预测制模型用于风险预测和秩序控制,在实际社会信用治理中产生过度收集数据、模型黑箱化、难以有效预测等问题。究其原因,在于技术设计过程未能有效反映治理需求,反而消解了人的主体性地位。社会信用治理需要从行为改变出发,迈向行为导向,转换治理范式。行为积分制模型以行为理论为支撑,通过将社区治理过程中的日常事项转化为积分激励事项,推动社区居民积极参与到社区经济、政治、社会、文化、生态等建设中,以共建推动共同治理,将共治行为转化为积分,进而实现治理成果的共享,提供了民主治理的新范式。在推动信用体系建设过程中,应更多考虑行为积分制模型的应用和推广。