Pricing Catastrophe Options with Credit Risk in a Regime-Switching Model

In this paper,we consider the price of catastrophe options with credit risk in a regime-switching model.We assume that the macroeconomic states are described by a continuous-time Markov chain with a finite state space.By using the measure change technique,we derive the price expressions of catastrophe put options.Moreover,we conduct some numerical analysis to demonstrate how the parameters of the model affect the price of the catastrophe put option.

基于损失程度变化的CreditRisk^+的鞍点逼近

传统的CreditRisk+模型在度量信用风险过程,假定违约损失是给定不变的,而近年的实证研究表明在实际的金融市场中违约损失是变化的。针对传统模型这一假设的不合理性,本文对模型作了发展。新模型充分考虑了违约时损失程度的变化,用β分布来刻画这种变化,并利用鞍点逼近给出了信用风险的度量,改进了传统递推算法的不足。最后进行数值模拟以说明方法的有效性。

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。

A credit risk assessment model based on SVM for small and medium enterprises in supply chain finance

Background:Supply chain finance(SCF)is a series of financial solutions provided by financial institutions to suppliers and customers facing demands on their working capital.As a systematic arrangement,SCF utilizes the authenticity of the trade between(SMEs)and their“counterparties”,which are usually the leading enterprises in their supply chains.Because in these arrangements the leading enterprises are the guarantors for the SMEs,the credit levels of such counterparties are becoming important factors of concern to financial institutions’risk management(i.e.,commercial banks offering SCF services).Thus,these institutions need to assess the credit risks of the SMEs from a view of the supply chain,rather than only assessing an SME’s repayment ability.The aim of this paper is to research credit risk assessment models for SCF.Methods:We establish an index system for credit risk assessment,adopting a view of the supply chain that considers the leading enterprise’s credit status and the relationships developed in the supply chain.Furthermore,We conducted two credit risk assessment models based on support vector machine(SVM)technique and BP neural network respectly.Results:(1)The SCF credit risk assessment index system designed in this paper,which contained supply chain leading enterprise’s credit status and cooperative relationships between SMEs and leading enterprises,can help banks to raise their accuracy on predicting a small and medium enterprise whether default or not.Therefore,more SMEs can obtain loans from banks through SCF.(2)The SCF credit risk assessment model based on SVM is of good generalization ability and robustness,which is more effective than BP neural network assessment model.Hence,Banks can raise the accuracy of credit risk assessment on SMEs by applying the SVM model,which can alleviate credit rationing on SMEs.Conclusions:(1)The SCF credit risk assessment index system can solve the problem of banks incorrectly labeling a creditworthy enterprise as a default enterprise,and thereby improve the credit rating status in the process of SME financing.(2)By analyzing and comparing the empirical results,we find that the SVM assessment model,on evaluating the SME credit risk,is more effective than the BP neural network assessment model.This new assessment model based on SVM can raise the accuracy of classification between good credit and bad credit SMEs.(3)Therefore,the SCF credit risk assessment index system and the assessment model based on SVM,is the optimal combination for commercial banks to use to evaluate SMEs’credit risk.

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.

A dynamic credit risk assessment model with data mining techniques:evidence from Iranian banks

Giving loans and issuing credit cards are two of the main concerns of banks in that they include the risks of non-payment.According to the Basel 2 guidelines,banks need to develop their own credit risk assessment systems.Some banks have such systems;nevertheless they have lost a large amount of money simply because the models they used failed to accurately predict customers’defaults.Traditionally,banks have used static models with demographic or static factors to model credit risk patterns.However,economic factors are not independent of political fluctuations,and as the political environment changes,the economic environment evolves with it.This has been especially evident in Iran after the 2008-2016 USA sanctions,as many previously reliable customers became unable to repay their debt(i.e.,became bad customers).Nevertheless,a dynamic model that can accommodate fluctuating politicoeconomic factors has never been developed.In this paper,we propose a model that can accommodate factors associated with politico-economic crises.Human judgement is removed from the customer evaluation process.We used a fuzzy inference system to create a rule base using a set of uncertainty predictors.First,we train an adaptive network-based fuzzy inference system(ANFIS)using monthly data from a customer profile dataset.Then,using the newly defined factors and their underlying rules,a second round of assessment begins in a fuzzy inference system.Thus,we present a model that is both more flexible to politico-economic factors and can yield results that are max compatible with real-life situations.Comparison between the prediction made by proposed model and a real non-performing loan indicates little difference between them.Credit risk specialists also approve the results.The major innovation of this research is producing a table of bad customers on a monthly basis and creating a dynamic model based on the table.The latest created model is used for assessing customers henceforth,so the whole process of customer assessment need not be repeated.We assert that this model is a good substitute for the static models currently in use as it can outperform traditional models,especially in the face of economic crisis.

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.

Credit risk evaluation using adaptive Lq penalty SVM with Gauss kernel

In order to improve the performance of support vector machine (SVM) applications in the field of credit risk evaluation, an adaptive Lq SVM model with Gauss kernel (ALqG-SVM) is proposed to evaluate credit risks. The non-adaptive penalty of the object function is extended to (0, 2] to increase classification accuracy. To further improve the generalization performance of the proposed model, the Gauss kernel is introduced, thus the non-linear classification problem can be linearly separated in higher dimensional feature space. Two UCI credit datasets and a real life credit dataset from a US major commercial bank are used to check the efficiency of this model. Compared with other popular methods, satisfactory results are obtained through a novel method in the area of credit risk evaluation. So the new model is an excellent choice.

Research on the Model of Household Credit Risk Evaluation of Rural Microfinance

Since rural microfinance is a credit which grants loans without collateral and guarantees to farmers,it is considerably important to evaluate and control the household credit risk.Through establishing the evaluation index system and then using catastrophe progression theory,three common types of catastrophe system and the normalization formula,we get the comprehensive evaluation.Finally,we take the empirical test and the result shows that this method is simpler and more objective which can be used by the credit cooperatives to decide whether to authorize the loans.

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.

MSEs Credit Risk Assessment Model Based on Federated Learning and Feature Selection

Federated learning has been used extensively in business inno-vation scenarios in various industries.This research adopts the federated learning approach for the first time to address the issue of bank-enterprise information asymmetry in the credit assessment scenario.First,this research designs a credit risk assessment model based on federated learning and feature selection for micro and small enterprises(MSEs)using multi-dimensional enterprise data and multi-perspective enterprise information.The proposed model includes four main processes:namely encrypted entity alignment,hybrid feature selection,secure multi-party computation,and global model updating.Secondly,a two-step feature selection algorithm based on wrapper and filter is designed to construct the optimal feature set in multi-source heterogeneous data,which can provide excellent accuracy and interpretability.In addition,a local update screening strategy is proposed to select trustworthy model parameters for aggregation each time to ensure the quality of the global model.The results of the study show that the model error rate is reduced by 6.22%and the recall rate is improved by 11.03%compared to the algorithms commonly used in credit risk research,significantly improving the ability to identify defaulters.Finally,the business operations of commercial banks are used to confirm the potential of the proposed model for real-world implementation.

Supply Chain Finance Credit Risk Evaluation Method Based on Self-Adaption Weight

Credit risk is the core issue of supply chain finance. In the supply chain, problems happened in different enterprises can influent the whole to different degrees through transferring, thus statuses of all enterprises and their different influences should be considered when evaluating the supply chain’s credit risk. We examine the characters of supply chain network and complex network, use the local growing complex network to simulate the real supply chain, use cluster analysis to classify the company into several levels;Introducing each level’s self-adaption weight formula according to the company’s quantity and degrees of this level and use the weight to improve the credit evaluation method. The research results indicate that complex network can be used to simulate the supply chain. The credit risk evaluation (CRE) of an enterprise level with bigger note degrees has a greater weight in the supply chain system’s CRE, thus has greater effect on the whole chain. Considering different influences of different enterprise levels can improve credit risk evaluation method’s sensitivity.

An Artificial Neural Network Approach for Credit Risk Management

The objective of the research is to analyze the ability of the artificial neural network model developed to forecast the credit risk of a panel of Italian manufacturing companies. In a theoretical point of view, this paper introduces a litera-ture review on the application of artificial intelligence systems for credit risk management. In an empirical point of view, this research compares the architecture of the artificial neural network model developed in this research to an-other one, built for a research conducted in 2004 with a similar panel of companies, showing the differences between the two neural network models.

Prioritizing real estate enterprises based on credit risk assessment:an integrated multi‑criteria group decision support framework

Credit risk assessment involves conducting a fair review and evaluation of an assessed subject’s solvency and creditworthiness.In the context of real estate enterprises,credit risk assessment provides a basis for banks and other financial institutions to choose suitable investment objects.Additionally,it encourages real estate enterprises to abide by market norms and provide reliable information for the standardized management of the real estate industry.However,Chinese real estate companies are hesitant to disclose their actual operating data due to privacy concerns,making subjective evalu-ation approaches inevitable,occupying important roles in accomplishing Chinese real estate enterprise credit risk assessment tasks.To improve the normative and reliability of credit risk assessment for Chinese real estate enterprises,this study proposes an integrated multi-criteria group decision-making approach.First,a credit risk assessment index for Chinese real estate enterprises is established.Then,the proposed framework combines proportional hesitant fuzzy linguistic term sets and preference ranking organization method for enrichment evaluation II methods.This approach is suitable for processing large amounts of data with high uncertainty,which is often the case in credit risk assessment tasks of Chinese real estate enterprises involving massive subjec-tive evaluation information.Finally,the proposed model is validated through a case study accompanied by sensitivity and comparative analyses to verify its rationality and feasibility.This study contributes to the research on credit assessment for Chinese real estate enterprises and provides a revised paradigm for real estate enterprise 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.

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 constraint mechanisms in rural financial reform

To research the operating mechanisms of rural financial reform, through setting up a contract model, the constraint roles of reputation and legal intervention on the default risk arising in the operating of the credit union funds are inspected. Analysis indicates that the increase in reputation cost can reduce the probability of union member default behavior and the probability of turning to the law for the credit union funds. Meanwhile, the amount of loans and the interest rates can increase the probability of turning to the law for the credit union funds. Below the marginal values, the penalty mechanisms can reduce the balancing probabilities of member default behavior and turning to the law for the credit union funds, namely, the penalty has some "substitution effect" for turning to the law for the credit union funds.