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。     

一个新的银行信用风险评估方法

本文以银行信用风险管理为例,将粗糙集和决策树两种具有互补优势的数据挖掘方法相结合,对客户信用做出归类分析判断,最后利用决策树生成决策规则.实践证明,这种方法忠于原始数据,提高了分类准确度,减小了决策树规模,具有良好的性能.     

Bagging方法在基于日常交易数据解决中小企业的信用评价问题中的应用

关于信用风险评价问题至今已经做了很多研究,各种信用评价模型与方法也已被开发.但是这些模型与方法几乎都是基于财务数据、股票价格或风险调研机构发表的各种调查结果.因为几乎所有的中小企业的财务数据都是非公开的,至今开发的信用评价模型与方法都不免成为无米之炊.为此,本文提出了一个新的途径,只需要根据销售额、顾客付款额、拖欠款额等日常业务处理数据来评价顾客企业的信用度.本文提出一个应用Bgging方法评价顾客信用的系统,其目的在于解决由于异常顾客数比正常顾客要少很多而带来的问题,提高分辨异常顾客的能力.本文所提出的信用评价系统将应用到一个实际企业的信用评价问题中,借此来验证系统的性能和效果.     

Examining evidence of financial and credit exclusion in Canada from 1999 to 2005

Changes are occurring in the provision of consumer credit, including the expansion of subprime and some fringe financial services (e.g., payday lending). We link two existing literatures on credit constraint and financial exclusion to assess the impact of these developments on the financial circumstances of low and modest-income consumers. We develop a model that identifies observable measures of credit constraint and financial exclusion and relate them to consumer characteristics and life cycle behaviour. We estimate this model using the two latest Surveys of Financial Security for 1999 and 2005, which provide consistent evidence of credit constraint and financial exclusion through time. We find modest overlap among our measures of financial exclusion, which include a zero balance/no account, credit card refusal, and using a pawnshop. Probit regression is used to investigate the factors influencing the incidence of financial exclusion. The results are similar for 1999 and 2005 and indicate rising incidence of financial exclusion as income and wealth fall, although the relationship is nonlinear such that incidence rises much faster at very low levels of income and wealth. Our analysis also suggests potentially important links between financial literacy, formal education, asset building, and financial exclusion and credit constraint. When we combine the samples, we find statistically significant evidence of growth in the incidence of each indicator of financial exclusion when other factors are held constant. Policy implications may include the strengthening of banking regulations that affect low-income Canadians and the promotion of universal financial literacy.     

信用评分模型的设计与决策分析

本文对商业银行信贷业务中的贷款者的信用问题,运用决策分析提出了信用评分的设计原理及临界分值的确定方法,给出了最优贷款策略的期望收益和风险的计算公式。最后,以一例说明之。     

内部评级法与我国商业银行信用风险管理

从上世纪九十年代中后期开始,我国商业银行的信用风险管理工作取得了一定的成绩。但是,还存在着很多的问题,诸如信用风险管理的技术还很不成熟、没有严格意义上的独立的风险管理部门和专职的风险经理来管理信用风险、并独立承担风险管理职责等,与内部评级法的要求存在较大的差距。为适应《巴塞尔新资本协议》中内部评级法的要求、加强信用风险管理能力,我国的商业银行应努力做好数据库的建设、信用风险模型的开发、加快金融改革,为信用衍生产品的推出创造良好的环境。     

倡导优势,开发潜能--浅论我校"奖励学分"制

高教法的颁布和新的本科专业目录的公布实施对高等学校的人才培养提出了新的要求.如何充分发挥学生学习的主动性和积极性、培养学生的创新意识,是当前高等学校面临的重要问题.北京航空航天大学在制定九七版教学计划时注重了学生自主学习和自主发展能力的培养,引入了"奖励学分"制度.     

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Relationship between Financial Stress and Workplace Absenteeism of Credit Counseling Clients

The researchers examined how financial stress was associated with absenteeism of credit counseling clients. Data were collected by a national non-profit credit counseling organization, from consumers who telephoned seeking assistance in debt management. The results indicate credit counseling clients’ financial stress affects their absenteeism at work. Clients with high levels of financial stress are more likely to experience higher levels of absenteeism; thus spending work hours handling personal finances, which decreases the time they are at work. The results suggest some insight into providing financial education and assistance for employees with financial strains as productivity loss might influence their pay.Appreciation is extended to the InCharge Institute of America and the InCharge Education Foundation for supporting this research. Dr. Kim served as an InCharge Scholar during this research effort.     

Do Forbearance Plans Help Mitigate Credit Card Losses?

In this paper, we examine how reinstated (i.e., re-aged) credit card accounts are likely to default again. Our sample data reveal that about 22% of the re-aged accounts default again, mostly in the first 24 months after reinstatement. We also find that a FICO score (public information) is a better predictor of a second default, while a payment behavioral score (private information) is a better predictor of a first default. Furthermore, the average FICO score of the 78% of the re-aged borrowers who did not default again rises about 20 points, an improvement in their relative risk profile overall. These findings suggest that the re-aging program provides a second chance for liquidity-constrained borrowers who would have otherwise defaulted on their debt.