摘要
信用等级划分旨在区分不同客户的风险水平,然现有大多数信用等级划分研究要么无法严格保证划分的信用等级满足“信用等级越高,损失率越低”标准,要么无法保证尽可能地区分违约可能性不相似的客户,而无论不符合哪种标准,划分的信用等级都不能作为贷款决策的有效依据.基于此,以上述两个标准为目标划分信用等级.创新与特色一是以客户的非违约累计频率与违约累计频率之差的最大绝对值的代数和最大为目标,以后一个信用等级损失率大于前面信用等级损失率为主要约束,确保划分的信用等级满足上述两个标准.二是提出通过设置随机分割点的区间来划分信用等级的新算法,避免随机赋予信用等级分割点时,靠前的信用等级分割点落到靠后的客户中,导致后面的信用等级无论怎样划分均划分不出来的弊端.最后,以中国某商业银行3045笔小企业贷款样本进行实证,结果表明本模型划分的信用等级满足“信用等级越高,损失率越低”标准,且其区分不同违约可能性客户的能力较强.
Credit rating is to differentiate the risk of different customers.However,most existing methods either fail to guarantee that the credit rating meets the criteria u the higher the credit rating,the lower the loss rate^,or fail to differentiate the customers with different default possibilities to the greatest possible degree.Therefore,the existing credit rating method cannot be used as an effective tool for loan decisions.This study gives credit grades according to the above criteria.The innovations are:firstly,credit grades are divided by taking the maximum difference between the cumulative frequency of non-defaulting customers and that of defaulted customers as the objective function and taking uthe higher the credit rating "the lower loss rate" as the main constraint,so that the credit grades meet the above dual criteria;Secondly,a new algorithm for credit rating by setting the intervals of random segmentation points is proposed.The algorithm avoids that problem that a front division point may be designated to customers in the back,resulting in the failure of grading customers in the back.Finally,this study uses 3045 small businesses from a Chinese bank for empirical study,and the results show that the credit grades thus derived meet the above dual criteria.
作者
迟国泰
于善丽
CHI Guo-tai;YU Shan-li(School of Economics and Management,Dalian University of Technology,Dalian 116024,China;Postdoctoral Research Station,Financial Research Institute,The People’s Bank of China,Beijing 100800,China;Inter Bank Market Clearing House Co.,Ltd,Shanghai 200002,China)
出处
《管理科学学报》
CSSCI
CSCD
北大核心
2019年第11期106-126,共21页
Journal of Management Sciences in China
基金
国家自然科学基金重点资助项目(71731003
71431002)
国家自然科学基金资助项目(71873103)
中国博士后科学基金资助项目(2018M641578).
