基于数据的银行信贷行业的信用风险研究

目前多数研究利用美国旧金山市KMV公司于1997年建立的模型(KMV模型)计算企业年违约距离来评估具体企业的信用风险,但缺乏信贷行业的信用风险评估方法,也不能给出随时间变化的信用风险.首先提出基于数据的信贷行业随时间动态演化的信用风险评估模型,然后利用2016年18个行业的数据得到了中国信贷行业动态演化的信用风险,该信用风险随时间演化特征可分为波动上升、下降后波动、下降后稳定、稳定四种类型.进一步研究发现金融业、科学研究和技术服务业、信息传输软件和技术服务业这三个行业动态演化的信用风险平均值高且不稳定,住宿和餐饮业的信用风险很高但是比较平稳,其他行业的信用风险较低且较平稳.

Research on Credit Risk of Bank Credit Industry Based on Data

At present, most researches use the KMV model established by KMV company in San Francisco in 1997 to calculate the annual default distance of enterprises and evaluate the credit risk of specific enterprises, but there is few researches on the credit risk assessment method of credit industry and few researches can give a time-evolving credit risk. Firstly, this paper proposes a dynamically time-evolving industry credit risk assessment model based on data, and then obtains the dynamically evolving credit risk of the Chinese credit industry using data of 18 industries in 2016. The results show that credit risk evolution characteristics of the Chinese credit industry can be divided into four types: fluctuation rising, fluctuation falling, stability falling, and stability. Further studies find that the average value of credit risk in the dynamic evolution of three industries, namely financial industry, scientific research and technical service industry, and information transmission software and technical service industry, are high and unstable. The credit risk in accommodation and catering industry is high but stable, while the credit risk in other industries are low and stable.