党的二十大报告指出,打好防范化解重大风险的攻坚战,重点是防控金融风险,信用风险是其最主要的风险之一,信用风险会导致银行破产,引发金融危机,甚至会导致全球经济动荡不安,因而信用风险是目前商业银行等金融机构所面临的重要的风险之一。本文选取国内7家上市股份制商业银行2013~2023年的数据,运用KMV模型测算违约距离与违约概率,从而度量其信用风险,并研究影响其信用风险的因素,研究结果表明,违约距离越小,信用风险越大,股权价值波动率对于违约概率存在着正向的显著影响,上市商业银行的股权价值波动率越高,违约概率就越大,信用风险越高,此外,不同股份制商业银行由于对外界风险因素的敏感度有所不同,加之其内部风险管理工作的成效各异,其信用风险存在差异。The report of the 20th National Congress of the Communist Party of China states that in the critical battle of preventing and defusing major risks, the focus is on preventing and controlling financial risks. Credit risk is one of the most prominent risks. Credit risk can lead to bank bankruptcies, trigger financial crises, and even cause global economic turmoil. Therefore, credit risk is one of the major risks faced by financial institutions such as commercial banks. This paper selects the data of 7 domestic listed joint-stock commercial banks from 2013 to 2023, uses the KMV model to calculate the distance to default and the probability of default, so as to measure their credit risks, and studies the factors influencing their credit risks. The research results show that the smaller the distance to default, the greater the credit risk. The volatility of equity value has a significant positive impact on the probability of default. The higher the volatility of the equity value of listed commercial banks, the greater the probability of default and the higher the credit risk. In addition, due to the different sensitivities of different join
近两年中国上市公司由于受到新的冠状物疫情的冲击,经济增速放缓,面临种种危机,信用风险受到很大考验,而规避违约风险对公司未来的发展不仅有好处,而且在制度稳定性方面也将起到明显的作用。因此,我们选择了KMV模型与GARCH模式和SV模式相结合,对上市公司的股票收益波动率进行重新拟合和估算,以此来衡量上市公司的资信管理,采用调整后的GARCH-KMV模型和SV-KMV模型,对上市公司中的9家ST企业与9家非ST企业的信用风险进行了对比研究。结果显示,传统的KMV可以更好地衡量上市公司的信用风险,在结合GARCH模型和SV模型后也可以衡量上市公司的信用风险,但SV模型对于信用风险的解释效果要好于GARCH模型。Impacted by the COVID-19 pandemic, the growth rate of China’s economy has slowed down, and listed companies in China are facing various crises. Credit risk is under significant testing, and avoiding default risk is not only beneficial for the future development of companies but also crucial for the stability of the system. Therefore, this study employs the KMV model to measure the credit quality of listed companies and combines the GARCH and SV models to re-estimate the volatility of equity value for these companies. The revised GARCH-KMV model and SV-KMV model are then applied to compare and analyze the credit risk of 9 ST companies and 9 non-ST companies in the listed market. The results indicate that the traditional KMV model can effectively measure the credit risk of listed companies, and incorporating the GARCH and SV models improves its credit risk measurement. Furthermore, the SV-KMV model demonstrates a better explanatory effect on credit risk compared to the GARCH-KMV model.
中小企业作为我国经济发展和社会稳定的重要支撑,对其违约风险进行全面有效评估有助于我国中小企业的发展。KMV模型作为风险度量方面的典型代表之一,为验证其在度量中小企业的违约风险方面的有效性,文章选取了180家上市中小企业2023年的财务数据,使用该模型进行了实证研究,并对违约距离DD进行描述性统计和ANVOA检验。ANVOA检验中p值为0.870,大于临界值0.05,发现高风险与低风险组即ST企业与非ST企业之间的违约距离不存在显著差异,这与我国资本市场还未进入有效市场有关,研究结果表明通过KMV模型分组度量并比较违约距离DD来度量我国中小企业信用风险的方法暂不可行。为有效管理防范我国中小企业信贷风险,首先,要加快完善我国资本市场的建设,加强对中小企业信息披露制度的管理,丰富相关监管部门的监管手段;其次,建立针对中小企业信用风险度量的专门体系,对中小企业的信用风险进行管理;最后,各中小企业应密切关注市场动向,引进相关人才和风险管理工具提高自身风险管理能力。As an important support for China’s economic development and social stability, a comprehensive and effective assessment of the default risk of small and medium-sized enterprises is conducive to the development of small and medium-sized enterprises in China. KMV model is one of the typical representatives of risk measurement, in order to verify its effectiveness in measuring the default risk of SMEs, this paper selects the financial data of 180 listed SMEs in 2023 and conducts an empirical study using the model, and conducts descriptive statistics and ANVOA test on the default distance DD. In the ANVOA test, the p-value is 0.870, which is greater than the critical value of 0.05, and it is found that there is no significant difference in the default distance between the high-risk and low-risk groups, that is, between ST enterprises and non-ST enterprises, w
