商业银行作为中国经济体系的重要组成部分,其稳定运行直接影响国家金融经济的健康、安全和持续发展。然而,近年来,随着商业银行的快速发展,面临的风险也日益增加。有效识别和防控信用风险是确保其平稳运行的关键。为了更好地评估我国上市商业银行的信用风险,本研究采用KMV模型来测量样本银行的违约距离,以违约距离和违约概率作为衡量中国上市商业银行信用风险的代理变量,选取了2016~2023年14家上市商业银行作为研究样本,构建面板数据进行信用风险评估。通过模型对风险的度量,得出以下结论:KMV模型能有效衡量上市商业银行的信用风险;违约距离DD与预期违约率EDF呈负相关;不同性质的样本银行之间的违约距离表现为国有银行 > 城市商业银行 > 股份制商业银行。根据研究结论,提出相关建议。As a crucial component of China’s economic system, the stable operation of commercial banks directly impacts the health, safety, and sustainable development of the national financial economy. However, with the rapid development of commercial banks in recent years, the risks they face have also increased. Effectively identifying and managing credit risk is key to ensuring their smooth operation. To better assess the credit risk of listed commercial banks in China, this study employs the KMV model to measure the default distance of sample banks, using default distance and default probability as proxy variables for measuring credit risk. The study selects 14 listed commercial banks from 2016 to 2023 as the research sample and constructs panel data for credit risk assessment. The model’s risk measurement leads to the following conclusions: the KMV model effectively measures the credit risk of listed commercial banks;default distance (DD) is negatively correlated with the expected default frequency (EDF);and the default distance varies among different types of sample banks, with state-owned banks having
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
