Credit Risk Measurement Study of Listed Commercial Banks in China

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 greater default distance than city commercial banks, and city commercial banks having greater default distance than joint-stock commercial banks. Based on these conclusions, relevant recommendations are provided.