金融学院知识库

信用风险作为金融市场上最重要且最古老的金融风险，对其进行预测和控制显得尤为重要。近些年受国内外因素的影响，我国上市公司信用风险问题暴露愈加严重，也备受瞩目。通过研究国际上流行的信用风险评价方法，试图找到适合我国上市公司信用风险管控的模型和评价体系，这不仅有助于我国信用风险管理水平的提升，还对我国金融市场的健康稳定发展具有重要的理论和实践意义。
本文首先对信用风险、信用风险度量模型及其发展演进、相关理论基础等进行详细阐述，并对信用风险度量模型进行综合比较分析，考虑KMV模型在我国的适用性。其次介绍了KMV模型的建立基础及理论，再针对传统KMV模型中估算股权价值波动率以及确定违约点系数方法在我国适用性不强的问题，分别应用EGARCH模型和遗传学算法（GA）对股权价值波动率和违约点系数重新确定，从而构建了精度更高的EGARCH-GA-KMV模型。实证方面，选择40家高风险组（ST类公司）和低风险组（非ST类公司）作为研究样本，并进行研究比较。结果证明，重构之后的模型能比较有效的辨别高低风险组，其中高风险组（ST类公司）的违约距离显著小于低风险组（非ST类公司），且经过验证，发现重构后的模型较传统KMV模型能够更好地体现信用风险高低的差异。因此，重构后改进的模型在我国上市公司的违约风险测度方面具有良好效果，适用于我国特定的国情。最后，对未来我国上市公司信用风险预测提出一些展望。

Credit risk, as the most important and oldest financial risk in the financial market, is particularly important to predict and control. In recent years, influenced by internal and external factors, credit risk issues of listed companies in China have become increasingly serious and have attracted significant attention. By studying internationally popular credit risk assessment methods, we attempt to find models and evaluation systems suitable for credit risk management of listed companies in China. This not only helps improve the level of credit risk management in China but also holds important theoretical and practical significance for the healthy and stable development of China's financial market.
This paper first elaborates on credit risk, credit risk measurement models, their development and evolution, relevant theoretical foundations, and conducts a comprehensive comparative analysis of credit risk measurement models, considering the applicability of the KMV model in China. Secondly, it introduces the establishment foundation and theory of the KMV model, and addresses the weak applicability of estimating equity value volatility and determining default point coefficient methods in the traditional KMV model in China. It applies the EGARCH model and Genetic Algorithm (GA) to re-determine equity value volatility and default point coefficients, thus constructing a more accurate EGARCH-GA-KMV model. In terms of empirical research, 40 high-risk group (ST companies) and low-risk group (non-ST companies) are selected as research samples for comparison. The results demonstrate that the reconstructed model can effectively distinguish between high and low-risk groups, with the default distance of the high-risk group (ST companies) significantly smaller than the low-risk group (non-ST companies). It is verified that the reconstructed model can better reflect the differences in credit risk levels compared to the traditional KMV model. Therefore, the improved model after reconstruction has a good effect on measuring default risk of listed companies in China and is suitable for the specific national conditions of China. Finally, some prospects for future credit risk prediction of listed companies in China are proposed.