基于KMV模型的汽车供应链金融违约风险测度

Measurement of Financial Default Risk of Automobile Supply Chain Based on KMV Model

摘要: 中小企业融资是现实的一大难题。供应链金融为缓解这一问题提供了新途径，但风险问题也不容忽视。当前，中国供应链金融风险量化识别体系尚不完善，商业银行难以准确评估风险。本文基于中国汽车工业现状，运用修正的KMV模型，选取9家代表性企业2022年财务信息，计算了核心制造商、零部件供应商与经销商的违约距离与概率。研究结果显示，修正后的模型能有效度量中国企业的信用风险，为商业银行的风险识别提供支持，对供应链金融的健康发展有积极意义。这一研究不仅丰富了风险管理理论，也为供应链金融的实践提供了有价值的参考。

Abstract: Financing for small and medium-sized enterprises is a major challenge in reality. Supply chain finance provides new ways to alleviate this problem, but risk issues cannot be ignored. Currently, the quantitative identification system for supply chain finance risks in China is not yet perfect, and commercial banks find it difficult to accurately assess risks. This paper, grounded in the current state of China’s automotive industry and informed by supply chain finance theory, selects the financial information of nine representative enterprises from 2022, including core manufacturers, component suppliers, and dealers. Through the modification of the classical KMV model, the paper aims to make its parameters more applicable to the Chinese market. Subsequently, the financial data of these nine enterprises are incorporated into the model to calculate the default distances and probabilities of core manufacturers, component suppliers, and dealers. The research findings indicate that the modified KMV model can effectively measure the credit risks of individual financing enterprises in China, providing strong support for commercial banks in risk identification and facilitating the healthy development of supply chain finance. This study not only enriches the theory of supply chain financial risk management but also provides valuable references for practical applications.