金融学院知识库

  随着我国债券市场发展的不断深入，债券违约风险也逐渐开始暴露。自2014年我国首只公司债券违约以来，债券市场的违约事件就相继发生。截至2019年12月31日，我国总计发生违约的债券190只，涉及违约金额高达1142.38亿元，引发投资者对于未来市场的广泛担忧。对于发展不够完善的我国债券市场来说，提前预测和准确度量信用违约风险，对最大限度的减少违约风险带来的隐患，具有非常重要的意义。

  本文通过梳理国内外学者的研究成果，并比较了4种现代流行的信用风险度量模型，最后选择了具有前瞻性、数据易获取和实用性等优点的KMV模型作为度量工具。首先，在对 KMV 模型在我国的适用性进行实证检验时，选取 2018 年发生债券违约的上市公司作为高风险组，未发生债券违约的上市公司作为低风险组，实证表明计算出的违约距离能清楚地反映出上市公司违约风险的高低，即高风险组样本的平均违约距离显著低于低风险组样本的平均违约距离。说明 KMV 模型较为有效。然后，结合案例分析，利用KMV模型计算出2018年我国A股信息技术行业上市公司的违约距离，并与其他行业的违约平均距离进行比较，结果表明信息技术行业的违约距离与高风险组较为接近，该行业存在较大的违约风险。之后，选取2019年首次发生债券违约的信息技术类上市公司东旭光电作为案例，比较实证结果的违约距离之后，通过对违约事件的梳理和原因的总结，得出东旭光电发生债券违约的几点原因：激进扩张导致的经营业绩不佳；受限资金比例高导致存贷双高；股权质押率过高违约风险较大；宏观因素的影响等。

  最后，根据以上的分析来对信息技术行业的上市公司提出几点建议：公司在进行业务扩张时，要增加新拓展业务与主营业务的关联度；股权质押率过高时上市公司和金融机构都要采取相关措施；上市公司要加强自律意识并建立完善的风控制度；监督机构也应该加强监管，完善监管机制。

  As the bond market in my country grows deeper, the risk of bond defaults is gradually becoming apparent. Since my country's first corporate bond defaulted in 2014, there has been a series of defaults in the bond market. As of December 31, 2019, a total of 190 bonds have defaulted in China, and the amount involved in default has reached 114.23 billion yuan, raising concerns about the futures market among investors. For the underdeveloped bond market in my country, predicting and accurately measuring credit default risk is very important to minimize the hidden risks posed by credit risk.

  In this paper, we summarize the research results of domestic and foreign scholars, compare four latest popular credit risk measurement models, and finally, a future-oriented KMV model with the advantages of easy data collection and practicality. Select as a measurement tool. First, in a demonstration test of the applicability of the KMV model in my country, listed companies that experienced bond defaults in 2018 were selected as high-risk groups, and listed companies that did not have bond defaults were selected as low-risk groups. The calculated default distance clearly reflects the high default risk of listed companies, that is, the average default distance of the high-risk group sample is significantly lower than the average default distance of the low-risk group sample. Is empirically shown. It shows that the KMV model is valid. Then, in combination with case analysis, use the KMV model to calculate the default distances for companies listed in my country's A-share information technology industry in 2018 and compare them to the average default distances for other industries. The results show that the default distance of the information technology industry is relatively close to that of the high-risk group, and that this industry has a greater risk of default. After that, we selected Dongxu Optoelectronics, an information technology listed company that had its first bond default in 2019, as an example. After comparing the default distances of empirical results, you can summarize the reasons by combining default events to find out the number of defaults for Dongxu Optoelectronics bonds. Several reasons: Poor performance due to aggressive expansion , Higher percentage of restricted funds will result in higher deposits and loans, too high stock pledge rate will result in higher risk of default, macro factors, etc.

  Finally, based on the above analysis, some precautions have been proposed for listed companies in the information technology industry.When a company expands its business, it needs to increase the correlation between the newly expanded business and its core business. If the initial public offering rate is too high, listed companies and financial institutions will need to take appropriate action. Listed companies need to strengthen their self-consciousness. Establishing discipline and sound risk management systems; supervisors also need to strengthen supervisory and improve supervisory mechanisms.