KMV模型在中国债券信用评级市场中的有效性研究

Jifeng Sun, Tingwei Sun
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摘要

近年来,中国债券市场发展迅速,但信用风险监管的步伐却没有跟上。自2014年以来,国内信用债券违约数量有所增加。2016年国内违约债券79只,违约金额高达403亿元。从国内债券市场信用风险监管预警机制来看,评级不客观,跟踪不及时,评级方法落后。因此,随着大数据等技术的发展,迫切需要研究适合国内债券市场的信用风险监管方法。本文在梳理国内债券市场发展和分析国内信用评级现状的基础上,结合国内外理论研究成果、国内市场现有信息、大数据挖掘和自动化技术,以上市公司财务和证券交易所信息为基础,结合BS期权定价理论,构建了KMV模型。
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Research on the Effectiveness of KMV Model in China's Bond Credit Rating Market
In recent years, China's bond market has experienced rapid development, but the pace of credit risk supervision has not kept up. Since 2014, the number of domestic credit bond defaults has increased. In 2016, there were 79 domestic default bonds, with a default amount of up to 40.3 billion Yuan. From the perspective of domestic bond market credit risk supervision and early warning mechanism, rating is not objective, and tracking is not timely also rating methods are backward. Therefore, with the development of big data and other technologies, it is urgent to study credit risk supervision methods suitable for the domestic bond market. On the basis of combing the development of domestic bond market and analyzing the current situation of domestic credit rating, this paper combines the results of theoretical research at home and abroad, the information available in the domestic market, big data mining and automation technology, based on the financial and stock exchange information of listed companies, combined with BS option pricing theory, constructs KMV model.
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