Research on the Effectiveness of KMV Model in China's Bond Credit Rating Market

Sun Jifeng(School of statistics, Renmin University of China (Shenzhen))
Sun Tingwei(School of Data and Computer Science, Sun Yat-sen University)

Abstract

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.

Keywords

Credit risk; KMV model; Default distance

References

Guo L. Determinants of Credit Spreads: The Role of Ambiguity and Information Uncertainty[J]. North American Journal of Economics & Finance, 2013, 24(19):279-297．

Merton R C. On the Pricing of Corporate Debt: The Risk Structure of Interest Rates[J]. Journal of Finance, 1974, 29(2):449-470.

Shimko D, Tejima, Deventer D. The Pricing of Risky Debt when Interest Rates Are Stochastic[J]. Journal of Fixed Income, 1993, 3(2):58-66.

Longstaff F A, Schwartz E S. A Simple Approach to Valuing Risky Fixed and Floating Rate Debt[J]. Journal of Finance, 1995, 50(3):789-819.

Christoffersen P, Du D, Elkamhi R. Rare Disasters, Credit, and Option Market Puzzles[J]. Management Science, 2016, 63(5):1341-1364.

Kwon T Y. A Correlated Structural Credit Risk Model with Random Coefficients and Its Bayesian Estimation Using Stock and Credit Market Information[J]. Journal of Risk Model Validation, 2016, 10(3):21-48.

Liang J, Zhao Y, Zhang X. Utility Indifference Valuation of Corporate Bond with Credit Rating Migration by Structure Approach[J]. Economic Modelling, 2016, 54(2):339-339.

Jarrow R A, Turnbull S M. Pricing Derivatives on Financial Securities Subject to Credit Risk[J]. Journal of Finance, 1995, 50(1):53-85.

Das S, Peter T. Pricing Credit Sensitive Debt when Interest Rates, Credit Ratings and Credit Spreads Are Stochastic[J]. Journal of Financial Engineering, 1996, 5(2):125-136.

Wei D, Guo D. Pricing Risky Debt: An Empirical Comparison of the Longstaff and Schwartz and Merton Models[J]. The Journal of Fixed Income, 1997, 7(2):8-28.

DOI: http://dx.doi.org/10.26549/jfr.v4i1.3483