Measurement And Empirical Research On Default Risk Of Credit Bonds Based On KMV-LOGIT Hybrid Model

Since 2013,with the deepening of China's economic structural transformation and supply-side reforms,the systematic financial risks accumulating in the capital market have been exposed.In 2014,the breach of the " 112061.SZ" credit bond broke the myth of the rigid payment of bond market in China.From 2014 to the end of 2017,there were a total of 156 defaults on credit bonds in China,and the default amount reached 91.714 billion yuan,and the phenomenon of bond defaults gradually became normal.Therefore,the study of default risk measurement of credit bonds in China has important implications for companies,investors and regulatory agencies.In this paper,we build a KMV-LOGIT hybrid model to measures and predicts the default probability based on the sample of credit bonds that defaulted from 2014 to 2017 in China.The empirical results are as follows:(1)The classic KMV model constructed in this paper can well calculate the sample default distance and expected default probability;(2)The classic LOGIT model based on actual default data constructed in this paper can calculate the probability of default of the default sample.The accuracy of the annual model is above 90%.(3)The KMV-LOGIT hybrid model can calculate the default probability of the sample and perform dynamic monitoring without relying on the actual default data.The forecast results of the KMV-LOGIT hybrid model have high consistency with the actual default data and the classic LOGIT model.Finally,we do a validity detection for the hybrid model with the data set which covers ST listed companies and non-ST listed companies.The results show that it can identify the risk of ST listed companies and the accuracy rate of the model reaches 81.00%.Since the new model does not require actual default data of sample,it can well measure and dynamically monitor the default risk of credit bonds.