Communication Equipment Industry Credit Risk Measurement-Empirical Analysis Based On KMV Model

Since the default of ST Chaori Second phase substantially,which is the first default event of the Public Offering Bond in 2014,the rigid payment of the bond market won't be bullish on any more.Afterwards the defaults continue to proliferate.Under the background of the economic downturn and supply-side structural reform,the credit crisis has gradually expanded from upstream of the industrial chain to the middle and lower reaches of the industrial chain.The industries involved range from cyclical overcapacity industry(such as coal,steel,and nonferrous metals)to other non-cyclical industries(such as transportation,communications equipment,and textile and clothing).Both amount and amount of default are on the rise.Therefore,the appropriate risk measurement model is actually applied to the analysis of China's specific financial markets after coefficient adjustments and parameter adjustments.The risk measurement model can avoid the uncertainty of the equity value which is resulted from increased risk of deterioration in credit status of the credited company.At the same time,this risk measurement model has practical value not only on banks' risk control,but also on private investors' investments.What's more,it has certain significance for establishing a credit risk measurement model and a credit risk assessment system that are truly suitable for China's development.After briefly introducing qualitative and quantitative credit risk measurement models in foreign countries,the author chooses the KMV model that is more suitable for China's national conditions to conduct empirical analysis.The main research points are how to correct the default point of the KMV model and to examine the KMV model's ability to predict the credit risk of listed companies in China.In this paper,a total of 56 companies in the telecommunications equipment industry and ST-listed companies were selected as samples in 2016.The coefficient of long-term liabilities was adjusted according to the actual situation in China,five default points were set,and four inspection methods were applied to selected ones.The default point is checked to find the optimal solution.The test results show that the model is most effective when the coefficient default point is set as the sum of short-term liabilities and long-term liabilities.Then based on the selected default point data,the default distance and default probability of the KMV model from 2014 to 2016 were calculated,and descriptive statistical analysis was performed to verify the forward-lookingness of the model.Finally,it summarizes the empirical part,analyzes the limitations of the study and puts forward suggestions on how to improve China's credit risk management.