Measurement Of Credit Default Risk Of Listed Manufacturing Companies

With the deepening of supply-side reform,in the past five years many local governments launched special rectification work on enterprises with high pollution and excess production capacity.In order to survive and develop in the market,some enterprises consider financing to meet their fund demand for production and operation.For those enterprises,they tend to choose bond financing.While during the process of bond financing,credit risk is inevitable.Excessive corporate credit risk not only affects the interests of both sides of the financing,but threaten the stability of the whole industry and the society.With the development of the bond market,the problem of insufficient awareness of enterprise risk control has been highlighted.While a good risk control is essential for the healthy development of enterprises because of its reliable risk measurement method.Enterprises can reduce their credit risk in the financing process through implementing reliable risk measurement method.Because of continuous changes and development of the financial market,previous risk measurement methods have been unable to keep pace with the era.The frequent bond default events in recent years have alarmed investors and enterprises,therefore we must choose a more targeted and timely credit risk measurement method to measure the credit risk of enterprises.KMV model(the same below),was first introduced by three entrepreneurs(Kealhofer,McQuown and Vasicek)in 1993.The name of KMV model is originated from the initials of its creator's names,and the basic idea of this model derives from the pricing model of financial derivatives.Compared with the early credit risk measurement model,KMV model is more time-efficient.However since this model is generated from the statistics of foreign capital markets,but the number of financial emergencies in Chinese market is significantly higher.Enterprise assets value of abnormal volatility behavior happens frequently,applying the traditional KMV model can no longer effectively reflect the situation of Chinese market.Therefore,this paper introduces the jumping process,which fully considers the abnormal volatility behavior of assets value to improve the KMV model.This paper mainly focuses on the measurement method of credit risk of listed manufacturing companies,because manufacturing industry is the lifeblood of national economy.Firstly,this paper describes the KMV model and elaborates the basic idea of improving KMV model with jump process(hereinafter referred as KMV improved model).In this part,the parameter estimation method of jump frequency,jump height and its standard deviation is given under KMV improved model,and the basic idea of optimizing the default point based on genetic algorithm is also explicitly stated.Secondly,38 listed manufacturing companies which are implemented the special rectification(hereinafter referred as ST company)between 2018 and 2019 in A-share market are selected as samples.Besides that,another similar 38 listed manufacturing companies(hereinafter referred to non-ST company)are adopted as control group in the empirical research.Thirdly,after integrating and analyzing the financial data and stock market information of 76 companies in the sample with MATLAB,this paper respectively obtains the asset value and its volatility under traditional KMV model and KMV improved model.Subsequently,when the long-term and short-term debt coefficient are 0.5 and 1 respectively,the maximum,minimum and average value of the default distance obtained under traditional KMV model and KMV improved model are compared and analyzed.Finally,the optimal default point of KMV improved model was obtained by using the genetic algorithm.There are three main conclusions in this paper.First,for both under traditional KMV model and KMV improved model,the assets value obtained is higher than the default point when the long-term and short-term debt coefficient are 0.5 and 1 respectively,indicating that the model has no ability to identify credit risk and is not applicable to measure the credit risk of listed manufacturing companies under this condition,and it is necessary to determine the optimal long-term and short-term debt coefficient to optimize the credit risk identification ability of the model.Second,on the whole,the default distance of KMV improved model is smaller than that of traditional KMV model for listed manufacturing companies,suggesting that KMV improved model fully takes into account the jump risk of the assets value of listed manufacturing companies,and it more accurately predicts credit risk than traditional KMV model.Thirdly,when the genetic algorithm is adopted to solve the optimal default point of KMV improved model,the short-term debt coefficient is higher than the long-term debt coefficient,and short-term debt has as large proportion in the debt structure of listed manufacturing companies.However,the larger the proportion of short-term debt is,the more likely the company is to suffer from capital chain break,and investors should pay more attention to the credit risk of related enterprises.The main contribution of this paper is to put forward the parameter estimation method of jump risk parameters for KMV improved model.Introducing the related concepts of jump frequency,jump height and its standard deviation,and making parameter estimation for the company's equity value and its volatility.And in this paper,we have also corrected the corresponding stock return sequence.Utilizing the genetic algorithm to solve the optimal default point in KMV improved model,and proposing a new fitness function to evaluate the classification of listed companies.While the convergence of the solution of KMV model is proved theoretically in the paper.