Non-listed Companies' Credit Risk Evaluation Method Research Based On KMV Model

With the slow economic growth and increase of non-performing loan incidences in China,it is necessary to establish scientific credit risk evaluation system.KMV model is one of the four credit risk models that is currently used,and has been recognized as a significant result for its solid theoretical basis?clear framework and convenience.Since non-listed companies do not have market value and volatility of market value that is very necessary for KMV model,this article focuses on non-listed companies' fair market value pricing.Two methods are used previously to solve this problem.One is to use industry average asset value as an alternative.However,it ignores the differences between listed companies and non-listed one.The other is to use the regression estimation method finding the relationship between market value of listed companies' asset and financial indicators,and then calculating the non-listed company market value using its own financial index as input.However,those two methods will overestimate the non-listed company enterprise value due to the shell value and liquidity premium in some Chinese listed companies.DCF can reflect the long-term development of the enterprise along with short-term turnover ability,and it has higher market recognition.Non-listed companies used in this article belong to the utility,which have clear cash flow,so this article will use the DCF valuation method to estimate the value of non-listed companies.After analyzing the KMV model principle and Chinese non-listed company pricing problem,DCF valuation method is raised to estimate the value of non-listed companies.This article verifies the effectiveness of KMV model based on DCF valuation method for non-listed companies' risk assessment in China.In comparison with regression estimation model,method used in this article can better distinguish high-risk non-listed companies from low-risk one.Besides,it turns out that companies have higher default rate using this method,thus it is more rigorous in risk testing.