Research On Credit Risk Of China’s Listed Companies Based On Genetic Algorithm And EGARCH-M

After the reform and opening up,China’s financial market continued to develop,and various risk events also happened frequently.Among them,credit risk covers the widest range and is an important content to be managed and prevented.At the same time,the number of listed companies has been increasing in recent years.Against this background,the credit risk has been increased and the measurement of credit risk has become more difficult.The accurate measurement and prediction of the credit risk of listed companies have become the top priority.In the research process,this article refers to studying the credit risk technology at home and abroad,studies the applicability of different credit risk measurement models in China,and selects the credit risk measurement model suitable for the actual situation of China’s listed companies.This article is revised based on the actual situation of my country’s financial market.First,the genetic algorithm is introduced to re-correct the default points in the KMV model in order to eliminate the defects of subjectivity or local optimality in the traditional default point setting method.Secondly,the EGARCH-M method is introduced to estimate the equity value volatility to better fit the "spike and thick tail" characteristics of the stock return sequence.Using the data of Chinese listed companies in 2018 as sample data,calculate the default distance of each listed company,and compare the default distance of the listed company required by the revised model and the traditional KMV model,and finally summarize the application of the model in the Chinese market.The empirical results show that the revised KMV model has a good effect on the evaluation of the credit risk of sample companies,and effectively improves the accuracy of the credit risk measurement of listed companies in my country.