Abstract
This paper first analyses the current situation of credit risk in China’s real estate industry, and then compares the traditional and modern credit risk measurement models. On this basis, the KMV model is selected, and the artificial intelligence model Genetic Algorithm (GA) and GARCH model are introduced to improve the accuracy of the KMV model. Secondly, the annual financial data and stock trading data of 24 real estate listed companies for 2018 – 2022 are selected for empirical research. By analyzing the total default distance of the 24 companies and the actual economic development of China, it is proved that the results of the GA-GARCH-KMV model are 8% more correct than the classical KMV model, which indicates that the model has better applicability.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
