Abstract
The new version of Basel Capital Accord has been formally rectified in the end of June, 2004. In order to follow the worldwide trend, the new version will also be executed in Taiwan. Among all contemporary structural modules of credit risk assessing models, the most prevalently implemented model is the KMV model. However, because the economic system in Taiwan, which is mainly based on small and medium enterprises and thus is deprived of market information, is unique, private firm model (PFM) developed by Moody's KMV Company is therefore employed to calculate the probability of default (PD) so as to quantify credit risk. However, private firm model has a lot of defects and relevant literature has shown that the application of PFM is not effective. Thus, the purpose of the current paper is to improve the method of applying Moody's KMV on evaluating non-listed companies. This paper not only adds jump-diffusion model to stock random process but also uses neural network to evaluate the stock price of non-listed companies. Furthermore, the present paper also adopts Monte Carlo simulation to simulate asset value paths in an attempt to more accurately quantify credit risks. The results show that our model has better discriminatory power in terms of both listed and non-listed companies; moreover, the empirical finding on both types of companies is in higher jump-coefficients. It can be concluded that the capacity of neural network in evaluating stock prices is prominent. Thus, it is believed that the current research will provide the domestic banks with another recommendable method to assess the credit risk of small and medium enterprises.