KMV and Maximum Likelihood Methods for Structural Credit Risk Models: Evidence from Taiwan Market

• Main Authors: Hui-Ling Chen, 陳慧玲
• Format: Others
• Language: en_US
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/71662497095283402046

碩士 === 國立中央大學 === 統計研究所 === 95 === KMV method is a popular commercial implementation of Merton''s (1974) structural credit risk model. It is found in recent academic papers, but it is not clear as to whether it is statistically sound. Unlike the MLE method, the KMV method is speechless with the distributional properties of the estimates and it is unsuited for statistical inference. We follow Duan et al. (2004) to verify that the KMV estimate is identical to the MLE estimate in Merton''s (1974) model. Moreover, we perform the Monte Carlo simulation to show that the estimations of KMV and MLE are alike. We also use the data from Taiwan market to examine the accuracy of Merton''s (1974) model by both KMV and MLE methods. We find that Merton''s (1974) model provides about 60% of accuracy ratio no matter the KMV or MLE method is emplyed in predicting the default probabilities of Taiwan companies. It is notable that our result is somehow driven by the incompleteness for one-quarter prediction. If we are able to complement the missing data, we could expect to have an accuracy ratio higher than 60%.