||This study evaluates two kinds of credit risk models. First one is Moody’s KMV model, and the other is Logistic Model. First, in KMV model, we calculate the average default rate during 260 days before the event time. In advance, we replace the parameter of risk free rate by ROA and Asset Growth Rate to evaluate the effective of these three parameters in our KMV Model. We also collect samples include firms which have ever declared some financial distress firms and normal firms. Then, in Logistic Model, we imply model with only financial variables and model with both financial and non-financial variables to calculate the potential default rate during the sample period. And moreover, we further test if Logistic model can identify the default events in 2008.|
Our results suggest that both Logistic and KMV models can successfully identify the default firms. In Logistic Model, we find the default rate show a positive trend as the default time being close. On the other hand, although we cannot get a significant different default rate under models with only financial variables and with financial and non-financial variables, model includes non-financial variables can more exactly identify default firms in 2008.KMV model also suggests an increasing default rate on default samples as event time being close, while default rate keeps in consistently low level on normal firms. Besides, after we replace risk free rate by ROA and Asset Growth Rate, we get a higher default rate among total samples because of consideration about specific firm’s risk condition rather than risk-neutral assumption. Finally, by including ROA and Asset Growth Rate in the models, we find much significant difference of default rate among financial distress firms and non-distress firms.
陳思翰 (2003)，商業銀行如何利用Logit 及KMV 模型檢視授信政策，國立中央大學財金所碩士論文。
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