近年來國內金融機構的逾放問題，已成為其經營上的一大隱憂，為了提升其資產品質，財政部決定將於2006年實施新版巴賽爾資本協定，希望金融機構對其所面臨之風險能作有效的控管，但在信用風險方面除標準法外，國內銀行還未有一套完整的內部模型，評估銀行資產的信用風險。因此本研究試圖利用傳統Logit模型及現今普遍使用之KMV模型，嘗試先對國內上市櫃公司之違約機率加以估計，瞭解其在國內市場環境的適用性。研究所納入之樣本公司為85年至90年60家違約公司與其產業及資本規模相近之60家正常公司，先依據兩模型估算其違約機率，再利用平均數及中位數之檢定結果進行下列兩部分之實證分析：(1)兩模型方法論及預測能力之探討；(2)第一銀行及中國信託商業銀行授信政策之比較分析。 由實證結果發現:1.營業損益率、資產報酬率、淨值報酬率及存貨週轉率可有效區別違約公司及正常公司，其中營業損益率及淨值報酬率代表公司的獲利能力，資產報酬率涉及所得稅效果的利息支出，因而主要反映公司舉債融資的狀況，存貨週轉率則用來檢視公司的銷貨速度。2. Logit模型及KMV模型除了有區別正常與違約公司的效果之外，還有提前警示公司信用品質出現惡化的能力，為理想的事前預測工具，皆可適用於國內的市場環境。3.因台灣的股票市場仍長期存在資訊不對稱的問題，所以當股價涉及人為操作致未能正確反映公司實際價值時，使用KMV模型並無法發揮良好的預警效果。因此本研究建議可將兩模型合併使用，一方面藉由KMV模型逐日監控信用風險，另一方面定期檢視客戶各項財務比率，除了防止誤判的情況發生之外，利用較為保守的估計方式，以確實掌握可能的信用損失。4.第一銀行及中國信託商業銀行授信政策的分析結果，顯示兩家銀行皆能有效控管授信客戶的信用風險，並且針對客戶信用品質的優劣，訂定合適的放款條件。在第一銀行方面，雖然能針對違約風險較高之傳統及建築業逐年降低放款比重，但其主要之信用風險來源仍以此兩種產業的放款客戶為主，因此，未來應加強對其信用狀況的審核，俾能提升整體的放款品質；就中信銀而言，則是應對傳統產業放款客戶的信用品質多加留意，並適度調整對該產業的放款比重。 The increasing non-performing loans has become a serious problem in the operation of commercial banks. In order to improve the quality of assets in Taiwan local banks, The Ministry of Finance has decided to implement the「New Basel Capital Accord」in 2006. But there is no completed internal rating-based approach (IRB Approach) to evaluate the credit risk except for the standard method. For the reason to understand the suitability of IRB Models in Taiwan financial environment, this study try to use the traditional Logit and current KMV models to calculate the default probability of public firms in Taiwan and to analyze the lending policy of First Bank and Chinatrust Bank. Major research findings include: (1) there are four financial ratios could effectively tell default and normal companies which are operating profit, return on assets, return on equity and inventory turnover. (2) except for the effect of differentiating between default and normal companies, using Logit and KMV models can gives a warning in advance to the deterioration of credit quality. (3) because of the information asymmetry in Taiwan stock market, KMV model can not work out when the stock price does not represent the real value about a company. To sum up, this study recommends that commercial banks can use both models simultaneously when assessing the credit quality of a company. They should monitor the credit condition by KMV model in daily term, while keep evaluating the operating performance regularly to do double check. (4) when applying both models on the lending policy of First Bank and Chinatrust Bank, the result shows that both banks can manage effectively the credit risk of their lending customers and provide appropriate loan terms based on their credit levels. For First Bank, the main cause of credit risk results from loans to traditional and architectural industries. It should examine severely the credit condition of these two industries in the future. Chinatrust Bank should revise the loan proportion properly and pay more attention to the customers in traditional industry.
