| 摘要(英) |
Credit risk management is one of the most critical issues of the commercial banking business. The contribution of the research is to provide the technique of conditional transition matrix which improves the accuracy of credit loss simulation. This research was divided into two sections. One part based on Kim (1999) is to describe a model for estimating the conditional matrix. The main idea is to adopt an established framework with four financial parameters, which we could incorporate credit cycle dynamics into the transition matrix. To implement the technique, we first build a credit cycle index, which indicates the credit state of the financial market as a whole. Furthermore, we adjust the transition matrix in terms of the credit cycle index. Besides, we use backtesting to check out the validity of the model. The other part is that we employ CreditMetricsTM model to count Credit VaR on the loan position of commercial banks in accordance with the unconditional, conditional, and realized transition matrix.
The major empirical results are as follows: First, the Credit VaR of these four banks (First Bank, FB; Hua Nan Commercial Bank, HNCB; Chinatrust Commercial Bank, CTCB; Land Bank, LB) are consistent and close to the trend of the reality. It indicates that the model could really improve the appropriateness of the required capital. Second, we use the ratio (the Credit VaR to the total loans) to analyze the quality of extending credit for banks. The result shows that CTCB has the smallest risk due to well credit risk management that could supervise the credit quality of a borrowing enterprise efficiently. Third, we use K-W test to verify that four banks may take the difference degree risk owing to the different industries. The result indicates that only the LB is different from other banks. It maybe caused by the poor credit quality of construction industry and the most companies are speculators. |
| 參考文獻 |
書籍部分
1. 周大慶、沈大白等人（民91），風險管理新標竿—風險值理論與應用，智勝文化事業有限公司。
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4. 郭敏華（民89），債信評等，智勝文化事業有限公司。
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期刊部分
1. 白鎮維、趙令斌（民90），「信用風險之評估-介紹KMV之EDF及若干國內實證」，貨幣觀測與信用評等，頁131-146。
2. 沈大白、蔡嘉倩（民91），「TCRI與累積倒帳機率之關係」，貨幣觀測與信用評等，頁127-135。
3. 葉金江（民87），「退票率與經濟景氣之關係-影響退票率因素之實證分析」，貨幣觀測與信用評等，第十一期，頁78-85。
4. 賴柏志（民91），「台灣地區信用循環指標的建立」，貨幣觀測與信用評等，頁123-127。
5. 賴柏志、白鎮維、張嘉娥（民91），「信用循環指標應用於信用風險修正之研究」，貨幣觀測與信用評等，頁123-127。
網路部分
1. 中央銀行網站：http://www.cbc.gov.tw/
2. 中國民國證券櫃臺買賣中心網站：http://www.otc.org.tw/c_index.htm
3. 台灣經濟新報網站：http:/ www.tej.com.tw/
4. 央行：國銀應加強風險模型研究，中時電子報，91/08/25，http://news.sina.com.tw/sinaNews/chinatimes/twHeadline/2002/0825/10624853.html.
5. 行政院主計處網站：http://www.dgbasey.gov.tw/
6. 行政院經濟建設委員會網站：http://www.cepd.gov.tw/index.jsp
7. 財政部金融局網站：http://www.boma.gov.tw/
8. 網址：http://www.tytrust.com.tw/report9011_2.html.
9. 蘇金祥，「利率模型參數估計：利率期間結構之估計」，http://www.bondnet.com.tw/doc/parestimate.htm.
英文部分
1. Allen, L.（2002）, “A survey of cyclical effects in credit risk measurement models, ” Working Paper.
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10. Gupton, Greg M., Finger, Christopher C. and Mickey Bhatia（1997）, CreditMetrics™ － Technical document, New York: Morgan Guaranty Trust Company.
11. Kim, J.（1999）, “A way to condition the transition matrix on wind, ” Credit Risk Supplement, pp.1-12.
12. Marrison, C.（2002）, The fundamentals of risk measurement, The McGraw-Hill Companies, Inc.
13. Merton, Robert C. (1974), “On the pricing of corporate debt: The risk structure of interest rates, ” Journal of Finance, 28, pp. 449-470.
14. Wilson, Thomas C.（1997）, “Credit portfolio risk Ⅰ, ” Risk Magazine October .
15. Wilson, Thomas C.（1997）, “Credit portfolio risk Ⅱ, ” Risk Magazine November. |
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