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研究生:鄭郁翰
研究生(外文):Yu-Han Jeng
論文名稱:新巴賽爾資本協定之信用風險評分卡之建置與研究
論文名稱(外文):The Construction of Credit Scorecard for New Basel Capital Accord
指導教授:朱美珍朱美珍引用關係李御璽李御璽引用關係
指導教授(外文):Mei-Chen ChuYue-Shi Lee
學位類別:碩士
校院名稱:銘傳大學
系所名稱:資訊管理學系碩士班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:中文
論文頁數:91
中文關鍵詞:新巴賽爾資本協定信用風險羅吉斯迴歸信用評分卡
外文關鍵詞:New Basel Capital AccordCredit RiskCredit ScorecardLogistic Regression
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  • 被引用被引用:0
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  近年來,金融自由化使國內外金融機構面臨了許多風險的問題。為了有效控管其風險,國際清算銀行旗下的巴賽爾銀行監理委員會(Basel Committee on Banking Supervision)提出了新巴賽爾資本協定(Basel Capital Accord II)。我國也已於2006年年底全面實施此協定。在銀行所面臨的風險中,以信用風險(Credit Risk)所佔的比重最高。因此,如何適當的判定一個銀行客戶的違約機率(Probability of Default)是十分重要的課題。本研究提出一個以羅吉斯迴歸為基礎之新的信用評分(Credit Scorecard)機制,此機制的建構除了考量借款人的資料外,並以模組化的方式加入景氣循環及信用歷史這兩項要素,透過此機制可增加信用評分模型之正確性,並可有效的預測借款人未來可能發生之風險,並降低銀行可能發生之損失和建構模型的成本。
  In recent years, financial liberalization makes the domestic and international financial institution face a lot of problems in risk. To efficiently manage the risk, the Basel Committee on Banking Supervision of the bank for international settlements proposes the Basel Capital Accord II. Taiwan also follows it at the end of 2006. Because the proportion of credit risk is the highest than the other risks, it is an important issue for properly evaluate the probability of default (PD) for each customer in a bank. Many researches focused on constructing the credit scoring model (Credit Scorecard). This study proposes a new credit scoring model based on logistic regression. Besides considering the debtor’s information, our model joins the module of prosperous circulation and debtor’s previous behavior to increase the accuracy of the model. Our methodology can predict debtor’s risk in the future efficiently, and decrease the cost of the bank and the model building.
摘 要 i
Abstract ii
目 錄 iii
表目錄 iv
第1章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究架構 4
第2章 文獻探討 5
2.1 新巴賽爾資本協定 5
2.2 信用風險(Credit Risk) 9
2.3 相關研究 12
第3章 新建評分卡(Pseudo Scorecard) 25
第4章 實驗結果 57
4.1 資料來源 57
4.2 屬性篩選與模型建立 58
4.3 景氣循環與信用歷史要素 61
4.4 實驗結果 66
第5章 結論 79
參考文獻 80
中文部分:
1.行政院金融監督管理委員會,金融機構家數,http://www.banking.gov.tw/public/data/boma/stat/index/index-1.xls,民國96年。
2.李三榮,Basel II,台灣金融財務季刊,第三輯,第二期,民國91年,頁117-118。
3.周大慶,風險管理新標竿:風險值理論與應用,智勝文化出版,民國96年,頁268-271。
4.陳錦村、陳木在,商業銀行風險管理,新陸出版,民國90年,頁329~360。
英文部分:
1.Angelini, E., et al., “A Neural Network Approach for Credit Risk Evaluation,” The Quarterly Review of Economics and Finance, doi:10.1016/j. ref.2007.04.001.
2.Basel Committee on Banking Supervision, “Basel II: International Convergence of Capital Measurement and Capital Standards: A Revised Framework,” Bank for International Settlements, pp. 1-239, 2004.
3.Basel Committee on Banking Supervision, “The New Basel Capital Accord: an explanatory note,” Basel Report, pp. 1-16, 2001.
4.Chiu, C. C., Shao, Y. J., Lee, T. S. and Lee, K. M., “Identification of Process Disturbance Using SPC/EPC and Neural Networks,” Journal of Intelligent Manufacturing, Vol. 14, No. 3, pp. 379-388, 2003.
5.Cox, D. R. and Snell, E. J., Analysis of Binary Data, London: Chapman and Hall, 1989.
6.Desai, V. S., Crook, J. N. and Overstreet Jr, G. A., “A Comparison of Neural Networks and Linear Scoring Models in the Credit Union Environment,” European Journal of Operational Research, Vol. 95, No. 1, pp. 24-37, 1996.
7.Fish, K. E., Barnes, J. H. and Aiken, M. W., “Artificial neural networks: a new methodology for industrial market segmentation,” Industrial Marketing Management, Vol. 24, No. 1, pp. 431-438, 1995.
8.Fisher, R. A., “The Use of Multiple Measurements in Taxonomic Problems,” Annals of Eugenics, Vol. 7, No. 2, pp. 179–188, 1936.
9.Guption, G. M. and Stein, R. M., “Loss Calc TM: Moody''s Model for Predicting Loss Given Default,” Moody''s KMV Company, pp. 1-32, 2002.
10.Hosmer, D. W. and Lemeshow, S., Applied Logistic Regression, New York: Wiley, 1989.
11.Hsieh, N. C., “Hybrid mining approach in the design of credit scoring models,” Expert Systems with Applications, Vol. 28, No.4, pp. 655-665, 2005.
12.Kuo, R. J., Ho, L. M. and Hu, C. M., “Integration of self-organizing feature map and K-means algorithm for market segmentation,” Computers & Operations Research, Vol. 29, No. 11, pp. 1475-1493, 2002.
13.Laitinen, E. K., “Predicting a Corporate Credit Analyst’s Risk Estimate by Logistic and Linear Models,” International Review of Financial Analysis, Vol. 8, No. 2, pp. 97-121, 1999.
14.Lee, G., Sung, T. K. and Chang, N., “Dynamics of Modeling in Data Mining: Interpretive Approach to Bankruptcy Prediction,” Journal of Management Information Systems, Vol. 16, No. 1, pp. 63-85, 1999.
15.Lee, T. S., Chiu, C. C., Lu, C. J. and Chen, I. F., “Credit Scoring Using the Hybrid Neural Discriminate Technique,” Expert Systems with Applications, Vol. 23, Issue 3, pp. 245-254, 2002.
16.Lee, T. S., Chiu, C. C., Chou, Y. C. and Lu, Chi Jie, “Mining the Customer Credit Using Classification and Regression Tree and Multivariate Adaptive Regression Splines,” Computational Statistics and Data Analysis, Vol. 50, Issue 4, pp. 1113-1130, 2006.
17.Lee, T. S. and Chen, I. F., “A Two-stage Hybrid Credit Scoring Model Using Artificial Neural Networks and Multivariate Adaptive Regression Splines,” Expert Systems with Applications, Vol. 28, Issue 4, pp. 743-752, 2005.
18.Li, X., Ying, W., Tuo, J., Li, B. and Liu, W., “Applications of classification trees to consumer credit scoring methods in commercial banks,” IEEE International Conference on Systems, Man and Cybernetics, Vol. 5, pp. 4112-4117, 2004.
19.Marijana, Z. S., Natasa, S. and Mirta, B., “Small Business Credit Scoring: A Comparison of Logistic Regression, Neural Network, and Decision Tree Models,” Proceedings of the 26th International Conference on Information Technology Interfaces, pp. 265-270, 2004.
20.Svoronos and Jean-Philippe, “Risk-Focused Supervision. How is Risk Defined?,” Financial Stability Institute and the Committee of Banking supervisors of West and Central Africa, Basel Committee on Banking Supervision, 2002.
21.Thomas, L. C., “A Survey of Credit and Behavioral Scoring: Forecasting financial Risks of Lending to Customers,” International Journal of Forecasting, Vol. 16, Issue 2, pp. 149-172, 2000.
22.Vellido, P. J., Lisboa, G. and Vaughan, J., “Neural Networks in Business: A Survey of Applications (1992-1998),” Expert Systems With Applications, Vol. 17, Issue 1, pp.51-70, 1999.
23.Westgaard, S. and van der Wijst, N., “Default Probabilities in a Corporate Bank Portfolio: A Logistic Model Approach,” European Journal of Operational Research, Vol. 135, Issue 2, pp. 338-349, 2001.
24.Zhang, G., Patuwo, B. E. and Hu, M. Y., “Forecasting with artificial neural networks: The state of the art,” International Journal of Forecasting, Vol. 14, No. 1, 1998, pp. 35-62.
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