政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/50854

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/50854

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政大典藏 > College of Commerce > Department of Money and Banking > Theses > Item 140.119/50854

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/50854

Title:	建構台灣銀行業預警系統-貝氏網路模型之運用 Bayesian model for bank failure risk in Taiwan
Authors:	黃薰儀 Huang, Hsun Yi
Contributors:	李桐豪黃薰儀 Huang, Hsun Yi
Keywords:	貝氏網路模型銀行危機預警系統銀行倒閉風險 Bayesian Network Bank Crisis Early Warning System Bank Failure Risk
Date:	2010
Issue Date:	2011-09-29 16:50:41 (UTC+8)
Abstract:	國際研究中雖有針對國家級的銀行脆弱性作分析，卻並未定義或預測台灣系統性危機，本研究在這樣的背景下，決定建構台灣本土的銀行業預警系統，建立銀行危機的領先指標，希望不只順應國際潮流，更能發展適合台灣特殊性的模型。本研究利用貝氏網路模型的特殊性: (1)事後值(2)機率特性，以個體化資料著手，建構一總體性模型。故研究者能確切了解個別銀行財務狀況，對個別銀行發出預警。事後值的特性使研究者能同時考慮多項財務比率。另外，利用機率特性，可幫助研究者了解危機的程度，且能做總體的延伸運用。本研究發展出兩種方法建構總體模型。第一種為百分比法，以危機銀行佔總銀行個數的比率為基礎；第二種為加權平均法，讓機率值高者有較大權數，機率小者有較小權數去建立一加權平均機率值。將本研究的推論結果和「台灣金融服務業聯合總會委託計畫-台灣金融危機領先指標之研究」比較，顯示本模型的兩種方法皆與危機之發生有相同趨勢，而考慮危機訊號的設定後，方法二加權平均法顯然具備較佳的預測結果。此外相較總體面衝擊產生的危機，本模型在預測能力上，對來自銀行個體面造成的危機預測明顯較優異。 International organizations defined and predicted country bank crises events without Taiwan, but they happened in Taiwan in the past twenty years. We construct the early warning system for banking crises in Taiwan and develop the specific model suited to our country. Using Bayesian Model’s specialities: (1) posterior value; (2) probability, we build a systematic model based on microeconomic data. So researcher can understand all financial conditions and predict the financial distresses of individual banks. The concept of posteriority lets researchers can consider a lot of financial ratio at the same time. The characteristic of probability makes researcher to extend the model to macroeconomic. We develop two methods to build systematic model. One is Percentage method which is based on the percentage of financial distress banks to all banks. The other one is weighted average method which used large weight in financial distress bank and small weight in financial sound banks. Comparing our results with the report that Taiwan Financial Services Roundtable issued in 2009, our methods have distress trends which link with crisis directly. But weighted average method has a better predict power than percentage method after considering the signals of distress we specify. Besides, our model has a stronger predictive power in crises from individual effect than crises from macroeconomic shocks.
Reference:	中文文獻吳懿娟 (2003)，”我國金融危機預警系統之研究”，中央銀行季刊，第二十五卷第三期，9月，5-42頁。陳順宇（2005），多變量分析。華泰書局，2005 年四版。李桐豪、江永裕(2009) ，” 台灣金融服務業聯合總會委託計畫-台灣金融危機領先指標之研究-台灣金融危機領先指標之研究”，台灣金融服務業聯合總會董瑞斌,”後金融海嘯時代金融發展現況與展望--引言報告”,台灣金融論壇系列(2009) 英文文獻 A. N. Terent’yev and P. I. Bidyuk, “Method of Probabilistic inference from learning data in Bayesian Networks” ,Cybernetics and Systems Analysis, Vol. 43, No. 3 (2007) Benedict Kemmerer, Sanjay Mishra and Prakash Shenoy, “Bayesian Causal Maps as Decision Aids in Venture Capital Decision Making: Method and Applications” (2001) Bongini, Paola & Ferri, Giovanni & Tae Soo Kang, "Financial intermediary distress in the Republic of Korea - Small is beautiful?," Policy Research Working Paper Series 2332, The World Bank (2000). Bongini, Paola & Stijn Claessens & Giovanni Ferri, "The Political Economy of Distress in East Asian Financial Institutions," Journal of Financial Services Research, Springer, vol. 19(1), pages 5-25, February(2001). Demirgüç-Kunt, A and Detragiache, E , "The Determinants of Banking Crisis in Developing and Developed Countries", IMF Staff Papers, Vol. 45, No.1. (1998) International Monetary Fund,” IMF-FSB Early Warning Exercise: Design and Methodological Toolkit”(2010) Manuel Ammann and Michael Verhofen,” Prior Performance and Risk-Taking of Mutual Fund Managers: A Dynamic Bayesian Network Approach”, The Journal of Behavioral Finance; Vol. 8 ,No. 1, 20–34 (2007) P. I. Bidyuk, A. N. Terent’ev, and A. S. Gasanov, “Construction and Methods of learning of Bayesian Networks”, Cybernetics and Systems Analysis, Vol. 41, No. 4(2005) Riza Demirer, Ronald R Mau,and Catherine Shenoy,” Bayesian Networks: A Decision Tool to Improve Portfolio Risk Analysis”, The Journal of Applied Finance, Vol. 16, No. 2 (2006) Sumit Sarkar, Ram S. Sriram, Shibu Joykutty, and Ishwar Murthy,” An information theoretic technique to design belief network based expert systems”, Decision Support Systems 17 (1996) Sumit Sarkar, Ram S. Sriram, Shibu Joykutty,” Belief Networks for Expert System, Development in Auditing” ,Intelligent Systems in Accounting, Finance and Management, Vol.5, P.147-163 (1996) Tatjana Pavlenko and Oleksandr Chernyak,” Credit Risk Modeling Using Bayesian Networks”, International Journal of Intelligent Systems, Vol. 25, No. 4 (2010)
Description:	碩士國立政治大學金融研究所 97352014 99
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0973520141
Data Type:	thesis
Appears in Collections:	[Department of Money and Banking] Theses

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