| dc.contributor.advisor | 李桐豪 | zh_TW |
| dc.contributor.author (Authors) | 黃薰儀 | zh_TW |
| dc.contributor.author (Authors) | Huang, Hsun Yi | en_US |
| dc.creator (作者) | 黃薰儀 | zh_TW |
| dc.creator (作者) | Huang, Hsun Yi | en_US |
| dc.date (日期) | 2010 | en_US |
| dc.date.accessioned | 29-Sep-2011 16:50:41 (UTC+8) | - |
| dc.date.available | 29-Sep-2011 16:50:41 (UTC+8) | - |
| dc.date.issued (上傳時間) | 29-Sep-2011 16:50:41 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0973520141 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/50854 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 金融研究所 | zh_TW |
| dc.description (描述) | 97352014 | zh_TW |
| dc.description (描述) | 99 | zh_TW |
| dc.description.abstract (摘要) | 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。 | zh_TW |
| dc.description.abstract (摘要) | 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. | en_US |
| dc.description.tableofcontents | 第一章、研究背景 6 1.導論 6 2.台灣時空背景 7 第二章、文獻回顧 10 1.金融危機文獻回顧 10 2.貝氏網路模型文獻回顧 13 第三章、研究範圍與限制 16 1.變數選擇 16 2.模型限制 19 第四章、研究方法 20 1.方法說明 20 第五章、實證結果 32 1.貝氏網路模型(BBN)表現 32 2.建立台灣銀行業系統性危機預警系統 40 3.結果分析 45 第六章、結論與後續研究建議 47 1.結論 47 2.後續研究建議 47 參考文獻 49 附錄 51 | zh_TW |
| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0973520141 | en_US |
| dc.subject (關鍵詞) | 貝氏網路模型 | zh_TW |
| dc.subject (關鍵詞) | 銀行危機 | zh_TW |
| dc.subject (關鍵詞) | 預警系統 | zh_TW |
| dc.subject (關鍵詞) | 銀行倒閉風險 | zh_TW |
| dc.subject (關鍵詞) | Bayesian Network | en_US |
| dc.subject (關鍵詞) | Bank Crisis | en_US |
| dc.subject (關鍵詞) | Early Warning System | en_US |
| dc.subject (關鍵詞) | Bank Failure Risk | en_US |
| dc.title (題名) | 建構台灣銀行業預警系統-貝氏網路模型之運用 | zh_TW |
| dc.title (題名) | Bayesian model for bank failure risk in Taiwan | en_US |
| dc.type (資料類型) | thesis | en |
| dc.relation.reference (參考文獻) | 中文文獻 | zh_TW |
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| dc.relation.reference (參考文獻) | 英文文獻 | zh_TW |
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