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研究生:吳巽剛
研究生(外文):Hsun-Kang, Wu
論文名稱:考慮金融海嘯因素之美國銀行危機預警模型
論文名稱(外文):Early Warning Systems of Banks in United States: After the Subprime Crisis.
指導教授:陳美玲陳美玲引用關係
指導教授(外文):Mei-Ling, Chen
口試委員:王凱立林福來
口試委員(外文):Kai-Li, WangFu-Lai, Lin
口試日期:98年7月28日
學位類別:碩士
校院名稱:大葉大學
系所名稱:國際企業管理學系碩士班
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:英文
論文頁數:33
中文關鍵詞:危機預警次貸風暴
外文關鍵詞:Early Warning SystemSubprime Crisis
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財務穩定是經濟成長的首要條件。次貸風暴自2007年8月爆發以來,被認為是史上最嚴重的財務危機。我們試著分析此次次貸風暴影響銀行風險有多麼嚴重,提升或是降低了?首先我們建立了銀行系統的風險預測模型。接著,為了檢視銀行風險如何對市場改變作反應,我們套用了數個市場變數來完成此一模型。我們使用的是MDA分析,一種最早被利用來預測銀行危機方式,也是最多後續研究的預測模型。另外,我們也使用了邏輯分析及概率分析。
Financial stability is an important prerequisite for economic growth and stability. The subprime crisis that began in August 2007 has been called the worst financial crisis since the Great Depression by George Soros, Joseph Stieglitz, the IMF (International Monetary Fund), and other commentators. We try to analyze how serious does subprime crisis affect the risk of banking system, is the risk rise or fall? First we would like to build models of risk prediction for banking system. To examine how the risk of banking system reacts to market changes, we would employ several market variables to fulfill our models. We use multivariate discriminant analysis, one of the earliest implications be applied to predict bank failures and most continued researches are based on this implication. Logistic regression analysis and probabilistic analysis are also employed in this study.
Contents

Chapter I. Introduction˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙ 1
Chapter II. Literature Review ˙˙˙˙˙˙˙˙˙˙˙˙˙˙ 7
2.1 Banking crises˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙ 7
2.2 Early warning systems˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙ 8
2.2.1 Multivariate discriminant analysis˙˙˙˙˙˙˙˙˙˙˙˙˙ 8
2.2.2 Logistic regression analysis and Probabilistic analysis ˙˙˙˙˙˙ 9
2.3 EWS variables˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙ 9
2.3.1 CAMEL ratings ˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙ 9
2.3.2 Macroeconomic variables ˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙ 10
2.3.3 Volatility Index˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙ 11
Chapter III. Methodology˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙ 12
3.1 Variables selection ˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙ 12
3.2 Applied models ˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙ 14
3.2.1 Multivariate Discriminant Analysis ˙˙˙˙˙˙˙˙˙˙˙˙ 15
3.2.2 Logit analysis ˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙ 15
3.2.3 Probit analysis˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙ 16
Chapter IV. Empirical results ˙˙˙˙˙˙˙˙˙˙˙˙˙˙ 18
3.1 Variables analysis˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙ 18
3.2 Models performance analysis ˙˙˙˙˙˙˙˙˙˙˙˙˙ 21
Chapter V Conclusions˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙ 23
Reference ˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙ 24


Tables

Table 3.1 Selected variables˙˙˙˙˙˙˙˙˙˙˙˙˙˙˙ 12
Table 4.1 Coefficients of α models ˙˙˙˙˙˙˙˙˙˙˙˙ 19
Table 4.2 Coefficients of β models ˙˙˙˙˙˙˙˙˙˙˙˙ 20
Table 4.3 Accuracy of α models ˙˙˙˙˙˙˙˙˙˙˙˙˙ 22
Table 4.4 Accuracy of β models ˙˙˙˙˙˙˙˙˙˙˙˙˙ 22

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