臺灣博碩士論文加值系統

2007年美國次貸風暴引發了金融海嘯，造成全球金融市場信用緊縮及金融體系流動性嚴重不足，一連串國際金融危機的爆發，突顯出金融機構「大到不能倒」所隱含的影響，使得大型金融機構可能帶給整個金融體系的系統風險，成為各國金融監理機關必須正視的議題。
過去傳統的風險衡量方法是使用VaR值，主要係將金融機構資產組合所面對的風險量化為一個數值，用來說明該金融機構預期的最大損失，以此衡量金融機構自身的風險，但是VaR有其侷限性，因為自身風險的計算無法反應出系統風險。
Adrian and Brunnermeier（2014）認為系統風險可以藉由個別金融機構的風險來辨識，因為這些金融機構的個別風險間會相互聯結而使風險大到可以影響其他金融機構，甚至整個金融體系，延伸此一概念提出CoVaR法。本論文就Adrian and Brunnermeier所提出的CoVaR模型，利用分量迴歸對不同分量的邊際效果來作解釋，以台灣上市上櫃金融機構為實例，衡量該金融機構於台灣金融市場的系統風險以及個別金融機構間交叉影響的系統風險貢獻。

In 2007, Subprime Mortgage Crisis exploded Financial Tsunami, resulting in the contraction of credit in the global financial market and severely insufficient liquidity of the financial system. A series of international financial crisis explosion shows the implied influence of the financial institutes that is "too big to fail". In other words, the large-scaled financial institutes may bring risk to the while financial system, which becomes the issue for the financial supervisory organs in each nation to face and take action.
In the past, people used VaR as the traditional risk measurement method. It can quantify the risk that the asset portfolio of the financial institute may encounter to illustrate the biggest loss predicted by that financial institute to take a thorough considerations on the risks of the financial institute itself. Unfortunately, VaR has the shortage of restrictive property, due to risk calculation made by itself, system risk cannot be reflected.
Adrian and Brunnermeier (2014) claimed that system risk can be identified by means of those in the individual financial institute, since the individual risk of those financial institutes inter-connect with one another, so that the risk become large enough to affect other financial institutes, or even the whole financial system. Therefore, by extending such concept, CoVaR method was proposed by this research. In this research, for the CoVaR model proposed by Adrian and Brunnermeier, by means of quantile regression's marginal effect on different components.this research attempted to explain CoVaR. Take the listed and public financial institutes as example, we consider the systematic risks from that financial institute in the financial market in Taiwan, as well as the systematic risk cross-influenced by the individual financial institute.

目錄
第 1 章 緒論 1
1.1研究背景 1
1.2研究動機與目的 2
1.3 研究架構 6
第 2 章 文獻探討 7
2.1 風險值相關文獻 7
2.2 CoVaR相關文獻 10
第 3 章 研究方法 13
3.1 CoVaR估計模型 14
3.1.1VaR定義： 14
3.1.2 CoVaR定義： 14
3.1.3 估計CoVaR 15
3.2 分量迴歸 16
3.3資料敘述與說明 18
3.3.1 研究對象 18
3.3.2 變數定義說明 23
3.3.3 資料來源 33
3.3.4帳面資產價值轉換為市值 33
第 4 章 實證結果與分析 35
4.1 金融機構系統風險分析 35
4.2 各金融機構間相互交叉的系統風險 44
4.3 以台中銀行為例 54
第 5 章 結論 59
參考文獻 61
附錄 A: 65

圖目錄
圖3 1：各期間信用利差走勢圖（月資料） 30
圖3 2：長短期利率與利差走勢圖（月資料） 31
圖3 3：各期間流動性利差走勢圖（月資料） 32
圖3 4：新台幣對美元匯率走勢圖（月資料） 33
圖3 5：台指選擇權波動率指數與發行量加權股價指數走勢圖（月資料） 34
圖3 6：各金融機構VaR1%及VaR5%大小排序 38
圖4 1：21家上市上櫃金融機構加權平均∆CoVaR1%system|i走勢圖 49
圖4 2：21家上市上櫃金融機構加權平均∆CoVaR5%system|i走勢圖 49
圖4 3：2008年-2014年台中銀VaR1%及CoVaR1%台中銀|system走勢圖 60
圖4 4：2008年-2014年台中銀VaR5%及CoVaR5%台中銀|system走勢圖 60

表目錄
表1 1：總體審慎與個體審慎監理比較表 8
表1 2：2014年G-SIBs全球系統性重要銀行 9
表3 1：本國銀行彙整表 26
表3 2：研究對象資產市值報酬之基本統計量 28
表3 3：狀態變數彙整 29
表3 4：各金融機構VaR5%估計模型 35
表3 5：各金融機構VaR1%、VaR5%及VaR50%平均值 37
表4 1：各金融機構VaR1%,ti係數－β1%system|i 41
表4 2：各金融機構VaR5%,ti係數－β5%system|i 42
表4 3：各金融機構Xi、VaR1%i及∆CoVaR1%system|i平均值 44
表4 4：各金融機構Xi、VaR5%i及∆CoVaR5%system|i平均值 45
表4 5：各金融機構2008年-2014年平均∆CoVaR1%system|i 47
表4 6：各金融機構2008年-2014年平均∆CoVaR5%system|i 48
表4 7：各金融機構交叉組合之平均CoVaR1%,tj|i 53
表4 8：各金融機構交叉組合之平均CoVaR5%,tj|i 54
表4 9：各金融機構交叉組合之平均∆CoVaR1%,tj|i 55
表4 10：各金融機構交叉組合之平均∆CoVaR5%,tj|i 56
表4 11：金融機構間交互影響之平均CoVaRθ,tj|i排名簡表 58
表4 12：金融機構間交互影響之平均∆CoVaRθ,tj|i排名簡表 59
表4 13：2008年-2014年各金融機構對台中銀的平均∆CoVaR1%,t 62
表4 14：2008年-2014年各金融機構對台中銀的平均∆CoVaR5%,t 63
表A 1：各金融機構VaR1%估計模型 70

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