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系統識別號 U0002-0906201001564000
中文論文名稱 外匯投資組合之風險值估計-分量迴歸的應用
英文論文名稱 Application of Quantile Regression to Estimating Value at Risk of Foreign Exchange Portfolio
校院名稱 淡江大學
系所名稱(中) 財務金融學系碩士班
系所名稱(英) Department of Banking and Finance
學年度 98
學期 2
出版年 99
研究生中文姓名 柯中偉
研究生英文姓名 Zhong-Wei Ke
學號 697530482
學位類別 碩士
語文別 中文
口試日期 2010-05-20
論文頁數 65頁
口試委員 指導教授-李沃牆
委員-沈大白
委員-何宗武
委員-池秉聰
中文關鍵字 分量迴歸  風險值  投資組合  GARCH  回溯測試 
英文關鍵字 Quantile Regression  VaR  Portfolio  GARCH  Back-Testing 
學科別分類 學科別社會科學商學
中文摘要 本研究以Koenker與Bassett(1978)提出的分量迴歸(Quantile Regression)導入風險值模型,經Markowitz投資組合理論篩選出最佳的外匯投資組合,日圓、美元、新加坡幣及里亞爾。比較GARCH、tGARCH、EGARCH及多變量CCC-GARCH在傳統變異–共變數法之風險值估計能力與加入分量迴歸後的差異;另外,比較個別外匯風險值與投資組合風險值。並以Kupeic和Christofferson二種回溯測試方法檢定風險值模型績效。
實證結果發現,VaR.tGARCH(1, 1)模型算出的每日外幣報酬率風險值較其他模型低,估計能力最差。分量迴歸結合單變量GARCH所算出的個別外匯風險值十分接近,平均較VaR.GARCH-type高,經回溯測試後,加入分量迴歸能夠做出準確的估計,充分展現不需任何分配假設即能捕捉金融資產厚尾、峰態及自我相關的特性。投資組合風險值模型回溯測試結果顯示,適當的投資組合確實能有效降低風險,同時VaR.QR.CCC-GARCH模型在外匯投資組合的績效明顯優於VaR.CCC-GARCH 模型。
英文摘要 We applied quantile regression proposed by Koenker and Bassett (1978) to value at risk model in this study. After selecting the best foreign exchange portfolio by Markowitz's portfolio theory, the JPY, USD, SGD and SAR was selected. We compared GARCH, tGARCH, EGARCH and multivariate CCC-GARCH in traditional variance-covariance method with quantile regression to estimating value at risk. And using two kinds of back-testing which includes Kupeic and Christofferson test the performance of value at risk models.
Empirical results, VaR.tGARCH (1, 1) model worst estimated the daily foreign return of value at risk. Combining quantile regression with GARCH-type to calculate the value at risk of individual foreign exchange is very close, where the average is higher than VaR.GARCH-type. After back-testing, adding quantile regression indeed is able to make accurate estimation. It fully shows that without any assumption of distribution surely capture fat-tail, kurtosis and correlation of financial assets characteristics. The back-testing results of portfolio's value at risk models show that the appropriate portfolio can actually reduce risk, while VaR.QR.CCC-GARCH model perform better than VaR.CCC-GARCH model in this foreign exchange portfolio.
論文目次 目 錄
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 3
第三節 研究架構與流程 4
第二章 理論介紹及文獻回顧 5
第一節 風險值 5
第二節 文獻回顧 11
第三章 研究方法 17
第一節 Markowitz投資組合 17
第二節 波動性估計 20
第三節 分量迴歸 28
第四節 風險值模型 31
第五節 回溯測試 36
第四章 實證結果與分析 39
第一節 資料選取與說明 39
第二節 基本統計分析 43
第三節 風險值模型結果分析 46
第四節 回溯測試結果 54
第五章 結論與建議 59
第一節 結論 59
第二節 研究限制與建議 60
參考文獻 61

表 目 錄
表2-1 風險值估計方法比較表 10
表4-1 外匯初步篩選排行計分 41
表4-2 外匯投資組合最適投資權重 41
表4-3 滿足不同風險偏好時之風險、報酬率及投資比重 42
表4-4 外幣價格的敘述統計量 43
表4-5 外幣相關系數 44
表4-6 單根檢定 45
表4-7 風險值水準-日幣 46
表4-8 風險值水準-美元 48
表4-9 風險值水準-新加坡幣 49
表4-10 風險值水準-里亞爾 50
表4-11 變異-共變數投資組合風險值 52
表4-12 回溯測試500天-日幣 54
表4-13 回溯測試500天-美元 55
表4-14 回溯測試500天-新加坡幣 56
表4-15 回溯測試500天-里亞爾 56
表4-16 回溯測試500天-投資組合風險值 57
表4-17 外幣個別風險值總和V.S.投資組合風險值 58

圖 目 錄
圖4-1 風險性資產組合之效率前緣 42
圖4-2 考慮風險偏好下之最適投資組合 42
圖4-3 外幣價格走勢 44
圖4-4 日幣投資期間內之風險值變化 47
圖4-5 美元投資期間內之風險值變化 48
圖4-6 新加坡幣投資期間內之風險值變化 50
圖4-7 里亞爾投資期間內之風險值變化 51
圖4-8 投資組合投資期間內風險值變化 52
圖4-9 共變數與顯著水準(常態分配值)及投資組合風險值的關係 53
參考文獻 一、中文部份
1. 李登賀(2002),加入新資訊之歷史模擬法的風險值衡量,國立中正大學財務金融學系碩士論文。
2. 李進生、謝文良、林允永、蔣照坪、陳達新、盧陽正(2001),風險管理-風險值理論與應用,清蔚科技出版。
3. 林淑蓉(2006),風險值與風險管理策略之研究,國立中央大學財務金融學系碩士論文。
4. 洪瑞成(2002),風險值之探討-對稱與不對稱波動GARCH模型之應用,淡江大學財務金融學系碩士論文。
5. 紀舒文(2000),VaR風險管理之保守性、精確度與效率性研究,國立臺灣大學商學研究所碩士論文。
6. 張宏政(2004),風險值(VaR)之估計-分位數迴歸之應用,淡江大學財務金融學系碩士論文。
7. 許傑翔(2004),多變量財務時間數列模型之風險值計算,東吳大學商學數學系碩士論文。
8. 郭逎鋒、李麗華、柯佩璇、張佩惠、謝雨豆(2008),台灣股票市場報酬率波動來源之探討:分量迴歸分析,2009行為財務學暨新興市場理論與實證研討會論文,世新大學。
9. 陳志偉(2005),外匯投資組合風險值之估計-DCC多變量GARCH模型之應用,淡江大學財務金融學系碩士論文。
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12. 黃卉芊(1998),台灣股匯市投資組合風險值之計算與評估,國立中央大學財務金融學系碩士論文。
13. 蔡秀霞(2006),風險值之應用-外匯投資組合實證研究,淡江大學財務金融學系碩士論文。
14. 蔡榮全(2005),比較分量迴歸與極值理論在風險值估計之績效,銘傳大學財務金融學系碩士論文。

二、英文部分
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10. Chen, M. Y. and J. E. Chen (2005) “Application of Quantile Regression to Estimation of Value at Risk,” Department of Economics, National Chung Cheng University, Taiwan.
11. Christoffersen, P. F. (1998) “Evaluating Interval Forecasts,” International Economic Review, Vol.39, No.4.
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