臺灣博碩士論文加值系統

擔保債權憑證（Collateralized Debt Obligations；CDOs）的發行加速金融市場債權的流動性，使金融機構有效的處理債權，降低資產的風險，同時提供投資人更多的金融工具，做為投機、套利、或避險等不同目的之選擇。然而，個別資產的違約機率，與不同資產之間的違約相關性，決定衡量CDO風險的複雜度，進而影響CDO的分券利差（Tranche Spread）。因此，本文探討CDO資產之間的違約相關性與利差的關係，以關聯結構（Copula）方法評價CDO之分券利差，分析不同資產之間的違約相關性與違約強度對CDO分券利差的影響，做為CDO的發行或投資的參考資訊。

Issuing collateralized debt obligations (CDOs) accelerates the liquidity of debt in the financial market. Financial institutions can efficiently manage debt and reduce the risks of assets by using CDO products. Meanwhile, CDO is another financial instrument for speculators, arbitrageurs, and hedgers. However, the default probability for each individual assets and the dependence of the assets determine the complexity of the measurement for CDO risks. Additionally, the default factors affect the spreads of CDO tranches. Therefore, this article studies the default probability and dependence for various CDO tranches. We evaluate various tranche spreads of CDOs with copula methods. Then the effects of default dependence and intensity for spreads of CDO tranches are analyzed. The results provide information for issuing and investing CDOs.

目 錄
摘 要 i
Abstract ii
誌 謝 iii
目 錄 iv
表目錄 vi
圖目錄 vii
第一章 前言 1
1.1 簡介 1
1.2 擔保債權憑證介紹 5
第二章 文獻探討 10
2.1 風險評價模型 10
2.1.1 單一金融商品評價模型 10
2.1.2 組合金融商品評價模型 11
2.2 違約強度模型 14
2.3 違約損失估計方法 16
第三章 研究方法 17
3.1 Copula方法介紹 17
3.2 單因子模型 20
3.3 違約機率設定 22
3.3.1違約事件 22
3.3.2 條件違約機率分配 23
3.3.3 違約強度模型 24
3.4 CDO評價 26
3.4.1 損失機率分配 26
3.4.2 分券利差 29
第四章 數值結果與分析 32
4.1 模型數值假設 32
4.2 危險率為常數 33
4.3 無跳躍過程之隨機危險率 35
4.4 有跳躍過程之隨機危險率 37
4.4.1跳躍幅度服從指數分配 37
4.4.2跳躍幅度服從常態分配 39
4.4.3跳躍幅度服從不同分配之利差比較 41
第五章 結論與建議 42
5.1結論 42
5.2建議 43
參考文獻 44
附錄1. 47
附錄2. 48
表目錄
表4.1 不同危險率與相關係數之分券利差 34
表4.2 分券利差之變化率 34
表4.3 隨機危險率模型之分券利差（ρ=0.1） 36
表4.4 隨機危險率模型之分券利差（ρ=0.9） 36
表4.5 指數跳躍過程之隨機危險率模型分券利差（ρ=0.1） 38
表4.6 指數跳躍過程之隨機危險率模型分券利差（ρ=0.9） 38
表4.7 常態跳躍過程之隨機危險率模型分券利差（ρ=0.1） 40
表4.8 常態跳躍過程之隨機危險率模型分券利差（ρ=0.9） 40
圖目錄
圖1.1 美國國庫券利率 1
圖1.2 美國債券商品年度總發行量 2
圖1.3 全球CDO發行量 3
圖1.4 CDO架構圖 5
圖1.5 CDO償還機制 6
圖1.6 CDO分類 8

參考文獻
中文部分
1. 江彌修，岳夢蘭，林恩平，2009，「條件獨立假設下合成型擔保債權憑證之評價與避險」，財務金融學刊，第十七卷，第一期，頁1-40。

2. 段登宇，2008，「擔保債權憑證CDO訂價與分析-單因子模型及機率水桶法之應用」，世新大學財務金融學系碩士論文。

3. 陳仕泓，2004，「具有 T-copula 形式之單因子模型抵押債權證券利差之敏感度分析」，東吳大學商用數學系碩士論文。

4. 廖四郎，李福慶，2005，「擔保債權憑證之評價-Copula分析法」，台灣金融財務季刊，第六輯，第二期，頁53-84。

英文部分
1. Bluhm, C., L. Overbeck, and C. Wanger, 2002, An Introduction to Credit Risk
Modeling, Chapman & Hall, New York.

2. Cherbini, U., E. Luciano, and W. Vechiato, 2004, Copula Method in Finance, Wiley, New York.

3. Cox, J. C., J. E. Ingersoll, and S. A. Ross, 1985, “A Theory of the Term Structure of Interest Rates,” Econometrica, Vol. 53 No. 2, 385-407.

4. Duffie, D., J. Pan, and K. Singleton, 1999, “Transform Analysis and Asset Pricing for Affine Jump-Diffusions,” Econometrica, Vol. 68, No. 6, 1343-1376.

5. Duffie, D. and N. Garleanu, 2001, “Risk and Valuation of Collateralized Debt Obligations,” Finance Analysis Journal, Vol. 57, No. 1, 41-59.

6. Errais, E., K. Giesecke, and L. R. Goldberg, 2007, “Pricing Credit from the Top Down with Affine Point Process,” working paper.

7. Hull, J. and A. White, 2004, “Valuation of a CDO and an N-th to Default CDS without Monte Carlo Simulation,” Journal of Derivatives, Vol. 12, No. 2, 8-23.

8. Hull, J., 2006, Options, Futures and Other Derivatives, 6/E, Prentice Hall.

9. Hull, J. and A. White, 2008, “Dynamic Models of Portfolio Credit Risk: A
Simplified Approach,” Journal of Derivatives, Vol. 15, No. 4, 9-28.

10. Jarrow, R. and S. Turnbull, 1995, “Pricing Derivatives on Financial Securities Subject to Credit Risk,” Journal of Finance, Vol. 50, 53-86.

11. Jarrow, R., D. Lando, and S. Turnbull, 1997, “A Markov Model for the Term
Structure of Credit Risk Spreads,” Review of Financial Studies, Vol.10, No. 2, 481-523.

12. Kalemanova, A., B. Schmid, and R. Werne, 2007, “The Normal Inverse Gaussian Distribution for Synthetic CDO Pricing,” Journal of Derivatives, Vol.14, 80-93.

13. Lando, D., 1998, “On Cox Processes and Credit Risky Securities,” Review of Derivatives Research, Vol. 2, 99-120.

14. Li, D. X., 2000, “On Default Correlation: a Copula Approach,” Journal of Fixed Income, Vol. 9, No. 4, 43-54

15. Mashal, R. and A. Zeevi, 2002, “Beyond Correlation: Extreme Co-movements
between Financial Assets,” working paper.

16. Mashal, R., M. Naldi, and A. Zeevi, 2003, “Extreme Events and Multi-Name
Credit Derivatives,” working paper.

17. Merton, R. C., 1974, “On the pricing of corporate debt：The Risk Structure of Interest Rates,” Journal of Finance, Vol. 29, 449-470.

18. Meneguzzo, D. and W. Vecchiato, 2004,”Copula Sensitivity in Collateralized Debt Obligations and Basket Swaps,” Working Paper, Risk Management Dept., Intesa Bank, Mila.

19. Nakagawa, H., 1999, “Valuation of Default Swap with Affine-type Hazard Rate,” Proc. Japan Acad. Ser. A. math Sci., Vol. 75, No. 3, 43-46.

20. Schloegl , L. and D. O’Kane, 2005, “A Note on the Large Homogenous Portfolio Approximation with the Student-t Copula,” Finance and Stochastic, Vol. 9, No. 4, 577-584.

21. Schonbucher, P. J. and D. Schubert, 2001, “Copula-dependent Default Risk in Intensity Models,” working paper.

22. Schonbucher, P.J., 2002, “Taken to the Limit: Simple and Not-so-simple Loan Loss Distributions,” Working Paper, Department of Statistics, University of Bonn.

23. Schonbucher, P. J., 2003, Credit Derivatives Pricing Models, Wiley, New York.

24. Turnbull, S. M., 2005, “Unresolved Issues in Modeling Credit-Risky Assets,” The Journal of Fixed Income, Vol.15, 68-87.

25. Vasicek, O.A, 1987, “Probability of Loss on Loan Portfolio,” KMV Corporation.

26. Vasicek, O.A, 1991, “Limiting Loan Loss Probability Distribution,” KMV
Corporation.

27. Vasicek, O.A, 2002, “The Distribution of Loan Portfolio Value,” Risk , Vol.15, 160-162.