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題名 單因子模型下信用損失分配尾端機率估計與合成型擔保債務憑證評價
Estimating Tail Probability of Credit Loss Distribution and Pricing CDOs with One Factor Copula Model
作者 紀宛汝
Chi, Wan Ju
貢獻者 劉惠美
Liu, Hui Mei
紀宛汝
Chi, Wan Ju
關鍵詞 關聯結構
重點抽樣方法
合成型擔保債務憑證
封閉偏斜常態分配
Copula model
Importance sampling method
Collateralized debt obligation
Closed skew normal distribution
日期 2014
上傳時間 22-Aug-2016 10:42:28 (UTC+8)
摘要 本文利用Bassamboo et al. (2008)提出有極值相依的t關聯結構模型，結合Chiang et al. (2007)所提出之重點抽樣方法，延伸出兩種估計信用損失分配尾端損失機率之重點抽樣方法，結果顯示模擬速度迅速，且其變異數縮減表現良好。另外，在評價合成型擔保債務憑證方面，由於在Kalemanova (2007)中，常態逆轉高斯模型對於擔保債務憑證之高級等級有良好的估計，本文提出利用封閉偏斜常態分配與常態逆轉高斯分配之混合分配對合成型擔保債務憑證做評價，其評價結果表現優異，較常態逆轉高斯模型表現更好。
參考文獻 1. Andersen, L., and J. Sidenius. (2005). Extensions to the Gaussian Copula: Random Recovery and Random Factor Loadings. Journal of Credit Risk, 1, 29-70.
     2. Bassamboo, A, S. Juneja, and A. Zeevi (2008). Portfolio Credit Risk with Extremal Dependence: Asymptotic Analysis and Efficient Simulation. Operations Research, 56, 593-606.
     3. Chiang, M. H, M. L. Yueh, and M.H. Hsieh (2007). An Efficient Algorithm for Basket Default Swap Valuation. Journal of Derivatives 15, 8-19.
     4. Capriotti, L. (2008). Least-Squares Importance Sampling for Monte Carlo Security Pricing. Quantitative Finance 8, 485-497.
     5. Chan, J C.C and D P. Kroese (2010). Efficient Estimation of Large Portfolio Loss Probabilities in T-Copula Models. European Journal of Operational Research 205, 361-367.
     6. Chen, Z. , Q. Bao, S. Li and J. Chen (2012). Pricing CDO Tranches with Stochastic Correlation and Random Factor Loadings in a Mixture Copula Model . Applied Mathematics and Computation 219, 2909-2916.
     7. Glasserman, P. (2004). Tail Approximations for Portfolio Credit Risk. The Journal of Derivatives 12, 24-42.
     8. Glasserman, P. and J. Li (2005). Importance Sampling for Portfolio Risk. Management Science 51, 1643-1656.
     9. Grundke, P. (2009). Importance Sampling for Integrated Market and Credit Portfolio Models. European Journal of Operational Research 194, 206-226.
     10. Hull J. and A. White (winter 2004). Valuation of a CDO and an n-th to Default CDS Without Monte Carlo Simulation. The Journal of Derivatives, 8-23.
     11. Kalemanove A., B. Schmid, and R. Werner (spring 2007). The Normal Inverse Gaussian Distribution for Synthetic CDO Pricing. The Journal of Derivatives, 80-93.
     12.Li, D. (2000) On Default Correlation: A Copula Function Approach. Journal of Fixed Income, 9, 43-54.
     13. Lüscher A. (December 2005). Synthetic CDO Pricing Using the Double Normal Inverse Gaussian Copula with Stochastic Factor Loadings. Master Thesis, Zürich University of Mathematics.
     14. Yang, R. , X. Qin and T. Chen (2009). CDO Pricing Using Single Factor M_(G-NIG) Copula Model with Stochastic Correlation and Random Loading. Journal of Mathematical Analysis and Applications 350, 73-80.
     15. Zheng, H. (2006). Efficient Hybrid Methods for Portfolio Credit Derivatives. Quantitative Finance 6, 349-357.
描述 博士
國立政治大學
統計學系
94354502
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0094354502
資料類型 thesis
dc.contributor.advisor 劉惠美zh_TW
dc.contributor.advisor Liu, Hui Meien_US
dc.contributor.author (Authors) 紀宛汝zh_TW
dc.contributor.author (Authors) Chi, Wan Juen_US
dc.creator (作者) 紀宛汝zh_TW
dc.creator (作者) Chi, Wan Juen_US
dc.date (日期) 2014en_US
dc.date.accessioned 22-Aug-2016 10:42:28 (UTC+8)-
dc.date.available 22-Aug-2016 10:42:28 (UTC+8)-
dc.date.issued (上傳時間) 22-Aug-2016 10:42:28 (UTC+8)-
dc.identifier (Other Identifiers) G0094354502en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/100452-
dc.description (描述) 博士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 統計學系zh_TW
dc.description (描述) 94354502zh_TW
dc.description.abstract (摘要) 本文利用Bassamboo et al. (2008)提出有極值相依的t關聯結構模型，結合Chiang et al. (2007)所提出之重點抽樣方法，延伸出兩種估計信用損失分配尾端損失機率之重點抽樣方法，結果顯示模擬速度迅速，且其變異數縮減表現良好。另外，在評價合成型擔保債務憑證方面，由於在Kalemanova (2007)中，常態逆轉高斯模型對於擔保債務憑證之高級等級有良好的估計，本文提出利用封閉偏斜常態分配與常態逆轉高斯分配之混合分配對合成型擔保債務憑證做評價，其評價結果表現優異，較常態逆轉高斯模型表現更好。zh_TW
dc.description.tableofcontents 摘 要 3
     Abstract 4
     第一章 緒論 5
     第二章 文獻探討 7
     第一節 關聯結構 7
     第二節 重點抽樣方法 9
     第三節 擔保債務憑證之評價 10
     第三章 信用損失分配尾端機率估計 12
     第一節 模型及重點抽樣方法 12
     第二節 數值結果 18
     第四章 合成型擔保債務憑證評價 20
     第一節 合成型擔保債務憑證之介紹 20
     第二節 損失分配與和擔保債務憑證之信用價差 21
     第三節 模型介紹 23
     第四節 數值結果與結論 31
     第五章 結論 36
     參考文獻 37		zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0094354502en_US
dc.subject (關鍵詞) 關聯結構zh_TW
dc.subject (關鍵詞) 重點抽樣方法zh_TW
dc.subject (關鍵詞) 合成型擔保債務憑證zh_TW
dc.subject (關鍵詞) 封閉偏斜常態分配zh_TW
dc.subject (關鍵詞) Copula modelen_US
dc.subject (關鍵詞) Importance sampling methoden_US
dc.subject (關鍵詞) Collateralized debt obligationen_US
dc.subject (關鍵詞) Closed skew normal distributionen_US
dc.title (題名) 單因子模型下信用損失分配尾端機率估計與合成型擔保債務憑證評價zh_TW
dc.title (題名) Estimating Tail Probability of Credit Loss Distribution and Pricing CDOs with One Factor Copula Modelen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 1. Andersen, L., and J. Sidenius. (2005). Extensions to the Gaussian Copula: Random Recovery and Random Factor Loadings. Journal of Credit Risk, 1, 29-70.
     2. Bassamboo, A, S. Juneja, and A. Zeevi (2008). Portfolio Credit Risk with Extremal Dependence: Asymptotic Analysis and Efficient Simulation. Operations Research, 56, 593-606.
     3. Chiang, M. H, M. L. Yueh, and M.H. Hsieh (2007). An Efficient Algorithm for Basket Default Swap Valuation. Journal of Derivatives 15, 8-19.
     4. Capriotti, L. (2008). Least-Squares Importance Sampling for Monte Carlo Security Pricing. Quantitative Finance 8, 485-497.
     5. Chan, J C.C and D P. Kroese (2010). Efficient Estimation of Large Portfolio Loss Probabilities in T-Copula Models. European Journal of Operational Research 205, 361-367.
     6. Chen, Z. , Q. Bao, S. Li and J. Chen (2012). Pricing CDO Tranches with Stochastic Correlation and Random Factor Loadings in a Mixture Copula Model . Applied Mathematics and Computation 219, 2909-2916.
     7. Glasserman, P. (2004). Tail Approximations for Portfolio Credit Risk. The Journal of Derivatives 12, 24-42.
     8. Glasserman, P. and J. Li (2005). Importance Sampling for Portfolio Risk. Management Science 51, 1643-1656.
     9. Grundke, P. (2009). Importance Sampling for Integrated Market and Credit Portfolio Models. European Journal of Operational Research 194, 206-226.
     10. Hull J. and A. White (winter 2004). Valuation of a CDO and an n-th to Default CDS Without Monte Carlo Simulation. The Journal of Derivatives, 8-23.
     11. Kalemanove A., B. Schmid, and R. Werner (spring 2007). The Normal Inverse Gaussian Distribution for Synthetic CDO Pricing. The Journal of Derivatives, 80-93.
     12.Li, D. (2000) On Default Correlation: A Copula Function Approach. Journal of Fixed Income, 9, 43-54.
     13. Lüscher A. (December 2005). Synthetic CDO Pricing Using the Double Normal Inverse Gaussian Copula with Stochastic Factor Loadings. Master Thesis, Zürich University of Mathematics.
     14. Yang, R. , X. Qin and T. Chen (2009). CDO Pricing Using Single Factor M_(G-NIG) Copula Model with Stochastic Correlation and Random Loading. Journal of Mathematical Analysis and Applications 350, 73-80.
     15. Zheng, H. (2006). Efficient Hybrid Methods for Portfolio Credit Derivatives. Quantitative Finance 6, 349-357.		zh_TW