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    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/100452


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/100452


    Title: 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
    Authors: 紀宛汝
    Chi, Wan Ju
    Contributors: 劉惠美
    Liu, Hui Mei
    紀宛汝
    Chi, Wan Ju
    Keywords: 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
    Date: 2014
    Issue Date: 2016-08-22 10:42:28 (UTC+8)
    Abstract: 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。
    Reference: 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.
    Description: 博士
    國立政治大學
    統計學系
    94354502
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0094354502
    Data Type: thesis
    Appears in Collections:[統計學系] 學位論文

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