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    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/59288
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/59288


    Title: 探討標準化偏斜Student-t分配關聯結構模型之抵押債務債券之評價
    Pricing CDOs with Standardized Skew Student-t Distribution Copula Model
    Authors: 黃于騰
    Huang, Yu Teng
    Contributors: 劉惠美
    Liu, Hui Mei
    黃于騰
    Huang, Yu Teng
    Keywords: 抵押債務債券
    單因子關聯結構模型
    標準化偏斜Student-t分配
    collateralized debt obligation
    one factor copula model
    standardized skew student-t distribution
    Date: 2012
    Issue Date: 2013-09-02 15:36:54 (UTC+8)
    Abstract: 在市場上最常被用來評價抵押債務債券(Collateralized Debt Obligation, CDO)的分析方法即為應用大樣本同質性資產組合(Large Homogeneous Portfolio, LHP)假設之單因子關聯結構模型(One Factor Copula Model)。由過去文獻指出,自2008年起,抵押債務債券的商品結構已漸漸出現改變,而目前所延伸之各種單因子關聯結構模型在新型商品的評價結果中皆仍有改善空間。
    在本文中使用標準化偏斜Student-t分配(Standardized Skew Student-t distribution, SSTD)取代傳統的高斯分配進行抵押債務債券之分券的評價,此分配擁有控制分配偏態與峰態的參數。但是與Student-t分配相同,SSTD同樣不具備穩定的摺積(convolution)性質,因此在評價過程中會額外消耗部分時間。而在實證分析中,以單因子SSTD關聯結構模型評價擔保債務債券新型商品之分券時得到了較佳的結果,並且比單因子高斯關聯結構模型擁有更多參數以符合實際需求。
    The most widely used method for pricing collateralized debt obligation(CDO) is the one factor copula model with Large Homogeneous Portfolio assumption. Based on the literature of discussing, the structure of CDO had been changed gradually since 2008. The effects for pricing new type CDO tranches in the current extended one factor copula models are still improvable.
    In this article, we substitute the Gaussian distribution with the Standardized Skew Student-t distribution(SSTD) for pricing CDO tranches, and it has the features of heavy-tail and skewness. However, similar to the Student-t distribution, the SSTD is not stable under convolution as well. For this reason, it takes extra time in the pricing process. The empirical analysis shows that the one factor SSTD copula model has a good effect for pricing new type CDO tranches, and furthermore it brings more flexibility to the one factor Gaussian copula model.
    Reference: 1. Amato, J.D. and Gyntelberg, J. (March 2005). CDS Index Tranches and The pricing of Credit Risk Correlations. BIS Quarterly Review.
    2. Andersen, L. and Sidenius, J. (Winter 2004). Extensions to the Gaussian Copula: Random Recovery and Random Factor Loadings. Journal of Credit Risk, Vol. 1, pp. 29-71.
    3. Burtschell, X., Gregory, J. and Laurent, L.-P. (April 2005). A Comparative Analysis of CDO Pricing Models. Working Paper.
    4. Dezhong, W., Rachev, S.T. and Fabozzi, F.J. (October 2006). Pricing Tranches of a CDO and a CDS Index: Resent Advances and Future Research. Working Paper.
    5. Dezhong, W., Rachev, S.T. and Fabozzi, F.J. (November 2006). Pricing if Credit Default Index Swap Tranches with One-Factor Heavy-Tailed Copula Models. Working Paper.
    6. Fernández, C. and Steel, M.F.J. (1998). On Bayesian modeling of fat tails and skewness. Journal of the American Statistical Association, 93(441), pp.359–371.
    7. Giot, P. and S. Laurent (2003). Value-at-Risk for Long and Short Trading Positions. Journal of Applied Econometrics, 18, pp. 641-664.
    8. Hansen, B.E. (1994). Autoregressive Conditional Density Estimation. International Economic Review, 35, pp. 705-730.
    9. Hull, J. and White, A. (Winter 2004). Valuation of a CDO and an n-th to Default CDS without a Monte Carlo Simulation. Journal of Derivatives, Vol. 12, No. 2, pp. 8-23.
    10. Kalemanova, A., Schmid, B. and Werner, R. (Spring 2007). The Normal Inverse Gaussian Distribution for Synthetic CDO Pricing. The Journal of Derivatives, Vol. 14, pp. 80-93.
    11. Lambert, P. and Laurent, S. (2001). Modelling financial time series using GARCH-type models with a skewed Student distribution for the innovations. Discussion Paper 01-25, Institut de Statistique, Université catholique de Louvain, Louvain-la-Neuve, Belgium.
    12. Li, D.X. (April 2000). On Default Correlation: A Copula Function Approach. Journal of Fixed Income, 9(4), pp. 43-54.
    13. Nelsen, R.B. (2005). An Introduction to Copulas. Springer. Second Edition.
    14. O'Kane, D. and Livasey, M. (2004). Base Correlation Explained. Technical report, Quantitative Credit Research, Lehman Brothers.
    15. O'Kane, D. and Schlögl, L. (February 2001). Modelling Credit: Theory and Practice. Quantitative Credit Research, Lehman Brothers.
    16. Sklar, A. (1959). Fonctions de répartition à n dimensions et leurs marges. Publications de l'Institut de Statistique de L'Université de Paris 8, pp. 229-231.
    17. Torresetti, R., Brigo, D. and Pallavicini, A. (November 2006). Implied correlation in CDO tranches: a Paradigm to be handled with care. Working Paper.
    18. Vasicek, O. (2002). Loan PortfoUo Value. Risk, Vol. 12, pp. 160-162.
    19. Willemann, S. (2004). An Evaluation of the Base Correlation FrameWork for Synthetic CDOs. Working Paper.
    20. 林聖航(2012) 探討合成型抵押擔保債券憑證之評價,碩士學位論文
    21. 邱嬿燁(2007) 探討單因子複合分配關聯結構模型之擔保債權憑證之評價,碩士學位論文
    Description: 碩士
    國立政治大學
    統計研究所
    100354020
    101
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0100354020
    Data Type: thesis
    Appears in Collections:[統計學系] 學位論文

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