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    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/67589
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/67589


    Title: 以單因子拉普拉斯關聯結構模型評價合成型抵押擔保債券憑證
    Pricing the Synthetic CDOs for One Factor models in Laplace Distributions
    Authors: 黃信源
    Contributors: 劉惠美
    黃信源
    Keywords: 合成型抵押擔保債券
    單因子關聯結構模型
    NIG分配
    拉普拉斯分配
    Date: 2013
    Issue Date: 2014-07-21 15:36:53 (UTC+8)
    Abstract: 以往在評價合成型抵押擔保債券時,最常使用的方法為應用大樣本一致性資產組合(large homogeneous portfolio, LHP)假設的單因子常態關聯結構,但會造成評價與市場報價間差異過大的問題,且會造成相關性微笑曲線現象。單因子常態關聯結構模型會有上述的問題是因為其缺少厚尾度或偏斜性,因此若能在單因子關聯結構模型中加入具厚尾度或偏斜性的分配,就能改善以上的問題,像是Kalemanova et al.(2007)提出應用LHP假設之單因子NIG關聯結構模型來對合成型CDO進行評價,該模型的評價結果遠優於單因子常態關聯結構模型。本文使用單因子拉普拉斯關聯結構模型對合成型抵押擔保債券作評價,並與其它單因子關聯結構模型的配適結果做比較,探討在評價上單因子拉普拉斯(Laplace)關聯結構模型是否能有其特有的優點與更好的配適效果。由最後的實證分析結果可得到幾個結論,第一,高斯(Gaussian)及拉普拉斯(Laplace)關聯結構模型皆能反應負報價。第二,單因子NIG(2)關聯結構模型是本文使用的關聯模型中配適結果最好的,其在各年度合成型抵押擔保債券商品的絕對誤差和都是最小。第三,對於本文所使用的全部模型,它們在各分券的隱含相關震盪幅度很大,因此這些模型都不符合LHP假設。
    Reference: 1. Dezhong, W. Rachev S.T., Fabozzi F.J. (October
    2006).Pricing Tranches of a CDO and a CDS Index: Resent
    Advances and Future Research. Working paper.
    2. Dezhong W., Rachev S.T., Fabozzi F.J. (November 2006).
    Pricing of Credit Default Index Swap Tranches with One-
    Factor Heavy-Tailed Copula Models. Working paper.
    3. Kalemanove, A., Schmid, B., and Werner, R
    (spring2007). “The Normal Inverse Gaussian Distribution
    for Synthetic CDO pricing.” The Journal of Derivatives,
    Vol. 14, pp. 80-93.
    4. Karlis, D. and Papadimitriou, A. (2004). Inference for
    the Multivariate Normal Inverse Gaussian Model. Working
    paper
    5. Hull, J. and White, A. (winter 2004) “Valuation of a
    CDO and an n-th to Default CDS without Monte Carlo
    Simulation.” The Journal of Derivatives, Vol. 12, pp. 8- 23.
    6. Li, D.X. (April 2000). On Default Correlation: A Copula
    Function Approach. Working Paper.
    7. D. O’Kane and L. Schloegl., M. (2001). Modeling Credit:
    Theory and Practice. Quantitative Credit Research,
    Lehman Brothers.
    8. Black, Fischer and John C. Cox, "Valuing Corporate
    Securities: Some Effects of Bond Indenture Provisions",
    Journal of Finance, Vol. 31, No. 2, (May 1976), pp. 351-
    367.
    9. Merton, R. C (1974) . On the Pricing of Corporate Debt:
    The Risk Structure of Interest Rates, Journal of
    Finance, 29, pp. 449-470.
    10. O.E. Barndor®-Nielsen. (1978). Hyperbolic distributions
    and distributions on hyperbolae. Scandinavian. Journal
    of statistics, 5, 151-157.
    11. Karlis. D. (2000). An EM type algorithm for maximum
    likelihood estimation of the normal–inverse Gaussian
    distribution. Statistic & Probability Letters , 57,
    43-52.
    12. 林聖航(民101)。探討合成型抵押擔保債券憑證之評價。國立政治大學統
    計學系碩士論文,台北市。
    13. 邱嬿燁 (民 97) 。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 。國立政治大學統計學系碩士論文,台北市。
    Description: 碩士
    國立政治大學
    統計研究所
    101354021
    102
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0101354021
    Data Type: thesis
    Appears in Collections:[統計學系] 學位論文

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