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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 王之彥(Jr-Yan Wang) | |
| dc.contributor.author | Pi-Ling Chen | en |
| dc.contributor.author | 陳碧玲 | zh_TW |
| dc.date.accessioned | 2021-06-08T04:19:52Z | - |
| dc.date.copyright | 2010-07-23 | |
| dc.date.issued | 2010 | |
| dc.date.submitted | 2010-07-21 | |
| dc.identifier.citation | [1] Abe Sklar, 1973, Random variables, joint distribution functions, and copulas, Kybernetika, 9, 449– 460.
[2] Anna Kalemanova, Bernd Schmid, and Ralf Werner, 2007, The normal inverse Gaussian distribution for synthetic CDO pricing, Journal of Derivatives, 14, 80–93. [3] Darrell Duffie, Kenneth J. Singleton, 1999, Modeling term structures of defaultable bonds, Review of Financial Studies, 12, 687–720. [4] David X. Li, 2000, On default correlation: a copula function approach, Journal of Fixed Income, 9, 43–54. [5] Fischer Black, John C. Cox, 1976, Valuing corporate securities: some effects of bond indenture provisions, Journal of Finance, 31, 351–367. [6] John C. Cox, Stephen A. Ross, and Mark Rubinstein, 1979, Option pricing: a simplified approach, Journal of Financial Economics, 7, 229–263. [7] John Hull, Alan White, 2000, Valuing credit default Swaps I: no counterparty default risk, Journal of Derivatives, 8, 29–40. [8] John Hull, Alan White, 2004, Valuation of a CDO and nth to default CDS without Monte Carlo simulation, Journal of Derivatives, 12, 8–23. [9] John Hull, Mirela Pedrescu, and Alan White, 2005, The valuation of correlation- dependent credit derivatives using a structural model, working paper, University of Toronto. [10] John Hull, Alan White, 2006, Valuing credit derivatives using an implied copula approach, Journal of Derivatives, 14, 8–28. [11] Lutz Schloegl, Dominic O’Kane, 2005, A note on the large homogeneous portfolio approximation with the student-t copula, Finance and Stochastics, 9, 577–584. [12] Martijn van der Voort, 2007, Factor copulas: external defaults, Journal of Derivatives, 14, 94–102 [13] Ole E. Barndorff-Nielsen, 1997, Processes of normal inverse Gaussian type, Finance and Stochastics, 2, 41–68. [14] Robert A. Jarrow, Donald R. Van Deventer, 2008, Synthetic CDO equity: short or long correlation Risk?, Journal of Fixed Income, 17, 31–41. [15] Robert A. Jarrow, Stuart M. Turnbull, 1992, Credit risk: drawing the analogy, Risk Magazine, 5, 51–56. [16] Robert A. Jarrow, Stuart M. Turnbull, 1995, Pricing derivatives on financial securities subject to credit risk, Journal of Finance, 50, 53–86. [17] Robert C. Merton, 1974, On the pricing of corporate debt: the risk structure of interest rates, Journal of Finance, 29, 449–470. [18] Sanjiv Ranjan Das, Rangarajan K. Sundaram, and Suresh M. Sundaresan, 2004, A simple model for pricing derivative securities with equity, interest-rate, default and liquidity risk, working paper, Santan Clara University. [19] Sanjiv Ranjan Das, Gary Geng, 2004, Correlated default processes: a criterion-based copula approach, Journal of International Management, 2, 44–70. [20] Santhosh Bandreddi, Sanjiv Das, and Rong Fan, 2007, Correlated default modeling with a forest of binomial trees, Journal of Fixed Income, 17, 38–56. [21] Xavier Burtschell, Jonathan Gregory, and Jean-Paul Laurent, 2009, A comparative analysis of CDO pricing models under the factor copula framework, Journal of Derivatives, 16, 9–37. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/22523 | - |
| dc.description.abstract | The objective of this thesis is to provide a comparative analysis of four representative models in pricing CDOs. The models compared in this thesis are the Gaussian copula, one-factor Gaussian copula, normal inverse Gaussian copula, and the defaultable-CRR models. The first three models are based on the factor copula pricing framework, but the last model is a characteristic combination of structural models and reduced form default models implemented on binomial trees. The effectiveness of these four models during the subprime crisis will be examined by comparing the fitness of these models with the market data and by evaluating the stability of parameter values over time. The market quotes of the tranche iTraxx Europe with five-year time to maturity are considered as examples for numerical evaluation in the thesis. The results indicate that the normal inverse Gaussian copula model outperforms other models before the crisis, especially for equity tranche, the 3~6 % tranche, and the most senior tranche, but after the crisis, the Gaussian copula model performs better. The performance of the defaultable-CRR model is poor due to the some fundamental problems. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T04:19:52Z (GMT). No. of bitstreams: 1 ntu-99-R96724091-1.pdf: 4190280 bytes, checksum: 12dba02714ca8e4bcc4a39217b5c0013 (MD5) Previous issue date: 2010 | en |
| dc.description.tableofcontents | Abstract………………………………………..……………………………….iii
Chapter I Introduction……………………………..………………………..……1 Chapter II CDS, CDO, Synthetic CDOs, and Index Tranches……………...…….4 2.1 Credit Default Swaps……………………………………………………..4 2.2 Collateralized Debt Obligations………………………………………….5 2.3 Synthetic CDOs……………………………………..……………………..7 2.4 Index Tranches…………………………………………...………………..8 Chapter III Models Review………………………………………………………12 3.1 General Semi-analytic Approach for Pricing Synthetic CDOs…….…...12 3.2 Review of Gaussian Copula Model…………………………………….14 3.3 One-Factor Gaussian Copula Model……………………………………..17 3.4 Normal Inverse Gaussian Copula Model…………………….………18 3.5 Defaultable-CRR Model....…………………………………….………21 3.5.1 Building a Binomial Tree with Default Risk………………………22 3.5.2 Calibrating parameters………………………………………………..23 3.5.3 Simulating Correlated Default………………………………………..25 Chapter IV Numerical Evaluation…………………………….………………..26 4.1 Comparison of the Models: Pricing iTraxx Europe…………….………26 4.2 Model Fitness..……………………………………………………………28 4.2.1 Results Analysis…………………….………………..………………28 4.2.2 Stability of Parameters……………………………………………….38 Chapter V Conclusion……………………………………………………………41 | |
| dc.language.iso | en | |
| dc.subject | 高斯關聯結構 | zh_TW |
| dc.subject | 一因子高斯關聯結構 | zh_TW |
| dc.subject | 擔保債券憑證 | zh_TW |
| dc.subject | one-factor Gaussian copula | en |
| dc.subject | Tranche spread | en |
| dc.subject | iTraxx Europe | en |
| dc.subject | defaultable-CRR model | en |
| dc.subject | CDO | en |
| dc.subject | Gaussian copula | en |
| dc.subject | normal inverse Gaussian copula | en |
| dc.title | 擔保債券憑證評價模型之比較分析 | zh_TW |
| dc.title | A Comparative Analysis of CDO Pricing Models | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 98-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 郭家豪(Jia-hau Guo),戴天時(Tian-Shyr Dai) | |
| dc.subject.keyword | 擔保債券憑證,高斯關聯結構,一因子高斯關聯結構, | zh_TW |
| dc.subject.keyword | CDO,Gaussian copula,one-factor Gaussian copula,normal inverse Gaussian copula,defaultable-CRR model,iTraxx Europe,Tranche spread, | en |
| dc.relation.page | 44 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2010-07-21 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 國際企業學研究所 | zh_TW |
| 顯示於系所單位： | 國際企業學系 | |
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