| dc.contributor.advisor | 劉惠美 | zh_TW |
| dc.contributor.author (作者) | 黃繼緯 | zh_TW |
| dc.contributor.author (作者) | Huang, Chi Wei | en_US |
| dc.creator (作者) | 黃繼緯 | zh_TW |
| dc.creator (作者) | Huang, Chi Wei | en_US |
| dc.date (日期) | 2012 | en_US |
| dc.date.accessioned | 2-九月-2013 15:36:20 (UTC+8) | - |
| dc.date.available | 2-九月-2013 15:36:20 (UTC+8) | - |
| dc.date.issued (上傳時間) | 2-九月-2013 15:36:20 (UTC+8) | - |
| dc.identifier (其他 識別碼) | G0100354005 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/59285 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 統計研究所 | zh_TW |
| dc.description (描述) | 100354005 | zh_TW |
| dc.description (描述) | 101 | zh_TW |
| dc.description.abstract (摘要) | 1990年代中期信用衍生信商品開始發展，隨著時代變遷，演化出信用違約交換(Credit Default Swaps, CDS)、擔保債權憑證(Collateralized Debt Obligation, CDO)、合成型擔保債權憑證(Synthetic CDO)等商品，其可以分散風險的特性廣受歡迎，並且成為完備金融市場中重要的一環。在2007年金融海嘯中，信用衍生性商品扮演相當關鍵的角色，所以如何合理定價各類信用衍生性商品就變成相當重要的議題以往在定價合成型擔保債權憑證時，多採取單因子結構模型來做為報酬函數的主要架構，並假設模型分配為常態分配、t分配、NIG分配等，但單因子結構模型的隱含相關係數具有波動性微笑現象，所以容易造成定價偏誤。為了解決此問題，本文將引用常態分配假設與NIG分配假設下的隨機風險因子負荷模型(Random Factor Loading Model)，觀察隨機風險因子負荷模型是否對於定價偏誤較其他模型有所改善，並且比較各模型在最佳化參數與定價時的效率，藉此歸納出較佳的合成型擔保債權憑證定價模型。 | zh_TW |
| dc.description.abstract (摘要) | During the mid-1990s, credit-derivatives began to be popular and evolved into credit default swaps (CDS), collateralized debt obligation (CDO), and synthetic collateralized debt obligation (Synthetic CDO). Because of the feature of risk sharing, credit-derivatives became an important part of financial market and played the key role in the financial crisis of 2007. So how to price credit-derivatives is a very important issue.When pricing Synthetic CDO, most people use the one-factor coupla model as the structure of reward function, and suppose the distribution of model is Normal distribution, t- distribution or Normal Inverse Gaussian distribution(NIG). But the volatility smile of implied volatility always causes the pricing inaccurate.For solving the problem, I use the random factor loading model under Normal distribution and NIG distribution in this study to test whether the random factor loading model is better than one-factor coupla model in pricing, and compare the efficience of optimization parameters. In conclusion, I will induct the best model of Synthetic CDO pricing. | en_US |
| dc.description.tableofcontents | 摘要...................................................... .IAbstract...................................................II第一章 緒論....................................... .........1第一節 研究動機........................................ ....1第二節 研究目的.................................... ........2第三節 擔保債權憑證.........................................2第四節 信用違約交換與CDS Index..............................3第五節 合成型擔保債權憑證...................................4第六節 論文架構............................ ................5第二章 文獻回顧.............................................6第一節 單因子關聯結構................................ .....6第二節 隨機風險因子負荷模型.......................... ......8第三節 Normal Inverse Gaussian Distribution.................9第三章 研究方法............................................10第一節 合成型擔保債權憑證定價方式..........................10第二節 單因子結構模型與LHP近似計算.........................13第三節 NIG分配的特性與單因子結構NIG分配模型................17第四節 單因子RFL結構模型...................................20第四章 實證分析............................................23第一節 DJ iTraxx Europe指數................................23第二節 合成型CDO各分券定價.................................24第五章 結論與建議..........................................38參考文獻...................................................30 | zh_TW |
| dc.format.extent | 987817 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0100354005 | en_US |
| dc.subject (關鍵詞) | 合成型擔保債權憑證 | zh_TW |
| dc.subject (關鍵詞) | 單因子結構模型 | zh_TW |
| dc.subject (關鍵詞) | synthetic CDOs | en_US |
| dc.subject (關鍵詞) | one-factor copula model | en_US |
| dc.title (題名) | 不同單因子結構模型下合成型擔保債權憑證定價之研究 | zh_TW |
| dc.title (題名) | Comparison between different one-factor copula models of synthetic CDOs pricing | en_US |
| dc.type (資料類型) | thesis | en |
| dc.relation.reference (參考文獻) | [1] Chunfa Wang and Dingwei Huang. (2009). “Double Normal Inverse Gaussian Copula with Random Factor Loadings Model for Synthetic CDO Pricing.” Management and Service Science, 2009. MASS `09. International Conference on.[2] Li. David X. (2000). “On Default Correlation: A Copula Function Approach.”Journal of Fixed Income, Vol. 9, Issue 4, pages 43–54.[3] Hull, J. and White, A. (2004). “Valuation of a CDO and an n-th to Default CDS Without Monte Carlo Simulation.” Journal of Derivatives, Vol. 12, No. 2, pp. 8–23.[4] Kalemanova, A., Schmid, B. Werner, R. (2007). “The normal inverse gaussian distribution for synthetic CDO pricing.” Journal of derivatives, Vol 14, pp. 80-93.[5] Barndorff-Nielsen O. E. (1987). “Hyperbolic distributions and distributions on hyperbolae.” Scandinavian Journal of Statistics 5, pp. 151-157.[6] O`kane, D., and Livesey, M. (2001). “Modeling Credit: Theory and Practice.”Quantitative Credit Research, Lehman Brothers. [7] Vasicek , O. (2002). “Loan Portfolio Value.” Risk, Vol. 12, pp.160-162[8]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,國立政治大學博士學位論文[9]林聖航 (2012) 探討合成型抵押擔保債券憑證之評價, 國立政治大學碩士學位論文 | zh_TW |
