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題名 Variance-Gamma因子聯繫結構模型於違約相關性之描述及應用
作者 賴興展
貢獻者 江彌修
賴興展
關鍵詞 合成型擔保債權憑證
因子聯繫結構
iTraxx指數分券
日期 2009
上傳時間 4-Sep-2013 10:05:09 (UTC+8)
摘要 本文在大樣本同質性(LHP)假設下，架構出Variance-Gamma因子聯繫結構模型。建立債權群組損失分配時，由於Variance-Gamma分配與常態分配相同皆具有累加性，因此作為因子結構模型會比起Double-t因子聯繫結構模型具較佳解析性。本文進一步比較Variance-Gamma因子聯繫結構模型與高斯因子聯繫結構模型以及Double-t因子聯繫結構模型。iTraxx指數分券實證結果顯示，Variance-Gamma因子聯繫結構模型最為精確，能有效刻劃高斯因子聯繫結構模型所缺少之尾端損失機率機率分配，以及改正Double-t因子聯繫結構模型過份高估尾端損失之缺點。此外利用調整Variance-Gamma分配之偏態及峰態係數，可以求出更精準的評價結果。最後本文介紹iTraxx分券的交易策略，並且針對不同風險予以避險，研究結果顯示，規避標的債權群組之信用價差風險後，往往無法規避違約相關性變化的風險，投資人在進行策略交易時應更審慎評估。
參考文獻 1. Altman, E.I., B. Brady, A. Resti and A. Sironi, 2005, “The link between default and recovery rate: theory, empirical evidence and implications,” Journal of Business 78, 2203-2228.
2. Andersen, L., and J. Sidenius, 2005, “Extensions to the Gaussian Copula: Random Recovery and Random Factor Loadings.” Journal of Credit Risk.
3. Andersen, L., J. Sidenius, and S. Basu, 2003,”All Your Hedges in One Basket." Risk, 11.
4. Arnaud De Servigny, Norbert Jobst,2008, “The Handbook of Structured Finance”, McGraw-Hill
5. Burtschell, X., J. Gregory, and L.-P. Laurent, 2005, “A Comparative Analysis of CDO PricingModels.” , working paper.
6. Craig Mlunfield, 2009 , ” Synthetic CDOs Modeling, Valuation and Risk Management”, Cambridge University Press
7. David Li, Ratul Roy, Jure Skarabot, 2004 , ” A Primer on Single Tranche CDOs”, Citi Bank Global Structured Credit Research.
8. Hull, John and White, Alan (2004), “Valuation of a CDO and nth to default CDS without Monte Carlo simulation”, Journal of Derivatives, 12, 8–23.
9. Joshi, Mark S. and Stacey, AlanM, 2006, Intensity Gamma: A New Approach To Pricing Portfolio Credit Derivatives. May 16,
10. Kalemanova, A., and R. Werner., 2006 ,”A Short Note on the Efficient Implementation of the Normal Inverse Gaussian Distribution." working paper.
11. Kalemanova, Anna, Schmid, Bernd and Werner, Ralf, 2007, “The Normal inverse Gaussian distribution for synthetic CDO pricing”. Journal of Derivatives, Spring,
12. Li, David (2000), “On default correlations: a copula approach”, Journal of Fixed Income, 9, 43–54
13. Thomas Moosbrucker, 2006, “Pricing CDOs with Correlated Variance Gamma Distributions.” working paper.
14. Vasicek, Oldrich,1987, “Probability of Loss on Loan Portfolio. Memo, KMV Corporation”, available at www.moodyskmv.com, 1987.
15. 林恩平, 江彌修,2009, “條件獨立假設下合成行擔保債權憑證之評價與避險”,財務金融學刊
描述 碩士
國立政治大學
金融研究所
96352033
98
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0096352033
資料類型 thesis
dc.contributor.advisor 江彌修zh_TW
dc.contributor.author (Authors) 賴興展zh_TW
dc.creator (作者) 賴興展zh_TW
dc.date (日期) 2009en_US
dc.date.accessioned 4-Sep-2013 10:05:09 (UTC+8)-
dc.date.available 4-Sep-2013 10:05:09 (UTC+8)-
dc.date.issued (上傳時間) 4-Sep-2013 10:05:09 (UTC+8)-
dc.identifier (Other Identifiers) G0096352033en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/59958-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 金融研究所zh_TW
dc.description (描述) 96352033zh_TW
dc.description (描述) 98zh_TW
dc.description.abstract (摘要) 本文在大樣本同質性(LHP)假設下，架構出Variance-Gamma因子聯繫結構模型。建立債權群組損失分配時，由於Variance-Gamma分配與常態分配相同皆具有累加性，因此作為因子結構模型會比起Double-t因子聯繫結構模型具較佳解析性。本文進一步比較Variance-Gamma因子聯繫結構模型與高斯因子聯繫結構模型以及Double-t因子聯繫結構模型。iTraxx指數分券實證結果顯示，Variance-Gamma因子聯繫結構模型最為精確，能有效刻劃高斯因子聯繫結構模型所缺少之尾端損失機率機率分配，以及改正Double-t因子聯繫結構模型過份高估尾端損失之缺點。此外利用調整Variance-Gamma分配之偏態及峰態係數，可以求出更精準的評價結果。最後本文介紹iTraxx分券的交易策略，並且針對不同風險予以避險，研究結果顯示，規避標的債權群組之信用價差風險後，往往無法規避違約相關性變化的風險，投資人在進行策略交易時應更審慎評估。zh_TW
dc.description.tableofcontents 壹、 緒論 7
貳、 文獻回顧 10
參、 基本假設與模型設定 13
3.1 合成型擔保債權憑證分券評價模型 14
3.2 Variance-Gamma因子連繫結構模型 21
3.3 iTraxx Tranche 0~3%分券價值求算 27
3.4 避險參數以及求法 29
肆、 數值結果與分析 33
4.1.1 損失分配之厚尾性描述 33
4.1.2 iTraxx指數分券評價結果 36
4.2 iTraxx指數分卷之風險分析 42
4.3 iTraxx Tranche Index交易策略及獲利分析 46
伍、 結論 60
陸、 參考文獻 63		zh_TW
dc.format.extent 1136281 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/#G0096352033en_US
dc.subject (關鍵詞) 合成型擔保債權憑證zh_TW
dc.subject (關鍵詞) 因子聯繫結構zh_TW
dc.subject (關鍵詞) iTraxx指數分券zh_TW
dc.title (題名) Variance-Gamma因子聯繫結構模型於違約相關性之描述及應用zh_TW
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) 1. Altman, E.I., B. Brady, A. Resti and A. Sironi, 2005, “The link between default and recovery rate: theory, empirical evidence and implications,” Journal of Business 78, 2203-2228.
2. Andersen, L., and J. Sidenius, 2005, “Extensions to the Gaussian Copula: Random Recovery and Random Factor Loadings.” Journal of Credit Risk.
3. Andersen, L., J. Sidenius, and S. Basu, 2003,”All Your Hedges in One Basket." Risk, 11.
4. Arnaud De Servigny, Norbert Jobst,2008, “The Handbook of Structured Finance”, McGraw-Hill
5. Burtschell, X., J. Gregory, and L.-P. Laurent, 2005, “A Comparative Analysis of CDO PricingModels.” , working paper.
6. Craig Mlunfield, 2009 , ” Synthetic CDOs Modeling, Valuation and Risk Management”, Cambridge University Press
7. David Li, Ratul Roy, Jure Skarabot, 2004 , ” A Primer on Single Tranche CDOs”, Citi Bank Global Structured Credit Research.
8. Hull, John and White, Alan (2004), “Valuation of a CDO and nth to default CDS without Monte Carlo simulation”, Journal of Derivatives, 12, 8–23.
9. Joshi, Mark S. and Stacey, AlanM, 2006, Intensity Gamma: A New Approach To Pricing Portfolio Credit Derivatives. May 16,
10. Kalemanova, A., and R. Werner., 2006 ,”A Short Note on the Efficient Implementation of the Normal Inverse Gaussian Distribution." working paper.
11. Kalemanova, Anna, Schmid, Bernd and Werner, Ralf, 2007, “The Normal inverse Gaussian distribution for synthetic CDO pricing”. Journal of Derivatives, Spring,
12. Li, David (2000), “On default correlations: a copula approach”, Journal of Fixed Income, 9, 43–54
13. Thomas Moosbrucker, 2006, “Pricing CDOs with Correlated Variance Gamma Distributions.” working paper.
14. Vasicek, Oldrich,1987, “Probability of Loss on Loan Portfolio. Memo, KMV Corporation”, available at www.moodyskmv.com, 1987.
15. 林恩平, 江彌修,2009, “條件獨立假設下合成行擔保債權憑證之評價與避險”,財務金融學刊		zh_TW