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題名 擔保貸款憑證之評價：使用Factor Copula方法
Using Factor Copula Method to Price Collateralized Loan Obligation作者 吳佳璇 貢獻者 林士貴<br>蔡政憲
吳佳璇關鍵詞 關聯函數
因子關聯函數
單因子模型
擔保債權憑證
擔保貸款憑證
Copula
One factor model
Factor copula
CDO
CLO日期 2018 上傳時間 3-Jul-2018 17:26:46 (UTC+8) 摘要 擔保債權憑證在1996後蓬勃發展，但卻在2008年成為全球金融風暴的問題之一，在沉寂幾年後近幾年來德意志銀行（Deutsche Bank）、高盛集團（Goldman Sachs）、摩根大通（JP Morgan）、法國興業銀行（Societe Generale）及花旗銀行（Citi Bank）等都曾嘗試復推擔保債權憑證商品，顯示這類商品其對於買賣方皆是吸引人的。隨著擔保債權憑證發行量的攀升，此商品的評價更顯其重要，本文使用因子關聯函數模型（Factor Copula），其優點為計算快速，並加入市場因子來當客觀標準，避免關聯函數法應用在不同市場資產情況下的不合理假設。實證部分以Venture在2016年發行的擔保貸款憑證為例，進行評價求得分券之公平溢酬，並針對評價過程提出可改善的地方。
The Collateralized Debt Obligation（CDO） boomed after 1996, but it became one of the problems of the global financial crisis in 2008. After a few years of silence, Deutsche Bank, Goldman Sachs, JPMorgan, France Societe Generale and Citi Bank have tried to reintroduce CDO, showing that CDO are attractive to buyers and sellers. With the increase in the issuance of CDO, the pricing of this commodity is even more important. This paper uses Factor Copula model, which has the advantages of fast calculation, adds market factors as objective criteria, and avoid the unreasonable assumption that the correlation function method is applied to different market assets. The empirical part uses the CLO issued by Venture in 2016 as an example to evaluate the fair premium of the tranche, and propose improvements to the pricing process.參考文獻 [1] 林彥儒(2015)。Copula模型在信用連結債券的評價與實證分析。未出版之博（碩士）論文，國立政治大學，金融學系研究所，台北市。[2] 段登宇(2008)。擔保債權憑證CDO之訂價與分析－單因子模型及機率水桶法之應用未出版之博（碩士）論文，世新大學，財務金融學系，台北市。[3] 廖四郎、李福慶，(2005)。擔保債權憑證之評價－Copula分析法。[4] 蔡宗翰，(2006)。抵押債權憑證之評價：Factor Copula 與JLT模型之應用。未出版之博（碩士）論文，國立清華大學，統計學研究所，新竹市。[5] 戴嘉雄，(2006)。擔保債權憑證之信用價差評價－ Copula分析法。未出版之博（碩士）論文，國立中山大學，財務管理學系碩士在職專班，高雄市。[1] Andersen, L. and J. Sidenius(2004). “Extensions to the Gaussian copula：random recovery and random factor loadings”, working paper, Bank of America.[2] Anson M. J. P., F. J. Fabozzi, M. Choudhry and R. R. Chen(2004). “Credit derivatives-instruments, applications, and pricing”, John Wiley &Sons, Incorporated.[3] Belkin, B., S. Suchover, and L. Forest (1998). “A one-parameter representation of credit risk and transition matrices”, Credit Metrics Monitor, 1(3), pages 46-56.[4] Black, F. and Cox, J. C. (1976). “Valuing corporate securities:some effects of bond indenture provisions”, Journal of Finance ,31, pages 351-367.[5] Carey M.(1998). “Creditrisk in private debt portfolios,” Journal of Finance, 53, pages1363-1387.[6] Cifuentes, A.,and Connor G.O. (1996) . “The Binomial expansion method applied to CBO/CLO analysis”, Moody`s Investors Service Special Report.[7] Cifuentes, A.,and Connor G.O. (1998) . “The double Binomial method and it’s application to a special case of CBO structures”, Moody`s Investors Service Special Report.[8] Das, S., Fong, G. and Geng, G. (2001). “The impact of correlated default risk on credit portfolios,＂Journal of Fixed Income, 11, pages 9-19.[9] Duffie, D. and K. Singleton (1999). “Modeling term structure of defaultable bonds”, Review of Financial Studies, 12, pages 687-720.[10] Darrell, D. and Gârleanu N., (2001). “Risk and valuation of collateralized debt obligations”, Financial Analysts Journal, pages 41-59.[11] Galiani, S.S. (2003). “Copula functions and their application in pricing and risk managing multiname credit derivative product”, working paper.[12] Gregory, J. and J. P. Laurent (2004). “In the core of correlation”, Risk, pages 87-91.[13] Gibson, M. (2004). ”Understanding the risk of synthetic CDOs”, Finance and Economics Discussion Series, 36, Federal Reserve Board.[14] Giesecke, K.(2001). “Structural modeling of correlate defaults with incomplete information,＂working paper, Humboldt University.[15] Goodman, L.S. (2002). “Synthetic CDOs: an introduction”, The Journal of Derivatives, 9(3), pages 60-72.[16] Gupton, G.M., C.C. Finger, and M. Bhatia (1997). “CreditMetrics-technical document”, Morgan Guaranty Trust Company.[17] Hull, J. and A. White (2004). “Valuation of a CDO and an n-th to default CDS without Monte Carlo simulation”, The Journal of Derivatives, 12(2), pages 8-48.[18] Introduction of Credit Derivatives, Chuang, M.C. , Retrieved June 06 2018, from https://drive.google.com/file/d/0B1qrE20J1V2YdGw4V05IeThURmM/view[19] Jarrow, R., D. Lando, and S. Turnbull (1997). “A Markov model for the term structure of credit spread”, The Review of Financial Studies, 10, pages 481-523.[20] Jarrow, R., and S. Turnbull (1995). “Pricing Derivatives on Financial Subject to Credit Risk”, Journal of Finance, 50, pages 53-68.[21] Jarrow, R., and F. Yu (2001). “Counterparty risk and pricing of defaultable securities”, Journal of Finance, 56, pages 1765-1799.[22] Lando, D. (1998). “On Cox processes and credit risky securities,” Review of Derivatives Research, Vol.2, pages 99-120.[23] Laurent, J-P. and J. Gregory (2003). “Basket default swaps, CDO’s and factor copulas”, Working Paper, ISFA Actuarial School, University of Lyon.[24] Li, D.X. (2000). “On Default Correlation: A Copula approach”, Journal of Fixed Income, 9, pages 43-54.[25] Li, D.X. (2002). “Valuing synthetic CDO tranches using copula function approach”, The RiskMetrics Group working paper.[26] Merton, R.C., (1974). “On the pricing of corporate debt: The risk structure of interest rates”, Journal of Finance, 29, pages 449-470.[27] Schonbucher J. and D. Schubert (2001), “Copula-dependent default risk in intensity models”, working paper, Department of Statistics, Bonn University.[28] Sklar, A. (1959). “Fonctions de repartition an dimensions et leurs marges”, Publication of the Institute of Statistics of the University of Paris, 8, pages 229-231.[29] Vasicek, O. A. (1997). “The loan loss distribution”, Working Paper, KMV. 描述 碩士
國立政治大學
金融學系
105352031資料來源 http://thesis.lib.nccu.edu.tw/record/#G0105352031 資料類型 thesis dc.contributor.advisor 林士貴<br>蔡政憲 zh_TW dc.contributor.author (Authors) 吳佳璇 zh_TW dc.creator (作者) 吳佳璇 zh_TW dc.date (日期) 2018 en_US dc.date.accessioned 3-Jul-2018 17:26:46 (UTC+8) - dc.date.available 3-Jul-2018 17:26:46 (UTC+8) - dc.date.issued (上傳時間) 3-Jul-2018 17:26:46 (UTC+8) - dc.identifier (Other Identifiers) G0105352031 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/118241 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 金融學系 zh_TW dc.description (描述) 105352031 zh_TW dc.description.abstract (摘要) 擔保債權憑證在1996後蓬勃發展，但卻在2008年成為全球金融風暴的問題之一，在沉寂幾年後近幾年來德意志銀行（Deutsche Bank）、高盛集團（Goldman Sachs）、摩根大通（JP Morgan）、法國興業銀行（Societe Generale）及花旗銀行（Citi Bank）等都曾嘗試復推擔保債權憑證商品，顯示這類商品其對於買賣方皆是吸引人的。隨著擔保債權憑證發行量的攀升，此商品的評價更顯其重要，本文使用因子關聯函數模型（Factor Copula），其優點為計算快速，並加入市場因子來當客觀標準，避免關聯函數法應用在不同市場資產情況下的不合理假設。實證部分以Venture在2016年發行的擔保貸款憑證為例，進行評價求得分券之公平溢酬，並針對評價過程提出可改善的地方。 zh_TW dc.description.abstract (摘要) The Collateralized Debt Obligation（CDO） boomed after 1996, but it became one of the problems of the global financial crisis in 2008. After a few years of silence, Deutsche Bank, Goldman Sachs, JPMorgan, France Societe Generale and Citi Bank have tried to reintroduce CDO, showing that CDO are attractive to buyers and sellers. With the increase in the issuance of CDO, the pricing of this commodity is even more important. This paper uses Factor Copula model, which has the advantages of fast calculation, adds market factors as objective criteria, and avoid the unreasonable assumption that the correlation function method is applied to different market assets. The empirical part uses the CLO issued by Venture in 2016 as an example to evaluate the fair premium of the tranche, and propose improvements to the pricing process. en_US dc.description.tableofcontents 第一章 緒論……………………………………………………………………………………………11.1 研究背景…………………………………………………………………………………………11.2 研究動機與目的……………………………………………………………………………2第二章 文獻回顧……………………………………………………………………………………42.1 信用風險評價模型………………………………………………………………………42.1.1 結構式模型………………………………………………………………………………42.1.2 縮減式模型………………………………………………………………………………52.2 擔保債權憑證評價模型……………………………………………………………6第三章 擔保債權憑證評價………………………………………………………………83.1 擔保債權憑證………………………………………………………………………………83.1.1 擔保債權憑證之架構……………………………………………………………93.1.2 擔保債權憑證之類型……………………………………………………………93.2違約強度模型…………………………………………………………………………………113.3 因子高斯關聯函數（Factor Gaussian Copula）…123.3.1 關聯函數……………………………………………………………………………………123.3.2 因子高斯關聯函數……………………………………………………………………133.3.3 擔保債權憑證評價……………………………………………………………………163.4 評價流程……………………………………………………………………………………………18第四章 利率模型與參數估計……………………………………………………………194.1 Hull and White 利率模型…………………………………………194.2參數估計……………………………………………………………………………………214.3評價流程示範…………………………………………………………………………21第五章 實證評價結果……………………………………………………………………………245.1 實證商品……………………………………………………………………………………………245.2 資料來源與限制………………………………………………………………………………265.3評價結果…………………………………………………………………………………………………27第六章 結論…………………………………………………………………………………………………28參考文獻…………………………………………………………………………………………………………30中文文獻…………………………………………………………………………………………………………30英文文獻…………………………………………………………………………………………………………30 zh_TW dc.format.extent 1106449 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0105352031 en_US dc.subject (關鍵詞) 關聯函數 zh_TW dc.subject (關鍵詞) 因子關聯函數 zh_TW dc.subject (關鍵詞) 單因子模型 zh_TW dc.subject (關鍵詞) 擔保債權憑證 zh_TW dc.subject (關鍵詞) 擔保貸款憑證 zh_TW dc.subject (關鍵詞) Copula en_US dc.subject (關鍵詞) One factor model en_US dc.subject (關鍵詞) Factor copula en_US dc.subject (關鍵詞) CDO en_US dc.subject (關鍵詞) CLO en_US dc.title (題名) 擔保貸款憑證之評價：使用Factor Copula方法 zh_TW dc.title (題名) Using Factor Copula Method to Price Collateralized Loan Obligation en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] 林彥儒(2015)。Copula模型在信用連結債券的評價與實證分析。未出版之博（碩士）論文，國立政治大學，金融學系研究所，台北市。[2] 段登宇(2008)。擔保債權憑證CDO之訂價與分析－單因子模型及機率水桶法之應用未出版之博（碩士）論文，世新大學，財務金融學系，台北市。[3] 廖四郎、李福慶，(2005)。擔保債權憑證之評價－Copula分析法。[4] 蔡宗翰，(2006)。抵押債權憑證之評價：Factor Copula 與JLT模型之應用。未出版之博（碩士）論文，國立清華大學，統計學研究所，新竹市。[5] 戴嘉雄，(2006)。擔保債權憑證之信用價差評價－ Copula分析法。未出版之博（碩士）論文，國立中山大學，財務管理學系碩士在職專班，高雄市。[1] Andersen, L. and J. Sidenius(2004). “Extensions to the Gaussian copula：random recovery and random factor loadings”, working paper, Bank of America.[2] Anson M. J. P., F. J. Fabozzi, M. Choudhry and R. R. Chen(2004). “Credit derivatives-instruments, applications, and pricing”, John Wiley &Sons, Incorporated.[3] Belkin, B., S. Suchover, and L. Forest (1998). “A one-parameter representation of credit risk and transition matrices”, Credit Metrics Monitor, 1(3), pages 46-56.[4] Black, F. and Cox, J. C. (1976). “Valuing corporate securities:some effects of bond indenture provisions”, Journal of Finance ,31, pages 351-367.[5] Carey M.(1998). “Creditrisk in private debt portfolios,” Journal of Finance, 53, pages1363-1387.[6] Cifuentes, A.,and Connor G.O. (1996) . “The Binomial expansion method applied to CBO/CLO analysis”, Moody`s Investors Service Special Report.[7] Cifuentes, A.,and Connor G.O. (1998) . “The double Binomial method and it’s application to a special case of CBO structures”, Moody`s Investors Service Special Report.[8] Das, S., Fong, G. and Geng, G. (2001). “The impact of correlated default risk on credit portfolios,＂Journal of Fixed Income, 11, pages 9-19.[9] Duffie, D. and K. Singleton (1999). “Modeling term structure of defaultable bonds”, Review of Financial Studies, 12, pages 687-720.[10] Darrell, D. and Gârleanu N., (2001). “Risk and valuation of collateralized debt obligations”, Financial Analysts Journal, pages 41-59.[11] Galiani, S.S. (2003). “Copula functions and their application in pricing and risk managing multiname credit derivative product”, working paper.[12] Gregory, J. and J. P. Laurent (2004). “In the core of correlation”, Risk, pages 87-91.[13] Gibson, M. (2004). ”Understanding the risk of synthetic CDOs”, Finance and Economics Discussion Series, 36, Federal Reserve Board.[14] Giesecke, K.(2001). “Structural modeling of correlate defaults with incomplete information,＂working paper, Humboldt University.[15] Goodman, L.S. (2002). “Synthetic CDOs: an introduction”, The Journal of Derivatives, 9(3), pages 60-72.[16] Gupton, G.M., C.C. Finger, and M. Bhatia (1997). “CreditMetrics-technical document”, Morgan Guaranty Trust Company.[17] Hull, J. and A. White (2004). “Valuation of a CDO and an n-th to default CDS without Monte Carlo simulation”, The Journal of Derivatives, 12(2), pages 8-48.[18] Introduction of Credit Derivatives, Chuang, M.C. , Retrieved June 06 2018, from https://drive.google.com/file/d/0B1qrE20J1V2YdGw4V05IeThURmM/view[19] Jarrow, R., D. Lando, and S. Turnbull (1997). “A Markov model for the term structure of credit spread”, The Review of Financial Studies, 10, pages 481-523.[20] Jarrow, R., and S. Turnbull (1995). “Pricing Derivatives on Financial Subject to Credit Risk”, Journal of Finance, 50, pages 53-68.[21] Jarrow, R., and F. Yu (2001). “Counterparty risk and pricing of defaultable securities”, Journal of Finance, 56, pages 1765-1799.[22] Lando, D. (1998). “On Cox processes and credit risky securities,” Review of Derivatives Research, Vol.2, pages 99-120.[23] Laurent, J-P. and J. Gregory (2003). “Basket default swaps, CDO’s and factor copulas”, Working Paper, ISFA Actuarial School, University of Lyon.[24] Li, D.X. (2000). “On Default Correlation: A Copula approach”, Journal of Fixed Income, 9, pages 43-54.[25] Li, D.X. (2002). “Valuing synthetic CDO tranches using copula function approach”, The RiskMetrics Group working paper.[26] Merton, R.C., (1974). “On the pricing of corporate debt: The risk structure of interest rates”, Journal of Finance, 29, pages 449-470.[27] Schonbucher J. and D. Schubert (2001), “Copula-dependent default risk in intensity models”, working paper, Department of Statistics, Bonn University.[28] Sklar, A. (1959). “Fonctions de repartition an dimensions et leurs marges”, Publication of the Institute of Statistics of the University of Paris, 8, pages 229-231.[29] Vasicek, O. A. (1997). “The loan loss distribution”, Working Paper, KMV. zh_TW dc.identifier.doi (DOI) 10.6814/THE.NCCU.MB.006.2018.F06 -
