| dc.contributor.advisor | 劉惠美 | zh_TW |
| dc.contributor.author (Authors) | 謝旻娟 | zh_TW |
| dc.contributor.author (Authors) | Hsieh, Min Jyuan | en_US |
| dc.creator (作者) | 謝旻娟 | zh_TW |
| dc.creator (作者) | Hsieh, Min Jyuan | en_US |
| dc.date (日期) | 2009 | en_US |
| dc.date.accessioned | 9-May-2016 15:11:31 (UTC+8) | - |
| dc.date.available | 9-May-2016 15:11:31 (UTC+8) | - |
| dc.date.issued (上傳時間) | 9-May-2016 15:11:31 (UTC+8) | - |
| dc.date.issued (上傳時間) | 9-May-2016 15:11:31 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0096354021 | en_US |
| dc.identifier (Other Identifiers) | G0096354021 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/95121 | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/95121 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 統計學系 | zh_TW |
| dc.description (描述) | 統計學系 | zh_TW |
| dc.description (描述) | 96354021 | zh_TW |
| dc.description (描述) | 96354021 | zh_TW |
| dc.description.abstract (摘要) | 相較於單一信用違約交換只能對單一信用標的進行信用保護，一籃子信用違約交換則能對一籃子的信用標的進行信用保護。此種產品的評價決定於一籃子信用標的實體的聯合機率分配，因此多個標的資產間違約相關性的衡量，對於一籃子信用違約交換的評價和風險管理是相當重要的課題。 在一個資產池中，有時可以將其切割成兩個以上的群體，各群體間彼此相互獨立，而在各群內彼此相依。我們將其視為在多因子模型下的特例，此模型提供我們更具彈性的方式去建立資產之間彼此的相關性。 在這篇文章中，我們主要以 Chiang, Yueh, and Hsieh (2007) 在單因子模型下所提出來的方法為基礎，將其延伸至多因子的模型下的特例。藉由選擇一個合適的(IS)分配，在每一次的模擬中必定會有k個違約事件發生；因此我們獲得一個有效率的方法對一籃子違約交換進行評價，此演算法不僅簡單並且其變異數較蒙地卡羅小。 | zh_TW |
| dc.description.abstract (摘要) | 相較於單一信用違約交換只能對單一信用標的進行信用保護，一籃子信用違約交換則能對一籃子的信用標的進行信用保護。此種產品的評價決定於一籃子信用標的實體的聯合機率分配，因此多個標的資產間違約相關性的衡量，對於一籃子信用違約交換的評價和風險管理是相當重要的課題。 在一個資產池中，有時可以將其切割成兩個以上的群體，各群體間彼此相互獨立，而在各群內彼此相依。我們將其視為在多因子模型下的特例，此模型提供我們更具彈性的方式去建立資產之間彼此的相關性。 在這篇文章中，我們主要以 Chiang, Yueh, and Hsieh (2007) 在單因子模型下所提出來的方法為基礎，將其延伸至多因子的模型下的特例。藉由選擇一個合適的(IS)分配，在每一次的模擬中必定會有k個違約事件發生；因此我們獲得一個有效率的方法對一籃子違約交換進行評價，此演算法不僅簡單並且其變異數較蒙地卡羅小。 | zh_TW |
| dc.description.abstract (摘要) | In contrast to a single name credit default swaps which provides credit protection for a single underlying, a basket credit default swap extends the credit protection to portfolio of obligors with the restriction that the default of only one underlying is compensated. The price of the products depends on the joint default probability of the underlying in the credit portfolio. Thus, the modeling of default correlation, default risk and expected loss is a key issue for the valuation and risk management of basket default swaps. Sometimes a pool of underlying obligors can have two or more separate groups, between those they are unrelated, but in each part they are related. The special cases provide more flexible way to construct the correlation between two or more underlying obligors. In this paper, our approach is based on the construction of importance sampling (IS) method proposed by Chiang, Yueh and Hsieh (2007) under one-factor model, and then we extend the model to a special case under the multi-factor model. By the appropriate choice of the importance sampling distribution, we establish a way of ensuring that for every path generated, k default events always take place. Then we can obtain an efficiency algorithm for basket default swap valuation. The algorithm is simple to implement and it also guarantees variance reduction. | en_US |
| dc.description.abstract (摘要) | In contrast to a single name credit default swaps which provides credit protection for a single underlying, a basket credit default swap extends the credit protection to portfolio of obligors with the restriction that the default of only one underlying is compensated. The price of the products depends on the joint default probability of the underlying in the credit portfolio. Thus, the modeling of default correlation, default risk and expected loss is a key issue for the valuation and risk management of basket default swaps. Sometimes a pool of underlying obligors can have two or more separate groups, between those they are unrelated, but in each part they are related. The special cases provide more flexible way to construct the correlation between two or more underlying obligors. In this paper, our approach is based on the construction of importance sampling (IS) method proposed by Chiang, Yueh and Hsieh (2007) under one-factor model, and then we extend the model to a special case under the multi-factor model. By the appropriate choice of the importance sampling distribution, we establish a way of ensuring that for every path generated, k default events always take place. Then we can obtain an efficiency algorithm for basket default swap valuation. The algorithm is simple to implement and it also guarantees variance reduction. | en_US |
| dc.description.tableofcontents | 1 Introduction..........................................2 2 Literature Review.....................................5 2.1 Basket Default Swap.............................5 2.2 Portfolio Credit Risk...........................7 3 Joint-Default Time Model..............................9 3.1 Copula Model....................................9 3.2 Joint-Default Time Model.......................10 4 Valuation of k-th to Default BDSs....................12 4.1 BDSs Valuation.................................12 4.2 Valuation of BDS under One-Factor Model........13 5 The Proposed Method..................................17 5.1 Special Cases under the Two-Factor Model.......17 5.2 The Proposed Method I..........................23 5.3 The Proposed Method II.........................25 6 Numerical Results....................................30 7 Conclusion...........................................39 References.............................................40 | zh_TW |
| dc.description.tableofcontents | 1 Introduction..........................................2 2 Literature Review.....................................5 2.1 Basket Default Swap.............................5 2.2 Portfolio Credit Risk...........................7 3 Joint-Default Time Model..............................9 3.1 Copula Model....................................9 3.2 Joint-Default Time Model.......................10 4 Valuation of k-th to Default BDSs....................12 4.1 BDSs Valuation.................................12 4.2 Valuation of BDS under One-Factor Model........13 5 The Proposed Method..................................17 5.1 Special Cases under the Two-Factor Model.......17 5.2 The Proposed Method I..........................23 5.3 The Proposed Method II.........................25 6 Numerical Results....................................30 7 Conclusion...........................................39 References.............................................40 | zh_TW |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0096354021 | en_US |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0096354021 | en_US |
| dc.subject (關鍵詞) | 一籃子信用違約交換 | zh_TW |
| dc.subject (關鍵詞) | 一籃子信用違約交換 | zh_TW |
| dc.subject (關鍵詞) | 關聯結構 | zh_TW |
| dc.subject (關鍵詞) | 關聯結構 | zh_TW |
| dc.subject (關鍵詞) | 變異數縮減 | zh_TW |
| dc.subject (關鍵詞) | 變異數縮減 | zh_TW |
| dc.subject (關鍵詞) | Basket Default Swap | en_US |
| dc.subject (關鍵詞) | Basket Default Swap | en_US |
| dc.subject (關鍵詞) | Copula | en_US |
| dc.subject (關鍵詞) | Copula | en_US |
| dc.subject (關鍵詞) | Variance Reduction | en_US |
| dc.subject (關鍵詞) | Variance Reduction | en_US |
| dc.title (題名) | 以有效率的方法進行一籃子違約交換之評價 | zh_TW |
| dc.title (題名) | 以有效率的方法進行一籃子違約交換之評價 | zh_TW |
| dc.title (題名) | Efficient algorithms for basket default swap valuation | en_US |
| dc.title (題名) | Efficient algorithms for basket default swap valuation | en_US |
| dc.type (資料類型) | thesis | en_US |
| dc.type (資料類型) | thesis | en_US |
| dc.relation.reference (參考文獻) | Chiang, M.H. and Yueh, M.L. and Hsieh, M.H. (2007). "An Efficient Algorithm for Basket Default Swap Valuation. "Journal of Derivatives, pp. 8-19. Glasserman, P. (2004). Monye Carlo Methods in Financial Engineering. New York: Springer Verlag. Glynn, P.W. and Iglehart, D.L. (1989). "Importance Sampling for Stochastic Simulations. "Mangement Science, 35, pp. 1367-1392. Hull, J. and White, A. (2000). "Valuing Credit Default Swaps I: No Counterparty Default Risk. "Journal of Derivatives, Vol. 8, No.1 , pp. 29-40. -----------------------(2001). "Valuing Credit Default Swaps II: Modeling Default Correlations. "Journal of Derivatives, Vol. 8, No.3 , pp. 12-22. Laurent, J.P. and Gregory, J. (2005). "Basket Default Swaps, CDOs and Factor Copulas." Journal of Risk, 7, pp. 103-122. Li, D.X. (1998). "Constructing a credit curve." Risk, pp. 40-44. ---------(2000). "On Default Correlation: A Copula Function Approach." Journal of Fixed Income, 9, pp. 43-54. Zhou, C. (2001). "An Analysis of Default Correlations and Multiple Defaults." Review of Financial Studies, Vol. 14, No.2 , pp. 555-576. | zh_TW |
| dc.relation.reference (參考文獻) | Chiang, M.H. and Yueh, M.L. and Hsieh, M.H. (2007). "An Efficient Algorithm for Basket Default Swap Valuation. "Journal of Derivatives, pp. 8-19. Glasserman, P. (2004). Monye Carlo Methods in Financial Engineering. New York: Springer Verlag. Glynn, P.W. and Iglehart, D.L. (1989). "Importance Sampling for Stochastic Simulations. "Mangement Science, 35, pp. 1367-1392. Hull, J. and White, A. (2000). "Valuing Credit Default Swaps I: No Counterparty Default Risk. "Journal of Derivatives, Vol. 8, No.1 , pp. 29-40. -----------------------(2001). "Valuing Credit Default Swaps II: Modeling Default Correlations. "Journal of Derivatives, Vol. 8, No.3 , pp. 12-22. Laurent, J.P. and Gregory, J. (2005). "Basket Default Swaps, CDOs and Factor Copulas." Journal of Risk, 7, pp. 103-122. Li, D.X. (1998). "Constructing a credit curve." Risk, pp. 40-44. ---------(2000). "On Default Correlation: A Copula Function Approach." Journal of Fixed Income, 9, pp. 43-54. Zhou, C. (2001). "An Analysis of Default Correlations and Multiple Defaults." Review of Financial Studies, Vol. 14, No.2 , pp. 555-576. | zh_TW |
