| dc.contributor | 政大統計系 | en_US |
| dc.creator (作者) | 施明儒;劉惠美;林永忠 | zh_TW |
| dc.date (日期) | 2011-10 | en_US |
| dc.date.accessioned | 12-Dec-2013 18:08:32 (UTC+8) | - |
| dc.date.available | 12-Dec-2013 18:08:32 (UTC+8) | - |
| dc.date.issued (上傳時間) | 12-Dec-2013 18:08:32 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/62444 | - |
| dc.description.abstract (摘要) | 蒙地卡羅模擬是在組合信用風險的管理上相當實用的計算工具。衡量組合信用風險時，必須以適當的模型描述資產間的相依性。常態關聯結構是目前最廣為使用的模型，但實證研究認為t關聯結構更適合用於配適金融市場的資料。在本文中，我們採用 Bassamboo et al. (2008) 提出的極值相依模型建立t關聯結構用以捕捉資產之間的相關性。同時，為增進蒙地卡羅法之收斂速度，我們以 Chiang et al. (2007) 的重要性取樣法為基礎，將其拓展到極值相依模型下，提出用以估計組合信用風險的演算法，並且以數值結果呈現演算法的估計效率。數值結果顯示所提出的演算法有著相當優異的估計效率，可有效地縮短評價組合信用風險的時間。 | en_US |
| dc.description.abstract (摘要) | Monte Carlo simulation is a useful tool on portfolio credit risk management. When measuring portfolio credit risk, one should choose an appropriate model to characterize the dependence among all assets. Normal copula is the most widely used mechanism to capture this dependence structure, however, some emperical studies suggest that t-copula provides a better fit to market data than normal copula does. In this article, we use extremal depence model proposed by Bassamboo et al. (2008) to construct t-copula. We also extend the importance sampling (IS) procedure proposed by Chiang et al. (2007) and propose an algorithm to evaluate portfolio credit risk with extremal dependence. We use several numerical example to show case the efficiency of the proposed algorithm. Numerical results show that the proposed algorithm has an outstanding efficiency. | en_US |
| dc.format.extent | 3132471 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.language.iso | en_US | - |
| dc.relation (關聯) | 主計季刊, 52(3), 29-39 | en_US |
| dc.subject (關鍵詞) | 蒙地卡羅法;組合信用風險;t關聯結構;極值相依;重要性取樣;變異數縮減 | en_US |
| dc.subject (關鍵詞) | Monte Carlo method;Portfolio credit risk;t-copula;Extremal dependence;Importance sampling;Variance reduction | en_US |
| dc.title (題名) | 應用變異數縮減技巧估計極值相依下之組合信用風險 | zh_TW |
| dc.type (資料類型) | article | en |
