| dc.contributor | 風管系 | zh_TW |
| dc.creator (作者) | 謝明華 | zh_TW |
| dc.creator (作者) | Hsieh, Ming-Hua | en_US |
| dc.creator (作者) | Lee, Yi-Hsi;Shyu, So-De;Chiu, Yu-Fen | zh_TW |
| dc.date (日期) | 2018 | |
| dc.date.accessioned | 19-Dec-2018 16:36:05 (UTC+8) | - |
| dc.date.available | 19-Dec-2018 16:36:05 (UTC+8) | - |
| dc.date.issued (上傳時間) | 19-Dec-2018 16:36:05 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/121447 | - |
| dc.description.abstract (摘要) | The importance of credit markets in China and Asia Pacific has been increased significantly in the past decade and international regulation demands high standard in credit risk quantification for financial institutions. Computation for credit risk measures is a challenge problem. Hence this study aims to develop a fast Monte Carlo approach of estimating portfolio credit risk. The method could be applied to estimate the probability of large losses and the expected excess loss above a large threshold of a credit portfolio, which has a dependence structure driven by general factor copula models. Except for the assumption that a global common factor driving the default events of all defaultable obligors exists, the study does not impose any restrictions on the composition of the portfolio (e.g., stochastic recovery rates). Hence, this method can therefore be applied to a wide range of credit risk models. Numerical results demonstrate that the proposed method is efficient under general market conditions. In the high market impact condition, in credit contagion or market collapse environments, the proposed method is even more efficient. | en_US |
| dc.format.extent | 807832 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (關聯) | Pacific-Basin Finance Journal | |
| dc.subject (關鍵詞) | Portfolio credit risk; Monte Carlo simulation; Variance reduction; Importance sampling; Factor copula models | en_US |
| dc.title (題名) | Estimating multifactor portfolio credit risk: A variance reduction approach | en_US |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.1016/j.pacfin.2018.08.001 | |
| dc.doi.uri (DOI) | https://doi.org/10.1016/j.pacfin.2018.08.001 | |
