| dc.contributor | 風管系 | |
| dc.creator (作者) | 謝明華 | zh_TW |
| dc.creator (作者) | CHUNG, Ming-Tao | en_US |
| dc.creator (作者) | HSIEH, Ming-Hua | en_US |
| dc.creator (作者) | CHI, Yan-Ping | en_US |
| dc.date (日期) | 2017-09 | |
| dc.date.accessioned | 23-Feb-2018 16:19:57 (UTC+8) | - |
| dc.date.available | 23-Feb-2018 16:19:57 (UTC+8) | - |
| dc.date.issued (上傳時間) | 23-Feb-2018 16:19:57 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/115984 | - |
| dc.description.abstract (摘要) | Quantification of operational risk has led to significant concern regarding regulation in the financial industry. Basel Accord II and III for banks and Solvency II for insurers require insurance companies and banks to allocate capital for operation risk. Because the risk measure used for Basel regulatory capital purposes reflects a confidence level of 99.9% during one year and the loss distribution of operational risk has high skewness and kurtosis, it is almost infeasible to get an accurate estimate of such a risk measure if a crude Monte Carlo approach is used. Therefore, we develop a novel importance sampling method for estimating such a risk measure. Numerical results demonstrate that the proposed method is very efficient and robust. The main contribution of this method is to provide a feasible and flexible numerical approach that delivers highly accurate estimates of operational risk with a high confidence level and meets the high international regulatory standard for quantification of operational risk. | en_US |
| dc.format.extent | 394354 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (關聯) | Romanian Journal of Economic Forecasting, Vol.20, No.3, pp.78-89 | |
| dc.subject (關鍵詞) | operational risk; advanced measurement approaches; loss distribution approach; Monte Carlo simulation; variance reduction | en_US |
| dc.title (題名) | Computation of Operational Risk for Financial Institutions. | en_US |
| dc.type (資料類型) | article | |
