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題名 Estimating multifactor portfolio credit risk: A variance reduction approach
作者 謝明華
Hsieh, Ming-Hua
Lee, Yi-Hsi;Shyu, So-De;Chiu, Yu-Fen
貢獻者 風管系
關鍵詞 Portfolio credit risk; Monte Carlo simulation; Variance reduction; Importance sampling; Factor copula models
日期 2018
上傳時間 19-十二月-2018 16:36:05 (UTC+8)
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.
關聯 Pacific-Basin Finance Journal
資料類型 article
DOI https://doi.org/10.1016/j.pacfin.2018.08.001
dc.contributor 風管系zh_TW
dc.creator (作者) 謝明華zh_TW
dc.creator (作者) Hsieh, Ming-Huaen_US
dc.creator (作者) Lee, Yi-Hsi;Shyu, So-De;Chiu, Yu-Fenzh_TW
dc.date (日期) 2018
dc.date.accessioned 19-十二月-2018 16:36:05 (UTC+8)-
dc.date.available 19-十二月-2018 16:36:05 (UTC+8)-
dc.date.issued (上傳時間) 19-十二月-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 modelsen_US
dc.title (題名) Estimating multifactor portfolio credit risk: A variance reduction approachen_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