{"title":"如何估计根据IFRS 9计提的预期信贷损失(ECL)","authors":"Mariya Gubareva","doi":"10.1108/JRF-05-2020-0094","DOIUrl":null,"url":null,"abstract":"PurposeThis paper provides an objective approach based on available market information capable of reducing subjectivity, inherently present in the process of expected loss provisioning under the IFRS 9.Design/methodology/approachThis paper develops the two-step methodology. Calibrating the Credit Default Swap (CDS)-implied default probabilities to the through-the-cycle default frequencies provides average weights of default component in the spread for each forward term. Then, the impairment provisions are calculated for a sample of investment grade and high yield obligors by distilling their pure default-risk term-structures from the respective term-structures of spreads. This research demonstrates how to estimate credit impairment allowances compliant with IFRS 9 framework.FindingsThis study finds that for both investment grade and high yield exposures, the weights of default component in the credit spreads always remain inferior to 33%. The research's outcomes contrast with several previous results stating that the default risk premium accounts at least for 40% of CDS spreads. The proposed methodology is applied to calculate IFRS 9 compliant provisions for a sample of investment grade and high yield obligors.Research limitations/implicationsMany issuers are not covered by individual Bloomberg valuation curves. However, the way to overcome this limitation is proposed.Practical implicationsThe proposed approach offers a clue for a better alignment of accounting practices, financial regulation and credit risk management, using expected loss metrics across diverse silos inside organizations. It encourages adopting the proposed methodology, illustrating its application to a set of bond exposures.Originality/valueNo previous research addresses impairment provisioning employing Bloomberg valuation curves. The study fills this gap.","PeriodicalId":46579,"journal":{"name":"Journal of Risk Finance","volume":"22 1","pages":"169-190"},"PeriodicalIF":5.7000,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"How to estimate expected credit losses – ECL – for provisioning under IFRS 9\",\"authors\":\"Mariya Gubareva\",\"doi\":\"10.1108/JRF-05-2020-0094\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"PurposeThis paper provides an objective approach based on available market information capable of reducing subjectivity, inherently present in the process of expected loss provisioning under the IFRS 9.Design/methodology/approachThis paper develops the two-step methodology. Calibrating the Credit Default Swap (CDS)-implied default probabilities to the through-the-cycle default frequencies provides average weights of default component in the spread for each forward term. Then, the impairment provisions are calculated for a sample of investment grade and high yield obligors by distilling their pure default-risk term-structures from the respective term-structures of spreads. This research demonstrates how to estimate credit impairment allowances compliant with IFRS 9 framework.FindingsThis study finds that for both investment grade and high yield exposures, the weights of default component in the credit spreads always remain inferior to 33%. The research's outcomes contrast with several previous results stating that the default risk premium accounts at least for 40% of CDS spreads. The proposed methodology is applied to calculate IFRS 9 compliant provisions for a sample of investment grade and high yield obligors.Research limitations/implicationsMany issuers are not covered by individual Bloomberg valuation curves. However, the way to overcome this limitation is proposed.Practical implicationsThe proposed approach offers a clue for a better alignment of accounting practices, financial regulation and credit risk management, using expected loss metrics across diverse silos inside organizations. It encourages adopting the proposed methodology, illustrating its application to a set of bond exposures.Originality/valueNo previous research addresses impairment provisioning employing Bloomberg valuation curves. The study fills this gap.\",\"PeriodicalId\":46579,\"journal\":{\"name\":\"Journal of Risk Finance\",\"volume\":\"22 1\",\"pages\":\"169-190\"},\"PeriodicalIF\":5.7000,\"publicationDate\":\"2021-06-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Risk Finance\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1108/JRF-05-2020-0094\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BUSINESS, FINANCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Risk Finance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/JRF-05-2020-0094","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
How to estimate expected credit losses – ECL – for provisioning under IFRS 9
PurposeThis paper provides an objective approach based on available market information capable of reducing subjectivity, inherently present in the process of expected loss provisioning under the IFRS 9.Design/methodology/approachThis paper develops the two-step methodology. Calibrating the Credit Default Swap (CDS)-implied default probabilities to the through-the-cycle default frequencies provides average weights of default component in the spread for each forward term. Then, the impairment provisions are calculated for a sample of investment grade and high yield obligors by distilling their pure default-risk term-structures from the respective term-structures of spreads. This research demonstrates how to estimate credit impairment allowances compliant with IFRS 9 framework.FindingsThis study finds that for both investment grade and high yield exposures, the weights of default component in the credit spreads always remain inferior to 33%. The research's outcomes contrast with several previous results stating that the default risk premium accounts at least for 40% of CDS spreads. The proposed methodology is applied to calculate IFRS 9 compliant provisions for a sample of investment grade and high yield obligors.Research limitations/implicationsMany issuers are not covered by individual Bloomberg valuation curves. However, the way to overcome this limitation is proposed.Practical implicationsThe proposed approach offers a clue for a better alignment of accounting practices, financial regulation and credit risk management, using expected loss metrics across diverse silos inside organizations. It encourages adopting the proposed methodology, illustrating its application to a set of bond exposures.Originality/valueNo previous research addresses impairment provisioning employing Bloomberg valuation curves. The study fills this gap.
期刊介绍:
The Journal of Risk Finance provides a rigorous forum for the publication of high quality peer-reviewed theoretical and empirical research articles, by both academic and industry experts, related to financial risks and risk management. Articles, including review articles, empirical and conceptual, which display thoughtful, accurate research and be rigorous in all regards, are most welcome on the following topics: -Securitization; derivatives and structured financial products -Financial risk management -Regulation of risk management -Risk and corporate governance -Liability management -Systemic risk -Cryptocurrency and risk management -Credit arbitrage methods -Corporate social responsibility and risk management -Enterprise risk management -FinTech and risk -Insurtech -Regtech -Blockchain and risk -Climate change and risk