{"title":"信用计量方法和风险信用价值(Credit VaR)","authors":"Y. Malhotra","doi":"10.2139/ssrn.3783490","DOIUrl":null,"url":null,"abstract":"Described by Hull (2011, 2012) as ‘a procedure for calculating credit value at risk’, CreditMetrics methodology (RiskMetrics Group 2007) is used for assessing portfolio risk due to changes in bond or debt value caused by credit quality changes including credit migration (upgrades and downgrades), as well as, default. It measures the uncertainty in forward value of the bond portfolio at the risk horizon caused by such credit events. Changes in debt value could be small in case of credit quality ratings change; however, they could be enormous, 50% to 90%, in case of default. Characterized by a long downside tail, credit-returns are highly-skewed and fat-tailed and thus far from the Gaussian normal distribution assumptions about market risk in VaR (Fig. 1). In the portfolio context, based upon correlation of credit quality moves across obligors, CreditMetrics assesses both value-at-risk (VaR), i.e., the volatility of value, as well as expected losses (EL). By distinguishing high quality well-diversified portfolios from low-quality concentrated portfolios, it offers better understanding of credit risk in terms of diversification benefits and concentration risk compared to mandated standard capital adequacy measures.","PeriodicalId":284021,"journal":{"name":"International Political Economy: Investment & Finance eJournal","volume":"59 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"CreditMetrics Methodology and Credit Value at Risk (Credit VaR)\",\"authors\":\"Y. Malhotra\",\"doi\":\"10.2139/ssrn.3783490\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Described by Hull (2011, 2012) as ‘a procedure for calculating credit value at risk’, CreditMetrics methodology (RiskMetrics Group 2007) is used for assessing portfolio risk due to changes in bond or debt value caused by credit quality changes including credit migration (upgrades and downgrades), as well as, default. It measures the uncertainty in forward value of the bond portfolio at the risk horizon caused by such credit events. Changes in debt value could be small in case of credit quality ratings change; however, they could be enormous, 50% to 90%, in case of default. Characterized by a long downside tail, credit-returns are highly-skewed and fat-tailed and thus far from the Gaussian normal distribution assumptions about market risk in VaR (Fig. 1). In the portfolio context, based upon correlation of credit quality moves across obligors, CreditMetrics assesses both value-at-risk (VaR), i.e., the volatility of value, as well as expected losses (EL). By distinguishing high quality well-diversified portfolios from low-quality concentrated portfolios, it offers better understanding of credit risk in terms of diversification benefits and concentration risk compared to mandated standard capital adequacy measures.\",\"PeriodicalId\":284021,\"journal\":{\"name\":\"International Political Economy: Investment & Finance eJournal\",\"volume\":\"59 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-02-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Political Economy: Investment & Finance eJournal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.3783490\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Political Economy: Investment & Finance eJournal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3783490","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
赫尔(2011年、2012年)将CreditMetrics方法描述为“计算风险中的信用价值的程序”,该方法(RiskMetrics Group 2007年)用于评估因信用质量变化(包括信用迁移(升级和降级)以及违约)引起的债券或债务价值变化而导致的投资组合风险。它衡量由此类信用事件引起的风险水平上债券投资组合远期价值的不确定性。如果信用质量评级发生变化,债务价值的变化可能很小;然而,在违约的情况下,它们可能是巨大的,50%到90%。以长下行尾部为特征,信贷回报是高度倾斜和厚尾的,因此与VaR中市场风险的高斯正态分布假设相距甚远(图1)。在投资组合背景下,基于债务人之间信贷质量移动的相关性,CreditMetrics评估风险价值(VaR),即价值的波动性,以及预期损失(EL)。通过区分高质量的分散投资组合和低质量的集中投资组合,与强制性标准资本充足率指标相比,它可以更好地理解分散收益和集中风险方面的信用风险。
CreditMetrics Methodology and Credit Value at Risk (Credit VaR)
Described by Hull (2011, 2012) as ‘a procedure for calculating credit value at risk’, CreditMetrics methodology (RiskMetrics Group 2007) is used for assessing portfolio risk due to changes in bond or debt value caused by credit quality changes including credit migration (upgrades and downgrades), as well as, default. It measures the uncertainty in forward value of the bond portfolio at the risk horizon caused by such credit events. Changes in debt value could be small in case of credit quality ratings change; however, they could be enormous, 50% to 90%, in case of default. Characterized by a long downside tail, credit-returns are highly-skewed and fat-tailed and thus far from the Gaussian normal distribution assumptions about market risk in VaR (Fig. 1). In the portfolio context, based upon correlation of credit quality moves across obligors, CreditMetrics assesses both value-at-risk (VaR), i.e., the volatility of value, as well as expected losses (EL). By distinguishing high quality well-diversified portfolios from low-quality concentrated portfolios, it offers better understanding of credit risk in terms of diversification benefits and concentration risk compared to mandated standard capital adequacy measures.