{"title":"2019冠状病毒病、信贷风险和宏观基本面","authors":"A. Dubinova, A. Lucas, Sean Telg","doi":"10.2139/ssrn.3875628","DOIUrl":null,"url":null,"abstract":"We investigate the relationship between macro fundamentals and credit risk, rating migrations and defaults during the start of the COVID-19 pandemic. We find that credit risk models that use macro fundamentals as covariates overestimate credit risk incidence due to the unprecedented drops in economic activity in the first lockdowns. We argue that this break in the macro-credit linkage is less affected if we take an unobserved components modeling framework, both at shorter and longer credit risk horizons.<br>","PeriodicalId":414741,"journal":{"name":"Econometric Modeling: Financial Markets Regulation eJournal","volume":"71 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"COVID-19, Credit Risk and Macro Fundamentals\",\"authors\":\"A. Dubinova, A. Lucas, Sean Telg\",\"doi\":\"10.2139/ssrn.3875628\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We investigate the relationship between macro fundamentals and credit risk, rating migrations and defaults during the start of the COVID-19 pandemic. We find that credit risk models that use macro fundamentals as covariates overestimate credit risk incidence due to the unprecedented drops in economic activity in the first lockdowns. We argue that this break in the macro-credit linkage is less affected if we take an unobserved components modeling framework, both at shorter and longer credit risk horizons.<br>\",\"PeriodicalId\":414741,\"journal\":{\"name\":\"Econometric Modeling: Financial Markets Regulation eJournal\",\"volume\":\"71 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Econometric Modeling: Financial Markets Regulation eJournal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.3875628\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Econometric Modeling: Financial Markets Regulation eJournal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3875628","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We investigate the relationship between macro fundamentals and credit risk, rating migrations and defaults during the start of the COVID-19 pandemic. We find that credit risk models that use macro fundamentals as covariates overestimate credit risk incidence due to the unprecedented drops in economic activity in the first lockdowns. We argue that this break in the macro-credit linkage is less affected if we take an unobserved components modeling framework, both at shorter and longer credit risk horizons.
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