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COVID-19 and credit risk: A long memory perspective.
Yin, Jie; Han, Bingyan; Wong, Hoi Ying.
  • Yin J; Department of Statistics, The Chinese University of Hong Kong, Hong Kong.
  • Han B; Division of Science and Technology, BNU-HKBU United International College, Zhuhai, Guangdong, China.
  • Wong HY; Department of Statistics, The Chinese University of Hong Kong, Hong Kong.
Insur Math Econ ; 104: 15-34, 2022 May.
Article in English | MEDLINE | ID: covidwho-1670602
ABSTRACT
The COVID-19 pandemic shows significant impacts on credit risk, which is the key concern of corporate bond holders such as insurance companies. Credit risk, quantified by agency credit ratings and credit default swaps (CDS), usually exhibits long-range dependence (LRD) due to potential credit rating persistence. With rescaled range analysis and a novel affine forward intensity model embracing a flexible range of Hurst parameters, our studies on Moody's rating data and CDS prices reveal that default intensities have shifted from the long-range to the short-range dependence regime during the COVID-19 period, implying that the historical credit performance becomes much less relevant for credit prediction during the pandemic. This phenomenon contrasts sharply with previous financial-related crises. Specifically, both the 2008 subprime mortgage and the Eurozone crises did not experience such a great decline in the level of LRD in sovereign CDS. Our work also sheds light on the use of historical series in credit risk prediction for insurers' investment.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Topics: Long Covid Language: English Journal: Insur Math Econ Year: 2022 Document Type: Article Affiliation country: J.insmatheco.2022.01.008

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Topics: Long Covid Language: English Journal: Insur Math Econ Year: 2022 Document Type: Article Affiliation country: J.insmatheco.2022.01.008