Yin Jie, Han Bingyan, Wong Hoi Ying
Department of Statistics, The Chinese University of Hong Kong, Hong Kong.
Division of Science and Technology, BNU-HKBU United International College, Zhuhai, Guangdong, China.
Insur Math Econ. 2022 May;104:15-34. doi: 10.1016/j.insmatheco.2022.01.008. Epub 2022 Feb 7.
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.
新冠疫情对信用风险产生了重大影响，而信用风险是保险公司等公司债券持有者的关键关注点。由机构信用评级和信用违约互换（CDS）量化的信用风险，由于潜在的信用评级持续性，通常表现出长期依赖性（LRD）。通过重标极差分析和一个包含灵活范围的赫斯特参数的新型仿射远期强度模型，我们对穆迪评级数据和CDS价格的研究表明，在新冠疫情期间，违约强度已从长期依赖状态转变为短期依赖状态，这意味着在疫情期间，历史信用表现对于信用预测的相关性大大降低。这种现象与以往的金融相关危机形成鲜明对比。具体而言，2008年次贷危机和欧元区危机在主权CDS中都没有经历如此大幅的长期依赖性水平下降。我们的工作还为保险公司投资的信用风险预测中历史序列的使用提供了启示。
