期限(时间)
气候变化
过渡(遗传学)
掉期(金融)
信用违约掉期
要价
业务
经济
精算学
货币经济学
金融经济学
信用风险
财务
生态学
生物化学
化学
物理
量子力学
生物
基因
作者
Julian F Kölbel,Markus Leippold,Jordy Rillaerts,Qian Wang
标识
DOI:10.1093/jjfinec/nbac027
摘要
Abstract We use BERT, an AI-based algorithm for language understanding, to quantify regulatory climate risk disclosures and analyze their impact on the term structure in the credit default swap (CDS) market. Risk disclosures can either increase or decrease CDS spreads, depending on whether the disclosure reveals new risks or reduces uncertainty. Training BERT to differentiate between transition and physical climate risks, we find that disclosing transition risks increases CDS spreads after the Paris Climate Agreement of 2015, while disclosing physical risks decreases the spreads. In addition, we also find that the election of Trump had a negative impact on CDS spreads for firms exposed to transition risk. These impacts are consistent with theoretical predictions and economically and statistically significant.
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