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.

中文翻译：

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问 BERT：转型和物理气候风险的监管披露如何影响 CDS 期限结构

我们使用基于人工智能的语言理解算法 BERT 来量化监管气候风险披露并分析其对信用违约互换 (CDS) 市场中期限结构的影响。风险披露可以增加或减少 CDS 利差，这取决于披露是否揭示了新风险或减少了不确定性。训练 BERT 以区分转型和物理气候风险，我们发现在 2015 年巴黎气候协议之后，披露转型风险会增加 CDS 利差，而披露物理风险会降低利差。此外，我们还发现，特朗普的选举对暴露于过渡风险的公司的CDS差异产生了负面影响。这些影响与理论预测一致，具有经济和统计意义。