关键词:
KMV模型
气候金融
银行
信用风险
摘要:
在人类社会经济发展至今,环境风险被列为全球经济面临的首要问题之一,而与气候变化相关的金融风险也被认为是系统性金融风险的重要来源之一。由于中国银行业在金融体系中占据着主导地位,气候风险将如何影响银行信用风险,如何管理风险以防该影响溢出到金融系统造成系统性金融风险显得尤为重要。本文基于15家国内A股上市银行2016~2021年的面板数据,用KMV模型中的违约距离评估银行信用风险,根据总部所在城市区域的气候波动数据,构建了固定效应模型,实证分析了气候变化对银行信用风险的影响。研究结果表明:以极端降水为主的气候变化将缩短银行预期违约距离,加大预期违约概率,增加银行所面临的信用风险。基于这一结论,本文给相关部门提出了加强气候风险识别和监管、鼓励发展绿色金融等政策建议,为管理气候变化导致的银行业信用风险提供了启示和参考。Environmental risks have been recognized as one of the most significant challenges facing the global economy in the course of human socio-economic development. Financial risks related to climate change are considered to be one of the most important sources of systemic financial risks. Given the dominant position of the Chinese banking industry in the financial system, it is crucial to understand how climate risks can affect bank credit risk and how to manage these risks to prevent their spill-over into the financial system and the emergence of systemic financial risks. Based on panel data from 15 Chinese A-share listed banks from 2016 to 2021, this article uses KMV model to evaluate bank credit risk by measuring default distance. The result indicates that climate change will shorten the expected default distance, increase the expected default probability, and raise the credit risk faced by banks. Based on these conclusions, this study provides insights and recommendations to relevant authorities, including strengthening the identification and regulation of climate risk and promoting the development of green finance, in order to manage the credit risk in the banking industry resulting from climate change.