近两年中国上市公司由于受到新的冠状物疫情的冲击,经济增速放缓,面临种种危机,信用风险受到很大考验,而规避违约风险对公司未来的发展不仅有好处,而且在制度稳定性方面也将起到明显的作用。因此,我们选择了KMV模型与GARCH模式和SV模式...近两年中国上市公司由于受到新的冠状物疫情的冲击,经济增速放缓,面临种种危机,信用风险受到很大考验,而规避违约风险对公司未来的发展不仅有好处,而且在制度稳定性方面也将起到明显的作用。因此,我们选择了KMV模型与GARCH模式和SV模式相结合,对上市公司的股票收益波动率进行重新拟合和估算,以此来衡量上市公司的资信管理,采用调整后的GARCH-KMV模型和SV-KMV模型,对上市公司中的9家ST企业与9家非ST企业的信用风险进行了对比研究。结果显示,传统的KMV可以更好地衡量上市公司的信用风险,在结合GARCH模型和SV模型后也可以衡量上市公司的信用风险,但SV模型对于信用风险的解释效果要好于GARCH模型。Impacted by the COVID-19 pandemic, the growth rate of China’s economy has slowed down, and listed companies in China are facing various crises. Credit risk is under significant testing, and avoiding default risk is not only beneficial for the future development of companies but also crucial for the stability of the system. Therefore, this study employs the KMV model to measure the credit quality of listed companies and combines the GARCH and SV models to re-estimate the volatility of equity value for these companies. The revised GARCH-KMV model and SV-KMV model are then applied to compare and analyze the credit risk of 9 ST companies and 9 non-ST companies in the listed market. The results indicate that the traditional KMV model can effectively measure the credit risk of listed companies, and incorporating the GARCH and SV models improves its credit risk measurement. Furthermore, the SV-KMV model demonstrates a better explanatory effect on credit risk compared to the GARCH-KMV model.展开更多
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...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.展开更多
文摘近两年中国上市公司由于受到新的冠状物疫情的冲击,经济增速放缓,面临种种危机,信用风险受到很大考验,而规避违约风险对公司未来的发展不仅有好处,而且在制度稳定性方面也将起到明显的作用。因此,我们选择了KMV模型与GARCH模式和SV模式相结合,对上市公司的股票收益波动率进行重新拟合和估算,以此来衡量上市公司的资信管理,采用调整后的GARCH-KMV模型和SV-KMV模型,对上市公司中的9家ST企业与9家非ST企业的信用风险进行了对比研究。结果显示,传统的KMV可以更好地衡量上市公司的信用风险,在结合GARCH模型和SV模型后也可以衡量上市公司的信用风险,但SV模型对于信用风险的解释效果要好于GARCH模型。Impacted by the COVID-19 pandemic, the growth rate of China’s economy has slowed down, and listed companies in China are facing various crises. Credit risk is under significant testing, and avoiding default risk is not only beneficial for the future development of companies but also crucial for the stability of the system. Therefore, this study employs the KMV model to measure the credit quality of listed companies and combines the GARCH and SV models to re-estimate the volatility of equity value for these companies. The revised GARCH-KMV model and SV-KMV model are then applied to compare and analyze the credit risk of 9 ST companies and 9 non-ST companies in the listed market. The results indicate that the traditional KMV model can effectively measure the credit risk of listed companies, and incorporating the GARCH and SV models improves its credit risk measurement. Furthermore, the SV-KMV model demonstrates a better explanatory effect on credit risk compared to the GARCH-KMV model.
文摘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.
