摘要
为了更加准确地度量金融系统性风险，预防灾难性金融风险事件发生，本文基于尾部损失的均值提出了一个新的度量系统性风险的方法——CoES模型，相对于传统的CoVaR模型来说，该方法更关注尾部损失的均值而不仅仅是单一分位点上的期望损失，能够更加准确地捕捉系统性风险，为金融系统监管提供更为有效的信息．最后，本文将该方法用于度量2007-2016年间共21个金融机构对我国金融市场系统性风险的贡献．研究结果发现：1）CoVaR模型可能低估了金融机构的系统性风险；2）当银行行业受到冲击时，其对整个金融系统造成的风险最大，其次是保险，房地产和多元金融行业；3）在银行行业中，对系统性风险贡献最大的当属工商银行和中国银行，应对其进行重点监管；4）相对于银行和房地产行业，保险行业和多元金融行业自身的VaR值较高，但对金融系统性风险的贡献较低，因此应注意对其自身风险的管理．
To measure the systemic risk contribution of the financial institutes in China more accurately and to avoid financial risk events, a new method named CoES model is proposed based on the mean value of tail loss. Compared to the traditional CoVaR model, this method pays more attention to the mean value of tail loss than the expected loss on one single quantile, which could provide more accurate information for the supervision when capturing the systemic risk of financial system. The new method is utilized to measure the systemic risk contributions of 21 financial institutions in China from 2007 to 2016. The empirical results show that： 1） the CoVaR model may underestimate the systemic risk of financial institutes; 2） the banking industry brings the largest systemic risk contribution to the whole financial system, followed by insurance, real estate and diversified financial industry; 3） among banking industry, the systemic risk contributions of Industrial and Commercial Bank of China and Bank of China are the largest, which should be the key regulatory objects; 4） compared with the banking and real estate industry, the insurance industry and the diversified financial industry own higher VaR value and their systemic risk contributions are relatively lower, so the regulators could pay more attention to their own risk management.
出处
《系统工程理论与实践》
EI
CSSCI
CSCD
北大核心
2018年第3期565-575,共11页
Systems Engineering-Theory & Practice
基金
国家自然科学基金(71390330,71390331)~~
