【摘要】 为实现在险价值VaR和风险溢出效应ΔCoVaR的准确度量，考虑到波动聚集、厚尾与非对称等碳金融市场的典型特征，基于神经网络分位数回归(QRNN)模型，并利用GARCH模型拟合波动聚集性方面的优势，构建QRNN-GARCH-CoVaR模型。选取北京、广东、湖北、伦敦碳交易收益率作为研究对象，实证结果表明，QRNN-GARCH-CoVaR模型不仅在度量VaR方面优于传统模型，而且捕捉了金融风险溢出效应；国内各市场风险传递方向及其敏感度各异，湖北市场稳定性高，吸收风险能力强，北京和广东市场波动大，北京市场易受国外市场影响。更多还原

【Abstract】 To achieve accurate measurement of VaR and risk spillover effect ΔCoVaR, considering the typical characteristics of carbon financial markets such as volatility aggregation, thick tail and asymmetry, QRNN-GARCH-CoVaR model is constructed based on neural network quantile regression(QRNN) model and GARCH model to fit the advantages of volatility aggregation. Taking carbon trading returns from Beijing, Guangdong, Hubei and London as the research objects, the empirical results show that, first, QRNN-GARCH-CoVaR model is not only better than the traditional model in measuring VaR, but also captures the financial risk spillover effect. Second, the risk transmission direction and sensitivity of domestic markets are different. Hubei market has high stability and strong risk absorption ability. Beijing and Guangdong markets fluctuate greatly, and Beijing market is vulnerable to foreign markets.更多还原