Risk spillover measurement of carbon trading market considering susceptible factors: A network perspective
提出一种基于网络的数据驱动策略，通过模糊认知图构建碳市场与关联市场的因果关系网络，识别与欧盟配额（EUA）同社区的市场因素，并测度EUA的尾部风险溢出水平，发现OILFUTURE对EUA有显著上尾溢出效应，EURUSD是EUA期货的最佳对冲工具。
Abstract An objective and robust network‐based data‐driven strategy is proposed to analyze risk spillovers in carbon markets. First, we characterize the causality network between the carbon market and potential associated markets using a data‐driven fuzzy cognitive map approach. Second, network‐based community detection is conducted to explore community structures that include carbon trading markets, and five market factors belonging to the same community as EU Allowances (EUA) are identified. Next, we conduct downside and upside‐tail measurements of EUA risk spillover levels within the community based on estimates and fits of marginal and joint distributions for different market pairs. Finally, we point out that the market factor having the most significant upper‐tail spillover effects on EUA is OILFUTURE, besides, EURUSD asset is found to be the best hedge for EUA futures among the detected market factors.