Tail dependence network of new energy vehicle industry in mainland China

The emerging new energy vehicles (NEV) industry is strategically important for China. How to capture its operating characteristics is a challenging but meaningful work. Considering that physical network (e.g. buyer–supplier) or correlation network (e.g. financial contagion) can provide the effective market information for enterprises in the operations management, we first construct the stock returns-based tail dependence network of the NEV industry by combining the Delta conditional value-at-risk (CoVaR) measure and the triangulated maximally filtered graph (TMFG) algorithm. We then explore the topological structure of the constructed network and obtain the operating characteristics for each enterprise in the whole industrial supply chain and at different levels. The empirical results show that the dependence and influence of different enterprises in the whole industrial supply chain are heterogeneous. In particular, upstream enterprises have closer dependence and faster influence power at all levels. These findings from the NEV industry with 71 listed enterprises would not only help regulators identify enterprises that affect the industry stability, but also help investors reduce risk across different enterprises, and managers can adjust operation strategies to reduce operating risks. On the theoretical side, we extend the network theory to the NEV industry. On the practical side, it is the first to capture the operating characteristics of the NEV industry in mainland China. In addition, on the methodological side, it constructs a new TMFG-CoVaR network.