Multiscale systemic risk spillovers in Chinese energy market: Evidence from a tail-event driven network analysis

Abstract

This paper investigates multiscale systemic risk spillovers in the Chinese energy market by combing the maximal overlap discrete wavelet transform (MODWT) with the tail-event driven network (TENET) model, which could analyze tail events and multiscale network dynamics. It creates multiscale extreme risk spillover networks consisting of energy companies in the coal, oil, natural gas, and new energy. To examine the transmission of risk between different subsectors and companies within the network, we assess overall, subsector, and company-level connectedness across different time scales, respectively. Empirical results show the OPEC oil production cut in 2017, the Sino-US trade war in 2018, the COVID-19 outbreak in 2020, and the Russia-Ukraine conflict started in 2022 boosted overall connectedness and concentration, suggesting that systemic risk connectedness tends to be intersectoral during market crises. The coal subsector appears to be the most integrated and exposed to systemic risk in the short term, while the oil subsector acts as the most influential subsector in the long term. The short-term risk spillover of the new energy subsector from 2018 to 2020 affects all energy subsectors, but it has moderated. The new energy subsector has a greater long-term impact on the energy market after 2020. Brent crude oil and domestic coal and LNG prices in China significantly drive systemic risk transmission in the Chinese energy sector, especially during the COVID-19 pandemic and the Russia-Ukraine conflict. This research may benefit governments, regulators, energy companies, and investors under different market conditions and investment horizons.