Risk contagion and tail risk in energy markets: A time-frequency network approach

This paper investigates risk contagion across traditional energy, clean energy, electricity, and carbon markets over multiple time scales. Employing a time-varying parameter vector autoregressive (TVP-VAR) model integrated with the Diebold–Yılmaz (DY) and Baruník–Křehlík (BK) spillover indices, we construct total, short-term, and long-term risk contagion networks using data from 2010 to 2024. The results reveal that risk contagion is predominantly long-term in nature and exhibits pronounced time-varying dynamics, particularly during periods of market stress. WTI crude oil emerges as the dominant risk-spillover node across all network layers, whereas conventional electricity predominantly functions as a risk-absorbing node, notably in total and long-term networks. Regression analysis linking network topology to asset tail risk reveals heterogeneous effects of different centrality indicators: net out-degree and eigenvector centralities are associated with lower tail risk, whereas betweenness centrality corresponds to higher tail risk. These findings highlight the structural resilience of the energy market network and its potential to mitigate systemic risk through the dynamic balance between risk-spillover and risk-absorbing entities.