Tail risk spillover network among green bond, energy and agricultural markets under extreme weather scenarios

This paper explores the tail risk spillover patterns among eight US green bond, energy, and agricultural commodity markets conditional on different extreme weather conditions, using a proposed conditional tail Diebold Yilmaz spillover network. The results of the empirical analysis, conducted using data from 2013 to 2023, indicates that, firstly, as weather conditions transit from normal to extreme risk state, the complexity and magnitude of tail risk spillovers increase, particularly affecting the relationships between different markets and sectors. Under most extreme weather risk scenarios, the energy market predominantly acts as a risk transmitter. Conversely, the agricultural market, more often emerges as a risk receiver. Secondly, of all the extreme weather scenarios considered, the US commodity markets achieve lowest in total risk spillover (TRS) in the extreme snow weather scenarios and this is the only case that is almost no more than the unconditional weather scenario over the sample period. Finally, the tail returns of most US commodity markets are more sensitive to tail movements in extreme total precipitation and extreme runoff weather conditions.