Abstract: Abstract: Amidst the global turmoil and the intensification of economic anti-globalization, it is of paramount significance to study the risk-contagion effect of the global foreign exchange market under various major shocks for maintaining the stability of the global exchange rate. Based on 27 major currencies worldwide from 2005 to 2023, this paper respectively constructs the R-Vine Copula model and the Elastic Net-VAR model, and comprehensively examines the risk contagion effect among global exchange rate markets under the influence of different extreme events from two dimensions: the linkage network and the spillover network. The empirical findings indicate that the US dollar and the Saudi riyal are consistently significant nodes in the global exchange rate risk linkage network and also demonstrate strong risk transmission characteristics in the spillover network. Simultaneously, the alterations of the risk linkage network and the risk spillover network between exchange rate markets are event-driven, and the impact of extreme events is more likely to trigger cross-infection of exchange rate risks among currencies in different geographical regions. Particularly, after the outbreak of the Russia-Ukraine conflict, the overall exchange rate pattern began to exhibit a trend of blockage, the influence of economic anti-globalization on the global exchange rate pattern gradually became prominent, the cross-regional linkages and risk spillovers between Europe and Asia weakened, and the US dollar displayed a robust risk spillover feature against the currencies of other geographical regions. Cross-regional risk linkages and risk spillovers are more prevalent among regions with cooperation, regions with similar economic volumes, or regions with close geographical proximities. Additionally, the topological grouping of the risk spillover network further reveals that the cross-regional risk spillover effect is the most significant during major public health security events, followed by the subprime crisis and the Russia-Ukraine conflict.

Key words: Keywords: Exchange rate, Extreme events, Risk transmission, Complex networks, R-Vine Copula model