Modeling the global sovereign credit network under climate change

Abstract

Climate change is becoming an urgent issue for the global economy. Our study employs a multivariate extreme value regression model that incorporates a LASSO-type estimator to investigate the tail dependence of the global sovereign credit default swap market conditional on climate change. Herein, we propose an extremal connectedness measure based on tail dependence to construct a sovereign credit network. The findings show that extreme weather or climate disasters significantly impact country-specific sovereign risk with heterogeneous network structure outcomes. Specifically, extreme weather conditions have a strong impact on countries' sovereign credit and magnify their influence on the global sovereign credit network. Furthermore, we identify an asymmetric risk spillover effect in the global sovereign credit network, where the degree of risk spillover is higher under extremely hot weather conditions. Our analysis provides new insights into the role of climate change in sovereign risk.