This paper explores how climate risks impact the overall systemic stress levels in the United States (US). We initially apply the TrAffic Light System for Systemic Stress (TALIS3) approach that classifies the stock markets across all 50 states based on their stress levels, to create an aggregate stress measure called ATALIS3. Then, we utilize a nonparametric causality-in-quantiles approach to thoroughly assess the predictive power of climate risks across the entire conditional distribution of ATALIS3, accounting for any data nonlinearity and structural changes. Our analysis covers daily data from July 1996 to March 2023, reveals that various climate risk indicators can predict the entire conditional distribution of ATALIS3, particularly around its median. 

The full-sample result also carries over time, when the nonparametric causality-in-quantiles test is conducted based on a rolling-window. Our findings showing that climate risks are positively associated with ATALIS3 over its entire conditional distribution, provide crucial insights for investors and policymakers regarding the economic impact of environmental changes, especially since we confirm that the results continue to be robust in an international-setting involving 11 important stock markets of the European Union.