Physical and Transition Climate Risks and Financial Risk Predictability in the US Banking Sector#

In this paper, we relate physical and transition climate risks of the United States (US) to systemic risk of the US banking sector. We start by estimating the systemic risk of 128 US bank stock prices from 26 th May 2008 to 30 th June 2023 using the time-varying financial risk meter (FRM) approach, which relies on a Lasso quantile regression model. The FRM for the overall system of banks, and for large, medium, and small banks separately, exhibits notable peaks during COVID-19 in particular, and the global financial and European sovereign debt crises. Subsequently, a nonparametric causality-in-quantiles test, robust to misspecification from nonlinearity and structural breaks, is employed to show that news-based metrics of physical and transition risks significantly predict the entire conditional distribution of the FRMs over the full-sample and in a time-varying manner. News related to international summits exert the strongest causal impact, surpassing that of natural disasters, global warming, and US climate policies. Further analysis demonstrates that all four climate risk factors consistently exert a positive impact on the conditional quantiles of the FRMs, thereby supporting the premise that climate risks can damage assets and augment operating costs in the banking sector. These findings have important policy implications for the stability of the US banking sector. • Build systemic risk for 128 US large, medium, and small banks via time-varying FRM • Apply dynamic causality-in-quantiles test on daily data from May 2008 to June 2023 • Physical and transition climate risks impact the conditional distribution of FRMs • Strongest causal flow runs from the news on international summits to FRM • Four climate risks factors positively impact the conditional quantiles of FRMs