US Climate Shocks: The Double Risk for the Global Financial Stability
Brahim Gaies & Maria Giuseppina Bruna
Abstract
The persistent uncertainty surrounding the Trump administration's commitment to addressing climate change raises concerns that global financial stability could be compromised through spillovers from US climate‐related risks. This study examines this hypothesis by analyzing the dynamic interactions between global financial instability and US climate risks using nonlinear time‐varying connectedness based on a TVP‐VAR framework and VAR‐based local projection causality methods. It distinguishes between physical and transition risks and considers various forms of systemic financial stress at the global level, including banking and funding stress, equity market stress, safe asset stress, and market volatility, in order to inform the prediction of future global financial instability. Our findings highlight that global financial instability reacts differently depending on the nature of US climate risks. We show that transition risks have stronger predictive relationships than physical risks on global financial stability, especially after major events such as the Paris Agreement and the COVID‐19 pandemic. Whereas US physical risks tend to propagate instability via safe asset markets, transition risks exert more direct pressure on credit and equity markets. In particular, safe asset markets, which absorb shocks under US physical risks, can also transmit these shocks to credit and equity markets under transition risks. This suggests the presence of flight‐to‐safety versus flight‐to‐quality behaviors among global investors, depending on the specific type of US climate risks. Overall, these findings show how climate‐based financial mechanisms can strengthen early‐warning signals that inform the forecasting of systemic financial risk.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.