Artificial Intelligence and Financial Tail Risks

Staff working paper 2026-40 (English) Javier Ojea Ferreiro, Andrea Ugolini, Juan C. Reboredo October 2026 Available as: PDF

Artificial intelligence (AI) has a double-edged relationship with financial stability: it may improve efficiency and risk management, while also creating new vulnerabilities and amplification channels. This paper studies whether market-perceived exposure to AI is reflected in the tails of financial-sector returns, and whether this relationship depends on macroeconomic conditions. We develop a framework that decomposes financial-sector tail outcomes across contemporaneous AI and macroeconomic scenarios, and identifies whether the AI-related episodes are primarily associated with extreme gains or losses. Using daily equity returns for financial sectors in Canada, the United States, Europe, the United Kingdom, and China, we document substantial heterogeneity. Financial-sector-specific scenarios dominate tail outcomes across markets, while the contribution of AI-related scenarios to return asymmetry differs substantially across countries. AI-sector scenarios contribute to downside asymmetry in Europe, China, and especially the United Kingdom, but make only a limited contribution in Canada and have the smallest tail weights in the United States. China is the only market exhibiting positive average skewness, driven by the financial and systemic scenarios rather than by AI-related outcomes. Overall, AI-related market movements are neither uniformly stabilizing nor destabilizing; their financial-stability interpretation depends on the market considered and on whether AI-sector tail events occur in isolation or jointly with broader macroeconomic movements.

Content Type(s): Staff research, Staff working papers JEL Code(s): C, C5, C58, G, G0, G01, G2, G21, G3, G32 Research Theme(s): Financial system, Financial institutions and intermediation, Financial stability and systemic risk, Models and tools, Econometric, statistical and computational methods, Structural challenges, Digitalization and productivity

DOI: https://doi.org/10.34989/swp-2026-40