Credit Risk Scenario Analysis: Super El Niño

Extreme weather events are becoming an increasingly important consideration in credit risk assessment. Floods, hurricanes, wildfires, droughts, and heatwaves can disrupt operations, damage assets, weaken supply chains, reduce revenues, and increase insurance and financing costs. These effects can place pressure on companies’ cash flows, liquidity, asset values, and debt-servicing capacity, with broader implications for lenders and investors. However, quantifying the impact remains challenging because weather events are highly localized, their frequency and severity are evolving, and their effects can propagate indirectly through interconnected economies and financial markets.

As part of the Credit Risk Scenario Analysis Series, this blog assesses the potential impact of extreme weather using a two-step approach. First, the impact of extreme weather on macroeconomic environment is projected using the Global Link Model (GLM). The resulting macroeconomic effects are then translated into changes in credit risk across countries and industries using Macro-Scenario Model (MSM).

Under the hypothetical Super El Niño scenario, the key assumptions and quantification approach are as follows:

• The scenario is designed to resemble the 2015-16 El Niño event, with an extended phase assumed to run from the second quarter of 2026 to the third quarter of 2027.
• Historical relationships between the Relative Oceanic Niño Index (RONI), agricultural prices, and macroeconomic variables are estimated using the vector autoregression approach of Cashin et al. (2015).¹
• An autoregressive integrated moving average (ARIMA) forecast of RONI is used to project the scenario’s effects on relevant economic indicators.
• Country risk scores are set at the maximum levels observed during the 2015-16 El Niño event to reflect the potential for heightened social unrest.

We compared companies’ current probability of default (PD), as of August 2026, with their scenario PD² to estimate the expected deterioration or improvement in credit risk over the subsequent one-year period. The results indicate that Germany, France and the United Kingdom are more likely to experience the strongest effects among the major European economies, followed by the United States, while the impact across Asia-Pacific countries is comparatively milder. At the industry level, Consumer & Services and Energy sectors are expected to be the most affected. In Australia, South Korea and Sweden, the scenario PD is expected to decline slightly, driven primarily by the Information Technology, Utility and Financial Institutions sector, respectively.

Figure 1: Relative difference between current and scenario median PD by country