European Central Bank Climate Risk Stress Tests: The Need to Fill the Gaps
European Central Bank (ECB) climate risk stress test results published in July 2022 show that banks have not yet sufficiently integrated climate risk into their stress test frameworks and internal models, despite some progress made since 2020.
A total of 104 significant banks took part in the ECB stress test, which consisted of three modules in which banks provided information on their: (i) own climate stress testing capabilities, (ii) their dependence on regarding carbon-emitting sectors, and (iii) performance under different scenarios over several time horizons. The bottom-up stress test of the third module was limited to 41 directly supervised banks to ensure proportionality vis-à-vis smaller banks. The test required banks to project losses from extreme weather events and under transition scenarios with short and long-term time horizons.
The exercise provided the ECB with a first insight into banks’ weather-related credit risk modeling capabilities. Overall, it appears that climate risk factors have not yet been fully taken into account. This may explain the limited differentiation in projected credit risk parameters of institutions under different long-term climate risk scenarios.
More specifically, the ECB found that: (i) the sectoral dimension is often not correctly reflected in banks’ credit risk models, since the asymmetric shocks between industrial sectors assumed in the scenarios have not led to notable differences in the projected sector risk parameters, (ii) climate risk variables were mostly captured using proxies (e.g. with respect to emissions), the quality of which appears to vary widely from institution to institution, and (iii) since carbon prices are often the only climate-related explanatory variable, existing credit risk models do not. do not appear to incorporate all relevant climate risk channels – i.e. direct channels (e.g. carbon price shocks and emissions trajectories) and indirect channels (e.g. macroeconomic) – which could affect the credit quality of each counterparty.
Banks, in turn, mentioned challenges in how to: i) model loss projections over a 30-year time horizon and relate scenario assumptions to credit risk parameters (i.e. probability of default (PD) and loss given default (LGD)), ii) characterize extreme weather events (i.e., incorporation of physical risks), and iii) anticipate changes in customer behavior, which is the one of the main triggers of transition risk.
Naturally, a framework that can address these gaps and challenges and help reliably quantify the financial and credit impact of climate risk is highly desired.
To support these efforts, S&P Global Market Intelligence and Oliver Wyman have developed Climate Credit Analytics, a suite of climate scenario analysis and credit analysis models that is now used by some of the major systemically important financial institutions (SIFIs) to support stress testing, as well as to inform their credit policy and net-zero strategy. These tools combine the data resources and credit analysis capabilities of S&P Global Market Intelligence with Oliver Wyman’s expertise in climate scenarios and stress testing.
Figure 1: Methodology for analyzing climate credits
Source: S&P Global Market Intelligence. As of: July 2021. For illustrative purposes only.
Analysis of climate credits provides a comprehensive and personalized approach to assess credit risk under multiple climate scenarios by providing:
A sectoral methodology adapted to the real economy in all sectors and adaptive business modelsthus balancing the risks and opportunities that climate transition can offer.
- Bottom-up modeling with a clear link between the main transition variables, drivers and resulting financial impacts.
- Compatibility with several climate scenariosincluding those recommended by the Network for Greening the Financial System (NGFS), as well as regulatory climate stress test scenarios, such as those of the ECB.
- Top-notch data leveraging S&P Global’s extensive and proprietary datasets and credit models.
- Easy implementation for seamless integration into existing processes and workflows, with data that doesn’t leave an institution.
- A user-friendly service model which includes the incorporation of future evolving climate scenarios.
The offering is designed to meet regulators’ expectations of financial institutions on this important but complex subject and provide the information necessary to make financial and credit decisions with conviction.
Figure 2: Fundamentals-based approach for high-emission sectors (oil and gas example)
Source: S&P Global Market Intelligence. To: August 16, 2022. For illustrative purposes only.
For more information, you can consult: https://www.spglobal.com/marketintelligence/en/solutions/climate-credit-analytics.
Note: S&P Global Ratings does not contribute to or participate in the creation of credit ratings generated by S&P Global Market Intelligence. Lowercase nomenclature is used to differentiate S&P Global Market Intelligence credit model scores from credit ratings issued by S&P Global Ratings.
 Oliver Wyman is an independent third-party company and is not affiliated with S&P Global or any of its divisions.