Financial Institutions: Risk of Factor Transition to Climate Stress Testing

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Climate change has become an important strategic issue for financial institutions around the world as concerns grow about lending, investing or insuring companies that are not taking steps to transition to a low-carbon economy. Research shows that in the five years since the signing of the Paris Agreement, the world 60 largest banks only financed fossil fuels in the amount of US $ 3.8 trillion[1] As stakeholders continue to focus more on sustainability issues, this becomes important for financial institutions to better understand the potential climate risks they face.

Climate change can lead to physical risks associated with severe weather conditions such as droughts, wildfires and hurricanes, as well as transient risks associated with policy changes, market dynamics, technological innovation and consumer sentiment. For banks, transient risks could trigger the revaluation of a large number of assets if the company’s business models are not aligned with the energy transition and face subsequent pressure on their corporate earnings. In addition, the ongoing shit with investor sentiment, consumer demand and public expectations could significantly alter the bank’s lending and investment strategies. Transition risks can also affect both the liabilities and the balance sheet assets of the insurance company. The transition to a low-carbon economy could affect liabilities by reducing premiums associated with changes in business activity, for example, if energy assets turn out to be uncollectible.[2] In addition, a significant technological breakthrough could lead to losses of financial assets for insurers in carbon-intensive industries if pricing does not fully account for risks. On the investment side, there are also risks from the undervalued value of carbon-intensive assets.

Checking the resilience of financial institutions to climate risks

Macroeconomic stress testing gained prominence in the wake of the 2007-2008 financial crisis by using predictive scenarios to understand how adverse market conditions might affect an organization. The growing threats from climate change have generated a lot of interest in developing stress tests to assess the financial stability risks associated with the transition to a low-carbon economy. In fact, a number of central banks are planning to stress climate change transitions in 2021. The Bank of England, for example, is using its 2021 two-year research scenario to test the resilience of the current business models of the largest banks and insurers to climate change. associated risks to determine the scale of adjustments that will be required over the coming decades to keep the system sustainable.

Problems of Accounting for Climate Risks in Stress Tests

However, stress testing for climate change is very different from existing macrostress testing and poses a number of challenges:

  • Lack of quality historical data makes it difficult to model interactions between climate, macroeconomics and industries, and requires data gaps to be filled with reasonable, informed and transparent assumptions.
  • Long time horizon for climatic stress tests which measures results over 30-50 years, rather than the typical nine quarters for macroeconomic stress testing, requires a methodological transformation to identify a set of prudent financial assumptions that can be used over these long periods.
  • At the same time, the ability to integrate the assessment of transition risks in the short term, as the impact of the risks of transition to climate begins to materialize faster requires modeling capabilities that support less ordered transitions.
  • The need to record carbon emissions by type of energy, direct and indirect emissions, as well as by country of origin requires detailed data for specific sectors.
  • Different tax regimes by country requires understanding of specific policies and the ability to reflect them in the analysis.
  • Different impacts on commodity production and elasticity of demand in response to price changes due to carbon tax increases requires the use of scientific / integrated models that take into account the energy transition and changes in energy demand by country / region.
  • Integration of analysis tools at the portfolio and borrower level related to climate scenarios requires an understanding of both the sector effect at the portfolio level and the various counterparty response metrics.
  • Assessment of alternative options for the counterparty’s behavior in the future requires assumptions for adaptation, business as usual, and asset transfer.
  • Understanding the implications for full financial reporting to get a 360 score0 Look requires sound financial assumptions.
  • The need to conduct a sensitivity analysis of the scenario output to identify the uncertainty inherent in the results. requires the ability to edit inputs, assumptions and financial implications.

Given these challenges, quantification using advanced analytics is required to perform climate stress testing. For this purpose, Climate Credit Analytics was developed by S&P Global Market Intelligence and Oliver Wyman. The suite of solutions combines the data assets and credit analytics capabilities of S&P Global Market Intelligence with Oliver Wyman’s climate scenario and stress testing experience.

Solving problems with climate credit analytics

With a highly dynamic industry-specific approach, Climate Credit Analytics allows you to analyze financial and credit climate-related risks at the counterparty and portfolio level for thousands of public and private companies in various sectors around the world. Users can perform the required stress testing with options to:

  • Time horizons up to 2050.
  • Multiple target temperatures and transition paths.
  • Variety of carbon pricing levels.
  • Transition possibilities.

A comprehensive analysis of all non-financial sectors is possible at the counterparty or portfolio level covering 141 GICS sub-sectors using a bottom-up approach that includes six different models:

  1. Metals and mining
  2. Oil and gas
  3. Power generation
  4. Automotive OEM
  5. Airlines
  6. Generic / other sectors

Climate Credit Analytics uses a fundamentals-based view to provide a credit rating for a specific company.[3] estimates for public and private companies with sufficient company financial and industry data to be able to model from the bottom up. In addition, the solution suite provides a name-level extrapolation module in each model to predict the likely impact on companies that lack the necessary financial data but have some basic credit risk information. Finally, users can overwrite or enter financial and industry data for a company to provide portfolio-level analysis.

The solution, available through S&P Global Market Intelligence, automatically extracts relevant company financial data, borrower-level credit ratings and industry data from S&P Global’s proprietary datasets. It includes:

  • Financial and industry data.
  • Complex quantitative credit scoring methodologies.
  • Data on greenhouse gas (GHG) emissions and environmental impact at the company level.

The analysis begins by converting different climate scenarios, sector-specific demand and supply elasticities, and market dynamics into industry-specific financial factors such as production volumes, fuel costs and capital expenditures. These drivers are then used to predict the company’s financial statements under different climate scenarios.

Finally, financial forecasts are analyzed using S&P Global Market Intelligence Credit Analytics models to calculate the impact on credit ratings and the likelihood of default. Alternatively, the projected financials can be used independently with the user’s internal credit scoring platform.

Figure 1: Climate Credit Analytics Methodology

Source: Methodology: Climate Credit Analytics, S&P Global Market Intelligence. For illustrative purposes only.

Climatic scenarios:

Users can run one of eight long-term climate change transition scenarios developed by the climate scientists community, or assess the impact of a globally applied tax that is phased in over three years. Users also have the ability to overwrite script variables with their own data to run custom scripts.

The integrated scenarios are consistent with the scenarios provided by the Greening the Financial Network, a group of central banks and supervisors that agreed on Integrated Assessment Models (IAMs) developed by the three climatology research groups. NGFS scenarios allow users to compare impacts across multiple hot, disruptive and orderly transition paths, estimating financial implications through 2050 across multiple transitions. The carbon tax scenario provides a short-term estimate of the financial implications of the immediate imposition of a global carbon tax at a user-selected level. This allows users to assess the potential impact over the next three years due to one triggering event.

Main outputs:

Key model results include financial statements for the forecast period (three years under the carbon tax scenario and up to 2050 under the integrated scenario), as well as the corresponding credit rating obtained using credit analytics from S&P Global Market Intelligence. The output also includes key inputs to the credit rating, allowing you to quickly see the factors that affect your credit rating.

In addition, the model allows the user to perform a detailed analysis of the sensitivity and contribution of a particular financial factor to a credit rating. This helps to determine the impact of the climate scenario on creditworthiness using the model drivers and the financial ratios involved. Finally, users can download batch financial data and process the results using their own internal credit rating models.

As more regulators push for banks and insurers to include climate change in stress testing, there is an urgent need for a solution like Credit Climate Analytics that is robust, flexible and transparent to address the many challenges associated with assessing this. a new stream of potential opportunities. risks.

For more information on climate credit analytics, contact



[2] The Impact of Climate Change on the UK Insurance Sector, Bank of England, September 2015.

[3] S&P Global Ratings does not or does not participate in the creation of credit ratings generated by S&P Global Market Intelligence. The string nomenclature is used to differentiate the ratings of the S&P Global Market Intelligence PD credit model from the credit ratings assigned by S&P Global Ratings.

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