Assessing and managing climate-related financial risks has quickly become a top priority for financial institutions worldwide. As detailed in our guide, "Overcoming the Challenges of Climate Risk Regulations – A Guide to Climate Stress Test Implementation," implementing climate stress tests accurately is crucial for aligning with new global standards and local regulations. Moreover, it plays a key role in enhancing strategic risk management, focusing primarily on transition risk.
A strong climate risk management solution not only meets compliance requirements but also provides a competitive edge. It allows users to evaluate the effects of climate scenarios on financial instruments with remarkable precision, enabling them to distinguish investment assets based on their unique climate change sensitivities. This makes it possible to enhance asset allocation and achieve sustainability goals, such as the Net-Zero 2050 commitments.
Financial institutions need state-of-the-art risk management software to prepare for current and future challenges. While mapping climate scenarios to traditional risk factor stress testing is a preliminary step, it’s limited in accuracy, granularity, and flexibility. Let’s explore how integrating complex climate scenario-based projections into the core valuation and simulation engines can address these limitations.
Mapping Climate Scenarios to Risk Factor Shocks
To comply with quantitative regulations like the Own Risk and Solvency Assessment reports, financial firms must conduct climate stress tests. A straightforward approach, where climate scenarios are linked to existing market risk factors, has been common in early regulatory exercises such as the European Insurance and Occupational Pensions Authority’s (EIOPA) 2022 climate stress tests. However, this method falls short of meeting future requirements, including those rooted in the Network for Greening the Financial System (NGFS) framework, as its results are too broad to effectively guide business decisions.
Typically, shocks are broken down by country and sector categories. For instance, in the "D – Electricity, Gas, Steam and Air Conditioning Supply" category, all industries receive similar adjustments despite their activities’ differences.
As illustrated in Figure 1, comparing NGFS scenarios with a traditional stress test in the electricity sector shows significant variations in subsector adjustments. Most policy pathways lead to a mix of positive and negative adjustments, depending on activity and technology specifics.
To enhance granularity, sector classification can go deeper into 4-digit NACE codes, allowing more precise distinctions, such as between electricity production and distribution. While this adds complexity to risk factor mapping, it enables a more precise understanding of impacts.
Increasing Granularity from Sectors to Legal Entities
For investment portfolios, even greater detail is required to generate distinct climate adjustments for each security. Climate models should consider each Legal Entity’s (LE) specific characteristics and the instruments it issues. For instance, distinguishing the impacts of policies on different electricity companies depends on their technologies and emission reduction capabilities.
For physical risks, it’s crucial to overlay the geolocation of physical assets with local damage functions to quantify hazards’ impacts accurately. Similarly, a granular approach benefits transition risk assessments.
Many firms operate across sectors, necessitating a model that considers weighted NACE code vectors per LE, mapping them to relevant CPRS categories. Additional LE-level features should refine climate impacts, such as credit risk indicators and technology mix.
Through the NGFS framework, tailored trajectories for variables like energy demand and carbon prices can shock creditworthiness, spreads, and equity values. The whitepaper "Overcoming the Challenges of Climate Risk Regulations: A Guide to Climate Stress Test Implementation" highlights the importance of the specific model used in this process and will be explored further in future publications.
Benefits of an Integrated Approach
Traditional market risk factors stress tests cannot support the granularity required for precise calculations. Additional factors for every LE would lead to countless model parameters and functional limitations, making a simple approach insufficient.
The solution is to focus on the simulation and pricing stages, leveraging instrument-level details. This is akin to incorporating betas and idiosyncratic risk when projecting a portfolio based on limited market risk factors. With a robust risk management calculation engine, real-time application of market shocks and time evolution is possible.
SS&C Algorithmics and CLIMAFIN offer such an integrated solution, providing:
- Granular scenario-contingent adjustments for instruments down to LEs and individual assets, based on systematic and idiosyncratic factors.
- Precise valuation adjustments for various assets like equities and bonds, integrating them over portfolio hierarchies.
- Real-time shock application and what-if analyses under different climate scenarios.
- Calculations of climate Value at Risk (VaR) using stochastic measures conditional on climate shocks.
- Preservation of existing risk factor universes and data size with maintained calculation performance.
Clients using Algorithmics for market and credit risk management experience unmatched accuracy in climate risk assessments. With CLIMAFIN-powered climate scenario libraries, these adjustments can scale consistently to future time points, supported by automated data sourcing.
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