1in1000 Model Suite
The 1in1000 Stress Testing Model is an asset-level climate credit risk and climate market risk model, suitable for the analysis of corporate loan books and equity portfolios. It can be used for sensitivtiy analysis using multiple different sets of climate scenarios and financial input parameters. The methodology has been co-developed by THEIA Finance Labs (former 2DII-Germany) and the Oxford Sustainable Finance group and builds on the well-established PACTA-alignment practice.
As the foundation of our analysis, we use 5 year ahead forward-looking projections of production (2021-2026), which we have available for over 80.000 companies globally and 8+ sectors, covering the majority of global emissions. . This data is provided by our strategic partner asset resolution.
Our model is suited to analyse the potential impacts of three types of Climate related risks:
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Our model for Transition risk highlights the effect of a major policy shock in the future, which would force companies to shift their production on a paris aligned trajectory.
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Our Physical risk model (under development) aims to capture the effect of major natural hazards on firm production outputs, based on the asset level geographic location.
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Our model for litigation risk (under development) aims to cover the potential effects of climate litigation actions on the profitability of specific firms.
Three Interlinked Layers combine into one Model Logic
1. Scenarios
Scenarios describes the source of transition risk, represented by a set of climate-adjusted economic parameters, designed and curated in a set of severe but plausible transition scenarios. Currently, we are working with scenarios from the International Energy Agency (IEA), the Network for Greening the financial System (NGFS) , the Institute of New Economic Thinking (INET) and the UN PRI Inevitable Policy Response (IPR) scenarios.
2. Economy
This layer describes the real economy, as well as individual firms’ asset values under different scenarios. It represents a firm by its physical economic assets and the associated ownership structure.
3. Financial System
This layer describes the financial system, represented by heterogeneous financial institutions and their balance sheets.
Transition Risk Workflow
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In the model, transition scenarios feature decarbonisation pathways and technological change affecting the unit cost of technologies (e.g. solar, oil extraction or electricity generated from coal-fired power plants).
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The climate-adjusted economic parameters affect the physical production of firms, induce additional costs and shifts market shares according to the alignment of firms and emission intensities of their physical assets.
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This alters the firms cost structure and production mix across technologies and business units and impacts their income and profitability.
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Firm impacts’ are then translated into equity valuation changes, through a discounted dividend flow model. Using a time-horizon adjusted Merton credit risk framework, equity impacts are subsequently translated into changes of the probability of default.
Advantages
Compared to traditional climate stress testing, our model has several key advantages
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It offers high granularity of underlying asset-level data, allowing for a more precise assessment of risk.
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It links financial assets to economic activity, giving you a better understanding of the broader context in which your assets operate.
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It provides forward-looking company information, enabling you to plan for the future with greater certainty.
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It allows for a precise distinction between high-carbon assets and low-carbon assets across a range of sectors, giving you greater control over your portfolio.
Illustrative Output of the Model
Company specific stress test results
Our Model is able to generate company specific shocks on market and credit risk level. These shocks are affected by (1) the company specific forward looking production and (2) the assumptions of the climate scenario that generates the Stress Test shock.
Sector results and scenario comparisons
Apart from company specific results, our model is able to aggregate the analysis on sector level. Furthermore, the 1in1000 model stands out as one of the few that can incorporate multiple sources of climate scenarios in a single stress testing framework, shedding light on overlooked distinctions in scenario assumptions and calibrations.