To provide some context, the ACPR orderly scenario reflects the French roadmap designed to fulfil the commitments made under the Paris Agreement. Forecast of 12-month PD by sector for two ACPR scenariosįigure 1 illustrates the 12-month PD forecast at each reporting date for two ACPR scenarios: orderly and sudden. Applying the PD model (obtained in step 1) to the ACPR sectoral forecasts to derive results from the sectoral approach.įigure 1. Calibrating a PD model to the historical default rate series of the global corporates segment, which, by design, included a GDP variable. In practice, our study consisted of the following steps: Mazars implemented both methodologies in an attempt to understand whether the methodologies yield consistent results. the requirement of sectoral macro-economic forecasts.a strong modelling assumption: “models are still pertinent with sectoral data” and,.On the other hand, the disadvantages include: the model does not introduce additional parameters.generally, results can be generated with slight modifications to existing models and.the work performed by the ACPR can be used as a reference.Similarly, the BoE produced a set of sectoral GVA forecasts for its 2021 Climate Biennial Exploratory Scenario (CBES), accounting for different economic sectors’ varying degrees of exposure to climate risks. This variable was necessary to produce forecasts with the sectoral granularity required for the climate risk stress testing assessment. It is worth noting that when the bank’s PD models did not include GDP as a default risk driver, new models were developed to add it. Climate PD forecasts were produced using the sectoral forecasts given by the ACPR along with either the bank’s pre-existing models or with newly developed models. The French regulator, the ACPR, produced a set of sectoral Gross Value Added (GVA) forecasts and existing leveraged models from banks when building its climate risk stress testing framework. However, a key drawback is the lack of guidance or references for the level of the sensitivities, so the determination of these inputs is based on expert judgement. which sectors have a high climate risk impact vs low). one can leverage the work performed by the UNEP when determining the hierarchy of the sensitivities (i.e.it requires few additional inputs and,.As previously mentioned, the method was developed by the UNEP, and we refer to it as the “sensitivity approach” throughout this article. In this approach, climate variables and their respective sensitivity coefficients -estimated using a scorecard – are added into the sectoral PD Merton models. The results obtained by Mazars show that these two distinct approaches can yield similar results. In particular, the approaches differ in the mode of incorporation of climate risk factors into the PD calculations. With this foundation, the two approaches differ in the way the framework has been adapted for climate stress testing. The Merton model has been widely used in the industry to derive the IFRS 9 forward-looking and point in time PDs and perform stress testing. This allows one to project the expected default rate according to the anticipated movements of these risk drivers. īoth approaches are built upon a well-established PD methodology, known as the Merton Framework, where the default dynamics are captured via macroeconomic and financial risk drivers. the second corresponds to the approach followed by the Autorité de Contrôle Prudentiel et de Résolution (ACPR) in its 2020 climate risk stress test pilot exercise.The first methodology studied was developed as part of the UN Environmental Programme (UNEP) Finance Initiative when piloting the implementation of recommendations outlined by Task Force on Climate-related Financial Disclosures (TCFD) and.In this article, Mazars presents the results obtained by implementing two methodological approaches to estimate the sectoral probability of default (PD) parameters in the context of climate risk stress testing: However, no consensus regarding the best methodology to use in this context has been reached as of today. Countries like the United Kingdom and France, having started working on pilot climate stress test exercises, are leading by example. Stress testing and scenario analysis are a common framework proposed by different regulatory and supervisory bodies across various countries to assess the impact of climate-related risks on the financial system. These initiatives followed the adoption of the United Nations Paris Agreement on climate change, the 2030 agenda for Sustainable Development and the European Green Deal. Recently, initiatives to tackle climate-related and environmental risks in the financial services industry have begun across the world. Benchmark study of approaches to estimate probability of default in the context of climate risk
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