Taming the Green Swan: How to improve climate-related financial risk assessments
156 Pages Posted: 22 Mar 2021
Date Written: 2020
Climate-related financial risks might have the potential to trigger the next systemic financial crisis, as recently stated by the Bank for International Settlements. In consequence, understanding these so-called Green Swan risks should be a key priority in financial decision-making and supervision. However, a systematic approach and a comprehensive theory on climate-adjusted financial risk metrics is still missing. This study is a first step to fill this gap, with a focus on transition risks. Drawing on insights from climate science, economics and finance research, we derive a set of important criteria to ensure that climate risk tools provide high quality, comparable, and decision-relevant results. We then use a sample of 16 climate transition risk tools and conduct two lines of research: First, by aid of descriptive analysis, we assess the tools’ coverage of risk sources and financial assets, their inputs (i.e. underlying climate scenarios), and their outputs (i.e. climate-adjusted financial metrics). Second, we use the previously defined criteria for an in-depth analysis of the quality, comparability and decision-relevance of the tools. The results will be presented at the individual tool level, and at the meta level. Based on the results of our descriptive and criteria-based analysis, we derive potential next steps for tool provider, conclusions for potential tool users, and guidelines for supervisory authorities. The analysis could be used as starting point for building a comprehensive theory of meaningful climate-related financial risk indicators, aid practitioners to select the tools best suited to their needs and use cases, and inform regulatory processes on financial climate risk assessment principles.
Keywords: Financial risk models, Financial pricing models, Transition risks, Climate risk tools, Climate-adjusted financial risk indicators, Criteria-based analysis, Forward-looking scenario analyses, Investment decisions under deep uncertainty
JEL Classification: C83, D53, D81, G12, G32, Q54
Suggested Citation: Suggested Citation