8 August 2023

How to Choose the Right Climate Model

Climate risk has caused significant challenges for life insurance financial risk modelers. There is a lot of noise in the market, with emphasis on either very precise or very complex modelling. However, it is important to also look at the broader, systematic impacts on financial markets exposures; such as yield curves, inflation, credit spreads, risk premia, and asset class returns.

How do you find the balance and a sufficient level of granularity in your modeling? How do you select and assess the impact of different climate models? Are there risks we are missing? Moody's Analytics have created a series of short articles to help those responsible for climate modeling in the actuarial, risk, and strategic asset allocation functions of a life insurance company. These articles begin to address and simplify the complexities of climate change in processes such as the Own Risk and Solvency Assessment (ORSA), stress testing, and strategic asset allocation.

How to Choose the Right Climate Model

Discussions around the pros and cons of different climate models and scenario sources focus more on the differences than the commonalities. Sustainability and environmentalism are factionalized.

Unfortunately, normative divisions also lead to a tendency for climate modeling and scenario analysis to be explained or presented badly, if not outright dismissed. For experienced users of quantitative risk models, the maxim 'all models are wrong, some are useful' is a good guide. This suggests that looking for the perfect model is not the end goal. The first step in choosing the right climate model is to try to avoid being drawn into a 'which side are you on' debate.

One way to do this, is to start with a focus on the end goal of your analysis. Most actuaries and risk managers working for insurers, fall into three different groups shown in the following figure. What defines the 'right climate model', is if you are able to use and interpret it appropriately to your end goal.

To date, most climate risk asset modeling has focused on the 'bottom-up approach'; to model real asset and credit risk. Typically, this is achieved by using either publicly available climate modeling—for example provided by the Network for Greening the Financial System (NGFS) or Principles for Responsible Investment (PRI)—or proprietary commercial modeling capabilities. Often, the bottom-up modeling will miss some key strategic risks, for example it may assume that yield curves and credit spread levels are held constant. The top-down approach to modeling climate risk can be used to fill that gap by applying either macroeconomic modeling capabilities, or financial risk modeling (using scenario generators). For general insurers, liability modeling will leverage physical climate and natural catastrophe modeling to understand how claims frequency/severity and underwriting could be impacted.

For many risk managers and actuaries, moving away from thinking about short-term risks, to longer-term uncertainties is the biggest mindset change required. Most models that explore climate change have been developed with the latter aim in mind.

Ideally, the right choice of climate models leads to a comprehensive view of the uncertainties faced by insurers. It should be able to be run on a consistent basis to understand how the entire business might be impacted. Basing the models on a consistent set of underlying scenarios that leverage the broader SSP-RCP (Shared Socio-economic Pathway – Representative Concentration Pathway) framework, such as NGFS, can help to ensure comparability. Many insurers may go further and customize scenarios to reflect their own risk exposures, or own management views.

Unlike risk or economic capital models, it is important to recognize that most climate models have not been developed to produce worst case outcomes or case studies. Most climate scenario exercises have focused on 'exploratory' analysis of possible future outcomes. These exploratory analyses focus on understanding the impact of climate change on expected business outcomes, rather than stress testing or tail risk analysis. To generate climate 'stress tests' in the traditional sense, it is likely that the industry will need to switch to focusing on existing catastrophe risk, economic capital, and stochastic scenario modeling capabilities.

For more insights on this topic, and to listen to the climate risk for insurers podcast series, visit https://www.moodysanalytics.com/microsites/climate-risk-for-insurers

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