Board of Governors of the Federal Reserve System

Transition Risk Module

The transition risk module required participants to estimate the credit risk impact of two macroeconomic pathways with different combinations of economic, technological, and policy assumptions and different estimates for economic and financial variables like GDP growth and carbon prices. Participants used the scenarios to estimate the credit impact on their corporate and CRE loan portfolios over a 10-year horizon.

Scenario Design

The transition risk module centered on two scenarios developed by the NGFS: Current Policies and Net Zero 2050. These scenarios include pathways for emissions, the energy system, and financial and macroeconomic conditions that participants were instructed to use for the pilot CSA exercise. Most participants identified a common set of key variables from the NGFS that they viewed as critical for determining the impact of the transition risk scenarios on their in-scope corporate and CRE exposures. Participants then used these variables to estimate climate-adjusted credit risk parameters. Figure 9 shows a stylized representation of this process.

Key Variables

As shown in table 9, participants used GHG emissions, GDP, equity prices, carbon prices, and energy prices from the NGFS for the United States. Many participants also used additional MEVs from the NGFS scenarios similar to those used in traditional stress testing, such as unemployment, inflation, and interest rates, as detailed below.

Table 9. Common macroeconomic variables used by participants

Participants translated certain NGFS variables to observable variables and typical modeling frequencies. For example, internal credit risk models generally use specific variables (e.g., U.S. 10-year Treasury rate, S&P equity index), while the NGFS scenarios include some generalized variables that are not necessarily tied to a specific time series (e.g., long-term interest rates, equity prices). Thus, participants linked the NGFS variables to observable time series by converting NGFS variable trajectories from levels to growth rates and applying these to the observable time series. This approach also helped to smooth the discontinuities between the current macroeconomic environment and the initial scenario projection year.¹¹

Participants noted challenges in interpreting the macroeconomic consistency of the transition risk scenarios. These included reconciling MEV relationships, understanding variable definitions, interpreting variable pathways, and estimating technological assumptions.

Variable Expansion

Most participants' modeling approaches incorporated additional variables not included in the NGFS Scenario Database, including additional macroeconomic, state- or MSA-level, and sector-level variables, like CRE indexes, credit spreads, and sector gross value added (GVA).¹² To fill these variable gaps, most participants expanded the NGFS scenarios in two ways: first to derive additional macroeconomic and regional variables that were inputs to internal stress testing models, and second to downscale macroeconomic and energy system variables to a more granular level to enable greater differentiation in impact across sectors.

In order to estimate the impact of transition risk across sectors, most participants needed to further downscale the NGFS scenarios to economic sectors that were not covered by the NGFS variables or to a greater level of granularity for economic sectors that were covered. These sectoral variables included production, prices, consumption, capital expenditure, and sector GVA. Participants either developed these variables using internal models or used vendor models to estimate the direct impact of transition risk on certain sectors. Differences in approaches led to differences in estimates of sectoral variables (e.g., sector GVA), both across sectors and for a given sector across participants.

Measurement Methodologies

To estimate the impact of the NGFS scenarios on credit risk parameters, most participants largely relied on existing wholesale credit risk modeling frameworks used in stress testing and regulatory capital requirements. These frameworks generally first estimated the direct credit risk impact at the exposure level (i.e., obligor for corporate loans and property for CRE), then forecasted the impact of the scenario's macroeconomic conditions across the 10-year projection horizon.

Corporate Exposures Methodology

All participants applied transition risk effects at the obligor level to estimate the impacts of the transition on obligors' business models. Many participants segmented their corporate exposures by sector. For some sectors, participants developed granular methods to project the impact of transition risks on obligor financials, cash flows, and internal risk ratings. For other sectors, participants used more generalized approaches with less granularity to project transition risk impacts at the obligor level or used unadjusted obligor financial estimates or ratings. Several participants assumed some sectors were not impacted, and they did not estimate transition risk impacts on obligors within those sectors.

The granular methodologies for estimating transition risk impacts typically augmented traditional obligor risk rating systems. Some participants contracted vendor models, while others developed methodologies in-house. Most methodologies calculated the potential impact of the transition scenarios' carbon prices and resultant energy system effects on obligors' financial estimates (e.g., revenue, operating costs, capital expenditures, dividend payouts, debt, equity, cash flows) that are inputs to credit risk models. Others used statistical methods that related ratings to MEVs.

Once obligor risk parameters were adjusted for transition risk impacts, participants used a range of approaches to integrate these adjusted credit risk parameters into their PD rating transition modeling frameworks. Some participants condensed obligors' 10-year adjusted risk parameter impacts into a single adjustment to risk ratings at the beginning of the scenario. Others dynamically integrated the impact across the projection horizon either by using the adjusted risk ratings at each projection period or by using the adjusted ratings to update the quarterly transition matrices used by traditional stress testing models.

Nearly all participants applied the rating transition models from their existing stress testing modeling frameworks to migrate obligor ratings or PDs across the projection horizon using the MEV pathways from the NGFS scenarios. Some participants updated their existing ratings transition models to incorporate greater sectoral granularity, for example, by replacing macroeconomy-level variables with regional or sectoral variables. Other participants used their stress testing ratings transition models without alteration.

Emissions data were broadly used for credit risk modeling by all participants. Participants used similar emissions data sources for public companies, while using a greater range of proxies (either vendor or internally developed) for private or nonreporting public companies. Proxy methodologies for estimating missing emissions data included extrapolating emissions from regional industry averages or predicting emissions from regression or machine learning models.

In the pilot CSA exercise, participants had the option to incorporate information about an obligor's "transition capacity" into their estimation approaches. All participants considered obligors' plans to manage transition risks to some degree. In some cases, participants used this information in connection with an obligor's financial projections or risk assessment.

Commercial Real Estate Methodologies

Generally, participants adapted their existing CRE stress testing approaches to model transition risk. Several participants also applied new vendor or internally developed approaches to target property-level estimates of transition risk that were integrated with these existing stress testing models.

Most participants considered the impact of transition risk drivers through adjustments to property-level NOI pathways, cap rates, or loan and property valuation estimates. Participants used different methodologies that focused on estimating revenues based on estimated vacancy rates, rental rates, and lost revenue; operating costs based on higher utility prices; and capital improvements needed to retrofit properties for carbon abatement or increased energy efficiency. These transition risk-adjusted estimates were used to project DSCR and LTV values for each property across the projection horizon. These projected values were then input into the main risk parameter models used in participants' existing stress testing approaches for estimating PD and LGD parameters.

To obtain estimates of energy efficiency or capital expenditure needs, many participants required property-level information on emissions or energy usage that is not currently collected or systematically available. Emissions data facilitate estimates of potential carbon abatement costs or upper bounds of potential retrofitting capital expenditure, while energy usage and efficiency metrics facilitate estimates of the change in relative energy costs across properties. Participants acquired property-level information where available and used proxy estimates, sample methodologies, or client surveys to derive missing information. Examples of data needed and/or proxied include property characteristics (e.g., property location, square footage, number of floors, construction materials, renovation, age of building since construction), energy efficiency ratings (e.g., Energy Star score, LEED ratings), municipal regulatory data, and retrofitting costs.

Impact Estimates from Participants

This section summarizes estimates of average loan-level PDs across participants for the transition risk module. Similar to the physical risk impact estimates, the transition risk impact estimates were produced by participants using their own models and assumptions. Similar caveats as those described under "Impact Estimates from Participants" in the "Physical Risk Module" section apply.

In the transition risk module, participants estimated the impact of two NGFS scenarios on their in-scope corporate and CRE exposures. As shown in table 10, in-scope corporate loans represent about 158,000 loans. In-scope CRE loans represent nearly 37,000 loans.

Table 10. Transition risk summary

Participants estimated that average PDs were higher in the Net Zero 2050 scenario for corporate and CRE loans relative to the Current Policies scenario. As shown in figure 10, the estimate of average PD for in-scope corporate loans was about 30 bps higher in the Net Zero 2050 scenario relative to the Current Policies scenario. The average PD for in-scope CRE loans was about 100 bps higher in the Net Zero 2050 scenario relative to the Current Policies scenario.¹³

Similar to the physical risk module, the distributions of PDs in both scenarios were concentrated in the 0-50 bps range with a small fraction of loans with PDs greater than 500 bps. The underlying distribution of PDs for both corporate and CRE loans shifts to the right in the Net Zero 2050 scenario relative to the Current Policies scenario, and there is an increase in the share of loans with PDs equal to or above 500 bps.

The transition risk impact can be estimated as the difference in the PD between the Net Zero 2050 scenario, which reflects higher carbon prices and related transition effects, and the Current Policy scenario, which doesn't. Box 2 describes the approach to define the transition risk impact for individual loans.

Corporate Estimates

Figure 11 shows the distribution of transition risk impact across all corporate loans. The distribution shows that the majority of loans had a 50 bps or less transition risk impact, while nearly 2 percent of loans had a transition risk impact of more than 500 bps. Participants' estimates also show significant heterogeneity across and within sectors.

One way to compare the impact of differences in methodologies and assumptions across participants is to look at the range of estimated transition risk impacts across participants for common obligors, that is, obligors that obtained credit from multiple participants (figure 12). Participants reported wide variation in estimates for some common obligors. For example, more than 20 percent of loans to common obligors had differences in estimates of transition risk impact of more than 100 bps across participants. Almost 6 percent of loans to common obligors had differences in transition risk impact of more than 500 bps.

CRE Estimates

The average transition risk impact for CRE loans was about 100 bps across all property types. Participants saw meaningful heterogeneity in impact across CRE property types with property types with higher energy intensity showing the largest impact.

 

References

11. As noted in the Participant Instructions, variable pathways from the NiGEM model begin in 2022, and participants were instructed to view the year 2022 within the NGFS scenarios as projection "Year 1" for purposes of the pilot CSA exercise. While participants were able to overcome most discontinuities through the growth rate approach, most participants required bespoke approaches for interest rate variables, given the magnitude of the discontinuity and resulting uneconomic outcomes. Return to text

12. Sector GVA shows the economic value of a sector's output less the value of that sector's input. Return to text

13. The estimated CRE credit risk parameters reported in figure 10 reflect different loan populations and measurement methodologies compared to the CRE credit risk parameters reported in figures 6 and 7. Return to text