Appendix A: Model Changes for DFAST 2019
Each year, the Federal Reserve has refined both the substance and process of the Dodd-Frank Act supervisory stress tests, including its development and enhancement of independent supervisory models. The supervisory stress test models may be enhanced to reflect advances in modeling techniques; enhancements in response to model validation findings; incorporation of richer and more detailed data; and identification of more stable models or models with improved performance, particularly under stressful economic conditions.
For DFAST 2019, the Federal Reserve enhanced the models that project auto loan losses, credit card losses, corporate loan losses, fair value for debt securities, and commercial real estate loan losses. In addition to these model changes, the Federal Reserve made other less material enhancements to simplify the models and account for changes in the historical data used to estimate the models.99
Enhancements to the Auto Loan Model
The Federal Reserve enhanced the PD and LGD components of the auto loan model. These refinements include changes to the way certain risk drivers are captured in the model, which reduces volatility from historical macroeconomic movements, and an adjustment to newly originated accounts to better reflect their higher credit risk compared to otherwise similar accounts.
Collectively, the enhancements are expected to result in a small increase in overall projected auto loan losses; however, for firms with large domestic auto loan portfolios, the changes may result in materially higher projected losses.100 Consistent with the Federal Reserve's stated policy for material model changes, the auto loan loss estimates for DFAST 2019 will be the average of the model used in DFAST 2018 and the updated model. Auto loan loss estimates for DFAST 2020 will only reflect the updated model.101
Phase-In and Additional Refinements to the Credit Card Model
The Federal Reserve began a two-year transition to an updated credit card model in DFAST 2018, and the updated model will be fully in effect for DFAST 2019. The two-year phase-in policy was employed because the credit card model refinements materially affected the forecasted credit card losses for a number of firms.102 The 2018 changes to the credit card model were described in the 2018 model change disclosure letter.103
Additionally, the Federal Reserve refined the way the model treats uncollected interest and fees in the EAD of the model. Data from more recent periods that includes a larger set of firms supports a slight decrease in the assumed percentage of uncollected interest and fee income.
The collective impact is expected to result in a slight increase in overall losses projected by the domestic credit card model, with larger increases for firms with material bank card exposures.104
Refined Treatment of Missing Firm-Reported Corporate-Loan Data
The Federal Reserve refined the treatment of missing firm-reported corporate loan data to better align the treatment of missing data in the corporate loan portfolio with other portfolios. Under the refined treatment, the Federal Reserve assigns a conservative loss rate for an entire portfolio when a certain proportion of the loans are missing required model inputs. Analysis suggests the refined treatment remains appropriately conservative.
The refinement is expected to result in a small decrease in overall losses projected by the corporate loan model. However, for certain firms that are unable to report variables required by the corporate loan model, the change is expected to be material.105 This refinement will be implemented with immediate effect.106
Enhancement to the Risk Drivers in the Debt Fair Value Model
Certain models used to project fair value for debt securities were enhanced to increase modeling flexibility and better align with historical trends. The risk drivers for agency MBS, such as OAS, now flexibly vary over the planning horizon.
The Federal Reserve also adopted a new model to project the OAS for sovereign bonds. In DFAST 2018, the OAS was projected using a scenario-based regression model. The new model projects the OAS based on high-percentile historical movements in sovereign bond spreads.
Collectively, the enhancements to the risk drivers in the debt fair value model are expected to result in a modest increase in overall OCI, with material increases for firms with large holdings of sovereign bonds. Consistent with the Federal Reserve's stated policy for material model changes, projections of OCI for DFAST 2019 will reflect the average OCI under the two approaches for projecting sovereign bond OAS, and projections for DFAST 2020 will only reflect the new model.
Refinements to the Commercial Real Estate Loan Model
The Federal Reserve refined the CRE LGD model and made a number of other minor changes to the CRE loan-loss model. The previous LGD model relied on reported charge-off and loan reserve data, which led to idiosyncratic reporting differences across firms. The change improves consistency by using a common data source and framework for the projection of LGD. Additionally, the Federal Reserve simplified the process for calculating auxiliary risk drivers. Under the new approach, a single conceptual framework is used to project auxiliary risk drivers, which increases consistency and decreases complexity.
The refinements are expected to result in a slight increase in overall projected CRE loan losses with modestly larger increases and decreases for firms depending on the risk characteristics of their portfolio.107
Re-estimation of and Refinements to Other Supervisory Models
Each year, the Federal Reserve makes a number of relatively minor refinements to models that may include re-estimation with new data, re-specification based on performance testing, and other refinements to the code used to produce supervisory projections. In 2019, models most affected by these refinements are the models for certain components of PPNR, first- and second-lien mortgages, trading and counterparty, other retail, operational risk, and the calculation of regulatory capital ratios. With the exception of the changes to certain components of PPNR, the refinements collectively resulted in a minimal change in post-stress capital ratios with no material impacts on any firm.
The Federal Reserve re-estimated the PPNR models with more data to better reflect recent performance in PPNR while keeping the structure of the model unchanged. For this cycle, the re-estimation is expected to result in a small decrease in aggregate PPNR forecasts due to weaker PPNR performance in the most recent year, particularly for net interest income. Additionally, new IHCs and historical data revisions also changed the estimation data.
References
99. Portfolios with material model changes are defined as those in which the change in revenue or losses exceeds 50 basis points for any firm individually under the severely adverse scenario, expressed as a percentage of RWAs, based on data and scenarios from DFAST 2018. In cases in which a portfolio contains more than one change, materiality is defined by the net change. Return to text
100. Analysis was conducted using data and scenarios from DFAST 2018. The effect on projections for DFAST 2019 and future years is uncertain and will depend on changes in firm portfolios, data, and scenarios. Return to text
101. Starting in DFAST 2017, the Federal Reserve began to adhere to a policy of phasing in the most material model enhancements over two stress test cycles to smooth the effect on post-stress capital ratios. See 82 Fed. Reg. 59528 (Dec. 15, 2017). Return to text
102. Starting in DFAST 2017, the Federal Reserve began to adhere to a policy of phasing in the most material model enhancements over two stress test cycles to smooth the effect on post-stress capital ratios. See 82 Fed. Reg. 59528 (Dec. 15, 2017). Return to text
103. See "Enhancements to Federal Reserve Models Used to Estimate Post-Stress Capital Ratios," March 2, 2018, https://www.federalreserve.gov/supervisionreg/files/model-change-letter-20180302.pdf. Return to text
104. Analysis conducted using data and scenarios from DFAST 2018. The effect on projections for DFAST 2019 and future years is uncertain and will depend on changes in firm portfolios, data, and scenarios. Return to text
105. Portfolios with material model changes are defined as those in which the change in revenue or losses exceeds 50 basis points for any BHC individually under the severely adverse scenario, expressed as a percentage of risk-weighted assets, based on the data and scenarios from DFAST 2018. In cases where a portfolio contains more than one change, materiality is defined by the net change. Return to text
106. In general, the phase-in threshold for material model changes applies only to conceptual changes to models. Model changes related to changes in accounting or regulatory capital rules and model parameter re-estimation based on newly available data are implemented with immediate effect. See 82 Fed. Reg. 59528 (Dec. 15, 2017). Return to text
107. Analysis was conducted using data and scenarios from DFAST 2018. The effect on projections for DFAST 2019 and future years is uncertain and will depend on changes in firm portfolios, data, and scenarios. Return to text