Dodd-Frank Act Stress Test 2016: Supervisory Stress Test Methodology and Results
PrintSupervisory Stress Test Framework and Model Methodology
Analytical Framework
The Federal Reserve estimated the effect of the supervisory scenarios on the regulatory capital ratios of the 33 BHCs participating in DFAST 2016 by projecting the balance sheet, RWAs, net income, and resulting capital for each BHC over a nine-quarter planning horizon, which for DFAST 2016 begins in the first quarter of 2016 and ends in the first quarter of 2018. Projected net income, adjusted for the effect of taxes, is combined with capital action assumptions to project changes in equity capital. The approach followed take into account U.S. generally accepted accounting principles (GAAP) and regulatory capital rules.18 Figure 8 illustrates the framework used to calculate changes in net income and regulatory capital.
Projected net income for the 33 BHCs is generated from projections of revenue, expenses, and various types of losses and provisions that flow into pre-tax net income, including
- PPNR;
- loan losses and changes in the allowance for loan and lease losses (ALLL);
- losses on loans held for sale or for investment and measured under the fair-value option;
- other-than-temporary impairment (OTTI) losses on investment securities in the available-for-sale (AFS) and held-to-maturity (HTM) portfolios;
- losses on exposures resulting from a global market shock for BHCs with large trading and private equity exposures; and
- losses from the default of the largest counterparty of BHCs with substantial trading, processing, or custodial operations.
The projection of PPNR includes net interest income plus noninterest income minus noninterest expense. Consistent with U.S. GAAP, the projection of PPNR incorporates projected losses generated by operational-risk events such as fraud, computer system or other operating disruptions, and litigation-related costs; mortgage repurchase related losses; and expenses related to the disposition of foreclosed properties (other real estate owned (OREO) expenses).
Provisions for loan and lease losses equal projected loan losses for the quarter plus the amount needed for the ending ALLL to be at an appropriate level to account for projected future loan losses. The amount of provisions over and above loan losses may be negative--representing a drawdown of the ALLL (an ALLL release, increasing net income)--or positive--representing a need to build the ALLL (an additional provision, decreasing net income).
Because the loss projections follow U.S. GAAP and regulatory guidelines, they incorporate any differences in the way these guidelines recognize income and losses based on where assets are held on the BHCs' balance sheets. As a result, losses projected for similar or identical assets held in different portfolios can sometimes differ. For example, losses on loans held in the accrual portfolio equal credit losses due to failure to pay obligations (cash flow losses resulting in net charge-offs). For similar loans that are held for sale or held for investment and classified as fair value loans, projected losses represent the change in fair value of the underlying assets in the supervisory scenario.
Following this approach, changes in the fair value of AFS securities and OTTI losses on securities are separately projected over the nine-quarter planning horizon. Under U.S. GAAP, changes in the fair value of AFS securities are reflected in changes in accumulated other comprehensive income (AOCI) but do not flow through net income. In addition, if a security becomes OTTI, all or a portion of the difference between the fair value and amortized cost of the security must be recognized in earnings.19 Consistent with U.S. GAAP, OTTI projections incorporate other-than-temporary differences between book value and fair value due to credit impairment but generally do not incorporate differences reflecting changes in liquidity or market conditions.
For the six BHCs subject to the global market shock, the losses on trading and private equity positions as well as the credit valuation adjustment are projected assuming an instantaneous re-pricing of these positions under the global market shock (see Global Market Shock and Counterparty Default Components). Losses from the global market shock are assumed to occur in the first quarter of the planning horizon. No subsequent recoveries on these positions are assumed, nor are there offsetting changes such as reductions in compensation or other expenses in reaction to the global market shock. In addition, incremental losses from potential defaults of obligors underlying BHCs' trading positions are projected over the planning horizon.
For the eight BHCs subject to the counterparty default component, the losses associated with the instantaneous and unexpected default of the largest counterparty across derivatives and securities financing transaction (SFT) activities are projected. These losses are assumed to occur in the first quarter of the planning horizon.
Over the planning horizon, the Federal Reserve projects quarter-end amounts for the components of the balance sheet. These projections are made under the assumption that BHCs maintain their willingness to lend subject to the demand characteristics embedded in the scenario. BHCs are assumed to use lending standards in line with their long-run behavior. Any new balances implied by these projections are assumed to have the same risk characteristics as those held by the BHC at the start of the planning horizon except for loan age. Where applicable, new loans are assumed to be current, and BHCs are assumed not to originate types of loans that are no longer allowed under various regulations. The Federal Reserve also incorporates material changes in a BHC's business plan, such as a planned merger, acquisition, consolidation, or divestiture.20 Only divestitures that had been completed or contractually agreed to prior to April 5, 2016, are incorporated. Once adjusted, assets are assumed to grow at the same rate as the pre-adjusted balance sheet.
Modeling Approach
The Federal Reserve's projections of revenue, expenses, and various types of losses and provisions that flow into pre-tax net income are based on data provided by the 33 BHCs participating in DFAST 2016 and on models developed or selected by Federal Reserve staff and evaluated by an independent team of Federal Reserve model reviewers. The models are intended to capture how the balance sheet, RWAs, and net income of each BHC would be affected by the macroeconomic and financial conditions described in the supervisory scenarios, given the characteristics of the BHCs' loans and securities portfolios; trading, private equity, and counterparty exposures from derivatives and SFTs; business activities; and other relevant factors.21
Detail of model-specific methodology is provided in appendix B.
The Federal Reserve's approach to model design reflects the desire to produce supervisory stress test projections that
- are based on the same set of models and assumptions across BHCs;
- reflect an independent supervisory perspective;
- are forward-looking and may incorporate outcomes outside of historical experience; and
- are appropriately conservative and consistent with the purpose of a stress testing exercise.
With these objectives in mind, the models were developed using multiple data sources, including pooled historical data from financial institutions. The estimated model parameters are the same for all BHCs and reflect the industrywide, portfolio-specific, and instrument-specific response to variation in the macroeconomic and financial market variables. This industrywide approach reflects both the challenge in estimating separate, statistically robust models for each of the 33 BHCs and the desire of the Federal Reserve not to assume that historical BHC-specific results will prevail in the future. This means that the projections made by the Federal Reserve will not necessarily match similar projections made by individual BHCs.
The Federal Reserve deviated from the industrywide modeling approach only in a very limited number of cases in which the historical data used to estimate the model were not sufficiently granular to capture the impact of firm-specific risk factors. In these cases, BHC-specific indicator variables (fixed effects) were included in the models. Additionally, in some cases, the projections of certain types of losses use sensitivities generated by the BHCs from their internal pricing models.
Model Methodology and Validation
A large majority of the models used for the supervisory stress test were developed by Federal Reserve analysts and economists, but a few are third-party models.22 Internally developed models draw on economic research and analysis as well as industry practice in modeling the effect of borrower, instrument, collateral characteristics, and macroeconomic factors on revenue, expenses, and losses. The approaches mostly build on work done by the Federal Reserve in previous stress tests. Models are refined for a variety of reasons, including to incorporate a greater range of data, to improve model stability, and to incorporate greater precision in calculation (see box 1). The performance of each model is assessed using a variety of metrics and benchmarks, including benchmark model results, where applicable. A central oversight group consisting of senior-level Federal Reserve experts closely scrutinized the models and assumptions used in the supervisory stress test and model outputs.
As in prior years, all models used for this year's supervisory stress test were also reviewed by an independent model validation team with a focus on the design, estimation, and implementation of the models. Model reviewers were Federal Reserve staff who are not involved in model development and who report to a different oversight group than model developers. Additionally, control procedures surrounding the model design and implementation processes were reviewed by process control experts.
Loan losses are estimated separately for different categories of loans, based on the type of obligor (e.g., consumer or commercial and industrial), collateral (e.g., residential real estate, commercial real estate), loan structure (e.g., revolving credit lines), and accounting treatment (accrual or fair value). These categories generally follow the classifications of the Consolidated Financial Statements for Holding Companies (FR Y-9C) regulatory report, though some loss projections are made for more granular loan categories.23
Two general approaches are taken to model losses on the accrual loan portfolio. In the first approach, the models estimate expected losses under the macroeconomic scenario. These models generally involve projections of the probability of default, loss given default, and exposure at default for each loan or segment of loans in the portfolio, given conditions in the scenario. In the second approach, the models capture the historical behavior of net charge-offs relative to changes in macroeconomic and financial market variables.
Accrual loan losses are projected using detailed loan information, including borrower characteristics, collateral characteristics, characteristics of the loans or credit facilities, amounts outstanding and yet to be drawn down (for credit lines), payment history, and current payment status.
Data are collected on individual loans or credit facilities for wholesale loan, domestic retail credit card, and residential mortgage portfolios. For other domestic and international retail loans, the data are collected based on segments of the portfolio (e.g., segments defined by borrower credit score, geographic location, and loan-to-value (LTV) ratio).
Losses on retail loans for which a BHC chose the fair-value option accounting treatment and loans carried at the lower of cost or market value (i.e., loans held for sale and held for investment) are estimated over the nine quarters of the planning horizon using a duration-based approach. Losses on wholesale loans held for sale or measured under the fair-value option are estimated by revaluing each loan or commitment each quarter of the planning horizon.
Losses on securities held in the AFS and HTM portfolios are estimated using models that incorporate other-than-temporary differences between amortized cost and fair market value due to credit impairment but generally do not incorporate differences reflecting changes in liquidity or market conditions. Some securities, including U.S. Treasury and U.S. government agency obligations and U.S. government agency mortgage-backed securities, are assumed not to be at risk for the kind of credit impairment that results in OTTI charges. For securitized obligations, models estimate delinquency, default, severity, and prepayment on the underlying pool of collateral. OTTI on direct obligations such as corporate bonds is based on an assessment of the probability of default or severe credit deterioration of the security issuer or group of issuers over the planning horizon. The models use securities data collected at the individual security (CUSIP) level, including the amortized cost, market value, and any OTTI taken on the security to date.
Losses related to the global market shock and the counterparty default components are estimated based on BHC-estimated sensitivities to various market risk factors, market values, and revaluations of counterparty exposures and credit valuation adjustment under the global market shock.
PPNR is projected using a series of models that relate the components of a BHC's revenues and non-credit-related expenses, expressed as a share of relevant asset or liability balances, to BHC characteristics and to macroeconomic variables. Most components are projected using data on historical revenues and operating and other non-credit-related expenses reported on the FR Y-9C report. Separate data are collected about mortgage loans that were sold or securitized but expose a BHC to potential put-back risk and the BHCs' historical losses related to operational-risk events, which are modeled separately from other components of PPNR.
The balance sheet projections are derived using a common framework for determining the effect of the scenarios on balance sheet growth, and, as noted, incorporate assumptions about credit supply that limit aggregate credit contraction. These sets of projections are based on historical data from the Federal Reserve's Financial Accounts of the United States (Z.1) statistical release, which is a quarterly publication by the Federal Reserve of national flow of funds, consolidated balance sheet information for each BHC, and additional data collected by the Federal Reserve.24
Once pre-tax net income is determined using the above components, a consistent tax rate is applied to calculate after-tax net income. After-tax net income also includes other tax effects, such as changes in the valuation allowance applied to deferred tax assets and BHC-reported information about extraordinary income items and income attributable to minority interests.
Box 1. Model Changes for DFAST 2016
Each year, the Federal Reserve has refined elements of both the substance and process of the Dodd-Frank Act supervisory stress tests, including the continued development and enhancement of independent supervisory models. Reasons for revisions to the Federal Reserve's supervisory stress test models may include 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 adverse economic conditions.
In 2016, the Federal Reserve did not introduce significant changes to its modeling framework, and, overall, revisions to most supervisory models were relatively incremental. Where appropriate, models have been re-estimated with more comprehensive data.
Changes to loan loss, mortgage repurchase, and securities models were generally modest and did not have a large net effect on aggregate estimates of total losses. The model used to estimate losses due to operational risk, the model used to estimate the path of market risk-weighted assets (MRWA), and the capital calculation underwent modifications that had a greater impact on aggregate results and on capital ratios for individual firms.
Operational Risk Model Enhancement
The projections of operational risk losses included in the estimates of PPNR include both historically based loss estimates, based on an average of multiple approaches, and estimates of potential costs from unfavorable litigation outcomes. In DFAST 2015 and in prior years, the Federal Reserve projected operational risk losses using the average of three models--a historical simulation model, in which loss severity is drawn from historical realized loss data; a loss distribution approach model, in which loss severity is drawn from a parametric distribution fit into historical data; and a panel regression model, which relates operational risk losses to macroeconomic conditions.1 For DFAST 2016, the Federal Reserve used an average of the historical simulation and panel regression models to project operational risk losses.
Additionally, the historical simulation model underwent two main modifications. Projections for each BHC incorporated large historical losses (in terms of severity and frequency) observed across all BHCs, scaled to firm size, rather than an individual firm's own historical data. Additionally, projections of losses from the historical simulation model were set at percentiles of the loss distribution that correspond to the severity of the supervisory scenarios.2
Collectively, these changes are expected to improve model stability and reduce year-over-year variation in projected operational risk losses. In the aggregate, these changes led to a moderate increase in operational risk losses, excluding estimates of costs related to unfavorable litigation outcomes, as a percentage of risk-weighted assets, and resulted in higher projected losses for firms that have reported fewer tail events historically but are still vulnerable to such loss events.
MRWA Model Enhancement
The models used to estimate several components of MRWA were modified in order to better differentiate the sensitivity of each component to scenario variables and to align the estimation more closely to the market risk rule in the Board's Regulation Q.3 With the modifications, the estimates of the incremental risk charge and comprehensive risk measure components of MRWA move together with the projected volatility in the credit market.
These changes, before accounting for new data and scenario, led to a moderate decline in projected MRWAs in the aggregate, and resulted in lower MRWAs for firms with more incremental risk charge, and higher MRWAs for firms with more comprehensive risk measure.
Supervisory Capital Calculation Enhancement
The Federal Reserve has made several changes to the supervisory capital calculation to improve precision. The main model enhancements include:
- Incorporating greater precision in the adjustments to the regulatory capital ratio denominators.
- Modifying assumptions regarding the relationship between mortgage servicing assets (MSAs) and associated deferred tax liabilities (DTLs).
Large balances of items that are fully deducted from regulatory capital, particularly goodwill, resulted in lower regulatory capital ratios due to this calculation change. The change in assumption underlying the relationship between MSAs and associated DTLs in the capital calculation resulted in higher capital ratios for firms with MSAs. The impact of these enhancements varies across BHCs depending on balances of items fully deducted from regulatory capital and the amount of MSAs held. The enhancements can result in higher or lower capital ratios, depending on the particular combination of such factors.
1. For more detail on the supervisory models used in DFAST 2015, see Appendix B of Board of Governors of the Federal Reserve System (2015), "Dodd-Frank Act Stress Test 2015: Supervisory Stress Test Methodology and Results." Return to text
2. The percentile for the severely adverse scenario corresponds to the frequency of severe recessions over a 60-year historical span. Return to text
3. See 12 CFR part 217, subpart F. Return to text
Data Inputs
The models are developed and implemented with data collected by the Federal Reserve on regulatory reports as well as proprietary third-party industry data.
Certain projections rely on aggregate information from the Financial Accounts of the United States (Z.1) statistical release. Others rely on the FR Y-9C report, which contains consolidated income statement and balance sheet information for each BHC. Additionally, FR Y-9C includes off-balance sheet items and other supporting schedules, such as the components of RWAs and regulatory capital.
Most of the data used in the Federal Reserve's stress test projections are collected through the Capital Assessments and Stress Testing (FR Y-14A/Q/M) reports, which include a set of annual, quarterly, or monthly schedules.25 These reports collect detailed data on PPNR, loans, securities, trading and counterparty risk, losses related to operational-risk events, and business plan changes. Each of the 33 BHCs participating in DFAST 2016 submitted data as of December 31, 2015, through the FR Y-14M and FR Y-14Q schedules in February, March, and April 2016. The same BHC submitted the FR Y-14A schedules, which also include projected data, on April 5, 2016.
BHCs were required to submit detailed loan and securities information for all material portfolios, where the portfolio is deemed to be "material" if the size of the portfolio exceeds either 5 percent of the BHC's tier 1 capital or $5 billion. The portfolio categories are defined in the FR Y-14M and Y-14Q instructions. Each BHC has the option to either submit or not submit the relevant data schedule for a given portfolio that does not meet the materiality threshold (as defined in the FR Y-14Q and FR Y-14M instructions). If the BHC does not submit data on its immaterial portfolio(s), the Federal Reserve will assign a conservative loss rate (e.g., 75th percentile), based on the estimates for other BHCs. Otherwise, the Federal Reserve will estimate losses using data submitted by the BHC.
While BHCs are responsible for ensuring the completeness and accuracy of data reported on the FR Y-14, the Federal Reserve made considerable efforts to validate BHC-reported data and requested resubmissions of data where errors were identified. If data quality remained deficient after resubmissions, conservative assumptions were applied to a particular portfolio or specific data, depending on the severity of deficiencies. If the quality of a BHC's submitted data was deemed too deficient to produce a supervisory model estimate for a particular portfolio, the Federal Reserve assigned a high loss rate (e.g., 90th percentile) or a conservative PPNR rate (e.g., 10th percentile) to the portfolio balances based on supervisory projections of portfolio losses or PPNR estimated for other BHCs. If data that are direct inputs to supervisory models were missing or reported erroneously but the problem was isolated in such a way that the existing supervisory framework could still be used, a conservative value (e.g., 10th or 90th percentile) based on all available data reported by BHCs was assigned to the specific data. These assumptions are intended to reflect a conservative view of the risk characteristics of the portfolios given insufficient information to make more risk-sensitive projections.
Capital Action Assumptions and Regulatory Capital Ratios
After-tax net income and AOCI are combined with prescribed capital actions to estimate components of regulatory capital. Changes in the regulatory capital components are the primary drivers in changes in capital levels and ratios over the planning horizon. In addition to the regulatory capital components, the calculation of regulatory capital ratios accounts for taxes and items subject to adjustment or deduction in regulatory capital, limits the recognition of certain assets that are less loss-absorbing, and imposes other restrictions as specified in the Board's regulatory capital rules.
The Dodd-Frank Act company-run stress test rules prescribe consistent capital action assumptions for all BHCs.26 In its supervisory stress tests, the Board generally followed these capital action assumptions. For the first quarter of the planning horizon, capital actions for each BHC are assumed to be the actual actions taken by the BHC during that quarter. Over the remaining eight quarters, common stock dividend payments are generally assumed to be the average of the first quarter of the planning horizon and the three preceding calendar quarters.27 Also, BHCs are assumed to pay scheduled dividend, interest, or principal payments on any other capital instrument eligible for inclusion in the numerator of a regulatory capital ratio. However, repurchases of such capital instruments and issuance of stock are assumed to be zero, except for issuance of common or preferred stock associated with expensed employee compensation or in connection with a planned merger or acquisition.
The four regulatory capital ratios in DFAST 2016 are common equity tier 1, tier 1 risk-based capital, total risk-based capital, and tier 1 leverage ratios. A BHC's regulatory capital ratios are calculated in accordance with the Board's regulatory capital rules using Federal Reserve projections of assets and RWAs.28
The denominator of each BHC's regulatory capital ratios, other than the tier 1 leverage ratio, was calculated using the standardized approach for calculating RWAs for each quarter of the planning horizon in accordance with the transition arrangements in the Board's capital rules.29
| Capital ratio | Aspect of ratio | Calculation |
|---|---|---|
| BHCs | ||
| Common equity tier 1 ratio | Capital in numerator | Revised capital framework |
| Denominator | Standardized approach RWAs | |
| Tier 1 ratio | Capital in numerator | Revised capital framework |
| Denominator | Standardized approach RWAs | |
| Total capital ratio | Capital in numerator | Revised capital framework |
| Denominator | Standardized approach RWAs | |
| Tier 1 leverage ratio | Capital in numerator | Revised capital framework |
| Denominator | Average assets | |
References
18. 12 CFR part 217. Return to text
19. A security is considered impaired when the fair value of the security falls below its amortized cost. Return to text
20. The inclusion of the effects of such expected changes to a BHC's business plan does not--and is not intended to--express a view on the merits of such proposals and is not an approval or non-objection to such plans. Return to text
21. In some cases, the loss models estimated the effect of local-level macroeconomic data, which were projected based on their historical covariance with national variables included in the supervisory scenarios. Return to text
22. A list of providers of the proprietary models and data used by the Federal Reserve in connection with DFAST 2016 is available in appendix B. Return to text
23. Consolidated Financial Statements for Holding Companies (FR Y-9C) is available on the Federal Reserve website at www.federalreserve.gov. Return to text
24. Financial Accounts of the United States (Z.1) is available on the Federal Reserve website at www.federalreserve.gov/releases/z1/. Return to text
25. The FR Y-14 schedules are available on the Federal Reserve website at www.federalreserve.gov/apps/reportforms/default.aspx. Return to text
26. 12 CFR 252.56(b). Return to text
27. Additionally, common stock dividends attributable to issuances related to expensed employee compensation or in connection with a planned merger or acquisition are included to the extent that they are reflected in the BHC's pro forma balance sheet estimates. This assumption provides consistency with assumptions regarding issuance of common stock. Return to text
28. See 12 CFR 252.44 and 252.42(m). The tier 1 common capital ratio requirement was introduced in 2009 as part of the Supervisory Capital Assessment Program to assess the level of high-quality, loss-absorbing capital held at the largest BHCs. At the time, the Board noted that it expected the tier 1 common capital ratio requirement to remain in force until the Board adopted a minimum common equity capital requirement. The Board proposed that the tier 1 common ratio be removed in light of the implementation of the minimum CET1 capital requirement, which became effective January 1, 2015. On November 25, 2015, the final rule removing the requirement for firms to calculate a tier 1 common ratio was approved. See 80 Fed. Reg. 75,419. Return to text
29. See 12 CFR 252.42(m); 80 Fed. Reg. 75,419; 12 CFR part 217, subpart G. Return to text
