Board of Governors of the Federal Reserve System

Appendix B: Models to Project Net Income and Stressed Capital

This appendix describes the models used to project stressed capital ratios and pre-tax net income and its components for the 35 firms subject to DFAST 2018.⁴⁰ The models fall into five broad categories:

1. Models to project losses on loans held in the accrual loan portfolio; loans in the accrual loan portfolio are those measured under accrual accounting, rather than fair-value accounting.
2. Models to project other types of losses, including those from changes in fair value on loans held for sale or measured under the fair-value option; losses on securities, trading, and counterparty exposures.
3. Models to project the components of PPNR (revenues and non-credit-related expenses) and losses related to operational-risk events that are part of PPNR.
4. Models to project balance sheet items and risk-weighted assets (RWAs).
5. The calculations to project capital ratios, given projections of pre-tax net income, assumptions for determining provisions into the allowance for loan and lease losses (ALLL), and prescribed capital actions.

For DFAST 2018, the Federal Reserve materially enhanced its models for projecting PPNR and other-than-temporary impairments for debt securities. The Federal Reserve re-estimated and refined models for projecting domestic credit card losses, auto loan losses, first-lien residential mortgage losses, and home equity losses. The Federal Reserve also made material changes to its capital calculations to account for changes in the tax law due to the Tax Cuts and Jobs Act. See box 1 for more details on material model changes for DFAST 2018.

Losses on the Accrual Loan Portfolio

More than a dozen individual models are used to project losses on loans held in the accrual loan portfolio. The individual loan types modeled can broadly be divided into wholesale loans, such as commercial and industrial (C&I) loans and commercial real estate loans, and retail loans, including various types of residential mortgages, credit cards, student loans, auto loans, small business loans, and other consumer loans.

The models project losses using detailed loan portfolio data provided by the firms on the FR Y-14 information collection.

Within larger loan categories, loan portfolios may be subcategorized by loan type, each with their own specific loss projection model. Generally, the loss models for all loan portfolios belonging to a larger category are similar. For example, losses on first-lien mortgage loans, home equity loans, and home equity lines of credit are each estimated by separate models, but each model follows the same structural form.

Two general approaches are taken to model losses on the accrual loan portfolio. In the first approach--an approach broadly used for DFAST 2017--the models estimate expected losses under the macroeconomic scenario; that is, they project the probability of default (PD), loss given default (LGD), and exposure at default (EAD) for each quarter of the planning horizon. Expected losses in quarter $$ t$$ are the product of these three components:

$$$$ {Loss}_t = {PD}_t \ast {LGD}_t \ast {EAD}_t $$$$

PD is generally modeled as part of a transition process in which loans move from one payment status to another (e.g., from current to delinquent) in response to economic conditions. Default is a terminal transition, and PD represents the likelihood that a loan will default during a given period. The number of payment statuses and the transition paths modeled differ by loan type.

LGD is typically defined as a percentage of EAD and is modeled based on historical data. For some loan types, LGD is modeled as a function of borrower, collateral, or loan characteristics and the macroeconomic variables from the supervisory scenarios. For other loan types, LGD is assumed to be a fixed percentage for all loans in a category. Finally, the approach to modeling EAD varies by loan type and depends on whether the outstanding loan amount can change between the current period and the period in which the loan defaults (e.g., for lines of credit).

In the second approach, the models capture the historical behavior of net charge-offs relative to changes in macroeconomic and financial market variables and loan portfolio characteristics.

The loss models primarily focus on losses arising from loans in the accrual loan portfolio as of December 31, 2017. The loss projections also incorporate losses on loans originated after the planning horizon begins. These incremental loan balances are calculated based on the Federal Reserve's projections of loan balances over the planning horizon. These new balances are assumed to have the same risk characteristics as those of the loan portfolio as of December 31, 2017, with the exception of loan age in the retail and CRE portfolios, where seasoning is incorporated. Where applicable, new loans are assumed to be current, and firms are assumed not to originate types of loans that are no longer permitted under various regulations. Loss projections also incorporate losses on loans acquired through mergers or purchase after the planning horizon begins. Additional information provided by the firms about the size and composition of acquired loan portfolios is used to estimate losses on acquired portfolios.

Loss projections generated by the models are adjusted to take account of purchase accounting treatment, which recognizes discounts on impaired loans acquired during mergers and any other write-downs already taken on loans held in the accrual loan portfolio. This latter adjustment ensures that losses related to these loans are not double counted in the projections. Loss projections do not take private loss-sharing agreements into account, reflecting the complexity and heterogeneity of such agreements and their uncertainty under stress.

Wholesale Loans: Corporate Loans ⁴¹

Losses stemming from default on corporate loans are projected at the loan level using an expected loss modeling framework.⁴¹ Corporate loans consist of a number of different categories of loans, as defined by the Consolidated Financial Statements for Holding Companies--FR Y-9C report (FR Y-9C). The largest group of these loans includes C&I loans, which are generally defined as loans to corporate or commercial borrowers with more than $1 million in committed balances that are "graded" using a firm's corporate loan rating process.⁴² Small business loans with less than $1 million in committed balances are included in other retail loans and are modeled separately.

The PD for a C&I loan is projected over the planning horizon by first calculating the loan's PD at the beginning of the planning horizon and then projecting it forward using an equation that relates historical changes in PD to changes in the macroeconomic environment. The PD as of December 31, 2017, is calculated for every C&I loan in a firm's portfolio using detailed, loan-level information submitted by the firm. For publicly traded borrowers, a borrower-specific PD, based on the expected default frequency, is used. For other borrowers, the PD is calculated based on the borrower's industry category and the firm's internal credit rating for the borrower, which is converted to a standardized rating scale and then mapped to a PD. Loans that are 90 days past due, in non-accrual status, or that have a Financial Accounting Standards Board Accounting Standards Codification Subtopic 310-10 (ASC 310-10) reserve as of December 31, 2017, are considered in default.

PDs are projected over the planning horizon using a series of equations fitted to the historical relationship between changes in the PD and macroeconomic variables, including growth in real gross domestic product (GDP), changes in the unemployment rate, and changes in the spread on BBB-rated corporate bonds. The equations are estimated separately by borrower industries, credit quality categories, and whether the borrower is foreign or domestic.

The LGD for a C&I loan at the beginning of the planning horizon is determined by the line of business, seniority of lien (if secured), country, and ASC 310-10 reserve, if applicable. The LGD is then projected forward by relating the change in the LGD to changes in the PD. In the model, the PD is used as a proxy for economic conditions, and, by construct, increases in PD generally lead to higher LGDs.

The EAD for C&I lines of credit and other revolving commitments equals the sum of the funded balance and a portion of the unfunded commitment, which reflects the amount that is likely to be drawn down by the borrower in advance of a future default. This drawdown amount is calibrated to the historical drawdown experience for defaulted U.S. syndicated revolving lines of credit that are in the Shared National Credit (SNC) database.⁴³ In the case of closed-end C&I loans, the funded balance and the corresponding EAD equals the outstanding balance. The EAD for standby letters of credit and trade finance credit are conservatively assumed to equal the total commitment.

Other corporate loans that are similar in some respects to C&I loans are modeled using the same framework. These loans include owner-occupied CRE loans, capital equipment leases, loans to depositories, syndication pipeline loans, non-purpose margin loans (net of loans to purchase and carry securities), and other loans.⁴⁴ Projected losses on owner-occupied CRE loans are disclosed in total CRE losses, while projected losses for the remaining other corporate loans are disclosed in the other loans category.

Wholesale Loans: CRE

CRE loans are loans collateralized by domestic and international non-owner occupied multifamily or nonfarm, nonresidential properties, and construction and land development loans (C&LD), as defined by the FR Y-9C report. Losses stemming from default on CRE loans are projected at the loan level using an expected-loss modeling framework.

The PD model for CRE loans is a hazard model of the probability that a loan transitions from current to default status, given the characteristics of the loan as well as macroeconomic variables, including commercial and residential property price indices and unemployment rates, at both the geographic market and national level. Once defaulted, the model assumes the loan does not re-perform; the effect of re-performance on the estimated loan loss is captured in the LGD model. A CRE loan is considered in default if it is 90 days past due, in non-accrual status, has an ASC 310-10 reserve, or had a very low internal credit rating at the most recent time its maturity was extended. The PD model also incorporates a nonlinear increase in PD as the loan maturity nears. The effect of loan age on the PD is calculated jointly for income-producing and C&LD loans. However, controls are included for income-producing and C&LD loans, and the effect of reaching loan maturity on the PD is allowed to vary for each type of loan. The model is estimated using the historical FR Y-14Q information collection and historical commercial mortgage-backed security (CMBS) data pooled together. The model also includes controls for the types of property (for example, multifamily) to account for differences in risk characteristics.

The LGD for CRE loans is calculated using FR Y-14Q report data on ASC 310-10 reserves. The model first estimates the probability that a defaulted loan will have losses as a function of loan characteristics and macroeconomic variables, including commercial property prices, residential house prices, and the unemployment rate. Then, using loans with losses, the model estimates the loss on the CRE mortgage as a function of the expected probability of loss, characteristics of the loan, and residential house prices and the unemployment rate. Finally, the EAD for CRE loans is assumed to equal the loan's full committed balance for both income producing and C&LD loans.

Retail Loans: Residential Mortgages

Residential mortgages held in firm portfolios include first and junior liens--both closed-end loans and revolving credits--that are secured by one- to four-family residential real estate as defined by the FR Y-9C report. Losses stemming from default on residential mortgages are projected at the loan level using an expected-loss modeling framework.⁴⁵

The PD model for first-lien residential mortgages estimates the probability that a loan transitions to different payment statuses, including current, delinquent, servicing transfer, default, and paid off. Separate PD models are estimated for closed-end fixed-rate mortgages and adjustable-rate mortgages. The PD model specification varies somewhat by loan type; however, in general, each model estimates the probability that a loan transitions from one payment state to another (e.g., from current to delinquent or from delinquent to default) over a single quarter, given the characteristics of the loan, borrower, and underlying property as well as macroeconomic variables, including local house prices, the statewide unemployment rate, and interest rates.⁴⁶ Origination vintage effects are also included in the estimation in part to capture unobserved characteristics of loan quality.

The historical data used to estimate this model are industrywide, loan-level data from many banks and mortgage loan originators. These estimated PD models are used to predict default for each loan reported by each firm under the supervisory scenarios. Residential mortgage loans are considered in default when they are 180 days or more past due.

The LGD for residential mortgages is estimated using two models. One model estimates the amount of time that elapses between default and real estate owned (REO) disposition (timeline model), while the other relates characteristics of the defaulted loan, such as the property value at default, to loss severity (in the case of prime loans) or dollars of losses (in the case of subprime and alt-A loans).⁴⁷

For prime loans, loss amounts and severities are calculated from historical data on loan balances, servicer advances, and losses from defaulted loans in both private-label, residential mortgage-backed securities (RMBS) and Freddie Mac securities. For subprime and alt-A loans, only RMBS data are used.⁴⁸

Finally, using the elapsed time between default and REO disposition estimated in the timeline model, total estimated losses are allocated into credit losses on the defaulted loans, which are fully written down at the time of default, or net losses arising from the eventual sale of the underlying property (other real estate owned--or OREO--expenses), which flow through PPNR. House price changes from the time of default to foreclosure completion (REO acquisition) are captured in LGD, while house price changes after foreclosure completion and before sale of the property are captured in OREO expenses. The LGD for loans already in default as of December 31, 2017, includes further home price declines through the point of foreclosure.

Home equity loans (HELs) are junior-lien, closed-end loans, and home equity lines of credit (HELOCs) are revolving open-end loans extended under lines of credit. Both are secured by one- to four-family residential real estate as defined by the FR Y-9C report. Losses stemming from default on HELs and HELOCs are projected at the loan level in an expected loss framework that is similar to first-lien mortgages, with a few differences. The PD model for HELOCs estimates the probability that a loan transitions to different payment statuses, including current, impaired, default, and paid off. At each point in time, each transition model is a function of account characteristics, customer characteristics, economic environment, and past delinquency history. Economic drivers include interest rates, home prices, and the unemployment rate.

For second-lien HELs and HELOCs that are current as of December 31, 2017, but are junior to a seriously delinquent first-lien mortgage, the model assumes elevated default rates under the supervisory scenarios. In addition, most HELOC contracts require only payment of interest on the outstanding line balance during the period when the line can be drawn upon (draw period). When the line reaches the end of its draw period (end-of-draw), the outstanding line balance either becomes immediately payable or converts to a fully amortizing loan. HELOCs that reach the end-of-draw period are assumed to prepay at a higher rate just prior to end-of-draw and to default at a higher rate just after end-of-draw than HELOCs that are still in their draw period.

The LGD for HELs and HELOCs is estimated using data from private-label mortgage-backed securities, using models used for closed-end first-lien mortgages, but the estimated total mortgage losses for properties with a defaulted HEL or HELOC are allocated based on the lien position. Finally, for HELOCs, EAD is assumed to equal the maximum of the outstanding line balance and the credit limit for lines that are open and have not reached the end-of-draw period, and equal to the outstanding line balance for loans that have been closed by the servicer or are past the end-of-draw period.

Retail Loans: Credit Cards

Credit cards include both general purpose and private-label credit cards, as well as charge cards, as defined by the FR Y-9C report. Credit card loans extended to individuals are included in retail credit cards, while credit cards loans extended to businesses and corporations are included in other retail lending and are modeled separately. Losses stemming from defaults on credit cards are projected at the loan level using an expected-loss modeling framework.

The PD model for credit cards estimates the probability that a loan transitions from delinquency status to default status, given the characteristics of the account and borrower as well as macroeconomic conditions, including the level and changes in the unemployment rate. When an account defaults, it is assumed to be closed and does not return to current status. Credit card loans are considered in default when they are 120 days or more past due. Because the relationship between the PD and its determinants can vary with the initial status of the account, separate transition models are estimated for accounts that are current and active, current and inactive accounts, and delinquent accounts. In addition, because this relationship can also vary by time horizon, separate transition models are estimated for short-, medium-, and long-term horizons. The historical data used to estimate this model are industrywide, loan-level data from many banks, and separate models were estimated for bank cards and charge cards. The PD model is used to forecast the PD for each loan reported by each firm in the FR Y-14 information collection.

The LGD for credit cards is assumed to be a fixed percentage of EAD and is calculated separately for bank cards and charge cards based on historical industry data on LGD during the most recent economic downturn. The EAD for credit cards equals the sum of the amount outstanding on the account and the estimated amount of the available credit line that is likely to be drawn down by the borrower between the beginning of the planning horizon and the time of default. This drawdown amount is estimated as a function of account and borrower characteristics. Because this relationship can vary with the initial status of the account and time to default, separate models are used to estimate the drawdown amount for current and delinquent accounts and for accounts with short-, medium-, and long-term transition to default. For accounts that are current, separate models were also estimated for different credit-line-size segments. The estimated EAD is further adjusted to better reflect typical accounting practice for accrued, unpaid interest and fees.

For DFAST 2018, the estimation sample for the PD model was expanded, and the historical data used to estimate LGD were updated to include a larger set of firms and a slightly longer downturn period to better capture lags in the recovery of credit card losses. For DFAST 2018, credit card loss projections are calculated as the arithmetic average of the projection from the models used in DFAST 2017 and the enhanced models, consistent with the Federal Reserve's policy of phasing in the most material model enhancements over two stress test cycles to smooth the effect on post-stress capital ratios.⁴⁹

Retail Loans: Auto

Auto loans are consumer loans extended for the purpose of purchasing new and used automobiles and light motor vehicles as defined by the FR Y-9C report. Losses stemming from default in auto retail loan portfolios are projected at the portfolio segment level using an expected loss framework.

The PD model for auto loans estimates the probability that a loan transitions from either a current or delinquent status to default status, given the characteristics of the loan and borrower as well as macroeconomic variables, including house prices and the unemployment rate. Auto loans are defined as being in default based on either the payment status (120 days or more past due), actions of the borrower (bankruptcy), or actions of the lender (repossession). Because the relationship between the PD and its determinants can vary with the initial status of the account, separate transition models are estimated for accounts that are current and for those that are delinquent. The historical data used to estimate this model are loan-level, credit bureau data.

The LGD for auto loans is modeled as a function of the characteristics of the loan as well as macroeconomic variables, such as the unemployment rate and used car prices. The historical data used to estimate this model are pooled, segment-level data provided by the firms on the FR Y-14 information collection. The EAD for auto loans is based on the typical pattern of amortization of loans that ultimately defaulted in historical credit bureau data. The estimated EAD model captures the average amortization by loan age for current and delinquent loans over nine quarters.

Retail Loans: Other Retail Loans

The other retail loans category includes the small business loan portfolio, the other consumer loan portfolio, the student loan portfolio, the business and corporate credit card portfolio, and international retail portfolio. Losses due to default on other retail loans are forecast by modeling as a function of portfolio risk characteristics and macroeconomic variables. This model is then used to predict future charge-offs consistent with the evolution of economic conditions under the supervisory scenarios.⁵⁰ The predicted net charge-off rate is applied to balances projected by the Federal Reserve to estimate projected losses.

The net charge-off rate is projected using a system of equations that also generates projections of the delinquency rate and the default rate. Default is defined as 90 days or more past due for domestic and international other consumer loans and 120 days or more past due for student loans, small business loans, corporate cards, and international retail portfolios. In general, each rate is modeled in an autoregressive specification that also includes the rate in the previous delinquency state, characteristics of the underlying loans, macroeconomic variables, such as changes in the unemployment rate and disposable personal income growth, and, in some cases, seasonal factors. The models are specified to implicitly capture roll-rate dynamics.⁵¹ Each retail product type is modeled separately, and, for each product type, economic theory and the institutional characteristics of the product guide the inclusion and lag structure of the macroeconomic variables in the model.

Because of data limitations and the relatively small size of these portfolios, the net charge-off rate for each loan type is modeled using industrywide, monthly data at the segment level. For most portfolios, these data are collected on the FR Y-14Q Retail schedule, which segments each portfolio by characteristics such as borrower credit score; loan vintage; type of facility (e.g., installment versus revolving); and, for international portfolios, geographic region.⁵²

Charge-off rates are projected by applying the estimated system of equations to each segment of the firm's loan portfolio as of December 31, 2017. The portfolio-level charge-off rate equals the dollar-weighted average of the segment-level charge-off rates.⁵³

Loan-Loss Provisions for the Accrual Loan Portfolio

Losses on the accrual loan portfolio flow into net income through provisions for loan and lease losses. Provisions for loan and lease losses equal projected loan losses for the quarter plus the amount needed for the ALLL to be at an appropriate level at the end of the quarter, which is a function of projected future loan losses. The appropriate level of ALLL at the end of a given quarter is generally assumed to be the amount needed to cover projected loan losses over the next four quarters.⁵⁴ Because this calculation of ALLL is based on projected losses under the adverse or severely adverse scenarios, it may differ from a firm's actual level of ALLL at the beginning of the planning horizon, which is based on the firm's estimate of incurred losses as of the balance sheet date.⁵⁵ Any difference between these two measures of ALLL is smoothed into the provisions projection over the nine quarters of the planning horizon. Because projected loan losses include off-balance sheet commitments, the firm's allowance at the beginning of the planning horizon for credit losses on off-balance sheet exposures (as reported on the FR Y-9C report) is subtracted from the provisions projection in equal amounts each quarter.

Other Losses

Loans Held for Sale or Measured under the Fair-Value Option

Certain loans are not accounted for on an accrual basis. Loans to which the fair-value option (FVO) is applied are valued as mark-to-market assets. Loans that are held-for-sale (HFS) are carried at the lower of cost or market value.

FVO/HFS loan portfolios are identified by firms and reported on the FR Y-14Q report. Losses related to FVO/HFS loans are recognized in earnings on the income statement at the time of the devaluation and are calculated by applying scenario-specific interest rate and credit spread projections.

Losses on C&I and CRE loans and commitments are calculated by revaluing each loan or commitment each quarter using a stressed discount yield. The initial discount yield is based on the loan or commitment's initial fair value, settlement date, maturity date, and interest rate. Quarterly movements in the discount yield over the planning horizon are assumed to equal the stressed change in corporate bond yields of the same credit rating and maturity, adjusted for potential changes in credit ratings. The models estimate changes in the fair value of the loan in a given scenario on a committed-balance basis.

Losses on retail loans held under FVO/HFS accounting are calculated over the nine quarters of the planning horizon using a duration-based approach. This approach uses balances on these loans reported on the FR Y-14Q report, estimates of portfolio-weighted duration, and quarterly changes in stressed spreads from the macroeconomic scenario. Estimates are calculated separately by vintage and loan type. No losses are assumed for residential mortgage loans under forward contract with the government-sponsored enterprises (GSEs).

Gains and losses on FVO loan hedges are modeled on a quarterly basis, using a set of scenario-specific factor projections and factor sensitivities submitted by firms. Profits and losses are calculated for a variety of hedge types, including corporate credit, rates, equities, and CMBS. These profits and losses are netted from estimated losses on the FVO loans.

Projections of fair value losses assume that each position has a constant maturity over the projection horizon. Aggregate gains and losses on hedges at the firm level are netted against projected gains and losses on wholesale and retail exposures in order to arrive at final estimates.

Securities in the Available-for-Sale and Held-to-Maturity Portfolios

Securities in the available-for-sale and held-to-maturity (AFS/HTM) portfolios include U.S. Treasury, U.S. Agency, municipal, mortgage-backed, asset-backed, corporate debt, sovereign debt, collateralized loan obligation, collateralized debt obligation, and equity securities. The AFS/HTM portfolio does not include securities held for trading; losses on these securities are projected separately. Changes in the value of the AFS/HTM portfolio can potentially impact a firm's capital in two ways. First, other-than-temporary impairment (OTTI) losses on AFS/HTM securities, realized gains and losses on sold securities, and unrealized gains and losses on equity securities are recognized in the net income of all firms. Second, under regulatory capital rules, accumulated other comprehensive income (AOCI) that arises from unrealized changes in the value of AFS securities must be incorporated into the Common Equity Tier 1 (CET1) for advanced approaches firms (and other firms that do not opt out of including AOCI in regulatory capital). Both OTTI and unrealized gains and losses on securities are projected at the security level, based on FR Y-14Q data, and aggregated up to the firm-level.

Other comprehensive income (OCI) associated with AFS securities arises from changes in their unrealized gains and losses, which are calculated as the difference between each security's fair value and its amortized cost. The amortized cost of each AFS security is collected by the Federal Reserve and equals the purchase price of a debt security that is periodically adjusted if the debt security (1) was purchased at a price other than par or face value or (2) has had a prior impairment recognized in earnings. The fair value of each AFS security is projected over the nine-quarter planning horizon using one of three methods: a present-value calculation, a full revaluation, or a duration-based approach. The simple present-value calculation is used to directly re-price U.S. Treasury securities. This calculation incorporates both the timing and amount of contractual cash flows and quarterly Treasury yields from the macroeconomic scenario. Full revaluation uses a security-specific discounted cash flow model to re-price agency MBS. Finally, the duration-based approach is used for all other debt securities. The duration-based approach forecasts the quarterly price path based on an approximation of the relationship between the securities price and its yield, taking into account security-specific information. Separate spread projections are calculated for securities in each asset class using projections of interest rates, corporate credit spreads, volatility, and asset prices included in the supervisory scenarios. Final projections of OCI take into account applicable interest rate hedges on the securities.

Securities experiencing an impairment over the forecast horizon may be at risk of an OTTI, which affects earnings and regulatory capital.⁵⁶ An impairment occurs when the fair value of a security falls below its amortized cost. If the firm intends to sell a security, or if it is more likely than not that the firm will have to sell without recovering its investment, then any impairment on that security will flow through the firm's earnings. The full write-down to fair value is recognized periodically as OTTI until the quarter in which the security is sold. Otherwise, a firm must recognize as a charge to earnings only the credit component of OTTI, which reflects a non-temporary decline in present value below amortized cost. The supervisory OTTI models are designed to incorporate the credit component only.

U.S. Treasury and U.S. government agency obligations and U.S. government agency or GSE mortgage-backed securities are assumed not to be at risk for the kind of credit impairment that results in credit-related OTTI charges. The supervisory OTTI model estimates OTTI for all other debt securities under the stress scenario. For all securities at risk for impairment that would result in credit-related OTTI charges, future balances are assumed to have risk characteristics similar to those of the initial balances.

Securities at risk of an impairment that would result in credit-related OTTI charges consist of securitizations and direct debt obligations. Securitized obligations include mortgage-backed, asset-backed, collateralized loan obligation, and collateralized debt obligation securities. Direct debt obligations are issued by a single issuer with recourse and include municipal, corporate, and sovereign debt securities.

As described in box 1, the model used to project OTTI charges for securitizations and direct debt obligations has changed for DFAST 2018. For all debt securities, OTTI charges for these securities are projected using the statistical relationship between historically observed OTTI write-downs and measures of the fair value of the securities. The model estimates this relationship separately for securitized obligations and direct debt obligations and accounts for each security's current amortized cost basis. Historical data on securities' amortized cost, fair values, and OTTI write-downs are obtained from the FR Y-14Q report and additional data from filings by U.S. life insurance companies. OTTI charges for each security are projected using this estimated relationship, the security's current amortized cost, current fair value, and projected fair value over the scenario from the supervisory fair value model.

Fair value losses and gains on equity securities are based on the projected fair value of each security as determined by the U.S. equities index in the scenario and the sensitivity of each security's returns to the overall returns of the index. Unrealized losses and gains for equity securities are recognized in net income and affect capital directly for all firms.⁵⁷

Balances at risk of OTTI are assumed not to decrease. After a security is written down as OTTI, the difference between its original value and its post-OTTI value is assumed to be invested in securities with the same risk characteristics. Similarly, the fair value projections assume that duration and remaining life remain constant. Net increases projected by the Federal Reserve in a firm's securities portfolio after December 31, 2017, are assumed to be in short-term, riskless assets, and no OTTI or OCI are projected on these securities.

Trading and Private Equity

Losses related to trading positions that are included in the supervisory stress test are of two primary types. The first type arises from changes in the mark-to-market value of the trading positions. The second type is associated with either the potential or the realized default of obligors and counterparties. The models used to project losses on trading positions under the global market shock account for both types of losses and rely on the market values and stressed revaluation of positions provided by firms on the FR Y-14Q report.

Mark-to-market gains and losses on trading positions are calculated by applying the movements in the global market shock factors to the associated market values or market value movements provided by firms. The global market shock specifies movements in numerous market factors, such as equity prices, foreign exchange rates, interest rates and spreads, commodity prices, securitized product prices, and private equity values. Firms provide the market value of their securitized products and private equity positions. For all other market factors, firms provide the estimated market value change (i.e., the profit or loss) across the trading book associated with a single, limited movement in a market factor (e.g., +1 basis point movement in a foreign exchange rate) or a range of positive and negative movements in a market factor (e.g., -30 percent, -25 percent, +30 percent for a foreign exchange rate). These market values and market value changes are collected for the same factors specified in the global market shock. The computation of gains and losses is performed by applying the market factor movements specified in the global market shock to the information reported by firms. For securitized products and private equity positions, the market values are multiplied by the global market shock market value movements. For all other market factors the gains and losses are computed by either multiplication of the global market shock movement and a single associated market value change or by interpolation using the range of associated market value changes. The ranges are used to capture the non-linear market value changes associated with certain assets.

Credit Valuation Adjustments

Losses that are related to the potential adverse changes in credit quality of a counterparty to derivatives positions are captured through credit valuation adjustments (CVA). CVA is an adjustment to the mark-to-market valuation of a firm's trading position that accounts for the risk of adverse changes in a counterparty's credit quality. Firms report their baseline and stressed CVA at the counterparty-level on the FR Y-14Q report as well as the associated baseline and stressed values of the components of CVA (i.e., counterparty expected exposure, probability of default (PD), and loss given default (LGD). The loss estimate is computed as the difference between the baseline and the stressed CVA aggregated across all counterparties.

Incremental Default Risk

In addition to mark-to-market and CVA losses on trading positions, the losses associated with the explicit default of issuers of credit instruments are captured through an incremental default risk (IDR) model. The IDR model estimates the credit losses in excess of mark-to-market losses subsequent to default of an issuer. The exposure types captured through this issuer default-loss estimate include single-name products (e.g., corporate bonds and single name CDS), index and index-tranche products, and securitized products.⁵⁸ A distribution of simulated sets of issuer defaults is created through a random jump-to-default framework that is based on factors such as PD and obligor correlations. Default distributions are simulated at the level of individual obligors or at the instrument and rating level, depending on exposure type. Losses associated with each default are derived from exposure at default, which is based on position information reported on the FR Y-14Q report, and loss given default (LGD), which is based on historical information. The loss estimate is the loss associated with a tail percentile of the distribution, which is calibrated to the severity of the macroeconomic scenario.

Largest Counterparty Default

To estimate losses from the default of counterparties to derivatives and securities financing transactions, the Federal Reserve applied a counterparty default scenario component to the eight firms that have substantial trading or custodial operations. The loss is based on the assumed instantaneous and unexpected default of a firm's largest counterparty, defined as the counterparty that would produce the largest total net stressed loss if it were to default on all of its derivatives and securities financing transactions. Net stressed loss was calculated using net stressed current exposure (CE), which is derived by applying the global market shock to the unstressed positions as well as any collateral posted or received and reported by firms. For derivative agreements, net stressed current exposure was calculated net of any stressed credit valuation adjustment (CVA) losses and any gains from CVA hedges not included in the calculation of trading gains or losses. A recovery rate of 10 percent is assumed for both net stressed CE and applicable CDS hedges.

Similar to the global market shock component, the loss associated with the counterparty default component occurs in the first quarter of the projection and is an add-on to the economic conditions and financial market environment in the supervisory scenarios. Certain sovereign entities (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States) and designated clearing counterparties were excluded when selecting the largest counterparty.

PPNR

PPNR is the sum of net interest income (interest income minus interest expense) plus noninterest income less noninterest expense, including losses from operational risk events and OREO expenses. PPNR does not include provisions for credit losses on loans or losses from other than temporary impairments on securities.

Core components of PPNR

Core components of PPNR are forecast separately, using autoregressive models, aggregate models, simple nonparametric models, or structural models.

Significant efforts are made to eliminate or minimize potential double-counting of losses. For example, in estimating certain components of PPNR, historical data series are adjusted, where appropriate, to exclude losses from operational-risk events and OREO expenses, which are modeled separately, as described below. In addition, the modeling approach for trading revenue (described below) limits the influence of severe market events captured in the global market shock. Finally, PPNR projections do not include debt valuation adjustments (DVA), which is not included in regulatory capital.

When choosing the level of detail at which to model the components of PPNR, consideration is given both to firms' business models and the ability to model the individual income or expense component accurately. Separate models are estimated for 24 different components of PPNR:

• The eight components of interest income modeled include income on loans, interest bearing balances, Treasury securities, mortgage-backed securities, federal funds and repurchase agreements, other securities, trading assets, and all other interest income.
• The seven components of interest expense modeled include interest expense on domestic time deposits, non-time deposits, foreign deposits, federal funds and repurchase agreements, trading liabilities and other borrowed money, subordinated debt, and all other interest expenses.
• The six components of noninterest income include trading revenue, and five components of noninterest, non-trading income: service charges on deposits, fiduciary income and insurance and annuity fees, investment banking fees, net servicing fees, and all other noninterest income.
• Finally, the three components of noninterest expense include compensation expense, fixed asset expense, and all other noninterest expense (excluding losses from operational-risk events or OREO expenses).

Each model generates projections of the PPNR component scaled by a relevant asset or liability balance (e.g., interest income on Treasury securities as a ratio of the book value of Treasury securities). The ratio forecast is then multiplied by the firm's projected asset or liability balance to generate a projection of the dollars of PPNR for that component.

The types of models used to forecast various components of PPNR include:

• autoregressive models that relate the components of a firm's revenues and non-provision-related expenses, expressed as a share of relevant asset or liability balances, to firm characteristics and to macroeconomic variables;
• aggregate models in which the revenue or expense is allocated to each firm based on a measure of the firm's market share;
• simple nonparametric models based on recent firm-level performance; and
• structural models that use granular data on individual positions.

The specific macroeconomic variables included in the models differ across equations based on statistical predictive power and economic theory. Macroeconomic variables used to project interest income and interest expense include yields on short- and long-term Treasury securities and corporate bond spreads. Noninterest non-trading income and noninterest expense are projected as a function of GDP growth, stock market volatility, stock returns, and home price growth.

Trading revenues are volatile because they include both changes in the market value of trading assets and fees from market-making activities. Forecasts of PPNR from trading activities at the firms subject to the global market shock are modeled in the aggregate, as a function of stock market returns and changes in stock market volatility, and then allocated to each firm based on a measure of the firm's market share. Net trading revenue is modeled using a median regression approach to lessen the influence of extreme movements in trading revenue, and, thereby, to avoid double-counting of trading losses that are captured under the global market shock. Trading revenues for the remaining firms are modeled as a function of corporate bond yields and long-term Treasury yields, in an autoregressive framework similar to that of other PPNR components. Some noninterest income and noninterest expense components are highly volatile quarter-to-quarter but do not exhibit a clear cyclical pattern. As a result, these components are modeled as a constant forecast ratio to reflect median performance over the past eight quarters.

The estimate of interest expenses on subordinated debt is based on security-level information and takes into account differences in the maturity schedule and debt pricing across firms. The estimate also reflects yields on short-term and long-term Treasury securities, and corporate bond spreads under various scenarios.

In the autoregressive models, projections for PPNR components (expressed as a share of a relevant asset or liability balance) converge over time towards the firm's own post-crisis average performance for that revenue or expense category, while still varying in response to changes in macroeconomic conditions. The post-crisis period in this context is defined as the time period from 2009:Q4 onwards. This enhanced modeling approach was introduced in DFAST 2017. For DFAST 2018, PPNR projections reflect only the enhanced models. For DFAST 2017, PPNR projections were calculated as the arithmetic average of the enhanced models and the prior modeling approach.⁵⁹

Losses Related to Operational-Risk Events

Losses related to operational-risk events are a component of PPNR and include losses stemming from events such as fraud, computer system failures, process errors, and lawsuits by employees, customers or other parties. Operational-risk loss estimates include the average of loss estimates from two modeling approaches and estimates of potential costs from unfavorable litigation outcomes.

Both modeling approaches--a historical simulation approach and a regression model--project operational losses for the 35 firms and are based on historical operational-loss data submitted by the firms on the FR Y-14A/Q reports.

In the historical simulation model, losses at different percentiles of simulated, nine-quarter loss distributions are used as a proxy for the expected operational losses conditional on the macroeconomic scenarios. Losses are modeled for each firm and each of the seven operational-risk categories identified in the Board's regulatory capital rule.⁶⁰ The historical simulation approach models the loss frequency and loss severity separately. The tails of the loss severity and frequency distributions are informed by historical industry loss severity and frequency scaled to the assets of individual firms, while the bodies of these distributions are informed by each firm's historical loss severity and frequency. The distribution of aggregate losses is then simulated by repeatedly drawing the nine-quarter event frequency from this frequency distribution, and the severity of those events from the severity distribution. The percentiles of loss distributions, which are used to estimate stressed losses, are tied to the frequency of severe recessions for the severely adverse scenario and to the frequency of all recessions for the adverse scenario. Loss forecasts for an individual firm are the sum of the firm's loss estimates for each event type.

The regression model is a two-step model. The first step projects the industry aggregate operational losses conditional on macroeconomic factors over the nine-quarter horizon.⁶¹ A regression approach is used to model industry operational losses as a function of macroeconomic variables, including measures of economic activity, financial conditions, and interest rate environment, and to produce industry aggregate projected losses for each of the different scenarios. Finally, the second step estimates weights to distribute industry losses to individual firms based on each firm's size.

Balance-Sheet Items and Risk-Weighted Assets

The size of each firm's balance sheet is projected based on a model that relates industrywide loan and non-loan asset growth to each other and to broader economic variables, including a proxy for loan supply. The model allows for both long-run relationships between the industry aggregates and macroeconomic variables, as well as short-term dynamics that cause deviations from these relationships. It is calculated using aggregate data from the Federal Reserve's Financial Accounts of the United States and the Bureau of Economic Analysis.

Industry loan and asset growth rates are projected over the planning horizon using the macroeconomic variables prescribed in the supervisory scenario. The growth rates embed the assumption that the industry will continue to lend using standards that are consistent with long-run behavior. This tends to raise the projected growth of lending by removing the effects of firm tightening that often occur in stressful periods. Over this horizon, each firm is assumed to maintain a constant share of the industry's total assets, total loans, and total trading assets. In addition, each firm is assumed to maintain a constant mix within their loan and trading asset categories. These assumptions are applied as follows:

• Each category of loans at a firm is assumed to grow at the projected rate of total loans in the industry.
• Each category of trading assets at a firm is assumed to grow as a function of both the projected rate of total assets and the projected market value of trading assets in the industry.
• All other assets of a firm, including securities, are assumed to grow at the projected rate of non-loan assets in the industry.
• A firm's cash holdings level, the residual category, is set such that the sum of cash and noncash assets grows at the projected rate of total assets.
• Growth in securities is assumed to be in short-term, riskless assets.

Balance sheet projections incorporate expected changes to a firm's business plan, such as mergers, acquisitions, and divestitures that are likely to have a material impact on its capital adequacy and funding profile. Firm-submitted data are used to adjust the projected balance sheet in the quarter when the change is expected to occur. Once adjusted, assets are assumed to grow at the same rate as the pre-adjusted balance sheet. Only submitted divestitures that are either completed or contractually agreed upon before April 5, 2018, are incorporated.

The projection of RWAs is accomplished in two parts and is based on the tenets of the standardized approach and market-risk rule in the Board's regulatory capital rule.⁶² The first part requires estimating the path of credit RWAs based on exposures from loans and securities.⁶³ The second requires estimating the path of market risk-weighted assets (MRWAs) based on exposures under the market-risk rule.⁶⁴

Credit RWA projection is a straightforward implementation of the standardized approach. Most risk weights are imputed from the FR Y-9C report and held fixed throughout the projection horizon. Risk weights are applied to appropriate balance paths and summed across categories. This treatment is consistent with the assumption that the general features of the credit portfolio and non-trading book assets remain constant during the projection period.

MRWA projections incorporate the assumption that market risk is sensitive to the economic scenario. In particular, the path of MRWAs is sensitive to changes in the projected volatility of the underlying mix of trading assets. While the underlying mix of exposures subject to the market-risk rule is assumed to remain constant throughout the scenario, some elements of MRWAs are affected by changes in market conditions assumed in supervisory scenarios. For example, projected value-at-risk (VaR) calculations--an important element of MRWAs--rise as the volatility of the portfolio's underlying assets increases. Similarly, a firm's incremental risk charge and its comprehensive risk measure are affected by the volatility of credit products. The remaining categories of MRWAs are assumed to evolve according to projections of a firm's trading assets. These properties make the trajectory of MRWAs more dynamic than credit RWAs because both the underlying path of trading assets and the volatility associated with the portfolio evolve.

Regulatory Capital

The final modeling step translates the projections of revenues, expenses, losses, provisions, balances, and RWAs from the models described above into calculations of regulatory capital for each firm under the supervisory scenarios. Regulatory capital is calculated using the definitions of capital in the Board's regulatory capital rule.⁶⁵ Regulatory capital is calculated consistent with the requirements that will be in effect during the projected quarter of the planning horizon.⁶⁶ The definition of regulatory capital changes throughout the planning horizon in accordance with the transition arrangements in the revised regulatory capital framework, where applicable.⁶⁷

Regulatory capital incorporates estimates of net income from supervisory projections of revenues, total losses, and tax expense. Current and deferred tax expenses, and related changes to net deferred tax assets (DTAs), are calculated by applying a consistent tax rate of 21 percent⁶⁸ to pretax income or loss. The tax expense includes changes to DTA valuation allowance, which is comprised of a calculation that evaluates whether a firm will have sufficient taxable income to realize its DTAs. Finally, projected after-tax income adjusts for income attributable to minority interests.

For DFAST 2018, the quarterly change in CET1 capital before adjustments and deductions equals projected after-tax net income minus capital distributions (dividends and any other actions that disperse equity), plus any issuance or other corporate actions that increase equity, plus other changes in equity capital such as other comprehensive income, and changes incident to business combinations.⁶⁹

Projected regulatory capital levels are calculated under the applicable regulatory capital framework to incorporate, as appropriate, projected levels of non-common capital and certain items that are subject to adjustment or deduction in capital. Some items, such as debt valuation adjustments (DVA), goodwill, and intangible assets (other than mortgage servicing assets), and components of accumulated other comprehensive income (AOCI) other than unrealized gains (losses) on available-for-sale (AFS) securities, are generally assumed to remain constant at their starting value over the planning horizon. For other items, firm projections--with supervisory adjustments--are factored into the regulatory capital calculation. Those items include the reported path of additional tier 1 and tier 2 capital and significant investments in the capital of unconsolidated financial institutions in the form of common stock. Other items subject to deduction, including DTAs and mortgage servicing assets, are projected under each supervisory scenario. The Federal Reserve also includes the effects of certain planned mergers, acquisitions, or divestitures in its projections of capital and the components of capital.

The projections of regulatory capital levels are combined with Federal Reserve projections of total assets for the leverage ratio, total assets and off-balance sheet exposures for the supplementary leverage ratio, and RWAs to calculate regulatory capital ratios. The risk-based regulatory capital ratios use RWAs calculated under the standardized approach.⁷⁰ RWAs and on-balance sheet total assets for the leverage ratio are projected based on supervisory projections of each firm's balance sheet. Off-balance sheet items are projected based on the bank-reported off-balance sheet SLR exposure and are assumed to grow at the supervisory model-projected total asset growth rate. The capital ratio denominators are adjusted for items subject to adjustment or deduction from capital, consistent with the projection of each item in the numerator of the regulatory capital ratios and the regulatory capital requirements. Projected capital levels and ratios are not adjusted to account for any differences between projected and actual performance of the firms observed at the time the supervisory stress test results were being produced in the second quarter of 2018.

Modeling Approaches for IHCs Newly Subject to the Supervisory Stress Test

Six IHCs (new entrant IHCs) became subject to the supervisory stress test for the first time in 2018.⁷¹ For these firms, the Federal Reserve modified its approach for modeling revenues and certain types of losses when the data required to produce a modeled estimate were not available from the firms. Specifically, the estimates of PPNR, trading and counterparty losses, and operational risk losses reflect different treatment of these IHCs compared to the other firms subject to the supervisory stress test. In each case, the modified approach utilized estimates produced by these models for the remaining firms in the stress test. The Federal Reserve used the same models it uses for all other firms to estimate loan losses for new entrant IHCs, based on the data the firms provided.

Since the new entrant IHCs were formed as of July 1, 2016, the historical data reported for the legal entity are insufficient to apply the supervisory models of core PPNR components to those firms. The modified PPNR projection for the new entrant IHCs is based on the industry aggregate performance for each revenue and expense component. The ratio for each PPNR component to the relevant asset or liability balance as of December 31, 2017, is generally set equal to its median historical value between the first and fourth quarters of 2017. Over the projection horizon, this ratio is assumed to move by the same number of basis points as the aggregate ratio for the industry excluding the six new entrant IHCs.

Aggregate PPNR exclusive of operational risk in the severely adverse scenario was $20.2 billion for the six IHCs newly subject to the supervisory stress test.

Five of the new entrant IHCs have significant trading activity and will be subject to the full global market shock component in DFAST 2019.⁷² In DFAST 2018, the Federal Reserve applied a supervisory market risk component to the five new entrant IHCs and HSBC. Specifically, the Federal Reserve applied loss rates to certain exposures, based on the losses resulting from the global market shock and large counterparty default components for the six domestic firms in 2014-17. The following loss rates were applied to the applicable measure of exposures as of December 31, 2017:

• Securitized products losses: 22.1 percent loss rate in the adverse scenario and 46.4 percent loss rate in the severely adverse scenario to certain loans and credits held for trading.⁷³
• Trading mark-to-market and trading incremental default risk losses: 1.4 percent loss rate in the adverse scenario and 1.8 percent loss rate in the severely adverse scenario to market risk-weighted assets.⁷⁴
• Credit valuation adjustments: 1.3 percent loss rate in the adverse scenario and 2.8 percent loss rate in the severely adverse scenario to over-the-counter derivatives risk-weighted assets.⁷⁵
• Large counterparty default losses: 1.0 percent loss rate in the adverse scenario and 1.5 percent loss rate in the severely adverse scenario to repo-style transactions and over-the-counter derivatives risk-weighted assets.⁷⁶

Operational-risk losses were also projected using a modified approach for the six new entrant IHCs, given the lack of a historical measure of total assets that is consistent over time and across firms. The historical simulation model applied to firms with sufficient historical operational-loss data submitted on the FR Y-14A and FR Y-14Q reports cannot be applied to these firms. In the place of the historical simulation model, a modified model assigned each IHC newly subject to the supervisory stress test the average projected loss produced by the historical simulation model, normalized by total assets. This modified model scales the average projected loss by these firms' total assets as of December 31, 2017.

Instead of calculating each firm's projected losses as the average of the regression model and historical simulation approach described earlier, projected operational-risk losses for these firms are calculated as the average of the regression model and the modified model described above.

 

References

 

 40. In connection with DFAST 2018, and in addition to the models developed and data collected by federal banking regulators, the Federal Reserve used proprietary models or data licensed from the following providers: Andrew Davidson & Co., Inc.; ICE Data Services; Bloomberg L.P.; CB Richard Ellis, Inc.; CoreLogic Inc.; CoStar Group, Inc.; Equifax Information Services LLC; Fitch Ratings, Inc.; Haver Analytics; Kenneth French; IDC Financial Publishing, Inc.; Intex Solutions, Inc.; Black Knight McDash Data from Black Knight IP Holding Company, LLC; Markit Group; Moody's Analytics, Inc.; Moody's Investors Service, Inc.; Mergent, Inc.; Morningstar, Inc.; MSCI, Inc.; Municipal Securities Rulemaking Board; SNL Financial; StataCorp LP; S&P Global Market Intelligence: S&P Capital IQ Estimates; Standard & Poor's Financial Services LLC; and World Bank Group. In addition, with respect to the global market shock component of the adverse and severely adverse scenarios, the Federal Reserve used proprietary data licensed from the following providers: Bloomberg L.P.; Intercontinental Exchange; JPMorgan Chase & Co.; Markit Group; and MSCI, Inc. Return to text

 41. This description is consistent with the more detailed description of the DFAST 2017 corporate loan model provided in 82 Fed. Reg. 59547 (December 15, 2017) because the corporate loan model did not change between DFAST 2017 and DFAST 2018. The more detailed description is part of a rulemaking that has yet to be finalized. For more on the status of that rulemaking see box 3. Return to text

 42. All definitions of loan categories and default in this appendix are definitions used for the purposes of the supervisory stress test models and do not necessarily align with general industry definitions or classifications. Return to text

 43. SNCs have commitments of greater than $20 million and are held by three or more regulated participating entities. For additional information, see "Shared National Credit Program," Board of Governors of the Federal Reserve System, www.federalreserve.gov/supervisionreg/snc.htm. Return to text

 44. The corporate loan category also includes loans that are dissimilar from typical corporate loans, such as securities lending and farmland loans, which are generally a small share of firm portfolios. For these loans, a conservative and uniform loss rate based on analysis of historical data is assigned. Return to text

 45. To predict losses on new originations over the planning horizon, newly originated loans are assumed to have the same risk characteristics as the existing portfolio, with the exception of loan age and delinquency status. Return to text

 46. The effects of loan modification and evolving modification practices are captured in the probability that a delinquent loan transitions back to current status (re-performing loans). Return to text

 47. Private mortgage insurance is not incorporated into the LGD models. Industry data suggest that insurance coverage on portfolio loans is infrequent and cancellation or nullification of guarantees was a common occurrence during the recent downturn. Return to text

 48. The differences between characteristics of mortgages in MBS and mortgages in firm portfolios, such as loan-to-value (LTV) ratio, are controlled for by including various risk characteristics in the LGD model, such as original LTV ratio, credit score, and credit quality segment (prime, alt-A, and subprime). Return to text

 49. For more information on the Federal Reserve's policy regarding material model changes, see the proposed Stress Testing Policy Statement (82 Fed. Reg. 59528, December 15, 2017). Projections of credit card losses in DFAST 2019 will reflect the enhanced models only. Return to text

 50. For the government-guaranteed portion of firms' student loan portfolios, an assumption of low PD and LGD is applied. Return to text

 51. "Roll-rate dynamics" refers to how delinquent loans in one period transition to defaulted loans in the next, and how defaulted loans in a period transition to net charge-offs in the next. Return to text

 52. Business and corporate credit card portfolio data, which were previously collected on the FR Y-14Q Retail schedule, are now collected at the loan level on the FR Y-14M Credit Card schedule and subsequently aggregated to the segment level. Return to text

 53. The dollar weights used are based on the distribution reported during the previous observation period. This method assumes that the distribution of loans across risk segments, other than delinquency status segments, remains constant over the projection period. Return to text

 54. For loan types modeled in a charge-off framework, the appropriate level of ALLL is adjusted to reflect the difference in timing between the recognition of expected losses and that of charge-offs. Return to text

 55. With regard to Accounting Standards Update No. 2016-13, Financial Instruments - Credit Losses (Topic 326): Measurement of Credit Losses on Financial Instruments (CECL), the Federal Reserve has not included the potential effect of CECL in the 2018 stress testing exercise. Covered banking organizations that have adopted ASU 2016-13 would not include the effect of ASU 2016-13 on their provisioning for purposes of stress testing until the 2020 stress testing cycle. Return to text

 56. Recognition and Presentation of Other-Than-Temporary-Impairments, Financial Accounting Standards Board, Staff Position No. FAS 115-2 and FAS 124-2 (April 9, 2009), www.fasb.org/jsp/FASB/Document_C/DocumentPage?cid=1176154545419&acceptedDisclaimer=true. Return to text

 57. Financial Accounting Standards Board Accounting Standards Update No. 2016-01. Return to text

 58. These exposures are distinct from the bilateral derivatives agreements and securities financing transactions included in the largest counterparty default scenario component (described below). Return to text

 59. For more details, see box 1 and box 2 of the disclosure document: Dodd-Frank Act Stress Test 2017: Supervisory Stress Test Methodology and Results. Return to text

 60. The seven operational-loss event type categories identified in the Federal Reserve's regulatory capital rule are internal fraud; external fraud; employment practices and workplace safety; clients, products, and business practices; damage to physical assets; business disruption and system failures; and execution, delivery, and process management. See 12 CFR 217.101(b). Return to text

 61. Losses are aggregated for six of the seven operational-risk categories. Operational-risk losses due to damage to physical assets are the exception. These losses are not expected to be dependent on the macroeconomic environment and therefore are modeled separately only as a function of size, but not of macroeconomic variables. Return to text

 62. 12 CFR part 217, subparts D and F. The use of the advanced approaches for calculating risk-based capital ratios under the capital plan and stress test rules has been deferred indefinitely. See 80 Fed. Reg. 75419 (Dec. 2, 2015); 12 CFR 225.8(d)(10); and 252.42(m). Return to text

 63. CFR part 217, subpart D. Return to text

 64. CFR part 217, subpart F. Return to text

 65. See 12 CFR part 217. Return to text

 66. See 12 CFR 225.8(e)(2)(i)(A) and 12 CFR 252.56(a)(2). Return to text

 67. See 12 CFR part 217, subpart G. Return to text

 68. For a discussion of the effect of changing this tax rate assumption, see Board of Governors of the Federal Reserve System, Dodd-Frank Act Stress Test 2013: Supervisory Stress Test Methodology and Results, (Washington, DC: Board of Governors, March 2013), www.federalreserve.gov/newsevents/press/bcreg/dfast_2013_results_20130314.pdf, box 2. Return to text

 69. The Federal Reserve used the following capital action assumptions in projecting post-stress capital levels and ratios: (1) for the first quarter of 2018, each company's actual capital actions as of the end of that quarter; and (2) for each quarter from the second quarter of 2018 through the first quarter of 2020, each company's projections of capital included (i) common stock dividends equal to the quarterly average dollar amount of common stock dividends that the company paid in the previous year (that is, from the second quarter of 2017 through the first quarter of 2018) plus common stock dividends attributable to issuances related to expensed employee compensation or in connection with a planned merger or acquisition to the extent that the merger or acquisition is reflected in the covered company's pro forma balance sheet estimates; (ii) payments on any other instrument that is eligible for inclusion in the numerator of a regulatory capital ratio equal to the stated dividend, interest, or principal due on such instrument during the quarter; (iii) an assumption of no redemption or repurchase of any capital instrument that is eligible for inclusion in the numerator of a regulatory capital ratio; and (iv) an assumption of no issuances of common stock or preferred stock, except for issuances related to expensed employee compensation or in connection with a planned merger or acquisition to the extent that the merger or acquisition is reflected in the covered company's pro forma balance sheet estimates. Return to text

 70. See 12 CFR part 217. Return to text

 71. The firms that are newly subject to the supervisory stress test in 2018 are Barclays US LLC; BNP Paribas USA, Inc.; Credit Suisse Holdings (USA), Inc.; Deutsche Bank USA Corp; RBC USA Holdco Corporation; and UBS Americas Holding LLC. These firms became subject to the capital plan rule and were required to submit capital plans to the Federal Reserve for the first time in 2017. The Board's decisions regarding the capital plans of these firms and the results of these firms' company-run stress tests were not disclosed. Return to text

 72. BNP Paribas USA, Inc., was not subject to the supervisory market risk component. HSBC North America Holdings Inc. will be subject to the global market shock component and was subject to the supervisory market risk component in DFAST 2018. Return to text

 73. Securitized products exposure equals the sum of FR Y-9C HC-D Column A, lines 4.c.; 4.e.; 5.a.(1); 5.a.(2); 5.a.(3); 6.a; 6.c.(1); 6.c.(2); 6.c.(3); and 6.c.(4). Return to text

 74. Trading mark-to-market and trading incremental default risk exposures equals FR Y-9C HC-R.II line 27. Return to text

 75. Credit valuation adjustment exposure equals the risk-weighted amounts of FR Y-9C HC-R.II line 20. Return to text

 76. Large counterparty default exposure will equal the risk-weighted amounts of FR Y-9C HC-R.II lines 16 and 20. Return to text