Appendix A: Common Themes from CCAR 2014

Introduction

This appendix describes some of the common themes identified by supervisors during CCAR 2014 that were broadly applicable to the bank holding companies (BHCs) involved in the program. The Federal Reserve provided these commonly observed themes to the BHCs as part of the CCAR 2014 supervisory feedback communicated in April 2014 to build upon expectations outlined in previous guidance and to provide additional clarification in specific areas where BHCs continue to experience challenges. The topics covered here were all outlined in the Federal Reserve's Capital Planning at Large Bank Holding Companies: Supervisory Expectations and Range of Current Practice, published in August 2013.⁵²

In subsequent communication, the Federal Reserve further clarified its expectations for modeling changes in the fair value of available-for-sale (AFS) securities to project other comprehensive income (OCI). In addition, certain information was updated regarding the threshold of trading assets and liabilities that trigger specific expectations for projecting market risk-weighted assets (RWAs). The RWA Methodologies and AFS Fair Value OCI sections of this appendix include those subsequent communications. The following nine themes came out of the CCAR 2014 program and are described further below: (1) sensitivity analysis, (2) assumptions management, (3) model overlays, (4) model risk management, (5) capital policy, (6) presentation of consolidated pro forma financial results, (7) RWA projection methodologies, (8) operational risk loss-estimation methodologies, and (9) AFS Fair Value OCI.

Before discussing each of these themes in more detail, it is important to reiterate one theme that is generally applicable to all of the issues below. While supervisors generally expect that BHCs use independently validated quantitative methods as the basis for their estimates, BHCs should not rely on weak or poorly specified models. Instead, qualitative approaches or adjustments to quantitative results should be used, for example, to address data limitations, material changes in a BHC's business, or unique risks of a certain portfolio (including fundamental changes to markets, products and businesses) that are not well represented in reference data and therefore not well captured in a model.

Most BHCs use some form of expert judgment--often as a management adjustment overlay to modeled outputs. Supervisors prefer that BHCs use management overlays to compensate for model limitations. Regardless of the estimation methodology, BHCs should have a transparent, repeatable, well-supported process that generates credible estimates that are consistent with assumed scenario conditions. (For more on model overlays see section 3 of this appendix and also the discussion of model risk management on page 19).

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1. Sensitivity Analysis

Having an understanding of the sensitivity of pro forma financial estimates to the various inputs and assumptions developed to support the forecasting process is an important aspect of developing sound stress scenario analysis projections. Sensitivity analysis is an important tool that tests the robustness of models and enhances reporting for BHC management, the board of directors, and supervisors. Based on observations in CCAR 2014, there is a continued need for BHCs to expand the use of sensitivity analysis to understand the range of potential estimates based on changes to inputs and key assumptions as well as the uncertainties associated with those estimates. Most notably, BHCs did not conduct sufficient sensitivity analysis during model development, and instead relied on the model validation function to carry it out. BHCs should expand the use of sensitivity analysis around both individual loss, revenue, and balance sheet component estimates as well as aggregate estimates at various levels of the consolidation process.

All key assumptions and input variables should be candidates for sensitivity testing. While not all assumptions and inputs will prove to have a material impact on estimates, BHCs should conduct sensitivity analysis to determine which inputs and assumptions can materially alter results. Some foundational assumptions that are common to most BHCs and should be subject to sensitivity analysis include projected market share, size of the mortgage market, cost and flow of deposits, utilization rate of credit lines, discount rates, or level and composition of trading assets. However, this list is not exhaustive and conducting sensitivity testing only on these assumptions will not be sufficient to meet supervisors' expectations in this area.

Sensitivity testing can also be particularly helpful in understanding the range of possible results of vendor-provided scenario forecasts and vendor models with less transparent or proprietary elements. Furthermore, sensitivity analysis can be an important tool to assess stress testing models and the credibility of stress projections, given the inherent challenges in conducting outcomes analysis of these models.

Overall, BHCs should ensure that model developers and model owners conduct sensitivity analysis, in addition to the testing performed by the model validation function. BHCs should also conduct sensitivity analysis as part of the aggregation process to understand the sensitivity of material components of the consolidated pro forma financials, as well as the post-stress pro forma capital ratios to material assumptions and inputs. By understanding and documenting a range of potential outcomes, BHCs can ensure there is a clear understanding of the inherent uncertainty and imprecision around pro forma results. Importantly, management should have a full understanding of key sensitivities in estimates and highlight those to the board so that the directors understand the sensitivity of capital to alternative inputs and assumptions and can make informed capital decisions.

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2. Assumptions Management

BHCs are expected to clearly document key assumptions used to estimate losses, revenues, expenses, asset and liability balances, and RWA. Documentation should provide the rationale and any empirical support for assumptions and specifically address how they are consistent with scenario conditions. Assumptions should generally be conservative, particularly in areas of high uncertainty, and should be well supported and subject to close oversight and scrutiny.

Given the significant number of assumptions required for capital planning and stress testing, one of the most common issues across firms is unclear or unsubstantiated assumptions. While this issue spans all areas of capital planning, it was among the most common issues for PPNR projections in CCAR 2013 and again in CCAR 2014. In particular, loan and deposit pricing assumptions were, in many instances, not well documented nor adequately supported, and in some cases they appeared inconsistent with the expected impact of scenario conditions, shift in portfolio mix, or growth or decline in balances over the planning horizon. Similarly, in certain instances, assumptions were made that provided a clear benefit to the BHC without consideration of strategic initiatives or achievability under a given scenario.

Overall, assumptions that may materially affect capital estimates should be consistent with scenario conditions, challenged across the enterprise, and internally consistent within each scenario. Where possible, assumptions should be supported by quantitative analysis or empirical evidence, and as discussed in the preceding section, augmented with sensitivity analysis to assess whether deviations from assumed values could have a material impact on post-stress, pro forma capital levels. That said, assumptions do not have to be anchored in historical experience. Historical experience may not be relevant if a BHC has gone through significant structural changes, or if the economic environment changes dramatically. Assumptions not based in historical experience can be acceptable, if BHCs provide sufficient support and rationale for why the assumptions are plausible, internally consistent with assumed scenario conditions, and conservative (i.e., they generate more losses or fewer revenues than strict adherence to historical experience).

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3. Model Overlays

As noted, most BHCs use some form of expert judgment--often as a management adjustment overlay to modeled outputs. In developing management overlays, BHCs should ensure that they have a transparent and repeatable process; that assumptions are clearly outlined and consistent with assumed scenario conditions; and that results are provided with and without adjustments.

In general, the purpose and impact of specific management overlays should be communicated in a way that facilitates a thorough understanding by the BHC's senior management. Senior management should be able to independently assess the reasonableness of using an overlay to capture a particular risk or compensate for a known limitation. Significant management overlays should receive a heightened level of support and scrutiny, up to and including review by the board of directors in instances where the impact to pro forma results is sufficiently material. Extensive use of management overlays should also trigger discussion as to whether new or improved modeling approaches are needed.

While improved support for management overlays was apparent during CCAR 2014, some BHCs' approach to overlays did not meet supervisory expectations. Specifically, a number of BHCs failed to tie management overlays to specific model weaknesses or identified issues and used a general "catch-all" adjustment to influence aggregate modeled losses in the interest of conservatism. In addition, several BHCs relied exclusively on a capital buffer and/or the capital targets to account for model limitations, rather than using a specific adjustment to model output, which directly impacts capital levels. To the extent possible, BHCs should incorporate the impact of all risk exposures into their projections of net income over the nine-quarter planning horizon rather than trying to address certain risks and model limitations by adding buffers on top of internally defined capital goals and targets.

In certain cases, BHCs made adjustments within the model (e.g., changes to parameter estimates) that were independently reviewed as part of the overall model validation process. However, post-validation management overlays applied to model outcomes to account for risks not captured by the model or to compensate for model limitations often failed to receive an adequate level of independent review (see "Model Risk Management"). In addition to being clearly documented and well-supported, supervisors expect all management overlays and adjustments to be reviewed in detail and approved at the appropriate level given their materiality/impact to the overall pro forma financial results.

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4. Model Risk Management

BHCs should ensure that they have sound model risk management, including independent review and validation of all models used in internal capital planning, consistent with existing supervisory guidance on model risk management (SR letter 11-7). Most BHCs involved in CCAR 2014 have made progress in enhancing their model risk management practices for models used in their capital planning processes. However, some BHCs still fell substantially short of supervisory expectations, and all BHCs still have room for improvement, most notably in the area of conducting more rigorous evaluations of the conceptual soundness of modeling approaches applied to stress testing use.

Supervisors observed that validation activities conducted by some BHCs were rigorous and appropriately resulted in required enhancements, restrictions on use, or rejection. However, there were numerous cases in which validation activities were not in line with supervisory expectations or effective challenge was not exercised.⁵³ For instance, some validation activities were only cursory in nature; did not probe key assumptions or model sensitivities; and perhaps most critically, did not evaluate models for their intended use (including vendor models). Supervisors also expect that model overrides or overlays--including those based solely on expert judgment--will be subject to oversight and review by validation staff or other independent reviewers, with the recognition that the work done to evaluate overlays to model output may be different than the validation work to evaluate and test the model and model output. BHCs should also ensure that challenger or benchmark models used as part of the capital planning processes are subject to validation, with the intensity and frequency of validation work a function of the importance of those models in generating estimates (per SR 11-7).

Supervisors recognize that not all validation activities can be conducted before each model is used, especially certain types of outcomes analysis given the lack of realized outcomes against which to assess projections generated under stressful scenarios. That said, at a minimum, BHCs should make every effort to conduct the conceptual soundness evaluation of a model prior to its use. An important aspect of model risk management governance is clearly identifying whenever any validation activities are not able to be conducted prior to use, making those shortcomings in validation transparent to users of model output, developing remediation plans, and applying compensating controls--such as conducting additional sensitivity analysis or using benchmarks. Any cases in which certain model risk management activities--not just validation activities--are not completed could suggest high levels of model uncertainty and call into question a model's effectiveness. BHCs should ensure that the output from models for which there are model risk management shortcomings are treated with greater caution (e.g., by applying compensating controls and conservative adjustments to model results) than output from models for which all model risk management activities have been conducted in line with supervisory expectations.

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5. Capital Policy

A BHC's capital policy should be a distinct, comprehensive written document that addresses the major components of the BHC's capital planning processes and links to and is supported by other policies. The policy should provide details on how the board and senior management manage, monitor, and make decisions regarding all aspects of capital planning and lay out expectations for the information included in the BHC's capital plan. During CCAR 2014, supervisors observed many cases in which BHCs' capital policies did not meet expectations. For instance, at some BHCs, capital policies provided insufficient detail, particularly as it pertained to the decisionmaking process around the level and composition of capital distributions. Supervisors expect capital policies to include explicit limits on aggregate capital distributions and to outline the type of analysis the BHC must provide in its capital plan to support its proposed capital actions.

Many BHCs' capital policies lacked a comprehensive suite of payout ratio targets or limits; an explanation for how the BHC arrived at those targets or limits; and, where they did exist, lacked defined response actions to be taken in case of breaches of dividend and/or repurchase payout targets or limits. Some BHCs included general considerations for decisionmaking, such as review of capital ratios under stress scenarios, but offered no explanation of how the BHC would arrive at planned distribution amounts or the form of capital distributions.

During CCAR 2014, supervisors also observed that some BHCs did not define and set capital goals and targets in a manner consistent with supervisory expectations. For example, some BHCs did not demonstrate that their internal capital goals were aligned with the expectations of all relevant stakeholders (including, but not limited to, shareholders, rating agencies, counterparties, and creditors) to help ensure that the BHC could continue as a viable entity during and after periods of stress. Some BHCs did not consider or clearly incorporate the impact of stress test results and uncertainty around those results in the determination of capital targets. In other cases, capital goals and targets did not incorporate expectations of changes to regulatory standards (e.g., they were solely based on Basel I metrics). Furthermore, some BHCs used a poorly defined capital buffer, ostensibly to capture a range of additional risks or uncertainties, but without clear attribution or sufficient analysis.

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6. Presentation of Consolidated Pro Forma Results

BHCs should ensure that they have sound processes for review, challenge, and aggregation of estimates used in their capital planning processes. Based on supervisory evaluations from CCAR 2014, there is evidence that processes for review, challenge, and aggregation contained significant shortcomings at several BHCs and that all BHCs should continue to enhance these processes. In some cases, BHCs had satisfactory review and challenge processes for some of their pro forma estimates, but not for others.

Satisfactory processes for review, challenge, and aggregation should include

• an effective internal review of processes used at both the line of business/sub-aggregated and enterprise level, with final review and sign off completed by an informed party not directly involved in those processes;
• policies and procedures documenting the process from end to end that include a clear articulation of accountability for credibility of results at each stage of the challenge process;
• evidence of clear communication among the different functions involved in drawing together estimates from across the organization to promote consistency and to ensure that those functions are operating under the same guidelines and assumptions;
• set processes for aggregating and finalizing results, including appropriate review and oversight of aggregate results to ensure coherence and consistency of projected outcomes sourced from various forecast providers;
• clear identification and documentation of key assumptions, sensitivities, limitations, and judgment applied at all levels of the processes used to generate estimates, as well as communication of these items to relevant senior management--and the board of directors, when necessary; and
• evidence of oversight and challenge to both processes and outcomes at the appropriate level of management, including documentation of actions taken as a result of questions, issues, or requests that came up during such review and discussions.

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7. RWA Methodologies

Many BHCs faced challenges with their methodologies for projecting RWAs. Given that the as-of-date RWA calculated for regulatory reporting serves as the foundation for RWA projections in scenario analysis, BHC management should ensure, and provide evidence of, an independent review of RWA regulatory reporting by either internal audit or another control function. Independent reviews should ensure point-in-time RWA processes appropriately capture all relevant on- and off-balance sheet exposures and are consistent with the various risk-weighting frameworks to which the BHC is subject. For CCAR 2014, the level of independent review for point-in-time RWA accuracy for many BHCs was not always evident, as reviews were either dated, under the guise of general regulatory reporting audits that lacked detail specific to RWA coverage, inferred as part of CCAR review process, or nonexistent.

For BHCs subject to the Market Risk Capital Rule, supervisors expect management to ensure that projections of market risk RWAs appropriately reflect the level of risk in the BHC's trading book and the contribution market risk RWA makes to the firm's total RWA. BHCs should document the rationale for any significant changes in risk weighting assigned to the trading book, particularly in cases where projections show the ratio of trading book RWA-to-trading exposures declining over time or under stress conditions. All else equal, RWA per notional dollar of trading asset is generally expected to increase over the projection horizon in response to the heightened market volatility assumed in many firms' stress scenarios, and any deviations from that relationship should be well supported.

Although some BHCs subject to the Market Risk Capital Rule report market risk RWAs that represent a relatively small proportion of total RWAs, all BHCs should ensure that their reported projections of market risk RWAs sufficiently consider the impact of each scenario. In general, all BHCs in the LISCC portfolio as well as any BHCs subject to the Market Risk Capital Rule that report (1) trading assets and liabilities of greater than $10 billion or (2) trading assets and liabilities of greater than 10 percent of total assets at the as-of date for reporting should project market risk RWAs using a quantitative methodology that captures both changes in exposures and changes in volatility implied by stress conditions over time.

Providing overall support and documentation for RWA methodologies was a shortcoming among BHCs in CCAR 2014. BHCs should provide evidence for the appropriateness of assumptions regarding the following:

• any aggregation of balance projections by exposure type or characteristic (e.g., balances for exposures that do not distinguish between amounts that are considered past due) for purposes of applying corresponding risk weights
• any uses of average or effective risk weights based on the BHC's as-of date portfolio composition or historical trend (and evidence of the appropriateness of basing RWA projections on historical trend, given the potential for changes in portfolio composition over time and under different stress conditions)
• support for any exposure types for which RWA is held constant over the projection horizon

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8. Operational Risk Loss Estimation

BHCs have found it challenging to identify meaningful relationships between operational losses and macroeconomic factors. Limited datasets and potential problems classifying and reporting events contribute to the difficulties. Specifically, the limited length of operational risk datasets makes finding robust correlations to macroeconomic and financial variables difficult for many firms. Compounding this problem, BHCs use extensive judgment to assign dates to loss events that unfold over time, such as legal losses. Given these challenges, correlation analysis can result in loss projections that are unstable or invariant to scenario conditions, and are thus inconsistent with the expectation that BHCs significantly stress their operational risk exposures.

Given the challenges noted above, BHCs should not try to force the use of unstable and/or unobservable correlations and should instead use a conservative approach to project increased operational risk losses from significant operational risk events that could plausibly occur during a stressed economic and financial environment. In other words, the use of scenario analysis may provide a more conceptually sound basis for assessing potential operational losses under stress.

The BHC stress scenario should capture significant operational risks that could occur over the nine quarters of the BHC scenario and translate them into loss estimates, regardless of whether or not they are directly linked to the stressed economic environment. The BHC stress scenario should be designed with the BHC's particular vulnerabilities in mind and include potential BHC-specific events such as system failures, litigation related losses, or rogue trading. BHCs internally identify operational risks using tools such as risk assessments and key risk reports. Material risks identified through these risk-management tools should be considered and captured in the scenario analysis supporting stress test estimates. While operational risk events may not be caused by the economic environment, firms should assume that they will occur during the nine-quarter period for the purpose of stress testing.

Methodology Guidelines

Operational risk scenario analysis should cover a myriad of potential losses characterized by differing event types and business lines, despite limited historical data. Various techniques and methodologies can be used based on the particular losses to be stressed, as long as they are logical, well supported, and effectively stress material, inherent risks. For example, a bank with limited internal data could supplement its analysis through the use of external data, using such data in both operational loss scenario analysis as well as other complementary approaches to operational risk quantification. BHCs are encouraged to explore multiple loss-projection techniques as long as the overall methodology ultimately leads to reasonable, significant loss projections.

In previous CCAR programs, four methodologies emerged: regression analysis, loss-distribution approaches, historical averages, and scenario analysis. Regardless of the methodology or combination of methodologies a BHC ultimately uses, it should justify its choice. In addition, when using a given methodology, BHCs should adhere to the supervisory expectations described below.

• When using a regression model, BHCs should have a clear understanding of data and model limitations and make compensating adjustments that are well supported and documented. BHCs should also balance goodness of fit considerations with over-fitting and stability concerns in variable selection criteria.
• When using a loss-distribution approach, BHCs should provide reasoning and justification for percentiles chosen as well as sensitivity analysis around the percentiles.
• When using historical averages, BHCs should justify the date range chosen through extensive sensitivity analysis, including exploring moving averages, averages during stressed periods, rolling averages, and worst-quarter results. BHCs should also stress averages for both frequency and severity when computing stressed operational risk loss estimates.
• When using scenario analysis, BHCs should have a structured, transparent, well-supported, and repeatable process subject to independent validation and review. BHCs should document and support the scenarios chosen and the resulting loss estimates and describe reasons why some scenarios may have been considered but then were rejected from the stress estimates. Furthermore, BHCs should consider all large historical events the BHC has experienced as well as external losses experienced by peer firms and hypothetical events the BHC is exposed to but may have not yet experienced.

Other Guidelines

The majority of operational risk shortcomings observed in CCAR 2014 related to data-capture, documentation, validation, and litigation-related losses. Data-capture issues typically included immature data-collection methods, use of net losses, subjective exclusion of large historical losses, truncated date ranges, and scaled-down internal losses.

BHCs should not assume that if they have scaled down certain businesses, the associated operational risk is necessarily eliminated and, thus, historical losses can be removed from data used for operational risk projections. Large historical events should not be excluded from a BHC's dataset unless soundly justified with evidence and analysis. Date ranges used in any empirical analysis should be justified, and BHCs should not selectively exclude time periods with relevant loss data. Relatively recent data may be more representative of a bank's current risk profile, but larger datasets usually facilitate a more stable model. BHCs should balance this tradeoff and conduct sensitivity analysis to confirm any choices around date ranges. In addition, the use of losses net of recoveries or insurance must be particularly well supported (i.e., inclusive of an assessment of the likelihood and timing of claims fulfillment), as such recoveries may not occur during a stressed economic environment.

Finally, many BHCs did not provide detailed and transparent information on the process used to estimate legal losses and how these losses factor into overall estimates of losses stemming from operational risks. Some firms only considered settled losses and did not incorporate forward-looking potential losses. Supervisors expects firms to estimate legal costs (including expenses, judgments, fines, and settlements) associated with the baseline and stressful outcomes. In baseline scenarios, firms should use expected litigation-related losses. Under stress scenarios, firms should estimate potential losses by assuming unfavorable, stressed outcomes on current, pending, threatened, or otherwise possible claims of all types. Estimates of stressed legal losses and other costs and expenses should be well supported by detailed underlying analysis and, while considered as a part of operational losses, should be broken out in their own subcategory, to the extent possible.

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9. AFS Fair Value OCI

As noted previously, this section on common themes identified by supervisors in CCAR 2014 regarding BHC practices for AFS Fair Value OCI was communicated to the BHCs subsequent to the other sections of this appendix.

Under U.S. Generally Accepted Accounting Principles (GAAP), changes in the fair value of AFS securities are reflected in changes in accumulated other comprehensive income (AOCI); however, prior to issuance of the revised capital framework, these changes were not reflected in the calculation of regulatory capital. In accordance with the revised capital framework, BHCs with total consolidated assets of $250 billion or more or on-balance-sheet foreign exposures of $10 billion or more (advanced approaches BHCs) must reflect AOCI items in their regulatory capital beginning in the second quarter of the planning horizon (the first quarter of 2014).⁵⁴ Under the transition provisions of the revised capital framework, regulatory capital for advanced approaches BHCs must include 20 percent of eligible AOCI in 2014, 40 percent in 2015, and 60 percent in 2016.⁵⁵ This guidance applies only to advanced approaches BHCs; it does not apply to BHCs with total consolidated assets of $50 billion or more that are not advanced approaches BHCs.

Advanced approaches BHCs are expected to evaluate all AFS (and impaired HTM) securities for changes in unrealized gains and losses that flow through OCI under stress scenarios. Stressing fair value is expected to reflect movements in projected spreads, interest rates, foreign exchange rates, and any other relevant factors specific to each asset class. Historical spread and price data may be sourced externally or internally; however, information utilized should be representative of the BHC's portfolio at a sufficiently granular level to capture the inherent risks of the assets. Additionally, the data utilized for projection is expected to span a sufficient period of time that includes a period of vulnerability for that asset class. Advanced approaches BHCs with weaker practices either chose historical data from indices that did not represent the inherent risk in their portfolios or evaluated a too limited a time frame of spread movements.

In order to appropriately capture the risks inherent in AFS agency mortgage-backed securities (MBS), advanced approaches BHCs should stress assets at a security level and substantially all risk be subject to cashflow modeling. The stress test should capture changes in prepayments, interest rates, and spreads. BHCs with better practices used cashflow models to project losses on every asset in their portfolio, while BHCs with lagging practices utilized sensitivity-based approaches (on a security and portfolio-level basis). Changes in fair value of securities should be projected using scenario-derived interest rates and spreads projected over the planning horizon. Advanced approaches BHCs should use the spreads that are consistent with scenario conditions. Better practices reflected forecasting agency MBS fair value changes through a forward full revaluation, repricing at every quarter or at multiple points in the time horizon.

There was limited variation across BHCs in approaches for stressing Treasury securities. Advanced approaches BHCs should explicitly link interest rate moves to scenario conditions. BHCs with better practices utilized full revaluation. Lagging BHCs did not holistically capture future price changes and instead projected price movements based only upon sensitivities.

For AFS credit sensitive assets, advanced approaches BHCs are expected to project changes in fair value consistent with assumed scenario conditions. Better practices included a projection of interest rate and spread changes using cashflow modeling with explicit linkage to the projected scenario horizon. Advanced approaches BHCs are expected to support the appropriateness of scenario variables specifically for each asset class. For example, if a BHC utilizes the same key explanatory variable for every asset class, there should be empirical support of a strong relationship between the explanatory variable and each asset class. The BHCs with the better practices utilized a regression-based methodology that captured the risk characteristics of the portfolio at a granular level, with clear documentation of key assumptions, limitations, and other considerations.

If an advanced approaches BHC contemplates reinvestments, investments should be clearly articulated with supporting rationale that is consistent with scenario conditions. New purchases and reallocations should also be subject to fair value changes across the remaining time horizon. BHCs with lagging practices did not contemplate any future changes in unrealized gains and losses for new asset purchases.

Consistent with expectations as laid out in Capital Planning at Large Bank Holding Companies: Supervisory Expectations and Range of Current Practice, all models utilized to project unrealized gains and losses should be independently validated. Any judgment used, including choice of data and key explanatory variables, should be well supported and subject to independent challenge.

In order to transparently evaluate the full functionality of AFS fair value OCI models, the Federal Reserve expects advanced approaches BHCs to clearly document their key methodologies and assumptions used in estimating unrealized gains and losses. Documentation should concisely explain methodologies used for each asset class, with relevant macroeconomic or other risk drivers, and demonstrate relationships between these drivers and estimates. The source and time frame of historical data utilized should also be clearly detailed, including support for the dataset chosen relative to the appropriate risk inherent in the portfolio. Documentation should also be developed and maintained to detail how the projections are consistent with the BHC's scenario conditions.

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References

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