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This article discusses two conceptual approaches for modeling stressed credit losses: top-down and bottom-up. It highlights the benefits and challenges of using each approach and regulatory expectations.

As stress testing requirements in the US mature and the next “batch” of institutions begin to comply with Dodd-Frank Act Stress Testing requirements, methodologies for loss estimation will continue to evolve. While standards of practice are beginning to form, guidance on methodological and modeling approaches to date is creating confusion. It is also causing a divergence of practices and a variety of modeling approaches among financial institutions – the benefits and pitfalls of each are widely debated.

Often lost in the discussion is that the Interagency Guidance on Stress Testing (SR 12-7) suggests multiple approaches to properly manage and control model risk: “an effective stress testing framework employs multiple conceptually sound stress testing activities and approaches.” This guidance applies across the capital planning process, including credit loss estimation, liabilities, new business volumes, and pro-forma balance sheet and income statements. This article focuses on two conceptual approaches for modeling stressed credit losses: top-down and bottom-up.

A top-down modeling approach

In top-down modeling, exposures are treated as pools with homogeneous characteristics. Scenarios (i.e., macroeconomic or idiosyncratic event-driven) are correlated to historical portfolio experiences. Examples of such approaches include transition matrices, roll-rate models, and vintage loss models. The outputs from this approach are intuitive and easily understood outside of the credit risk function and can be readily calibrated and back-tested against ongoing actual and projected performance. This is increasingly important, as stress testing and capital planning requirements are forcing stress testing analytics to be coordinated among the treasury, finance, and risk groups. Such top-down approaches can also be easier to develop as pool modeling is not exposed to the idiosyncrasies or noise of modeling single firm financial statements. Additionally, historical data is readily available at most institutions, as the same type of data is needed for modeling allowances for loan losses. A bank’s own loss experience can, therefore, be incorporated into the analysis, satisfying an element of the “use-test” criteria for model validation.

"…an effective stress testing framework employs multiple conceptually sound stress testing activities and approaches.” (Interagency Guidance on Stress Testing – SR 12-7)

Top-down modeling has been widely adopted for some retail portfolios as both champion and challenger models, where homogeneous groupings are more easily identifiable. At the same time, this approach can ignore important risk contributors and nuances for more heterogeneous portfolios (e.g., commercial real estate, commercial and industrial loans, project finance, and municipal exposures). For these portfolios, top-down models serve better as a secondary or “challenger” modeling approach, rather than a firm’s primary modeling methodology.

A bottom-up modeling approach

Bottom-up modeling refers to counterparty or borrower-level analyses. Typically, the risk drivers for a specific segment or industry are correlated to macroeconomic variables. Granular, borrower-level analysis goes beyond regulatory-mandated stress testing and can serve as a foundation for risk-based pricing, improved budgeting and planning, economic capital modeling, and limit- and risk-appetite setting. It can also highlight the most desirable banking relationships while isolating the riskiest relationships and concentrations.

While expediency to meet requirements is critical, it is equally important to ensure the firm’s modeling architecture is designed to be leveraged and re-used once the firm is ready to graduate to a more comprehensive and holistic approach.

Methodologically, there are several approaches to bottom-up modeling. Many banks use actuarial modeling to determine credit risk transition, delinquency, and default, as well as loss frequency and magnitude. However, they often miss critical factors such as the timing of delinquency, default, and losses, which require cash flow based approaches. One major challenge is that many organizations do not possess the required data necessary to calibrate credit-adjusted, cash flow models. Few institutions have systemically collected borrower-level financial statements and default and loss data over several business cycles. Many treasury and asset-liability committee (ALCO) members, however, prefer to think of balance sheet risk in a cash flow (i.e., option-adjusted) fashion. As a result, many organizations are required to supplement internal modeling with external data, modeling, and model calibration techniques from third parties, leading to longer development cycles.

Bottom-up modeling for stress testing will soon be applied to Basel III, potentially making it the preferred methodology in the long-term. For bank officers embarking on developing a stress testing program who are less familiar with data and risk quantification requirements associated with bottom-up modeling, development, and firm-wide adoption of obligor-level analysis may require additional time and cross-organizational buy-in. While rapid implementation timelines driven by regulation, flexibility, and intuitiveness of the approach may make top-down modeling more attractive in the short-term for many banks, it is equally important to ensure a firm’s modeling architecture is designed to be leveraged and reused once they are ready to graduate to a more comprehensive and holistic bottom-up modeling approach.

Using multiple approaches

While no single modeling approach has been blessed by the regulatory agencies or emerged as a best practice, two things have become clear. First, the use of multiple, conceptually sound approaches is prudent given the imprecision of existing “state-of-the-art” modeling techniques. And second, selected developmental data samples should have sufficient granularity and robust timelines appropriate for the portfolio being modeled.

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