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Strengthening Financial Risk Management at the FDIC

Improving Financial Reporting – Horizon 1


The 2-year Projection of failed bank assets is the FDIC’s estimate of the sum of the assets of all FDIC-insured institutions whose failure is reasonably possible in the coming 24 months. Compared with the CLR, the 2-year Projection provides a slightly longer-term perspective and a somewhat weaker threshold for the likelihood of failure. Until recently, its use was twofold: as a key component of the semiannual rate case, and as a basis for a footnote to the Annual Report describing “reasonably probable” losses to the deposit insurance funds. Today, it is used only for the latter purpose.

The methodology of the 2-year Projection could be improved, but it is fundamentally not the best estimate for FDIC’s needs, and it should be replaced. The first step in this reasoning is to explain how the 2-year Projection is calculated.

How the 2-year Projection is calculated

The 2-year Projection is based on the DSC’s 8-Quarter List and three DIR models, with six different scenarios between them. Each of these seven inputs (DSC’s list and six model scenarios) yields a list of institutions expected to fail in the coming 2 years, which in turn yields seven estimates of the sum of 2-year failed-bank assets.

The FRC creates a “model range” as the band between the largest and smallest of these seven estimates. The FRC participants then use their judgment to determine whether that range should be altered, for example by raising the upper end of the range. It is this modified range, the “reported range,” that the FRC disseminates to the rest of the FDIC and that is used in the FDIC’s annual report. As depicted in Exhibit 1-6, the current model range for BIF-insured institutions is $0 to $22 billion, and the FRC currently has raised and widened its reported range, to $2 billion to $35 billion.

Exhibit 1-6


    Model projections, BIF example, 2002, $B Exhibit 1-6D
Source: FDIC; McKinsey analysis

The seven input estimates of failed-bank assets are derived from the DSC 8-Quarter List and three DIR models. Those models are the Pro Forma Model, the Stress Analysis Model (SAM), and the Proportional Hazards and Logistic Model (treated here as a single model, the “PH Model”).

DSC’s 8-Quarter List contains the FDIC’s best supervisory assessment of which institutions are likely to fail in the coming 24 months, along with the quarter of expected failure. An institution is put on the list whenever supervisors judge its likelihood of failure to be more than 50 percent.

The Pro Forma Model was discussed above. This model provides fairly straightforward forecasts of the balance sheets of depository institutions. It uses accounting ratios and rules to estimate an institution’s future financial condition based on its current financial condition. For the 2-year Projection, Pro Forma is run in two different scenarios: “Optimistic” and “Pessimistic.” The primary difference between the two scenarios is in their assumptions about the share of an institution’s assets that are non-performing. In either scenario, an institution is projected to fail if the Pro Forma forecast of the institution’s capital falls below two percent of its assets.

SAM, like Pro Forma, is a balance-sheet simulation model, but SAM incorporates additional financial data and has more-sophisticated rules about how balance sheets evolve. Furthermore, SAM has 14 parameters (e.g., loan charge-off rates) that are calibrated to fit historical data. Like Pro Forma, SAM projects that an institution will fail whenever its capital falls below 2 percent of assets.

The PH Model is based on a set of statistical regressions about the causes and timing of failures of depository institutions. Specifically, it uses a logistic regression to estimate the probability that an institution will fail, based on nine measures of its current condition. These variables range from CAMELS ratings to the change in its capital over time. For the 2-year Projection, the PH Model is run in three different scenarios: “Optimistic,” “Baseline,” and “Pessimistic,” where the optimistic and pessimistic scenarios are 90 percent confidence bounds around the baseline scenario. In each case, the PH Model reports an institution as expected to fail whenever the failure probability from the regressions is above 50 percent.

Performance of the 2-year Projection

The performance of the 2-year Projection should be measured based on how the estimate is actually used. The 2-year Projection is referred to in the Rate Case and is an input to a DOF calculation that is reported in Note 6 of the FDIC’s Annual Report. In crafting Note 6, the Division of Finance uses the upper end of the reported range ($35 billion for the BIF example), multiplies by an assumed 20 percent loss rate on assets ($35 billion x 0.2 = $7 billion) and subtracts the current CLR ($7 billion – $1 billion = $6 billion). The result is explained as follows:

“Due to the uncertainty surrounding future economic and market conditions, there are other banks for which the risk of failure is less certain, but still considered reasonably possible. Should these banks fail, the BIF could incur additional estimated losses up to $6.0 billion.”14
The 2-year Projection and the calculations described above are not an ideal way to arrive at “possible” losses over and above the CLR, for three reasons. First, the seven input estimates are not equally valid measures of possible losses, and using the largest of them takes no account of the meaningful information contained in the others. As depicted in Exhibit 1-7, all the input estimates except SAM generally underestimate actual losses, so they would be poor estimates of the FDIC’s downside risk. Second, the 1-year loss estimate in Note 6 is derived from a longer, 2-year estimate. Third, the current method for arriving at Note 6 assumes a flat 20 percent loss rate on failed assets, while average loss rates are typically lower and depend on an institution’s size, asset composition, and liability structure.

Exhibit 1-7


Assets in failing depository institutions in subsequent 2 years, 1997-2001, $B

    Exhibit 1-7D
    Note: Scale for SAM . Mild Stress differs from other three scatter plots
Source: FDIC; McKinsey analysis

Specifics of Recommendation 1.2 (Replacing the 2-year Projection)

Since the 2-year Projection is not well suited to the FDIC’s needs, the FRC should not expend its limited resources improving the estimate. Instead, the 2-year Projection should be abandoned, and two better-suited estimates calculated in its place.

1.2.a. The FDIC should no longer calculate the 2-year Projection. The 2-year Projection should be replaced with two other estimates, detailed below, that will better serve FDIC’s needs.

1.2.b. DIR should decide whether to keep, revise, or eliminate the three models supporting the 2-year Projection:

  • DIR should keep the Pro Forma model as it is today. That model is inexpensive to maintain and is used appropriately in the risk groupings for the CLR calculations.

  • SAM would be better suited to offsite monitoring and should be migrated to that use. SAM is a useful research model and shows promise as a tool to screen for at-risk institutions that deserve further attention, which is precisely the objective of offsite models.

  • The PH Model should be abandoned. The statistical methods that it uses have some merit, but those methods can and should be implemented afresh with the new credit risk model that DIR is developing as its next-generation risk management model (described in the second chapter). While the methods embodied should be retained in this manner, the model itself should be abandoned as a vehicle for supporting risk reporting requirements.15
1.2.c. DIR should calculate a confidence interval around the CLR, and DOF should use the upper end of this confidence interval in Note 6 to the FDIC’s annual report. There are two stated objectives in Note 6 of the FDIC’s annual report. The first is to report losses that are “probable and reasonably estimable.” This is met by the CLR itself. The second objective is to report additional losses that are “possible.” A confidence interval about the CLR would be the natural basis for such possible losses. For example, suppose the CLR was $1 billion and the upper 90 percent confidence bound for the CLR was $7 billion. Then 90 percent of the time the true 1-year loss would be less than $7 billion, so DOF would report $6 billion ($7 billion less the $1 billion CLR) as possible additional losses.

There are a variety of ways to calculate a confidence interval around the CLR. The simplest and most robust method would be to track the differences between the CLR and the FDIC’s actual losses over time. Sometimes the CLR will be very close to the actual loss. Other times, the CLR and actual losses will diverge. The distribution of these errors gives a sense for how likely it is for the actual loss to be far from the reported CLR. Exhibit 1-8 depicts this methodology in more detail.

Exhibit 1-8


Source: FDIC; McKinsey analysis

The first step in creating such a confidence interval will be for DIR to compute pro forma CLRs for as far back as possible, e.g., to 1990, using the revised methodology described in the previous section. DIR should then tabulate the distribution of errors between this CLR and the FDIC’s actual losses over that time Confidential period.16 This computation would then form the basis for a robust confidence interval around the CLR going forward.

1.2.d. DIR should create a 2-year loss estimate using methodology similar to that of the CLR. The calculations will be identical to those of the CLR, but using 2-year rather than 1-year failure rates. Such an estimate can be used to inform the internal budgetary planning process, the semi-annual rate assessment, and/or other organizational processes that may benefit from an extended loss outlook.

1.2.e. DIR should investigate the sensitivity of such a 2-year loss estimate to changes in the reserve list. Since the CLR methodology estimates losses only for institutions with current CAMELS ratings of 4 and 5, the proposed 2-year loss estimate would not account for failures that were unanticipated a full two years in advance. As such, it may be necessary to adjust the proposed 2-year loss estimate upward to account for institutions that may have their CAMELS ratings downgraded from 1-to-3 to 4-to-5 after the first year.

14 FDIC 2002 Annual Report, at 51.

15 This model and others may well have value in other contexts, but a decision by DIR to retain such models should hinge on an explicit identification of what those contexts may be and a clear description of what corresponding value the models will create.

16 The methodology in Exhibit 1-9 assumes that the distribution of errors in percentage terms does not depend on the level of the CLR, e.g., that the FRC is just as likely to be 10 percent off when the CLR is $1 billion as when it is $1 million. In statistical terms, Exhibit 1-9 assumes that the errors are homoskedastic, when in fact they may be heteroskedastic. After calculating a larger set of historical errors (e.g., by back-testing the CLR to 1990), DIR should explore whether there is a systematic relationship between the level of the CLR and the size of the error. If such differences exist, DIR should control for them, either by a) creating error distributions for CLRs in different size bands or b) using statistics to fit a functional relationship between the variance of the error and the level of the CLR (e.g., the variance of the errors might be a linear function of the CLR).


Last Updated 08/21/2003 insurance-research@fdic.gov
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