While efforts are underway to improve financial reporting in Horizon 1, the FDIC
should continue to move toward best practices in risk management more broadly in
Horizon 2. In particular, it should accelerate development of its new integrated
model for financial risk management and strengthen its risk organization and
processes, focusing primarily on the NRC, RAC, and FRC.
To reach best practice risk management techniques over the next 12 to 18 months,
the FDIC should aggressively develop an integrated model for financial risk
management (Exhibit 21). By combining and synthesizing models of bank
failures, investment income, deposit growth, and premium income, such an
integrated model will enable the FDIC to monitor and manage its overall financial
risks (e.g., declines in the deposittoreserve ratio). The outputs of this integrated
model should be captured in userfriendly “dashboard” formats with appropriate
detail for the NRC, RAC, and FRC. These dashboards, detailed below, will help
focus the organization on a timely basis on risk metrics that are significant, relevant,
and actionable within its current risk management environment.
The most important component of the FDIC’s integrated model will be its model of
projected losses from bank failures. DIR is currently beginning development of a
credit risk model for this purpose. This will be a sophisticated and flexible
econometric model that will generate a loss distribution – the likelihood of
experiencing any given insurance loss over a particular time horizon – based on a
simulation engine and a set of assumptions about failure probabilities, loss rates,
and correlation structures. The credit risk model will also provide the ability to run
a broad range of scenario and ‘what if’ analyses quickly and easily without
extensive human intervention, capabilities that can facilitate resource planning and
allocation across the organization. DIR has already committed some initial
resources to developing its credit risk model. As detailed below, these efforts
should be accelerated and expanded over the next 18 months.
In order to complete its integrated model of financial risk management, the FDIC
will need to complement the credit risk model with additional models of investment
income, deposit growth, and premium income. These three auxiliary models should
be developed and refined in parallel. Once all four pieces are in place, the integrated model will give a comprehensive picture of the FDIC’s overall risks, allowing it to manage and mitigate those risks in the future.
The credit risk model concept
A credit risk model is a method of producing a statistically valid distribution of
credit losses over various time horizons. In FDIC’s case, such a model should
incorporate four essential elements:
 A statistical model of the probability of failure for individual institutions. The FDIC should develop a stateoftheart econometric model that estimates bank failure probabilities on a per bank basis using CAMELS ratings, pertinent financial metrics drawn from Call Report data (e.g., capital ratios), and relevant market data (e.g., bond yield spreads and equity volatility).
 A statistical model of loss given failure for individual institution. The model should incorporate institution size, asset composition (e.g., cash, securities, consumer loans, commercial loans), liability structure (e.g., quantity of insured deposits), and speed of deterioration of the institution.
 A correlation structure reflecting the relationships between bank failures, as such events are not truly independent. A deterioration of consumer credit, for example, is likely to cause subprime institutions as a group to face an increased chance of failure. An understanding of how and to what extent the fortunes of particular groupings of banks are correlated is crucial for accurately estimating the likelihood of the most severe systemic outcomes (i.e., the tail of the distribution), itself a critical component of scenario analysis.
 A rigorous Monte Carlobased simulation engine to integrate these three components into a distribution of credit losses over any given time period. The simulation engine must be capable of handling correlations of arbitrary complexity, in a reasonable amount of time, with a workable user interface, and thorough integration into FDIC IT infrastructure.
Specifics of Recommendation 2.1 (Accelerate development of
the integrated model)
DIR has in place plans to develop a basic credit risk model to replace the CLR and
2year failedasset models. These plans represent a significant first step toward Horizon 2, and this effort should be accelerated and expanded into the fullblown
credit risk model, such as that described above. The FDIC should develop the
credit risk model in three phases over the next 18 months: A working prototype by
the end of 2003, an “intermediate” model by June 2004, and an “advanced” model
by December 2004. The FDIC should simultaneously develop the three auxiliary
models of investment income, deposit growth, and premium income.
2.1.a. DIR should build a working prototype capable of generating a basic loss
distribution by December 2003. Although the tails of the resulting distribution will
contain limited information due to the need to employ a basic correlation structure
at this stage, the prototype will demonstrate the feasibility of the model and serve as
a platform for future improvements. Failure probabilities should be based on an
implementation of the work now being performed through collaboration between
the FDIC and Robert Jarrow, an economics professor at Cornell University, but
extended to include CAMELS ratings as a variable. (The ongoing work is aimed at
developing estimates of failure probabilities using market and Call Report data.)
Loss rates should be derived not from the current methodology but from the
Research Model, as revised and recalibrated in Horizon 1 and extended to cover not
only institutions on the problem list (i.e., those rated CAMELS 4 or 5, and failing
institutions) but all banks. A simple correlation structure that includes a limited
degree of bucketing (e.g., up to 25 categories) should be employed. Similarly, the
simulation engine should be basic – either an inexpensive offtheshelf solution or
an inhouse approach (e.g., built with SAS or MATLAB). The ongoing work with
Professor Jarrow is relevant to all of these components, and should enable a
working prototype to be created in 90 days with only a modest shift of existing DIR
staff and within the existing DIR budget.
2.1.b. DIR should develop an intermediate credit risk model to replace the
current CLR methodology by June 2004. The prototype will need to be backtested,
and any obvious problems resolved to develop an intermediate model
suitable for use in operations. Integrated and flexible, the intermediate model will
combine updated approaches to failure probabilities, loss rates, and the other
components into a single architecture with an enhanced user interface and
convenient reportgeneration capability. The integrated model will provide a
greatly enhanced ability to conduct “what if” analyses through its use of more sophisticated
correlation structures, while the associated loss distributions – the tails
in particular – will provide additional insight into risks to the insurance funds. The
model should be used for several months to shadow the current CLR methodology
before being adopted for that use by June 2004.
Within the model itself, failure probabilities should represent an extension of those
used in the prototype, with additional testing and calibration to more systematically
incorporate marketbased offerings such as KMV for public institutions and
RiskCalc for private institutions. Loss rates should be refined to incorporate
institution size as a variable.
The limited correlation structure used by the prototype should be expanded to
include multiple scenarios and refined to incorporate additional data. A more
sophisticated correlation structure could, for example, shed light on how proposed
consumerbankruptcy legislation would affect the likelihood of subprime lenders
failing as a group. Another possible correlation structure might reflect the possible
progression of a liquidity crisis through the banking sector and how that might
impact failure probabilities. These efforts to improve the model’s correlation
structures will require both statistical analysis (e.g., examining historical data for
potential relationships between depository institutions) and qualitative, forward looking
analysis of potential future correlations.
Even with these improvements, enhanced credit loss distributions will not be
assured without a more robust simulation engine. The increasing need to run
simulations frequently and the “curse of dimensionality” (i.e., the size of the
problem increases exponentially with the number of correlations) will require a
solution more sophisticated than the ad hoc simulation engine crafted out of SAS,
MATLAB, or similar software for the prototype. The FDIC should purchase an
advanced commercial Monte Carlo simulation package or build a similarly capable
simulation program inhouse.
2.1.c. By December 2004, DIR should develop and adopt an advanced credit risk
model capable of reliably estimating loss distributions over multiple years. Once
firmly established as the basis for estimating necessary oneyear reserves (the
CLR), the intermediate model should be updated with new and ongoing FDIC
research into model inputs, such as the impact of region and size on correlations
among bank failure probabilities. Loss rates similarly should be refined to add new
variables, such as the rate at which an institution deteriorates and its assetto liability
ratio. Also, the model should be extended to project losses over multiple
years.
Pursuing this coordinated threephased approach to the development of a credit risk
model will require a considerable degree of project management and planning that
may strain DIR’s existing organizational structure. DIR should create a risk
management modeling group, with detailed work schedules and regular progress
review meetings, to ensure the delivery and continuous improvement of the various
models used in Horizon 2. Much as Exhibit 22 presents a highlevel summary of the credit risk model schedule, detailed supporting schedules should be created for
each of the model components. In addition, the risk management modeling group
should be charged with conducting further research into available outside solutions
– not as replacements for internal products, but as additional benchmarks for the
FDIC’s own work on an ongoing basis in the future.
Exhibit 22
DEVELOPING THE CREDIT RISK MODEL





Key characteristic 

Capability to systematically
generate loss distributions, but
supporting models unintegrated
and relatively inflexible

 Models partially integrated,
improved user interface allow staff
with different skills to produce
reports in less time


Models are increasingly
integrated/interactive and
run in closer to real time


Default
rates 
 Implementation of the Jarrow
work including CAMELS ratings

 Continued extensions, testing and
calibration against RiskCalc, KMV
and others

 Continuous improvement of
models, with particular
emphasis on speed, flexibility

Loss
rates 
 Implementation of the
Research Model

 Extension of loss rates model to
incorporate new variables, testing
of model against AVR database

 Continued enhancement of
loss rate estimates

Correlation
structure 
 Assumed that all banks are
independent or limited bucketing
(e.g.,as many as 25 buckets)

 Increasing use of correlation
structures, 23 standard
variations developed by
statistical analysis

 Procedures and computer
programs to facilitate creating
correlation structures to express
views of DSC and others

Simulation
engine 
 MATLAB, SAS, or similar 'Cheap
and cheerful' offtheshelf solution

 Limitations of 'cheap and cheerful'
solution increasingly become binding constraint

 Increasing need for fast
commercial Monte Carlo package





Output/
capability 
 Basic loss distribution, tails will
contain limited information due to
simple correlation structure
 Improved CLR and multiyear
projections

 Tails of loss distribution provide
real insights for FDIC decision
making
 Detailed analysis at least quarterly

 Detailed analysis now routinely
available, perhaps at daily RAC
meeting and on intranet
 Some risk reports can be
created at intranet


2.1.d. Simultaneous to the development of the credit risk model, DIR should
develop auxiliary models of investment results, premium growth, and deposit
growth. While the credit risk model will be the most difficult and complex
component to deliver, other risk management metrics will be required to produce a
fully integrated risk model. Although these are simpler problems, they will
nonetheless require specific efforts. These efforts will allow the FDIC to generate
simulations beyond the credit risk model’s loss distributions, to include the
probability of falling below (within) the designated reserve ratio (range), as well as
the probability of losses exceeding a critical threshold level of the funds. These two
probabilities represent the core risk metrics that the FDIC should monitor on a
regular basis.
Further, the resulting models should be coordinated with the credit risk modeling
effort. Specific, actionoriented plans should be developed by the credit risk
modeling group to ensure model compatibility and to help overcome what are likely
to be challenging issues of implementation.
The contemplated frequency of risk reporting, and increased use of scenario
analysis in Horizon 2 will create increased demands on DIR. The division should
assume that one FTE would be dedicated to report production by yearend 2003 and
possibly two to three FTEs by the end of 2004. The greater the investment in
building robust, userfriendly systems, the less the need for dedicated report
production staff.
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