JOSF-CCAR:DFAST

THE JOURNAL OF STRUCTURED FINANCE 51FALL 2015
Successfully Navigating
CCAR and DFAST
LARRY LEE
LARRY LEE
is a capital markets strat-
egist at Black Knight
Financial Services Data &
Analytics in Irvine, CA.
larry.lee@bkfs.com
T
he Federal Reserve Board’s (FRB)
complementary Dodd–Frank Act
stress testing (DFAST) and Com-
prehensive Capital Analysis and
Review (CCAR) exercises are meant to
assess whether large bank holding companies
(BHCs)—those with $50 billion or more in
consolidated assets—have sufficient capital
to absorb losses through stressed economic
conditions.
In addition, Dodd–Frank requires
annual, company-run stress testing for insti-
tutions whose consolidated assets are at least
$10 billion but which still fall below the
$50 billion threshold that would mandate
CCAR/DFAST participation.
Regardless of on which side of that
dividing line an institution finds itself, the
process can be complex and often onerous. It
can pull bank executives away from mission-
critical business objectives and instead thrust
them into months of data compilation, dispa-
rate calculation processes, report generation
and submission, and potentially tense meet-
ings with regulators. It is also a fact of life.
It is no surprise that in the finan-
cial services industry, CCAR and DFAST
requirements have proven to be a source of
frustration, but they have also proven to be
one of innovation.
Stress testing proves to regulators that
an institution is well equipped—in terms of
both capital reserves and processes—to deal
with a variety of financial and regulatory sce-
narios. At the same time, the measures used
to demonstrate compliance to regulators also
provide the institution itself with a compre-
hensive view of its own financial well-being,
allowing for better risk management, plan-
ning, and decision making.
Today, even organizations below the
$10 billion threshold for compliance with
DFAST are recognizing the importance of
stress testing and frequently completing it
without a statutory mandate as part of readi-
ness for possible merger or acquisition oppor-
tunities. Although the challenges presented
are real, they are not insurmountable—given
a strategic plan for successful stress testing
that emphasizes industry best practices and is
supported by the right quantitative analytics
and reporting resources.
A HIGH LEVEL OVERVIEW
The aim of both CCAR and DFAST is
clear: to ensure that in the event of a potential
financial crisis, damage does not snowball
and spread throughout the wider economy as
a result of BHCs and other financial institu-
tions having insufficient capital on hand to
continue operations and weather the crisis. In
the initial round of testing, only BHCs with
consolidated assets of $50 billion or more
were required to demonstrate robust, for-
ward-looking capital-planning processes that
JSF-LEE.indd 51JSF-LEE.indd 51 10/19/15 3:58:54 PM10/19/15 3:58:54 PM
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52 SUCCESSFULLY NAVIGATING CCAR AND DFAST FALL 2015
accounted for their risks. Since 2013, however, financial
institutions with average total consolidated assets of $10
billion to $50 billion have also had to conduct annual
stress tests in accordance with Section 165(i)(2) of the
Dodd–Frank Wall Street Reform and Consumer Pro-
tection Act (Public law 111-203, 124 Stat. 1376, July
2010) and the regulations issued by the Office of the
Comptroller of Currency (OCC).
Passing the stress tests is not supposed to be easy.
If it were, the DFAST requirements might very well
be too weak and the Fed’s financial models too sim-
plistic to achieve their purpose of protecting financial
markets and consumers. However, banks below the $50
billion threshold obviously do not have the same level of
resources as large BHCs. Addressing this disparity can
require looking outside of the institution and gaining
assistance from qualified third-party partners that can
provide the analytics and reporting resources necessary,
as well as help with industry benchmarking data.
Federal rules require running two stress tests each
year. When an institution runs the “annual” test, it is
supposed to use its data as of September 30 and report its
results to the Federal Reserve by January 5. In addition,
it must conduct a “mid-cycle” test and report the results
to the Federal Reserve by July 5.
Each year, approximately three months in advance
of deadlines, the Fed issues scenarios for stress testing:
in general, at least a baseline, an adverse, and a severely
adverse scenario. Financial modeling is then applied (by
the Fed directly or by the institution itself, depending
upon which exercise it is required to take part in) to
simulate the scenarios and observe the outcome.
INDUSTRY OBSERVATIONS
The mortgage industry is but a single piece of
the wider financial sector that is subject to CCAR and
DFAST requirements. However, its influence is con-
siderable both because of its size—$9.4 trillion in out-
standing mortgage debt on roughly $12 trillion worth
of housing stock, according to an analysis performed by
Freddie Mac—and because of the outsized role mort-
gages and mortgage-backed securities (MBS) played in
the most recent financial crisis. It was, of course, this
crisis that eventually led to the passage of the Dodd–
Frank Act in 2010 and, ultimately, to the creation of
both CCAR and DFAST in the first place.
Looking at CCAR/DFAST mandated stress-
test requirements as they stand as of this writing, six
key observations can be drawn from the experience of
industry lenders and represent a path forward in terms
of best practices. These are as follows:
1. Process. The stress-testing process itself requires an
unprecedented amount of coordination and collab-
oration across the enterprise, pulling in numerous
front, middle, and back office functions.
2. Documentation. Communication, documentation,
and well-defined business processes are required,
and assumptions made for conditional forecasts
require justification.
3. Governance. Governance of the internal stress-testing
process can be as important as the result(s).
4. Risk. Risk quantification is critical at all levels,
with challenger approaches considered sound
practice.
5. Policy. Pre-provision net revenue (PPNR) esti-
mates are notoriously complex in that centralized
estimates may miss necessary small- to medium-
sized entity (SME) input from lines of business
(e.g., how the business or businesses would actually
react under stress). Quantification processes are
generally preferred, with overlays well justified.
6. Efficiency. Creating increased efficiency in the pro-
cess is necessary to create cost savings and improve
operational resilience.
Looking at process in more detail, we see that within
the institution, there are any number of key groups that
must be party to the stress-testing process—treasury,
asset liability management, credit, risk management, and
economics, just to name a few. A well-defined pro-
cess around the testing will contribute to efficient and
fluent communication among and between each internal
group, a crucial component to smooth and successful
testing.
It’s important to remember that regulators will
be conducting interviews among these various groups
throughout the process review, so it is absolutely essen-
tial that each of the parties understands what the others
are doing, is able to communicate those activities, and
supports each other so there are no gaps to be exploited.
If partnering with a third party for all or part of the stress
testing, make sure that party has robust internal controls

over data aggregation and reporting processes, including
strong governance and internal controls.
Turning to documentation, we see that efficiency
in documentation is proving to be essential. While it
may seem that when in doubt, providing regulators with
more information rather than less would seem sensible,
in actuality, many institutions have been flagged for
providing unclear or overwhelming documentation.
Remember that regulators have a mere two to four
weeks to complete their review, not two to four years.
This means that a document thousands of pages in length
is essentially useless to them—there is simply no way
to process and review something that long within the
available timeframe.
In addition, even a short document that is poorly
organized and confusing may be more harmful than
helpful to both regulators and a given institution’s lead-
ership, who must be able to confidently explain and
defend the data in face-to-face meetings with regulators.
While the Federal Reserve will never issue guidelines
that indicate what constitutes the perfect amount of doc-
umentation, the key is that participants to the process
must be able to gauge what level of detail is needed to
efficiently communicate the institution’s methodology
and findings to the regulators.
Technology is crucial to meeting the documen-
tation requirements successfully. How functional an
institution’s database is for quantifying assets, stratifying
products, and stratifying historical performance comes
into play here. Larger institutions traditionally have the
budget resources to reconfigure databases and write
new internal software tools to add new functionality to
existing databases. However, institutions of $25 billion
or less cannot as easily change existing technology to
meet new documentation and reporting requirements.
Rather, third-party tools that can generate clear and
comprehensive DFAST documentation that stands the
test of regulatory scrutiny are often the best option.
It should be noted at this point that efficiency in
communication, process, and documentation is not just
for the benefit of the regulators. The third observation,
governance, describes the absolutely crucial element for
the “C-suite” to have a strong grasp of the information
being presented to regulators. At the end of the day,
these leaders will be the ones in the regulatory meetings
and must therefore know—and be able to communi-
cate—the process, the methodology, and the execution
of the process to perform the stress testing and complete
the CCAR layout. Hired third parties can only provide
guidance; they will not be in the regulatory meetings
with the institution’s leadership. However, if staff and
third-party vendors have executed their responsibility
for clear documentation and process, leadership will be
adequately prepared with information in a format that
allows them to fully understand the entirety of the pro-
cess and defend it.
Another key observation is how critical risk quan-
tification is proving to be for all the components within
a bank. There must be an institutional understanding of
how the credit risk team handles the bank’s portfolio.
That includes understanding what the parameters are,
what scenarios are being modeled internally, and how
their organization compares with peers. This requires
more than simply meeting the three risk scenarios pro-
vided annually by the Fed. Risk quantification demands
understanding these scenarios in depth, the market con-
ditions the institution operates within, and how peer
organizations would handle the same scenario.
For banks of less than $25 billion, employing the
type of elaborate datasets necessary to quantify risk at
the level of granularity required for DFAST reporting
can be a daunting experience. They’ve had to deliver
traditional datasets to the Federal Deposit Insurance
Corporation (FDIC), but this is not close to the speci-
ficity necessary for DFAST. Enterprise-level risk man-
agement tools—of the sort that the larger BHCs have
developed in house and used for years—may not be
readily available.
In such situations, employing third-party risk
assessment and modeling tools can greatly assist with
compliance. A good tool can provide a comprehensive
identification of risks and documented risk appetite, with
linking of risks to stress-testing processes and capital
plans. It can also capture the bank’s full range of mate-
rial exposures, activities, and risks, while simultaneously
employing multiple, conceptually sound, stress-testing
activities and approaches. The tool—or tool set—should
incorporate comprehensive peer datasets and both pri-
mary as well as challenger models. These challenger
models—which allow banks to simultaneously model
alternate, even conflicting, scenarios alongside primary
“production” models—should be built and maintained
outside of the organization’s own processes, data limita-
tions, and approach.
Likewise, an experienced third-party partner can
help provide and/or develop engaged and knowledgeable

54 SUCCESSFULLY NAVIGATING CCAR AND DFAST FALL 2015
individuals and committees to provide governance over
the capital planning and stress-testing processes. These
individuals and committees can spearhead the devel-
opment of stress models to cover material risks, help
validate models, and apply the right technical skills and
capacity to deliver timely DFAST results, including fur-
ther model development and fine tuning as necessary.
Additionally, previously un-modeled areas—such as
operations risk—can be quantitatively modeled to give
a 360-degree view of operations, helping to inform
capital policy.
Ultimately, all these components translate into an
institution’s policy and business strategy. In a doomsday
scenario, how much capital and liquidity would the
institution actually have to hold to weather that storm,
and if they have to borrow, what are the additional
warehouse credit lines available to help organization
weather that stress? All those elements must be doc-
umented and established in a uniform policy for the
organization. Again, for those that need it, third-party
tools are available to assist with capital policy, including
ones that establish an effective risk-management policy
model and framework, assess capital impact and actions,
set up an end-to-end internal audit process that aligns
with DFAST requirements, and effectively integrate the
DFAST activities into the financial institution’s strategic
and operational processes.
The final key observation is the important role of
efficiency in all of this. Even if an institution performs
each of the these five components very well, it will be
difficult for it to navigate through a period of stress—
either hypothetical as in DFAST or actual—if the com-
ponents do not work together and cannot be activated
in a logical order.
STRUCTURED FINANCE IN STRESS
TEST WORLD
Turning to how these six observations play out in
the realm of structured finance, one sees how necessary
it is to effectively use quantitative analytics to very pre-
cisely model risk in order to accurately calculate capital
reserve requirements. Structured finance arrangements,
such as MBS, collateralized debt obligations (CDOs),
and collateralized loan obligations (CLOs), receive
added scrutiny from regulators because of the role they
played in the most recent financial crisis. The Basel III
accords—which form the basis for much of the criteria
that mark CCAR and DFAST outcomes—specifically
require structured financial instruments like these now
to be 100% risk weighted. Obviously, this requires a
higher reserve be set aside for these assets.
To determine the right amount to hold in reserve
and be able to defend this amount to regulators, an insti-
tution’s financial modeling must be designed to pre-
dict across alternative environments; not just what has
happened historically. Predictive analytics must be very
precise in this regard. Whatever underlying calculation
or methodology is used to forecast the performance of
structured finance instruments within the parameters
of the Fed’s scenarios, it must be reliable in its ability
to produce an accurate, defensible reserve number. It
should:
• be based on sound economic principles and intui-
tive factors;
• stay consistent with observed history, but not
include unexplained adjustments simply to improve
that historical fit;
• apply a single model structure across all types of
mortgage products;
• have loan data that drive results and not arbitrary
loan definitions;
• continually update and revise assumptions con-
cerning mortgage behavior and market structure;
• be robust to unknown or “dirty” input data.
A key assumption of modeling for MBS is that it
must account for actual market conditions. There are any
number of assumptions that must be built into predic-
tive analytics that are specific to the mortgage space. For
example, any modeling around MBS performance must
account for such industry-specific phenomena as loan
modifications—both proprietary within an individual
lender shop and those structured by the government’s
Home Affordable Modification Program (HAMP) or
other similar programs that may come down the pike in
response to particularly adverse economic conditions.
Likewise, it needs to address the availability of refinancing
via the Home Affordable Refinance Program (HARP)
for homeowners current on their mortgage payments but
in negative equity positions. This means the model must
be able to accurately predict the probability that a given
eligible loan will be modified or refinanced.
Furthermore, the model should address some
unknowns, such as whether there are unknown second

liens, and be linked with the ability to identify the exis-
tence of those second liens via public records. It should
also be able to account for the potential impact of regu-
latory changes and moratoria.
Loss severity that includes state-level fixed and
variable costs also should be accounted for, as should
the possibility of appraisal bias. In addition, the model
should apply a distribution around calculated current
loan-to-value (CLTV) ratio and be able to provide broad
context of how comparable assets have performed under
similar circumstances.
Finally, a good model also must account for the
human element inherent to the mortgages underlying an
MBS and weigh behavioral factors to generate monthly
transition probabilities for prepayment (through tradi-
tional refinancing or sale of a property), delinquency,
default, and loss for each loan.
Considering all of these various and complex ele-
ments in modeling, it is clear that it cannot be fully
accomplished within the walls of any single institu-
tion. Accurate models require large datasets to deter-
mine probabilities, which can make employing existing
products based on such large datasets attractive. In addi-
tion, all institutions need to benchmark against peers to
ensure their approach to modeling risk and stress testing
is workable. Contributory databases—to which institu-
tions provide loan-level portfolio data in exchange for
access to the same from the wider industry—provide
the perfect means for acquiring peer datasets to model
against.
DE-STRESSING STRESS TESTING
There is no doubt that the challenges inherent in
DFAST and CCAR requirements are substantial. How-
ever, meeting these obligations is not impossible and
indeed can be a healthy exercise for even small com-
munity banks.
Since 2012, Federal Reserve System agencies have
emphasized “that all banking organizations, regardless
of size, should have the capacity to analyze the potential
impact of adverse outcomes on their financial condition”
(Board of Governors of the Federal Reserve System
[2012]). Particularly as institutions prepare to merge or
be acquired, conducting pre-testing of both parties to
the merger/acquisition, as well as pre-testing a modeled
version of what the joint entity would look like, is an
important part of risk management.
The key to successfully navigating the stress testing
process—be it government mandated or an internal due
diligence exercise—is to consider the six key observa-
tions discussed here and rely on industry best practices
for meeting the challenges presented. Leveraging these
best practices, accurately gauging an organization’s appe-
tite for risk, and strategically employing the tools and
resources available in the marketplace will all aid in
providing an accurate number for reserve capital that sat-
isfies regulators, while ultimately working to the long-
term advantage of the institution, no matter its size.
REFERENCES
Board of Governors of the Federal Reserve System, Fed-
eral Deposit Insurance Corporation, Office of the Comp-
troller of the Currency. “Statement to Clarify Supervisory
Expectations for Stress Testing by Community Banks.” May
14, 2012. Available at www.federalreserve.gov/newsevents/
press/bcreg/bcreg20120514b1.pdf.
To order reprints of this article, please contact Dewey Palmieri
at dpalmieri@iijournals.com or 212-224-3675.

JOSF-CCAR:DFAST

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