Key learnings of recent AQR & CCAR exercises suggest that some significant moves are required to fulfill market & regulators expectations. For Institutions, in the short-term the main challenges are threefold: - Methodology: quickly adopt and implement new approaches / scenarios proposed by supervisors - Project implementation: identify work blocks, wisely plan and provide with adequate resources - Time (submission): submit in time, under tight deadlines and with the appropriate quality of outputs

1. Regulatory Stress-testing | EBA, CCAR …
Implementing an optimized Stress testing process
May 2014

2. 2
Overview
Why a dedicated offer now ?
THE RIGHT TIME
THE RIGHT
EXPERTISE
THE RIGHT ANSWER
The difficulties encountered during the AQR
exercise led to a new way of thinking the stress
tests. Time is come to rethink it
All the results from AQR have been challenged.
Each bank’s maturity would be judged on the
capacity to face these new challenges (Data
challenger models, PIT Parameters …)
Banks shouldn’t focus only on stress test results.
Internal processes are also under judgment to
ensure high quality and the timely delivery of the
assessment (quality assurance process)
Key learnings of recent AQR & CCAR exercises suggest that some significant moves are
required to fulfill market & regulators expectations
That is why, based on our recent dialogues with the Financial industry, we have come to
the conclusion that new objectives came to light:

3. Stress-testing | Overview – timeline of recent exercises
Focus | 2014 EBA stress test
Stakes | Key challenges and success factors
Contacts
Appendix
Agenda
1
2
3
4
3
5

4. 4
Stress-testing| Overview – timeline of recent exercises
Some background1.
Stress testing is a key component of financial institutions
risk management framework, helping them determine
capital levels , but also spot emerging risks and take
preventive actions
The 2008-2009 financial crisis highlighted some
shortcomings in practices (i.e. less severe scenarios
based on historical data, limited involvement of the top
management, etc.) that prompted the Basel Committee
to issue in May 2009 recommendations on how to
conduct stress-tests*
For Institutions, in the short-term the main challenges are threefold:
- Methodology: quickly adopt and implement new approaches / scenarios proposed by supervisors
- Project implementation: identify work blocks, wisely plan and provide with adequate resources
- Time (submission): submit in time, under tight deadlines and with the appropriate quality of outputs
Increasingintensity,scopeandfrequency
* “Principles for sound stress testing practices and supervision”, Basel Committee on Banking Supervision, May 2009
Between 2008 and 2013, many stress-tests have been
held to cope with the sovereign crisis or before a bail
out of financial sectors in troubled countries: Greece,
Slovenia, Spain, Portugal, Cyprus, Ireland.
Stress-testing has become a regular regulatory process
and used as a tool to test resilience of financial sectors In 2014, the CCAR in US demonstrated that new
generation of stress-tests are much more intensive and
broader. The EBA stress-test will follow suit before the
enforcement of the SSM (ECB new central supervisor).
For banks, it’s no longer enough to meet current
regulatory requirements (e.g. for ICAAP purposes)
2010/2011
EBA Macro Stress Tests
Today

5. 5
Stress-testing| Overview – timeline of recent exercises
Mounting regulatory pressure on financial institutions around the world1.
 BIS principles for
sound stress
testing practices
and supervision
 US Supervisory
Capital
Assessment
Program (SCAP)
 UK liquidity and
reverse stress-
testing
2009
 Collapse of
Lehman-Brothers:
financial crisis hits
US
 The crisis spreads
to Europe
2008
 2nd EBA stress
tests
 US CCAR + CPAR
(≥$10bn)
2011
 US CCAR + DFAST
Company stress-
tests (≥$50bn
and $10bn-
$50bn)
 UK FDSF
2013
 US CCAR + DFAST
Company stress-
tests
(≥$50bn)
2012
 Enforcement of
Basel III
framework
2015
 AQR + EBA
stress-tests
 BoE stress tests
 HKMA liquidity
stress-tests
 CHINA ST
 US CCAR + DFAST
Company stress-
tests
2014
 1st EBA stress
tests
2010
Stress-testing progressively has become a cornerstone amid increasing regulatory expectations

6. • Core templates: Minimum Data required by the EBA
- Advance Data Collection (ADC): collected prior to commencing the stress test
- Calculation Support and Validation data (CSV): supplied to CAs as input to their quality
assurance process; Also used to automatically populate transparency templates
- Transparency (TR): Data on stress test outcomes to be disclosed on a bank‐by‐bank basis.
• Additional templates: not required by the EBA but can be required by NCAs
- Advance Data Collection (ADC)
6
Key
aspects
• The process is not a substitute to existing obligations regarding stress-testing (i.e. ICAAP pillar 2)
• Risk coverage : Credit risk, Market risk, Sovereign risk, Securitization, Cost of funding, operational risk (standard approach);
CAs may include additional risks
• Assumptions : a static balance sheet , prescribed approaches to market risk and securitization, and a series of caps and floors on net interest
income, risk weighted assets (RWAs) and net trading income
• Horizon : over the period 2014-2016 with 31/12/2013 as a starting point
• Regulatory (capital) hurdles :
- 8% Common Equity Tier 1 ratio for the baseline scenario
- 5.5% Common Equity Tier 1 ratio for the adverse scenario
• Banks will have a maximum of 4 months to complete the process (results due in October 2014)
Scenarios
Disclosures
• Baseline scenario :
- Based on the winter 2014 forecast (EU) extended through a
model-based approach to cover the year 2006 (2016 is
outside the 2-year horizon of the winter forecast)
• The results will be disclosed on a bank by bank basis consistent at least with 2011 EU-wide stress test. Components: the capital position of banks, risk
exposures and sovereign holdings
• Banks are expected to cover potential capital shortfalls within 6 to 9 months after the release of the results
Focus| 2014 EBA stress – test (1/2)
The most complex and comprehensive test to date2.
Data
Risk
modeling
Credit risk
• Perimeter: banking book excluding
counterparty credit risk.
• Calculation of Point-in-time PD and LGD
• ECB EL benchmarks available for banks
with no point-in-time models
• Application of macro-economic scenario to
PIT and regulatory parameters
• Regulatory risk parameters to be used for
stressed RWA calculation
Market risk
• Simplified approach (Var-banks & Non-Var
banks) : projection of NTI based on bank’s
historical loss (2009-2013)
• Comprehensive approach (Var-banks) :
translation of macro-economic scenarios to
project gains & losses on FV positions using
internal models
• CVA and IRC also stressed
• RWA: SVar used in adverse scenario
Securitization risk
• FV positions : market risk methodology
• Impairment estimates for positions not
held for trading
• RWA based on risk profile (3 risk buckets)
Sovereign risk
• FV positions : market risk methodology
• Banking book: credit risk methodology
for impairment estimates based on rating
migration
• Stress-scenario :
- Macro-eco: global debt markets sell-off, a rise in funding
costs, a new recession, and deep dives in property and equity
prices.
- Market shocks: set of common stressed market parameters
Key elements
• New Segmentation for Clients & Exposures in
the Banking book
• Trading Book notional amounts to be re-valued
using IFRS 13 hierarchy
• Higher granularity for asset classifications and
the Real Estate portfolio
• ‘Simplified version’ of 2013 EBA Forbearance &
Performing/NPLs definitions to be used

7. 7
Project
Implementation
/Governance/
Resources
Scenarios
Results/
Documentation
Focus| 2014 EBA stress – test (2/2)
Preliminary questions that the banks start to ask2.
Data
Risk modeling
• What processes? How to ensure involvement of the top management in the end-
to-end process?
• What is the optimal mix of competences?
• What coverage of risks? Which portfolios? Which entities?
• What scenarios to be used? At which level of severity? What is the planning
horizon?
• What models? What parameters to be stressed? How to translate macro-
scenarios into risk factors? What level of sophistication? How to value capital
impact?
• How to leverage on existing documentation? What are the new requirements?
• How to align the task of documentation with actual performance of the test? How
to dot it on time ?
• What data are necessary inputs for scenarios and stress-tests? How to respond to
additional data requests from regulators?
• What level of industrialization achieved by implementing (or not) a stress library?

8. 8
3.
Stakes | Key challenges and success factors (1/2)
Key issues
Successful
completion
of a stress-
testing
process
Data, systems &
disclosures
Project
implementation
Resources and
capabilities
Methodology
Governance &
communication
 The limited period allowed for the
exercises, require a very efficient
project management to meet
regulatory tight deadlines
 Tasks need to be clearly defined,
streamlined and rigorously
monitored
 Supervisors assess results as well as
the way they are produced
 Completeness, consistency (e.g.
finance vs. risk data) and (more
importantly) quality
 Massive data from different
sources
 Compliance with stress test
requirements (e.g. AQR results
used as inputs)
 Consistency with external
definitions (e.g. EBA definitions of
forbearance and NPLs) and
accounting principles in force
 Heavy documentation, flexibility
to answer additional data
requests from supervisors
 Stress-testing is a very burdensome process..
Recent CCAR exercises suggest that banks will
need more people dedicated to the process
 The increased complexity and scope require
a mix of quantitative, financial, IT and/or
economic skills  Excellent capacity for the analysis of
regulatory guidelines and identify which
texts apply to the bank
 Flexibility in incorporating new
approaches in ST framework is key
 Optimize internal modeling since
supervisors increasingly rely on internal
models and assess their quality
 Translate macro-scenarios into risk factors
 Leverage on benchmark
 Detailed documentation of modeling
approaches used by the bank
 More integrated approach
across all areas and business
lines of the bank (front office,
finance, risk, etc.)
 Board and senior management
need to be involved in the
development and operation of
the stress-testing : close
oversight and communication
throughout the process
Failure to pass the tests, and the way to process the stress exercise, can lead to an
unexpected impact on the firm’s reputation vis–à-vis the market or investors

9. 9
3.
Stakes | Key challenges and success factors (2/2)
Why CH&Cie?
CH&Cie CREDENTIALS
 We accompanied several tier 1
Investment banks in the
development of their ICAAP / Stress-
testing and risk appetite frameworks
 Our experts performed several
projects in response to the EBA
stress test (design, implementation,
impact calculation…) for leading
actors of the industry
 We are proud to leverage on our
internal “Global research Analytics”
quantitative department, and have
realized extensive works on stress
testing methodologies (Sensitivity
test, Scenario analysis – historical &
hypothetical - , Maximum Loss,
Extreme Value Theory…)
 Based on our extensive experience
in the industry, we understand
several banks individual set-ups,
know the teams and specific
constraints /obligations and
modeling approaches
 We can also provide with benchmark
for our clients to access best
practices (see appendix 5B & 5C)
Beyond the 2014 exercise requirements, our work will be designed
in order to support periodic needs
A STATE of THE ART EXPERTISE
USED TO WORK UNDER HIGH PRESSURE
RESPECT of DEADLINES & FLEXIBILITY
A RESULTS-DRIVEN TEAM
HIGH QUALITY DELIVERY & COMPLIANCE WITH
REGULATORY REQUIREMENTS
Key learnings of the AQR demonstrate the needs of a new approach
combining strong and tailored skills

10. London
Paris
Hong
Kong
10
4. Contacts
Our experts will remain at your disposal to discuss further the aforementioned topics
We will be very pleased to share with you the latest developments in implementing stress
testing as well as best practices
Stéphane EYRAUD, CEO
E-mail: seyraudt@chappuishalder.com
Phone number : + 44 78 34 55 03 98
+ 33 (0)6 12 41 64 06
Benoit GENEST, Partner and Head of GRA
E-mail: bgenest@chappuishalder.com
Phone number : +33 (0)7 87 68 81 77
Ziad FARES, Manager
E-mail: zfares@chappuishalder.com
Phone number +33 (0)6 62 96 25 00
Matthieu SACHOT, Director
E-mail: sachot@chappuishalder.com
Phone number +852 9433 0753

11.  Appendix A – Stress parameters – Methodologies
 Appendix B – Benchmark on supervisory requirements
 Appendix C – Benchmark on central bank models
 Appendix D – Regulatory Stress testing - What is required from banks?
11
5. Appendix

12. Stress parameters - Methodologies
Illustrative examples on PD
Different kinds of models can be used to translate a shift in PD or LGD parameters from macro
economics data
In terms of benchmarking, 5 types of methods are usually implemented (or derivative models)
Method Description Illustration
1
2
3
4
5
Diffusion Models
Regression models
Interpolation models
EVT (Extreme Value Theory)
Bayesian networks
• The model is based on a differential equation of the variable to
be explained following the explanatory variables in order to
translate the dynamics of evolution of this variable
• ARCH , GARCH models are part of this family
2
2
( )
( ( ), ( ), ( ))
PD
f X t Y t Z t
t



PD differential equation Explanatory variable
functions (GDP …)
• The objective is to determine a causal relation between the PD
and explanatory variables
• In other terms, the goal is to put into equation the PD based on a
combination of selected explanatory variables, which will lead to
the projection of the PD
( ) (0,893. ( ( 1))
0,062. ( 2)
0,02. ( ) 0,54)
PD t InvLogit Logit PD t
Inflation t
Chômage t
  
 

• It’s an iterative method for projecting the PD based on the
maximum of likelihood
• It’s done through an intermediary stage of assessment of the
expectation and then of the maximization of the expectation
• The method is based on extreme values of the variables
• Answers to the question: How will evolve the PD if a extreme
though plausible phenomenon occurs?
Change in
initial
trend–
Extreme
event /
Outliers
• Probabilistic model based on Bayes theory and conditional
probabilities
• Thus it is used to infer the relation between the PD and the
evolution of risk parameters
GDP
Unemploy
-ment
Interpolation
Oil
PD
12
5A

13. Bank of Greece
Regulator
13
Theme Benchmark
Definition of default In models based on loan performance, the key dependent variables are the NPL
ratio, the LLP ratio and the historical default frequencies
Model used Vector autoregressive model using a Logit transformation
Sample used [2000Q1 - 2007Q4] : First, given our data length and the asymptotic properties of
the VAR analysis, a re-estimation of the model is necessary once a new/revised data
set comes available
Finally, only one economic indicator is modeled, yet the shock may be directly
generated through a range of indicators that influence the level of the NPLs and
interact with economic growth
Acknowledging the problems of inference associated with a VAR on a short data
series
We find a significant effect of the changes in the euro exchange rates and the
Euribor interest rates on the non-performing loan ratio while the effect of GDP
growth, albeit small, is found to be significant too
Explanatory variables
Sample used
Explanatory variables
its Financial System Report (Bank of Japan 2007), the BoJ estimates a VAR model
comprising five macroeconomic variables (GDP, inflation rate, bank loans
outstanding, effective exchange rate, and the overnight call rate)
Explanatory variables
The model analyzes the relationship between a logit transformation of Canadian
sectoral default rates and two macroeconomic variables (GDP and interest rate).
particular, in stressful periods, when the default rate reaches its historical peak;
without nonlinearities, even the extreme shocks would have had a very limited
impact on default rates.
Explanatory variables
Bank of Japan
Bank of Canada
5B Appendix | Benchmark on supervisory requirements (1/2)

14. Bank of Italy
Regulator
14
Theme Benchmark
Model used In fact, almost all the studies reviewed here, following Wilson (1997), have used
nonlinear specifications, such as the logit and probit transformation, to model the
default rate. A Logit transformation of default rates is used
Sample used Q1-1990 to Q3-2006
Variables such as economic growth, unemployment, interest rates, equity prices,
and corporate bond spreads contribute to default risk. In particular, interest rates
are a crucial variable, as they represent the direct cost of borrowing.
Explanatory variables
We consider the multifactor probit model of Jimenez and Mencıa to explain the
evolution of the probabilities of default, using default frequencies
Model used
Bank of Spain
For example, in the OeNB’s SRM model, the number of statistically and
economically most reasonable explanatory macroeconomic variables ranges from
two to four depending on the sector, with some variables common to all the sectors
Methodology Another frequent problem in interpreting macroeconomic models of credit risk
concerns the use of linear statistical models: the linear approximation may be
reasonable then shocks are small, but when they are large, nonlinearities are likely
to be important
As our database we use quarterly series of sectoral default frequencies pk,t from
1984.Q1 to 2006.Q4 from the Spanish central credit register
Sample used
This credit register contains information about all the loans with volumes higher
than €6,000. Since this threshold is very small, we can safely assume that we are
modeling the whole Spanish credit market
Appendix | Benchmark on supervisory requirements (2/2)5B

15. Bank
15
Model
Bank of Canada
Explanatory variables Data
Logit transformation of default
rates
- GDP Growth rate
- Unemployment rate
- Medium-term loans rate
Q1-1988 -> Q4-2005
Bank of England Logit transformation of default
rates
- GDP Growth rate
- Short term interest rate
- Equity return
No info
Bank of Italy Logit transformation of default
rates
- GDP Growth rate
- Interest rate
- Equity index
- Competitiveness index
Q1-1990 -> Q3-2006
Bank of Japan Probit transformation of the
probability of rating transition
- GDP Growth rate
- Interest rate
Q1-1985 -> Q4-2005
Bank of Spain Probit transformation of the
default rate
- Quarterely change in real GDP
Growth
- Variation of 3-month real IR
- Term spread
Q4-1984 -> Q4-2006
Bank of Netherlands Logit Transformation of default
rates
- Real GDP growth
- Term spread
Q1-1990 -> Q4-2004
Appendix | Benchmark on central bank models (1/2)5C

16. Bank
16
Model
Deutsche Bundesbank
Explanatory variables Data
Logit Transformation of Loan Loss
Provisions
- Lagged dependent variable
- Credit Growth
- Real GDP Growth
- Variation short-term IR
Q1-1993 -> Q4-2006
ECB EDF or euro-area corporates - Euro-area real GDP
- CPI inflation
- Real equity prices
- Real euro/US$ exchange rate
- Short term interest rate
Q1-1992 -> Q4-2005
Banque de France Logit transformation of the
probability of a rating transition
- GDP
- Short-term interest rate
- Long-term interest rate
No info
Oesterreichische
National Bank
First difference of the Logit
transforamtion of default rates
- Real GDP
- Unemployment rate
- Real short-term IR
- Real five-year IRrate
Q1-1969 -> Q4-2007
Swiss National Bank Logit Transformation of Loan Loss
Provisions
- GDP growth
- Unemployment rate
- Level of three month IR
Q1-1987 -> Q4-2004
Appendix | Benchmark on central bank models (2/2)5C

17. 17
 ECB, EBA, NCA
 EC (economic scenario),
ESRB
Supervisor (s) / regulatory
bodies
** China stress-tests details are set to be released in July 2014. At this point no relevant information on the process, methodology or scope are available
Eurozone
Regulatory Stress testing - What is required from banks?
Stress tests approaches are aligned across regions
 BoE / PRA / FPC
UK*
 Federal Reserve
US
 HKMA
HK**
 EBA FINAL draft ITS
(forbearance and NPLs
exposures ), 20/02/2014
 For IFRS banks: IAS 39, IAS
37, IFRS 13
Scope
 Stress testing the UK
banking system: guidance
for participating firms,
April 2014
 CRD IV, IAS19
 Dodd-Frank Act Stress-
tests
 TBD
 At least 50% of each
national banking sector,
 At the highest level of
consolidation
 128 banks
Data requirements
 8 major UK banks &
building societies
 At the highest level of UK
consolidation
 TBD
 Historical/AQR Data –
Core (ADC, TR, CSV) &
Additional (CSV)
Templates2,3
Risks covered (major)
 FDSF (Firm Data
Submission Framework) –
Historical, Year-End Data
& P/L Projections
 FRY Reports – A/Q/M
Data; P/L Projections
 TBD
 Credit and market risks,
securitization, sovereign
and funding risks
Scenarios
 Credit and market risks,
securitization, operational
risk and conduct costs,
Pension risk, funding risks
 “all potential sources of
losses from all on/off
balance sheet positions…
potential to impact
capital”
 Liquidity risk (personal
loan portfolios)
 Regulatory Baseline
 Stress Scenario
 Common EBA Baseline
(except dynamic balance
sheet)
 Variant Stress scenario
 Bespoke Firm Stress
 Baseline, Adverse,
Severely Adverse;
 Firms’ Scenarios
 Personal loan consultation
: 3% rise in interest rates
 “different degrees of
capital outflow”
Relevant regulations /
accounting standards
 CCAR : Large BHCs & FBO
( ≥ $50 bn in total
consolidated assets)
 DFAST : BHCs & FBO
( ≥ $10 bn)
Source : EBA, HKMA, Fed, BoE, Moody’s
* UK ST will complement those of the EBA with a more severe and UK-specific stress scenario (e.g. house prices down 35%, unemployment rising to 12% and interest rate to 4%) and four additional firms
in the scope
5D

18. 18
 Bottom-Up & Top-Down;
Firms’ Own Models
Modeling approach
Eurozone
Regulatory Stress testing - What is required from banks? (2)
Stress tests approaches are aligned across regions
 Bottom-Up /Granular;
Firms’ Own Models
UK
 Bottom-Up; Firms’ Own
Models; Dynamic
Projections
US
 TBD
HK
Planning horizon  12 quarters (2014-2016)
Frequency
 12 quarters (2014-2016)
 9 quarters (30 sept.14-
Dec.15)
 TBD
 Annual (2009-2011 EBA);
2014 (ECB)
Hurdles’ Requirements
 Annual
 Annual (regulator-led)
 Semi-annual (bank-led)
 Annual
 8% CET1 for the baseline
scenario
 5.5% CET1 for the adverse
scenario
Disclosure
 7% CET1 for the baseline
scenario (3% Tier 1
leverage ratio)
 4.5% CET1 for the variant
Stress scenario
 CET1 ≥ 5% and above the
required regulatory
minimum levels in effect
 TBD
 Results in Oct. 14 (with
AQR results)
 Results towards end of Q4
2014
 Annual submission: 31/03
(disclosure in June)
 Semi-Annual submission:
31/03 and 05/07(March
and September for
disclosure)
 TBD
Source : EBA, HKMA, Fed, BoE, Moody’s
5D