The document provides details about a web conference on capital adequacy stress tests with a focus on pre-provision net revenue (PPNR) modeling and scenario design. It includes dial-in details for participants to join the audio portion of the web conference, which will be presented by Joshua Hancher from CRISIL Global Research & Analytics. The agenda covers PPNR modeling components like balance sheet projections, net interest income, noninterest income and expenses. It also discusses scenario development and case studies from CRISIL GR&A on commercial loan forecasts and fair value of loans held-for-sale.

1. ©2014CRISILLtd.Allrightsreserved.
Dial-in Details
 Welcome to the CRISIL web conference on “Capital Adequacy Stress Tests:
Pre-Provision Net Revenue and Scenario Design”. We will commence the
session shortly.
 Please join the audio conference by dialing the number relevant to your
location.
1
Toll Free Numbers
USA 1 866 746 2133
UK 0 808 101 1573
Singapore 800 101 2045
Hong Kong 800 964 448
India 1 800 209 1221
Australia 1 800 053 698
Poland 00 800 112 4248
Germany 800 142 43444
UAE 800 017 5282
Argentina 0 800 142 43444
China - North 10 800 713 1853
China - South 10 800 130 1814
South Korea 800 1424 3444
Canada 800 1424 3444
Switzerland 800 564 911
Netherlands 0 800 022 9808

2. ©2014CRISILLtd.Allrightsreserved.
Gurpreet Chhatwal,
Global Head of Risk & Analytics,
CRISIL Global Research & Analytics
PPNR Modeling & Scenario Development
Capital Adequacy Stress Tests
March 2014
Speakers:
Joshua Hancher,
Senior Consultant, Risk and Analytics,
CRISIL Global Research & Analytics

3. ©2014CRISILLtd.Allrightsreserved.
CRISIL GR&A Summary
3
NASSCOM® Exemplary Talent
Awards: 2012 (The Talent
Magnets), 2011(Skill
Enhancement Initiatives)
Top Research Outsourcer for
Financial Services Industry
Analytics, 2006, 2007 and 2009
'Top Green IT Enterprise Award
2012' from CIO Magazine
Roopa Kudva named one of the 20
most powerful women in Business
by Fortune India in 2011
'Best Leadership Training
Programme' award hosted by
World HRD Congress, 2012
Awards & RecognitionsAwards & Recognitions
Roopa Kudva named one of the
30 most powerful women in
Indian business by Business
Today magazine, for the third
time 2009-2011
Mumbai Corporate office - LEED
certified “GREEN BUILDING" 2010
Global Leader in
Research &
Analytics Space
Global Leader in
Research &
Analytics Space
Regulated Entity
with Strong
Compliance
Culture
Regulated Entity
with Strong
Compliance
Culture
High Quality
Skill Sets
High Quality
Skill Sets
Rigorous &
Scalable
Processes
Rigorous &
Scalable
Processes
Unmatched
Financial
Business
Strength
Unmatched
Financial
Business
Strength
Partnerships
with Expert
Networks
Partnerships
with Expert
Networks
 25 year track record
 Pioneered Global Research &
Analytics
 Team of more than 2,300 analysts
 25 year track record
 Pioneered Global Research &
Analytics
 Team of more than 2,300 analysts
 Serve 12 of the top 15 global investment
banks
 Serve 2 of the top 10 global consulting groups
 Serve 3 of the top 15 global insurance
companies
 Serve 12 of the top 15 global investment
banks
 Serve 2 of the top 10 global consulting groups
 Serve 3 of the top 15 global insurance
companies
 Regulated by SEBI, an SEC-
equivalent
 The only regulated entity in this space
 Regulated by SEBI, an SEC-
equivalent
 The only regulated entity in this space
 Highest standards pertaining to regulatory
requirements, compliance, and confidentiality
 Highest standards pertaining to regulatory
requirements, compliance, and confidentiality
 Strong project management
capabilities
 Quality Assurance and Governance
 Information Security
 Strong project management
capabilities
 Quality Assurance and Governance
 Information Security
 Robust training programmes
 Business Continuity
 Inbuilt processes and tools to enhance
productivity
 Robust training programmes
 Business Continuity
 Inbuilt processes and tools to enhance
productivity
 Well-diversified business
 Strong balance sheet and zero debt
 Market reputation and global reach
 Well-diversified business
 Strong balance sheet and zero debt
 Market reputation and global reach
 30% CAGR over the past 10 years
 Consistent growth across business cycles
that provides stability to deliver high quality
services to our clients
 30% CAGR over the past 10 years
 Consistent growth across business cycles
that provides stability to deliver high quality
services to our clients
 Recognized for high quality service – Our tagline “The best people to work with” is a
verbatim reproduction of what a client said in a survey
 Highly experienced and stable management team to ensure high-touch engagement
 Large talent pool comprising MBAs/CAs/CFA/FRMs/PhDs and other university graduates
 Recognized for high quality service – Our tagline “The best people to work with” is a
verbatim reproduction of what a client said in a survey
 Highly experienced and stable management team to ensure high-touch engagement
 Large talent pool comprising MBAs/CAs/CFA/FRMs/PhDs and other university graduates
CRISIL GR&A joined the FINCAD Alliance Program in order to leverage
various FINCAD Analytics tool that meets the derivatives and fixed income
needs of clients. FINCAD is the market leader for innovative derivatives
solutions.
CRISIL GR&A joined the FINCAD Alliance Program in order to leverage
various FINCAD Analytics tool that meets the derivatives and fixed income
needs of clients. FINCAD is the market leader for innovative derivatives
solutions.

4. ©2014CRISILLtd.Allrightsreserved.
Executive Summary
 PPNR projections have assumed critical importance since the initial
Fed stress tests
 One of the most common themes for PPNR modeling improvement
is integration with balance sheet and credit projections
 Bottom-up model approaches have become crucial to achieve
integration and robust model specification
 Shifts in regulatory focus relating to scenario development have
brought into question many existing industry practices on variable
coverage and the incorporation of bank-specific/idiosyncratic risk
4

5. ©2014CRISILLtd.Allrightsreserved.
Agenda
 Pre-Provision Net Revenue Modeling
– Regulatory Landscape
– What is PPNR?
– Walkthrough PPNR components
• Balance Sheet Forecasting
• Net Interest Income
• Noninterest Income & Expense
– Qualitative Adjustments to Model Output
– Documentation of Stress Test Results
 Scenario Development
– Regulatory Landscape
– UK versus US Stress Tests
– Expansion of Supervisory Scenarios
– Internal Scenario Development
 CRISIL GR&A Case Studies
– C&I Loan Balance Forecast Model
– Fair Value of Loans Held-for-Sale
5

6. ©2014CRISILLtd.Allrightsreserved.
Pre-Provision Net Revenue – Regulatory Landscape
6
 Linkage between noninterest income & expense, balance sheet projections, and credit
forecasts
 Emphasis on bottom-up forecast methodologies
 Divergence in budgeting / strategic planning and stress test forecast methods
 Effective documentation of methodologies and results
 Linkage between noninterest income & expense, balance sheet projections, and credit
forecasts
 Emphasis on bottom-up forecast methodologies
 Divergence in budgeting / strategic planning and stress test forecast methods
 Effective documentation of methodologies and results
PPNR modeling has gained in relative importance, but remains the most
difficult modeled component of enterprise stress tests
CRISIL GR&A believes regulators are looking for:
1. Before Enterprise Stress Tests
 Budgeting and strategic planning processes
 Reliance on expert judgment
2. Initial Stress Tests (SCAP 2009)
 Banks must project capital based on
adverse economic scenarios
 Primary focus is credit quality
3. CCAR 2011-2014/CapPR 2011-2013
 PPNR gains in relative importance
− Credit quality stabilizes
− Investor emphasis on approval of
Planned Capital Actions

7. ©2014CRISILLtd.Allrightsreserved.
What is Pre-Provision Net Revenue?
7
In short, it is a lot of things…
EarningAssetYieldEarningAssetYieldLiabilityRatesLiabilityRates
NetInterestMarginNetInterestMargin
Other Consumer
Commercial &
Industrial
Commercial
Real Estate
International
Residential
Mortgage
Auto Loans
Student Loans
Loans & LeasesLoans & Leases
Credit Cards
Deposits & Interest Bearing
Liabilities
Deposits & Interest Bearing
Liabilities
Customer
Deposits
Short-term Debt
and Borrowings
Investment SecuritiesInvestment Securities
(+)
=
Total Earning AssetsTotal Earning Assets
Loan Fees
Syndication
Fees
Capital Markets
Fees
Fair Value of
HFS Assets
Credit Card
Fees
Fiduciary
Income
COLI
Balance Sheet-Related Fee IncomeBalance Sheet-Related Fee Income
Venture
Capital
Additional Noninterest IncomeAdditional Noninterest Income
OREO &
Collections
Environmental ExpenseEnvironmental Expense
FDIC
Assessment
Specific
Operational Risk
Goodwill
Impairment
Salaries
Personnel Related ExpensePersonnel Related Expense
Employee
Benefits
Relocation &
Recruiting
Meetings &
Training
Software &
Equipment
Depreciation
Operating
Lease Expense
Occupancy
Expense
Other ExpenseOther Expense
Net Interest IncomeNet Interest Income Noninterest IncomeNoninterest Income Noninterest ExpenseNoninterest Expense(-)
MSR
Valuation
Deposit
Service Charges
(+)

8. ©2014CRISILLtd.Allrightsreserved.
Balance Sheet Projections
8
Balance Sheet
Projections
Net Interest
Income
Noninterest Income /
Expense
Documentation of
Results
Qualitative
Adjustments
Modeling Approach
Economic
Theory
Economic
Theory
 Banks serve as credit intermediaries to businesses and households
− Commercial credit used primarily to finance investment
− Consumer credit used primarily to finance consumption and residential investment
 Banks serve as credit intermediaries to businesses and households
− Commercial credit used primarily to finance investment
− Consumer credit used primarily to finance consumption and residential investment
Variable
Selection
Variable
Selection
 Common economic variables provided in regulatory scenarios
 Additional variable needs may arise to capture aggregate demand for loanable funds and supply side
effects of credit availability in stress scenarios
 Common economic variables provided in regulatory scenarios
 Additional variable needs may arise to capture aggregate demand for loanable funds and supply side
effects of credit availability in stress scenarios
Modeling
Techniques
Modeling
Techniques
 Panel Regression: Easily understood and straight-forward scenario results
 Vector Auto Regression: Stands up well in both benign and stress periods
 ARIMA: Enables modeling of nominal terms instead of growth rates by addressing non-stationarity
 Panel Regression: Easily understood and straight-forward scenario results
 Vector Auto Regression: Stands up well in both benign and stress periods
 ARIMA: Enables modeling of nominal terms instead of growth rates by addressing non-stationarity
 Modeling new originations versus loan balances
 Linkage with other stress test processes
 Liquidity stress events
 Stress testing versus budgeting process
 Modeling new originations versus loan balances
 Linkage with other stress test processes
 Liquidity stress events
 Stress testing versus budgeting process
CRISIL GR&A sees the key challenges as:CRISIL GR&A sees the key challenges as:

9. ©2014CRISILLtd.Allrightsreserved.
 Granular pricing assumptions commensurate with credit risk
 More defendable loan balance projections
 Explicit volume assumptions allow for direct modeling of related noninterest income items
 Granular pricing assumptions commensurate with credit risk
 More defendable loan balance projections
 Explicit volume assumptions allow for direct modeling of related noninterest income items
Balance Sheet Projections
9
 Most banks lack historical new origination data
 Existing cash flow systems geared toward “balance targeting”
 Budgeting and strategic planning groups more accustomed to ending loan balances
 Most banks lack historical new origination data
 Existing cash flow systems geared toward “balance targeting”
 Budgeting and strategic planning groups more accustomed to ending loan balances
Modeled ApproachModeled Approach
New
Originations
Distinct Pricing
(Spread to Index)
Obligor Risk
Rating
Interest Income
Expected Loss,
NPLs
Credit Projections
Pre-Provision Net Revenue
Commensurate with
Balance Sheet
Projections
Net Interest
Income
Noninterest Income /
Expense
Documentation of
Results
Qualitative
Adjustments
Modeling of new originations preferred…Modeling of new originations preferred…
…but remains a long-term solution…but remains a long-term solution
New Loan
Originations Balance
Beginning Balance t0 Ending Balance t‐1 Ending Balance t‐1
plus: New Originations t0 Modeled Calculated
less: Contractual Payments t0 Modeled Modeled
less: Prepayments t0 Modeled Modeled
less: Credit Losses t0 Modeled Modeled
plus: Change in Utilization t0 Modeled Modeled
Ending Balance t0 Calculated Modeled

10. ©2014CRISILLtd.Allrightsreserved.
Net Interest Income
10
 Justifying ability to maintain a given loan spread in stress scenarios
 Granularity of portfolio segmentation
 Consistency of spread to index with assumed credit quality for new originations
 Justifying ability to maintain a given loan spread in stress scenarios
 Granularity of portfolio segmentation
 Consistency of spread to index with assumed credit quality for new originations
Net Interest Income =
∑ (Balance Earning Asset i ) * (Index + Spread) * (days / interest day count) –
∑ (Balance Interest Bearing Liability i ) * (Index + Spread) * (days / interest day count)
Modeling needs are two-fold:
1. Interpolation of yield curves and credit spreads provided in supervisory scenarios
2. Modeling of spread to index and deposit pricing beta models
Balance Sheet
Projections
Net Interest
Income
Noninterest Income /
Expense
Documentation of
Results
Qualitative
Adjustments
Net Interest Income Calculation
 Interest rates and credit spreads applied to earning asset and interest-bearing liabilities to
derive net interest income
CRISIL GR&A sees the key challenges as:CRISIL GR&A sees the key challenges as:

11. ©2014CRISILLtd.Allrightsreserved.
Noninterest Income & Expense
11
Balance Sheet
Projections
Net Interest
Income
Noninterest Income /
Expense
Documentation of
Results
Qualitative
Adjustments
 Lack of correlation with macroeconomic variables
 Econometric analysis not suitable for all line items
 Linkage between balance sheet and credit forecasts
 Varying data availability for non-GAAP/IFRS information
 Lack of correlation with macroeconomic variables
 Econometric analysis not suitable for all line items
 Linkage between balance sheet and credit forecasts
 Varying data availability for non-GAAP/IFRS information
CRISIL GR&A sees the key challenges as:CRISIL GR&A sees the key challenges as:
Modeling Approach
Economic
Theory
Economic
Theory
 Banks generate higher levels of net revenues during periods of strong economic growth through greater
lending activities, fiduciary income and lower environmental expense
– Fiduciary & capital markets fees correlated with business investment and market indices
– Fewer FTEs required to support lending activities during periods of weak economic growth
 Banks generate higher levels of net revenues during periods of strong economic growth through greater
lending activities, fiduciary income and lower environmental expense
– Fiduciary & capital markets fees correlated with business investment and market indices
– Fewer FTEs required to support lending activities during periods of weak economic growth
Variable
Selection
Variable
Selection
 Common economic variables provided in regulatory scenarios
 Additional variables needed to capture the relationship between banking activities and economic cycles
 Some components are largely balance sheet and credit driven
 Common economic variables provided in regulatory scenarios
 Additional variables needed to capture the relationship between banking activities and economic cycles
 Some components are largely balance sheet and credit driven
Modeling
Techniques
Modeling
Techniques
 Quantile Regression – Well-suited for capturing regime change during economic stress
 Generalized Linear Models – Useful for noninterest expense items that display bimodal distributions
 ARIMA – Enables modeling of nominal terms instead of growth rates by addressing nonstationarity
 Linear Regression – Can be ideal for generating Betas as a starting point for expert judgment
 Quantile Regression – Well-suited for capturing regime change during economic stress
 Generalized Linear Models – Useful for noninterest expense items that display bimodal distributions
 ARIMA – Enables modeling of nominal terms instead of growth rates by addressing nonstationarity
 Linear Regression – Can be ideal for generating Betas as a starting point for expert judgment

12. ©2014CRISILLtd.Allrightsreserved.
12
Qualitative Adjustments to Model Output
Balance Sheet
Projections
Net Interest
Income
Noninterest Income /
Expense
Documentation of
Results
Qualitative
Adjustments
Expert judgment should be used to incorporate forward-looking
business intelligence and address model weaknesses and limitations.
Strong Practice
Forecasts driven by
quantitative models
with expert judgment
when necessary
Weak Practice:
Sole reliance on
model output
Weak Practice:
Sole reliance on
expert judgment
 Some acceptable ways to size qualitative adjustments include
– Out of sample backtesting and challenger models
– Confidence intervals
– Quantification of enterprise-wide sensitivity to model assumptions
 Use of qualitative adjustments and associated governance framework must be
abundantly documented
 Some acceptable ways to size qualitative adjustments include
– Out of sample backtesting and challenger models
– Confidence intervals
– Quantification of enterprise-wide sensitivity to model assumptions
 Use of qualitative adjustments and associated governance framework must be
abundantly documented

13. ©2014CRISILLtd.Allrightsreserved.
Documentation of Results
13
 Inadequate documentation was cited by the Fed as one of the most prevalent
problems in PPNR submissions across all banks
 Documentation should be concise and provide relevant information for credible
challenge and review
– Clear delineation between model output and qualitative adjustments
– Logic and economic rationale for variable selection clearly explained
– Inundating regulators with information is never a good strategy
 Results versus Model Development Documentation – there is a big difference.
 FR Y-14A reporting templates versus modeled account line structure – must
reconcile the two.
 Inadequate documentation was cited by the Fed as one of the most prevalent
problems in PPNR submissions across all banks
 Documentation should be concise and provide relevant information for credible
challenge and review
– Clear delineation between model output and qualitative adjustments
– Logic and economic rationale for variable selection clearly explained
– Inundating regulators with information is never a good strategy
 Results versus Model Development Documentation – there is a big difference.
 FR Y-14A reporting templates versus modeled account line structure – must
reconcile the two.
Stress Tests are like standardized tests:
there is a math and written portion of the exam
Balance Sheet
Projections
Net Interest
Income
Noninterest Income /
Expense
Documentation of
Results
Qualitative
Adjustments

14. ©2014CRISILLtd.Allrightsreserved.
Scenario Development – Regulatory Landscape
14
Major learnings on scenario development
1. Additional variables are needed to build robust models
2. Sole reliance on macroeconomic scenarios does not adequately stress the risk
profiles of all bank holding companies
Major learnings on scenario development
1. Additional variables are needed to build robust models
2. Sole reliance on macroeconomic scenarios does not adequately stress the risk
profiles of all bank holding companies
These learnings are reflected in new PRA and EBA stress test designThese learnings are reflected in new PRA and EBA stress test design
 Greater expectations for variable selection beyond core set provided in supervisory
scenarios
 Use of standard off-the-shelf scenarios not suitable for capturing bank-specific risks and
vulnerabilities
 Robust risk-identification processes to feed idiosyncratic scenario development
 Greater expectations for variable selection beyond core set provided in supervisory
scenarios
 Use of standard off-the-shelf scenarios not suitable for capturing bank-specific risks and
vulnerabilities
 Robust risk-identification processes to feed idiosyncratic scenario development
CRISIL GR&A believes regulators are looking for:CRISIL GR&A believes regulators are looking for:

15. ©2014CRISILLtd.Allrightsreserved.
Both Stress Testing Frameworks Require Two Sets of Scenarios
Scenario Development – UK versus US Stress Tests
15
The final shape of UK stress tests is yet to be finalized, but the proposed
framework has more similarities than differences with US CCAR
requirements – particularly with respect to scenario design.
Risk Factors Included in Common and Internal Scenarios
Internally
Generated
Bespoke
Scenarios
Supervisory
Baseline & Stress
Scenarios
Bank-specific/
Idiosyncratic risk
Bank-specific/
Idiosyncratic risk
Systemic
Risk
Systemic
Risk
Total Risk
Exposure
=
Systemic
Risk
+
Idiosyncratic
Risk
Total Risk
Exposure
=
Systemic
Risk
+
Idiosyncratic
Risk
 Supervisory Stress Scenarios
Applied across all bank participants
Additional variables often required
 Supervisory Stress Scenarios
Applied across all bank participants
Additional variables often required
 Internally generated scenarios
Designed individually by each bank
Typically result in greater capital
depletion than supervisory scenarios
 Internally generated scenarios
Designed individually by each bank
Typically result in greater capital
depletion than supervisory scenarios
1 2

16. ©2014CRISILLtd.Allrightsreserved.
 Modeling the components of aggregate variables like GDP (provided in supervisory scenarios) commonly
needed
 Geographic and industry-specific variables may improve model performance and also incorporate
idiosyncratic risk
 Most variables can be forecast using structural and agnostic models
 Modeling the components of aggregate variables like GDP (provided in supervisory scenarios) commonly
needed
 Geographic and industry-specific variables may improve model performance and also incorporate
idiosyncratic risk
 Most variables can be forecast using structural and agnostic models
Identification & Forecasting of Additional VariablesIdentification & Forecasting of Additional Variables
Addressing Scenario Development Issues
16
Univariate
Models
Univariate
Models
 ARMA, ARIMA, ARCH/GARCH, Interpolation, Exponential Smoothing Models with/without seasonality
 Provide easily interpretable equations and useful for short term forecasting
 Fail to provide an explanation of the causal structure behind the evolution of a time series
 E.g. Volatility, stock price, interest rates, market illiquidity could be forecasted/filled-in using such methods
Multivariate
Models
Multivariate
Models
 Multiple Regression, Logit and Probit Model, VAR/VECM, Generalised Linear Model, ARIMAX, Multivariate
GARCH, Copula, Factor Analysis, Dynamic Stochastic Models
 Exploit the relationship between the dependent variable and several independent variables and their lags
 Framework provides a systematic way to capture rich dynamics in multiple time series
Machine
Learning
Models
Machine
Learning
Models
 Artificial Neural Networks, Support Vector Machines, Random Forests, Decision Trees, Cluster Analysis
 Used for forecasting and classification purposes primarily
 Exploits the non-linearity and nonlinear relationships among the time series, requires huge amount of data
for training purposes

17. ©2014CRISILLtd.Allrightsreserved.
(15%)
(10%)
(5%)
0%
5%
10%
4Q07
1Q08
2Q08
3Q08
4Q08
1Q09
2Q09
3Q09
4Q09
1Q10
2Q10
3Q10
4Q10
1Q11
2Q11
3Q11
4Q11
1Q12
2Q12
3Q12
Quarterly % Change
C&I Loan Model
Out of sample backtest (2008‐2013)
Recession Dates Actual Champion Challenger
Case Study 1
Forecasting C&I Loan Balances
17
Champion Model
Independent Variables Coefficient T-stats
Constant 0.0170 1.50
SPREAD1 -0.0097 -2.25
US Real GDP (Lag 2) 0.0040 1.70
Dow Jones (Lag 5) 0.1467 2.44
Adjusted R^2 = 0.30 AIC = 3.93
Log-Likelihood = 102.50 Schwarz Criteria = 3.78
F-Statistics = 8.01 Durbin-Watson Stats = 1.91
 Champion Model output closely tracked actual C&I
loan growth in a sample backtest
 High model fit (Mean Average Error = 0.0244)
 Directionally correct (Hit ratio = 79%)
 Model benchmarking shows that the champion
model has been developed with adequate variable
coverage to inform C&I model coefficients that result
in reasonable projections
 Champion Model output closely tracked actual C&I
loan growth in a sample backtest
 High model fit (Mean Average Error = 0.0244)
 Directionally correct (Hit ratio = 79%)
 Model benchmarking shows that the champion
model has been developed with adequate variable
coverage to inform C&I model coefficients that result
in reasonable projections
 Macroeconomic drivers from the Fed’s supervisory
scenarios were used to model commercial loan
balances as part of the PPNR modeling process
 Vector Auto Regression was the chosen
methodology for its ease of use and suitability for
forecasting both benign and stress scenarios
 Challenger Model was also developed using
supplemental macroeconomic variables to help
ensure the reasonableness of the primary model
output
 Macroeconomic drivers from the Fed’s supervisory
scenarios were used to model commercial loan
balances as part of the PPNR modeling process
 Vector Auto Regression was the chosen
methodology for its ease of use and suitability for
forecasting both benign and stress scenarios
 Challenger Model was also developed using
supplemental macroeconomic variables to help
ensure the reasonableness of the primary model
output
CRISIL GR&A Approach Insights

18. ©2014CRISILLtd.Allrightsreserved.
Case Study 2
Fair Value of Loans Held-for-Sale (HFS)
18
Macro to Micro Model
 Significantly reduced market risk-related losses by
separately taking into account underlying exposure
and credit spread indices (i.e., bottoms-up
approach)
 Significantly reduced market risk-related losses by
separately taking into account underlying exposure
and credit spread indices (i.e., bottoms-up
approach)
 Bank had a substantial loan warehouse subject to
fair value accounting
 Top-down approach was previously used by
modeling dollar losses on the loan warehouse
 Bank had a substantial loan warehouse subject to
fair value accounting
 Top-down approach was previously used by
modeling dollar losses on the loan warehouse
 Macro-to-micro model was developed to forecast
credit index spreads widely used by the market to
price and hedge CMBS loans
 Multiple Regression was used to model CMBS
spreads using the Fed’s macroeconomic scenario
variables
 Noninterest income impact was then calculated
using macro-to-micro model output to value
warehouse loans in supervisory base, adverse, and
severely adverse scenarios
 Macro-to-micro model was developed to forecast
credit index spreads widely used by the market to
price and hedge CMBS loans
 Multiple Regression was used to model CMBS
spreads using the Fed’s macroeconomic scenario
variables
 Noninterest income impact was then calculated
using macro-to-micro model output to value
warehouse loans in supervisory base, adverse, and
severely adverse scenarios
Fair Value Gain/Loss:
Loans HFS
2.0
2.2
2.4
2.6
2.8
3.0
3.2
3Q13A
4Q13F
1Q14F
2Q14F
3Q14F
4Q14F
1Q15F
2Q15F
3Q15F
4Q15F
Credit Spreads
Base Adverse Severely Adverse
CMBS Credit Spreads
Coefficient T-Stats
Intercept .73 2.66
Market Volatility Index (Qtrly % Change) .03 3.61
Commercial Real Estate Price Index (Qtrly % Change) (2.94) (1.26)
Background
Client Impact
CRISIL GR&A Approach

19. ©2014CRISILLtd.Allrightsreserved.
www.crisil.com/gra