capital adequacy stress tests: pre-provision net revenue and scenario design
DESCRIPTION
CRISIL Global Research & Analytics (GR&A) conducted a web-conference on March 26, 2014 on Capital Adequacy Stress Testing. The web-conference, attended by risk and stress testing practitioners from around the world, focused on why banks need risk models with greater integration across projections. It threw light on how Pre-Provision Net Revenue (PPNR) modelling specifically has assumed critical importance since the original stress tests by the US Federal Reserve following the 2008 economic crisis.TRANSCRIPT
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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.
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Toll Free NumbersUSA 1 866 746 2133UK 0 808 101 1573Singapore 800 101 2045Hong Kong 800 964 448India 1 800 209 1221Australia 1 800 053 698Poland 00 800 112 4248Germany 800 142 43444UAE 800 017 5282Argentina 0 800 142 43444China - North 10 800 713 1853China - South 10 800 130 1814South Korea 800 1424 3444Canada 800 1424 3444Switzerland 800 564 911Netherlands 0 800 022 9808
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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
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CRISIL GR&A Summary
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Serve 12 of the top 15 global investment banks
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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
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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
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Pre-Provision Net Revenue – Regulatory Landscape
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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
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What is Pre-Provision Net Revenue?
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In short, it is a lot of things…Ea
rnin
g As
set Y
ield
Earn
ing
Asse
t Yie
ldLi
abilit
y R
ates
Liab
ility
Rat
es
Net
Inte
rest
Mar
gin
Net
Inte
rest
Mar
gin
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
VentureCapital
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 OperatingLease Expense
Occupancy Expense
Other ExpenseOther Expense
Net Interest IncomeNet Interest Income Noninterest IncomeNoninterest Income Noninterest ExpenseNoninterest Expense(-)
MSRValuation
Deposit Service Charges
(+)
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Balance Sheet Projections
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Balance Sheet Projections
Net InterestIncome
Noninterest Income / Expense
Documentation of Results
Qualitative Adjustments
Modeling Approach
Economic TheoryEconomic 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 SelectionVariable 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 TechniquesModeling 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:
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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
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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 InterestIncome
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 LoanOriginations Balance
Beginning Balance t0 Ending Balance t‐1 Ending Balance t‐1plus: 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
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Net Interest Income
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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 scenarios2. Modeling of spread to index and deposit pricing beta models
Balance Sheet Projections
Net InterestIncome
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:
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Noninterest Income & Expense
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Balance Sheet Projections
Net InterestIncome
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 TheoryEconomic 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 SelectionVariable 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 TechniquesModeling 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
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Qualitative Adjustments to Model Output
Balance Sheet Projections
Net InterestIncome
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 PracticeForecasts driven by quantitative models
with expert judgment when necessary
Weak Practice:Sole reliance on
model output
Weak Practice:Sole reliance onexpert 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
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Documentation of Results
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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 InterestIncome
Noninterest Income / Expense
Documentation of Results
Qualitative Adjustments
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Scenario Development – Regulatory Landscape
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Major learnings on scenario development1. Additional variables are needed to build robust models2. Sole reliance on macroeconomic scenarios does not adequately stress the risk
profiles of all bank holding companies
Major learnings on scenario development1. Additional variables are needed to build robust models2. 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:
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Both Stress Testing Frameworks Require Two Sets of Scenarios
Scenario Development – UK versus US Stress Tests
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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
ScenariosSupervisory
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
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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 Variables Identification & Forecasting of Additional Variables
Addressing Scenario Development Issues
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Univariate ModelsUnivariate 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 ModelsMultivariate Models
Multiple Regression, Logit and Probit Model, VAR/VECM, Generalised Linear Model, ARIMAX, MultivariateGARCH, 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
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(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 ModelOut of sample backtest (2008‐2013)
Recession Dates Actual Champion Challenger
Case Study 1Forecasting C&I Loan Balances
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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
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Case Study 2Fair Value of Loans Held-for-Sale (HFS)
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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
3Q13
A
4Q13
F
1Q14
F
2Q14
F
3Q14
F
4Q14
F
1Q15
F
2Q15
F
3Q15
F
4Q15
F
Cred
it Spreads
Base Adverse Severely Adverse
CMBS Credit SpreadsCoefficient T-Stats
Intercept .73 2.66Market Volatility Index (Qtrly % Change) .03 3.61Commercial Real Estate Price Index (Qtrly % Change) (2.94) (1.26)
Background
Client Impact
CRISIL GR&A Approach
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