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Capital Adequacy Stress Tests: Pre-Provision Net Revenue and Scenario Design

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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 …

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.

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  • 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

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