Internal Risk Ratings Approaches for Commercial Loans and Structured Securities. Chris Henkel, Sr. Director David Kurnov, Sr.

Transcription

1 Internal Risk Ratings Approaches for Commercial Loans and Structured Securities Chris Henkel, Sr. Director David Kurnov, Sr. Director October 2015

2 Introduction Chris Henkel, Senior Director» Leads the Risk Measurement Services team (Americas) within the Enterprise Risk Solutions group» Works directly with financial institutions to help them improve how they measure and manage credit risk» The RMS team delivers solutions related to risk modeling and rating, ALLL quantification, stress testing, portfolio management, credit administration, and regulatory compliance David Kurnov, Senior Director» Manages the US operations for the Structured Finance Valuations & Advisory team» Team develops and implements analytical models for valuing and stress testing securities across structured asset classes» Provides advisory support for risk analysis and regulatory submission. 2

3 Interactive Poll Question 1 Access the live polling page in the RPC app to participate Which asset class is your primary focus for internal risk ratings? 1) Commercial Loans 2) Securities 3) Both 3

4 Interactive Poll- Question 2 Access the live polling page in the RPC app to participate Does your firm use dual risk ratings for commercial loans? 1) Yes 2) No 4

5 Interactive Poll Question 3 Access the live polling page in the RPC app to participate How are internal ratings primarily used at your firm? 1) Pricing 2) ALLL 3) Credit decisions 4) Early warnings 5) Capital allocation 6) Buy and sell decisions 5

6 Agenda 1. Developing Ratings for Commercial Loans 2. Developing Ratings for Structured Securities 3. Designing a PD/LGD Rating Scale 4. Rating Scale for Structured Securities 5. Q&A 6

7 1 Developing Ratings for Commercial Loans

8 Annualized NCO Rate (%) In terms of credit risk, we ve come a long way since the Great Recession Quarterly Charge-Off Rates: C&I & CRE Loans ( ) C&I CRE 3.26% % NCOs posted a YoY decline for the 20 th consecutive quarter. Noncurrent loans and loan-loss reserves for fell a 21st consecutive quarter. Reserves/TLs is 1.40%, lowest since % 0.04% Source: Federal Reserve, All Banks, NSA; NBER 8

9 Consequently, banks are once again relaxing underwriting standards in the face of increased competition Lending standards are easing Loan demand is up Spreads continue to decline relative to COFs Source: Federal Reserve 9

10 The story is somewhat similar for CRE loans Lending standards are easing but more measured for CLD Loan demand is up for all three major CRE types Standards easier at larger banks Source: Federal Reserve 10

11 If credit risk is declining, why are we discussing risk ratings today?» Relaxed underwriting standards are generally followed by a rise in problem loans» Increased competition from other banks make it more difficult to attract or retain good customers» The industry s net interest margin of 3.06% is slightly above its the 30-year low, set in 1Q15 (3.02%)» Credit risk is cyclical, it can only go down by so much» Expectations from regulators and accountants are requiring improved risk-based practices (e.g., ALLL, risk rating, loan review, stress testing, validation, etc.)» You can t manage what you can t measure! 11

12 There is no one-size-fits-all approach for effective ratings, but there are common attributes found across banks Attributes of Deficient Ratings» Too few risk grades and / or excessive concentration in just a few risk grades» Lack of consistent risk grading approach across portfolios (e.g., a 4 in CRE does not present the same risk as a 4 in C&I)» Inconsistent interpretation or unclear definition across internal risk grades» Lack of clear written policies describing what each risk grade actually means» Failure to decompose risk into key drivers separating borrower risk from facility risk» Lack of independence across those who assign ratings and those who use ratings Attributes of Best Practice Ratings» Universal, consistent and uniformly applied risk grades serving as common language across institution (e.g., EL)» Risk grades mapped to quantified absolute risk parameters (e.g., PD)» Sufficient granularity across the master rating scale» Calibrated to observed or benchmarked experience» Grades assigned based on objective (measurable) versus subjective criteria» Actionable and applicable to other creditrelated activities (e.g., ALLL, stress testing, pricing) 12

13 Count of Loans Ratings allow us to differentiate the credit risk in the portfolio so you can make more informed decisions 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% Distribution of Risk Ratings Scale 1 Scale Rating Which set of bars best resembles your institutions distribution of rating? 13

14 Cumulative 1-Yr. EDF Cumulative 1-Yr. EDF Cumulative 1-Yr. EDF Cumulative 1-Yr. EDF Effective risk measurement should provide banks with actionable information 20% % th Percentile 15% 15% 50 th Percentile 10% 5% 10% 5% 25 th Percentile 0% 20% % 20% % 15% 10% 10% 5% 5% 0% %

15 Testing the Performance of a Ratio or Model A POWER CURVE MEASURES HOW RAPIDLY DEFAULTS WOULD BE EXCLUDED THE RELATION BETWEEN THE ACCURACY RATIO AND A POWER CURVE % Defaults % Defaults Perfect Model A 60% B B A.R.= A+B Random Model Perfect Model: 100% Random Model: 0% 20% % Sample % Sample 15

16 As a refresher, dual risk ratings involve separating the credit risk of a loan into two components 1 2 On average, the amount we could potentially lose depends on three things $6M how likely the borrower is to go into default the estimate of loss (1-recovery) should default occur = x x 3 % 20 % likelihood of exposure the exposure amount at the time of default $1MM Expected Loss Probability of Default Loss Given Default Exposure at Default EL = PD x LGD x EAD M = thousand; MM = million 16

17 Hypothetical Risk Rating Scorecard: What do you observe? Leverage Ratio (Funded Debt/EBITDA) - 10% Management Character - 30% Ratio Score Rating Response Score Rating < Aa Excellent 100 Aa Baa Above Average 75 Baa Ba Average 50 Ba B Below Average 25 B > Ca Poor 0 Ca Liquidity Ratio (Current Ratio) - 15% DSCR (EBITDA/CPLTD+Int Exp) - 25% Ratio Score Rating Ratio Score Rating > Aa > Aa Baa Baa Ba Ba B B < Ca < Ca Access to Capital - 10% Guarantees - 10% Response Score Rating Response Score Rating Excellent 100 Aa Excellent 100 Aa Above Average 75 Baa Above Average 75 Baa Average 50 Ba Average 50 Ba Below Average 25 B Below Average 25 B Poor 0 Ca Poor 0 Ca 17

18 Observed default rate Observed default rate Taking a more quantitative approach can improve the effectiveness of a bank s risk rating process High Liquidity Ratio High Leverage Low Low Percentile High Low Low Percentile High Each level of a ratio is associated with a different default rate, and their weights are chosen to maximize the fit between predicted default rate and observed default rate in the database Example: If the Liquidity ratio for a firm is in the 70 th percentile that means that 70% of the sample had a lower Liquidity ratio than that firm 18

19 What do you observe about the distribution of responses for these two scorecard factors? 100% Management Quality 80% 60% 40% 20% In this example, the Management Quality factor, as currently designed, has limited ability for discriminating risk between obligors in the portfolio. 0% 100% Excellent Good Fair Poor Barriers to Entry 80% 60% 40% 20% The Barriers to Entry factor, on the other hand, differentiates strongly between obligors in the portfolio. 0% Very High High Moderate Low 19

20 Moody s follows a well-established process when developing a risk rating scorecard Scorecard Development Process Data Preparation Single Factor Analysis (SFA) Multi-Factor Analysis (MFA) Financial Model Selection Preliminary Scorecard Validation Calibration Qualitative Factor Workshops Qualitative Data Collection Pilot Qualitative Factors Selection Final Scorecard 20

21 2 Developing Ratings for Structured Securities

22 Internal Ratings for Structured Securities Quantitative Approach» Complexity of underlying collateral and deal structure» Model-driven collateral forecasting» Analyst assumptions/overrides Ratings Analyst Override Analyst Override Based on loss/default Analyst Override Portfolio Behavior Model Cashflow Engine Tranche Results RMBS Residential Waterfall Structure Loss CMBS Commercial Loan/Pool Data WAL/Duration ABS ABS Price CLO Corporate 22

23 Mortgage Modeling Overview Mortgage Portfolio Analyzer (MPA) models default, prepayment, and severity for each loan, taking as inputs information on the borrower, loan, underlying property, and local & national macro-economic factors. FACTORS MODELS OUTPUT» Economic Data (Simulated or Realized)» Default» Loan Level Pool Data (User Data)» Supplemental User data (Pool Performance etc.)» Severity» Prepayment Loan Level E(L) S Pool Level E(L) 23

24 Commercial Mortgage Metrics (CMM) Model Configuration Real GDP growth Unemployment Rate Fed Funds Rate 10 Year Treasury Yield CPI Inflation Rate Population growth Number of Households Growth Retail Sales Growth Total Non-Farm Employment Growth Nominal Personal Income Growth Home Price Growth Baa Corporate Yield Macro Forecast National Level CRE Forecast Local Level CRE Forecasts Series of 20 Regression Equations Convert Macro Forecast to National Level CRE Forecast Vacancy, Rent, NOI, Cap Rate Office, Retail, Industrial, Multifamily, Hotel By MSA/submarket Forecast by Property Type and location CMM Macro Translation Engine CMM National to Regional Engine Loan & Property Details Forward-looking Volatility NOI & Property Value Forecast CMM PD/LGD Engine Risk Measures: PD, LGD, EL 24

25 25 Portfolio Analyzer ABS (PA-ABS)» PA-ABS is a tool for analyzing credit risk in ABS collateral» Models for a large number of US and non-us collateral» Risk vectors from models are combined with cashflow models to produce tranche results Deal Libraries» Regulatory» Built-In» User-Defined» Monte Carlo» Delinquency» CPR/CDR» Severity WSA Platform Tranche level cashflows and losses Macro Scenarios Credit Models Cashflow Engine Output 25

26 CreditEdge - Corporate Probability of Default Moody s Analytics utilizes CreditEdge for CLO issuer-level probabilities of default A firm defaults when the value of its business falls below what it owes PD is the probability that the asset value will fall below the default point Moody s Analytics approximates probabilities of default for non-public issuers:» Issuers are assigned an EDF based on the Industry, Region & Rating averages Modeldriven collateral forecasting 26

27 Static vs. Stochastic Static Approach» Easier to implement» Isolate economic/credit scenarios» Simple data/logic flow Tranche A Tranche B Tranche C Tranche D } Pool Losses Stochastic Approach Probability» Capture many states of the world» Generate tail losses and VaR» Essential for thin or cliff tranches Tranche C PD Tranche B PD Tranche A PD Tranche C Tranche B Tranche A Pool Losses 27

28 Stochastic Model Economic simulation» Each performance forecast driven by economic path» Common simulation across securities» Captures correlation Economic Simulator Economic Paths Pool Forecast 1 Pool Forecast 2 Pool Forecast 3 Performance simulation» Develop distribution of forecasts from historical performance» Captures idiosyncratic risk for each security» Stress test not tied to economic outlook Pool SImulator Performance Paths Pool Forecast 1 Pool Forecast 2 Pool Forecast 3 28

29 3 Designing a PD/LGD Rating Scale

30 Having an enterprise-wide rating scale helps to ensure a common language of risk across the bank 50% 45% 40% 35% Average Annual Default Rates by Alphanumeric Rating ( ) 46.94% 30% 25% 20% 15% 10% 5% 0% Half of the MIS scale is investment grade but most community and regional banks are concentrated here 0.00% 0.00% 0.00% 0.05% 0.15% 0.11% 0.07% 0.18% 0.19% 0.27% 0.42% 0.65% 1.06% 1.43% Aaa Aa1 Aa2 Aa3 A1 A2 A3 Baa1 Baa2 Baa3 Ba1 Ba2 Ba3 B1 B2 B3 Caa1 Caa2 Caa3 Ca-C 2.94% 4.00% 6.84% 16.35% 29.34% 30

31 Hypothetical EL-based Master Rating Scale A B C D E F G 5% 15% 20% 25% 35% 45% 55% 1 Pass 0.08% 0.00% 0.01% 0.02% 0.02% 0.03% 0.04% 0.04% 2 Pass 0.14% 0.01% 0.02% 0.03% 0.04% 0.05% 0.06% 0.08% 3 Pass 0.25% 0.01% 0.04% 0.05% 0.06% 0.09% 0.11% 0.13% 4 Pass 0.43% 0.02% 0.06% 0.09% 0.11% 0.15% 0.19% 0.24% 5 Pass 0.75% 0.04% 0.11% 0.15% 0.19% 0.26% 0.34% 0.41% 6 Pass 1.31% 0.07% 0.20% 0.26% 0.33% 0.46% 0.59% 0.72% 7 Pass 2.30% 0.11% 0.34% 0.46% 0.57% 0.80% 1.03% 1.26% 8 Pass 4.02% 0.20% 0.60% 0.80% 1.01% 1.41% 1.81% 2.21% 9 Pass 7.04% 0.35% 1.06% 1.41% 1.76% 2.46% 3.17% 3.87% 10 OAEM 12.31% 0.62% 1.85% 2.46% 3.08% 4.31% 5.54% 6.77% 11 Substandard - A 20.00% 1.00% 3.00% 4.00% 5.00% 7.00% 9.00% 11.00% 12 Substandard - NA 35.00% 1.75% 5.25% 7.00% 8.75% 12.25% 15.75% 19.25% 13 Doubtful 50.00% 2.50% 7.50% 10.00% 12.50% 17.50% 22.50% 27.50% 14 Loss % 5.00% 15.00% 20.00% 25.00% 35.00% 45.00% 55.00% 31

32 Developing a rating scale is an iterative process which involves art as much as it does science» Consider PD cutoffs where business decisions are made» The relationship of a score to PD can be represented as a curve: Default probability increases by some power of the score The number of grades and their definition affects the shape of the PD curve The more convex ( bowed ) the curve, the more sensitive the rating scale is to change in score The flatter the curve, the less sensitive the rating scale is to changes in score» What should be the PD associated with Criticized loans?» Stability through the cycle not just a 2015 rating scale» Bins with very large or very small proportion of total accounts should be avoided (more common at the lower end)» Technical Considerations: Common Considerations Scale should be approximately linear in log-odds (U-Shape of PDs) Tapering of scale to produce a somewhat normal distribution to the expected frequencies 32

33 Calibration is effectively a mapping exercise for assigning scores to ratings or PDs PD 1 Pass 0.08% 2 Pass 0.14% 3 Pass 0.25% 4 Pass 0.43% 5 Pass 0.75% 6 Pass 1.31% 7 Pass 2.30% 8 Pass 4.02% 9 Pass 7.04% 10 OAEM 12.31% 11 Substandard - A 20.00% 12 Substandard - NA 35.00% 13 Doubtful 50.00% 14 Loss % Max 0.99% Min 0.57% Grade 5 PD bound Calibration curve Grade 5 Score range Score 80 ~ 75 Score E.g., a company with a score between 75 and 80 is assigned grade 5. 33

34 4 Rating Scale for Structured Securities

35 Rating Considerations for Structured Securities Defining PD for a tranche» Positive cumulative loss at maturity or Year N» Cumulative loss exceeds a $ or % threshold» Unrealized loss or under-collateralization Rating based on PD or EL» PD captures likelihood of loss, but magnitude may be small» EL captures actual risk and relative riskiness between securities» Different results for thin vs. thick tranches Strategy based on ratings» Buy/sell decisions» Optimal portfolio mix 35

36 Rating Scale for Structured Securities Timing of default or loss» Rating based on lifetime or N Years PD/EL» Capturing individual risk vs. common approach across securities» What if EL = 0? Choosing a scale» Agency rating tables may be too front-loaded» Different scales for different asset classes?» Different scales for AFS vs. HTM? 36

37 Questions To learn more, please or visit us at the RPC Solutions Cafe 37

38 A Appendix

39 Commercial Mortgage Metrics (CMM) Inputs, Outputs & Uses» Loan Details CMM Inputs - Loan Amount, Term/Amort. - Rate: Fixed, Floating, Other» Structure» Property details» Property type, MSA/Submarket, Rent, Vacancy, Cap Rate, Lease rolls, Expenses, Value» Asset Volatility - Systematic and Idiosyncratic volatility CMM Outputs» Estimated Property Value» Estimated NOI» Expected Default Frequency (EDF)» Loss Given Default (LGD)» Expected Loss (EL)» Yield Degradation (YD)» Stressed Risk Measures - Stressed PD, LGD - Unexpected Loss» Moody s Rating» Customer Rating Scale CMM Uses» Identify sources and causes of risk» Price new loans» Monitor loan expected performance as markets change» Stress Testing» Early Warning System» Identify loans for potential sale» Identify periods of maximum risk» Respond to management and regulators» Efficiently size capital allocations vis-à-vis competing asset classes 39

40 CMM aims to quantify the risk of default» Cash flow from the property is inadequate to cover the scheduled debt service payment» The underlying commercial properties, which serve as the secured collateral, are worth less than the amount of the loan» If underwritten appropriately, in theory, CRE loans carry very little credit risk at origination. What drives the credit risk is the inherent future uncertainty which potentially can be quantified.» CMM is calibrated to observed data (e.g., NOI & DSCR to empirical default rate) 40

41 Collateral performance and credit risk go hand-in-hand Economic shocks Collateral Model Credit Risk Model Property-level NOI and Value Systematic factors Idiosyncratic factors Local CRE Market Info Volatility Current condition Forward-looking views Property Type, MSA/submarket Loan-level Characteristics PD and LGD Drivers DSCR LTV Market Vacancy Market Price Index Change Loan seasoning etc. 41

42 moodysanalytics.com Optional contact information or office address here

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