DFAST and Dual Ratings: Making Practical Use of Credit Loss Estimation Measures

Transcription

1 DFAST and Dual Ratings: Making Practical Use of Credit Loss Estimation Measures #RPC14 KEN CARSON, SENIOR VICE PRESIDENT, UMPQUA BANK CHRISTIAN HENKEL, SENIOR DIRECTOR, ENTERPRISE RISK SOLUTIONS October 2014

2 Introductions Ken Carson Senior Vice President Umpqua Bank Portland, OR Christian Henkel Senior Director Moody s Analytics New York, NY

3 Agenda 1. Overview of Dodd-Frank Act Stress Testing (DFAST) 2. Evaluating the relationship between loss forecasting and risk rating 3. Getting ready for DFAST 4. Using the DFAST investment to enhance credit risk measurement

4 1 Overview of Dodd-Frank Act Stress Testing (DFAST)

5 The Dodd-Frank Act is the primary impetus for bringing stress testing to the forefront for banking organizations» Initiated by SCAP in 2009, the Dodd-Frank Act ( DFA ) introduced CCAR in 2011 (about to start season four )» CCAR is an annual exercise by the Fed to assess capital adequacy, process, and planning during stressed economic times» DFAST is a complimentary exercise relying on similar processes, data, requirements, etc.» BHCs > $50B must submit by January 5 th with Fed results in March (30 BHCs in CCAR 2014; 31 in 2015)» BHCs $10B - $50B required to conduct annual company-run stress tests as part of DFA (not subject to CCAR)» Banks < $10B currently excluded Minimum Capital Ratios for Adv. Approach BHCs / All Other BHCs Category Q Tier 1 common ratio 5% 5% 5% Common equity tier 1 ratio N/A NA / 4% 4.5% Tier 1 RBC ratio 4% 4% / 5.5% 6% Total RBC ratio 8% 8% 8% Tier 1 leverage ratio 3% or 4% 3% or 4% / 4% 4%» In October, the Fed issued its final rule which included amendments to the Capital Plan and Stress Test Rules - Modify start date from October 1 st to January 1 st - BHCs > $50B must submit by April 5 th - BHCs $10B - $50B submit by July 31 st unchanged; effective cycle - Limits on capital distributions (BHC s > $50B)

6 Mid-sized and large banks continue to own a disproportionate share of the industry s assets 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Assets > $10 Billion Assets $1 Billion - $10 Billion Assets $100 Million - $1 Billion Assets < $100 Million Share of Industry Assets 104 BHCs currently own 81% (only 31 are CCAR banks) 84:1 84:4 85:3 86:2 87:1 87:4 88:3 89:2 90:1 90:4 91:3 92:2 93:1 93:4 94:3 95:2 96:1 96:4 97:3 98:2 99:1 99:4 00:3 01:2 02:1 02:4 03:3 04:2 05:1 05:4 06:3 07:2 08:1 08:4 09:3 10:2 11:1 11:4 12:3 13:2 14:1 Source: FDIC

7 Capital levels, even in stressed scenarios, are well above regulatory minimums for mid-size BHCs Capital Ratio DFAST 2014 Severely Adverse Tier 1 common ratio (%) 7.6 Common equity tier 1 ratio (%) 7.9 Tier 1 risk-based capital ratio (%) 8.5 Total-risk based capital ratio (%) 11.0 Tier 1 leverage ratio (%) 5.9 Average Capital Ratios for BHCs $10B - $50B Tier 1 Common Capital (CET1) RB Ratio (%) Tier 1 Risk-based Ratio (%) Leverage Ratio (%) Risk Based Capital Ratio (%) Source: SNL Financial. Includes BHC with total assets between $10B and $50B as of June 30, 2014; Federal Reserve

8 On Thursday, the Fed released scenarios for 2015 Highlights» The scenarios start in 4Q14 and extend through 4Q17 (13-quarter forecast)» Each scenario includes 28 variables» Two new variables» Sixteen domestic variables: - Six measures of economic activity and prices - Four aggregate measures of asset prices or financial conditions - Six measures of interest rates» Regardless of how plausible the scenarios are, they will impact risk and capital planning for all banks > $10B Category Economic Activity and Prices Interest Rates Market Price and Volatility Real estate Macrovariable Real GDP growth Nominal GDP growth Real disposable income growth Nominal disposable income growth Unemployment rate CPI inflation rate 3-month Treasury rate 5-year Treasury yield **new** 10-year Treasury yield BBB corporate yield Mortgage rate Prime rate **new** Dow Jones Total Stock Market Index Market Volatility Index (VIX) House Price Index Commercial Real Estate Price Index

9 The Dodd-Frank Act requires BHCs larger than $10 billion to conduct annual company-run stress tests Key Dates of Current and Revised Timeline for BHCs with $10B - $50B in Total Consolidated Assets 2015 Cycle (starting 10/01/14) 2016 Cycle (starting 01/01/16 and thereafter) Company-Run Stress Test Action September 30, 2014 December 31, 2015 As of date for the stress test cycle By September 30, 2014 By December 31, 2015 FRB notifies BHC if it will require add l scenarios By November 15, 2014 By February 15, 2016 By December 1, 2014 By March 1, 2016 By March 31, 2015 By July 31, 2016 June 15 30, 2015 October 15 31, 2016 FRB publishes scenarios for the upcoming annual stress test cycle FRB communicates description of any add l components or scenarios required BHCs submit required regulatory report to the FRB on their stress tests BHCs disclose summary results of the annual company-run stress test Note: The Federal Reserve s capital plan rule, annual Comprehensive Capital Analysis and Review, and supervisory stress tests for capital adequacy currently not applicable for BHC < $50B

10 Supervisors outlined five principles for an effective stress testing regime, which also hold true for risk rating Bank Specific 1 Activities and exercises should be tailored the BHC s exposures, activities, and risks A company s stress testing framework should include strong governance and effective internal controls Strong Governance & Controls 5 Conceptually Sound Framework 2 An effective stress testing framework should employ multiple conceptually sound stress testing activities and approaches Stress test results should be clear, actionable, well supported, and inform decision-making Produce Actionable Results 4 Forward- Looking & Flexible 3 An effective stress testing framework should be forward-looking and flexible Source: Federal Reserve guidance for $10B - $50B BHCs

11 2 Evaluating the relationship between loss forecasting and risk rating

12 In conducting a stress test, for each quarter of the planning horizon, a company must estimate the following for each required scenario: Losses, PPNR, provision for loan and lease losses, and net income and determine the impact on capital from the supervisory scenarios - Federal Reserve, on guidance for $10B - $50B banks

13 Today we focus on loan loss forecasting and how it relates to credit risk management 3.50 Quarterly Charge-Off Rates: C&I & CRE Loans ( ) 3.00 C&I CRE Annualized NCO Rate (%) Source: Federal Reserve, All Banks, NSA; NBER

14 During stressed economic times, the provisions for loan losses consume a considerable amount of revenues 100% 90% LLP/NOR* LLP/PPNR** 99.1% 80% 70% 60% 50% 40% 37.9% 30% 20% 10% 0% Source: FDIC (all insured institutions); NOR = Net Interest Margin + Noninterest Income. PPNR = Net Interest Margin + Noninterest Income Non Interest Expense

15 The estimate of future loan loss provisions will derive from an estimate of projected loan losses in the portfolio» The appropriate level of ALLL at the end of any given quarter (including forecast periods) is generally assumed to be the amount needed to cover projected loan losses over the next four quarters» Mathematically: Provisions(t) = ALLL(t) ALLL(t-1) + NCOs(t) NCO(t) ALLL(t) t0 t t+1 ALLL(t-1)

16 Models to project losses in the portfolio are an integral component for deriving projections under stress Two General Approaches in DFAST Capture the historical behavior of net charge-offs relative to changes in macroeconomic and financial market variables and loan portfolio characteristics 2. Estimate expected losses at the loan-level under the macroeconomic scenario for each quarter of the planning horizon, where expected losses in quarter t are the product of these three components: Loss t = PD t x LGD t x EAD t For C&I loans, the PD is the obligor s PD (internal rating) at the beginning of the planning horizon, subsequently projected forward using an equation that relates historical changes in PD to changes in the macroeconomic environment. For C&I loans, the EAD is assumed to equal the loan s outstanding balance, with an assumption (using SNC data) for add l draws in the event of default for RLOCs. For C&I loans, the LGD at the beginning of the planning horizon is determined by the line of business, seniority, and country. It is projected forward by relating the change in the LGD to changes in the PD (a proxy for economic conditions). In order to project quarterly provisions, a company should estimate the adequate level of the allowance for loan and lease losses ( ALLL ) to support remaining credit risk at the end of each. The ALLL projections for DFA stress testing should be generally consistent with a company s internal ALLL approach, which often relies upon internal ratings.

17 Losses stemming from default on CRE loans are also projected at the loan level using an EL modeling framework Similar to the approach used for C&I loans, the Fed estimates expected losses at the loan-level under the macroeconomic scenario for each quarter of the planning horizon, where expected losses in quarter t are the product of these three components: Loss t = PD t x LGD t x EAD t For CRE loans, the Fed uses a model that estimates the probability that a loan transitions from current to default status, given the characteristics of the loan as well as macroeconomic variables such as house prices and CRE vacancy rates, at both the geographic market and national level. For C&I loans, the EAD is assumed to equal the loan s outstanding balance for amortizing loans and the full committed balance for C&LD loans. For CRE loans, LGD is a function of the expected probability of loss, characteristics of the loan, and macroeconomic variables. This EL-based approach, which was broadly applied in DFAST 2014, is consistent with many banks internal ratings framework.

18 Dual risk ratings involve separating credit risk much the same a commonly applied approach in DFAST 2014 Separation of Obligor and Facility Risk Borrower Risk Exposure Risk Recovery Risk PD EAD LGD Loss = 0 NO Will the counterparty default? YES What will be the size of the exposure at default? If defaulted, what amount will be lost? Loss = 1 - % Recovered Borrower/ Obligor Rating Facility Rating

19 3 Case Study: Getting ready for DFAST

20 The Umpqua Bank Story» Founded in Canyonville, Oregon in 1953; merged with Sterling Bank in April 2014 Umpqua Loan and Leases Composition (in $ billions, as of 6/30/2014)» Headquartered in Portland, Oregon with 350+ branches in five states: California Idaho Nevada Oregon Washington» Total assets: $22 billion» Market capitalization: $3.8 billion» Total loans and leases (as of June 30, 2014): $14.8 billion Investor Real Estate, 6.2 Consumer, 3.1 C&I, 5.5

21 Umpqua s DFAST Submission» Dodd-Frank Act Stress Testing (DFAST) applies to all banks with more than $10 billion but less than $50 billion in assets» 2014 First time Umpqua Bank was designated to submit DFAST results; submission handled by Finance using a top down approach» 2015 Asset size doubles with merger with Sterling Bank; use top down approach; Moody s bottom up loan-level approach to be used as challenger model; no model validation» 2016 Use Moody s bottom up obligor-level approach as primary model and top down approach to be used as a challenger model

22 Umpqua s Stress Testing Framework» Umpqua purchased licenses to Moody s Analytics RiskCalc PD / LGD ST, CMM ST, Mortgage Portfolio Analyzer, and Scenario Analyzer to perform stress testing on the following portfolios: Commercial and Industrial (C&I) Commercial Real Estate (CRE) Residential Real Estate (RRE)» Stress testing data inputs are sourced from: Core Systems Loan Origination Systems (LOS)

23 Umpqua Bank Credit Stress Testing Model C&I LOS RiskCalc C&I PD & LGD Results C&I Estimation Matrix Scenario Analyzer C&I ALLL C/O Results ADR / DataMart CMM CRE Estimation Matrix CREPD & LGD Results Scenario Analyzer CREALLL C/O Results RRE LOS MPA RRE PD & LGD Results RRE Estimation Matrix Scenario Analyzer RRE ALLL C/O Results

24 Umpqua s Stress Testing Process» Step 1: Create baseline data files from internal data systems» Step 2: Estimate PD and LGD through models» Step 3: Create PD and LGD estimation matrix for loans with insufficient data, based on averages by segment» Step 4: Aggregate loan level results to portfolio level» Step 5: Develop ALLL framework using appropriate macroeconomic scenario and 9- quarter projection horizon» Step 6: Export ALLL and charge-off data for use by Capital Planning» Step 7: Repeat the process for all scenarios

25 4 Using the DFAST investment to enhance credit risk measurement

26 Umpqua s Current Risk Rating Model» 10-step judgment-based rating system» Combines risk of primary and second repayment sources» Rating distribution is a very narrow bell curve

27 While Basel II does not pertain directly to Umpqua Bank, it does provide guidance related to leading practices Basel II Requirements Pertaining to Mastering Rating Scales (MRS)» A dual-risk rating (DRR) measuring PD x LGD is necessary because underlying risks are different» MRS must have a minimum seven PD borrower grades for non-defaulted borrowers, and one for defaulters; no specific minimum for LGD grades» Excessive concentration (over 30% of total) in any single PD grade requires convincing empirical support that all borrowers do have comparable PDs» Each grade should represent markedly different risk characteristics from other grades» +/- modifiers do not qualify as grades unless the institution has developed unique rating descriptions for them» Validation of MRS must occur on a regular basis to ensure continued risk grading accuracy

28 Components of a dual risk rating framework include PD and LGD to arrive at EL Example Borrower / Obligor Rating Facility Rating Expected Loss PD LGD EL 2% 30% 0.60% Exposure $5MM = $30M EL

29 Illustrative Dual Risk Rating Scale Loss Given Default Rating A B C D E F 5% 10% 25% 35% 50% 75% 1.0 Pass 0.1% 0.0% 0.0% 0.0% 0.0% 0.1% 0.1% Probability of Default Rating 1.5 Pass 0.5% 0.0% 0.1% 0.1% 0.2% 0.3% 0.4% 2.0 Pass 1.0% 0.1% 0.1% 0.3% 0.4% 0.5% 0.8% 2.5 Pass 1.5% 0.1% 0.2% 0.4% 0.5% 0.8% 1.1% 3.0 Pass 2.0% 0.1% 0.2% 0.5% 0.7% 1.0% 1.5% 3.5 Pass 2.5% 0.1% 0.3% 0.6% 0.9% 1.3% 1.9% 4.0 Pass 3.0% 0.2% 0.3% 0.8% 1.1% 1.5% 2.3% 4.5 Pass 3.5% 0.2% 0.4% 0.9% 1.2% 1.8% 2.6% 5.0 Pass 4.5% 0.2% 0.5% 1.1% 1.6% 2.3% 3.4% 5.5 Pass 5.0% 0.3% 0.5% 1.3% 1.8% 2.5% 3.8% 6.0 Pass 10.0% 0.5% 1.0% 2.5% 3.5% 5.0% 7.5% 6.5 Pass 15.0% 0.8% 1.5% 3.8% 5.3% 7.5% 11.3% 7.0 OAEM-A 20.0% 1.0% 2.0% 5.0% 7.0% 10.0% 15.0% 7.5 OAEM-B 35.0% 1.8% 3.5% 8.8% 12.3% 17.5% 26.3% 8.0 Substandard 50.0% 2.5% 5.0% 12.5% 17.5% 25.0% 37.5% 9.0 Doubtful 90.0% 4.5% 9.0% 22.5% 31.5% 45.0% 67.5% 10 Loss 100.0% 5.0% 10.0% 25.0% 35.0% 50.0% 75.0%

30 Umpqua s Dual-Risk Rating Implementation Plan» Step 1: Develop and validate PD and LGD scorecards» Step 2: Calibrate scorecards» Step 3: Develop ALLL framework» Step 4: Phased roll out to business segments (Phase 1: CRE; Phase 2: C&I)» Step 5: Phase out legacy system Run old and new system in parallel for 3 to 6 months On-going validation between two systems Make decision to phase out» Step 6: Back Testing Establish on-going back testing Establish committee for scorecard refinement 1. PD / LGD Scorecard Development and Validation 2. Scorecard Calibration 3. Develop ALLL framework 4. Roll out to business segments 5. Legacy System Phase Out 6. Back Testing and Refinement

31 Among other things, an effective risk measurement framework can serve as an early warning system» Understand risks within specific segments View a single borrower s performance for specific groups across the portfolio Monitor over time for an early warning indicator and an effective approach toward risk rating Identify outliers in a portfolio and identify key trends and insights within important segments Limit settings (e.g., EDF levels)

32 Challenges» Data integrity and system integration» Model validation and documentation» Bottom-up approach requires dramatic shift of bank s credit culture» Substantial cost for additional staff and infrastructure

33 Questions

34 Thank you

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36 Appendix

37 Example of a C&I Firm under the Fed s Severely Adverse Scenario Starting 1 Yr CCA EDF is 1.88% with a $10MM loan commitment and $9.5MM outstanding; (000s) Measure Q1 Q2 Q3 Q4 Q5 Q9 Q7 Q8 Q9 Stressed EDF Stressed LGD 1.94% 2.62% 3.44% 4.40% 5.25% 5.47% 5.33% 1.94% 2.62% 47.40% 48.66% 51.30% 50.85% 43.37% 38.30% 32.29% 47.40% 48.66% Expected Losses ($) 11,002 15,232 20,990 26,446 26,712 24,297 19,650 11,002 15,232 Net Charge-offs ($) 2,750 6,558 11,806 18,417 22,345 24,611 24,276 2,750 6,558 ALLL ($) 35,487 54,502 77,676 98, ,515 97,254 81,366 35,487 54,502 Provisions ($) 24,535 25,573 34,980 39,528 27,073 18,350 8,389 24,535 25,573

38 Case Study: Baseline Scenario Example» Characteristics: Origination: July 2012 Maturity: June 2019 Exposure: $10 million Rate: Prime + 100bps; Reset: 3 mos Amortization Term: 300 months (25 yrs) DSCR: 1.25x LTV: 75% Property Type: Office Market: Dallas, Texas (CBD) Real Estate Forecast: Baseline Interest Rate Forecast: Baseline 0.90% 0.80% 0.70% 0.60% 0.50% 0.40% 0.30% 0.20% 0.79% 0.53% Annual PD 0.30% 0.21% 0.15% 0.16% 0.10% 0.00% Year 1 Year 2 Year 3 Year 4 Year 5 Year 6

39 Case Study: Fed s Severely Adverse Scenario (CCAR 2014)» Characteristics: Example Origination: July 2012 Maturity: June 2019 Exposure: $10 million Rate: Prime + 100bps; Reset: 3 mos Amortization Term: 300 months (25 yrs) DSCR: 1.25x LTV: 75% Property Type: Office Market: Dallas, Texas (CBD) Real Estate Forecast: Fed Severely Adverse (CCAR 2014) Interest Rate Forecast: Fed Severely Adverse (CCAR 2014)» Summary Under the Fed s Severely Adverse scenario, the annual PD is 3x higher, on average, over the life-of-the loan (4x in Year 2) 2.50% 2.00% 1.50% 1.00% 0.50% 0.00% 0.79% 1.13% 0.53% 2.17% Annual PD 0.30% 0.81% Baseline 0.21% Fed Sev Adv 0.53% 0.54% 0.59% 0.15% 0.16% Year 1 Year 2 Year 3 Year 4 Year 5 Year 6

40 Case Study: Comparison of the Impact by Scenario» Summary Example Under the Fed s Severely Adverse scenario, the annual PD is 3x higher, on average, over the life-of-the loan 4x higher in Year 2 The annual EL in Year 2 is 5x greater as a result of the change in scenario, resulting in an $80k increase 2.50% 2.00% 1.50% 1.00% 0.50% 0.00% 2.17% 1.13% 0.79% 0.53% Annual PD Baseline Fed Sev Adv 0.81% 0.53% 0.54% 0.59% 0.30% 0.21% 0.15% 0.16% Year 1 Year 2 Year 3 Year 4 Year 5 Year % 45.00% Annual LGD 45.42% 45.12% Baseline Fed Sev Adv 43.21% 43.58% 43.61% 1.20% 1.00% 0.99% Annual EL Baseline Fed Sev Adv 40.00% 35.00% 30.00% 25.00% 36.67% 35.17% 35.59% 34.61% 34.22% 33.67% 33.46% Year 1 Year 2 Year 3 Year 4 Year 5 Year % 0.60% 0.40% 0.20% 0.00% 0.41% 0.37% 0.28% 0.23% 0.24% 0.26% 0.19% 0.11% 0.07% 0.05% 0.05% Year 1 Year 2 Year 3 Year 4 Year 5 Year 6