Downside Risks to the Macro Outlook: Retail Credit Risk Implications

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In this presentation, we examine how to anticipate downside risks & identify different potential scenarios, the translation of macro scenarios to retail credit portfolios, and put forth a case study which demonstrates a vintage approach to modelling risk.

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Downside Risks to the Macro Outlook: Retail Credit Risk Implications

  1. 1. Dr. Juan M. Licari, Head of Economic & Credit Analytics – EMEA, Moody’s Analytics Originally presented at the EFMA Retail Credit Conference | June 20, 2013 | Amsterdam, The Netherlands Downside Risks to the Macro Outlook Retail Credit Risk Implications
  2. 2. 2 Today’s Agenda - How to anticipate downside risks & identify different potential scenarios Examples: (i) currency wars, (ii) eurozone breakdown, (iii) US fiscal situation, (iv) emerging markets hard landing, (v) oil price shock and stagflation - Translation of macro scenarios to retail credit portfolios: Stress Testing Challenges - UK mortgages case study: A vintage approach to modelling risk
  3. 3. 3 Macro Modelling & Scenario Analysis
  4. 4. Simulation-Based Scenarios 4 Weaker Economy Healthier Economy Baseline: Recession S3: Double Dip 1-in-10 S4: Severe Double Dip 1-in-25 Alternative Economic Scenarios S2: Mild Double Dip 1-in-4 S1: Stronger Recovery 1-in-4 Simulation-Based 1:100 1:25 1:20 1:10 1:4 Forecast 1:4 4 S6: Stagflation 1-in-15 S5: Global Slowdown 1-in-7
  5. 5. Current Economic Cycle 5 Expansion In recession At risk Recovery May 2013 Source: Moody’s Analytics
  6. 6. Baseline Outlook 6 95 100 105 110 115 120 125 12 13 14 15 16 12 13 14 15 16 12 13 14 15 16 12 13 14 15 16 Euro zone Real GDP, 2008Q1=100 World U.K. U.S. Source: National Statistical Offices, Moody’s Analytics
  7. 7. Baseline Outlook 7 0 2 4 6 8 10 12 13 14 15 16 12 13 14 15 16 12 13 14 15 16 12 13 14 15 16 Russia Real GDP growth China Brazil India Source: National Statistical Offices, Moody’s Analytics
  8. 8. Quantitative Models for Scenario Analysis 8 Inflation Rate, History & Forecasts, Euro- Zone Level Inflation Rate Distribution, Euro- Zone Level Inflation Rate Distribution, Euro- Zone Level
  9. 9. Developed Markets: GDP Growth -8 -6 -4 -2 0 2 4 6 Euro Zone Japan Germany Spain UK US BL 2013Q2 - 2014Q4 S4 (s-t-t) S6 (s-t-t) 14Q4 14Q3 14Q4 14Q3 14Q4 14Q4 Source: National Statistical Offices, Moody’s Analytics 9
  10. 10. Developed Markets: Inflation Rate % change on the previous year Source: National Statistical Offices, Moody’s Analytics -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3 3.5 Euro Zone Japan Germany Spain UK US 2014Q4 baseline 2014Q4 S4 2014Q4 S6 10
  11. 11. 11 Event-driven Scenarios
  12. 12. Event-Driven vs. Simulation-Based Scenarios 12 Weaker Economy Healthier Economy Baseline: Recession S3: Double Dip 1-in-10 S4: Severe Double Dip 1-in-25 S2: Mild Double Dip 1-in-4 S1: Stronger Recovery 1-in-4 Simulation-Based 1:100 1:25 1:20 1:10 1:4 Forecast 1:4 12 S6: Stagflation 1-in-15 S5: Global Slowdown 1-in-7 Emerging Markets Hard Landing Sovereign Default Shock In line with Regulatory Guidelines Event-Driven
  13. 13. 13 Alternative Macroeconomic Scenarios Stronger Near-Term ReboundS1 S2 Mild Second Recession S3 Deeper Second Recession Protracted SlumpS4 Baseline (most likely)BL Standard Below Trend, Long-Term GrowthS5 Oil Price ShockS6 Fed BaselineFB Fed AdverseFA EBA BaselineEB EBA AdverseES Regulatory-Driven Fed Severely AdverseFS Custom Euro Zone BreakupEB StagflationSF
  14. 14. 14 GDP at Market Prices, (Bil. 2000 EUR, SA) EU Harmonised Unemployment Rate, (%, NSA) Average Nominal House Price: Total (EUR)Interest Rate: 10-year Bond Yield, % Event-Driven Scenario: Italy Exit - Effect on Europe Source: Moody’s Analytics
  15. 15. GDP at Market Prices, (Bil. 2008 £, SAAR) UK Unemployment Rate, (%, SA) Halifax Average Nominal House Price, (£, SA)Interest Rate: 10-year Bond Yield, % Source: Moody’s Analytics Event-Driven Scenario: Italy Exit - Effect on UK 15
  16. 16. 16 Stress Testing Retail Credit Portfolios • Key Discussion Topics: 1- Dynamic vs. Static Approach to Stress Testing, 2- Partial vs. General Equilibrium, 3- Top-down vs. Bottom-up, 4- Modelling Methodologies: Stress Testing vs. Forecasting/Scoring,
  17. 17. Performance of Future Loans Forecasted Performance of Existing LoansPerformance History 17 Stress Testing: 1- Dynamic vs. Static Approach
  18. 18. 18 Performance of Future Loans Forecasted Performance of Existing LoansPerformance History Stress Testing: 1- Dynamic vs. Static Approach
  19. 19. 19 Stress Testing: 2- Partial vs. General Equilibrium Examples of collateral type for RMBS/ABS deals » Interest rates » Unemployment rates » Income growth » Profits (National Accounts) » Share market Small Business Loans » Interest rates » Unemployment rate » Commodity/oil prices » Price index for used cars Auto-Equipment Loan/Lease Illustrative » Mortgage rate difference from origination » Unemployment rate » Employment growth » Income growth » House price growth » Home equity RMBS 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 2009M 062009M 102010M 022010M 062010M 102011M 022011M 062011M 102012M 022012M 062012M 102013M 022013M 062013M 10 Baseline S3 S4 DD 0 10 20 30 40 50 60 70 80 90 100 2009M 06 2009M 11 2010M 04 2010M 09 2011M 02 2011M 07 2011M 12 2012M 05 2012M 10 2013M 03 2013M 08 2014M 01 2014M 06 2014M 11 2015M 04 2015M 09 Baseline S3 S4 DD PD term-structure LGD curves
  20. 20. 20 Stress Testing: 3- Top-down vs. Bottom-up Issue: Loan level model can miss correlations and feedback effects » Individual performance depends on other loans » Difficult to model individuals within a system Risk models could miss the forest for the trees – Why not model the forest, model the trees and then make sure the tree model agrees with forest projections? ≠
  21. 21. 21 Stress Testing: 4- Modelling Methodologies Table 1 Average probabilities (1983M1 - 2007M1) Aaa Aa A Baa Ba B Caa-c Def Aaa 92.10% 7.52% 0.33% 0.00% 0.04% 0.00% 0.00% 0.00% Aa 0.99% 90.49% 8.07% 0.37% 0.04% 0.03% 0.00% 0.02% A 0.07% 2.76% 90.65% 5.67% 0.65% 0.15% 0.03% 0.02% Baa 0.05% 0.24% 5.51% 87.91% 4.75% 1.14% 0.23% 0.17% Ba 0.01% 0.07% 0.47% 6.35% 82.56% 8.60% 0.60% 1.33% B 0.01% 0.05% 0.18% 0.52% 5.52% 82.90% 4.74% 6.08% Caa-c 0.00% 0.02% 0.10% 1.20% 1.19% 7.12% 69.42% 20.96% Table 2 Average probabilities (2007M6 - 2009M10) Aaa Aa A Baa Ba B Caa-c Def Aaa 78.15% 21.71% 0.04% 0.11% 0.00% 0.00% 0.00% 0.00% Aa 0.05% 82.65% 16.03% 0.99% 0.11% 0.02% 0.07% 0.09% A 0.00% 0.88% 89.58% 8.24% 0.44% 0.30% 0.15% 0.41% Baa 0.01% 0.14% 2.20% 91.95% 4.40% 0.72% 0.20% 0.38% Ba 0.00% 0.00% 0.04% 5.10% 81.25% 10.46% 1.83% 1.32% B 0.00% 0.00% 0.07% 0.17% 3.35% 78.31% 13.55% 4.55% Caa-c 0.00% 0.00% 0.00% 0.14% 0.23% 5.74% 71.19% 22.70%
  22. 22. 22 Stress Testing: 4- Modelling Methodologies Figure I: Bi-Modal Nature of Credit Transitions Bi-Modal Distribution of Baa to Ba Credit Migrations (Bar Chart) vs. a Normal, Symmetric Distribution (Green Solid Line) 0 10203040 Density 0 .02 .04 .06 .08 .1 baa_ba First Mode: Around Normal/Good Credit Conditions Second Mode: Around Stressed Credit Conditions
  23. 23. 23 Stress Testing: 4- Modelling Methodologies0 .2.4.6.8 1 2000m1 2002m1 2004m1 2006m1 2008m1 2010m1 2012m1 Month of Transition Binary_Probit_Regression O_1_Median_Variable 0 .2.4.6.8 1 Transition% 2000m1 2002m1 2004m1 2006m1 2008m1 2010m1 2012m1 Month of Transition Binary_Probit_Regression O_1_Median_Variable Binary (Probit) Model Downgrade 0 .1.2.3.4 2000m1 2002m1 2004m1 2006m1 2008m1 2010m1 2012m1 2014m1 2016m1 Month of Transition Actuals Baseline FSA Scenario4 Custom 0 .02.04.06.08 Transition% 2000m1 2002m1 2004m1 2006m1 2008m1 2010m1 2012m1 2014m1 2016m1 Month of Transition Actuals Baseline FSA Scenario4 Custom CaaC to Default Baa to A Binary (Probit) Model Upgrade
  24. 24. 24 Case Study: UK Mortgage Market
  25. 25. Econometric model – Dynamic Panel Data Techniques Time series performance for a given vintage and segment = f Lifecycle component » Dynamic evolution of vintages as they mature » Nonlinear model against “age" (1) Lifecycle component Pool-specific quality component » Vintage attributes (LTV, asset class/collateral type, geography, etc.) define heterogeneity across cohorts » Early arrears serve as proxies for underlying vintage quality » Economic conditions at origination matter » Econometric technique accounts for time-constant, unobserved effect (2) Vintage-quality component Business cycle exposure component » Sensitivity of performance to the evolution of macroeconomic and credit series (3) Business cycle exposure component 25
  26. 26. Lifecycle Component Modelling Approach Consumer Credit Forecasting Total delinquency rate (% of out. £) against months-on-book 26
  27. 27. Vintage-Quality Modelling Approach Consumer Credit Forecasting Vintage quality index (left) and Disposable Income Growth (right) against vintages 27
  28. 28. - Baseline Scenario - Stressed Scenario Exposure to the Business Cycle Modelling Approach Consumer Credit Forecasting Total delinquency rate (% of out. £) under different economic scenarios 28
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