Stress Testing Handling low default portfolios under stress Part of the Moody s Analytics Stress Testing Webinar Series Thanks for joining today s webinar. It will begin shortly. Dial-in details: Call-in toll-free number: 0808 238 9692 (United Kingdom) Call-in local number(0)2034333531 (United Kingdom) Passcode: 529 869 2163 Please ensure your phone is on mute!! 0
Handling low default portfolios under stress Manuele Iorio manuele.iorio@moodys.com Senior Director Dr. Juan M. Licari juan.licari@moodys.com Senior Director September 2 nd, 2014
2 Today s Presenters Dr. Juan Licari - Senior Director - Moody s Analytics EMEA juan.licari@moodys.com Dr. Juan Licari is a Senior Director at Moody s Analytics and the head of the Economics and Consumer Credit Analytics team for EMEA. Dr. Licari s team is responsible for generating alternative macroeconomic forecasts for Europe and for building econometric tools to model credit risk phenomena. His team is an industry leader in developing and implementing risk solutions that explicitly connect credit data to the underlying economic cycle, allowing portfolio managers to plan for alternative macroeconomic scenarios. These solutions are leveraged into stress testing and reverse stress testing practices. Manuele Iorio - Senior Director - Moody s Analytics EMEA manuele.iorio@moodys.com Manuele Iorio is a Senior Director of the EMEA Advisory Service Team at Moody s Analytics. The function is responsible for providing risk management solutions for financial institutions and corporates, encompassing the development and the validation of rating systems, portfolio models, stress testing and governance. 2
3 About Moody s Analytics Leading global provider of credit rating opinions, insight, and tools for credit risk measurement and management Independent provider of credit rating opinions and related information for nearly 100 years Research, data, software, and related professional services for financial risk management Moody's Analytics operates independently of the credit ratings activities of Moody's Investors Service. We do not comment on credit ratings or potential rating changes, and no opinion or analysis you hear during this presentation can be assumed to reflect those of the ratings agency. 3
4 Agenda Handling low default portfolios under stress Regulators are challenging how to perform stress testing on low default portfolios by reviewing bank s PD models for RWA stress testing, in the absence of data they need to be convinced of the methodology used. In this Moody s Analytics webinar we put forward a statistical approach to stress testing low default portfolios with practical case studies. This 30-min webinar will discuss: Introductions and definitions. Introducing the proposed statistical method: 2 stage approach with an example on how to stress credit migrations. Case study on corporate PD modelling. Q&A: stresstesting@moodys.com 4
5 Proposed Methodology: 2-stage Approach Stage 1: Discrete Choice Model Selecting states of nature Stressed vs. Normal states. Example: Probit or Logit models to assess the probabilities of either state. Stage 2: Conditional Estimations Examples: standard ML methods, quantile estimations. Model specifications can differ across the two states of nature. 5
6 Credit Migration Example 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% 6
0 0.05 Transition %.2.4.6 Transition %.1.15.2.25 0 0 Transition %.1.2.3.4 Transition %.2.4.6 7 Credit Migration Example (cont.) One Notch Downgrade Transitions from Investment Grades One Notch Upgrade Transitions from Investment Grades 1990m1 1995m1 2000m1 2005m1 2010m1 Month of Transition aaa_aa a_baa aa_a baa_ba 1990m1 1995m1 2000m1 2005m1 2010m1 Month of Transition ba_b caac_def b_caac One Notch Downgrade Transitions from Non-Investment Grades One Notch Upgrade Transitions from Non-Investment Grades 1990m1 1995m1 2000m1 2005m1 2010m1 Month of Transition ba_b caac_def b_caac 1990m1 1995m1 2000m1 2005m1 2010m1 Month of Transition ba_baa caac_b b_ba 7
0 Density 10 20 30 40 8 Credit Migration Example (cont.) 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) 0.02.04.06.08.1 baa_ba 8
0 0 Transition %.1.2.3.4 Transition %.02.04.06.08 0 0 Transition %.2.4.6.8 Transition %.2.4.6.8 1 1 9 Credit Migration Example (cont.) Binary (Probit) Model Downgrade Binary (Probit) Model Upgrade 2000m1 2002m1 2004m1 2006m1 2008m1 2010m1 2012m1 Month of Transition Binary_Probit_Regression O_1_Median_Variable 2000m1 2002m1 2004m1 2006m1 2008m1 2010m1 2012m1 Month of Transition Binary_Probit_Regression O_1_Median_Variable CaaC to Default Baa to A 2000m1 2002m1 2004m1 2006m1 2008m1 2010m1 2012m1 2014m1 2016m1 Month of Transition 2000m1 2002m1 2004m1 2006m1 2008m1 2010m1 2012m1 2014m1 2016m1 Month of Transition Actuals FSA Custom Baseline Scenario4 Actuals FSA Custom Baseline Scenario4 9
0 Transition %.02.04.06.08.1 10 Credit Migration Example (cont.) Figure II: B to Default Transitions Over Time Historical Data (Actuals and Fitted), Predictions under Baseline, FSA Anchor, Severe -Scenario4, and Custom Scenarios States of Nature Stressed in Red, Normal and/or Booming in Blue 2000m1 2002m1 2004m1 2006m1 2008m1 2010m1 2012m1 2014m1 2016m1 Month of Transition Actuals FSA Custom Baseline Scenario4 10
11 Case Study: Stress testing of European Large Corporates Case Study Outline» The case study we are presenting is aimed at testing the methodology presented in Stress Testing of Credit Migration. A Macroeconomic Approach to European Large Corporates» The objective is to estimate the yearly Default Probability of a portfolio exploiting the dual regime present for Normal/Stressed conditions» While the approach can be applied to any large corporate portfolios mapped onto External Ratings, the study examines the historical default rates of the European Rated universe» The results show that the model is sufficiently able to capture the relevant behaviour of Default rates during the financial crisis notwithstanding the limited number of data available 11
12 Default rate distribution The density plot and the histogram show the multimodal (dual) nature of the phenomenon. 80 70 60 DR Density (observed vs normal) 30 25 DR Histogram 50 20 40 15 30 20 10 10 5 0 0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 0 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 4.0% 5.0% 6.0% EU_DRs Normal 12
13 Regime Identification The identification of the regime is key in this framework. Several options can be used. In the present study an HP-filter has been used to identify the stressed conditions. 5.0% 4.5% 4.0% 3.5% 3.0% 2.5% 2.0% 1.5% 1.0% 0.5% 0.0% 1990 1995 2000 2005 2010 13
14 Proposed models In the plot two models are presented. The underlying binary model is obtained using a probit model with GDP Growth, CPI index and Unemployment rate as regressors. Forecasts are presented around the baseline scenario. 3.5% 3.5% 3.0% 3.0% 2.5% 2.5% 2.0% 2.0% 1.5% 1.5% 1.0% 1.0% 0.5% 0.5% 0.0% 2003 2005 2007 2009 2011 2013 0.0% 2009 2010 2011 2012 2013 2014 2015 2016 All Mod 1 Mod 2 14
15 Q&A Session Please use the chat function to ask questions Following the webinar send your questions to: stresstesting@moodys.com 15
Upcoming Moody s Analytics Stress Testing Webinars Stress Testing Model Validation Our expert Burcu Guner gives practical advice on how banks can challenge their internal methodologies in the context of stress testing. Date: Friday September 5th, 2014; Time: 9:30-10:00 AM GMT (London) Stress Testing as a catalyst for BCBS 239 or vice versa? Christian Thun discusses the need for banks to address data issues as part of the new principles for risk data aggregation (BCBS 239) and the potential of a recurring stress testing exercise. Date: Monday September 8th, 2014; Time: 9:30-10:00 AM GMT Leveraging Stressed EDFs to automate the Stress Testing process As the stress testing process evolves and regulatory scrutiny increases, banks will benefit from easily deployable tools that simplify and streamline that process. Date: Friday September 12th, 2014; Time: 2:00-2:30 PM GMT (London) Register by emailing: stresstesting@moodys.com
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