Loss Given Default What is your potential loss position in the current volatile environment? Complete your credit risk assessments with recovery analyses. Evaluate potential economic losses for a range of exposures in various asset classes with an extensive suite of Loss Given Default (LGD) benchmark data, models and services. A full suite of analytical services to help you assess and mitigate risk Meet Your Regulatory Requirements and More Develop and implement LGD methodologies to support the Advanced Internal Ratings Based approach (AIRB) under Basel II, or to simply comply with industry best practices Enhance existing LGD models and methodologies in preparation for AIRB recognition Validate and benchmark internally developed LGD models Build customized LGD/EAD estimation methodology using existing Decision-tree frameworks calibrated to S&P Capital IQ data and best practice approaches Access various LGD client data pooling and collection exercises by region and asset class including Project Finance and Leveraged Finance Access our LossStats model and database of North American corporate recoveries A Fully Transparent Methodology Extensive sector coverage includes mid-size to large Corporates, Financial Institutions, Insurance Companies, Project Finance, Asset Finance, Real Estate, Trade Finance, Local & Regional Governments and Sovereigns. Robust tools support peer and scenario analysis and active portfolio management based on recovery risk. Distinguish Good Loans From Bad Ones lower PD higher lower ACCEPT LOW RISK FOCUS ON COLLATERAL VALUE AND SECURITY ENFORCEABILITY LGD higher FOCUS ON ENSURING OBLIGOR RISK RATING IS CORRECT REJECT HIGH RISK Separation of the two credit risk dimensions probability of default (PD) risk and recovery risk enables you to both analyze and manage the distinct risks in different ways. Facilities can be structured most effectively by minimizing expected losses. Additional features include: Increased consistency, transparency and granularity of internal recovery ratings Fully transparent and intuitive methodologies with detailed regulatory related documentation, a user guide and on-site training A time-efficient and user-friendly interface with input factors either readily available from public or standard sources or easily determined Continuously reviewed testing results and models that ensure their conceptual soundness and strong performance These capabilities may also be effective when assessing unlisted companies. 1
Lgd Estimation Scorecards Scorecards are thoroughly tested internal ratings-based methodologies that produce facility-specific estimates of LGD in the low default environment. Cover a wide range of asset classes and sub-sectors More than a dozen industry- and asset-class specific off-the-shelf scorecards produce a point LGD estimate on a continuous scale, which can be mapped to any discrete recovery scale Consistent approach across asset classes via the same underlying statistical framework facilitates use of more inputs based on expert judgment whenever recovery data is limited Delivered in a user-friendly Excel spreadsheet framework for easy integration into your platform Get a fast and effective solution when there is insufficient internal data to build a statistical model. Our methodologies leverage the analytical process of Standard & Poor s Ratings Services, most recent regulatory guidance and best industry practices. Sample Lgd Corporates Scorecard Obligor Characteristics and Bank Policy Industry Enterprise/Asset Valuation Recovery Policy Jurisdiction Industry-Sector Type of Assets Volatility per Asset Type Stress Factor in Case of Default Workout LGD or Liquidation Scenario Location of Assets LGD Point Estimate Recovery Rating Facility Characteristics Debt Structure and Subordination Total Debt and Liabilities Debt Cushion Debt Priority Pledged Collateral Assets Pledged to Facility Assets Pledged to Other Facilities Recovery Enhancement Guarantees Insurances Decision-Tree Models This is a Facility Risk Rating methodology that captures information and calculation of LGD and Exposure at Default (EAD) for each facility. It enables you to adhere to Basel II AIRB principles and best industry practices and is employed by leading Basel II AIRB banks. Uses your own empirical loss rates to the furthest extent possible and blends the available empirical data with in-depth analysis; internal data can be supplemented with external sources to substantiate the level of chosen risk factors Incorporates your own expertise in development of the methodology, including choice of risk drivers, number and complexity of Decision-Tree paths in the model Includes a Lending Value Allocation methodology for facilities secured by one or more specific types of collateral; collateral gets reapportioned to all facilities that have access to it Solution has an empirical basis, but accommodates for the necessary level of analytical insight required to fully capture LGD risk drivers Supports compliance with new regulatory frameworks (such as Basel III), especially in stress testing 2
and model governance Borrower Type Model Base LGD Calculation Adjustments Collateral Value Small & Medium Enterprises Yes Collateralized? Collateral Cash Marketable Securities Accounts Receivable Inventory Lending Value Allocation Current Exposure/ Facility Type/Maturity EAD Estimate Qualitative Adjustments Jurisdiction/ Industry Adjustments Commercial & Industrial Equipment Real Estate Base LGD Factor Facility Coverage Ratio Base LGD Estimate Guarantee Adjustment No Seniority Secured All Assets Unsecured Subordinated Debt Above/ Debt Below Downturn Adjustment Overrides Banks/NBFIs Final LGD Risk Rating Other Sectors Specialized Sovereigns Public Sector Enterprise Sector Benchmark Project Finance Agriculture Sample Decision Tree Framework LossStats Model This model forecasts LGD values by applying a mathematical framework to the 4,000+ recovery estimates in the LossStats database of ultimate recovery data and distressed debt trading-price information. Enables estimation of potential ultimate recovery and 30-day post-default recovery (by selling the asset) for specified defaulted assets, using U.S. data Validation Validation provides the assessment of loss estimations of individual facilities as well as a review of the use of internal recovery ratings or LGD estimates in the credit process. Solutions include validation diagnosis, framework and process design, performance testing (benchmarking and backtesting where relevant) and review of your internal validation procedures 3
Benchmarking Benchmarks are developed by placing an objective, structured framework around our existing methodology rather than on the basis of heterogeneous historical data. Provides a solution to benchmarking in segments where there are few or no public recovery ratings Performs equally well for entities publicly rated by Standard & Poor s Ratings Services and those that are not publicly rated, as well as those that are listed and not listed, independent of the size of an entity or facility Once benchmarks are generated, various statistical tests are performed and reported comparing your facility ratings with Standard & Poor s assessments Sample Histogram of Debt Cushion and Collateral Percent of Observations (%) 18 16 14 12 10 8 6 4 2 0 0 to 10 10 to 20 20 to 30 30 to 40 40 to 50 50 to 60 60 to 70 70 to 80 80 to 90 90 to 100 >=100 Recovery Rate (%) Debt Cushion >50% with Collateral Debt Cushion <=50% with Collateral Debt Cushion >50% without Collateral Debt Cushion <=50% without Collateral For illustrative purpose only Using S&P Capital IQ s LossStats database of facilities, the bimodal behavior of recovery rates known from the retail sector appears to hold for large obligors as well. Depending on key facility characteristics (here, debt cushion and secured debt versus unsecured debt) actual recovery rates tend to be either very high (right-hand side) or very low (left-hand side). Relatively few observations are centered around mean values of recovery. Data Enhancement CreditPro Recovery Reports and Data This information provides LGD data based on more than 800 public and private U.S. companies that have defaulted since 1987. Sample Data Snapshot Debt Type Instrument Count Percentage of Debt Total Defaulted Amount Bank Debt 1,709 41.39 349,945,173,051 Senior Secured Bonds 391 7.05 59,633,976,858 Senior Unsecured Bonds 1,260 37.77 319,353,979,137 Senior Subordinated Bonds 564 9.02 76,243,751,663 Subordinated Bonds 434 4.34 36,676,855,714 Junior Subordinated Bonds 54 0.43 3,598,084,795 Grand Total 4,412 100.00 845,451,821,218 This includes credit loss information on more than 4,000 defaulted bank loans and high yield bonds, as well as other debt instruments of almost $1 trillion in defaulted principal. 4
Instrument Recoveries Report (2006 to 2012) Recovery Rate (%) 90 80 70 60 50 40 30 20 10 0 AVG: 72.7% AVG: 37.0% 2006 2007 2008 2009 2010 2011 2012 Year Bank Debt Senior Unsecured Bonds For illustrative purpose only Client Data Pooling Peer LGD data helps provide benchmarks as well as fills gaps in internal data for more comprehensive regulatory compliance and model building. S&P Capital IQ has successfully led data consortia in North America, Europe, the Middle East and Asia by providing the independence and objectivity that is necessary to make these collaborative projects successful Leverage the Power of S&P Capital IQ Solutions Explore the range of analytic tools available to help you assess and mitigate loss given default risk. S&P Capital IQ brings you a suite of products PD Scorecards Evaluation of the probability of default (PD) risk associated with asset finance (AF), project finance (PF) and trade finance (TF) transactions. Sector-Specific Corporate PD Rating Scorecards Sector-Specific Financial Institution PD Rating Scorecards Sovereign, Local Regional Government PD Scorecards AF PD Rating Scorecards Commodity Finance PD Rating Scorecards Commodity Trader PD Rating Scorecards PF PD Rating Scorecards LGD Scorecards Evaluation of the LGD risk associated with asset finance, project finance and trade finance transactions. Asset Based LGD Scorecard Decision Tree LGD Scorecard AF LGD Scorecards Commodity Finance LGD Scorecards PF LGD Scorecards DATA Differentiation of historical default and loss severity of your portfolio in comparison with the market. LGD Database CreditPro PD Data PF Data Pooling Consortium benchmarking Comparison or validation of internal ratings and PD or LDG generated by your model with an independent and reliable external reference LGD Benchmarking PD Benchmarking stress testing Validate PD, LGD and EAD Stress Testing Models Model Validation Backtesting Conceptual Soundness Benchmarking 5
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