An Integrated PIT/TTC Risk Rating & Loss Framework for Basel & Credit Risk Management

1. An Integrated PIT/TTC Risk Rating & Loss Framework for Basel & Credit Risk Management ISDA/PRMIA March 13, 2007 London Dr. Scott D. Aguais Director & Global Head of Credit Risk Methodology scott.aguais@barcap.com

2. Overview • Key Presentation Points Highlighted • PIT vs. TTC Ratings – Background, Concepts & Objectives • Existence of Statistical Credit Cycles Motivates PIT-TTC Distinctions • Implementing PIT/TTC Ratings in a Basel-Compliant Internal Rating Approach with Sector & Region Credit Factors • An Integrated PIT-TTC Approach for PD & LGD – Initial Ideas See, ‘Designing and Implementing a Basel Compliant PIT-TTC Ratings Framework’, Chapter 10, Basel II Handbook, Second Edition, M. Ong Editor, January 2007.

3. Key Presentation Points (1) Historical analysis of systematic credit risk factors (Zs) provides reasonable empirical evidence of recurring cycles – mean reversion & momentum are statistically observable (2) The existence of credit cycles makes PIT/TTC distinctions meaningful in assessing PDs – a fully integrated PIT/TTC approach: • Consistently supports multiple credit risk objectives – is required for Basel II • Improves on current legacy credit models that assume random walks for systematic factors • Consistently converts various credit indicators to the same like-for-like basis (3) Implementing a PIT/TTC framework with statistical credit cycles utilizes a more dynamic approach in both a ‘batch’ & ‘desktop’ environment using MKMV EDFs (4) A fully integrated PD & LGD framework is currently lacking in credit modeling – recognizing this in jointly calibrating PD/LGD models provides more accurate parameter estimates

4. Portfolio Management Counterparty Exposures/Limits Management Instrument Valuation Transaction Management Obligor Creditworthiness Analysis Using the Appropriate Time Horizons in Rating Systems • Credit risk business objectives supported by ratings require different measures & time horizons: • 1-Year expected loss prediction – 1-Yr PIT • Regulatory Capital under Basel II – 1-Yr TTC • Economic Capital (Aggregate) – 1-Yr TTC • Discretions/Limits – 1-Yr TTC • Credit Pricing – Yr-1 PIT then Credit Cycle Adjusted Term Structure Yr-2 onward • Issues in risk rating design: • Time horizons of objectives varies • Multiple credit indicators – but they aren’t all consistent with each other • Explicit, statistical credit cycles required to consistently assess “PIT” & “TTC”

5. We Find the Existence of Credit Cycles to be Plausible for the Following Reasons • Most Monetary Authorities are tasked to curtail both inflation & recessions – thus indirectly steering cyclical default rates • Unemployment rates, inflation rates, relative commodity prices, relative currency values & interest rates are often found to exhibit mean reversion • Recent evidence points to equity indexes also exhibiting mean reversion which implies a similar pattern for systematic credit indexes • Most importantly – forecast equations for systematic credit factors also exhibit statistically significant mean reversion & momentum

6. Historical Factors (Z) for Multiple Creditworthiness Measures Exhibit Clear Cycles Latent Creditworthiness Factors Derived from 6 Different Series For each annual series, a latent factor (Z) is estimated & then normalized to be (0,1) Source: Moody’s KMV, Standard & Poor’s, Federal Reserve Board & Barclays Capital Research

7. Defining PIT/TTC Terminology -- PIT PDs Measure Real Risk – TTC PDs Do Not • Fully PIT PDs: • Assesses potential default over 1 or more years • Starts from the current situation & describes an expectation of the future that integrates all relevant systematic cyclical & obligor idiosyncratic effects with appropriate probabilities • Corresponds to the usual meaning of PD & is unconditional w.r.t. unpredictable factors • Fully TTC PDs: • Can also be assessed over 1 or more years – but they must have an explicit horizon • Usually reflect very long-run circumstances where systematic credit cycle effects are assumed to average close to zero • Can also be determined using a specific ‘stress’ scenario • Assumes systematic credit factors stay at their historically observed averages • Are conditional w.r.t. credit conditions staying at either historical averages or a specific level of stress

8. TTC DD = PIT DD – Credit Cycle Adjustment Default Distance PIT for Borrower 2.6 2.2 TTC for Borrower Credit Cycle Adjustment t PIT for Population (Z) 2.4 TTC for Population (Z) 2.0 Time t Relationship Between PIT & TTC Default Distance TTC PDs Impacted by Only the Borrower Idiosyncratic Factor PIT PDs Impacted by Both the Systematic & Idiosyncratic Factors

9. 4 Legacy Credit Models Global Z Credit Index 2 Irrational exuberance? Predicted by credit-cycle model 0 Predicted by credit-cycle model -2 Legacy Credit Models Legacy Credit Models -4 Jan-91 Jan-04 Jan-94 Jan-95 Jan-97 Jan-98 Jan-99 Jan-00 Jan-96 Jan-01 Jan-02 Jan-03 Jan-90 Jan-93 Jan-92 Legacy Credit Models Are Blind To the Predictable Systematic Component of Credit Cycles Current Models Assume Credit Factors Follow a Random Walk

10. Barclays Capital Risk Rating Approach – One Rating – Two PDs -- Implemented Globally in Mid-2005 • Designed to be Basel-Compliant (Pro-Cyclicality) & consistently support multiple credit risk management objectives • Primary internal rating – 1-year, PIT rating & PD • Approach also calculates a 1-year, TTC PD: • Defined as ‘average credit conditions’ • Assessed using systematic sector & region credit cycle factors (Zs) • NOT average historical Agency Rating PDs which are NOT TTC • Incorporates statistical Z credit cycles: • Used to normalise credit indicators onto a ‘like-for-like’ basis • Also used to adjust forward PD term-structures for momentum & mean reversion in credit cycles

11. 3 Generally Used Types of Credit Indicators Are Not Comparable on a “Like-for-Like” Basis Credit Indicator Description Approach PIT Forward-looking, cardinal PDs based on MKMV model assumptions KMV EDFs ‘Hybrid’ 70/30 TTC/PIT Agency Grades Generally Ordinal ratings mapped to PDs using past average experience Internal (or other external) models that determine either PDs, implied ratings or “scores” (scorecards) which are then mapped to PDs or ratings TTC Internal Models

12. “Pure TTC” “Hybrid PIT/TTC” “Pure PIT” KMV EDFs KMV TTC EDF Remove Credit Cycle Agency Grades EDF Mapping Agency PIT EDFs Remove Credit Cycle Agency TTC EDFs Int. Model PIT EDFs Internal Models Add Credit Cycle Integrated Approach Consistently Assesses “PIT” & “TTC” PDs Across Different Credit Indicators

13. Examples of “Agency Z” Factors Used to Convert Agency Ratings to PIT PDs Source: Moody’s KMV , Moody’s Investors Services, Standard & Poor’s & Barclays Capital Research

14. Examples of “Z” Sector Credit Cycles Plus Forecasts Used to Form Forward PD Term-Structures Source: Moody’s KMV & Barclays Capital Research

15. Monthly Z Factors are Derived from Global MKMV EDFs Global MKMV EDF Data-Set has Roughly 28,000 EDFs REGIONS SECTORS Corporates & Financials All MKMV Companies are ‘Bucketed’ into the Various Region/Sector Z Categories • Sector 1 • Sector 2 • …. • …. • Region 1 • Region 2 • ….

16. Manual Process Automatic Process Risk Review Function Previous Z Impacts New Region & Sector Zs Each Month Previous Z measures New/ Z Previous PD Change MKMV EDFs Impact PD Calculator Analysis Batch New New PIT & TTC 1-YR PDs & PD Term Structures (YRs 2-5) Each Month Z measures New Z Impact PD Calibration Batch Stress Test & Analytics Environment Approved Z Downstream Credit Risk Uses Update New Z Measures PD Calculator Desk-top & Batch PD PD Risk Rating Application PD Monitoring Integrated PIT/TTC Requires ‘Batch’ & Desk-Top Implementations to Assess Zs & PDs on Monthly Basis

17. Portfolio Management Counterparty Exposures/Limits Management Instrument Valuation Transaction Management Obligor Creditworthiness Analysis PD & LGD Models Are Usually Developed & Calibrated on a ‘Standalone’ Basis PD Model Risk Factors Credit Risk Objectives PD Modelling ‘Stand-Alone’ PD Model Calibration Default/No-Default Outcomes Predicted PDs LGD Modelling Predicted LGDs ‘Stand-Alone’ LGD Model Calibration Default/Loss Outcomes LGD Model Risk Factors

18. Use of an Integrated PD/LGD Framework is Currently Lacking in Credit Modelling • PD & LGD models are calibrated on a ‘standalone’ basis • But loss rate data is available that reflects the combined effects of an underlying PD/LGD model • Recent work on co-dependency between PD & LGD (Altman et. al., Frye etc.) has focused on statistical relationships – this misses the need for an integrated conceptual framework for combined PD & LGD • The Basel II Capital calculation also misses the integrated approach as ‘normal’ PD is inconsistently combined with ‘stress’ LGD • The Merton framework provides a starting point for better understanding the implications of not using a fully integrated approach

19. Merton Model Implies Linkage Between PD and LGD Default occurs here . . . . .. . . . . . but not if the LGD rises in stress • Default occurs if the ratio of asset value (A) to liabilities (L) falls below the default point (DP) • Distance between DP and A=L point approximates LGD • Thus, if LGD rises, DP falls and the default rate falls For more, see George Pan’s discussion in the CreditGrades’ model documentation.

20. LGD = F( % Above, % Below, Z and other variables) % Above = % of Obligor’s Debt Capital Senor to the Obligation % Below = % of Obligor’s Debt Capital Subordinate to the Obligation Z = Credit-Cycle Index We Find that LGD Varies Statistically With Credit Cycles • We find that for each 1 standard deviation move down in Z, LGD increases by about 3 percentage points -- e.g. from 30 to 33% for a 1 standard deviation deterioration in credit conditions • Thus, in the Basel-II, -3.09 standard-deviation scenario, LGD rises by about 9 percentage points relative to its normal value • Our LGD model determines a probability distribution (not a point estimate) for LGD, but the expected value has the above properties

21. Calibration to Loss History Provides a Fix Stress LGD enlarges exaggeration of loss-rate cycles intrinsic in using Basel-prescribed PD correlations in a Credit Metrics’ model; Calibration to experience motivates some combination of lower PD & LGD volatilities

22. Short-Run Approach to PD/LGD Integration • Implement the integrated PIT/TTC PD & ratings framework • Incorporate statistical credit cycles (Z) in LGD modeling to achieve a correctly specified model • Utilize a ‘neutral’ value of Z (Z=0) in LGD model implementation • Put another way – don’t use ‘stress’ LGD in anything but Reg Cap !! • Dampening of LGD in this way gets closer the implied integrated PD/LGD framework volatilities • Consider a integrated calibration to joint loss (PD & LGD) data to continue to refine the parameter estimates

23. Summary Comments (1) Historical analysis of systematic credit factors implies recurring credit cycles are real & about 20% is “predictable” (2) Because Credit cycles exist – distinctions between PIT & TTC PDs have meaning (3) Satisfying both Basel II & sophisticated credit risk management objectives requires an integrated PIT/TTC rating framework (4) Only PIT PDs measure risk – TTC PDs do not directly measure real risk but they are used to manage decisions where volatility is problematic (4) Successfully implementing an integrated PIT/TTC approach requires a Kuhnian paradigm shift in culture, language, business process, technology, policy . . . . . . . . . . . (5) Credit risk modeling generally still lacks a fully specified approach for integrated PD & LGD – until that time, standalone credit model calibration will be jointly less accurate