Presentation on theme: "Director & Global Head of Credit Risk Methodology"— Presentation transcript:
1 Director & Global Head of Credit Risk Methodology An Integrated PIT/TTC Risk Rating & Loss Framework for Basel & Credit Risk ManagementISDA/PRMIAMarch 13, 2007LondonDr. Scott D. AguaisDirector & Global Head of Credit Risk Methodology
2 OverviewKey Presentation Points HighlightedPIT vs. TTC Ratings – Background, Concepts & ObjectivesExistence of Statistical Credit Cycles Motivates PIT-TTC DistinctionsImplementing PIT/TTC Ratings in a Basel-Compliant Internal Rating Approach with Sector & Region Credit FactorsAn Integrated PIT-TTC Approach for PD & LGD – Initial IdeasSee, ‘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 IIImproves on current legacy credit models that assume random walks for systematic factorsConsistently 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 Obligor Creditworthiness Analysis Transaction Management Using the Appropriate Time Horizons in Rating SystemsCredit risk business objectives supported by ratings require different measures & time horizons:1-Year expected loss prediction – 1-Yr PITRegulatory Capital under Basel II – 1-Yr TTCEconomic Capital (Aggregate) – 1-Yr TTCDiscretions/Limits – 1-Yr TTCCredit Pricing – Yr-1 PIT then Credit CycleAdjusted Term Structure Yr-2 onwardIssues in risk rating design:Time horizons of objectives variesMultiple credit indicators – but they aren’t all consistent with each otherExplicit, statistical credit cycles required to consistently assess “PIT” & “TTC”Obligor Creditworthiness AnalysisInstrument ValuationTransaction ManagementCounterpartyExposures/LimitsManagementPortfolio
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 ratesUnemployment rates, inflation rates, relative commodity prices, relative currency values & interest rates are often found to exhibit mean reversionRecent evidence points to equity indexes also exhibiting mean reversion which implies a similar pattern for systematic credit indexesMost importantly – forecast equations for systematic credit factors also exhibit statistically significant mean reversion & momentum
6 Latent Creditworthiness Factors Derived from 6 Different Series Historical Factors (Z) for Multiple Creditworthiness Measures Exhibit Clear CyclesLatent Creditworthiness Factors Derived from 6 Different SeriesFor 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 yearsStarts from the current situation & describes an expectation of the future that integrates all relevant systematic cyclical & obligor idiosyncratic effects with appropriate probabilitiesCorresponds to the usual meaning of PD & is unconditional w.r.t. unpredictable factorsFully TTC PDs:Can also be assessed over 1 or more years – but they must have an explicit horizonUsually reflect very long-run circumstances where systematic credit cycle effects are assumed to average close to zeroCan also be determined using a specific ‘stress’ scenarioAssumes systematic credit factors stay at their historically observed averagesAre conditional w.r.t. credit conditions staying at either historical averages or a specific level of stress
8 Relationship Between PIT & TTC Default Distance TTC PDs Impacted by Only the Borrower Idiosyncratic FactorPIT PDs Impacted by Both the Systematic & Idiosyncratic FactorsTTC DD = PIT DD – Credit Cycle AdjustmenttPIT for Population (Z)PIT for BorrowerTTC for Population (Z)TTC for BorrowerCredit CycleAdjustment2.62.22.02.4TimeDefaultDistance
9 Legacy Credit Models Are Blind To the Predictable Systematic Component of Credit Cycles Current Models Assume Credit Factors Follow a Random Walk-4-224Jan-90Jan-91Jan-92Jan-93Jan-94Jan-95Jan-96Jan-97Jan-98Jan-99Jan-00Jan-01Jan-02Jan-03Jan-04Legacy Credit ModelsPredicted by credit-cycle modelGlobal Z Credit IndexIrrational exuberance?
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 objectivesPrimary internal rating – 1-year, PIT rating & PDApproach 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 TTCIncorporates statistical Z credit cycles:Used to normalise credit indicators onto a ‘like-for-like’ basisAlso 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 DescriptionApproachForward-looking, cardinal PDs based on MKMV model assumptionsPITKMVEDFs‘Hybrid’ 70/30TTC/PITAgencyGradesGenerally Ordinal ratings mapped to PDs using past average experienceInternal (or other external) models that determine either PDs, implied ratings or “scores” (scorecards) which are then mapped to PDs or ratingsTTCInternalModels
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 Source: Moody’s KMV & Barclays Capital Research Examples of “Z” Sector Credit Cycles Plus Forecasts Used to Form Forward PD Term-StructuresSource: 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 EDFsREGIONSSECTORSCorporates & FinancialsAll MKMV Companies are ‘Bucketed’ into the Various Region/Sector Z CategoriesSector 1Sector 2….Region 1Region 2….
16 Stress Test & Analytics Environment Term Structures (YRs 2-5) Integrated PIT/TTC Requires ‘Batch’ & Desk-Top Implementations to Assess Zs & PDs on Monthly BasisManual ProcessAutomatic ProcessRisk RatingApplicationStress Test & Analytics EnvironmentNewZ measuresPD CalibrationBatchPreviousPrevious Z ImpactsZChangeImpactAnalysisNew/PDPD CalculatorZ MeasuresNew Z ImpactDesk-top&PD MonitoringApproved ZUpdateRisk Review FunctionDownstreamCredit RiskUsesNew Region &Sector ZsEach MonthNew PIT & TTC 1-YR PDs & PDTerm Structures (YRs 2-5)Each MonthMKMV EDFs
17 Obligor Creditworthiness Analysis Transaction Management PD & LGD Models Are Usually Developed & Calibrated on a ‘Standalone’ BasisPD ModelRisk FactorsCredit RiskObjectivesPD ModellingDefault/No-DefaultOutcomes‘Stand-Alone’PD Model CalibrationPredictedPDsObligor Creditworthiness AnalysisInstrument ValuationTransaction ManagementCounterpartyExposures/LimitsManagementPortfolioLGD ModellingDefault/LossOutcomes‘Stand-Alone’LGD Model CalibrationPredictedLGDsLGD ModelRisk Factors
18 Use of an Integrated PD/LGD Framework is Currently Lacking in Credit Modelling PD & LGD models are calibrated on a ‘standalone’ basisBut loss rate data is available that reflects the combined effects of an underlying PD/LGD modelRecent 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 & LGDThe Basel II Capital calculation also misses the integrated approach as ‘normal’ PD is inconsistently combined with ‘stress’ LGDThe 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 stressDefault 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 LGDThus, if LGD rises, DP falls and the default rate fallsFor more, see George Pan’s discussion in the CreditGrades’ model documentation.
20 We Find that LGD Varies Statistically With Credit Cycles 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 ObligationZ = Credit-Cycle IndexWe 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 conditionsThus, in the Basel-II, standard-deviation scenario, LGD rises by about 9 percentage points relative to its normal valueOur 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 frameworkIncorporate statistical credit cycles (Z) in LGD modeling to achieve a correctly specified modelUtilize a ‘neutral’ value of Z (Z=0) in LGD model implementationPut 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 volatilitiesConsider 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
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