IFRS 9 and CECL Credit Risk
Modelling and Validation
A Practical Guide with Examples
Worked in R and SAS
Tiziano Bellini
TizianoBellini
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Amiopapà, aduneroesilenzioso, allasuaencomiabilemoralità.
1.4 ECLandCapitalRequirements
1.4.1InternalRating-BasedCreditRisk-WeightedAssets................17
2.2 DefaultDefinitionandDataPreparation.
2.3 GeneralisedLinearModels(GLMs)
2.3.1GLM(Scorecard)Development..
2.4 MachineLearning(ML)Modelling
2.5 LowDefaultPortfolio,Market-Based,andScarceDataModelling
2.5.1LowDefaultPortfolioModelling.
2.5.2Market-BasedModelling
2.6
2.5.3ScarceDataModelling..
2.5.4HintsonLowDefaultPortfolio,Market-Based,andScarceDataModel Validation...............................................75
2.8 AppendixA.FromLinearRegressiontoGLM
2.9 AppendixB.DiscriminatoryPowerAssessment
3.1
3.2 DataPreparation...
3.2.1DefaultFlagCreation......................................93
3.2.2Account-Level(Panel)DatabaseStructure
3.3 LifetimeGLMFramework........................................98
3.3.1Portfolio-LevelGLMAnalysis...............................99
3.3.2Account-LevelGLMAnalysis
3.3.3LifetimeGLMValidation...................................108
3.4 SurvivalModelling.
3.4.1KMSurvivalAnalysis......................................112
3.4.2CPHSurvivalAnalysis.....................................116
3.4.3AFTSurvivalAnalysis.....................................121
3.4.4SurvivalModelValidation
3.5 LifetimeMachineLearning(ML)Modelling
3.5.1Bagging,RandomForest,andBoostingLifetimePD
3.5.2RandomSurvivalForestLifetimePD ..........................129
3.5.3LifetimeMLValidation.....................................133
3.6 TransitionMatrixModelling... ...................................134
3.6.1NaïveMarkovChainModelling
3.6.2Merton-LikeTransitionModelling..
3.6.3Multi-StateModelling...
3.6.4TransitionMatrixModelValidation..
3.7 SASLaboratory ................................................144 3.8 Summary.....................................................147 SuggestionsforFurtherReading ...................................148
4.1 Introduction
4.2 LGDDataPreparation
4.2.1LGDDataConceptualCharacteristics ..........................158
4.2.2LGDDatabaseElements ....................................160
4.3 LGDMicro-StructureApproach ...................................161
4.3.1ProbabilityofCure.. ......................................164
4.3.2Severity................................................167
4.3.3DefaultedAssetLGD......................................170
4.3.4Forward-LookingMicro-StructureLGDModelling ................173
4.3.5Micro-StructureRealEstateLGDModelling.
4.3.6Micro-StructureLGDValidation..............................178
4.4 LGDRegressionMethods.. ......................................182 4.4.1TobitRegression..........................................182
4.4.2BetaRegression..........................................186
4.4.3MixtureModelsandForward-LookingRegression ................189
4.4.4RegressionLGDValidation..................................190
4.5 LGDMachineLearning(ML)Modelling
4.5.1RegressionTreeLGD......................................191
4.5.2Bagging,RandomForest,andBoostingLGD
4.5.3Forward-LookingMachineLearningLGD..
4.5.4MachineLearningLGDValidation
4.6 HintsonLGDSurvivalAnalysis...................................203
4.7 ScarceDataandLowDefaultPortfolioLGDModelling
4.7.1ExpertJudgementLGDProcess. .............................204
4.7.2LowDefaultPortfolioLGD..................................207
4.7.3HintsonHowtoValidateScarceDataandLowDefaultPortfolioLGDs208
4.8 SASLaboratory. ...............................................208
4.9 Summary.....................................................212 SuggestionsforFurtherReading ...................................213
5.1 Introduction
5.2 DataPreparation ...............................................217
5.2.1HowtoOrganizeData......................................217
5.3 FullPrepaymentModelling. ......................................219
5.3.1FullPrepaymentviaGLM ...................................220
5.3.2MachineLearning(ML)FullPrepaymentModelling
5.3.3HintsonSurvivalAnalysis..................................227
5.3.4FullPrepaymentModelValidation
5.4 CompetingRiskModelling. ......................................231
5.4.1MultinomialRegressionCompetingRisksModelling...
5.4.2FullEvaluationProcedure ...................................239
5.4.3CompetingRiskModelValidation .............................243
5.5 EADModelling. ...............................................245
5.5.1ACompeting-Risk-LikeEADFramework.......................246
5.5.2HintsonEADEstimationviaMachineLearning(ML)
5.5.3EADModelValidation..
5.6 SASLaboratory ................................................252 5.7 Summary.....................................................254
6.1
6.2 ScenarioAnalysis..
6.2.1VectorAuto-RegressionandVectorError-CorrectionModelling
6.2.2VARandVECForecast..
6.2.3HintsonGVARModelling ..................................269
6.3 ECLComputationinPractice......................................270
6.3.1ScenarioDesignandSatelliteModels
6.3.2LifetimeECL............................................273
6.3.3IFRS9StagingAllocation...................................277
6.4 ECLValidation................................................280
6.4.1HistoricalandForward-LookingValidation
6.4.2CreditPortfolioModellingandECLEstimation.
TizianoBellini’sBiography
TizianoBellinireceivedhisPhDdegreeinstatisticsfromtheUniversityofMilanafterbeingavisiting PhDstudentattheLondonSchoolofEconomicsandPoliticalScience.HeisaQualifiedChartered AccountantandRegisteredAuditor.HegainedwideriskmanagementexperienceacrossEurope(includinginLondon)andinNewYork.HeiscurrentlyDirectoratBlackRockFinancialMarketAdvisory (FMA)inLondon.PreviouslyheworkedatBarclaysInvestmentBank,EYFinancialAdvisoryServices inLondon,HSBC’sheadquarters,PrometeiainBologna,andotherleadingItaliancompanies.Heis aguestlectureratImperialCollegeinLondon,andattheLondonSchoolofEconomicsandPolitical Science.Formerly,heservedasalecturerattheUniversityofBolognaandtheUniversityofParma. Tizianoistheauthorof StressTestingandRiskIntegrationinBanks,AStatisticalFrameworkandPracticalSoftwareGuide(inMatlabandR) editedbyAcademicPress.Hehaspublishedinthe European JournalofOperationalResearch, ComputationalStatisticsandDataAnalysis,andothertop-reviewed journals.Hehasgivennumeroustrainingcourses,seminars,andconferencepresentationsonstatistics, riskmanagement,andquantitativemethodsinEurope,Asia,andAfrica.
Preface
Aseriesofconcernshavebeenexpressedsincetheadoptionoftheincurredlossesparadigmbyboth theInternationalAccountingStandardBoard(IASB)andtheFinancialAccountingStandardBoard (FASB).Therecent(2007–2009)financialcrisisuncoveredthisissuebyinducingareviewofaccountingstandardswhichculminatedwiththeInternationalFinancialReportingStandardnumber9(IFRS9) andCurrentExpectedCreditLoss(CECL).
ThisbookprovidesacomprehensiveguideoncreditriskmodellingandvalidationforIFRS9and CECLexpectedcreditloss(ECL)estimates.Itisaimedatgraduate,masterstudentsandpractitioners. Asadistinctivepracticalimprint,softwareexamplesinRandSASaccompanythereaderthroughthe journey.Thechoiceofthesetoolsisdrivenbytheirwideusebothinbanksandacademia.
Despitethenon-prescriptivenatureofaccountingstandards,commonpracticesuggeststorelyon theso-calledprobabilityofdefault(PD),lossgivendefault(LGD),andexposureatdefault(EAD) framework.Othernon-complexmethodsbasedonloss-rate,vintage,cashflowsareconsideredasa corollary.Inpractice,banksestimatetheirECLsasthepresentvalueoftheabovethreeparameters’ productoveraone-yearorlifetimehorizon.Basedonthis,adistinctionarisesbetweenCECLand IFRS9.Iftheformerfollowsalifetimeperspectiveforallcredits,thelatterclassifiesaccountsinthree mainbuckets:stage1(one-yearECL),stage2(lifetimeECL),stage3(impairedcredits).Thekey innovationintroducedbythenewaccountingstandardssubsumesashifttowardsaforward-lookingand lifetimeperspective.Thereforealinkbetweenmacroeconomicvariables(MVs),behaviouralvariables (BVs),andtheabovethreeparametersiscrucialforourdissertation.Suchaframeworkisalsoanatural candidateforstresstestingprojections.
Fromanorganizationalstandpoint,Chapter 1 servesthepurposetointroduceIFRS9andCECL. Itpointsouttheirsimilaritiesanddifferences.Thenthefocusisonthelinkconnectingexpectedcredit lossestimatesandcapitalrequirements.Abookoverviewisprovidedasaguideforthereaderwilling tograspahigh-levelpictureoftheentireECLmodellingandvalidationjourney.
Chapter 2 focusesonone-yearPDmodelling.Twomainreasonssuggestourtreatingone-yearand lifetimeseparately.Firstly,bankshavebeendevelopingone-yearPDmodelsoverthelasttwodecades forBaselIIregulatoryrequirements.Secondly,abuilding-block-structuresplitinone-yearandlifetime PDfacilitatesthelearningprocess.Asastartingpoint,thischapterfocusesonhowdefaulteventsare definedforaccountingpurposesandhowtobuildaconsistentPDdatabase.Movingtowardsmodellingfeatures,firstly,generalizedlinearmodels(GLMs)areexploredasaparadigmforone-yearPD estimates.Secondly,machinelearning(ML)algorithmsarestudied.Classificationandregressiontrees (CARTs),bagging,randomforest,andboostingareinvestigatedbothtochallengeexistingmodels, andexplorenewPDmodellingsolutions.Inlinewiththemostrecentliterature,thechoiceofthese approachesisdrivenbothbytheireffectivenessandeasyimplementation.Ifawidedataavailability encouragestheuseofdatadrivenmethods,lowdefaultportfoliosanddatascarcityareotherchallenges onemayneedtoface.Bespokemethodsarescrutinizedtoaddressissuesrelatedtolimitednumberof defaults,andadhocproceduresareexploredtodealwithlackofdeephistoricaldata.
Oneofthekeyinnovationintroducedbythenewaccountingstandardsreferstolifetimelosses. Eventhoughthisconceptisnotnewinriskmanagement,itsimplementationinthefinancialindustry xiii
isextremelycontemporaryasdiscussedinChapter 3.Account-levelinformationisusuallyrequired todevelopacomprehensivemodellingframework.Basedondataavailability,onemayconsiderfew alternativemethods.Asafirstwaytotacklethechallenge,generalizedlinearmodels(GLMs)areexplored.Asasecondstep,survivalmodellingisintroducedbymeansofthreemaintechniques.The pioneeringKaplan–Meier(KP)framepavesthewaytolifetimePDmodellingbymeansofCoxProportionalHazard(CPH)andacceleratedfailuretime(AFT)models.Thirdly,machinelearning(ML) proceduresarescrutinized.Bagging,randomforestandboostingaredirectlyappliedonpaneldatato capturetherelationshipwithbothBVsandMVsovertime.Then,asanalternative,randomsurvival forestisexploredbyembeddingsurvivalmodellingintoaMLstructure.Fourthly,transitionmatrices arestudiedbyconsideringbothbespokeapproaches,basedontransitionmatrixadjustmentsovera multi-periodhorizonandmultistateMarkovmodels.
Chapter 4 focusesonlossgivendefault(LGD)representingtheportionofanon-recoveredcreditin caseofdefault.AsastartingpointtodevelopanLGDmodel,oneneedstorelyonasuitabledatabase. Itsgoalistocollectallinformationneededtoassessrecoveriesthroughouttheso-called“workout process”untilanaccountisfullycuredorwritten-off.Fromamodellingstandpoint,firstlyamicrostructureLGDmodellingisintroducedtoprovideacomprehensiveviewofthepost-defaultrecovery process.Thefocusisontwokeycomponents:probabilityofcureandseverity.Asanextstep,thefocus isonregressiontechniques.Tobit,betaandothermethodsarefirststudiedassilosapproaches,andthen combinedinmixturemodelstoimprovebothgoodness-of-fitandmodelpredictivepower.Thirdly,machinelearning(ML)modellingisexplored.Classificationandregressiontreesarenaturalcandidates tofitLGDs.Bagging,randomforestandboostingarealsostudiedasavalidenhancementofthemost traditionalMLmethods.SomehintsareprovidedonhowtoapplyCoxproportionalhazard(CPH),and acceleratedfailuretime(AFT)modelstoLGDs.Scarcedataissuesandlowdefaultportfoliosarethen investigatedbypointingouttheneedforsimplerapproaches.Qualitativeassessmentsplayakeyrole insuchasetting.
Chapter 5 isdevotedtoexposureatdefault(EAD)analysis.Akeydistinctionoperatesbetween committedproducts(forexample,loans)anduncommittedfacilities(forinstance,overdrafts).Loantypeproductsusuallycoveramulti-yearhorizon.Consequently,economicconditionsmaycausea deviationfromtheoriginallyagreedrepaymentscheme.Fullprepaymentsandoverpayments(partial prepayments)arefirstinvestigatedbymeansofgeneralizedlinearmodels(GLMs)andmachinelearning(ML).Hintsonsurvivalanalysisarealsoprovidedtoestimateandprojectprepaymentoutcomes. Growingattentionisdevotedbothbyresearchersandpractitionerstocompetingrisks.Asasecond stepofourinvestigation,thefocusisonaframeworktojointlymodelfullprepayments,defaults,and overpayments.Ontheonehand,wemodeltheseeventsbymeansofamultinomialregression.Inthis case,whentheoutcomeisnotbinary(forexample,overpayment)asecondstepisneeded.Tobitand betaregressionsareusedtocaptureoverpaymentspecificfeatures.Ontheotherhand,fullprepayments andoverpaymentsarejointlyinvestigatedbymeansofMLmodels.Asathirdfocusarea,uncommitted facilitiesareinspectedbymeansofabespokeframework.Oneneedstodealwithadditionalchallenges, comprisingaccountswithzeroornegativeutilizationatreportingdateandpositiveexposureatdefault. AllstatesoftheworldrelevantforECLcomputationarescrutinizedfromdifferentangles.
Finally,Chapter 6 bringstogetherallECLingredientsstudiedthroughoutthebook.Giventherole ofscenarios,multivariatetimeseriesareinvestigatedbymeansofvectorauto-regression(VAR)and vectorerror-correction(VEC)models.Informationregardingglobalvectorauto-regression(GVAR) modellingisalsoprovided.CasestudiesallowustograsphowtocomputeECLinpractice.Emphasis
Preface xv isplacedonIFRS9andCECLcomparison.Finally,fullECLvalidationisscrutinized.Indeed,the presumedindependencebetweenriskparametersandlackofconcentrationeffectsischallengedby meansofhistoricalvalidationandforward-lookingportfolioanalysis.
London,August2018
TizianoBellini
CREDITLOSSMODELLINGAND
VALIDATION
Asaresponsetoincurredlossescriticisms,boththeInternationalAccountingStandardBoard(IASB) andFinancialAccountingStandardBoard(FASB)workedtoredesignaccountingstandardstowards anexpectedcreditlossparadigm.Theaimwastoanticipatelossrecognitionbyavoidingissues experienced—inparticular—duringthe2007–2009financialcrisis.
Startingfromaninitialjointeffortforauniquesolution,IASBandFASBagreedoncommonprinciples,butthenissuedtwoseparatedstandards.IASB’sInternationalFinancialReportingStandard number9(IFRS9),issuedin2014,reliesonathree-bucketclassification,whereone-yearorlifetime expectedcreditlossesarecomputed.Onthecontrary,FASB’sCurrentExpectedCreditLoss(CECL) accountingstandardupdate2016–13(topic326:creditlosses)followsalifetimeperspectiveasageneralrule.
IFRS9andCECLareseparatelyintroducedinSections 1.2 and 1.3 topointouttheirsimilarities anddifferences.Thenthefocusisonthelinkconnectingexpectedcreditlossestimatesandcapital requirements,asdetailedinSection 1.4.Asafinalstep,abookoverviewisprovidedinSection 1.5 asaguideforthereaderwillingtograsponoverviewoftheentireexpectedcreditlossmodellingand validationjourney.
KEYABBREVIATIONSANDSYMBOLS
CECL Currentexpectedcreditloss
EADi,s,t Exposureatdefaultforaccount i insub-portfolio s attime t
ECL Expectedcreditloss
FASB FinancialAccountingStandardBoard
IASB InternationalAccountingStandardBoard
IFRS Internationalfinancialreportingstandard
LGDi,s,t Lossgivendefaultforaccount i insub-portfolio s attime t
PDi,s,t Probabilityofdefaultforaccount i insub-portfolio s attime t
PIT Point-in-time
RWA Riskweightedasset
TTC Throughthecycle
UL Unexpectedloss
IFRS9andCECLCreditRiskModellingandValidation. https://doi.org/10.1016/B978-0-12-814940-9.00009-8
Copyright©2019ElsevierInc.Allrightsreserved.
FIGURE1.1
ECLengine:topicsexploredthroughoutthechapter.
1.1 INTRODUCTION
SincetheadoptionoftheincurredlossesarchetypebybothIASBandFASB,aseriesofconcernshave beenexpressedabouttheinappropriatenessofdelayingtherecognitionofcreditlossesandbalance sheetfinancialassetsoverstatement.Therecent(2007–2009)financialcrisisuncoveredthisissuetoits broaderextentbyforcingaprofoundreviewofaccountingstandards,culminatingwiththeInternational FinancialReportingStandardnumber9(IFRS9)(IASB, 2014)andCurrentExpectedCreditLoss (CECL)(FASB, 2016).IFRS9goeslivein2018byconsideringnotonlyexpectedcreditloss(ECL) rules,butalsoclassificationmechanicsandhedgeaccounting.Hereafter,ourfocusislimitedtoECLs. Ontheotherhand,FASB’snewimpairmentstandardwillbeeffectiveforSECfilersforyearsbeginning onorafterDecember15,2019(withearlyadoptionpermittedoneyearearlier),andoneyearlaterfor otherentities.
Thekeyinnovationintroducedbynewaccountingstandardssubsumesashiftfromabackwardincurred-lossesperspectivetowardsaforward-lookingECLrepresentation.Thischangeimpliesadeep reviewintermsofbusinessinterpretation,computationalskillsandITinfrastructures.Furthermore, adeeperseniormanagementinvolvementisattheveryheartofnewaccountingstandardspractical implementation.Aholisticperspectiveisrequiredinsuchacomplexframeworkinvolvingwidespread competencestobealignedonacommongoal.
Figure 1.1 summarisesthekeyareastouchedinthischapterasanintroductiontothemaintopics discussedthroughoutthebook.
• IFRS9. Despitethenon-prescriptivenatureoftheaccountingprinciple,commonpracticesuggests relyingontheso-calledprobabilityofdefault(PD),lossgivendefault(LGD)andexposureatdefault (EAD)framework.BanksestimateECLasthepresentvalueoftheabovethreeparameters’product overaone-yearorlifetimehorizon,dependinguponexperiencingasignificantincreaseincredit risksinceorigination.Section 1.2 startsbydescribingthekeyprinciplesinformingthestaging allocationprocess.Threemainbucketsareconsidered:stage1(one-yearECL),stage2(lifetime
ECL),stage3(impairedcredits).Thefocus,then,movesonECLkeyingredients,thatis,PD,LGD, EAD.Aforward-lookingperspectiveinspiresIFRS9byemphasisingtheroleofeconomicscenarios asakeyingredientforECLcomputation.Basedonthis,aneasyparallelcanbedrawnbetweenECL andstresstesting.
• CECL. FewmethodologiesarementionedunderFASB(2016)tocomputeECLs.Inthisregard, Section 1.3 providesanoverviewofapproachesonemayadopttoalignwithCECLrequirements. Loss-rate,vintageandcashflowmethodsareinspectedbymeansofillustrativeexamples.These non-complexapproachesarenotfurtherinvestigatedthroughoutthebook.Indeed,thefocusof thebookisonmorecomplexmethodsbasedonPD,LGDandEADtoleveragesimilaritieswith IFRS9.
• ECLandcapitalrequirements. Section 1.4 highlightssomeofthekeyconnectionslinkingaccountingstandardsandregulatorycapitalrequirements.Firstly,internalrisk-based(IRB)weighted assetsareintroduced.Secondly,expectedcreditlossesarescrutinisedinthecontextofregulatory capitalquantification.IFRS9andCECLimpactoncommonequityTier1,Tier2andtotalcapital ratiosispointedoutbymeansofafewillustrativeexamples.
• Bookstructureataglance. BothIFRS9andCECLrequireanoutstandingeffortintermsofdata, modellingandinfrastructure.AdeepintegrationisrequiredtocoherentlyestimateECLs.Forthis reasonSection 1.5 providesaguideforthereaderthroughthejourney.Anintroductiontoeach chapterisprovidedtogetherwithanarrativehighlightingthekeychoicesmadeinpresentingeach topic.
1.2 IFRS9
Therecent(2007–2009)financialcrisisurgedaresponsenotonlyfromacapitalperspective,butalso fromanaccountingpointofview.Indeed,BIS(2011)introducednewconstraintstobankingactivity andenforcedexistingrules.Capitalratioswerestrengthenedbydefiningaconsistentsetofrules. Leverageratiowasintroducedasameasuretopreventbanksexpandingtheirassetswithoutlimit. Liquidityratios(thatis,liquiditycoverageratioandnetstablefundingratio)constitutedaresponseto liquidityproblemsexperiencedduringthecrisis.Finally,stresstestingwasusedasakeytooltoassess potentialrisksonawiderperspectivebyencompassingeconomic,liquidityandcapitalperspectivesall atonce.
Fromanaccountingperspective,IASBintroducedthenewprinciple,IFRS9(IASB, 2014).Itsmost importantinnovationreferstocreditlossesestimation.Intermsofscope,thenewmodelappliesto:
• Instrumentsmeasuredatamortisedcost. Assetsaremeasuredattheamountrecognisedatinitial recognitionminusprincipalrepayments,plusorminusthecumulativeamortisationofanydifferencebetweenthatinitialamountandthematurityamount,andanylossallowance.Interestincome iscalculatedusingtheeffectiveinterestmethodandisrecognisedinprofitandloss.
• Instrumentsmeasuredatfairvaluethroughothercomprehensiveincome(FVOCI). Loansand receivables,interestrevenue,impairmentgainsandlosses,andaportionofforeignexchangegains andlossesarerecognisedinprofitandlossonthesamebasisasforamortisedcostassets.Changes infairvaluearenotrecognisedinprofitandloss,butinothercomprehensiveincome(OCI).
AnecessaryconditionforclassifyingaloanorreceivableatamortisedcostorFVOCIiswhether theassetispartofagrouporportfoliothatisbeingmanagedwithinabusinessmodel,whoseobjective istocollectcontractualcashflows(thatis,amortisedcost),ortobothcollectcontractualcashflows andtosell(thatis,FVOCI).Otherwise,theassetismeasuredatfairvaluesprofitandloss,andECL modeldoesnotapplytoinstrumentsmeasuredatfairvalueprofitandloss(forexample,tradingbook assets).
Figure 1.2 summarisesthekeytopicsinvestigatedthroughoutSection 1.2.
FIGURE1.2 WorkflowdiagramforSection 1.2.
Section 1.2.1 introducesthestagingallocationprocess.Indeed,IASB(2014)reliesontheconcept ofsignificantincreaseincreditrisktodistinguishbetweenstage1andstage2credits.Forstage1, one-yearECLholds,whereasforstage2lifetimeECLneedstobecomputed.Stage3referstoimpaired creditsandlifetimeECLapplies.Section 1.2.2 providesanoverviewofthekeyingredientscommonly usedtocomputeECL.Indeed,apartfromsimplifiedapproaches,probabilityofdefault(PD),lossgiven default(LGD)andexposureatdefault(EAD)arekeyelementsforECLestimate.
1.2.1STAGINGALLOCATION
IFRS9standard(IASB, 2014)outlinesathree-stagemodelforimpairmentbasedonthefollowing:
• Stage1. Thisbucketincludesfinancialinstrumentsthathavenothadasignificantincreaseincredit risksinceinitialrecognitionorthathavelowcreditriskatthereportingdate.Fortheseassets, one-yearECLisrecognisedandinterestrevenueiscalculatedonthegrosscarryingamountofthe asset(thatis,withoutdeductionforcreditallowance).One-yearECListheexpectedlossthatresults fromdefaulteventsthatarepossiblewithinoneyearafterthereportingdate.
• Stage2. Financialinstrumentsthatexperiencedasignificantincreaseincreditrisksinceinitial recognition,butthatdonothaveobjectiveevidenceofimpairmentareallocatedtostage2.For theseassets,lifetimeECLisrecognised.Interestrevenueisstillcalculatedonthegrosscarrying amountoftheasset.LifetimeECLreferstoallpossibledefaulteventsovertheexpectedlifeofthe financialinstrument.
• Stage3. Assetsthathaveobjectiveevidenceofimpairmentatreportingdateareallocatedtostage3. Fortheseassets,lifetimeECLisrecognisedandinterestrevenueiscalculatedonthenetcarrying amount(thatis,netofcreditallowance).
Inlinewiththeabove,thedefinitionofsignificantincreaseincreditriskplaysakeyrolethroughout theentireIFRS9process.Indeed,thisisthetriggercausingECLtobecomputedoveraone-year insteadoflifetimehorizon.Reasonableandsupportableinformation—availablewithoutunduecost oreffort—includingpastandforward-lookinginformation,areattheveryrootofthedecision.Few presumptionsinformthisprocessaslistedbelow:
• Lowcreditrisk. Nosignificantincreaseincreditriskispresumedinthecaseoflowcreditriskatthe reportingdate.Asanexample,onemayconsideranexternallyratedinvestmentgradeinstrument. Abankcanuseinternalmethodstoidentifywhetheraninstrumenthasalowcreditrisk.
• Presumption30dayspastdue. Thereisarebuttablepresumptionthatthecreditriskhasincreased significantly,whencontractualpaymentsaremorethan30dayspastdue.
Abankcomparestheriskofadefaultoccurringovertheexpectedlifeofthefinancialinstrument, suchasatthereportingdatewiththeriskofdefaultandatthedateofinitialrecognition.Inlinewith IASB(2014),theassessmentofsignificantincreaseincreditriskreliesonarelativecomparison.Nevertheless,anabsolutecomparison(example,absolutethreshold)isviablewhenitprovidesaconsistent outcome,asunderarelativeapproach.Factorstoconsiderindeterminingtheoccurrenceofasignificantincreaseincreditriskinclude,butarenotlimitedto,thefollowing:
• Quantitativeindicators. Probabilityofdefaultisthemostcommonindicatoradoptedinpracticeto assesscreditriskincrease.AresiduallifetimePDshouldbeused.Itimpliesthatthesameremaining periodisconsideredforbothPDatoriginationandreportingdate.Asapracticalexpedient,aoneyearPDcanbeusedifchangesinone-yearPDareareasonableapproximationtochangesinthe lifetimePD.
• Qualitativeindicators. IFRS9providessomeexamples,including:creditspread,creditdefault swapprice,marketinformationrelatedtotheborrower,significantchangeinthecreditrating,internalcreditratingdowngradeandsignificantchangeinthevalueofthecollateral.Qualitativefactors shouldbeconsideredseparately,whentheyhavenotalreadybeenincludedinthequantitativeassessment.
Judgementisappliedindeterminingwhatthresholdwouldbeconsideredasignificantincrease increditrisk.Theriskofrecognisingexpectedlossestoolateshouldbebalancedagainstdefininga narrowbandtoavoidinstrumentsfrequentlymovinginandoutofthedifferentstageswithoutthis reflectingasignificantchangeincreditrisk.Whatisasignificantchangevaries,basedonaseries ofcircumstances.Thesameabsolutechangeintheriskofdefaultwillbemoresignificantforan instrumentwithalowerinitialcreditrisk,comparedtoaninstrumentwithahigherinitialriskof default.Asanexample,iforiginationPDis0.10%andreportingdatePDis0.20%,arelativeincrease of100.00%occurred.However,itaccountsforarelativelysmallchange,0.10%.Onthecontrary,if thePDatinitialrecognitionwas1.00%andthisincreasedbythesameabsoluteamountof0.10%to 1.10%,thisisanincreaseofonly10%.Thereforestagingmechanicsneedspecificbalancetoaccount forbothrelativeandabsolutefeatures.
Itisevidentthatakeyissueinmeasuringexpectedlossesistospecifywhenadefaultoccurs.IFRS 9doesnotprovideaspecificdefinition.Nevertheless,anentitymustapplyadefinitionthatisconsistent withinternalcreditriskmanagementpurposes.Thereisarebuttablepresumptionthatadefaultdoes notoccurlaterthan90dayspastdue.
Thefollowingsectionprovidesahigh-leveldescriptionofthekeyECLingredients:PD,LGD,and EAD.
1.2.2ECLINGREDIENTS
InlinewithIFRS9principles,ECLmustreflectanunbiasedevaluationofarangeofpossibleoutcomes andtheirprobabilitiesofoccurrence.Reasonableandsupportableinformationneedstobeusedwithout unduecostoreffortatthereportingdateaboutpastevents,currentconditionsandforecastsoffuture economicconditions.Estimatesalsoneedtoreflectthetimevalueofmoneybymeansofrelevant discounting.
Basedonwhatwasdescribedintheprevioussection,defaultdefinitionplaysacrucialrole.Sucha concept,togetherwithdatatouseforitspracticalimplementation,willbeinvestigatedthroughoutall chaptersofthebook.Inwhatfollows,ahigh-leveldescriptionofthemainingredientstouseforECL estimateisprovided.Itallowsustofamiliarisewiththeircharacteristicsandunderstandthereasonwhy adequatemodelsarenecessary.
• Probabilityofdefault(PD). Defaulteventscanbeinterpretedasrealisationsofarandomvariable.PDrepresentstheexpectationoftheseoccurrencesoveragiventimeframe.Whenapplied toafinancialinstrument,PDprovidesthelikelihoodthataborrowerwillbeunabletomeetdebt obligationswithinacertainperiod.OneofthekeyinnovationsintroducedbyIFRS9isextending toalifetimetimeframecreditriskestimates.Thedistinctionbetweenone-yearandlifetimeECL, referredtoasstage1and2,respectively,needstobereflectedintermsofone-yearandlifetime PDs.Inthisregard,startingfromalifetimeperspective,PDsmaybebrokendownintosub-period withintheremaininglifeterm.One-yearPDrelatestoaone-yearinterval. Itisworthnotingthatfromacreditriskmanagementorganisationalstandpoint,regulatorycapital reliesonone-yearestimates(BIS, 2006).Asaconsequence,banksfancytheideatouseacommon frameworkforbothcapitalrequirementsandaccountingpurposes.Astress-testing-likeframework mayserveasaviaticumforone-yearthrough-the-cycle(TTC)PDstowardspoint-in-time(PIT) forward-lookinglifetimeestimates,asdetailedinChapters 2 and 3 ofthebook.
• Lossgivendefault(LGD). LGDrepresentstheportionofanon-recoveredcreditincaseofdefault. Twoextremescaneasilybeidentified.Ontheonehand,afullrecoveryisassociatedwith0%LGD. Onthecontrary,azerorecoveryscenarioleadstoa100%LGD.Aseriesofpartialrecoveriesmay alsooccurinpractice,sothatLGDisusuallyboundedbetween0%and100%.Duetoitsnature, LGDisestimatedovertheentireworkoutprocess.Inotherwords,oneneedstoconsiderallrecoveriesoccurredafterdefaultwithoutimposingtimerestrictions.Inthisconnection,LGDmaybe regardedasalifetimemetric.Ontheotherhand,accuratelyinvestigatingwhetherandhowmacroeconomicconditionsaffectLGDsisnecessary.Indeed,aforward-lookingperspectiveisrequiredfor IFRS9andCECLestimates,asdetailedinChapter 4
• Exposureatdefault(EAD). Chapter 5 ofthebookfocusesonthebalanceexpectedcreditloss (ECL)iscomputedon.Akeydistinctionarisesbetweenloan-typeproducts(forexample,mortgages)anduncommittedfacilities(suchasoverdrafts).Intheformercase,amultiyearhorizon usuallyapplies.Asaconsequence,deviationsfromtheoriginallyagreedrepaymentschememay takeplace.Fullprepaymentsandoverpayments(partialprepayments)requireadequatemodelling. Withregardstouncommittedfacilities,firstlyoneneedstohighlightthedistinctionbetweencontractualandbehaviouralmaturity.Thendefaulteddrawnamountisthetargetvariableunderscrutiny.
Asanadditionalstep,acompetingriskframeworkisconsideredagainstthemoretraditionalcredit risksilosrepresentation.Indeed,growingattentionhasbeenaddressedbothbypractitionersand researcherstointegrateriskestimatesbycapturingtheirinterdependencies.Ourfocusisonprepayments,defaultsandoverpayments.
AllaboveingredientsarejointlyusedtocomputeECL,basedonstagingallocationrulesintroduced andscenarios,assummarisedinthefollowingsection.
1.2.3SCENARIOANALYSISANDECL
IASB(2014)requiresascenario-weightingscheme(thatis,probability-weighted).Insomecasesa simplemodellingmaybesufficientasperB5.5.42.Inothersituations,theidentificationofscenarios thatspecifytheamountandtimingofthecashflowsforparticularoutcomes,andtheirestimated probability,isrequired.Inthosesituations,theexpectedcreditlossesshallreflectatleasttwooutcomes (seeparagraph5.5.18).OnemayinferanupperlimittothenumberofscenariosfromSectionBC5.265. Consequently,therequirementofasimulation-basedapproachoverthousandsofscenarioscanbe disregarded.
Aconnectingframeworkisneededtolinkaccountinformation,macroeconomicscenarios,and creditrisksatellitemodels(forexample,PD,LGD,EAD).Thisisalsothetypicalhigh-levelframe usedforstresstesting.Indeed,inbothcases(thatis,ECLandstresstesting)creditriskmodelsare linkedtomacroeconomicscenariostoforecastasetofmetrics.InthecaseofECL,thefocusisonIFRS 9scope(thatis,credits),whereasstresstestingalsoinvolvestheentireassetandliabilitystructureofa bank.
Onemaysummarisetheprocessasfollows:
• Data. Atreportingdate,asetofinformationisneededasastartingpointtoassessECL;information regardingbothperforminganddefaultedportfoliosisneeded.
• Macroeconomicscenarios. GivenECLforward-lookingnature,scenariosarerequiredbothfor IFRS9andCECL.Inmoredetail,IFRS9explicitlyrequiredamultiscenarioframework;on theotherhand,CECLisnotprescriptive.Nevertheless,CECLmaybeorganisedbyrelyingona multiple-scenarioscheme,asperIFRS9.
• Satellitemodels. Creditriskmodels,suchasPD,LGDandEAD,inconjunctionwithasetof otherframes(forexample,effectiveinterestrate),relyonaccount-level,portfolio-leveldata,and scenarios.ProjectionsaredrawnoveralifetimehorizonbothforIFRS9andCECL.Aspartofthe lifetimecurve,IFRS9requiresone-yearparameterstoestimatestage1ECL.
• IFRS9stagingallocation. Stagingrulesapplytoeachfinancialinstrument,basedoncriteria alignedwithIASB(2014)requirements.
• ECLcomputationforeachscenario. TheoutputofcreditrisksatellitemodelsfeedtheECL engine.IfCECLreliesonlifetimeperspectiveforallaccounts,IFRS9requiresadistinctionbetween one-yearandlifetimeECLs,basedonthestagingallocationprocessdescribedabove.
• WeightingschemeandfinalECLestimation. Asafinalstepoftheprocess,ECLisobtainedasa weightedaverageofECLsestimatedunderalternativescenarios.ForCECL,ifonlyonescenariois used,ECLisestimatedaspartofthepreviousstep: ECLcomputationforeachscenario.
Example 1.2.1 helpsustograspinterconnectionsbetweencreditriskparameters,scenarios,staging allocation,andECLestimation.
EXAMPLE1.2.1STAGINGALLOCATIONANDECL
Letusfocusonagivenborrowerwithonlyoneloan.Twoillustrativestagingthresholdareconsidered,thatis,6.00%and5.00%.Threescenarios(A,B,andC)aredefined,assummarised inTable 1.1.Allcreditriskparameters(thatis,PD,LGD,andEAD)areaffectedbyscenarios.Theaccountisallocatedtoonestage,basedontheweightedaveragelifetimePD.Inour case,theaveragelifetimePDis5.55%(thatis,2.00% 30.00% + 5.50% 50.00% + 11.00% 20.00%).
• Hypothesisthreshold6.00%. Since5.55% < 6.00%,theaccountisallocatedtostage1. ThereforeECLis$19.28thousand(thatis,one-yearestimate).
• Hypothesisthreshold5.00%. Ifthestagingthresholdissetat5.00%,theaccountisallocatedtostage2,andtheECLis$28.84thousand(thatis,lifetimeestimate).
Scenario
ThefollowingsectionfocusesonCECL(FASB, 2016).
1.3 CECL
Inlinewiththegeneralprincipleinformingpost2007–2009financialcrisisaccountreform,CECL (FASB, 2016)goesinparallelwithIFRS9bymovingfromanincurredperspectivetowardsanexpectedlossframe.Oneneedstoincludeforward-lookinginformationandrecognisetheexpected lifetimelossesuponoriginationoracquisition.Off-balancesheetcommitments,whicharenotunconditionallycancellablealsorequireECLcomputation.Inabroadsense,CECLappliestofinancialassets measuredatamortisedcostbyincluding,amongothers,financingreceivables,heldtomaturity(HTM) securities,receivablesfromrepurchaseagreementsandsecuritieslendingtransactions,andreinsurance receivables.Itexcludes,amongothers,assetsmeasuredatfairvaluethroughnetincome,loansmadeto participantsbydefinedcontributionemployeebenefitplansandpolicyloanreceivablesofaninsurance company.
Intermsofmethodologies,thestandardisnotprescriptive.Inparticular,itisopentomethodsbased onloss-rate,probabilityofdefault(thatis,PD,LGD,EAD),discountedcashflows,roll-rate,orusean ageingschedule.WhendevelopingECLmethods,oneneedstoconsiderthefollowing:
Table1.1 Stagingallocation:ingredientsandECLoutcomes($Thousands)
• Historicallossinformation. InternalandexternalpastinformationareattheveryheartofECL estimate.Thisisthestartingpointfortheassessment.Portfoliosegmentationandpoolingarealso crucialtoidentifycommonriskcharacteristics.
• Currentconditions. Estimatesneedtoincludecurrentconditions.Thisisinlinewiththepoint-intimeprinciplealsoinspiringIFRS9computation.
• Reasonableandsupportableforecasts. Theforward-lookingperspectiveurgedbyIFRS9isalso attherootofCECL.ThereforeeconomicforecastsneedtobeembeddedintoECLestimates.
• Reversiontohistory. Banksneedtoreverttohistoricallossinformationwhenunabletomakereasonableandsupportableforecasts.Thisreversionneedstobeperformedsystematicallybystarting frominputs,orappliedatanaggregatedlevel.Itimpliesthatbanksneedto:
• Defineasustainableandsupportablehorizonformeanreversion.
• Estimatealong-termexpectedloss.
Figure 1.3 summarisesthekeytopicsinvestigatedthroughoutSection 1.3.
FIGURE1.3
WorkflowdiagramforSection 1.3
Section 1.3.1 providesanoverviewofloss-ratemethods.Cumulativecreditlossesareinspected fromtwostandpoints:collectiveandindividual.Section 1.3.2 describeshowtomeasureECLsbased ontheoriginationdateandhistoricalperformanceofassetswithsimilarvintagecharacteristics.Section 1.3.3 summarisesthekeyfeaturesofcashflowsmethods.Section 1.3.4 pointsoutthekeyelements toconsiderwhenaprobabilityofdefaultmethodisadopted.InlinewithIFRS9,PD,LGDandEAD arethekeyingredientstocomputeECL.Asapracticalchoice,thebookfocusesonthemostcomplexmodellingmethods.Forthisreason,loss-rate,vintage,cashflow,andotherswillnotbefurther investigatedinthefollowingchapters.AttentionwillbedrivenbyPD,LGDandEADmodelling. Finally,Section 1.3.5 pinpointsthekeysimilaritiesanddifferencesbetweenIFRS9andCECLframeworks.
1.3.1LOSS-RATEMETHODS
Loss-ratemethodscantakevariousforms.Inallcases,theyarebasedonhistoricalratesofloss.As astartingpoint,onemayconsiderthepercentageofreceivablesthathavehistoricallygonebad,and
thenmakeanynecessaryadjustmentsbasedonrelevantinformationaroundcurrentorfutureconditions.
Example 1.3.1 referstoFASB(2016)Example1bypointingoutthedifferencebetweenincurred lossandECLestimates.
EXAMPLE1.3.1LOSSRATEANALYSIS
Table 1.2 providesallinformationneededtoestimatelossesundertheincurredlossmethod. Foreachyearaone-yearemergingperiodisconsideredasincurredlossesestimate(thatis,the AnnualLosscolumn).Aconstant0.30%representstheloss-ratepercentage.Anextra0.50% adjustmentisappliedtoobtainatotal0.80%incurredlosspercentage.Thelatteristhenappliedtoyear2020exposure(thatis,$3,000.00thousand)tocompute$24.00thousandincurred loss.
Table1.2 Loss-rate:incurredlossmethod($Thousands)
MovingfromFASB(2016)Example1,onemayestimateECLassummarisedinTable 1.3.Aten-yearhistoricalincurredlossestrackisputinplace.Inourcase,thestartingpointistheamortisedcostbalanceof$1,500.00thousandasper2010.Lossesare trackedthroughouttheperiod2010–2020.Totalincurredlossesis$22.50thousandcorrespondingto1.50%oftheinitialbalance.Someadjustmentsareintroducedtotakeintoaccountexpectationsoneconomicconditionevolution.Theoverallexpectedlosspercentage is1.65%.Basedon2020exposureof$3,000.00thousand,ECLaccountsfor$49.50thousand.
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