ProposalforCreditRiskModelingUsingSta6s6calTechniques

I.   Interviewclient.Planstrategically:Takemanagement’svisionandconvertitintoanac6onablepathway.

II.   Determinehowrecommendedbankingguidelinesfitwiththeneedsoftheclientwithintheregulatoryframework.

III.   DiscusshowproposedCreditRiskModelingsta6s6caltechniquesforkeyCreditRiskparametersPD,LG,EADmaybeusedandsuggestmodelvalida6ontechniques.

IV.  Reviewhowstrategicobjec6vesweremetandwhythesepar6cularsta6s6calanalysismethodswouldfitwiththerecommenda6onsofBaselII.

Author:C-GStefanita 1

EstablishKeyRequirements

DevelopaPlan

AllocateResources

TrackProgressthroughMetrics Assess

Deliverablesagainst

Requirements

Author:C-GStefanita 2

PARTI.StrategicPlanning:•  DetermineClient’sNeeds•  PlananAc6onablePathway

PARTII.RegulatoryFramework&BankingGuidelines

June2004-BaselCommiXeeon

BankingSupervision(BCBS)

issuesBaselII

RevisedFrameworkonInterna6onalConvergenceof

CapitalMeasurement

ResearchTaskForce

(RTF)

whichisaresultsintheforma9onofa

AccordImplementa6onGroup(AIG)

Op6onsforimplemen6ng

BaselII

ComprehensiveCapitalAnalysisandReview(CCAR)regulatory

frameworkintroducedbytheFederalReserve

asubgroupofRTFgives

onafederallevelresultsin

CCARassessessoundnessof

internalcreditriskmeasurementbutbanksdeveloptheirownmethodologies

Bankshavetoworkwithintwointernal-ra6ngsbased(IRB)approaches:a

founda6onapproachandanadvanced

approach

DependingonIRBapproachfollowed,banksareallowedto

usetheirowninternalmeasuresforkeydriversofcredit

risk

asaresult nevertheless

BCBS–BaselII–CCAR–followedbyBaselIIIthatfocusesprimarilyoncreditrisk

Author:C-GStefanita 3

PARTII.–con6nued:

ClientNeed:Bankshavetodeveloptheirowncreditriskmodelssubjecttofederalscru6ny.

DependingonIRBapproach,banksreviewandanalyzevalida6on

methodologies&classifyra6ngssystems

Banksundertakevalida6ontoensureinternalra6ngssystemissuitablefor

internaluses

Banksneedtotakeintoaccountdifferencesin

quan6fica6onapproaches

Commonra6ngssystems:Point-in-6me(PIT)

Through-the-cycle(TTC)

SubjecttoFederalReserveapprovaluponmee6ng

certaincondi6ons

ClientNeed:Valida6onofInternalRa6ngsSystems

Ra6ngssystemsareacornerstoneforthecalcula6onofbanks’regulatorycapitalchargeintheIRBapproachofBaselII.

Ra6ngssystemsarethebasisforthedetermina6onofaborrower’sPD,alsotheothercreditriskfactorsLGD,EAD.Valida6oniskey.

Author:C-GStefanita 4

PARTII.–con6nued:

ModelDesign

RiskComponents

Valida6onofra6ngs

systembyabank

Banksmustdemonstratethattheycanassesstheperformanceoftheirinternalra6ngsandtheirriskes6ma6onssystemconsistently.

Realizeddefaultrateshavetobewithin

expectedrange

Banksmustuse

differentquan6ta6vevalida6on

tools

Internalstandardsmustexistfor

significantdevia6onsofobservedvaluesofriskcomponentsfromes6matedvalues.

2.

KeyDriversofCreditRisk:ProbabilityofDefault(PD)LossGivenDefault(LGD)ExposureatDefault(EAD)

3.

1.

Author:C-GStefanita 5

PARTIII-PlanSta6s6calTechniquesforKeyFactorsofCreditRisk

Author:C-GStefanita 6

1.Uselogis6cregression(logit)basedonanAltmanscoringmodel(oraprobitmodel)todeterminetheweightsofthefactors

contribu6ngtoPD;involvesMaximumLikelihoodEs6ma6on(MLE)–seeseparatedocumentfora

solvedexample.

2.Testthevalidityofthemodelbyperformingaone-samplet-testforthees6matedbcoefficients.

Usemul$variateANOVAtocomparedifferentPD‘buckets’.Considerdifferentscenarioswhereonly

somebcoefficientsarezero.Thisisamoreadvancedanalysisthatdiffersfromthecommonlyusedtextbook

ANOVA.ThegoalistodeterminewhichfactorscontributemoretoPD.Analysisinvolvesusingmatrix

algebratoimplementANOVAtheory.

PartIII–con6nued:

1.Calculateobligor-specificPDswithina‘bucket’byes6ma6ngweightsofexplanatoryvariables.

2.Validatemodelifes6matedweightsaresta6s6callymeaningful.A.   WithinaPD‘Bucket’

LogitorProbit,MLE,t-test

B.BetweenPD‘Buckets’Mul6variateANOVA

1.

ProbabilityofDefault(PD)–Non-StructuralModel

Obligorsareassignedtoa‘bucket’dependingonobligor-specificPDs.

PooledPDsreflectwithin-’bucket’averagesofobligor-specificPDs.

Author:C-GStefanita 7

CreditPorgolioRiskModel

ProbabilityDistribu6onofLosses

AssetValueorLatentValueApproach

producesa

arisesfromaporMolioofcreditriskyinstruments

wecanuse

answersques9onofprobabilityoflossesonloanporMolioexceedingforexample$100Minayear

ObtainedthroughMonteCarloSimula6on

StartbyUsingSimplifiedApproach:ConsiderLossesfromDefaultOnly,notChangesinMarketValue

SpecifyPDsofindividualcredit

events

SpecifyLGDas%ofEADlostat

default

Specifycorrela6onsofindividualcredit

events

ObtainPorgolioValueDistribu6onthroughModeling

PartIII–con6nued:

MeasuringCreditPorgolioRiskwiththeAssetValueApproach

Author:C-GStefanita

LossGivenDefault(LGD)2.

MarketLGD

ImpliedMarketLGD

ImpliedHistorical

LGD

WorkoutLGD

FourModels

•  Mostcommonlyused•  Basedondiscountedcashflowsajerdefault

Basedonpricesoftradeddefaultedloans

Derivedfromnon-defaultedbondpricesthroughassetpricingmodel

•  Forretailporgolio•  BasedonexperienceoftotallossesandPDes6mates

Cri6calissues: •  Howrecoveriesaremeasured•  Howworkoutcostsareallocated•  Howappropriatediscountfactorsareallocated

8

PartIII–con6nued:

PDes6ma6onstronglydependsonquan6fica6ontechniques,andvalida6onhastwostages

PARTIV–Review:FitwithBaselIIRecommenda6ons?

PDes6ma6onfromhistoricaldefaultrates(scoringmodel)ismostmeaningful

whenpooledPDsareunstressed

Theseareunbiasedes6matesofthelikelihoodofdefaultinthefollowingyearfor‘bucket’PDs

Along-runTTC‘bucket’ofPDswillnotprovidegoodes6mates

ofunstressedpooledPDs

UnstressedpooledPDstendtobelowerthanlong-runaveragedefaultfrequencyduringcyclicalpeaksandhigherduring

cyclicaltroughs

Sta6s6calmodelmaybepreferredthatusesahybridbetweenstructural

(Merton)andnon-structural(Altman)modelswithupdatedmarketdata

Valida6onofdiscriminatory

powerofra6ngsystem

Valida6onofaccuracyof

quan6fica6on(calibra6on)

Author:C-GStefanita 9

PDEs6matesDetermineLGD,EAD