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1、PCreditRiskMeasurementand卜Management,83.i3.：2-203乌业llQTopicWeightingsinFRMPartIISessionNO.Contents%Session1MarketRiskMeasurementandManagement20Session2CreditRiskMeasurementandManagement20Session3OperationalRiskandResiliency20Session4LiquidityandTreasuryRiskMeasurementandManagement15Session5RiskManag
2、ementandInvestmentManagement15Session6CurrentIssuesinFinancialMarket10FrameworkIntroductionofCreditRiskCreditDecisionandCreditAnalystKeyCreditRiskIndicatorsCreditRiskMeasurementProbabilityofDefaultCreditExposuresCounterpartyRiskCapitalStructureinBanksCreditRiskManagementMitigationofCounterpartyRiskC
3、reditDerivativesSecuritizationRetailBankingRiskManagement4-203MSV!.IntroductionofCreditRiskTopic1:CreditDecisionandCreditAnalyst1.CreditDecision5-2032.CreditAnalystM亚OK“CreditDecisionCreditRiskThedefaultofacounterpartyonafundamentalfinancialobligation.Anincreasedprobabilityofdefault.Ahigherthanexpec
4、tedlossseverityarisingfromeitheralowerthanexpectedrecoveryorahigherthanexpectedexposureatthetimeofdefaultThedefaultofacounterpartywithrespecttothepaymentoffundsforgoodsorservicesthathavealreadybeenadvanced(settlementrisk).FourPrimaryComponentsofCreditRiskEvaluationTheobligorrscapacityandwillingnesst
5、oRepay.Theexternalconditions.Theattributesofobligationfromwhichcreditriskarises.Thecreditriskmitigants.尊曲*tsCreditDecisionCreditAnalysisTechniquesQualitativeCreditAnalysisTechniques-WillingnesstoRepayCharacterandreputationofaprospectiveborrower.Creditrecordofaprospectiveborrower.QuantitativeCreditAn
6、alysisTechniques-AbilitytoRepayEvaluatingthecapabilityofanentitytoperformitsfinancialobligationsthroughacloseexaminationofnumericaldataderivedfromitsmostrecentandpastfinancialstatementsforms.7-203M业倒舞IMCreditDecisionCategoriesofCreditAnalysisFormostindividuals,factorssuchasapersonsnetworth,salary,as
7、sets,reputation,andcreditscoreareusedasfundamentalcriteria.Fornonfinancialfirms,liquidity,cashflowtogetherwithearningscapacityandprofitability,capitalposition(solvency),stateoftheeconomy,andstrengthoftheindustryareused.Forfinancialfirms,bank-specificmeasuressuchascapitaladequacy,assetquality,andtheb
8、anksabilitytowithstandfinancialstressmustbeconsidered.Theimportanceofassetquality.Theomissionofcashflowasakeyindicator.BankInsolvencyvs.BankFailureInsolventbankscankeepgoingonandonsolongastheyhaveasourceofliquidity.8-203MW6NfiIMExercise1HBrentGulickracreditanalystwithHomeTownBankrisconsideringtheloa
9、napplicationofasmall,localcardealership.ThedealershiphasbeensolelyownedbyBobJusticeformorethan20yearsandsellsthreebrandsofAmericanautomobiles.Becauseoftherurallocation,mostofthecarssoldinthepastbythedealershiphavebeenlargepick-uptrucksandsportsutilityvehicles.However,saleshavedeclined,andgasolinepri
10、ceshavecontinuedtoincrease.Asaresult,Justiceisconsideringsellingalineofhybridcars.JusticehasborrowedfromHomeTownBankbeforebutcurrentlydoesnothaveabalanceoutstandingwiththebank.WhichofthefollowingstatementsisnotoneofthefourcomponentsofcreditanalysisGulickshouldbeevaluatingwhenperformingthecreditanaly
11、sisforthispotentialloan?Exercise1A.Thebusinessenvironment,competition,andeconomicclimateintheregion.B.Justicescharacterandpastpaymenthistorywiththebank.C.ThecardealershipsbalancesheetsandincomestatementsforthelastfewyearsaswellasJusticespersonalfinancialsituation.D.ThefinancialhealthofJusticesfriend
12、sandfamilywhocouldbecalledupontoguaranteetheloan.10-203Answer:DMWMfi!Exercise2RichardMarshallFRM,isaratingagencyanalystwhoiscurrentlyperformingfinancialstatementanalysisonamorbank.Whichofthefollowingfinancialstatementswouldbeleastusefulforbankcreditanalysis?A.Balancesheet.B.Incomestatement.C.Stateme
13、ntofcashflows.D.Statementofchangesincapitalfunds.11-203Answer:CMW/em!CreditAnalystCreditAnalysis:ToolsandMethodsQuantitativeElementsInvolvesthecomparisonoffinancialindicatorsandratios.Moreamenabletostatisticaltechniquesandautomation.NominallyobjectiveQualitativeElementsConcernsthoseattributesthataff
14、ecttheprobabilityofdefault,butwhichcannotbedirectlyreducedtonumbers.Consequently,theevaluationofsuchattributesmustbeprimarilyamatterofjudgment.Reliesheavilyonanalystsperceptions,experiencejudgment,reasoning,andintuition.Nominallysubjective.CreditAnalysis:ToolsandMethodsResearchSkillsPrimaryresearchs
15、killsincludedetailedanalysisofauditedfinancialstatementsforseveralyearstogetherwithannualreportsandrecentinterimfinancialstatements.Secondaryresearchskillsinvolveusingtheresearchpublishedbyothers(e.g.4ratingagencies).SourcesofInformationusedbyCreditAnalystAnnualreports;Interimfinancialstatements;Fin
16、ancialdatasources;Newsservices;Ratingagencyreportsandotherthird-partyresearch;Prospectusesandregulatoryfillings;Notesfromthebankvisitandthirdparties;Auditorsreportorstatement;Auditorsopinion;Thebankwebsite;News,theInternetandsecuritiespricingdataMwrelei“CreditAnalystCAMELSystemBankcreditanalystsuniv
17、ersallyemploytheCAMELsystemtoevaluatebankcreditrisk.Itcanbeseenasachecklistoftheattributesofabankthatareviewedascriticalinevaluatingitsfinancialperformance.FiveMostImportantAttributesofBankFinancialHealthC:CapitalA:AssetQualityM:ManagementE:Earnings1.Liquidity14-203Amenabletoratioanalysisswam*Introd
18、uctionofCreditRiskTopic2:KeyCreditRiskIndicators1.CreditRiskIdentification2.ThreeDrivers3.KeyIndicators4.CapitalStructure1S-2O3CreditRiskIdentificationCredit Risk of Different Financial ProductsLending RiskCounterparty Risk: risk to each party of a contract that the counterparty will not live up to
19、its contractual obligations.ForwardSWaPOptionExotic OptionThree DriversProbability of Default (PD)Exposure at Default (EAD)Loss given Default (LGD)Key IndicatorsExpected Loss and Unexpected Loss (Credit VaR) Three DriversProbability of Default (PD)Likelihood that a borrower will default within a spe
20、cified time horizon.Credit migrations or discrete changes in credit quality (such as those due to ratings changes) are crucial, since they influence the term structure of default probability.Exposure at Default (EAD)Amount of money lender can lose in the event of a borrower default.Loss given Defaul
21、t (LGD)The amount of creditor loss in the event of a defaultFraction of exposure recovered at default is recovery.recovery LGDRR =I-exposureexposure Key IndicatorsExpected Loss (EL)Expected value of credit Iossr and represents the portion of loss a creditor should provision for. If the only possible
22、 credit event is default, expected loss is equal to:EL=PDXa- RR) X EAD = PDX LGD X EADUnexpected Loss (Credit VaR)Is typically defined in terms of unexpected loss (UL) as the worst-case portfolio loss at a given confidence level over a specific holding period, minus the expected loss.UL = Credit VaR
23、 = WCL - ELKeyIndicatorsCreditVaRversusMarketVaRExtremeskewnessisamaterialconcernincreditrisk.Extremeskewnessarisesgiven,intherareeventthatdefaultdoesoccur,returnsareverylargeandnegative.SkewnessresultsinahigherconfidenceintervalformeasuringcreditVaRfusuallyat99thand99.9thpercentiles.Thetimehorizons
24、formarketriskarealmostalwaysbetweenonedayandonemonth.ButthetypicaltimehorizonformeasuringcreditriskismuchIOngeLoften,thecreditriskhorizonisoneyear.TyPeMarketRiskCreditRiskDistributionsSymmetricFattailsSkewedtotheleftTimeHorizonShortTerm(Days)LongTerm(Years)19-203M业倒舞IMKeyIndicators1.ossDistributionM
25、W6NfiIMKeyIndicatorsExampleCaseStudy1:Oneloanwithprincipalof!million,PD=8%,RR=40%.Howmuchshouldbankprovisionfor?CaseStudy2:Consideraportfolioof$100millionwith3bondsA,B.andCwithvariousprobabilitiesofdefault.Theexposuresareconstant.Therecoveryincaseofdefaultiszero.Defaulteventsareindependentacrossissu
26、ers.Thefollowingsdisplaytheexposuresanddefaultprobabilities.!issuerExposureProbabilityIA$250.05B$300,1C$4502KeyIndicators22-203ExampleIDefaultLossLiProbabilityP(Li)CumulativeProb.ExpectedLp(L)Variance(Li-Eli)2P(Li)None$00.6840.684$0.00120.08A$250.0360.720$0.9。4.97B$300.07607%$2.282132C$450.171S967$7
27、.7017238A,B$550.0040.971$0.226.97AfCB.C$70$750.0090.0190.9800.999$0.63$14328.9972.45ARC$1000.001LooO$0.10$13.257.53434.69M亚mIMKeyIndicatorsExampleTheexpectedcreditlossoftheportfoliois:E(CL)=pjCEi=0.0525+0.1030+0.2045=13.25L=WCL-EL=45m-13.25m=31.75mDistributionofCreditLosses0.60.70.60.50.40.30.20.10T
28、(X)=7570=553530由501.ossKeyIndicatorsPortfolioCreditVaRDefaultCorrelationEstimationDefaultCorrelationdrivesthelikelihoodofhavingmultipledefaultsinacreditportfolio.SimplestFrameworkTwofirms(orcountries,ifwehavepositionsinsovereigndebt).Withprobabilitiesofdefault11f=and2Oversometimehorizon=Andajointdef
29、aultprobability-theprobabilitythatbothdefaultover-equalton豆.KeyIndicatorsPortfolioCreditVaRDefaultCorrelationEstimationOutcomeXiX之XX2ProbabilityNodefault00o11-T一+五Firm1onlydefaults10CFirm2onlydefaults01CBothfirmsdefault111久整E(Xi)=i;E(X1X2)=K12E42V(XgE闾)-E(W-1(1FiROP=VlrWlCov(X1,X2)=E(X1X2)-E(X1)E(X2
30、)=12-12KeyIndicatorsPortfolioCreditVaREstimationofPortfolioCreditVaRDefaultcorrelationaffectstheextremequantilesoflossorworstcaselossratherthantheexpectedloss.Ifdefaultcorrelationinaportfolioofcreditsisequalto1,thentheportfoliobehavesasifitconsistedofjustonecredit.Nocreditdiversificationisachieved.2
31、6-203Ifdefaultcorrelationisequalto0,thenthenumberofdefaultsintheportfolioisabinomiallydistributedrandomvariable.Significantcreditdiversificationmaybeachieved.swam*11KeyIndicatorsPortfolioCreditVaREstimationofPortfolioCreditVaR(cont)p=l(theportfoliowillactasifthereisonlyonecredit)Givenaportfoliowithn
32、otionalvalueof$1,000,000and20creditpositions.EachcreditshasaPDof2%andaRRof0.Eachcreditpositionisanobligationfromthesameobligorsothatthecreditportfoliohasadefaultcorrelationequalto1.Whatisthecreditvalueatriskatthe99%confidencelevelforthisportfolio?EL=1,000,0002%=20,000WCL(99%)=1,(KX)zOOOCreditVaR=l,0
33、00,000-20,000=980,000PortfolioCreditVaREstimationofPortfolioCreditVaR=0(numberofdefaultsisbinomiallydistributed)Givenaportfoliowithavalueof$1,000,000and50credits.Eachcreditisequallyweightedandhasaterminalvalueof$20,000eachifnodefaultoccurs.EachcreditshasaPDofandaRRofzero.Whatisthecredit=VaRat95%conf
34、idencelevelifis2%andthedefaultcorrelationis0?=(the95thpercentileofthenumberofdefaultsbasedonthisdistributionis3)?EL=1,000,0002%=20,000WCL(95%)=320,000=60,000CreditVaR=60x000-20,000=40,000KeyIndicatorsEffectofGranularityonCreditVaRWhentheportfoliobecomesmoregranular,thatis,containsmoreindependentcred
35、its,eachofwhichisasmallerfractionoftheportfolio.TheCreditVaRis.naturally,higherforahigherprobabilityofdefault,giventheportfoliosize.Butitdecreasesasthecreditportfoliobecomesmoregranularforagivendefaultprobability.Butthathasanimportantconverse:ItishardertoreduceVaRbymakingtheportfoliomoregranular,ift
36、hedefaultprobabilityislow.Eventually,foracreditportfoliocontainingaverylargenumberofindependentsmallpositions,theprobabilityconvergesto100percentthatthecreditlosswillequaltheexpectedloss.Theportfoliothenhaszerovolatilityofcreditloss,andtheCreditVaRiszero.CapitalStructureStepstoDeriveEconomicCapitalf
37、orCreditRiskExpectedLosses(EL)UnexpectedLosses(UL-StandaIone)UnexpectedLossContribution(ULC)EconomicCapitalELandUL(instatisticalterms)EL三PDxEAxLRUL=EAJpd112r+LR2WhereOLR=standarddeviationofthelossrateLRD=standarddeviationofthedefaultprobabilityPD2=PD(1-PD)CapitalStructureExampleSupposeXYZbankhasbook
38、edaloanwiththefollowingcharacteristics:totalcommitmentof$2,000,000,ofwhich$1,200,000iscurrentlyoutstanding.Thebankhasassessedaninternalcreditratingequivalenttoa1%defaultprobabilityoverthenextyear.Drawdownupondefaultisassumedtobe75%.Thebankhasadditionallyestimateda40%lossgivendefault.Thestandarddevia
39、tionofEDFandLGDis5%and30%,respectively.CalculatetheunexpectedlossforXYZbank.EA=1,200,000+800,00075%=lf800,000UL=1,800,0001%30%2+40%25%2=64,900CapitalStructureUnexpectedLossContributionOULP1ULMCi=xJPOULi2ULpULj1/(nPULUL)_y=JU02ULph1Cd!,，LUvTotalContributiontothePortfoliosULnULP=ULMCiXULii=ELUkULCi=UL
40、MClUL1=11ULi32-203SWrel!i31-203Mwrelei“CapitalStructureEconomicCapitalAsdefinedpreviously,theamountofeconomiccapitalneededisthedistancebetweentheexpectedoutcomeandtheunexpectedoutcomeatacertainconfidencelevel.Unexpectedlossistranslatedintoeconomiccapitalforcreditriskinthreesteps:First,thestandaloneu
41、nexpectedlossiscalculated.Then,thecontributionofthestandaloneULtotheULofthebankportfolioisdetermined.Finally,thisunexpectedlosscontribution(ULC)istranslatedintoeconomiccapital.CapitalStructureEconomic CapitalEconomicCapitalp=ULpCMEConomiCC叩ital=ULCCM1ICM=capitalmultiplier与业色iIVCapitalStructureChalle
42、ngestoQuantifyingCreditRiskThisapproachassumesthatcreditsareilliquidassets.Sincethecreditriskofbankloansbecomesmoreandmoreliquidandistradedinthecapitalmarkets,avalueapproachwouldbemoresuitable.35-203Thiswouldrequiremodelingthemulti-periodnatureofcreditsand,hence,theexpectedandunexpectedchangesinthec
43、reditqualityoftheborrowers(andtheircorrelations).Themoreprecisenumericalsolutionsgetverycomplexandcumbersome.Therefore,almostallinternalcreditriskmodelsusedinpracticeuseonlyaone-yearestimationhorizon.Althoughthisapproachconsiderscorrelationsatapracticablelevelwithinthesamerisktype,itassumes,whenmeas
44、uring,thatallotherriskcomponents(suchasmarketandoperationalrisk)areseparatedandaremeasuredandmanagedindifferentdepartmentswithinthebank.MW/em“Exercise1SupposeBankZlendsEUR1milliontoXandEUR5milliontoY.Overthenextyear,thePDforXis0.2andforYis03.ThePDofjointdefaultis0.1.Thelossgivendefaultis40%forXand60
45、%forY.Whatistheexpectedlossofdefaultinoneyearforthebank?A.EUR0.72millionB.EUR0.98millionC.EUR0.46millionD.EUR0.64millionAnswer:BCreditRiskMeasurement1. Z3.4.6.Topic 1: Probability of DefaultBasic Approaches used to Predicting DefaultRating SystemMeasurement from Market PricesExponential DistributionSingle Factor ModelOther Models37-203Mwrelei “BasicApproachesusedtoPredictingDefaultExperts-Based,Statistical-basedandNumericalApproachesExperts-BasedStatistical-BasedHeuristicandNumericalApproachStructuralApproachesandReduced-FormApproachesStru
