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FRM二級培訓講義-基礎班CreditRiskMeasurement
andManagementTopicWeightingsinFRMPart
II3-203Session
NO.Contents%Session1MarketRiskMeasurementand
Management20Session2CreditRiskMeasurementand
Management20Session3OperationalRiskand
Resiliency20Session4LiquidityandTreasuryRiskMeasurementandManagement15Session5RiskManagementandInvestment
Management15Session6CurrentIssuesinFinancial
Market10FrameworkIntroductionofCredit
RiskCreditDecisionandCredit
AnalystKeyCreditRisk
IndicatorsCreditRisk
MeasurementProbabilityof
DefaultCredit
ExposuresCounterparty
RiskCapitalStructurein
BanksCreditRisk
ManagementMitigationofCounterparty
RiskCredit
DerivativesSecuritizationRetailBankingRisk
Management4-203Introduction
ofCredit
Risk5-203Topic1:CreditDecisionandCredit
AnalystCredit
DecisionCredit
AnalystCredit
Decision6-203Credit
RiskThe
default
of
a
counterparty
on
a
fundamental
financial
obligation.Anincreasedprobabilityof
default.Ahigherthanexpectedlossseverityarisingfromeitheralowerthanexpected
recovery
or
a
higher
than
expected
exposure
at
the
time
ofdefault.The
default
of
a
counterparty
with
respect
to
the
payment
of
funds
forgoods
or
services
that
have
already
been
advanced
(settlement
risk).FourPrimaryComponentsofCreditRisk
EvaluationTheobligor’scapacityandwillingness
to
Repay.Theexternal
conditions.Theattributesofobligationfromwhichcreditrisk
arises.Thecreditrisk
mitigants.Credit
Decision7-203CreditAnalysis
TechniquesQualitativeCreditAnalysisTechniques–Willingnessto
RepayCharacterandreputation
ofaprospective
borrower.Creditrecord
ofaprospective
borrower.QuantitativeCreditAnalysisTechniques–Abilityto
RepayEvaluatingthecapabilityofanentitytoperformitsfinancial
obligationsthroughacloseexaminationofnumerical
dataderivedfromitsmostrecentandpastfinancialstatements
forms.Credit
Decision8-203CategoriesofCredit
AnalysisFormostindividuals,factorssuchasaperson’snetworth,salary,assets,reputation,andcreditscore
areusedasfundamental
criteria.Fornonfinancialfirms,liquidity,cashflowtogetherwithearningscapacityandprofitability,capitalposition(solvency),stateoftheeconomy,andstrengthoftheindustry
are
used.Forfinancialfirms,
bank-specificmeasuressuchascapitaladequacy,assetquality,andthebank’sabilitytowithstandfinancialstressmustbeconsidered.Theimportanceofasset
quality.Theomissionofcashflowasakey
indicator.BankInsolvencyvs.Bank
FailureInsolventbankscankeepgoingonandonsolongastheyhaveasourceofliquidity.Exercise
1BrentGulick,acreditanalystwithHomeTownBank,isconsideringtheloanapplicationofasmall,localcardealership.ThedealershiphasbeensolelyownedbyBobJusticeformorethan20yearsandsellsthreebrandsofAmericanautomobiles.Becauseoftherurallocation,mostofthecarssoldinthepastbythedealershiphavebeenlargepick-uptrucksandsportsutilityvehicles.However,saleshave
declined,andgasolinepriceshavecontinuedtoincrease.Asaresult,Justiceisconsideringsellingalineofhybridcars.JusticehasborrowedfromHomeTownBankbeforebutcurrentlydoesnothaveabalanceoutstandingwiththebank.WhichofthefollowingstatementsisnotoneofthefourcomponentsofcreditanalysisGulickshouldbeevaluatingwhenperformingthecreditanalysisforthispotential
loan?9-203Exercise
1The
business
environment,
competition,
and
economic
climate
inthe
region.Justice'scharacterandpastpaymenthistory
withthebank.The
car
dealership's
balance
sheets
and
income
statements
for
thelastfewyearsaswellasJustice'spersonal
financialsituation.ThefinancialhealthofJustice'sfriendsandfamilywhocould
becalledupontoguaranteethe
loan.Answer:
D10-203Exercise
2RichardMarshall,FRM,isaratingagencyanalystwhoiscurrentlyperformingfinancialstatementanalysisonamajorbank.Whichof
thefollowingfinancialstatementswouldbeleastusefulforbankcreditanalysis?Balance
sheet.Income
statement.Statementofcash
flows.Statementofchangesincapital
funds.Answer:
C2021CFA&FRM11-203Credit
Analyst12-203CreditAnalysis:Toolsand
MethodsQuantitative
ElementsInvolvesthecomparisonoffinancialindicatorsand
ratios.Moreamenabletostatisticaltechniquesand
automation.Nominally
objectiveQualitative
ElementsConcernsthoseattributesthataffecttheprobabilityofdefault,but
whichcannotbedirectlyreducedtonumbers.Consequently,theevaluationofsuchattributesmustbeprimarilyamatterof
judgment.Relies
heavily
on
analyst’s
perceptions,
experience,
judgment,
reasoning,and
intuition.Nominally
subjective.Credit
Analyst13-203CreditAnalysis:Toolsand
MethodsResearch
SkillsPrimary
researchskills includedetailedanalysisofaudited
financialstatements
for
several
years
together
with
annual
reports
and
recent
interimfinancial
statements.Secondaryresearchskillsinvolveusingtheresearchpublished
byothers(e.g.,rating
agencies).SourcesofInformationusedbyCredit
AnalystAnnualreports;Interimfinancialstatements;Financialdatasources;Newsservices;Ratingagencyreportsandother
third-partyresearch;Prospectusesandregulatoryfillings;Notesfromthebankvisitand
thirdparties;Auditor’sreportorstatement;Auditor’sopinion;Thebankwebsite;
News,
the
Internet,
and
securities
pricing
dataCredit
Analyst14-203CAMEL
SystemBankcreditanalystsuniversallyemploytheCAMELsystemtoevaluatebankcreditrisk.Itcanbeseen
asachecklistoftheattributesofabankthatareviewedascriticalinevaluatingitsfinancial
performance.FiveMostImportantAttributesofBankFinancial
HealthC:
CapitalA:Asset
QualityM:
ManagementE:
EarningsL:
LiquidityAmenabletoratio
analysisIntroduction
ofCredit
Risk15-203Topic2:KeyCreditRisk
IndicatorsCreditRisk
IdentificationThree
DriversKey
IndicatorsCapital
StructureCreditRisk
IdentificationCreditRiskofDifferentFinancial
ProductsLoanForwardSwapOptionExotic
OptionThree
DriversProbabilityofDefault
(PD)ExposureatDefault
(EAD)LossgivenDefault
(LGD)Key
IndicatorsExpectedLossandUnexpected
Loss(CreditVaR)Lending
RiskCounterpartyRisk:risktoeachpartyofacontractthatthecounterpartywillnotliveuptoitscontractual
obligations.16-203Three
DriversProbabilityofDefault
(PD)Likelihoodthataborrowerwilldefaultwithinaspecifiedtime
horizon.Creditmigrationsordiscretechangesincreditquality(suchas
thosedue
to
ratings
changes)
are
crucial,
since
they
influence
the
termstructureofdefault
probability.ExposureatDefault
(EAD)Amount
of
money
lender
can
lose
in
the
event
of
a
borrower’s
default.LossgivenDefault
(LGD)Theamountofcreditorlossintheevent
ofadefault.Fractionofexposurerecoveredatdefaultis
recovery.RR=recovery=
1
? LGDexposure exposure17-203Key
IndicatorsExpectedLoss
(EL)Expectedvalueofcreditloss,andrepresentstheportionoflossacreditor
should
provision
for.If
the
only
possible
credit
event
is
default,expectedlossisequal
to:UnexpectedLoss(Credit
VaR)Istypicallydefinedintermsofunexpectedloss(UL)astheworst-caseportfoliolossatagivenconfidenceleveloveraspecificholding
period,minustheexpected
loss.EL=
PD
× 1
?
RR ×EAD=PD×LGD×
EAD18-203UL=CreditVaR=WCL?
ELKey
Indicators19-203CreditVaRversusMarket
VaRExtremeskewness
isamaterialconcern
increditrisk.Extremeskewnessarises
given,
in
the
rare
event
that
default
does
occur,
returns
are
verylarge
and
negative.
Skewness
results
in
a
higher
confidence
interval
for
measuringcreditVaR,usuallyat99thand99.9th
percentiles.The
time
horizons
for
market
risk
are
almost
always
between
one
dayand
one
month.
But
the
typical
time
horizon
for
measuring
creditrisk
ismuch
longer,
often,
the
credit
risk
horizon
is
one
year.TypeMarket
RiskCredit
RiskDistributionsSymmetricFat
tailsSkewedtothe
leftTime
HorizonShortTerm
(Days)LongTerm
(Years)Loss
DistributionKey
Indicators20-203Key
Indicators21-203ExampleCaseStudy1:Oneloanwithprincipalof1million,PD=8%,RR=
40%.Howmuchshouldbankprovision
for?CaseStudy2:Consideraportfolioof$100millionwith3bondsA,
B,andC,withvariousprobabilitiesof
default.Theexposuresare
constant.Therecoveryincaseofdefaultis
zero.Defaulteventsareindependentacross
issuers.Thefollowingsdisplaytheexposuresanddefault
probabilities.IssuerExposureProbabilityA$250.05B$300.1C$450.2Key
Indicators22-203ExampleDefaultiLossLiProbabilityp(Li)CumulativeProb.ExpectedLip(Li)Variance(Li-Eli)2p(Li)None$00.6840.684$0.00120.08A$250.0360.720$0.904.97B$300.0760.796$2.2821.32C$450.1710.967$7.70172.38A,B$550.0040.971$0.226.97A,C$700.0090.980$0.6328.99B,C$750.0190.999$1.4372.45A,B,C$1000.0011.000$0.107.53$13.25434.69Key
Indicators0.80.70.60.50.40.30.20.10-100-75 -70 -55 -45 -30 -25 0FrequencyLossProbabilityExpected
LossUnexpected
LossP
CL
CLi
95%Credit
VaRTheDeviationsfromthe
Mean23-203ExampleTheexpectedcreditlossoftheportfolio
is:E(CL)=∑pi×CEi=0.05×25+0.10×30+0.20×45=
13.25UL=WCL-EL=45m-13.25m=31.75mDistributionofCredit
LossesKey
Indicators24-203PortfolioCreditVaRDefaultCorrelation
EstimationDefaultCorrelationdrivesthelikelihoodofhavingmultipledefaultsinacredit
portfolio.Simplest
FrameworkTwofirms(orcountries,
ifwehavepositionsinsovereigndebt).Withprobabilitiesofdefaultπ1and
π2.Oversometimehorizon
τAndajointdefaultprobability–theprobabilitythatbothdefaultoverτ
–equalto
π12.Key
IndicatorsPortfolioCreditVaRDefaultCorrelation
EstimationOutcomeX1X2X1X2ProbabilityNo
default0001–π1–π2+
π12Firm1only
defaults100π1–
π12Firm2only
defaults010π2–
π12Bothfirms
default111π12E
Xi =πi;
E(X1X2)=π12V(Xi)=E(X2)?[E(X
)]2=π 1
?
π i=1,2i i i iCov(X1,X2)=E(X1X2)-E(X1)E(X2)=π12?
π1π2ρ
=π12?
π1π2π1
1
?
π1 π2(1?
π2)25-203Key
Indicators26-203PortfolioCreditVaREstimationofPortfolioCredit
VaRDefault
correlation
affects
the
extreme
quantiles
of
loss
or
worst
caselossratherthantheexpected
loss.Ifdefaultcorrelationinaportfolioofcreditsisequalto1,then
theportfoliobehavesasifitconsistedofjustonecredit.No
creditdiversificationis
achieved.Ifdefaultcorrelationisequalto0,thenthenumberofdefaults
intheportfolioisabinomiallydistributedrandomvariable.Significant
creditdiversificationmaybe
achieved.Key
Indicators27-203PortfolioCreditVaREstimationofPortfolioCreditVaR
(con’t)ρ=1(theportfoliowillactasifthereisonlyone
credit)Givenaportfoliowithnotionalvalueof$1,000,000and20creditpositions.EachcreditshasaPDof2%andaRRof0.Each
creditpositionisanobligationfromthesameobligorsothatthecreditportfoliohasadefaultcorrelationequalto1.Whatisthecreditvalueatriskatthe99%confidencelevelforthis
portfolio?EL=1,000,000×2%=
20,000WCL(99%)=1,000,000Credit
VaR=1,000,000-20,000=980,000Key
Indicators28-203PortfolioCreditVaREstimationofPortfolioCredit
VaRρ=0(numberofdefaultsisbinomially
distributed)Givenaportfoliowithavalueof$1,000,000and50credits.Eachcreditisequallyweightedandhasaterminalvalueof$20,000eachifnodefaultoccurs.EachcreditshasaPDofπandaRRofzero.WhatisthecreditVaRat95%confidencelevelifπis2%andthedefaultcorrelationis0?(the95thpercentileofthenumberofdefaultsbasedonthisdistributionis
3)?EL=1,000,000×2%=
20,000WCL(95%)=3×20,000=
60,000Credit
VaR=60,000-20,000=40,000Key
Indicators29-203EffectofGranularityonCreditVaRWhentheportfoliobecomesmoregranular,thatis,containsmoreindependent
credits,
each
of
which
is
a
smaller
fraction
ofthe
portfolio.TheCreditVaR
is,naturally,higherforahigherprobabilityofdefault,giventheportfoliosize.Butitdecreasesasthecreditportfolio
becomes
moregranularforagivendefaultprobability.Butthathasan
importantconverse:ItishardertoreduceVaRbymakingtheportfolio
moregranular,ifthedefaultprobabilityis
low.Eventually,foracreditportfoliocontainingaverylargenumber
ofindependentsmallpositions,theprobabilityconvergesto100
percentthatthe
credit
loss
willequal
the
expected
loss.
The
portfolio
then
has
zerovolatilityofcreditloss,andtheCreditVaRis
zero.Capital
StructurePDσ2=PD×(1?
PD)StepstoDeriveEconomicCapitalforCredit
RiskExpectedLosses
(EL)UnexpectedLosses
(UL-Standalone)UnexpectedLossContribution
(ULC)Economic
CapitalELandUL(instatistical
terms)EL=PD×EA×
LRUL=
EA
× PD
×σ2 +LR2×
σ2LR PDWhereσLR=standarddeviationofthelossrate
LRσPD=standarddeviationofthedefaultprobability
PD30-203Capital
StructureExampleSupposeXYZbankhasbookedaloanwiththefollowing
characteristics:totalcommitmentof$2,000,000,ofwhich$1,200,000is
currentlyoutstanding.
The
bankhas
assessed
an
internal
credit
rating
equivalenttoa1%defaultprobabilityoverthenextyear.Drawdownupon
defaultisassumedtobe75%.Thebankhasadditionallyestimateda40%lossgivendefault.ThestandarddeviationofEDFandLGDis5%and30%,respectively.Calculatetheunexpected
lossforXYZbank.EA=1,200,000+800,000×75%=
1,800,000UL=
1,800,000
× 1%×30%2+40%2×5%2=
64,90031-203Capital
StructureUnexpectedLoss
ContributionULMCi
==??????LP 1??????Li
2ULP×
P
????
UL2??????Li=12ULP×i=
j=????????????????=????=????
∑n1∑n
1
ρijULiULj ∑????1
????????????
????????????????????????TotalContributiontothePortfolio’s
ULnULP=?ULMCi×
ULii=1ULCi=ULMCi×ULi
=i=
∑n1ULj×
ρijULP×
ULi32-203Capital
Structure33-203Economic
CapitalAsdefinedpreviously,theamountofeconomiccapitalneededisthe
distance
between
the
expected
outcome
and
the
unexpected
outcomeatacertainconfidence
level.Unexpected
loss
is
translated
into
economic
capital
for
credit
risk
inthree
steps:First,thestandaloneunexpectedlossis
calculated.Then,thecontributionofthestandalone
ULtotheULofthebankportfoliois
determined.Finally,thisunexpectedlosscontribution(ULC)istranslated
intoeconomic
capital.Capital
StructureEconomic
CapitalEconomicCapitali=ULCi×
CMEconomicCapitalP=ULp×
CMCM=capital
multiplier34-203Capital
Structure35-203ChallengestoQuantifyingCredit
RiskThisapproachassumesthatcreditsareilliquidassets.Sincethecreditriskofbankloansbecomesmoreandmoreliquid
andistradedin
thecapitalmarkets,avalueapproachwouldbemore
suitable.Thiswouldrequiremodelingthemulti-periodnatureofcredits
and,hence,
the
expected
and
unexpected
changes
in
the
credit
quality
oftheborrowers
(and
their
correlations).
The
more
precise
numerical
solutionsgetverycomplexandcumbersome.Therefore,almostallinternalcreditrisk
models
used
in
practice
use
only
aone-year
estimation
horizon.Althoughthisapproachconsiderscorrelations
atapracticablelevelwithin
the
same
risk
type,
it
assumes,
when
measuring,
thatallother
risk
components
(such
as
market
and
operational
risk)are
separated
and
are
measured
and
managed
in
different
departments
within
the
bank.Exercise
1SupposeBankZlendsEUR1milliontoXandEUR5milliontoY.Overthenextyear,thePDforXis0.2andforYis0.3.ThePDofjointdefaultis0.1.Thelossgivendefaultis40%forXand60%for
Y.Whatis
the
expected
loss
ofdefault
in
one
year
for
the
bank?EUR0.72
millionEUR0.98
millionEUR0.46
millionEUR0.64
millionAnswer:
B2021CFA&FRM
36-203CreditRiskMeasurement37-203Topic1:Probabilityof
DefaultBasicApproachesusedtoPredicting
DefaultRating
SystemMeasurementfromMarket
PricesExponential
DistributionSingleFactor
ModelOther
ModelsBasicApproachesusedtoPredicting
Default38-203Experts-Based,Statistical-basedandNumerical
ApproachesExperts-BasedStatistical-BasedHeuristicandNumerical
ApproachStructuralApproachesandReduced-Form
ApproachesStructural
Approaches:
based
on
economic
and
financial
theoreticalassumptionsdescribingthepathtodefault.Modelbuildingisanestimateoftheformalrelationshipsthatassociate
therelevantvariablesofthetheoreticalmodel.(e.g.,
Merton)Reduced-Form
Approaches:
the
final
solution
is
reaches
using
the
moststatisticallysuitablesetofvariables
anddisregardingthetheoreticalandconceptualcausalrelationsamong
them.Rating
System39-203KeyFeaturesofaGoodRating
SystemMeasurabilityand
VerifiabilityObjectivityand
HomogeneityRatingAgencies’Assignment
MethodologiesMoody’s
releases
mainly
issues
ratings
and
far
less
issuers’
rating.S&Pconcentratesonprovidingacreditqualityvaluationreferredto
theissuer,
despite
the
fact
that
the
counterparty
could
be
selectively
insolventonpubliclistedbondsoronprivate
liabilities.FITCH
adopts
an
intermediate
solution,
offering
an
issuer
rating,
limitedtothepotentialinsolvencyonpubliclylistedbonds,without
consideringthecounterparty’sprivateandcommercialbank
borrowings.Rating
released
by
the
three
international
rating
agencies
are
not
directlycomparable.SpecificityRating
System40-203FromBorrowerRatingsto
PDInitialRatingAverageCumulatedAnnualDefaultRatesattheEndofEachYear
(%)Year
1Year
2Year
3Year
4Year
5Year
6Year
7Year
8Year
9Year
10Aaa0.000.000.000.000.000.000.000.000.000.00Aa10.000.000.000.000.000.000.000.000.000.00Aa20.000.000.000.000.000.000.000.000.000.00Aa30.000.000.000.000.000.060.170.170.170.17A10.000.000.000.000.040.060.060.060.060.06A20.050.110.250.350.460.520.520.520.520.52A30.050.190.330.430.520.540.540.540.540.54Baa10.210.490.760.900.951.041.261.581.661.66Baa20.190.460.821.311.661.982.212.352.582.58Baa30.390.931.542.213.003.423.854.334.494.49Ba10.431.262.112.493.163.653.683.683.683.68Ba20.771.712.814.034.785.065.456.487.5310.16Ba31.063.015.798.5210.2411.7613.2514.6716.1217.79Rating
System41-203Transition
MatrixRating
agencies
also
assess
changes
in
ratings.
Probability
estimates
aresummarized
in
transition
matrices,
which
show
the
estimated
likelihoodofaratingchangeforacompanywithinaspecifiedtime
period.One-YearFinalRating
Class(%)AaaAaABaaBaBCaaCa-CDefaultWRInitialRating
ClassAaa89.17.10.60.00.00.00.00.00.03.2Aa1.087.46.80.30.10.00.00.00.04.5A0.12.787.54.90.50.10.00.00.04.1Baa0.00.24.884.34.30.80.20.00.25.1Ba0.00.10.45.775.77.70.50.01.18.8B0.00.00.20.45.573.64.90.64.510.4Caa0.00.00.00.20.79.958.13.614.712.8Ca-C0.00.00.00.00.42.68.538.730.019.8Rating
SystemMeasurementofPDinRating
SystemCumulativeDefault
ProbabilityProbabilitythataborrowerwilldefaultoveraspecifiedmulti-year
period.kPDcumulated
=DefiNamestk42-203t+k
tNames:thenumberof
issuersDef:
the
number
of
names
that
have
defaulted
in
the
time
horizonMarginalDefault
ProbabilityProbabilitythataborrowerwilldefaultinanygiven
year.PDmarg=PDcumulated?
PDcumulatedRating
SystemMeasurementofPDinRating
SystemForwardProbability(ContingenttotheSurvival
Rate)t,t+kPDForw
=Deft+k?DeftNames
survivedtt,t+kSurvival
RateProbabilityaborrowerwillnotdefaultoveraspecifiedmulti-year
period.SRForw
= 1?
PDForwt43-2031?
PDcumulatedi=?
SRForwt;t+kti=1Rating
SystemMeasurementofPDinRating
SystemAnnualizedDefaultRate
(ADR)If
itis
necessary
to
price
a
credit
exposed
transaction
on
a
five
year
timehorizon,
itis
useful
to
reduce
the
five-year
cumulated
default
rate
to
anannualbasisforthepurposesof
calculation.t1?
PDcumulatedi=?
SRForw=(1?ADRt)ttt44-203i=11?
PDcumulated=
e?ADR×tRating
System45-203MeasurementofPDinRating
SystemExampleYears012345names1000990978965950930default1022355070PD(cumulated,%)1.002.203.505.007.00PD(marg,%)1.001.201.301.502.00PD(forw,%)1.001.211.331.552.11SR(cumul,%)99.0097.8096.5095.0093.00SR(forw,%)99.0098.7998.6798.4597.89ADR(discrete,%)1.001.111.181.271.44ADR(continuous,%)1.011.111.191.281.45Exercise
1Default
ProbabilityRating3
year5
yearAAA0.05%0.15%AA0.22%0.48%A0.30%0.72%BBB0.92%1.98%BB6.91%11.83%B20.37%28.00%CCC31.63%40.15%
Answer:
C46-203Usethefollowingtabletoanswerthequestionblow.Whichloanbelowhasthehighestexpectedcreditloss?(Assumethatalloftheloansaredueatmaturitywithoutamortizationandrecoveryrateis
zero).A 3-year loan of $50,000,000 to acounterpartywithacreditratingof
“A”.A 5-year loan
of $1,500,000 to acounterpartywithacreditratingof
“BB”.A 5-year loan of $40,000,000 to acounterpartywithacreditratingof
“AA”.A 3-year loan of $20,000,000 to acounterpartywithcreditratingof
“BBB”.Exercise
2Asaresultofthecreditcrunch,asmallretailbankwantstobetterpredictandmodelthelikelihoodthatitslargercommercialloansmightdefault.
It
is
developing
an
internal
ratings-based
approach
to
assess
itscommercialcustomers.Giventhisone-yeartransitionmatrix,whatistheprobabilitythataloancurrentlyratedatBwilldefaultoveratwo-year
period?A. 17.50%B. 20.0%C. 21.1%D. 23.5%
Answer:
D47-203RatingatBeginning
ofPeriodRatingatEndof
PeriodABCDA0.900.100.000.00B0.000.750.150.10C0.000.050.550.40MeasurementfromMarket
PricesInferPDfromCorporateBond
PricesRisk-Neutral
PDDefaultPayoff=
$1×RRP1–
PDt=
0NoDefaultPayoff=
$1t=
1PDp
=$1 $1×PD×RR+$1×(1?PD)1+YTM
=?PD
=(1+
Rf)1 YTM?
Rf
LGD 1+
YTM?YTM?Rf≈PD×
LGD48-203MeasurementfromMarket
Prices49-203InferPDfromCorporateBond
PricesCreditSpread–DVCS(spread
‘01)The
spread
’01
is
analogous
to
the
DV01.
It
measures
the
price
changeimpliedbyaonebasispointchangeinthecredit
spread.Z-spreadisthespreadthatmustbeadded
totheLIBORspotcurve/government
bond
curve
to
arrive
atthe
market
price
of
the
bond.We
calculate
the
DVCS
by
re-pricing
the
bond
after
shocking
the
z-spread.Example:currentbondprice:92,currentz-spread:
207;scenario1:bondprice:91.93,
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