Credit risk modeling using Excel and VBA pot

... performance as it is described in Chapter 7. Credit risk modeling using Excel and VBA Gunter LöfflerPeter N. Posch Credit risk modeling using Excel and VBA Gunter LöfflerPeter N. PoschFor other ... Gunter. Credit risk modeling using Excel and VBA / Gunter Löffler, Peter N. Posch.p. cm.Includes bibliographical references and index.ISBN 978-0-470-03157-5 (cloth : alk. paper)1. Credit Management ... inexplanatory variables Credit Risk Modeling using Excel and VBA 21Table 1.12 Default rate for percentiles of ME/TLThus, the examination of univariate relationships between default rates and explanatoryvariables...

... eigenvalues and othercalculation procedures.1.7 RELATED EXCEL WORKBOOKSPart I which concentrates on Excel functions and procedures and understanding VBA hasthree related workbooks, AMFEXCEL, VBSUB and ... areall Excel objects as is Excel itself (the Application object). You program in VBA tomanipulate the properties and apply methods to Excel objects.Many statements can be made about objects and VBA, ... q D 3% and  D 20%. Expressions d1 and d2are evaluated in cells G8 and G11, from which the cumulative normal probabilitiesNd1 and Nd2 in cells G9 and G12 are derived, using Excel s...

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... understood and documented.An essential modeling method is to use mathematical entities such as numbers,functions, and sets to describe properties and their relationships to problems and real-world ... concepts, principles and methods, such as:– Abstraction and decomposition– Programming principles– Data structures– Algorithm structures– Concurrency and synchronization– Modeling and simulation– ... simulation,multidisciplinary computational modeling, formal specification of real-time systems,language design and processors, and modeling systems performance. Dr. Garrido de-veloped the Psim3, PsimJ, and PsimJ2 simulation...

... value.Rating Models and Validation50 Guidelines on Credit Risk ManagementII ESTIMATING AND VALIDATINGPROBABILITY OF DEFAULT (PD)1 Defining Segments for Credit Assessment Credit assessments ... heuristic credit assessment models rely on the subjective experience of credit experts, statistical models attempt to verify hypotheses using statisticalprocedures on an empirical database.For credit ... p. 10.Rating Models and Validation42 Guidelines on Credit Risk ManagementDuring the Credit TermInstead of observing the customerÕs bank account activity patterns, the credit institution should...

... FIGURE1.7Three-levelfactorstructureinKMV’sGlobalCorrelationModelTM,seealsocomparablepresentationsintheliterature,e.g.Figure9.9.in[21].Firm Risk Systematic Risk Specific Risk Industry Risk Country Risk Industry-Specific Risk Country-Specific Risk Global Economic, Regional, and Industrial Sector Risk Level 1: Composite ... LLCReferences1.1.1TheDefaultProbabilityThetaskofassigningadefaultprobabilitytoeverycustomerinthebank’screditportfolioisfarfrombeingeasy.Thereareessentiallytwoapproachestodefaultprobabilities:•Calibrationofdefaultprobabilitiesfrommarketdata.Themostfamousrepresentativeofthistypeofdefaultprobabil-itiesistheconceptofExpectedDefaultFrequencies(EDF)fromKMV2Corporation.WewilldescribetheKMV-ModelinSection1.2.3andinChapter3.Anothermethodforcalibratingdefaultprobabilitiesfrommarketdataisbasedoncreditspreadsoftradedproductsbearingcredit risk, e.g.,corporatebondsandcreditderivatives(forexample,creditdefaultswaps;seethechapteroncreditderivatives).•Calibrationofdefaultprobabilitesfromratings.Inthisapproach,defaultprobabilitiesareassociatedwithratings,andratingsareassignedtocustomerseitherbyexternalratingagencieslikeMoody’sInvestorsServices,Standard&Poor’s(S&P),orFitch,orbybank-internalratingmethodologies.Be-causeratingsarenotsubjecttobediscussedinthisbook,wewillonlybrieflyexplainsomebasicsaboutratings.AnexcellenttreatmentofthistopiccanbefoundinasurveypaperbyCrouhyetal.[22].Theremainingpartofthissectionisintendedtogivesomebasicindicationaboutthecalibrationofdefaultprobabilitiestoratings.1.1.1.1RatingsBasicallyratingsdescribethecreditworthinessofcustomers.Herebyquantitativeaswellasqualitativeinformationisusedtoevaluateaclient.Inpractice,theratingprocedureisoftenmorebasedonthejudgementandexperienceoftheratinganalystthanonpuremathe-maticalprocedureswithstrictlydefinedoutcomes.ItturnsoutthatintheUSandCanada,mostissuersofpublicdebtareratedatleastbytwoofthethreemainratingagenciesMoody’s,S&P,andFitch.2KMVCorp.,founded13yearsago,headquarteredinSanFrancisco,developsanddis-tributescreditriskmanagementproducts;seewww.kmv.com.©2003 ... LLCInpractice,analyticalapproximationtechniquescanbeappliedquitesuccessfullytoso-calledhomogeneousportfolios.Theseareportfolioswherealltransactionsintheportfoliohavecomparableriskcharacter-istics,forexample,noexposureconcentrations,defaultprobabilitiesinabandwithmoderatebandwidth,onlyafew(better:onesingle!)industriesandcountries,andsoon.Therearemanyportfoliossatisfy-ingsuchconstraints.Forexample,manyretailbankingportfoliosandalsomanyportfoliosofsmallerbankscanbeevaluatedbyanalyticalapproximationswithsufficientprecision.Incontrast,afullMonteCarlosimulationofalargeportfoliocanlastseveralhours,dependingonthenumberofcounterpartiesandthenumberofscenariosnecessarytoobtainsufficientlyrichtailstatisticsforthechosenlevelofconfidence.ThemainadvantageofaMonteCarlosimulationisthatitaccuratelycapturesthecorrelationsinherentintheportfolioinsteadofrelyingonawholebunchofassumptions.Moreover,aMonteCarlosimulationtakesintoaccountallthedifferentriskcharacteristicsoftheloansintheportfolio.ThereforeitisclearthatMonteCarlosimulationisthe“state-of-the-art”increditriskmodeling,andwheneveraportfoliocon-tainsquitedifferenttransactionsfromthecreditriskpointofview,oneshouldnottrusttoomuchintheresultsofananalyticalapproximation.1.2.3ModelingCorrelationsbyMeansofFactorModelsFactormodelsareawellestablishedtechniquefrommultivariatestatistics,appliedincreditriskmodels,foridentifyingunderlyingdriversofcorrelateddefaultsandforreducingthecomputationaleffortregard-ingthecalculationofcorrelatedlosses.Westartbydiscussingthebasicmeaningofafactor.AssumewehavetwofirmsAandBwhicharepositivelycorrelated.Forexample,letAbeDaimlerChryslerandBstandforBMW.Then,itisquitenaturaltoexplainthepositivecorrelationbetweenAandBbythecorrelationofAandBwithanunderlyingfactor;seeFig-ure1.5.Inourexamplewecouldthinkoftheautomotiveindustryas...

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