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1、DefaultModelingandCreditGradingPreparedproperly,acreditscoreorriskgradeforaborrowerwillprovideasufficientstatisticforevaluatingthelikelihoodofdefaultoverachosentimeintervalsuchasayear.Thispaperdiscussesthestepsinvolvedindevelopingadefaultmodelthatallowsfor
2、suchcreditgrading.Wedistinguish3essentialsteps,namely·Specifyingalogicallysoundmodel·Estimatingthemodel’sparameters·Testingforecastingperformance.Wedescribeeachofthesestepsbelow.Wepresentthestepsintheirusualorder,thoughtheactualprocessmayinvolvemorethanone
3、passthroughthesequence.Initialempiricalresultsoftenmotivatechangestoamodel.Wheneverpossible,webelievethatcreditratingsshouldderivefromanempiricalstudyofthefactorsaffectingdefaultrisk.Weexpectthatbothnumerical,financialmeasuresandcategorical,subjectiveonesw
4、illhelpexplaindefault.Ineithercase,we’llmakeeveryefforttoassessavariable’sinfluencethroughstatisticalestimationandtesting.Thisempiricalapproachcontrastswithschemesthatconsolidateaseriesofjudgmentalevaluationsusingjudgmentalweights.Westronglybelieveinstatis
5、ticalestimation,sinceotherwiseonehasnofirmbasisforclaimingthatacreditscorehasmorepredictivepowerthanarandom-numbergenerator.Observethatifratingsderivefromastatisticaldefaultmodel,onehasmoreflexibilityandgranularitythanofferedbyasmallsetofdiscreteriskgrades
6、.Forsomepurposessuchasmigrationanalysisorlimitsetting,onemightwanttoclassifytogetherborrowerswithsimilardefaultrates.However,thecontinuousindicatorprovidedbytheempiricalmodelstandsoutasthemoreinformativeriskgradeforaborrower.Wefocushereonborrowerratings.St
7、artingwithaborrowerrating,onemaycharacterizefacilityriskbyappendingdescriptionsofcollateral,covenants,guarantees,amortizationschedules,andotherfeaturesoftheloanagreement.Weprefersuchamultifaceteddescriptionoffacilityrisktoasummaryindicatorthatostensiblyacc
8、ountsforalloftherelevantcharacteristics.Webelievethatinstitutionswillfinditvaluabletokeeprecordsonallofthedistinctfactorsthatinfluenceborrowerandfacilityrisk.Thecreditratingrepresentsbyfarthemostimportantinpu