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1、7CurrentIssues:2021.,8 3lt 31 FrameworkCurrent Issues1.BeyondLIBOR2.ReplacingLlBOR3.MachineLearning4.AIandMLinfinancialservices5.ClimateChange-PhysicalRiskandEquity6.TheGreenSwan7.WhenSellingBecomesViral8.MarketsintheTimeofCOVID-199.FinancialCrimeinTimesofCOVID-1910.CyberRiskandtheUSFinancialSystem专
2、业事新tt1.BeyondLIBOR4-84Muy色嘛！A.AnIdealReferenceRateAnIdealReferenceRateNotsusceptibletomanipulation.Derivedfromactualtransactionsinliquidmarkets.Serveasabenchmarkforbothtermlendingandfunding.5-84MwrrmaB.ProblemswithLIBoRIssuesthatledtothereplacementofLIBOR3Constructedfromasurveyofbanksreporting.Thisc
3、reatedamplescopeforpanelbankstomanipulateLIBORsubmissions.Sparseactivityininterbankdepositmarkets.1.Thedispersionofindividualbankcreditrisk.1.IBORaimstocapturecommonbankrisk.iRegulatoryandthemarketwanttoreducecounterpartycreditriskininterbankexposures,bankshavealsotiltedtheirfundingmixtowardslessris
4、kysourcesofwholesalefunding.uy*at*maD.RisksofRFRsintheRepoMarketRisksofRFRSintheRepoMarket0/Nreporatecannotreflectbanksmarginalfundingcosts.Banksasset-liabilitymanagementischallenging.Whenunderstress,reporatescanmoveintheoppositewayofunsecuredrates.Theforcesdrivingunsecured0/Nrates(includingcreditri
5、sk)pulledtheserateshigherastheunsecuredinterbankmarketsfroze.Atthesametime,theforcesdrivingsecured0/NrateswerepullingthemlowerowingtoacollateralshortageandflighttosafetyForlongertenors,termratesbasedonnewRFRsarelikelytodeviatepersistentlyfromtheirLIBORcounterpartseveninnormaltimes.TransitionIssues:t
6、hemigrationoflegacyLIBOR-Iinkedexposurestothenewbenchmarksafter2021.M亚倒舞m2.ReplacingLIBOR10-84Muy 色嘛 ！A.TheFactsPublicationofUBOR-theLondonInterbankOfferedRate-willlikelyceaseattheendof2021.after202LtheFCAwouldnolongercompelreluctantbankstorespondtotheLIBORsurvey.11 84then,theFCAcoulddeclareLIBORrat
7、esUnrepresentative*offinancialrealityanditwillvanish.MwrrmaB.RisksWhenLIBOREndsThesystemicriskposedbythecessationofLIBOR.3ThefirstarisesfromthelegacycontractsreferencingLIBOR.WhenpublicationofLIBORstops(orisexpectedtostop),contractsthatlackadequatefallbackprovisionsmayplungeinvalue.Totheextentthatla
8、rge,leveragedintermediariesareexposed,theresultinglossescouldimpairtheircapital,leavingusinthedarkaboutwhichinstitutionsarehealthyandwhicharenot.3Thesecondissueiswhether,whenLIBORceases,therewillbeanadequatesubstitutethatallowsintermediariesbothtofundthemselvesinaliquidmarketandtoprovidecredit.C.Cur
9、rentProblemsWheredothingsstandnow?First,thereremainplentyofdollarLIBORlegacycontractsoutstanding,thelatestavailabledataarenearlythreeyearsoldrandtheindustrycontinuestocreateLIBOR-Iinkedcontracts,westronglysuspectthatthesenumbersunderstatethechallenge.Second,thereisnocentralrepositoryprovidinginforma
10、tionaboutwhat,ifany,fallbacklanguageexistsinthesecontracts.WithoutLIBOR,whathappens?Inadequatefallbacklanguagefostersuncertaintyaboutthevalueoftheassets,andcouldtriggerawaveoflawsuits.Third,whiletheprocessofcreatingasatisfactoryreplacementfordollarLIBORiswelladvanced,itisfarfromcomplete.Thereislittl
11、etimefortesting.D.TheGovernmentsRoleintheTransitionFourveryimportantrolesforgovernmentofficials.3Thegreaterthecertaintyabouttheenddate,thefastertheLIBORtransitionWillbe.AuthoritiescanfurtherintensifywarningsabouttheimprudenceofrelyingonLIBOR.3Supervisorsmustensurethatallsystemicallyimportantbanksand
12、financialmarketutilitiesarefullyprepared.?Thereremainsaremarkableabsenceofup-to-datedataonLIBOR-linkedinstruments.regulatorsshouldgatherandpublishdatashowingtheevolutionofLIBOReposure(includinginformationonfallbacklanguage)atleastquarterly.GBecausetheLlBORtransitionwilldirectlyaffectmanyhouseholdsan
13、dsmallbusinesseswithUBOR-Iinkeddebtitisimportantforauthorities(includingtheConsumerFinancialProtectionBureau)topromotepublicawarenessofthechangesunderway.14-84M皿施舞na3.MachineLearningA.IntroductionTheDrivingForcesMoredetailsofreporting.High-frequency,unstructuredlowqualityconsumerdata.BigdataPredicti
14、onversusExplanationStatisticalmethodsaregoodforexplanation.MLisgoodforprediction.16-84uy * a ! B. Background to MLSupervised LearningDependent variable y is known.Unsupervised LearningDependent variable y is lacking.17-84su n * maB.BackgroundtoMLMachineLearningMethodsRegressionAsupervisedMLproblem.T
15、opredictacontinuousdependentvariabley.Afactorisaddedtopenalisecomplexityinthemodel.ClassificationAdiscreteproblem.ClusteringAnunsupervisedMLproblem.B.BackgroundtoMLOverfittingFitthedatasampleverywell.Performpoorlywhentestedout-of-sample.Havingtoomanyparameters.WaystoDealwithOverfittingBoosting:overweightscarcerobservationsinatrainingdataset.Bagging:amodelisrunthousandsoftimes,eac
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