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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:overw
16、eightscarcerobservationsinatrainingdataset.Bagging:amodelisrunthousandsoftimes,eachonadifferentsubsampleofthedataset.Averagealltheruns.Randomforest:amodelconsistingofmanydifferentdecisiontrees.19-84Ensemble:averagetheresultingmodelwithmanyotherMLmodels.uya三!C.ThreeUseCasesCreditRiskandRevenueModelin
17、gDifficultiesinUsageModelscanbesensitivetooverfittingthedata.Hardforanyhumantounderstand.&FraudDetectionofCreditCardFraudClearhistoricaldatawithrelevantfraudlabelstotrainclassification.ZSurveillanceofConductandMarketAbuseinTradingApplication:Monitorthebehaviouroftraders.ChallengestoApplyingMLNolabel
18、eddatatotrainalgorithms.Blackboxes:hardtoexplaintoacomplianceofficer.Countermeasure:incorporateshumandecisions.BarriertotheImplementationofAutomatedSurveillanceInformationfromdifferentsourcescouldbemutuallyincompatible.2684乌皿*tma4.AIandMLinFinancialServicesA.BackgroundanddefinitionsAschematicviewofA
19、I,machinelearningandbigdataanalytics22-84Svpph facfo51 Deuund factonuya三!B.DriversAvarietyoffactorsthathavecontributedtothegrowinguseofFinTechgenerallyhavealsospurredadoptionofAIandmachinelearninginfinancialservices.sun三maC.SelectedFourUseCasesCustomer-focusedUsesCreditScoringApplicationsUseforPrici
20、ng,MarketingandManagingInsurancePoliciesClient-facingChatbotsz.Oerations-focusedUsesCapitalOptimisationUseCaseModelRiskManagementandStressTestingMarketImpactAnalysisC.SelectedUseCasesZTradingAndPortfolioManagementAIMLinTradingExecutionScopefortheUseofAIMLinPortfolioManagement3AIMLInRegulatoryComplia
21、nceAndSupervisionRegTech:ApplicationsbyFinancialInstitutionsforRegulatoryCompliance.UsesforMacroprudentialSurveillanceandDataQualityAssurance.SupTech:UsesandPotentialUsesbyCentralBanksandPrudentialAuthorities.2S-84UsesbyMarketRegulatorsforSurveillanceandFraudDetection.购课后务必加唯一售后微信;xuebajun888suya三!D
22、.Micro-financialAnalysisPossibleEffectsOfAIMLonFinancialMarketsImprovementCollectandanalyseinfoonagreaterscale.1.owertradingcosts.ConcernsSimilarAIMLprogrammes=correlatedrisks.26-84Couldbeusedbyinsiderstomanipulatemarket.MwrrmaD.Micro-financialAnalysisPossibleEffectsofAIMLonFinancialInstitutionsBene
23、fitingSystem-wideStabilityIncreaserevenuesandreducecosts.Earlierandmoreaccurateestimationofrisks.Collaborationbetweenfinancialinstitutionsandotherindustries.DrawbacksMissnewtypesofrisks.Blackboxesindecision-making.Forintermediaries:alackofclarityaroundresponsibility.Third-partydependencies.uy*at*maD
24、.Micro-financialAnalysisPossibleEffectsofAIMLonConsumersandInvestorsANumberofBenefitsConsumers:Iowerfeesandborrowingcosts.Wideraccesstofinancialservices.Facilitatemorecustomisedfinancialservices.ConcernsDataprivacyandinformationsecurity.Avoidingdiscrimination.28-84uy * a !E.Macro-financialAnalysisMa
25、rketConcentrationandSystemicImportanceofInstitutionsAffectthedegreeofconcentrationAsmallnumberofadvancedthird-partyproviders.Technologiesaffordableonlytolargecompanies.Reducethesystemicimportanceoflargeuniversalbanks.Universalbanksvulnerabilitytosystemicshocksmaygrow.NetworksandInterconnectednessGre
26、aterinterconnectednessinthefinancialsystem.Helptosharerisks.29-84Butalsospreadtheextremeshocks.乌皿rar!E.Macro-financialAnalysisPotentialMarketVulnerabilitiesGreaterdiversityinmarketmovements.1.esspredictabletradingalgorithms.Increaseliquidity.Moreeffectivehedgingstrategies.Reducerelianceonbankloans.M
27、inimisecapital=morerisk.OtherImplicationsOfAIMLApplicationsReducethedegreeofmoralhazardandadverseselection.Higherpremiumsforriskierconsumers.Entailbiases.ForRegTechandSupTech,Game,regulatoryrules.31-845.ClimateChange-PhysicalRiskandEquityM政*舞mA.IntroductionTheprojectedincreaseinthefrequencyandseveri
28、tyofdisastersduetoclimatechangeisapotentialthreattofinancialstability.Extremeweathereventsorclimatichazardscanturnintodisastersthatcauselossoflifeandcapitalstock,aswellasdisruptionstoeconomicactivity.thereactionsofeconomicagents(includinggovernments)tothesechanges,inparticularthroughadaptation32-84t
29、ransitionriskresultingfrompolicy,technology,legalandmarketchangesthatoccurduringthemovetoalow-carboneconomy乌皿rar!B.PhysicalRiskandFinancialStabilityFromtheperspectiveofphysicalrisk,climatechangecanaffectfinancialstabilitythroughtwomainchannels.3FirSLcurrentclimaticdisastersaffectcredit,underwriting,
30、market,operational,andliquidityrisks.WSecond,theshiftsinexpectationsandattentionaboutfutureclimaticdisasterscanaffectassetvaluestoday.C.PhysicalRiskandEquityPricesTheimpactonequitypricescaninformfinancialstabilityassessmentsforatleasttworeasons.kwidespreaddestructionoffirmsassetsandproductivecapacit
31、yoradropindemandfortheirproducts.thereactionofthestockpricesoffinancialinstitutionsprovidesasummarymeasureoftheextenttowhichtheseinstitutionsareaffectedbydisasters.34-84anopportunitytoincreaseunderwritingvolumesandpremiumsforinsurancecompanies.M政*舞fC.PhysicalRiskandEquityPricesEmpiricalevidence:3ona
32、verage,therehasbeenonlyamodestresponseofstockpricestolargeclimaticdisasters.Results,however,varyconsiderablyacrossdisasters.HurricaneKatrina(2005)v.s.the2011floodsinThailand.SAmongfinancialsectorfirms,largedisastershaveastatisticallysignificanteffectonthereturnsofnon-lifeinsurersinadvancedeconomies,
33、butnosignificantreactioninemergingmarketanddevelopingeconomies.apossiblereason:alargeshareofinsuranceinemergingmarketanddevelopingeconomiesisprovidedbysubsidiariesofinsurerslistedabroad.购课后务必加唯一售后微信;XUebajUn888sD.InsurancePenetrationandSovereignFinancialStrengthRegardlessofthesizeoffutureclimaticsho
34、cks,insurancecoverageandsovereignfinancialstrengthwillbekeyfactorsinmaintainingfinancialstability.InsurancepenetrationRisk-sharingmechanisms.E.g.insurance,weatherderivatives,andcatastrophebonds.SovereignfinancialstrengthIncreasetheabilityofthegovernmenttorespondtodisastersthroughfinancialreliefandre
35、constructionefforts.Increaseitscapacitytooffersomeformsofexplicitinsuranceprograms.37-846.TheGreenSwanM犯医嘛！A.BlackSwanandGreenSwanBlackswaneventshavethreecharacteristics3theyareunexpectedandrare,therebylyingoutsidetherealmofregularexpectations;3theirimpactsarewide-rangingorextreme;?theyCanonlybeexpl
36、ainedafterthefact.OthercharacteristicsofBlackswaneventsTakemanyshapesrfromaterroristattacktoadisruptivetechnologyoranaturalcatastrophe.Theseeventstypicallyfitfattailedprobabilitydistributions,cannotbepredictedbyrelyingonbackward-lookingprobabilisticapproachesassumingnormaldistributions(eg.value-at-r
37、iskmodels).Suchanepistemologicalpositioncanprovidesomeformofhedgingagainstextremerisks(turningblackswansintogreyones)butnotmakethemdisappear.38-84c_A.BlackSwanandGreenSwanGreenswans,orclimateblackswansr,presentmanyfeaturesoftypicalblackswans.Climate-relatedriskstypicallyfitfat-taileddistributions.th
38、eirchancesofoccurrencearenotreflectedinpastdata,andthepossibilityofextremevaluescannotberuledout.assessingclimate-relatedrisksrequiresanepistemologicalbreak*withregardtoriskmanagementA.BlackSwanandGreenSwanGreenswansaredifferentfromblackswansinthreeregards.3Firstalthoughtheimpactsofclimatechangeareh
39、ighlyuncertain,thereisahighdegreeofcertaintythatsomecombinationofphysicalandtransitionriskswillmaterializeinthefuture.aSecond,climatecatastrophesareevenmoreseriousthanmostsystemicfinancialcrises.40-84?Third,thecomplexityrelatedtoclimatechangeisofahigherorderthanforblackswans.M政*舞fB.MonetaryInstabili
40、tyClimate-relatedshocksarelikelytoaffectmonetarypolicythroughsupplysideanddemand-sideshocks,andtherebyaffectcentralbankspricestabilitymandate.Supply-sideshockspressuresonthesupplyofagriculturalproductsandenergyareparticularlypronetosharppriceadjustmentsandincreasedvolatility.reduceeconomiesproductiv
41、ecapacity.Demand-sideshocksreducinghouseholdwealthandconsumption.Climatemitigationpoliciescouldalsoaffectinvestmentinsomesectors.4184Insum,theimpactsofclimatechangeoninflationareunclearpartlybecauseclimatesupplyanddemandshocksmaypullinflationandoutputinoppositedirections,andgenerateatrade-offforcent
42、ralbanksbetweenstabilisinginflationandstabilisingoutputfluctuations.乌皿rar!B.MonetaryInstabilityTraditionally,ifthereisapresumptionthattheimpactistemporary,theresponsecanbetowaitandseeasitdoesnotaffectpricesandexpectationsonapermanentbasis.However,climate-relatedshockhasmorelastingeffects,therecouldb
43、emotivestoconsiderapolicyreaction,however,thereareatleastthreechallenges.3Climatechangeisexpectedtomaintainitstrectoryforlongperiodsoftime(cyclicalinstrumentscanleadtoStagflationarysupplyshocksthatmonetarypolicymaybeunabletofullyreverse).占Climatechangeisaglobalproblemthatdemandsaglobalsolution,where
44、asmonetarypolicyseems,currently,tobedifficulttocoordinatebetweencountries.?Evenifcentralbankswereabletore-establishpricestabilityafteraclimate-relatedinflationaryshock,thequestionremainswhethertheywouldbeabletotakepre-emptivemeasurestohedgeexanteagainstfat-tailclimaterisks.C.FinancialInstabilityClim
45、ate-relatedrisksareasourceoffinancialrisk.Therearetwomainchannelsthroughwhichclimatechangecanaffectfinancialstability.Physicalrisks:arethoserisksthatarisefromtheinteractionofclimate-relatedhazards.withthevulnerabilityofexposuretohumanandnaturalsystems*.Transitionrisk:areassociatedwiththeuncertainfin
46、ancialimpactsthatcouldresultfromarapidlow-carbontransition,includingpolicychanges,reputationalimpacts,technologicalbreakthroughsorlimitations,andshiftsinmarketpreferencesandsocialnorms.购课后务必加唯一售后微信；xuebajun888sC.FinancialInstabilityPhysicalrisks3Thedestructionofcapitalandthedeclineinprofitabilityofexposedfirmscouldinduceareallocationofhouseholdfinancialwealth.占affecttheexpectationoffuturelosses,whichinturnmayaffectcurrentriskpreferences.Znon-insuredlossescanthreatenthesolvencyofhouseholds,businessesandgovernments,andth
