FRM二级基础段培训课件：案例（打印版）.docx

7CurrentIssues':2021.,8 3lt 31 FrameworkCurrent Issues1.BeyondLIBOR2.ReplacingLlBOR3.MachineLearning4.AIandMLinfinancialservices5.ClimateChange-PhysicalRiskandEquity6.TheGreenSwan7.WhenSellingBecomesViral8.MarketsintheTimeofCOVID-199.FinancialCrimeinTimesofCOVID-1910.CyberRiskandtheUSFinancialSystem专业事新tt1.BeyondLIBOR4-84Muy色嘛！A.AnIdealReferenceRateAnIdealReferenceRateNotsusceptibletomanipulation.Derivedfromactualtransactionsinliquidmarkets.Serveasabenchmarkforbothtermlendingandfunding.5-84MwrrmaB.ProblemswithLIBoRIssuesthatledtothereplacementofLIBOR3Constructedfromasurveyofbanksreporting.ThiscreatedamplescopeforpanelbankstomanipulateLIBORsubmissions.Sparseactivityininterbankdepositmarkets.1.Thedispersionofindividualbankcreditrisk.1.IBORaimstocapturecommonbankrisk.iRegulatoryandthemarketwanttoreducecounterpartycreditriskininterbankexposures,bankshavealsotiltedtheirfundingmixtowardslessriskysourcesofwholesalefunding.uy*at*maD.RisksofRFRsintheRepoMarketRisksofRFRSintheRepoMarket0/Nreporatecannotreflectbanksmarginalfundingcosts.Banks'asset-liabilitymanagementischallenging.Whenunderstress,reporatescanmoveintheoppositewayofunsecuredrates.Theforcesdrivingunsecured0/Nrates(includingcreditrisk)pulledtheserateshigherastheunsecuredinterbankmarketsfroze.Atthesametime,theforcesdrivingsecured0/NrateswerepullingthemlowerowingtoacollateralshortageandflighttosafetyForlongertenors,termratesbasedonnewRFRsarelikelytodeviatepersistentlyfromtheirLIBORcounterpartseveninnormaltimes.TransitionIssues:themigrationoflegacyLIBOR-Iinkedexposurestothenewbenchmarksafter2021.M亚倒舞m2.ReplacingLIBOR10-84Muy 色嘛 ！A.TheFactsPublicationofUBOR-theLondonInterbankOfferedRate-willlikelyceaseattheendof2021.after202LtheFCAwouldnolongercompelreluctantbankstorespondtotheLIBORsurvey.11 84then,theFCAcoulddeclareLIBORratesUnrepresentative*offinancialrealityanditwillvanish.MwrrmaB.RisksWhenLIBOREndsThesystemicriskposedbythecessationofLIBOR.3ThefirstarisesfromthelegacycontractsreferencingLIBOR.WhenpublicationofLIBORstops(orisexpectedtostop),contractsthatlackadequatefallbackprovisionsmayplungeinvalue.Totheextentthatlarge,leveragedintermediariesareexposed,theresultinglossescouldimpairtheircapital,leavingusinthedarkaboutwhichinstitutionsarehealthyandwhicharenot.3Thesecondissueiswhether,whenLIBORceases,therewillbeanadequatesubstitutethatallowsintermediariesbothtofundthemselvesinaliquidmarketandtoprovidecredit.C.CurrentProblemsWheredothingsstandnow?First,thereremainplentyofdollarLIBORlegacycontractsoutstanding,thelatestavailabledataarenearlythreeyearsoldrandtheindustrycontinuestocreateLIBOR-Iinkedcontracts,westronglysuspectthatthesenumbersunderstatethechallenge.Second,thereisnocentralrepositoryprovidinginformationaboutwhat,ifany,fallbacklanguageexistsinthesecontracts.WithoutLIBOR,whathappens?Inadequatefallbacklanguagefostersuncertaintyaboutthevalueoftheassets,andcouldtriggerawaveoflawsuits.Third,whiletheprocessofcreatingasatisfactoryreplacementfordollarLIBORiswelladvanced,itisfarfromcomplete.Thereislittletimefortesting.D.TheGovernment'sRoleintheTransitionFourveryimportantrolesforgovernmentofficials.3Thegreaterthecertaintyabouttheenddate,thefastertheLIBORtransitionWillbe.AuthoritiescanfurtherintensifywarningsabouttheimprudenceofrelyingonLIBOR.3Supervisorsmustensurethatallsystemicallyimportantbanksandfinancialmarketutilitiesarefullyprepared.?Thereremainsaremarkableabsenceofup-to-datedataonLIBOR-linkedinstruments.regulatorsshouldgatherandpublishdatashowingtheevolutionofLIBORe×posure(includinginformationonfallbacklanguage)atleastquarterly.GBecausetheLlBORtransitionwilldirectlyaffectmanyhouseholdsandsmallbusinesseswithUBOR-Iinkeddebtitisimportantforauthorities(includingtheConsumerFinancialProtectionBureau)topromotepublicawarenessofthechangesunderway.14-84M皿施舞na3.MachineLearningA.IntroductionTheDrivingForcesMoredetailsofreporting.High-frequency,unstructuredlowqualityconsumerdata.BigdataPredictionversusExplanationStatisticalmethodsaregoodforexplanation.MLisgoodforprediction.16-84uy * a ! B. Background to MLSupervised LearningDependent variable y is known.Unsupervised LearningDependent variable y is lacking.17-84su n * maB.BackgroundtoMLMachineLearningMethodsRegressionAsupervisedMLproblem.Topredictacontinuousdependentvariabley.Afactorisaddedtopenalisecomplexityinthemodel.ClassificationAdiscreteproblem.ClusteringAnunsupervisedMLproblem.B.BackgroundtoMLOverfittingFitthedatasampleverywell.Performpoorlywhentestedout-of-sample.Havingtoomanyparameters.WaystoDealwithOverfittingBoosting:overweightscarcerobservationsinatrainingdataset.Bagging:amodelisrunthousandsoftimes,eachonadifferentsubsampleofthedataset.Averagealltheruns.Randomforest:amodelconsistingofmanydifferentdecisiontrees.19-84Ensemble:averagetheresultingmodelwithmanyotherMLmodels.uy'a三!C.ThreeUseCasesCreditRiskandRevenueModelingDifficultiesinUsageModelscanbesensitivetooverfittingthedata.Hardforanyhumantounderstand.&FraudDetectionofCreditCardFraudClearhistoricaldatawithrelevantfraudlabelstotrainclassification.ZSurveillanceofConductandMarketAbuseinTradingApplication:Monitorthebehaviouroftraders.ChallengestoApplyingMLNolabeleddatatotrainalgorithms.Blackboxes:hardtoexplaintoacomplianceofficer.Countermeasure:incorporateshumandecisions.BarriertotheImplementationofAutomatedSurveillanceInformationfromdifferentsourcescouldbemutuallyincompatible.2684乌皿*tma4.AIandMLinFinancialServicesA.BackgroundanddefinitionsAschematicviewofAI,machinelearningandbigdataanalytics22-84Svpph facfo51 Deuund factonuy'a三!B.DriversAvarietyoffactorsthathavecontributedtothegrowinguseofFinTechgenerallyhavealsospurredadoptionofAIandmachinelearninginfinancialservices.su'n三'maC.SelectedFourUseCasesCustomer-focusedUsesCreditScoringApplicationsUseforPricing,MarketingandManagingInsurancePoliciesClient-facingChatbotsz.Oerations-focusedUsesCapitalOptimisationUseCaseModelRiskManagementandStressTestingMarketImpactAnalysisC.SelectedUseCasesZTradingAndPortfolioManagementAIMLinTradingExecutionScopefortheUseofAIMLinPortfolioManagement3AIMLInRegulatoryComplianceAndSupervisionRegTech:ApplicationsbyFinancialInstitutionsforRegulatoryCompliance.UsesforMacroprudentialSurveillanceandDataQualityAssurance.SupTech:UsesandPotentialUsesbyCentralBanksandPrudentialAuthorities.2S-84UsesbyMarketRegulatorsforSurveillanceandFraudDetection.购课后务必加唯一售后微信;xuebajun888suy'a三!D.Micro-financialAnalysisPossibleEffectsOfAIMLonFinancialMarketsImprovementCollectandanalyseinfoonagreaterscale.1.owertradingcosts.ConcernsSimilarAIMLprogrammes=>correlatedrisks.26-84Couldbeusedbyinsiderstomanipulatemarket.MwrrmaD.Micro-financialAnalysisPossibleEffectsofAIMLonFinancialInstitutionsBenefitingSystem-wideStabilityIncreaserevenuesandreducecosts.Earlierandmoreaccurateestimationofrisks.Collaborationbetweenfinancialinstitutionsandotherindustries.DrawbacksMissnewtypesofrisks.Blackboxesindecision-making.Forintermediaries:alackofclarityaroundresponsibility.Third-partydependencies.uy*at*maD.Micro-financialAnalysisPossibleEffectsofAIMLonConsumersandInvestorsANumberofBenefitsConsumers:Iowerfeesandborrowingcosts.Wideraccesstofinancialservices.Facilitatemorecustomisedfinancialservices.ConcernsDataprivacyandinformationsecurity.Avoidingdiscrimination.28-84uy * a !E.Macro-financialAnalysisMarketConcentrationandSystemicImportanceofInstitutionsAffectthedegreeofconcentrationAsmallnumberofadvancedthird-partyproviders.Technologiesaffordableonlytolargecompanies.Reducethesystemicimportanceoflargeuniversalbanks.Universalbanks'vulnerabilitytosystemicshocksmaygrow.NetworksandInterconnectednessGreaterinterconnectednessinthefinancialsystem.Helptosharerisks.29-84Butalsospreadtheextremeshocks.乌皿rar!E.Macro-financialAnalysisPotentialMarketVulnerabilitiesGreaterdiversityinmarketmovements.1.esspredictabletradingalgorithms.Increaseliquidity.Moreeffectivehedgingstrategies.Reducerelianceonbankloans.Minimisecapital=>morerisk.OtherImplicationsOfAIMLApplicationsReducethedegreeofmoralhazardandadverseselection.Higherpremiumsforriskierconsumers.Entailbiases.ForRegTechandSupTech,Game,regulatoryrules.31-845.ClimateChange-PhysicalRiskandEquityM政*舞m«A.IntroductionTheprojectedincreaseinthefrequencyandseverityofdisastersduetoclimatechangeisapotentialthreattofinancialstability.Extremeweathereventsorclimatichazardscanturnintodisastersthatcauselossoflifeandcapitalstock,aswellasdisruptionstoeconomicactivity.thereactionsofeconomicagents(includinggovernments)tothesechanges,inparticularthroughadaptation32-84transitionriskresultingfrompolicy,technology,legalandmarketchangesthatoccurduringthemovetoalow-carboneconomy乌皿rar!B.PhysicalRiskandFinancialStabilityFromtheperspectiveofphysicalrisk,climatechangecanaffectfinancialstabilitythroughtwomainchannels.3FirSLcurrentclimaticdisastersaffectcredit,underwriting,market,operational,andliquidityrisks.WSecond,theshiftsinexpectationsandattentionaboutfutureclimaticdisasterscanaffectassetvaluestoday.C.PhysicalRiskandEquityPricesTheimpactonequitypricescaninformfinancialstabilityassessmentsforatleasttworeasons.kwidespreaddestructionoffirms'assetsandproductivecapacityoradropindemandfortheirproducts.thereactionofthestockpricesoffinancialinstitutionsprovidesasummarymeasureoftheextenttowhichtheseinstitutionsareaffectedbydisasters.34-84anopportunitytoincreaseunderwritingvolumesandpremiumsforinsurancecompanies.M政*舞f«C.PhysicalRiskandEquityPricesEmpiricalevidence:3onaverage,therehasbeenonlyamodestresponseofstockpricestolargeclimaticdisasters.Results,however,varyconsiderablyacrossdisasters.HurricaneKatrina(2005)v.s.the2011floodsinThailand.SAmongfinancialsectorfirms,largedisastershaveastatisticallysignificanteffectonthereturnsofnon-lifeinsurersinadvancedeconomies,butnosignificantreactioninemergingmarketanddevelopingeconomies.apossiblereason:alargeshareofinsuranceinemergingmarketanddevelopingeconomiesisprovidedbysubsidiariesofinsurerslistedabroad.购课后务必加唯一售后微信;XUebajUn888sD.InsurancePenetrationandSovereignFinancialStrengthRegardlessofthesizeoffutureclimaticshocks,insurancecoverageandsovereignfinancialstrengthwillbekeyfactorsinmaintainingfinancialstability.InsurancepenetrationRisk-sharingmechanisms.E.g.insurance,weatherderivatives,andcatastrophebonds.SovereignfinancialstrengthIncreasetheabilityofthegovernmenttorespondtodisastersthroughfinancialreliefandreconstructionefforts.Increaseitscapacitytooffersomeformsofexplicitinsuranceprograms.37-846.TheGreenSwanM犯医嘛！A.BlackSwanandGreenSwanBlackswaneventshavethreecharacteristics3theyareunexpectedandrare,therebylyingoutsidetherealmofregularexpectations;3theirimpactsarewide-rangingorextreme;?theyCanonlybeexplainedafterthefact.OthercharacteristicsofBlackswaneventsTakemanyshapesrfromaterroristattacktoadisruptivetechnologyoranaturalcatastrophe.Theseeventstypicallyfitfattailedprobabilitydistributions,cannotbepredictedbyrelyingonbackward-lookingprobabilisticapproachesassumingnormaldistributions(eg.value-at-riskmodels).Suchanepistemologicalpositioncanprovidesomeformofhedgingagainstextremerisks(turningblackswansinto"grey"ones)butnotmakethemdisappear.38-84c_A.BlackSwanandGreenSwanGreenswans,or'climateblackswansr,presentmanyfeaturesoftypicalblackswans.Climate-relatedriskstypicallyfitfat-taileddistributions.theirchancesofoccurrencearenotreflectedinpastdata,andthepossibilityofextremevaluescannotberuledout.assessingclimate-relatedrisksrequiresanepistemologicalbreak*withregardtoriskmanagementA.BlackSwanandGreenSwanGreenswansaredifferentfromblackswansinthreeregards.3Firstalthoughtheimpactsofclimatechangearehighlyuncertain,thereisahighdegreeofcertaintythatsomecombinationofphysicalandtransitionriskswillmaterializeinthefuture.aSecond,climatecatastrophesareevenmoreseriousthanmostsystemicfinancialcrises.40-84?Third,thecomplexityrelatedtoclimatechangeisofahigherorderthanforblackswans.M政*舞f«B.MonetaryInstabilityClimate-relatedshocksarelikelytoaffectmonetarypolicythroughsupplysideanddemand-sideshocks,andtherebyaffectcentralbanks'pricestabilitymandate.Supply-sideshockspressuresonthesupplyofagriculturalproductsandenergyareparticularlypronetosharppriceadjustmentsandincreasedvolatility.reduceeconomies'productivecapacity.Demand-sideshocksreducinghouseholdwealthandconsumption.Climatemitigationpoliciescouldalsoaffectinvestmentinsomesectors.4184Insum,theimpactsofclimatechangeoninflationareunclearpartlybecauseclimatesupplyanddemandshocksmaypullinflationandoutputinoppositedirections,andgenerateatrade-offforcentralbanksbetweenstabilisinginflationandstabilisingoutputfluctuations.乌皿rar!B.MonetaryInstabilityTraditionally,ifthereisapresumptionthattheimpactistemporary,theresponsecanbetowaitandseeasitdoesnotaffectpricesandexpectationsonapermanentbasis.However,climate-relatedshockhasmorelastingeffects,therecouldbemotivestoconsiderapolicyreaction,however,thereareatleastthreechallenges.3Climatechangeisexpectedtomaintainitstrectoryforlongperiodsoftime(cyclicalinstrumentscanleadtoStagflationarysupplyshocksthatmonetarypolicymaybeunabletofullyreverse).占Climatechangeisaglobalproblemthatdemandsaglobalsolution,whereasmonetarypolicyseems,currently,tobedifficulttocoordinatebetweencountries.?Evenifcentralbankswereabletore-establishpricestabilityafteraclimate-relatedinflationaryshock,thequestionremainswhethertheywouldbeabletotakepre-emptivemeasurestohedgeexanteagainstfat-tailclimaterisks.C.FinancialInstabilityClimate-relatedrisksareasourceoffinancialrisk.Therearetwomainchannelsthroughwhichclimatechangecanaffectfinancialstability.Physicalrisks:are"thoserisksthatarisefromtheinteractionofclimate-relatedhazards.withthevulnerabilityofexposuretohumanandnaturalsystems'*.Transitionrisk:areassociatedwiththeuncertainfinancialimpactsthatcouldresultfromarapidlow-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