Post on 04-Apr-2018
7/30/2019 SAFE an Early Warning System for Systemic Banking Risk
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SAFE:AnEarlyWarningSystemforSystemicBankingRiskMikhailV.Oet1,RyanEiben2,TimothyBianco3,DieterGramlich4,StephenJ.Ong5,andJingWang6*
Originalversion:December2009
Thisversion:August242011
Abstract
Fromthefinancialsupervisorspointofview,anearlywarningsysteminvolvesanexanteapproachtoregulation,
targetingtopredictandpreventcrises.AnefficientEWSallowstimelyexantepolicyactionandcanreducetheneedforexpostregulation. Thispaperbuildsonexistingmicroprudentialandmacroprudentialearlywarningsystems(EWSs)to
proposeahybridclassofmodelsforsystemicriskincorporatingthestructuralcharacteristicsofthefinancialsystemand
feedbackamplificationmechanism. Themodelsexplainfinancialstressusingdatafromfivelargestbankholding
companies,regressinginstitutionalimbalancesusinganoptimallagmethod. Zscoresofinstitutionaldataarejustifiedas
explanatoryimbalances. Themodelsutilizebothpublicandproprietarysupervisorydata.TheSAFEEWSmonitorsmicro
prudentialinformationfromsystemicallyimportantinstitutionstoanticipatebuildupofmacroeconomicstressesinthe
financialmarketsatlarge.Tothesupervisor,SAFEpresentsatoolkitofpossibleinstitutionalsupervisoryactionsthatcan
beusedtodiffusethebuildupofsystemicstressinthefinancialmarkets.Ahazardinherentforallexantemodelsis
thatthemodeluncertaintymayleadtowrongpolicychoices.Tomitigatethisrisk,SAFEdevelopstwomodeling
perspectives:asetofmediumterm(sixquarter)forecastingspecificationstoallowthepolicymakerssufficienttimefor
exantepolicyaction,andasetofshortterm(twoquarter)forecastingspecificationsforverificationandadjustmentofsupervisoryactions.IndividualfinancialinstitutionsmayutilizepublicversionofSAFEEWStoenhancesystemicrisk
stresstestingandscenarioanalysis.ThepapershowseconometricresultsandrobustnesssupportfortheSAFEsetof
models.Discussionofresultsaddressesusabilityandtestsofusefulnessofsupervisorydata. Inaddition,thepaper
investigatesandsuggestslevelsforactionthresholdsappropriateforthisEWS.
Keywords: Systemicrisk,earlywarningsystem,financialstressindex,microprudential,macroprudential,structural
characteristics,feedback,liquidityamplification,contagion.
JELclassification: G01,G21,G28,C25,C53
1 Economist,FederalReserveBankofCleveland. Email:mikhail.oet@clev.frb.org
2 Ph.D.candidateinEconomics,IndianaUniversityBloomington(formerly,EconomicConsultant,FederalReserveBankofCleveland. Email:reiben@indiana.edu)
3 EconomicAnalyst,FederalReserveBankofCleveland. Email:timothy.bianco@clev.frb.org
4
ProfessorofBanking,BadenWuerttembergCooperativeStateUniversity. Email:gramlich@dhbwheidenheim.de5 VicePresident,RiskSupervisionandPolicyDevelopment,FederalReserveBankofCleveland. Email:stephen.ong@clev.frb.org
6 DBAcandidateinFinance,ClevelandStateUniversity,EconomicConsultant,FederalReserveBankofCleveland. Email:jing.wang@clev.frb.org
* TheviewsexpressedinthispaperarethoseoftheauthorsandnotnecessarilythoseoftheFederalReserveBankofCleveland,theBoardofGovernors,orthe
FederalReserveSystem.
TheauthorswouldliketothankJosephHaubrich,BenCraig,andMarkSchweitzerforconstructiveguidance. Wearealsogratefultothefollowingpeoplewhohave
providedvaluablecomments:MarkSniderman,JamesThomson,TobiasAdrian,ViralAcharya,JohnSchindler,JonFrye,EdPelz,CraigMarchbanks,andAdrianDSilva.
Wewouldalsoliketoacknowledgeconstructivecommentsbytheparticipantsof2010DeutscheBundesbank/TechnischeUniversitatDresden,BeyondtheFinancial
Crisisconference,particularlyAndreasJobstandMarcellaLucchetta,2010CommitteeonFinancialStructureandRegulation,particularlyGustavoSuarezandWilliam
Keeton,2010FederalRegulatoryInteragencyRiskQuantificationForum,particularlyStevenBurton,WilliamLang,EvanSekeris,ChristopherHenderson,andScott
Chastain;feedbackbytheResearchseminarparticipantsattheFederalReserveBankofCleveland,aswellasbytheparticipantsoftheRiskCentralBankingNewYork
seminaronManagingSystemicRiskinFinancialInstitutions,FederalReserveBankofChicago2009CapitalMarketsConference,andtheNBERFRBCleveland
ResearchConferenceonQuantifyingSystemicRisk. Inaddition,wewouldliketothankthefollowingpeoplefordata,researchassistance,andhelpfulinsights:Chris
Lentz,TinaRicciardi,JuanCalzada,JasonAshenfelter,JuliePowell,andKentCherny.
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Contents(1)Introduction................................................................................................................................................................... 3
(2)EWSelements................................................................................................................................................................ 5
(2.1)Measureforfinancialstressdependentvariabledata...................................................................................... 6
(2.2)Driversofriskexplanatoryvariablesdata.......................................................................................................... 7
(3)Riskmodelandresults................................................................................................................................................... 7
(3.1)EWSmodels............................................................................................................................................................ 7
(3.2)Criteriaforvariableandlagselection..................................................................................................................... 9
(3.3)EWSmodelspecificationsandresults.................................................................................................................. 11
(4)Discussionandimplications......................................................................................................................................... 13
(4.1)PerformancesupervisoryEWSvs.publicEWS................................................................................................... 13
(4.2)Applicationstosupervisorypolicy........................................................................................................................ 14
(5)Conclusionsandfuturework....................................................................................................................................... 18
(6)References................................................................................................................................................................... 20
(7)TablesandFigures....................................................................................................................................................... 23
(8)Appendix...................................................................................................................................................................... 37
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reason,macroprudentialEWSmodelscannotprovideadistresswarningfromindividualinstitutionsthataresystemically
importantorfromthesystemsorganizationalpattern. Theauthorsarguethatthearchitectureofthesystemicrisk
EWScanovercomethefundamentallimitationsoftraditionalmodels,bothmicroandmacroandshouldcombine
boththeseclassesofexistingsupervisorymodels. Recentsystemicfinancialcrisesshowthatpropagationmechanisms
includestructuralandfeedbackfeatures. Thus,theproposedsupervisoryEWSforsystemicriskincorporatesboth
microprudentialandmacroprudentialperspectives,aswellasthestructuralcharacteristicsofthefinancialsystemand
feedbackamplificationmechanism.
ThedependentvariablefortheproposedSAFEEWS15
isdevelopedseparatelyasafinancialstressindex.16
ThemodelsintheSAFEEWSexplainstressindexusingdatafromfivelargestbankholdingcompanies,regressinginstitutional
imbalancesusinganoptimallagmethod. Zscoresofinstitutionaldataarejustifiedasexplanatoryimbalances. The
modelsutilizebothpublicandproprietarysupervisorydata.ThepaperprovidessomediscussionofhowtousetheEWS
andteststoseeifsupervisorydatahelps. Inaddition,thepaperinvestigatesandsuggestslevelsforactionthresholds
appropriateforthisEWS.
Tosimulatethemodels,weselectnotonlytheexplanatoryvariablesbutalsotheoptimallags,buildingonand
extendingprecedentideasfromliteraturewithourowninnovations. Mostofthelagselectionresearchemphasizesthe
importantcriteriaofgoodnessoffit,variablesstatisticssignificance(tstatistics),causality,etc. HanssensandLiu(1983)
presentmethodsforthepreliminaryspecificationofdistributedlagsinstructuralmodelsintheabsenceoftheoryor
information. Davies(1977)selectsoptimallagsbyfirstincludingallpossiblevariablelagschosenbasedontheoretical
considerations. DavisfurthernarrowsthelagselectionbybestresultsintermsoftstatisticsandR2. HolmesandHutton
(1992)andLeeandYang(2006)introducetechniquesofselectingoptimallagsbyconsideringcausality. Bahmani
OskooeeandBrooks(2003)demonstratethatwhengoodnessoffitisusedasacriterionforthechoiceoflaglengthand
thecointegratingvector,thesignandsizeoftheestimatedcoefficientsareinlinewiththeoreticalexpectations.
Jacobson(1995)slagstructureinVARmodelsisbasedontestsonresidualautocorrelationandWinker(2000)uses
informationcriterialikeAICandBICascriteria. MurrayandPapell(2001)chosethefollowinglaglengthkj selection
methodforsingleequationmodels.Theystartwithanupperboundkmaxonk. Ifthetstatisticonthecoefficientofthe
lastlagissignificantat10%valueoftheasymptoticdistribution(1.645),thenkmaxk. Ifitisnotsignificant,thenkisloweredbyone. Thisprocedureisrepeateduntilthelastlagbecomessignificant.
Recentresearchfocusesonautomationproceduresforoptimallagselection. DueckandScheuer(1990)applya
heuristicglobaloptimizationalgorithminthecontextofanautomaticselectionprocedureforthemultivariatelagstructureofaVARmodel. Winker(1995)andWinker(2000)developanautomaticlagselectionmethodasadiscrete
optimizationproblem. MaringerandWinker(2004)proposeamethodforautomaticidentificationofthedynamicpart
ofVECmodelsforthemodelingofeconomicandfinancialtimeseriesandaddressthenonstationaryissues. They
employamodifiedinformationcriteriondiscussedbyChaoandPhillips(1999)forthecaseofpartiallynonstationary
VARmodels. Inaddition,theyallowforholes"inthelagstructures,i.e.lagstructuresarenotconstrainedtosequences
oflagsuptolagk,butmightconsist,e.g.,ofthefirstandfourthlagonlyinanapplicationtoquarterlydata. Usingthis
approach,differentlagstructurescanbeusedfordifferentvariablesandindifferentequationsofthesystem. Borbly
andMeier(2003)arguethatestimatedforecastintervalsshouldaccountfortheuncertaintyarisingfromselectingthe
specificationofanempiricalforecastingmodelfromthesampledata. Toallowthisuncertaintytobeconsidered
systematically,theyformalizeamodelselectionprocedurethatspecifiesthelagstructureofamodelandaccountsfor
aberrantobservations. Theprocedurecanbeusedtobootstrapthecompletemodelselectionprocesswhenestimating
forecastintervals. Sharp,JeffressandFinnigan(2003)introducetheLagoMatic,aSASprogramthateliminatesmany
ofthedifficultiesassociatedwithlagselectionformultiplepredictorvariablesinthefaceofuncertainty. Theprocedure
(1)lagsthepredictorvariablesoverauserdefinedrange;(2)runsregressionsforallpossiblelagpermutationsinthe
predictors;(3)allowsuserstorestrictresultsaccordingtouserdefinedselectioncriteria(e.g.,facevalidity,significant
ttests,R2,etc.). LagoMaticoutputgenerallycontainsalistofmodelsfromwhichtheresearchercanmakequick
comparisonsandchoices.
15 Collectively,thesetofmodelsisconsideredtoformasupervisoryEWSframeworkcalledSAFE,shortforSystemicAssessmentof
FinancialEnvironment.16 OetandEiben(2009,2011).
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TheSAFEEWSmodelsarebasedonhighqualitydata. Thedependentdataishighfrequencywithover5000daily
observations,leadingtotheconstructionofaquarterlydependentvariableseries. MostdatacomesfromBloomberg
andFRED,supplementedbytheBankofEnglanddata. Theexplanatorydatacomesfrom77quarterlypanelsfrom1st
quarter1991to3rdquarter2010. Welookattoptierhistoricaltop20bankholdingcompaniesandaggregatetopfiveof
theseasaproxyforagroupofsystemicallyimportantinstitutions. Wespecifythemodelusing50insamplequarters. A
largecomponentofdatacomesfrompublicsources:mostlyfromtheFederalReserveSystem(FRS)microdatafromthe
bankholdingcompaniesandtheirbanksubsidiaries. ThepublicFederalReservedataissupplementedbyadditional
goodqualitysourcesaccessibletothepublicsuchasS&PCaseShillerandMITRealEstateCenter(forthereturndata),
Compustatdatabases(forsomestructuraldata),andMoodysKMV(forsomeriskdata). Wealsoreplicatedatafromsomepubliclydisclosedmodelsanddatasets,forexampletheCoVaRmodel
17 andtheFlowofFundsdata. Inaddition,
foreachofthefourclassesofexplanatoryimbalances,wedependtosomeextentonprivatesupervisorydata. Our
privatedatasetconsistsofdatathatisnotdisclosedtothepublicortheresultsoftheproprietarymodelsdevelopedat
theFederalReserve. Examplesofprivatedatasetsarethecross countryexposuresdata,supervisorysurveillance
models,aswellasseveralsubmodelsdevelopedspecificallyforthisEWS.18Additionaldatadescriptionsareprovidedin
Box1intheAppendix. DatasourcesfortheexplanatoryvariablesareshowninTable15.19 Thedefinitions,theoretical
expectations,andGrangerCausalityofexplanatoryvariablesaresummarizedinTable16throughTable19.
Therestofthispaperisstructuredasfollows. Insection2wediscusstheconceptualorganizationofelementsofthe
systemicbankingriskEWS. Section3discussesmethodologyoftheSAFEEWSmodelsandtheresults. Section4
discussestheresearchimplicationsandcasestudiesbasedonourmodels. Section5concludesbydiscussionof
interpretationsanddirectionsforfurtherpursuit.
(2)EWSelementsTheelementsofanEWSaredefinedbyameasureforfinancialstress,driversofrisk,andariskmodeltocombineboth.
Asameasureofstress,SAFEEWSusesthefinancialmarketsstressseriesbyOetandEiben(2009,2011). Thispaper
contributesanewtypologyforthedriversofriskintheEWS. Riskmodelappliesaregressionapproachtoexplainthe
financialmarketsstressindexusingoptimallylaggedinstitutionaldata.
Ourbasicconjecturesarethatsystemicfinancialstresscanbeinducedbyassetimbalancesandstructuralweakness.
Wecanviewtheimbalancesasthedeviationbetweenassetexpectationsandtheirfundamentals. Thelargerthe
deviation,thegreateristhepotentialshock(seeFigure1below). Therefore,systemicfinancialstresscanbeanticipatedtoincreasewiththeriseinimbalances.
InsertFigure1abouthere
Thesecondconjectureisthatstructuralweaknessinthefinancialsystemataparticularpointintimeincreasessystemic
financialstress.Toillustrate,considerthefollowingfinancialsystemasanetworkoffinancialintermediaries. The
financialsystemischaracterizedbyanabsenceofconcentrationsandiswelldiversified. Individualinstitutionsare
interconnectedtomultiplecounterpartiesofvaryingsizesacrossthesystem. Thissystemsentitiesareofvaryingsizes,
somequitelargeandsignificant,someintermediate,andsomesmall. Afailureofoneinstitution,evenalargeone,will
resultinseveranceofseriesofconnectionsandlocalstress. Thisfailure,however,haslimitedpotentialtoinducesystemicstressbecauseofthegreatnumberofnetworkredundanciesandcounterpartiesthatcantakeupthisstress.
Suchasystemhasinherentlystrongbalancingability.
Bycomparison,consideranalternativefinancialsystem. Here,individualinstitutionsareconcentratedinparticular
markets,interconnectedinlimitedwaysviaasmallnumberofintermediaries. Inthissystem,certainfinancial
17 AdrianandBrunnermeier(2008,2009).
18 Theliquidityfeedbackmodelandthestresshaircutmodel.
19 Toconservespace,thetablesshowonlyinformationfortheexplanatoryvariablesthatultimatelyentertheSAFEmodel.
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intermediariesactashighlyinterconnectedgatekeepers,dominatingcertainmarkets(institutionalgroups). Market
accessacrossthissystemforlessconnectedinstitutionsisonlypossiblethroughthesefewsignificantgatekeeper
institutions. Likethepreviousexample,thissystemisalsocharacterizedbyinstitutionsofvaryingsize. However,herea
limitednumberofinstitutionsdominateparticularmarketsandsomeinterlinktheentirenetwork. Thenumberof
structuralredundanciesinthissystemissmallerandperhapsminimalinsomemarkets. Afailureorhighstress
experiencedbyoneofthemoredominantinstitutionsinaparticularmarketcannotbeaseasilysustainedandthereby
increasespotentialforsystemicrisk. Inthissystem,afailureofoneofthegatekeeperinstitutionsthatinterlinks
severalmarketscanbecatastrophicleadingtoacollapseofamarketorevenofthesystem. Therefore,thissystemis
lesstolerantofstressandfailureofoneparticularsignificantmarketplayer.
TheconjectureofimportanceofstructuralcharacteristicsissupportedbyempiricalevidencediscussedinGramlichand
Oet(2011). Briefly,loanexposuresofUSbanksformahighlyheterogeneousstructurewithdistincttiering. The
structuralheterogeneityisclearlyobservedbothloantypeexposures(Figure2)andfinancialmarketsconcentrationsof
topfiveUSBHCs(Figure3).
InsertFigure2abouthere
InsertFigure3abouthere
(2.1)MeasureforfinancialstressdependentvariabledataBuildingonresearchprecedentbyIllingandLiu,inOetandEiben(2009,2011)wedefinesystemicriskasthecondition
whenobservedmovementsoffinancialmarketcomponentsreachcertainthresholdsandpersist. There,wedevelopthe
financialstressindexintheUS(CFSI21)asacontinuousindexconstructedofdailypublicmarketdata. Tobecertainthat
aversatileindexofstresshasbeenidentified,theresearcheraimstorepresentaspectrumofmarketsfromwhichstress
mayoriginate. Aspreviousresearchinthisfieldattests,conditionsincredit,foreignexchange,equity,andinterbank
marketsprovidesubstantialcoverageofpotentialstressorigination. TheCFSIusesdynamicweightingmethodanddaily
datafromthefollowingelevencomponents:1)financialbeta,2)bankbondspread,3)interbankliquidityspread,4)
interbankcostofborrowing,5)weighteddollarcrashes,6)coveredinterestspread,7)corporatebondspread,8)liquidityspread,9)commercialpaper TBillspread,10)treasuryyieldcurvespread,11)stockmarketcrashes. Thedata
issourcedfromBloombergandtheFederalReserveFREDdatabase.22
Itisimportanttonotethatin2008,atthetimeofSAFEEWSdevelopment,noseriesonfinancialstressintheUnited
Statesexisted. Interestingly,asof2010,12alternativefinancialstressindexesareavailable. ThecomparisonofCFSI
withalternativefinancialstressseriesisdiscussedinOetandEiben(2009,2011).23
ThefinancialstressseriesintheSAFEEWSisconstructedseparatelyas ,aquarterlyfinancialmarketsstressindex. Mathematically,thefinancialstressseriesisconstructedas:
100 (1Here,eachofjcomponentsoftheindexisobservableinthemarketswithhigh(daily)frequencybutresultsinaquarterlyseriesoffinancialstress. isanobservedvalueofmarketcomponentjattimet. Thefunctionistheprobabilitydensityfunctionthattheobservedvaluewillliebetweenand . Theintegralexpression isthecumulativedistributionfunction(cdf)ofthecomponentgivenasasummationoftheprobabilitydensityfunctionfromthelowestobservedvalueinthedomainofmarketcomponentjto. Thecdfdescribestheprecedentsetbythecomponentsvalueandhowmuchthatprecedentmatters. Thetermistheweightgivento21 [FederalReserveBankof]ClevelandFinancialStressIndex
22 SeeOet,Eiben(2011)fordescriptionofspecificCSFIdatasources.
23 Oet,Eiben(2009)discussesinitialCFSIconstruction.Oet,Eiben,(2011)includescomparisonswithalternativeindexes.
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indicatorjinthe attimet. Thekeytechnicalchallengeintheconstructionandvalidationofthefinancialstressseriesisthechoicefortheweightingmethodology. Inefficientchoiceoftheweightingmethodologywouldincreasethe
potentialforfalsealarmsgivenbytheseries. Seekingtominimizethefalsealarms,wewereagnostictothechoiceof
weightingtechniqueandtestedanumberofmethods,includingtheprincipalcomponentanalysis. Theapproach
ultimatelyselectedtominimizefalsealarmsisthecreditweightsmethodasexplainedinOetandEiben(2009,2011).
(2.2)DriversofriskexplanatoryvariablesdataToadvancefromthesepremiseswecomeupwithamethodologythatusesZscorestoexpresstheimbalances. Wedefineanimbalanceasdeviationsofsomeexplanatoryvariable fromitsmean. Weconstructitasastandardizedmeasure. Thatis,eachexplanatoryvariableisaggregated,deflated(typicallybypricebasedindex),demeaned,anddividedbyitscumulativestandarddeviationattimet. TheresultingZscoreisdesignated. Byconstructiondescribesimbalanceasthedistanceinstandarddeviationsfromthemeanoftheexplanatoryvariable.imbalanceshowspotentialforstress.ThedetailsofvariableconstructionaresummarizedinAppendixBox2.
SAFEEWSbuildsontheexistingtheoreticalprecedentsofTable1withanewtypologyofthesystemicriskEWS
explanatoryvariables,giveninTable2. Thedefinitions,theoreticalexpectationsandGrangerCausalityofexplanatory
variablesaresummarizedinTable16throughTable19.
InsertTable
2about
here
(3)RiskmodelandresultsTherearemanywaystoapproachamodelsuchasthis. Generally,wecanexpectthatexplanatoryvariablesdonotact
atapointintime,butareinfactdistributedintime. Theestimationbecomesverydifficultparticularlywhenthe
numberofobservationsissmallrelativetothenumberofvariables. Inpreferencetothedistributedestimation,an
optimallagapproachisusedinpractice. SAFEEWSconsistsofanumberofmodelsthatareeachanoptimallaglinear
regressionmodeloftraditionalform
, , , , (2
wherethe
dependent
variable
Ytis
constructed
separately
asaseries
ofsystemic
stress
inthe
U.S.
financial
markets,
andtheindependentvariables, aretypesofreturn,risk,liquidity24,andstructuralimbalancesaggregatedforthetopfiveUSBankHoldingCompanies.
(3.1)EWSmodelsBasedonthepremisethatfinancialstresscanbeexplainedbyimbalancesinassetsandstructuralfeaturesofthe
system,whatarethepossibleimbalancestoriesthatcanbeproposed? Atthemostbasiclevelandwithoutanyother
information,onecanexpectthatfinancialstressatapointintimemayberelatedtopaststress. Indeed,ausefulfinding
formodeldevelopmentwasthattheFinancialStressIndexappearedtobeanautoregressiveprocess,consistingofa
singlelagandaseasonallagofthefinancialstressseriesitself. Tothiseffect,theunderlyingARstructureofFSIformsa
benchmarkmodelonwhichtheresearcherhopestoimprove. Anymodelbasedonacredibleimbalancestoryshould
outperformthisnaivebenchmarkmodelovertime. ThegeneralstrategyforconstructingtheEWSmodels,then,would
betoidentifyotherexplanatoryvariablesthatimprovetheFSIforecastoverthebenchmark.
Fromadesignperspective,ahazardinherentforallexantemodelsisthatthemodeluncertaintymayleadtowrong
policychoices. Tomitigatethisrisk,SAFEdevelopstwomodelingperspectives:asetoflonglag(sixquartersandabove)
forecastingspecificationstoallowthepolicymakerssufficienttimeforexantepolicyaction,andasetofshortlag
forecastingspecificationsforverificationandadjustmentofsupervisoryactions.
24 Sinceweviewimbalancesasdeviationsfromfundamentalexpectations,wechoosetofurtherclassifytheassetimbalancesas
return,risk,andliquidityimbalances. Theclassificationisbasedonatypologyofthedemandforfinancialassetsasfunctionof
return,risk,andliquidityexpectations(Mishkin1992).
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increasedmismatchbyitselfindicatesanincreasedimbalanceinrepricingatparticularmaturityandreflectsincreased
interestrateriskexposure. Thus,thelargeristhemismatch,thelargeristhepotentialforsystemicstress.
Definedinastandardmanner,leverageisratioofdebttoequity. Useofleverageallowsaninstitutiontoincreasegains
onitsinherentequitypositionbytakingonriskydebt. Thus,leverageisadoubleedgemagnifierofreturns,increasing
bothpotentialgainsandpotentiallosses. Anincreaseinleveragedescribeshigherlevelsofriskydebtrelativetosafer
equity. Increasedleveragehasbeenwidelyassociatedwithfuelingthefireofmanyfinancialcrises. Thus,our
theoreticalexpectationforleverageispositive.
Shortlag
and
long
lag
base
models
Clearly,thecandidatebasemodeldescribedaboveisonlyoneofthepossibleparsimoniousmodelsandisformed
withoutaparticularconsiderationofthevariablelagstructure. Amorerigorousprocedureforformingtheshortlagand
longlagmodelsisasfollows. Toaidinidentifyingasetofkeyvariablesintheconstructionofbasemodel,wefirstutilize
thetoolofGrangerCausalitytofindthesetofvariableswithGrangerlagsappropriateforeachmodelingperspective:
exclusivelyfromlag6tolag12forthelonglagmodels,andinclusivelyfromlag1tolag12fortheshortlagmodels. We
thenexaminethecorrelationforallourvariablesandseparatethevariableswithhighcorrelation(morethan60%). For
eachgroupofpotentialvariableswithGrangerlags,weusestepwiseandmaxRsquareprocedurestosimulatethebase
models;identifythekeyimpactvariables,highrateofoccurrencevariables,thevariableswithlargecoefficientsandhigh
explanatorypower. Finally,ineachpotentialbasemodel,wetrytoselectthekeyvariableswithGrangerlagsfromeach
categoryofreturn,liquidity,structure,andriskimbalances. Ifanykeyvariablelosessignificanceafteritisenteredinto
thebasemodel,26
wereiteratethevariableoptimallagtogetthedesiredsignificanceandexpectedsign. Inaddition,asweintendtotestthemodelsontheoutofsampleperiodthatincludesthefinancialcrisisof2007,weonlyexaminethe
relationshipbetweenFSIandourXsthroughthefirstquarterof2007.
(3.2)CriteriaforvariableandlagselectionStartingfromtheshort andlonglagbasemodels,weformadditionalshort andlonglagEWSmodelsbyextendingthe
basemodelswithotherexplanatoryvariables. Weutilizethecriteriabelowtodeterminewhetheranewvariableshould
beincluded.
1) Theoreticalreview: Considerwhetherinclusionofthevariableintheequationisunambiguousandtheoreticallysound. Allthevariablesinthemodelshouldmeettheexpectedsign(seeAppendixTable16Table19for
theoreticalsign).
2) Hypothesistesting(tstatistics): Considerwhetherthecoefficientofthevariabletobeincludedissignificantintheexpecteddirection. Wegenerallyacceptthevariablessignificantat10%confidencelevel.Toavoid
heteroskedasticityproblem,wereportwhiletstatisticstoinvariableandlagselectionprocedure.
3) Stationarity: Considerationofstationarityisimportantfortimeseriesdata. Weconductthestationaritytestsfortheentiremodelandeachvariable. TheindividualseriesstationarityisverifiedviaAugmentedDickeyFuller
unitroottests. Ifthedependentvariableisfoundtobenonstationary,wecheckforcointegration,before
furtheradjustments. CointegrationofthetrialOLSspecificationsisverifiedviaAugmentedDickeyFullerunit
roottestsontheresiduals. Thetestsshowthatnullhypothesisofunitrootintheresidualsisstronglyrejected
inallthreeRWcases:randomwalk(RW1),randomwalkwithdrift(RW2),andrandomwalkwithdriftandtrend
(RW3),asADFteststatisticsineachcaseismorecriticalthenthetestcriticalvaluesevenat1%level. Fornon
stationaryvariables,weapplyfirstdifferencingandreverifytheabovecriteria.
4) GrangerCausality:Considerwhetherthevariabletobeincludedconsistentlyandpredictablychangesbeforethedependentvariable. AvariablethatGrangercausesfinancialstressonewayat20%significancecanberetainedforfurthertesting. Thusfar,weseektoretainthevariableswithsignificantGrangerlags,expectedsigns,and
significantcoefficients. However,ifuponinclusionintothemodel,thevariablecoefficientlosessignificanceor
changessign,wereiteratethevariablesoptimallag,seekingthereestablishmentofallthreecriteria:
theoreticalexpectation,significantcoefficient,andGrangercausality.
26 Forexample,duetothevariablemulticollinearityandholesinthelagstructure.
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5) Multicollinearity:Althoughmulticollinearityisnotaseriousissueforforecasting,toensurethatourtstatisticsarenotinflatedandtoimprovemodelstabilityovertime,wetrytominimizepotentialmulticollinearityissuesby
consideringthevarianceinflationfactor(VIF). WeseektoreplacethevariableswithVIFhigherthan10.
6) Optimallagselection: WeutilizeSASforautomaticlagselectionandthemodelsimulationprocess. Startingfromthebasemodels,weenternewcandidatevariablespassingtheabovetests,oneatatimefromthereturn,
risk,liquidity,andstructureimbalanceclasses. Foreachnewvariable,wetestandselecttheoptimallagamong
thevariablelagsinclusivelyfromonetotwelveforshortlagmodelsandexclusivelyfromsixtotwelveforlong
lagmodels. Theoptimalitycriteriaincludesignexpectations,tstatistics,Grangercausality,VIF,R2,andnumber
ofobservations.27
Ifnolagsforthatvariableshowsignificanceinthetheoreticallyexpecteddirection,weexcludethisvariablefromthemodel. Ifmorethanonelagmeetsourselectionrequirements,wenarrowthe
optimallagselectiontothelagwithGrangercausalityandmostadjustedR2increases. Insummary,the
variableslistedinthetwoGrangercausalitytables(seeAppendixTable16 Table19)formtheprincipal
regressorsintheEWSmodels. ThevariableswithGrangerlagssignificanceat10%levelareconsideredfirstas
theydemonstrateastrongerGrangerrelationshipwithFSIthanthosesignificantat20%level.
7) Forecastingaccuracyreview: Considerandcompareasetforecastingaccuracymetrics.o DoesAdjusted increasewhenthevariableisaddedtotheequation?o DoesMAPEdecreasewhenthevariableisaddedtotheequation?o DoesRMSEdecreasewhenthevariableisaddedtotheequation?o Dotheinformationcriteria(AICandSC)decreasewhenthevariableisaddedtotheequation?o DoesTheilUdecreasewhenthevariableisaddedtotheequation?
8) Reviewofbias: Doothervariablescoefficientschangesignificantlywhenthevariableisaddedtotheequation?o Functionalformbias: Theconsequencesofthisissuegenerallymanifestthemselvesinbiasedestimates,
poorfitanddifficultiesreconcilingtheoreticalexpectationsandempiricalresults. Forseveralvariablesin
themodel,thetransformationfromlevelrelationshiptochangesintheindependentvariableisfoundto
improvethefunctionalform.
o Omittedvariablebias: Thisbiastypicallyresultsinsignificantsignsoftheregressionvariablesthatcontradicttheoreticalexpectations. Whenmisspecificationbyomittedvariablesisdetectedintrialmodels,
wefurtheradjustthemodelbyseekingtoincludetheomittedvariable(oritsproxy)orreplacethe
misspecifiedvariables.
o Redundantvariable: Typically,thisissueresultsindecreasedprecisionintheformofhigherstandarderrorsandlowertscores.
28 Irrelevantvariablesinthemodelgenerallyfailmostofthefollowingcriteria:
failedtheoreticalexpectations,lackofGrangercausality,statisticalinsignificance,deterioratingforecastingperformance(e.g.RMSE,MAPEandTheilUbias),andlackofadditionalexplanatorypowertodeterminethe
dependentvariable(e.g.R2,AIC,andSC). Whenastrongtheoreticalcaseexistsforanindependentvariable
tobeincludedthatisnototherwiseproxiedbyanotherrelatedvariable,weseektofindaproxyvariable
thatisboththeoreticallysoundandisnotredundanttothetrialspecification.
9) Robustnesstesting: Totheextentthatviolationsofclassicallinearregressionmodel(CLRM)assumptionsarise,certainadjustmentsneedtobemadeinthemodelspecification.
o Treatmentofserialcorrelation:TheresultsoftheBreuschGodfreyLMtestsforshortlagdynamicmodelsshowevidenceofserialcorrelationinthreeofthesevendynamicspecifications(models(1),(5),and(8)in
Table6). Sincealloftheseequationsaretheoreticallycorrectlyspecified,theserialcorrelationispureand
doesnotcausebiasinthecoefficients. Thus,wecanapplyNeweyWeststandarderrorstothese
specifications,while
keeping
the
estimated
coefficients
intact.
Durbin
Watson
statistics
ofthe
long
lag
modelsshowinconclusiveevidenceofpositiveserialcorrelationandmanyrejectnegativeserialcorrelation
ata5percentsignificancelevelfortheestimationperiodofQ4:1991toQ1:2007. Anexpandedestimation
periodwhichincludesthefinancialcrisis(Q4:1991toQ4:2010)yieldsDurbinWatsonstatisticsthatconfirm
serialcorrelationoftheforecasterrors. TheadditionofAR,MA,orbothtermsasexplanatoryvariablesin
27 TheinnovationofouroptimallagselectionprocedureconsistsininclusionofGrangercausalityandmulticollinearitycriteria. In
addition,thenumberofobservationsservesasanoperationalthreshold:variableswithlessthan50insampleobservationsare
rejected.28 Studenmund(2006),p.394.
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thesemodelscanpotentiallyremedyserialcorrelation. Modelsestimatedwithanautoregressivetermas
anexplanatoryvariableweresuccessfulateliminatingserialcorrelationforshortlagmodels. Sinceweaim
toestimatemodelswithlongerforecastinghorizonwithoutautoregressivevariables,weincludeMAterms
asexplanatoryvariablestoremoveserialcorrelationandimproveourforecasts.
o Heteroskedasticity: Heteroskedasticitycanbeanadditionalpenaltyassociatedwithbaddataandinherentmeasurementerrorsinthefinancialtimeseriesdata. WeconductamodifiedWhiteandBreuschGodfrey
teststoinsurethatthevarianceoftheresidualisconstant(homoskedasticityCLRMassumption). Thetests
failtorejectthenullhypothesisofhomoskedasticityinallcases,awelcomefinding.
o Otherspecificationproblems: RamseyRESET(RegressionSpecificationErrorTest)29
iscommonlyusedasageneralcatchalltestformisspecificationthatcanbecausedbyalitanyofpossiblereasonsofthefollowing:
omittedvariables,incorrectfunctionalform,correlationbetweentheresidualandsomeexplanatory
variable,measurementerrorinsomeexplanatoryvariable,simultaneity,andserialcorrelation. Thevery
generalityofthetestmakesitausefulbottom linecheckforanyunrecognizedmisspecificationerrors.
WhileinacorrectlyspecifiedOLSregressiontheresidualfollowsamultivariatenormaldistribution,Ramsey
showedthattheaboveconditionscanleadtoanon zeromeanvectoroftheresidual. RamseyRESETtestis
setupasaversionofageneralspecificationFtestthatdetermineslikelihoodofsomeomittedvariableby
measuringwhetherthefitofagivenequationcanbeimprovedbytheadditionofsomepowersof . AlltheRamseyRESETtestsshowwelcomeresultwithsimilarfitbetweentheoriginalandtherespectivetest
equationwiththeFstatisticislessthanthecriticalFvalue. Providednootherspecificationproblemsare
highlightedbyearliertests,RamseyRESETtestsfurthersupporttheresearchclaimofabsenceof
specificationproblems.
(3.3)EWSmodelspecificationsandresultsInsampleresultsofthebenchmark(panelA),candidatebasemodel(panelB),shortlagbasemodel(panelC),andlong
lagbasemodel(panelD)aredetailedinTable3below. Informingabasemodel,weseektofindacorestoryof
theoreticallyconsistentlongtermrelationshipsbetweensystemicstressYt andinstitutionalimbalancesXt. Candidate
modelofpanelBisformedbyselectingrepresentativeimbalances,oneperexplanatoryvariableclass,discussedinthe
introduction. Inthiscandidatemodel,realequity,assetliabilitymismatch,andleverageincreasethepotentialfor
systemicstress,offsetbycreditriskimbalances. CandidatemodelinpanelBimprovesonthebenchmarkmodel
insample,asdemonstratedbytheadjustedcoefficientofdeterminationandtheAkaikeandSchwarzinformation
criteria. ShortlagbasemodelinpanelCisformedbyestablishingacorestory:positiveinfluencesofstructural
imbalancesandnegativeinfluencesofriskimbalances. IncreasingthepotentialforsystemicstressareimbalancesinFX
concentration,leverage,andequitymarketsconcentration. Theyareoffsetbytheimbalancesininterestraterisk
capitalandcreditriskdistancetosystemicstress. Theshortlagbasemodelfurtherimprovesonthebenchmarkand
candidatemodels. LonglagbasemodelinpanelDisformedbymodifyingthecorestoryforthelongerrun:positive
influencesofstructuralandriskimbalancesandnegativeinfluencesofriskandliquidityimbalances. Increasingthe
potentialforsystemicstressareimbalancesininterbankconcentration,leverage,andexpecteddefaultfrequency. They
areoffsetbytheimbalancesinfiresaleliquidityandcreditriskdistancetosystemicstress. Thelonglagbasemodel
providesausefulperformancetargetforthelonglagEWSmodels.
AllofthebasemodelsvariablesarestatisticallysignificantintheexpecteddirectionandshowsignificantGranger
causalitywiththedependentfinancialstressseries. Statisticalsignificanceat10%,5%and1%levelsisindicatedby*,
**,***,respectively. Significanceofcausalrelationshipsat20%and10%isindicatedby,,respectively. The
sampleperiodisfromOctober1991toMarch2007.
29 Ramsey(1969)
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InsertTable3abouthere
OutofsampleresultsofthebenchmarkandbasemodelsareshowninTable4below. Viewedoutofsample,the
candidatebasemodelfailstooutperformthebenchmarkingmodelinrootmeansquareerror(RMSE)andbias(TheilU)
measures,butoffersmodestimprovementinmeanabsolutepercentageerror. Theshortlagbasemodel,however,
consistentlyimprovesonthebenchmarkingmodelinallthreestatisticalmeasures.
InsertTable4abouthere
Table5belowsummarizestheshortlagmodelstoriesthatfurtherimproveonthecorestoryofthecorrespondingbase
modelinexplainingfinancialstressinsample. Itisclearthatthepositiveandnegativerelationshipswithfinancialstress
colorcodedastheyare,tendtofallintoessentiallytwostories:apositivestoryofstructureandnegativestoryofrisk30,
supplementedandenhancedbyadditionaltypesofreturnandliquidityimbalances,bothpositiveandnegative.31
InsertTable5abouthere
IntheTable5above,consider,forexample,model7. Onecanseethecorestoryinmodel7likeintheothermodelsis
thestoryofpositivestructureandnegativeriskinfluence. Wesupplementthisstoryforthismodelbycertainpositive
returnimbalancesandadditionalnegativeimpactofriskimbalancesbeyondthoseincludedinthecoremodel. Themost
significantvariableinthismodelthatincreasesthepotentialforsystemicriskistheinterestriskdistancetostress. Itisa
measurerelatedtobookvalueofequitythatexpressestheequitysusceptibilitytostressandconstructedthrougha
proprietarystressdiscountingmodel,sothisisnotanobservablemeasure. Thestoryofsusceptibleequityis
supplementedinthismodelbythestoryoftotalcreditdiscountedbyCPI,discussedabove,andbythestoryofchangein
foreignexchangeconcentrations. Decreasingthepotentialforsystemicstressaretheriskmeasures:solvencydistance
tosystemicstress,creditriskdistancetosystemicstress,andthechangeinthecreditriskdistancetostressallnot
directlyobservableandconstructedfortheSAFEEWS.
InsampleresultsoftheeightcompetingEWSspecificationsforeachforecastinghorizonaredetailedinthefourpart
Table6(shortlag)andTable7(forlonglags)below. OutofsampleresultsaregiveninTable8(shortlag)andTable9
(longlag).
InsertTable6abouthere
InsertTable7abouthere
InsertTable8abouthere
30 Thereasonthatriskimbalancesdescribeanegativerelationshipwithstressisthatbyconstructiontheyarepredominantly
defensivefunctionsofcapitalandsolvency.31 Thelonglagmodelstellfundamentallysimilarstoriesofpositivestructuralimbalancesandnegativeriskimbalances. The
correspondingtableisomittedforbrevity.
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InsertTable9abouthere
(4)Discussionandimplications(4.1)PerformancesupervisoryEWSvs.publicEWSThestoriestoldbythevariousshort andlonglagEWSmodelsdiffer. Therefore,weexpectthatsomeofthestoriestendtodobetterovertime,whileothersaremoresuitedtoparticulartypesofcrises. Ingeneral,thestoriesmighthave
differentperformance. Itisinstructivetolookatthestatisticalperformanceofthesemodelsinsample(Table6,Table7)
andtheiroutofsampleforecastingability(Table8,Table9). Theforecastingparametersaredefinedthroughthe
windowendingin2010. Someinterestingobservationsarise,suchthatsomemodelstendtobemorestableovertime.
Thatisanimportantconsideration,sincefinancialconditionschange,regulatoryregimechanges,newproductscome
andgo. Therefore,itisimportantfortheEWSresearchertoseekastablemodelortorecognizethedynamicsandto
adjustforit. Fromthiswork,itwouldappearthatthemodels2,4,and7maybeexpectedtobebothstableandpossess
attractiveexplanatorypowers.
Wecomparerelativeperformanceoftheeightshortlagspecificationsbyrunningaforecastinghorseracewithresults
showninTable10. Inthehorserace,welookatfourdifferentknownstressepisodes:LTCMcrisis,thedotcomcrisis,
thestockmarketdownturnof2002,andthesubprimecrisis. Wethenrankordertheperformanceofthemodelsbased
ontheRMSE. Somemodelsconsistentlydobetterinthishorserace,butotherswithlessshiningstatisticsalsoemerge
somewhatsurprisinglyasprovidingpowerfulinsights.
InsertTable10abouthere
Itmightbetemptingtothinkthatoneshouldseektofindthewinner,however,weargueagainstthis! Itisvery
importantforapolicymakerusingthisEWSframeworktoresistthetemptationtofindthebestmodelbecauseevery
crisisisdifferent! SAFEmodelsrepresentdistinctstoriesofcrisesthatovertimemostconsistentlyexplainfinancial
stressinthemarkets. Futurestressmayevolveinwaysnotseeninthepastorbedrivenbyimbalancecombinations
thatmayberelativelyrareanddifferentfromthebesthistoricmodel. Inordertostudyapossiblebuildupoffinancial
stressusingthisEWS,oneshouldthereforeconsideravarietyofplausiblestoriesthatmayberealizedthroughtime.
SinceSAFEEWSincorporatesbothpublicandsupervisorydata,animportantquestionthatmaybeaskediswhether
supervisoryinformationoffersadditionalvalue. WeaddressthisquestioninCaseStudy1,whichconsiderscompetitive
performanceofasystemicriskEWS,basedonpubliclyavailableinformationvs.anEWSusingprivateinformation.
CaseStudy1:supervisoryvs.publicEWSspecifications
Anassumptionoftheresearcheristhatnonpublicdataprovidesforamoreaccurateandamoreactionableearly
warningSystem. Totestthis,weremoveallsupervisoryFRSvariablesfromthemodelsuggestionstageandrespecify
theSAFEmodels.
Therearethreebroadcategoriesofexplanatoryldata:(1)confidentialinstitutionspecificdatainternaltotheFederal
ReserveSystem,(2)undisclosedFederalReservemodelsandtheiroutput,and(3)datafromthepublicdomain.
Category1consistsofconfidentialinstitutionaldatanototherwiseavailabletothepublic. Category2,theundisclosed
FRSmodelsmayuseeitherpubliclyavailabledataorFederalReservedata. Category3dataincludesrawdatafromthe
publicdomainaswellasoutputfrompubliclyavailablemodelsthatutilizesdatafromthepublicdomain. Wedefine
privatesupervisorydataasFRSinternaldata(category1)andtheundisclosedoutputofFRSmodels(category2).
Wecanexpectaqualitativedifferencebetweencategory1andcategory2supervisorydata. Theconfidentialdata(1),
althoughopaquetothepublic,isgenerallyofhighquality. Theconstructeddata(2)ispronetoanumberof
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measurementerrorsandisinherentlymuchmoreunstable. Manyofthepublicseriesfromtheoriginalspecifications
arepreserved. Removalofprivatesupervisoryseriesmostseverelyaffectstheriskvariables,andtoalesserextentthe
liquidityvariables. Thus,wecanexpectthatthosevariableswouldbemostaffectedwhenwetaketheprivatedataout
toonlyseethepublicformulationsoftheEWSmodels. Table11belowshowsthedistributionofcategory2data
(marketwith)andcategory3data(markedwith)amongtheimbalanceclasses. Table12showsproportionof
supervisoryvariablesamongthespecifiedindependentvariables.
InsertTable11abouthere
InsertTable12abouthere
ComparingthepublicdataonlyversionsofSAFEmodelswiththoseusingsupervisorydata(Table13andFigure4),we
findthatmodelsusingsupervisorydataoutperformthepublicformulations,bothinthegoodnessoffitandthe
forecastingabilityasseenintheRMSE,MAPEandthebiasstatistics. Whenappliedtotheoutofsample20072009
period,bothprivateandpublicspecificationscatchtheincreaseinstressduring2Q2007. However,whiletwoofthe
privatemodels
do
well
inprojecting
explanations
into
the
4th
quarter
2007,
the
public
models
fail
completely
in
explainingthelaterepisode. We,thus,findevidenceoftheimportanceandusefulnessofprivatedatainthecreationof
asystemicriskearlywarningsystem.
InsertTable13abouthere
Fromthepointofviewoffinancialinstitutions,itisclear,thatevenpublicdatabasedsystemicriskEWSmodelswould
allowinstitutionstostudythecorrelationsandsensitivitiesoftheirexposuresandstructuralpositionswithinthe
financialsystemandusetheframeworktoenhancesystemicriskstresstestingandscenarioanalysis.
ThiscasestudyonlyconsiderstherelativeoutofsampleperformanceofpublicandprivateSAFEmodels. Manyinterestingquestionslieaheadinthislineofinvestigation. Forexample,futureworkcanaddressadditionalanalytical
questions,suchas(a)whatfactorsmatteredmostintherecentcrisis,(b)whatmaybetheresultsoflikelihoodtestsfor
StructuralCs(concentration,connectivity,contagion),and(d)whatmaybetheresultsoflikelihoodtestsforblocksof
datatriggeredbybehavioraleffects.
InsertFigure4abouthere
(4.2)ApplicationstosupervisorypolicyHowcanSAFEfacilitatetheworkofpolicymakers? Oneofitskeybenefitsisraisingpolicymakersattentionto
imbalancesthathavestrongpositiveandnegativeassociationswithfinancialstress. SAFEEWSmodelshelpexplain
financialmarketstressintermsofseveralimbalancesbothescalatingstressandoffsettingit.
Anumberofquestionsspringimmediatelytomind. Cannottheimbalancesbeobservedreadilyinthefinancialsystem?
Howcananearlywarningsystemhelp? Afterall,weallknowthatwhatgoesupmusteventuallycomedown. Intuition
tellsusthatthelongerthegrowth,thecloseristheprecipice. Shouldnotbeanobservationofevenasingleimbalance
besufficientgroundforregulatoryaction? Indeed,inthecaseofarecentcrisis,afeweconomists,amongthemRobert
Shiller,observedthatthedifferencebetweenaresidentialhousingpricingindexanditslongtermaveragevaluehas
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reachednewheightsandcalledthisnotsustainable.Yetnoeconomicmodelprovidedarigorousforecastofthecoming
downturnandcrisis. Whycannooneanswerwhenthingswillcomedown?
Somesaysuchforecastisimpossible.Fromanefficientmarketperspective,financialcrisesareshockeventsand
thereforecannotbepredicted.Efficientmarketstheorytellsusthatitisimpossibletoknowthetimingoftheseshocks.
Furthermore,evenifthiswerepossible,thisperspectivetellsusthatbubbleprickingpolicywouldbeproblematic,
becauseitpresumesthatyouknowmorethanthemarket.32 Italsohighlightsaserioustechnicalchallengefor
monitoringassetbubbles,claimingabsolutelythatsinceembeddedpricingfactorsareunobservableinthemarkets,itis
empiricallyimpossibletoverifyassetpricebubbles.33 Furthermore,thedivergencemaybedueeithertotheembedded
pricefactorsorsomeunderlyingeconomicfundamentals(statevariables),andthatitisimpossibletodeterminewhich
oneisresponsibleforsuchdivergence.34 Economiststhatbelievethatmarketsarefundamentallyefficientarguethatit
isthereforebettertofocusoncrisisresolutionmechanismsoncetheyoccur.
Fromanempiricalperspective,however,thecrisesarenotstrictlyabouttimingofassetpricebubbles,butabouta
varietyoffactorsthatevolveslowlyovertime. Thesefactorsareobservable35andtendtohavecommonfactors:
Excessiveassetprices,relativetocentraltendencyortrendwhichimplicitlyrepresentalongertermequilibriumbasedonastablesetofexpectations,financialtechnology,etc.;
Lotsofleveragefuelingexcessiveassetprices. Becausefinancialinstitutionbalancesheetsandcertainassetclasses(e.g.realestate)arehighlyleveraged,theytendtoplayamajorpartinfinancialcrises;
Networkedfinancialsystem,combinedwithleveragedfinancialfirms,canspillassetlossesandfundingproblemsfromoneinstitutiontoanother,placingtheentiresystematrisk.36
Onepracticalconstraintinobservingimbalancesisthedifficultyofrelatingthemtotheeconomy. RobertSchiller
measureshousingimbalancesbydeflatingthembyaggregatehousingvalue.37 Borioandcolleaguesmeasure
imbalancesbydeflatingthembyGDP. SAFEEWSmeasuresimbalancesbydeflatingthembyaggregateassetsor
relevantpriceindexes.
Secondmajordifficultyisrelatinganobservedimbalancetoothers. Innormallyfunctioningmarkets,institutionscan
efficientlyestimateriskandhedgeit,whilesustainingthefinancialsystembalanceandgrowth. Howcanapolicymaker
makeaninformedjudgmentthatinstitutionsestimatesofriskarebecomingbiasedataparticulartime,andthe
marketsgrowthbecomesirrationallyexuberant? SAFEfacilitatesthischallengebyconsistentlyestimating
fundamentalsofvariousassetclassesandstructuralcharacteristicsofthesystem. Thus,anerrorinmeasurementofa
singleimbalanceduetoabiasedestimateofitsfundamentalvalueisminimizedwhencombininganumberofpositiveandnegativeimbalanceswithinaSAFEOLSmodel. Bylookingatseveraloffsettingimbalancestogether,SAFEOLS
estimatesareBLUEbestlinearunbiasedestimators.
Inaddition,SAFEEWSassistspolicymakersdecisionprocessbyallowingthemtotargetaparticularactionthreshold
abovethepreviousmeanofthefinancialstressseries. Whatshouldthethresholdbe? Shouldpolicymakerstargethalfa
standarddeviationoffinancialstress,oronestandarddeviation,oranotherthreshold? Inabsenceofamorerigorous
theoreticalframework,SAFEEWScanhelpempirically. Asweshowinthecasestudy2below,iterativereviewof
retrospectiveSAFEforecastsinaseriesofhistoricalstressepisodescanestablishthedifferenceinstandarddeviations
32 AlanGreenspan,quotedintheNewYorkTimes,November15,1998.
33 Acommonfeatureofassetbubblesisthatpricesincreaseatarategreaterthanexplainedbyunderlyingfundamentals
(Kindleberger,1992).34 Cogley(1999).
35 RobertShillernotesthatitissurprisingthattheexpertsfailedtorecognizethebubbleasitwasforming(Shiller,2008). Strictly
speakingthisisnotquiteaccurate. AsAlanGreenspantestifiedtoCongress,in2005thebuildupwasobservedandgave
policymakersseriousconcernsthattheprotractedperiodoftheunderpricingofriskwouldhavedireconsequences
(Greenspan,2008).36 TheabovefactorsarenotuniquetotheUnitedStatesandcanalsobeobservedindevelopingcountriesfinancialcrises. The
UnitedStatespossessesareservecurrencythatiscapableofstoppingspillovereffects.Bycontrast,adevelopingcountrymay
beforcedtoappealtotheIMFforhelpinstoppingcrisisspillover.37 Standard&Poors(2008),p.10.
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betweenSAFEEWSforecastsandthecoincidentfinancialstressatthetimeoftheforecast. Thepolicymakerwouldthen
formasetofstressepisodeswhenadditionalsupervisoryinvolvementcouldbecontemplatedtoreducetheeconomic
losses. ComparingthedifferencebetweenSAFEforecastsoffinancialstressandthecoincidentstressmeanforallstress
episodeswouldleadtoidentificationofthedesiredtargetlevelatwhichpolicymakerswouldbecomeinvolved. When
theforecastsofstressfallshortofthetargetactionlevel,thehistoricalevidencewouldsupportthecasethatmarkets
areabletoselfresolvetheparticularlevelofstress. Whenforecastofstressexceedsthetargetlevelofstress,the
policymakerscanweightheeconomiccostsofregulatorypreventiveactionagainsttheeconomiccostsofashock
bringingtheaggregateimbalancesbacktothefundamentals.
ThefollowingsimplifiedcasestudyillustratestheprocessbywhichSAFEEWScanfacilitatethepolicymakersselection
ofactionthresholds.
CaseStudy2:selectionofactionthresholdsinhistoricstressepisodes
Inthiscasestudy,wetesttheperformanceofSAFEagainstthreehistoricepisodes:DotComstressepisode(4Q1999/1Q
2000),StockMarketDownturnstressepisode(2Q2002/4Q2002),andSubprimestressepisode(4Q2007/1Q2008).
Consideringthesethreeepisodesexpostandtheireconomiccosts,thepolicymakerswilllikelyagreethatnoregulatory
actionwouldhavebeenneededduringthe2002stockmarketdownturn. Thepolicymakerswillbelikelytoagreethat
regulatorypreventiveactionpriortotheSubprimeepisodemaybeefficientinalleviatingtheeconomiccostsofthecrisis
andperhapsevenforestallingit. Thedecisionmaybelessclearinthecaseofthedotcomepisode. Thosethatwouldrejecttheideaofregulatoryinterventioncanpointoutthefactthestressepisodewasessentiallyastockmarket
correctionofovervaluedhightechnologyrelatedfirms. Thosethatwouldsupporttheideacanpointoutthatthe
correctionwasfarfromsoftandgavetheUSeconomyaprecipitouspushtowardtheEarly2000sRecession.
Table14showstheresultsofthepolicyhorseraceamongthemodels. Asthetableshows,thefinancialstressseriesz
scoredropped0.3standarddeviationsfromitslevelsixquartersaheadoftheStockMarketdownturn,supportingthe
notionthatepisodewasbenign. Bycontrast,thestressseriesmovedupalmost0.7standarddeviationsfrom2ndquarter
1998totheDotComcrisis,andmovedalmost2.9standarddeviationsfrom2ndquarter2006totheSubprimecrisis.
DependingonthepolicymakersbeliefinthecostefficiencyofpreventiveactionfortheDotcomcrisis,thepolicymakers
usingtheSAFEEWStohelpestablishatargetthresholdmightchooseanactionthresholdbeloworabove0.7standard
deviationsfromthefinancialstressseriesmeanatthetimeofaforecast.
TheresultsofthetablealsosupportourpreviousargumentthatselectingasinglebestSAFEmodelisnotwelladvised
Thepolicyhorseraceshowsthatbestmodelcontinuallychanges. ItalsoshowsthatsomeSAFEmodelsdoconsistently
well. ItisclearthatthecurrentsetofSAFEmodelscanbeusedinvariousways:forexample,thepolicymakerscan
consideronlythetopmodelatthetimeofeachquarterlyforecast,orseveraltopmodels.
InsertTable14abouthere
WeconcludethisCaseStudy2illustrationofapolicyapplicationbyaretrospectivecasestudyintheoutofsample,
Subprimeepisodestress(seeFigure5below). LetussupposethatthepolicymakershavetheuseofSAFEEWSduring
the2ndquarter2006. Observingthefinancialstressseriesatthistimewouldgiveregulatorsnoreasonsforconcern. In
fact,bythetimethedataforafreshquarterlyobservationofFSIisassembledfromthedailyobservations,onewould
observeevenashorttermtrenddownwardasthefinancialmarketscontinuetoboom. Thepolicymakerswouldliketo
anticipatepossiblescenariosoffuturestatesofthefinancialstresssixquartersforward:duringthe4thquarter2007and
1stquarter2008. Todothis,assuggestedbythepolicyhorseresultsabove,theywouldliketoconsideralternative
plausibleimbalancestoriesasgivenbyseveraltopSAFEEWSmodels. Calibratedupto2ndquarter2006,thetopthree
shortlagmodelsaremodels(2),(4),and(7). Astheforecastisrun,allthreemodelsshowsignificantriserelativetothe
currentlevelofstress. Moreover,allofthemshowthatthetrenddoesnotpeakattheforecasthorizon,butinfact
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originatesmuchearlierduring2ndquarter2007.38 Thisforecastposestwocriticalquestionstothepolicymakers.
First,istheanticipatedincreaseinfinancialstressrealorillusory? Second,iftheincreaseisreal,isitcriticalenoughto
riskintroductionofsomecorrectivemeasuresearlyin2006todiffusethisprojectedbuildupofstress? Ifthebuildupof
stressisillusoryandthepolicymakersintroducesomeprophylacticmeasurestoreducetheimbalances,theyrisk
crampingahealthyeconomy. Ifnothingisdone,thefinancialmarketsstressthreatenstobecomelarge. Thequestion
ofactionorinactionisthecriticalchoice. Inordertoprovidefurtherpolicymakinginsight,aEWSresearchermustalso
bereadytoanswerwhichchannelsofprophylacticactionshouldbeopentothepolicymakers. Weintendtoaddress
bothofthesequestionsfromamorerigoroustheoreticalfoundationinafollowuptothisstudy.
InsertFigure5abouthere
CaseStudy3:thefinancialcrisis
Thefinancialcrisisof2008offersatestoftheforecastingaccuracyofboththeshortlagandloglagmodels. Whilethe
pinnacleofthecrisiswillberememberedbythefailureofLehmanBrothersandtheresultingquantitativeeasing,there
mayhavebeensignsofstressasearlyasthefirstquarterof2007. Thiswouldhaveallowedtimetoconsidermonetary
and/orsupervisorypolicyactionspriortothecrisistohelpmitigatedevelopingstress. Wewillconsiderforecastsfrom
shortlagandlonglagmodels.
ShortLagForecasts
SeveralshortlagmodelspredictedtheadventofstressstartingQ2:2007andcontinuingthroughout2007insomecases.
Inparticular,sixofeightshortlagmodelspredictedstresswhichwassignificantlylargerthanstressobservedinthe
comparativelyquietyearsleadingtothecrisis. ThesepredictionscanbeseeninFigure6. Inparticular,models(2)and
(8)predictedearlystressinQ2:2007,whileothermodelssuchas(4)predictedstresswithalag.
Whilethemajorityoftheshortlagmodelscontainanautoregressiveexplanatoryvariable,severaladditionalkey
explanatoryvariableswerefoundtobevaluableatpredictingfinancialstress. Thedegreesofthecontributiontoearly
financialstressaredependentuponthechosenlagoftheexplanatoryvariablesandupontheactualvariablesincluded
intheforecast. Forexample,model(2)predictedrapidstressincreasebeginninginQ2:2007. Theobservedshrinking
valueofLiq_5(liquidity)andtheincreasingvalueofStr_4(FXcurrencymarketconcentration)inthismodelwerethe
leadingcontributorstotheincreaseinstressintheforecastperiod. ThisforecastindicatesthatpreviousvaluesofLiq_5
weredecreasingwhichisasignthatthemodelstopfiveinstitutionshadliquidityconstraints. Moreover,arisingvalue
ofStr_4indicatesanincreaseinfuturefinancialstressbecausethismeasuresthedegreetowhichlargerfirmsare
exposedrelativetotheaggregateforeignexchangecurrencymarkets(i.e.largerfirmsbearalargersegmentofrisk
associatedwiththismarket). Specifically,Liq_5andStr_4added29.1and22.5unitsrespectivelyinQ2:2007aswellas
adding28.9and21.5unitsinQ3:2007.
InsertFigure6abouthere
Othermodelssuchasmodel(4)predictedthatstresswouldbepresentatdifferenthorizons. Model(4)predictedthat
financialstresswouldbesubduedinthefirsttwoquartersbutwouldincreasesignificantlyinQ4:2007. Further,thiswas
drivenmainlybyslightlydifferentvariablesincludingLiq_6(stresssaleliquidity)andStr_4.1(interbankcurrencymarket
concentration). TheremainingmodelsrevealedothernoteworthyvariablessuchasRet_2cpi(capitalmarkets),Rsk_8a
(expecteddefaultfrequency),andRsk_L(solvencystressdistancetosystemicstress).
LongLagForecasts
38 Simulatingforecastsinsubsequentquarters,onecanobservethatastheforecastingwindownarrows,themodelstendto
converge,asexpected.
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Longlagmodelsallowustoforecaststressatlongerhorizonswhichissuitableforexantepolicyactions. Thevalueofa
forecastwithalongerhorizonisthatithighlightsfactorsthattendtocontributetostressinthelongerterm(atleast6
quarters).
Similartotheshorterhorizonforecasts,wecananalyzethevariableswhichwereimportantatsignalingfinancialstress.
Figure7illustratesthatseverallonglagforecastspredictedanotableincreaseinstressthroughQ3:2008. Two
significantdriversofstressthroughouttheforecastperiodareLiq_6(3monthforwardsale)andLiq_7(firesale). Similar
toLiq_5inshortlagmodel(2),adecreasingvalueofLiq_6andLiq_7signalsanincreaseinfuturefinancialstress
becausethisisasignthatthesefirmsarelackingliquidityrelativetothepast. Thesevariablesaddedasmuchas18units
tostressinthefirsttwoquartersoftheforecastperiod.
InsertFigure7abouthere
AnotherimportantdriverofstresswasRsk_8a(expecteddefaultfrequency)whichaddedasmuchas21unitstostress
inthefirstquarteroftheforecast(LL4)andasmuchas21unitstowardtheendoftheforecastperiod(LL3). Expected
defaultfrequency(EDF)isameasureoftheprobabilityofdefaultoftheinstitutionasdescribedbyMoodysKMV,anda
growingvalueofEDFsignalsfuturefinancialstress. Theincreasinglikelihoodofadefaulthasseveralcauseandeffect
connections. Forexample,anincreasingEDFcouldleadanincreaseincounterpartyriskwhichcouldleadtodifficulties
inraising
liquidity,
thus
accentuating
the
likelihood
ofstress.
We
see
similar
examples
ofthese
types
ofconnections
uponfurtheranalysisofthelonglagforecasts. AsEDFandliquidityvariablesleadtofinancialstress,weobservean
additionalincreaseinStr_9(leverage). Str_9becomesalargedriverofstresssolelytowardstheendoftheforecast
period. Thisimpliesthatfirmshadahigherdegreeofriskydebtrelativetosafercapital. Thishashistoricallybeena
criticaldriveroffinancialstressduringfinancialcrises. Theriseinleveragemayhavebeeninturnindirectlycausedby
previousincreasesinLiq_6,Liq_7,andRsk_8a.
(5)ConclusionsandfutureworkThemaincontributionofthispaperhasbeentodemonstratefirst,theexistenceofsignificantassociationbetween
institutionalimbalancesandfinancialmarketsstress. Furthermore,thepaperalsoshowsthatsignificantresultsare
obtainedwhentheseassociationsareexplainedintermsofinstitutionalreturn,risk,liquidity,andstructural
characteristics:bothintermsofstatisticalsignificanceinexpecteddirectionandGrangercausality.
Theresultsoftheearlywarningsystemdevelopedinthepaperraiseattentiontoimbalancesthathavestrongpositive
andnegativeassociationswithfinancialstress. TheSAFEEWSteststheoreticalexpectationsofpositiveandnegative
impactsonfinancialstressatthesametimeandallowsaconsistentapproachtoevaluationofthesystemicbankingrisk.
Bycomparingperformanceofmodelsbasedonpublicdataandthoseusingprivate(supervisory)information,thepaper
findsevidenceofvalueinsupervisorydata. Further,thestudydiscussestheuseandrelativeperformanceofSAFEEWS
calibratedusingonlydatapubliclyavailabletotheUSfinancialinstitutions.
Bycomparisonwithprecedentsinsystemicriskearlywarningsystems,SAFEEWSadditionallyoffersanumberof
innovativefeatures. Itisahybridearlywarningsystemframework,integratingbothmacroeconomicvariablesand
institutionspecificdata. SAFEEWSbenefitsfromaveryrichdatasetofpublicandprivatesupervisorydata,integratinga
numberofpreviouslystandalonesupervisorytoolsandsurveillancemodels. Fromthepointofviewofmethodology,
SAFEEWSextendstheoptimallagapproachandclarifiesthemodelselectioncriteria. Inaddition,SAFEEWSprovidesa
toolkitofalternativeimbalancestoriestomeetavarietyofpossiblepropagationmechanismsinagivensystemicstress
episode.
Intermsofitsarchitectureandtypology,SAFEextendsthetheoreticalprecedentsinEWSvariablesbysuggestingthat
theyfallintofourclassesofimbalances:return,risk,liquidity,andstructure. Althoughresearchershavelongrecognized
structuraleffects,theyhaveuptonownotbeenincorporatedintoanearlywarningsystemofsystemicrisk. Inaddition,
afeedbackamplificationmechanismhasbeenincorporated. Feedbackmechanismsaremodelsthatareparticularly
pronetomeasurementerrorandshouldbetreatedcautiouslybytheEWSresearcher. Nevertheless,asSAFEshowsin
theanalysisofpublicandprivatedatablocks,theamplificationmechanismcanaddsignificantexplanatorypowerand
7/30/2019 SAFE an Early Warning System for Systemic Banking Risk
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deservesfurtherconsideration. Inparticular,theliquidityfeedbackmechanismappearsinmostSAFEmodelsthrougha
liquidityindependentvariableandservesasacriticalvaluationengineforsomeofthemoredominantriskimbalance
variables. Fromthefinancialsupervisorspointofview,anEWSinvolvesanexanteapproachtoregulation,targetingto
predictandpreventcrises. Ahazardinherentforallexantemodelsisthatthemodeluncertaintymayleadtowrong
policychoices. Tomitigatethisrisk,SAFEdevelopstwomodelingperspectives:asetoflonglag(sixquartersandabove)
forecastingspecificationstoallowthepolicymakerssufficienttimeforexantepolicyaction,andasetofshortlag
forecastingspecificationsforverificationandadjustmentofsupervisoryactions.
Thispaperonlybeginstoaddresstheimportantanalyticalexerciseoftheperformanceofthevariousspecificationsin
varioushistoricperiodsoffinancialstress. Itcanbeextendedinseveralways. Forexample,itwouldbeusefultodiscuss
furthertheimportantvariablesselectedbythemodel,theirapplicabilityforuseinsupervisorypolicy,theirmarginal
impacts,andverificationthatthevariablesindeedmatteredornotandwhy. Specificattentionshouldbeattributedto
thetimepatternofevolvingfinancialstress,e.g.thespeedandamplificationdynamicofupcomingfinancialcrises. A
specialattentionshouldfurtherbedevotedtotheanalysisofthemodelperformanceoutofsamplewithconsideration
giventotheeconomicinterpretationoftheresults. Thismayalsoincludetestingthemodelfordifferentscenariosand
theinclusionofnewvariables. Toprovidefurtherpolicymakinginsights,EWSresearchershouldbereadytosupportthe
channelsofprophylacticaction,whichmaybeopengivenaparticularsetofimbalances,andbeabletoevaluatethe
impactofregulatorychangesonfinancialstressinrealtime. Importantly,theEWSmodelshouldbeextendedto
financialintermediariesotherthanbankholdingcompanies.
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(7)TablesandFiguresTable1Systemicriskexplanatoryvariablesinliterature
39
DemirgKuntandDet
ragiache1998
KaminskyandRein
hart1999
BorioandLowe20
02,
Asset
BorioandLowe20
02,
Crises
Edison200
3
HanschelandMon
nin2005
King,
Nuxoll,andYe
ager2006
Hendricks,Kambhu,andMosser2007
BorioandDrehmann2009
MoshirianandW
u2009
IMF,
April2009,Responding
ReinhartandRog
off2009
Nationaleconomic
a)GDPnational x x x x x
b)Credit/GDPnational x x x x x x x (x)
c)Equity x x x x x (x) x x x (x) x
d)Property x x (x) x x
e)Investments x x
Internationaleconomic
a)GDPinternational x
b)Credit/GDPinternational
c)Equity (x) x (x) (x) x
d)Foreignexchangerate (x) x x x (x) x
e)Exports/Imports (x) x x x
Financialsystem
a)Interbanklending x (x) (x) (x)
b)Leverage (x) x
c)Interestrate x x x x x x
d)Competition,concentration x x
e)Riskappetite,discipline x (x) x
f)Complexity x x
g)Dynamics,volatility x x x
39 ThetableistakenfromGramlich,Miller,Oet,andOng(2010),p.205.
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Figure1Imbalancesasdeviationsfromfundamentalsreflectpotentialshocks
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Figure2TopologyofloanUSDconcentrationsacrosstiersandloantypes
TierITierII
TierIII
TierIV
200
400
600
800
1,000
1,200
1,400
1,600
1,800
2,000
C&I
Consumer
Other
Depository
Institutions
LeaseFinancing
Agriculture
Construction
NF/NR
Multifamily
Farm
14Revolving
14Other
CRE
RealEstate
Billions
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Figure3TopologyoffinancialmarketconcentrationsoftopfiveUSBHCsacrossmarketsandtime
EquityMarkets
CreditMarkets
FXMarkets
CurrencyMarkets
InterbankMarkets
Securitization Markets
CreditDerivative Markets
InterestRateDerivative Markets
1.0
0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.54.0
6/30/1991
6/30/1992
6/30/1993
6/30/1994
6/30/1995
6/30/1996
6/30/1997
6/30/1998
6/30/1999
6/30/2000
6/30/2001
6/30/2002
6/30/2003
6/30/200
4
6/30/2005
6/30/20
06
6/30/2
007
6/30/
2008
STD
1.00.5 0.50.0 0.00.5 0.51.0 1.01.5 1.52.0 2.02.5 2.53.0 3.03.5 3.54.0
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Table2ExplanatoryvariableclassesintheSAFEmodel
ExplanatoryVariableClasses Constructionclasses
Returnimbalances
Throughassetpriceboom/bust
|Bymarkets/productsin:
CAPITALMARKETS
||Equitymarkets
||Creditmarkets
|||Propertymarkets:residential/commercial)CURRENCYMARKETS
||FX
||Interbank
RISKTRANSFER/DERIVATIVESMARKETS
||Securitizationsmarkets
||CreditDerivativesmarkets
||InterestRateDerivativesmarkets
RiskimbalancesCredit
Interestrate
Market
Solvency
Liquidityimbalances ThoughFundingLiquiditychannels
ThoughAsset
Liquidity
channels
Structuralimbalances ConnectivityConcentrationContagion
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Table3 BenchmarkandBasemodelsinsample
PanelA:
BenchmarkFSI
model
7.85 0.60 0.24DF=58 K=2
Constant LaggedFSI SeasonalFSI Adjusted
Rsquared
Akaikeinfo
criterion
Schwar
criterion
Estimates 7.85 0.60 0.24
0.49 6.72 6.82tvalue (1.44) (5.86) (2.31)
Granger
PanelB:
CandidateBase
Model
36.58 0.35 1.70_3 7.04_ 2.34 12.62_DF=61 K=5
Constant LaggedFSI AL
mismatch
Levera ge Rea lEquity CreditRisk Adjusted
Rsquared
Akaikeinfo
criterion
Schwarz
criterion
Estimates 36.58 0.35 1.70 7.04 2.34 12.62 0.60 6.51 6.71
tvalue (5.72) (3.24) (3.65) (2.97) (1.89) (2.29)
Granger
PanelC:Short
LagBaseModel
38.77 0.40 2.064 8.655 8.15_ 2.943 4.55_DF=61 K=6
Constant LaggedFSI FX
concentr.
Equity
Market
concentr.
Leverage Interest
RateRisk
capital
CreditRi sk Adjusted
Rsquared
Akaikeinfo
criterion
Schwarz
criterion
Estimates 38.77 0.40 2.06 8.65 8.15 2.94 4.55
0.63 6.49 6.74tvalue (5.65) (3.93) (2.78) (3.14) (3.38) (1.03) (3.16)
Granger
PanelD:Long
LagBaseModel
37.85 9.88_3 2.29 2.24_ 4.55_ 11.20_DF=57 K=5
Constant ALmismatch Expected
Default
Frequency
CreditRisk Currency
Market
concentr.
Lev erage Adjusted
Rsquared
Akaikeinfo
criterion
Schwar
criterio
Estimates 37.85 9.88 2.29 2.24 4.55 11.20
0.51 6.75 6.96tvalue (6.20) (3.05) (2.06) (1.85) (2.13) (3.68)
Granger
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Table4 BenchmarkandBasemodelsoutofsample
PanelA:
Benchmark
FSImodel
7.85 0.60 0.24DF=58 K=2
RMSE MAPE TheilU
8.35 12.42 0.081
PanelB:
Candidate
BaseModel
36.58 0.35 1.70_3 7.04_ 2.34 12.62_DF=61 K=5
RMSE MAPE TheilU
11.70 15.24 0.112
PanelC:
ShortLag
BaseModel
38.77 0.40 2.064 8.655 8.15_ 2.943 4.55_DF=61 K=6
RMSE MAPE TheilU
9.04 11.83 0.084
PanelD:
LongLag
BaseModel
37.85 9.88_3 2.29 2.24_ 4.55_ 11.20_DF=57 K=5
RMSE MAPE TheilU
14.62
16.73
0.138
20
30
40
50
60
70
80
90
100
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
0
20
40
60
80
100
120
140
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
20
40
60
80
100
120
140
160
1994 1996 1998 2000 2002 2004 2006 2008 2010
20
40
60
80
100
120
140
160
1994 1996 1998 2000 2002 2004 2006 2008 2010 201
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Table5Summaryofshortlagmodelstories
Model Story Positive Negative
(1)ASLSadjFSI
Structure+ Leverage CreditRiskcapital
Risk FXconcentration InterestRateRiskcapital
Return+
MarketCapitalization Commercialpropcredit
(2)ASLMRadj
Structure+ FXconcentration InterestRateRiskcapital
Risk EquityMktconcentration Shock_Liquidity
Liquidity Leverage Solvency
(3)BSLSadj
Structure+ FXconcentration Shock_Liquidity
Risk Leverage CreditRiskdisttosyststress
Return+ Liquidity
MarketCapitalization Solvency
(4)BSLMRadj
Structure+ FXconcentration InterestRateRiskcapital
Risk EquityMktconcentration CreditRiskcapital
Risk+ Return
ExpectedDefaultFrequency Commercialpropertycredit
(5)CSLSadj
Structure+ EquityMktconcentration CreditRiskdisttosyststress
Risk Connectivity Solvencydisttosyststress
Connectivity
(6)CSLMRadj
Structure+ EquityMktconcentration CreditRiskdisttosyststress
Risk+ Leverage InterestRateRiskcapital
Liquidity+ Return
ALmismatch InterestRiskDerivatives
(7)revDSLSadj2
Structure+ IntRateRiskdisttostress Solvencydisttosyststress
Risk TotalCreditcpi CreditRiskdisttosyststress
Risk+ Return
+ FXconcentration CreditRiskdisttostress
(8)DSLMRadj
Structure+ FXconcentration CommercialPropertycredit
Risk FXconcentration Solvencydisttosyststress
Return Interbankconcentration CreditRiskdisttosyststress
Legend: Structure Risk
Return Liquidity
7/30/2019 SAFE an Early Warning System for Systemic Banking Risk
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Table6InsampleregressionresultsforSAFEEWSshortlagmodels
VARIABLE SERIES EXPOSURE
(1)cpi
ASL
Sadj
(2)cpi
ASL
MRadj
(3)ta
BSL
Sadj
(4)ta
BSL
MRadj
(5)cpi
CSL
Sadj
(6)cpi
CSL
MRadj
(7)ta
DSL
Sadj
(8)ta
DSL
MRadj
RETURNVARIABLES
RET_1.1cpi 5 CapitalMarketsEquity(pricebased) 11.810(4.56) ***
RET_2cpi _ CapitalMarkets Bonds(pricebased) 7.723(4.16) ***
RET_4ta CapitalMarkets CommercialProperty(totalassetsbased) 7.958(6.93) ***
5.195
(2.74) ***
RET_4ta 5 CapitalMarkets CommercialProperty(totalassetsbased 10.673(5.06) ***
RET_5.2ta InterbankDerivativeExposure 1.192 (1.78) ***
RET_6cpi _ CurrencyMarkets InterbankExposures(pricebased) 3.076(2.86) ***RET_6ta CurrencyMarkets InterbankExposures(totalassetsbased) 2.193
(3.43) ***
3.686
(3.46) ***
1.023
(1.23)
3.686
(4.52) ***
2.600
(2.28) **
RET_9ta RiskTransferMarkets IRDerivatives(totalassetsbased) 4.298 (2.48) **
RISKVARIABLES
RSK_2 3 IRRIndicators throughthecyclefunction 11.536 (7.59) ***
RSK_2.1 _ IRRIndicators throughthecyclefunction 3.344(4.93) ***
1.655
(2.68) **
4.859
(5.40) ***
2.319
(9.07) ***
RSK_4 _ IRRIndicators pointintime/stressfunction 13.243 (4.33) ***
RSK_6 5_
IRRIndicators extremestress/crisisfunction 13.443
(4.63) ***
9.156
(2.66) **
5.095
(3.15) ***
RSK_7.1 _ CreditRiskIndicators throughthecyclefunction 13.191 (5.81) ***
7.290
(2.02) **
RSK_8a CreditRiskIndicators pointintime/stressfunction 3.281(4.38) ***
2.252
(2.81) ***
2.081
(2.66) **
1.301
(1.17)
2.588
(2.80) ***
2.809
(8.02) ***
RSK_9 _ EconomicValue:12callreportloanportfolios 99.5%BankCaR 2.588(2.16) ***
RSK_14 _ Solvency throughthecyclefunction 2.378 (3.42) ***
RSK_15 _ Solvency pointintime/stressfunction 3.514(1.74) *
RSK_16 _ Solvency extremestress/crisisfunction 4.554(3.90) ***
RSK_F _ InterestRateRisk normaldistancetosystemicstress 2.421(3.30) ***
RSK_G _ InterestRateRisk normaldistancetostress 2.811 (2.66) **
2.811
(10.32) ***
2.637
(2.73) ***
RSK_H _ CreditRisk stressdistancetosystemicstress 4.997 (3.86) ***
2.291
(1.46) ***
2.291
(1.78) *
53.223
(6.09) ***
RSK_H _ CreditRisk stressdistancetosystemicstress 8.422(1.70) *
8.422
1.86 *
12.133
(4.68) ***
RSK_I _ CreditRisk normaldistancetosystemicstress 4.036 (3.60) ***
RSK_I 5 CreditRisk normaldistancetosystemicstress 9.465(4.15) ***
9.465
(7.50) ***
5.924
(3.44) ***
RSK_K 4 CreditRisk normaldistancetostress 4.731(4.22) ***
RSK_L _