SAFE an Early Warning System for Systemic Banking Risk

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:[email protected]

2 Ph.D.candidateinEconomics,IndianaUniversityBloomington(formerly,EconomicConsultant,FederalReserveBankofCleveland. Email:[email protected])

3 EconomicAnalyst,FederalReserveBankofCleveland. Email:[email protected]

4

ProfessorofBanking,BadenWuerttembergCooperativeStateUniversity. Email:[email protected] VicePresident,RiskSupervisionandPolicyDevelopment,FederalReserveBankofCleveland. Email:[email protected]

6 DBAcandidateinFinance,ClevelandStateUniversity,EconomicConsultant,FederalReserveBankofCleveland. Email:[email protected]

* 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.


2/48Electronic copy available at: http://ssrn.com/abstract=1914862

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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).



(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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11

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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12

InsertTable3abouthere

OutofsampleresultsofthebenchmarkandbasemodelsareshowninTable4below. Viewedoutofsample,the

candidatebasemodelfailstooutperformthebenchmarkingmodelinrootmeansquareerror(RMSE)andbias(TheilU)

measures,butoffersmodestimprovementinmeanabsolutepercentageerror. Theshortlagbasemodel,however,

consistentlyimprovesonthebenchmarkingmodelinallthreestatisticalmeasures.


Table5belowsummarizestheshortlagmodelstoriesthatfurtherimproveonthecorestoryofthecorrespondingbase

modelinexplainingfinancialstressinsample. Itisclearthatthepositiveandnegativerelationshipswithfinancialstress

colorcodedastheyare,tendtofallintoessentiallytwostories:apositivestoryofstructureandnegativestoryofrisk30,

supplementedandenhancedbyadditionaltypesofreturnandliquidityimbalances,bothpositiveandnegative.31


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).




30 Thereasonthatriskimbalancesdescribeanegativerelationshipwithstressisthatbyconstructiontheyarepredominantly

defensivefunctionsofcapitalandsolvency.31 Thelonglagmodelstellfundamentallysimilarstoriesofpositivestructuralimbalancesandnegativeriskimbalances. The

correspondingtableisomittedforbrevity.


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13


(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.


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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14

measurementerrorsandisinherentlymuchmoreunstable. Manyofthepublicseriesfromtheoriginalspecifications

arepreserved. Removalofprivatesupervisoryseriesmostseverelyaffectstheriskvariables,andtoalesserextentthe

liquidityvariables. Thus,wecanexpectthatthosevariableswouldbemostaffectedwhenwetaketheprivatedataout

toonlyseethepublicformulationsoftheEWSmodels. Table11belowshowsthedistributionofcategory2data

(marketwith)andcategory3data(markedwith)amongtheimbalanceclasses. Table12showsproportionof

supervisoryvariablesamongthespecifiedindependentvariables.



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.


Fromthepointofviewoffinancialinstitutions,itisclear,thatevenpublicdatabasedsystemicriskEWSmodelswould

allowinstitutionstostudythecorrelationsandsensitivitiesoftheirexposuresandstructuralpositionswithinthe

financialsystemandusetheframeworktoenhancesystemicriskstresstestingandscenarioanalysis.

ThiscasestudyonlyconsiderstherelativeoutofsampleperformanceofpublicandprivateSAFEmodels. Manyinterestingquestionslieaheadinthislineofinvestigation. Forexample,futureworkcanaddressadditionalanalytical

questions,suchas(a)whatfactorsmatteredmostintherecentcrisis,(b)whatmaybetheresultsoflikelihoodtestsfor

StructuralCs(concentration,connectivity,contagion),and(d)whatmaybetheresultsoflikelihoodtestsforblocksof

datatriggeredbybehavioraleffects.


(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.


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.


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.


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.


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


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19

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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20

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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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25

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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26

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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29

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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30

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


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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 _

Documents

SAFE an Early Warning System for Systemic Banking Risk