What influences banks’ choice of credit risk management practices?

Contents lists available atScienceDirect

Journal of Financial Stability

journal homepage:www.elsevier.com/locate/jfstabil

What influences banks’ choice of credit risk management practices?

Theory and evidence 夽

Dilek Bülbül

^a

, Hendrik Hakenes

^b,∗

, Claudia Lambert

^c

aFrankfurtUniversityofAppliedSciences,Nibelungenplatz1,D-60318FrankfurtamMain,Germany

bUniversityofBonnandCEPR,InstituteofFinanceandStatistics,Adenauerallee24-42,D-53113Bonn,Germany

cEuropeanCentralBank,Sonnemannstr.20,60314D-Frankfurt,Germany

a r t i c l e i n f o

Articlehistory:

Received8December2014

Receivedinrevisedform7November2018 Accepted9November2018

Availableonline14November2018

JELclassification:

G21 L10 Keywords:

Banking Riskmanagement Creditrisk

Creditportfoliomodeling Creditrisktransfer

a b s t r a c t

Banksusedifferentriskmanagementpracticeswithvaryinglevelsofsophistication.Thispaperexamines thefactorsthatdeterminethechoiceofrisk-managementpractices.Inatheoreticalmodel,weidentify twomaindeterminantsforthechoiceofriskmanagementtools:bankcompetitionandsectorconcen- trationintheloanmarket.Weempiricallytestthepredictionsofourmodelusinghand-collecteddata onthecreditriskmanagementof249Germansavingsbanks.Theresultsareinlinewithourtheory:

Competitionpushesbankstoimplementadvancedriskmanagementpractices.Sectorconcentrationin theloanmarketpromotescreditportfoliomodeling,butitinhibitscreditrisktransfer.

©2018ElsevierB.V.Allrightsreserved.

1. Introduction

Credit risk management has evolved immensely in recent decades.Duetoadvancesininformation technology,themoni- toringofcreditriskhasimproved.Duetofinancialinnovations, risksharinghasbecomeeasiertoaccess.Somebasiclevelofrisk managementisimposeduponbanksbyfinancialregulation.Above this basiclevel,bankscan choosewhethertogatheradditional

夽WethanktheGermanSavingsBanksAssociation(DSGV)forprovidingthedata usedinthisstudy.WearegratefultotheparticipantsoftheSouthernFinance AssociationConferenceinAsheville,NorthCarolina,theINFINITIConferenceon InternationalFinanceinDublin,theInternationalRiskManagementConferencein Florence,theInternationalTorVergataConferenceinBankingandFinanceinRome, theConferenceoftheSwissSocietyforFinancialMarketResearch(SGF)inZurich, andtheFinancialEngineeringandBankingSocietyConference(FEBS)inLondonfor usefulcommentsandsuggestions.Thepaperrepresentstheauthors’personalopin- ionsanddoesnotnecessarilyreflecttheviewsoftheinstitutionswithwhichthey areaffiliated,orthoseoftheGermanSavingsBanksAssociation.Fundingfromthe CRCTR224isgratefullyacknowledged.Allremainingerrorsareourown.

∗Correspondingauthor.

E-mailaddresses:bulbul@fb3.fra-uas.de(D.Bülbül),hakenes@uni-bonn.de (H.Hakenes),claudia.lambert@ecb.europa.eu(C.Lambert).

informationoncreditrisk,andwhethertosharecreditrisk.The determinantsofthesechoicesarevirtuallyunexplored.

Attheheartofourpaperisourownsurvey,collectingdataon riskmanagementpracticesofGermansavingsbanks.Bankswere askedwhethertheygatherinformationoncreditrisk(creditport- foliomodeling,CPM),whethertheymitigatecreditriskbypooling loansorwithcreditderivatives(creditrisktransfer,CRT),orboth (advancedriskmanagement,ARM).Thesepracticesgobeyondthe riskmanagementmandatedbyfinancialregulation.

Inthis paper,weexaminethefactorsdrivingbanksdecision toimplementaspecificsetofriskmanagementinstruments.We aimtounderstandwhatexplainscross-sectionaldifferencesinthe depthofimplementationofriskmanagementinbanks.Forthispur- pose,wefirstconstructamodelthatcapturesCPMandCRT,and weshowunderwhichconditionsbanksaremorepronetoimple- mentCPMorCRT,orboth.Wethentestthesehypotheses,using ourhand-collecteddataset.

Inthemodel,abankhasaportfoliooftwoloansofunknown correlation.Usingcreditportfoliomodeling,itcanfindoutthecor- relationstructure,andusethisinformationtoadjustitsriskbuffer, forexampleliquidity,equityorreserves.Usingcreditrisktrans- fer,itcanpoolloans,thatwayspreadingthesameaggregaterisk overmorebanks.Advancedriskmanagementmeansthatthebanks https://doi.org/10.1016/j.jfs.2018.11.002

1572-3089/©2018ElsevierB.V.Allrightsreserved.

doesboth.Inthemodel,thehigherthesectorconcentrationinthe loanmarketthemorevaluabletheadditionalinformationbyCPM becomes.However,inaveryconcentratedregion,theadditional information byCPM becomes lessvaluable, asthe bankknows abouttheextremecorrelation.Withahigherlevelofcompetition betweenbanks,itismorecostlytobuildupabuffer,sofine-tuning becomesmorevaluable.Weprovethreegeneralpropositionsthat analyzehowthedesirabilityofthethreepracticesdependsoncom- petitionandsectorconcentration.

We then bring the implications of ourtheoretical model to thedata.Tothis end,we needdetailedbank-level data,inpar- ticularinformationontheexplicituseofcreditriskmanagement tools;datathatistypicallyhardtoobtain.Therefore,asmentioned above,weconducted apaper questionnairesurvey toelicitthe necessaryinformationoncreditriskmanagement.Thesurveywas distributedamong438savingsbanksoftheGermanSavingsBanks Finance Groupin 2009. In total, 279 completedquestionnaires werereturned;aresponserateof63.7%.Wecombinedthisdata withdetailedbalance-sheetdata,whichisnotpubliclyavailable, income-statementdataandregionaleconomicdata.

Consequently,we candirectlyrelatetheuseofdifferentrisk managementinstrumentstobankcharacteristics,andtomarket and regional conditions. Furthermore,our sample allows for a bank-levelanalysisofbankcompetitionandsectorconcentration becausethebusinessactivitiesofthebanksinoursamplearelim- itedtoaspecificgeographicalarea,followingtheso-called“regional principle”.Finally,weareabletoprovideunbiasedresultsbecause thebanksinoursamplefaceequalaccessconditionstoimplement creditriskmanagementinstrumentsandcanaccessthesamecredit riskmanagementinstruments.Theyoperatewithinthesamereg- ulatoryenvironmentandhaveacommonbusinessmodelbutare legallyandeconomicallyindependentintheirbusinessdecisions.

Our results show that differences in the implementation of advancedriskmanagementcanbeexplainedbydifferencesincom- petitionandsectorconcentration:Banksthatactinacompetitive environmentwithhighlyconcentratedsectorsaremorelikelyto implement sophisticated credit risk instrumentsand measures.

Wefindevidencethatmanagingriskviacreditportfoliomodels isprevalentwhenthesectorconcentrationisrelativelyhigh. In thiscase thebankcanlearn thecorrelationinitsportfolio and adjust(precautionary) capital buffers relative totherisk ofthe loanportfolio.Thedepthofimplementationandtheintegration of a credit transfer technologyis primarilydriven by competi- tionamongbanks.Ifinterestmarginsonoriginatedloansarehigh (whichistypicallythecaseinanenvironmentwithlowcompe- tition),then theprobability ofdefaultissmalleven ifloansare notsecuritized.Thebenefitofimplementingacreditrisktransfer technologyisthereforemostbeneficialwhencompetitionishigh.

Contrariwise,thelower thesectorconcentration,thelargerthe benefitofthecreditrisktransfertechnologysincetheprobability ofarrivingatabalancedportfoliobecomeslarger.

Ourpaperisrelatedtotwoseminaltheoreticalpapersonthe impactofcompetitiononbanks’risk-takingbehavior(Boydand DeNicolo,2005;Keeley,1990)thatarriveatdifferentconclusions:

Underthecompetitionfragilityviewbankschosemoreriskyport- foliosin anenvironment withhighcompetition(Keeley, 1990), whereasunderthecompetitionstability viewincentivemecha- nismsare disclosed that can explain riskierportfolios in more concentratedmarkets.Theseresultsaresupportedbyempirical papers,forexampleJiménezetal.(2013)andBergstresser(2004) whoidentifyanegativerelationshipbetweenmarketpowerand risk-taking,in linewiththe franchisevalueview.Hakenes and Schnabel(2010)showwhat happens whenthequalityof loans increditrisktransfermarketsisprivateinformation:banksissue unprofitableloansthatcontributetoaggregaterisk,asituationthat isevenexacerbatedwhencompetitionishigh.

Ourpapercomplementsthisliteraturebyexplicitlytestinghow concentrationinfluencesbanks’decisiontoengagein advanced creditriskmanagement.Closelyrelatedistheliteraturethatinves- tigatesconditionsunderwhichsophisticatedmanagementcontrols areextensivelyused,showingthatinparticularfirmsundercom- petitivepressureusesophisticatedcontrolsmoreextensivelyand more selectively than firms that face less intense competition.

Numerous(mainlyempirical)studiesexaminethefactorsunder- lyingbanks’decisionstousederivatives(SinkeyandCarter,2000;

Ashrafetal.,2007).SinkeyandCarter(2000)findthatuserbanks, comparedtononusers,areassociated withriskiercapitalstruc- tures,largermaturitymismatchesbetweenassetsandliabilities.

Otherrelevantstudiesidentifythecostoffinancialdistressandthe existenceofcapitalmarketimperfectionsasrationalestoactively managerisk(FrootandStein,1998;Frootetal.,1993;Stulz,1984).

Toourknowledge,therearenopapersspecificallyinvestigatingthe underlyingdecisionstoadoptcreditportfoliomodels.

Ourpaperisfurtherrelatedtovariouspapersonriskmanage- ment,studyingbothquantitativeandqualitativeaspectsagainst thebackgroundthatriskmanagementhasbecomeoneofbanks’

mainactivities(AllenandSantomero,1997)andessentiallyoneof banks’corecompetences(Hellwig,2010;Hakenes,2004).Papers thatstudyqualitativedimensionsofriskmanagementincludeEllul andYerramilli(2013),wholinkorganizationalriskcontrolsand risktaking atbank holdingleveland Fahlenbrachet al.(2012), whoshowthatpersistencein(bad)riskculturemakebankssensi- tivetocrises.Researchonquantitativeaspectsofriskmanagement (e.g.techniquesandinstruments)includeCebenoyanandStrahan (2004),whoshowhowdifferencesintheintensityofadvancedrisk managementaffectinvestmentdecisions,thevalueofafirm,and itsprofitability.Mintonetal.(2009)analyzewhethercreditderiva- tivesgenerallycontributetowardssounderbanks,a pointoften madebyregulatorspriortothecrisis.

Whereasearlier researchfocusedonindividualriskmanage- mentinstruments,ourstudyexpandsonpriorworkbymodeling and empirically investigating banks’ motivation to engage in advancedriskmanagementthroughbothcreditportfoliomodels andparticipationinthecreditrisktransfermarkets.Thismoreinte- gratedviewofadvancedriskmanagementprovidesinsightsofthe interplayofriskmanagementpractisesanditsdrivers.

Theremainderofthepaperisstructuredasfollows.Section2 developsthemodelandderivesthepredictions,whicharelater testedempirically.InSection3,we takeourmodeltothedata.

Section4concludesthestudy.

2. Amodelofbankriskmanagement

Inthissection,weconstructafrugaltheoreticalmodelonbank riskmanagement.Themainrequirementisthattheendogenous variables in ourdata,thechoiceof risk management tools,are alsoendogenousinthemodel.Therighthandsidevariablesinthe regression,sectorconcentrationandbankcompetition,areexoge- nousparametersinthemodel.Thatway,themodelcanbeusedto generatetestablehypotheses.

Theoriesonriskmanagement.Theliteratureoncredit risk transferis largeand growing, especiallydue tothesignificance ofCRTduringthe2007financialcrisis.Mostpapers,suchasthe seminalPennacchi(1988)andParlourandPlantin(2008),focuson banks’monitoringactivities,thusconcentratingmoreonexpected returnsratherthanonrisk.Onenotableexceptionisthecontribu- tionbyFeessandHege(2013),wherebankscanscreenloansand choosehowfartoparticipateinsystemicrisk.Banksthenspecial- ize:onlysomebanksimplementsophisticatedscoringsystems.

Some recent papers consider the effect of risk transfer and diversification onfinancial (in)stability (see (Allen and Carletti,

2006;Wagner,2010;Purnanandam,2011;Ibragimovetal.,2011;

Stephensand Thompson,2017;Van Oordt,2017),just toname afewimportantcontributions).Ourpaperfocusesonthedeter- minantsofasinglebank’sdecision,abstractingfromanymacro effects.

ThesecondtypeofriskmanagementinourmodelisCPM.Itcon- sidersabanks’benefitofsimplyassessingtheriskstructureofits portfolio.Ofcourse,thereisawealthofresearchonthestatistical andtechnicalaspectsofthequantificationofbankrisk.Inmanythe- oreticalmodels,banksscreentheirborrowers,hencetheycollect informationontheriskofasingleloan.Wearenotaware,how- ever,ofanytheoreticalmodelwherebanksgatherinformationon itsportfoliostructure.Inreality,riskmanagementcomprisesboth, informationonsingleassetsandoncorrelations.

2.1. Themodel

Competition.Inastaticmodel,thereisabankthatholdsaport- foliooftwoassets,eachwithavolumeof1.Theexpectedreturnof anassetisR>1,suchthatRcanbeinterpretedasameasureof competition,whichistreatedasexogenousinthemodel.AhighR denoteslowcompetition,andviceversa.Assetsarerisky.Forexpo- sition,assumethatthereturnisnormallydistributedwithstandard deviation.Henceforth,letuscalltheassetsloans,bearinginmind thattheycouldbeanytypeofriskyasset.

Thebankcanchooseitsbalancesheetstructure,consistingof depositsdandequityk.Thebalancesheetequationisd+k=2,thus d=2−k.Acapitalrequirementwouldstipulatek≥˛2underthe standardizedapproach,otherapproachesarediscussedbelow.A leverageratiowouldalsostipulatek≥˛¯·2.Depositorsdemanda constantgrossreturnofr_d(equalto1plusthenetrateofreturn).

Depositsarecoveredbydepositinsuranceandthedepositrateis normalizedtozero,thusr_d=1.Toobtainaninteriorsolutionforthe capitalstructure,andforsimplicity,assumethatthecostofequityis increasinginvolumeandtherateisr_k=1+k/2.Ifthebankcannot repaydepositsfromtheirloanportfolio,itdefaultsatacostc>0.

Notethatkcanalsobeinterpretedasabufferorreserveagainst potentialloanlosses.Forourresults,itiscrucialthatthebankhas achoicevariablewhoseoptimalchoicedependsoncreditrisk.

Sectorconcentration.Loanscomefromdifferentcorrelation classes,whichwecallindustrialsectors.Sectorshavemasses₁, ₂,...,suchthat

i_i=1.Thereturnsofloansfromthesamesec- torhaveahighercorrelationthanthereturnsofloansfromdifferent sector.For simplicity,set=_H withina sector,and =_L<_H betweensectors.Tobeprecise,assumethattheriskstemsfrom threefactors:onecommonfactorwiththedistributionN(0,0),one idiosyncraticfactorwiththedistributionN(0,_i),andonesector- specificfactorwiththedistributionN(0,_S).Fortwoloansfromthe samesector,thesector-specificfactorisidentical;otherwisethis factorisstochasticallyindependent.Thenthestandarddeviation ofaloanis

=

₀²+_i²+_S²,

andthecorrelationbetweentwoloansiseither

H= ²₀+_S²

₀²+_i²+_S² or L= ₀² ₀²+_i²+_S².

Sector concentration can be measured by the Herfindahl–Hirschman index (HHI),

i²_i. It is assumed to bepublicinformation,andhasasecondinterpretation.Ifthebank

pickedtwo loansatrandomout ofthepool,then theexpected correlationwouldbe

E[]=²₀+(

i,j_iji,j)_S²

²₀+_i²+_S² = ²₀+HHI_S² ₀²+_i²+²_S.

ThesectorconcentrationHHIisthussimultaneouslyameasurefor the(lackof)diversificationwithina“natural”loanportfolio.Finally, assumethatoneofthesectorshasdiscretemass>0,whereas theothershaveinfinitesimalmass.TheHHIthenequals².Aswe willsee,thisassumptionsimplifiesthediscussionofportfolioswith manyloans.

Riskmanagementtools.Thebankhasaccesstothreeriskman- agementtools.Firstofall,someriskmanagementismandatory.

Becauseweareinterestedinthebanks’choicebetweenriskman- agementtools,weassumethattheloanshavethecharacteristicsR andafterthebankhasappliedthesemandatorymethods.Next, creditportfoliomodeling(CPM),isapassiveriskmanagementtool.

Thebanklearnsthecorrelationstructureofitsloanportfolio,at a costc_CPM.WithoutCPM,thebankhasexpectationsaboutthe likelycorrelationbetweenitsloans.WithCPM,itlearnswhether thecorrelationisHorL.Intheabovecontext,thebanklearns whethertheloansareinthesamesectorornot.Bankscanusethe informationgeneratedbyCPMtofine-tunetheircapitalstructure, dependingontheirportfolio.WithouttheinformationfromCPM, theexpectedcorrelationoftwoloansisequaltoHHI;withCPM,it iseither_H(ifloansareinthesamesector)or_L.

Thesecondtooliscalledcreditrisktransfer(CRT).Itcostsc_CRT toimplement.Abankoriginallyhasabalancesheettotalof2,it cangranttwoloans.WithCRT,itcansellafractionoftheseloans, andusethereceiptstograntnewloans.Onecouldthinkofthe securitizationofloans,ortheuseofcreditderivativesinorderto recycleregulatorycapital.Letusassume,however,thatthisprocess cannotbedrivenadinfinitum.Forconcreteness,assumethatthe banksells50%ofeachloan,andgrantstwomoreloans,ofwhich againitsells50%.Thebalancesheettotalisthenagain2.Thesame allocationwouldbeobtainedfrominitiallygrantingtwoloans,then securitizingandselling50%ofeach,andthenusingthereceipts tobuysecuritizedloansfromanotherbank.CRTisthusawayto diversify.¹

Finally,thebankcanbemaximallyadvancedinitsmanagement ofcreditriskbyimplementingbothCPMandCRT.Thisiscalled advancedriskmanagement(ARM),itcomesatacostofc_ARM.Pos- sibly,c_ARM =/ c_CPM+c_CRTdueto(dis)economiesofscope.Thisway, abankcanbothdiversifyandfine-tunetheirbuffers.Notethatthe valueofCPMdependsonwhetherthebankalsousesCRTornot.

Hence,ARMismore(orless)thanthesumofitscomponents,CPM andCRT.Therelativevalueofeachstrategy,CRT,CPMorARM,will dependonparameters,especiallythelevelofcompetitionandthe sectorconcentration.

Asmentionedabove,thecapitalrequirementunderthestan- dardizedapproachwouldbek≥˛·2.UnderthefoundationalIRB approach,itwouldbealsobek≥˛·2inourmodel,possiblywith

˛ =/ ˛.Thereasonforthesimilarstructureis thatRand are assumedtobetheloancharacteristicsaftermandatoryriskman- agementprocedures,andthebankknowsbothRand.Underthe foundationalIRBapproach,thebankscanonlyestimatethePDfor individualclients,sotheinformationfromthevoluntarymethods isuseless.Inourmodel,theadvancedIRBwouldstipulatek≥˛(m₁, m₂,...)2,wherem₁,m₂,...arethevolumesofloansinthedifferent sectors1,2,....Becausethesectorsareassumedtobesymmetri-

1Inthemodelthereisonlyonebank,thusfinancialnetworksandcontagion cannotbemodeled.Inreality,ifbanksinsurecreditriskusingcreditderivatives,its individualriskmaydecrease,butfinancialfragilitymayincrease,seeKrauseand Giansante(2012)andothercontributionsonthatspecialissue.

cal,˛(·)willtypicallybeinvariantwithrespecttopermutations ofthevector(m1,m2,...).Therewillalsobesomemonotonicity, suchas˛(2,0,...)>˛(1,1,...).Inouranalysis,wewillconcentrate onbanksthatuseeitherthestandardizedofthefoundationalIRB approach,anddiscusstheadvancedIRBapproachattheendofthis section.

2.2. Theoptimalstrategy

Theoptimalriskmanagementstrategydependsonthecostof implementation,andonthevalueoftheaccordingstrategy.We startwithcalculatingtheexpectedprofitifthebenchmarkcase, wherethebankusesneithercreditportfoliomodeling(CPM)nor creditrisktransfer(CRT).Wethencalculatethebank’sexpected profitaftertheimplementationofcreditportfoliomodeling(CPM).

Ifthedifferencebetweenthetwoexceedsthecostc_CPM,thebank willpreferCPM,andviceversa.Wecontinuewiththesamecalcula- tionforcreditrisktransfer(CRT).ARMisthesumofCPMandCRT, hencewemustcompareittothebetterofthesetwo.Wederive comparativestaticresultsforasituationwhenthebankprefers CPM,CRTorARM.

Thebenchmarkcase.In thebenchmarkcase, thebankuses noneof the above risk management instruments. In reality, of course,banksarerequiredbylawtohavesomebasicriskman- agement.Weareinterestedintheendogenousmethodchoiceof banks,hencethesebasicinstrumentsareoutsidethefocusofour model.Thebankhasabalancesheettotalof2,henceitgrantstwo loanswiththemeanreturnof2R.Theseloanshavehighcorre- lation_HwithprobabilityHHI=²,theyhavelowcorrelation_L withprobability1−².Ifbothloansareinthediscretesector,the standarddeviationoftheaggregateportfoliois

_H²=²+²+2H²=2

1+ ₀²+_S² ₀²+²_i +_S²

(₀²+_i²+_S²)=4²₀+2_i²+4_S².

HencetheaggregatereturnYisnormallydistributedwithmean2R andstandarddeviation_H²,thusY∼N(2R,_H²).Thebankhasdebt (deposits)ofd=2−k.Wewanttodeterminetheprobabilitythat theyieldcannotcoverdepositrepayments,Y<2−k.Theprobability ofsuchfinancialdistressisgivenbytheprobabilitythatthe PD₁=Pr{Y<2−k}=

2−k−2R _H²

, (1)

where(·)isthestandardnormaldistributionfunction.Withprob- ability2(1−),oneoftheloansisinthediscretesector,theother isinoneoftheinfinitesimalsectors.Withprobability(1−)²,both loansareinoneoftheinfinitesimalsectors.Inbothcases(aggregate probability1−²),thestandarddeviationisthen

_L²=²+²+2L²=2

1+ ₀² ₀²+_i²+_S²

(₀²+_i²+_S²)=4²₀+2_i²+2_S²,

thusY∼N(2R,_L),andtheprobabilityofdistressis PD₀=Pr{Y<2−k}=

_2−k−2R

L

. (2)

Fromnowon,set₀=_i=0withoutlossofgenerality,toconcen- trateontheeffect ofthe largerorsmallercorrelationbetween sectors. The aggregate expected profit of the bank equals the expectedreturn,netofrefinancingcostsandtheexpectedcostof financialdistress,

=2R−drd−k(1+k/2)−k²/2−c(²PD1+(1−²)PD0)

=2R−2−k²/2−c(²PD1+(1−²)PD0).

(3)

Fig.1. Optimalcapitalbufferk^*dependingoncompetition(lowR)andsectorcon- centration.Thisfigureshowstheoptimalk^*dependingonRfortheextremecasesof HHI=²=1andHHI=²=0.Inthenumericalexample,c=10,=2,and=0.2.This numericalexampleisusedthroughoutthemodelingsection.Fordifferentparam- etervalues,thepictureisqualitativelyidentical.Twothingscanbeseenfromthis figure.First,thebankwillholdhigherequitybuffersforhighcompetition(lowR).

Forhighcompetition,thebank’sprofitsaresmall,sothebankpreferstoinsureitself againstdistresswithahighercapitalbufferk^*.Second,forahighsectorconcentra- tionHHI,thebankprefershigherbuffersk^*.Thereasonisthatthebankdoesnot knowtheprecisecorrelationstructurebetweenloans,butithasexpectationsfor agivensectorconcentration.Thehighertheconcentration,themorelikelyarethe loanstobecorrelated.Morebuffersarethenneeded.Bothcomparativestaticsare unsurprising.Themoreimportantquestionis,howcanbuffersbesavedbytheuse ofCPM,andunderwhatconditions(competition,sectorconcentration).

Thebankwillchoosethebufferktomaximizetheexpectedprofits,

∂

∂k =−k^∗+²X+√

2(1−²)X² 2√

2 c=0, where

X =exp

−(2−k^∗−2R)² 8²

(4)

is an auxiliary variable. There is no algebraic solution to this implicitdefinitionofk^*.However,theimplicitfunctiontheorem canbeusedtocomputesomecomparativestatics. Mostimpor- tantlyforthis paper,∂k^*/∂R<0.Themore competitionbetween banks,thesmallertheirinterestmargins,andthesmallertheR,the morebuffersbanksneedtoholdagainstfinancialdistress.Second,

∂k^*/∂>0.Intheabsenceofcreditportfoliomanagement,banks donotknowtheexactcorrelationstructureoftheirloanportfolio.

However,ifthesectorconcentrationishigh,theprobabilityofacor- relatedportfolioislarge,hence,thebankwillholdhigherbuffers (Fig.1).

Creditportfoliomodeling(CPM). By implementinga credit portfoliomodel(CPM),thebankfindsoutthecorrelationwithin theirloanportfolio.Inotherwords,itdetermineswhethereachof theloansisinthediscretesector.Usingthisinformation,itcan fine-tunethebuffer.Ifitfindsthecorrelationinitsportfoliotobe high,theaggregatestandarddeviationishigh,anditneedslarger buffers.

Wenowcalculatethebenefitofthispieceofinformation.With probability²,thebankfindsthatbothloansareinthediscretesec- tor,hence,theyareperfectlycorrelated.Theprobabilityofdefault isthenPD₁,asdefinedabovein(1).Thebankwillthenmaximize ₁=2R−2−k²/2−cPD₁. (5) Thisexpectedprofitismaximizedfork^∗₁,asdefinedby

∂₁

∂k =−k^∗₁+ c 2√

2 X=0, (6)

whereXistheauxiliaryvariabledefinedin(4).If,withprobability 1−²,thebankfindsthattheloansareuncorrelated,theprob- abilityofdefaultisPD0,asdefinedin(2).Theexpectedprofitis

0=2R−2−k²/2−cPD0,andthebankcanreducethebufferto k^∗₀,accordingtothefirstordercondition

∂₀

∂k =−k^∗₀+ c 2√

2

√2X²=0. (7)

Exante,theexpectedprofitisthentheaverageof₁and₀, _CPM=²₁+(1−²)₀. (8) Thus,thebenefitofcreditportfoliomodelingequalsthedifference betweentheexpectedprofitswithand withouttheinformation aboutcorrelations.Somefactsareintuitive.Forexample,if=0, thenalltheloansinaloanportfoliomustbeuncorrelated.Conse- quently,thecorrelationstructureisalreadyknown,andthevalue addedbyfurtherinformationiszero.For=1,alltheloansina portfolioareperfectlycorrelated,andnothingmorecanbelearned.

Again,thevalueofadditionalinformationiszero.Third,thevalue oftheinformationcanneverbenegative.Wearriveatthefollow- ingproposition,deliveringtwohypothesesthatwillbetestedin theempiricalsectionofthepaper.

Proposition1(Creditportfoliomodeling). Forhighercompetition (lowerR),CPMbecomes(weakly)moredesirable.Forlarger sector concentration(higher,uptoacertainlevel),CPMbecomes(weakly) moredesirable.

Theproof isin Appendix A.As a directconsequence, ceteris paribus,abankinaregionwithhighsectorconcentrationwilltend toimplementCPM.Abankundertensecompetitionwillalsotend toimplementCPM(Fig.2).Letusnowdiscusstheimplicationsfor thesecondriskmanagementtool,creditrisktransfer(CRT).

Creditrisktransfer(CRT).Assumenowthatthebankcanimple- mentacreditrisktransfer(CRT)technology.By doingso,itcan securitizesomepartofeachloan,thusincreasingthenumberof loansitcangrant.Asarguedabove,weassumethatthisprocess cannotbedrivenadinfinitum(otherwisebankswouldendupwith perfectlydiversifiedportfolios).Only50%ofeachloancanbesecu- ritized,thebankkeepstheother50%initsbooks.Thisimpliesthat, withabalancesheettotalof2,thebankcangrant4halfloans.With thecorrelationstructureasbefore,therearefivedifferentpossible constellations:(i)alltheloanscancomefromthediscretesector (probability⁴);(ii)allbutoneloancancomefromthediscrete sector(probability4³(1−));(iii)allbuttwoloanscancome fromthediscretesector(probability6²(1−)²);(iv)onlyone loancancomefromthediscretesector(probability4(1−)³);

and(v)allloanscanstemfromtheinfinitesimalsectors(probability (1−)⁴).Dependingonthecorrelationstructure,theprobability ofdefaultwilldiffer.However,thebenefitofCRTconsistsonlyin theincreaseddiversificationwithintheportfolio.Intheabsenceof furtherinformation(thatcouldstemfromCPM),thebankcannot adjustbufferstothedifferentconstellations.

Inthefirstscenario(case(i),probability⁴),thestandarddevia- tionoftheportfoliois2,hence,theprobabilityofdefaultisPr{Y<

2−k}=(^2−k−2₂ ^R).Inthesecondscenario(case (ii),probability 4³(1−)), three loans are correlated, thefourth is indepen- dent.Thestandarddeviationis

(3/2)²+(/2)²=

5/2,and accordingly,theprobabilityofdefaultis(²√^−k−2R

5/2).Inthethirdsce- nario(case(iii),probability6²(1−)²),twoloansarecorrelated, andallothersaremutuallyindependent.Thestandarddeviation is

(2/2)²+(/2)²+(/2)²=

3/2,and theprobabilityof defaultis(^2−k−2√ ^R

3/2).Finally,inthelattertwocases(iv)and(v), alltheloansarestochasticallyindependent,sowithprobability 4(1−)³+(1−)⁴, theportfoliohasmaximal diversification.

Thestandarddeviationis

(/2)²+(/2)²+(/2)²+(/2)²=, andtheaccordingprobabilityofdefaultis(^{2−k−2 R}).Takingthese defaultprobabilitiesintoaccount,thebankwillsettheoptimal

Fig.2.Differenceinprofits,creditportfoliomodeling(CPM)vs.benchmark.Param- etersinthisfigurearethesameasinFig.1.Itshowsthedifferencebetweenthe expectedprofitswithandwithoutCPM,fordifferentdegreesofcompetitionand sectorconcentration.Oneachcurve,thebenefitofimplementingCPMisconstant.

Thedifferenceinprofitsisplottedonthecontours(inpercentagepointsofthebal- ancesheettotal).LightshadingmeansthatthebenefitofCPMissmall,darkgray impliesthatthebenefitislarge.Forexample,takeR=1.0and=0.4.Thecorre- spondingpointinthefigureisexactlyonthe0.8-curve.Thisimpliesthatthebenefit ofhavingCPMimplementedis0.8%ofthebalancesheettotal.IfthecostofCPM werecCPM=0.8,thebankwouldbeindifferentwithrespecttoitsimplementation.

ForcCPM<0.8%·2=0.016,itwouldgoaheadandimplementCPM.Onecanthusread thefigureasfollows.ForagivencCPM,findtheaccordingcurve.Thebankwillimple- mentCPMforallparameterconstellationsnortheastofthiscurve.Therearetwo apparentproperties.First,CPMisespeciallyvaluableifcompetitionislarge,hence, Rissmall.IfRislarge,theprobabilityofdistressissmallevenintheabsenceof buffers.Regardlessofwhethertheportfolioiscorrelated,thebankwillholdonly smallbuffers.Therefore,theimpactofCPMinformationonthebank’sbehaviorwill bemarginal.Asaconsequence,theinformationisnotvaluable.Incontrast,ifcom- petitionishigh,thebankwilllikelysufferfinancialdistress,anditwillholdlarge bufferstoinsureagainstdistress.Bylearningthatitsportfolioisrelativelybalanced, thebankcansaveamajorfractionofthesebuffers.Hence,theCPMinformation isvaluableifcompetitionishigh.Second,CPMisespeciallyvaluableifthesector concentrationislarge.Thereason,asmentionedabove,isthatfor=0,thecorre- lationstructurecanbeguessedevenintheabsenceofCPM.(Thesamewouldtrue for=1,butgiventhat²equalsthesectorHerfindahl–Hirschmanindex,will realisticallybecloserto0thanto1.Therefore,wehaveplottedthisfigureonlyfor 0≤≤1.)Hence,thelargerthesectorconcentration,themorecanbelearnedabout theportfoliostructure,andthemorevaluableCPMbecomes.

bufferk^*.Again,wearriveatapropositioncontainingtwohypothe- ses,whichwillbetestedintheempiricalsectionofthepaper.

Proposition2(Creditrisktransfer). Forhighercompetition(lower R),CRTbecomes(weakly)moredesirable.Forlargersectorconcentra- tion(higher),CRTbecomes(weakly)lessdesirable(Fig.3).

Advancedriskmanagement(ARM).Wehaveconsideredthe benefitstobanksofgatheringinformation abouttheirportfolio structure(CPM),anddiversifyingtoreducethegranularityoftheir loanportfolio(CRT).Nowletusdefineadvancedriskmanagement (ARM)asthechoicetoimplementboth.Inourmodel,thisisthe mostsophisticatedlevelofriskmanagement:riskismeasuredand diversified,andthebuffersareadjusted.UsingARM,abankcan learnexactlyhowitsportfolioisstructuredwithinitsportfolio, endingupinfivecases,asdiscussedabove:(i)allfourloanscanbe correlated;(ii)allbutonecanbecorrelated;(iii)allbuttwocan becorrelated;or(ivandv)allmaybeuncorrelated.Ineachcase, thebankwillthensetadifferentbuffer.Inthefirstcase,thebuffer willberelativelyhigh,andinthelastcase,itwillberelativelylow.

Calculatingtheprofitsinallfourscenarios,weightingthemwith theaccordingprobabilities,andcalculatingtheaggregateexpected profits,wecancalculatethebenefitsofARMincomparisontothe second-bestalternative.BecauseCRTandCPMalwaysdominate

Fig.3. Differenceinprofits,creditrisktransfer(CRT)vs.benchmark.Thisplotshows thedifferenceoftheprofitswithCRTandthebenchmarkcase.Again,darkgray denotesforlargebenefitsofCRT,andwhitedenotesforsmallbenefits.Notethata morediversifiedportfolioalwayshasasmallerprobabilityofdefault.Therefore,the bankcaneconomizeonbuffers.Hence,theprofitswithCRTalwaysexceedthosein thebenchmarkcase.Weobserveanumberoffurtherproperties.First,thehigherthe competition(lowerR),themorebeneficialcreditrisktransferbecomes.Theintu- itionissimilartothatforCPM.IfRisratherlarge,thentheprobabilityofdefaultis smallevenintheabsenceofCRT.CRTthenlowerstheprobabilityofdefaulteven further.However,giventhatthePDisalreadyatalowlevel,thebenefitcannotbe large.Hence,ifcompetitionislow,thereisnotmuchscopeforlargebenefitsfrom CRT.Inthefigure,theshadingiswhiteforlargeR.ForsmallerR,theargumentgoesin theoppositedirection,hence,thebenefitsfromCRTcanbelarge,andtheshadingin thenumericalexampleisdarker.Second,thebenefitofCRTishighestifsectorcon- centrationislow.Tounderstandwhy,taketheextremeof=1.Then,bothloans areperfectlycorrelatedwithprobability1.Iftheseloansaresecuritized,thetwo newloanswillalsobeperfectlycorrelated.Thecorrelationstructureisunchanged byCRT.Thusfor=1,thebenefitofCRTisexactlyzero.Thelowerthesectorcon- centration,thelargerthebenefitofCRTisbecausetheprobabilityofarrivingata balancedportfoliobecomeslarger.Therefore,wehavedarkershadingespeciallyfor lowdegreesof.

thebenchmarkcase,onlyCRTorCPMcanbethebestalternative.

Fig.4showsanumericalsimulation.

Proposition3(Advancedriskmanagement). Forhighercompeti- tion(lowerR),ARMbecomes(weakly)moredesirable.Forlargersector concentration(higher),ARMbecomes(weakly)lessdesirable.

3. Empiricalevidence

Webringtheimplicationsofourtheoreticalmodeltothedata and provideempirical evidence onthevalidity of ourassump- tions. To test our theoretical model the banks in our sample belongingtotheGermanSavingsBanksFinanceGroup(Sparkassen- Finanzgruppe)provideanidealsetup.

TheGermanSavingsBanksFinanceGroup.Thesavingsbanks arenotonlyideallysuitedbecauseoftheirspecificfeaturesand organizationalstructurebutalsoforthefactthatwedealhereon theone handwithlegallyandeconomicallyindependentbanks withratherhomogeneousbusinessmodelsbutmoreimportantly ontheotherhandwithbankstakingindependentbusinessdeci- sions.Thisisofrelevanceasbanksindependentlytakethedecision foruseornon-useofparticularriskmanagementinstruments.

Moreover,weareabletoassessthecompetitiveenvironment andtodeterminethesectorconcentrationwhichreflectstheport- folioconcentrationofeachbankinoursampleastheirbusiness activitiesarelimitedtocertaindefinedregions.Infact,duetotheir

“regionalprinciple”theGermansavingsbanksarenotallowedto expandtheirbusinesstootherregions.Thisspecificfeatureamong othersallowsustoinvestigateempiricallythemaininfluencingfac-

torsidentifiedbyourmodel.Foracomprehensivelistofbanking groups’specialfeaturesseeBülbületal.(2014).

Theorganizationalstructureofthebankingnetworkisverycru- cialfortheunrestrictedaccessofeachbanktoriskmanagement instruments.Thesavingsbanksandotherfinancialinstitutionsin thegroupareorganizedandconnectedbyamulti-levelnetwork.

Inaddition,therealsoexistsa parallelstructureofassociations.

Thisstructureconsistsof 11regionalassociationsandcomprise allindependentsavingsbanksintheirrespectiveregionsastheir members.TheGermanSavings BanksAssociation(DSGV)isthe umbrellaorganizationoftheGermanSavingsBanksGroup.This associationtakesovercentraltasks.Inourspecificcase,theDSGV hasadaptedthecreditportfoliomodelcalledCreditPortfolioView tothespecificneedsofthesavingbanksandprovideaccesstoall banksinthegroup.Thecreditportfoliomodelisaffordablealsofor smallerbanksbecausethemonthlyfeeisproportionaltobanksize.

Sothebanksinoursamplefaceequalaccessconditions.

Furthermore,thebanksinoursampleparticipate ininternal risktransfermarketsthroughcreditpooling(Kreditpooling)orga- nizedinternallywithinthebankingnetworkorcreditrisktransfer throughinternationalcapitalmarkets.Thebankscanparticipate intheinternalloanpoolandexternalrisktransfermarket,bothas issuersofrisk(protectionbuyer)andasbuyersofrisk(protection seller).

Oursurvey.Apparentlytheinformation oftheusageof risk managementinstrumentsofeachbankisnotpubliclyavailable.

Theparticularinformationontheexplicituseofcreditriskman- agementtoolsistypicallyhardtoobtain.Therefore,weusedapaper questionnairesurveytoelicitthenecessaryinformationoncredit riskmanagement.

Inordertofindouttheinformationofusageofcreditportfolio modelsandrisktransferweconductedasurveyin2009amongthe Germansavingsbanks.Of438questionnairessenttoallsavings banksfromtheGermanSavingsBanksFinanceGroup,atotalof 279completedquestionnaireswerereturned.Forouranalyseswe used249responses(57%)becausesomebanksreturnedtheques- tionnairewithoutthefrontpagecontainingthenameofthebankor wereinvolvedinarecentmerger.Thus,comprising57%ofthebanks participatinginthesurvey,oursampleishighlyrepresentativeof allregionsandassetclasses.Formoredescriptiveinformation,see Bülbül(2013).

3.1. Riskmanagementstrategiesofbanks

Inthefollowingweexplainhowrisk managementstrategies ofbankscanbederivedfromthesurvey.Inthequestionnairethe bankswereaskedtoprovideinformation ontheirriskmanage- mentstrategiesintheirdailycorporatebusinesstomanagecredit risk,fromapplyingoftraditionalmethodssuchaslimitingtheir exposuretoregions,sectorsetc.uptotheuseofsophisticatedrisk managementtools.Thebankswereabletoclassifytheintensity oftheuseofriskmanagementinstrumentsasnouse,occasional use,orfrequentuse.²Concentratingonthemoresophisticatedrisk management strategieswe constructthree dependentvariables fromthesurvey data:CRT,CPMand ARMfollowingourmodel.

Thevariablesareconstructedfromthefollowingsurveyquestions, ARMbeingacombinationofCRTandCPM³:

2Inthesurveywedidnotrequiretheparticipantstoquantifytheirintensityofuse bythenumberofapplicationspermonth.Asthequantityisverymuchdependent onthespecificbusinessofthebank,webelievethatbanks’qualitativejudgement (ownjudgement)onthisissueismoreappropriatetouse.Foradetaileddescription ofthequestionnaireseeAppendixB.

3Weconstructthisvariablefromquestions12-10,12-11,13-3ofthesurveyin AppendixB.

•CRT:Whichofthefollowinginstrumentsforcreditriskmanage- mentareusedinyoursavingsbank?

–Creditrisktransfer(creditpooling) –Creditrisktransfer(creditderivatives)

•CPM: How intensively does your bank use the results from quantitativecreditportfolioanalyzes(CPV,other)foractiveman- agementofthecreditportfolio?

Creditrisktransfer(CRT).WedefinethebinaryvariableCRTto beonewheneitherinternalmarketsforcreditderivatives(credit pooling)orthemarket-basedsolutionforcreditderivativesisused frequentlyoroccasionally,andzerootherwise.Assuch,weimpose thateitherfrequentoroccasionaluseoftheseinstrumentsissuffi- cientforabanktobeclassifiedasbeingactiveincreditrisktransfer markets.Intuitively,thismakessense,asthefrequencyofpartic- ipationinthecreditrisktransfermarketdependsonthespecific businessofthebank.Consequently,eitherformofparticipationis recognized.

Creditportfoliomodeling(CRM).ThebinaryvariableCPMis onewhenthecreditportfoliomodelisemployedfrequentlytomea- sureandmanagecreditrisk,andzerootherwise.Employingacredit portfoliomodelformonitoringandactivelymanagingtheportfo- liooccasionallymeansusingtheinstrumentatmostonceamonth, whereasfrequent useimplies usingtheinstrumentmuch more often.Giventhatfrequentuseallowsthebanktoactivelymoni- torandmanagetheircreditportfolio,wetherefore,onlyinclude thesebanks.Reasonablelendingstrategiesofbankscanbederived ifbanksactivelymonitortheirportfolioandalsousetheresultsfor theirbusinessdecisions.

Advancedriskmanagement(ARM).Finally,ARMisabinary variableequal toone ifCRTand CPM areused simultaneously, andzerootherwise.Here,thebankengagesinthehighestlevel ofadvancedriskmanagementasdefinedinourtheoreticalmodel.

3.2. Potentialdeterminantsofriskmanagementstrategies

Inthissectionweprovideanoverviewofpotentialdeterminants influencingbanks’decisiontoimplementadvancedriskmanage- ment tools. We would like todevelop first themain variables identifiedbyourmodel,namelycompetitionand portfoliocon- centration and, second furthervariables tocontrol for (control variables).

Competition.WeusetheLernerindexasaproxyformarket power.Itmeasureshowfarbankscansetpricesabovemarginal costsandiscalculatedasLERNER_it=(P_it−MC_it)/P_itwhereP_itisthe priceproxied by the ratioof total revenues (interest and non- interestincome)tototalassetsandMC_itisthemarginalcost(Berger etal.,2009;Bülbül,2013).Marginalcostisderivedfromatranslog costfunction⁴wherebankingoutputisproxiedbythetotalassets TA_it(Carbóetal.,2009),andthreeinputpricesW_k,itaredefinedas theratioofpersonnelexpensestototalassets(priceoflabor),the ratioofinterestexpensestototaldeposits(priceoffunding)and theratioofoperatingandadministrativeexpensestototalassets (priceofcapital).WeaveragetheLernerindexfortheobservation periodbecauseweareinterestedinthecompetitivestanceofthe bank.

Portfolio concentration. Given that the banksin our sam- ple conduct business in a defined regional area according to the“regionalprinciple”, thesectorconcentrationintherespec-

4We estimate the equation lnCostit=ˇ0+ˇ1lnTAit+^ˇ₂²lnTAit2+

3

k=1ktlnWk,it+

3

k=1klnTAitlnWk,it+

3 k=1

3

j=1lnWk,itlnWj,it+it

byincludingyearlytime-fixedandbank-fixedeffectswithrobuststandarderrors usingpaneldatacoveringtheperiodfrom1996to2006.

tive region proxies the lending portfolio of each bank. The Herfindahl–Hirschmanindexisusedtoestimatethesectorconcen- trationinregioni(forbanki)andiscalculatedasHHI(x)_i=

N

n=jx²_j wherex_jistheshareofthenumberoffirmsconductingbusiness bythesectorsjoverallthefirmsintheregioniasof2005.⁵

Abankwithaconcentratedloanportfoorerisky.Thus,itisnot surprisingthatcreditriskconcentrationhasplayedacriticalrole inpastbankfailuliowilltypicallybemresinmatureeconomies (BaselCommitteeonBankingSupervision,2004).Bothadvanced instrumentscanbeusedtomanagecreditrisksuchthatalending portfolioisdiversifiedbyreducingitscreditriskconcentration.⁶ DüllmannandMasschelein(2007)showthatitisnecessarytotake inter-sectordependencyintoaccountwhenmeasuringcreditrisk, forwhichcreditportfoliomodelsareatypicalinstrument.Accord- ingtoBattenandHogan(2002)creditderivativeshaveamuchmore flexibleapproachtomanagingtherisksassociatedwithconcentra- tion.Weexpectbankswithcreditriskconcentrationproxiedby sectorconcentrationtobemorelikelytoinvolveinadvancedrisk management.

Controlvariables.Theremaybeotherdeterminantsinfluencing banks’decisionforriskmanagement besidesthemaindetermi- nants postulated by our model. We use the following control variables.Wemeasuretherisk-returnprofileofabankusingthree separate variables:net-interest income tototal income (Ashraf etal.,2007),net-commissionincome(non-interest)tototalincome (BeltrattiandStulz,2012)andtheratioofloanlossprovisionsto totalassets(Mintonetal.,2009).Weexpectbankswithlessinter- estincomeandhigher loanlossprovisions tobemorelikely to useadvancedcreditriskmanagement.Weincludetheequityto totalassetratio(Mintonetal.,2009)becausebanks’havetofulfill minimumcapitalrequirementsinaccordancewiththeriskthey carry.Asaconsequencethis mayinfluencea bank’sdecision to implementadvancedriskmanagementtools.Weproxybanksize bytotalassets.Toallowfornonlinearitiesbetweensizeandtheuse ofriskmanagementinstrumentswedefinefourassetclassesfol- lowingCebenoyanandStrahan(2004).Thesmallestquartileacts astheomittedcategory.Weexpectthesizeofthebanktohave asignificanteffectonbanks’decisionstoengageinadvancedrisk managementduetotheirpotentiallymoreopaquelendingbusi- nessandtypicallyaccesstolargerresourcestorunadequatelyrisk managementtools.Wealsoaccountforthelendingstructureand fundingstructureofthebank.Weproxythelendingstructureas theratioofcorporateloansovertotalnon-bankloans.Thefund- ingstructureisrepresentedbytotaldepositsovertotalnon-bank loans.Abank’sdecisiontoengageinadvancedriskmanagementis potentiallyrelatedtothecompositionoftheloanportfolioandthe bank’srefinancingsituation.Toaccountforregionaldisparitieson thebanklevel,weincluderegionalearningscalculatedbyGDPper capitainourmodel.Furthermore,tocaptureeffectswhichmaybe drivenbydisparitiesineconomicdevelopmentaftertheGerman reunification,wecontrolfortheregionalareabyincludingabinary variableeastbeingonewhenthebankisinformerEastGermany.

5AccordingtotheStatisticalClassificationofEconomicActivitiesintheEuro- pean Community,twelvesectors arespecified: (i) MiningandQuarrying;(ii) Manufacturing;(iii)Electricity,Gas,SteamandAirConditioningSupply;(iv)Con- struction;(v)WholesaleandRetailTrade,RepairofMotorVehiclesandMotorcycles TransportationandStorage;(vi)AccommodationandFoodServiceActivities;(vii) TransportationandStorage;(viii)FinancialandInsuranceActivities;(ix)RealEstate Activities;(x)Education;(xi)HumanHealthandSocialWorkActivities;and(xii) OtherServiceActivities.

6TheDeutscheBundesbank(2006)definescreditriskconcentrationas“concen- trationofloanstoindividualborrowers...andanunevendistributionacrosssectors ofindustryorgeographicalregions(sectoralconcentration).Afurtherriskcategory consistsofrisksarisingfromaconcentrationofexposurestoenterprisesconnected withoneanotherthroughbilateralbusinessrelations.”

Table1

Summarystatisticsanddifferencesinmeans.

(1)Allbanks (2)ARMusers (3)ARMnon-users Difference p-Values

Mean sd Mean sd Mean sd

HHI 0.1583 0.0133 0.1692 0.0146 0.1574 0.0128 −0.0118^*** (−3.73)

Lerner 0.2861 0.0711 0.2189 0.0622 0.2913 0.0692 0.0724^*** (4.31)

InterestIncome 0.4263 0.0352 0.4092 0.0334 0.4276 0.0351 0.0184^* (2.15)

Commission 0.0997 0.0162 0.1032 0.0097 0.0994 0.0166 −0.00383 (−0.97)

LLP 0.0206 0.0095 0.0225 0.0083 0.0204 0.0096 −0.00204 (−0.87)

CorporateLoans 0.3127 0.0652 0.3162 0.0684 0.3124 0.0651 −0.00382 (−0.24)

Equity 0.0469 0.0088 0.0441 0.0054 0.0472 0.0090 0.00309 (1.43)

Deposits 0.5583 0.2320 0.5527 0.2945 0.5588 0.2272 0.00611 (0.11)

East 0.1165 0.3214 0.1111 0.3234 0.1169 0.3220 0.00577 (0.07)

GDP 24.2590 7.7944 30.6938 16.8464 23.7576 6.4060 −6.936^*** (−3.73)

TotalAssets 14.2482 0.9369 15.0644 0.9066 14.1846 0.9108 −0.880^*** (−3.95)

No.Employees 459.8781 474.4064 861.0739 525.1171 428.6161 456.8537 −432.5^*** (−3.83)

Observations 249 18 231

(1)Allbanks (2)CPMusers (3)CPMnon-users Difference p-Values

Mean sd Mean sd Mean sd

HHI 0.1583 0.0133 0.1657 0.0162 0.1568 0.0122 −0.00889^*** (�