Nesrine Djebali , Khemais Zaghdoudi stability in the MENA region Threshold effects of liquidity risk and credit risk onbank ScienceDirect

JournalofPolicyModeling42(2020)1049–1063

ScienceDirect

Threshold effects of liquidity risk and credit risk on bank stability in the MENA region

Nesrine Djebali

^∗

, Khemais Zaghdoudi

LaboratoryofNaturalandCulturalPatrimonyValorization,FacultyofLaw,EconomicsandManagementofJendouba, UniversityofJendouba,Tunisia

Received24June2019;receivedinrevisedform25November2019;accepted5January2020 Availableonline9March2020

Abstract

Thispaperstudiestherelationshipsbetweenbothliquidityandcreditrisksonbankstabilityforapanel datasetof75conventionalbanksbelongingto11countriesoftheMENAregionobservedduringtheperiod 1999–2017.ByperformingaPanelSmoothThresholdRegression(PSTR)modeldevelopedbyGonzalez etal.(2005), estimationresultsshow thattherelationshipsbetweenbank stability-creditrisk andbank stability-liquidityriskarenon-linearandcharacterizedbythepresenceoftwooptimalthresholdswhichare equalto13.16%forcreditriskand19.03%forliquidityrisk.Contrarytotheirpositiveeffectsbelowthese optimalthresholds,creditriskandliquidityriskbecomedetrimentaltobankstabilityinhighregime.

Toensuretheirstability,banksareencouragedtorevisetheprimacygiventocreditactivityanddiversify theiractivitiestoimproveprofitability.Theyarealsorecommendedtostrengthentheirownfundsandopt forappropriaterestructuringtoeasetheirsmallsize.AsfortheStatesoftheselectedcountries,theyhaveto deeplyreformtheirfinancialsystemsanddevelopthelegalframeworkrelatingtonewtechniquesofexternal managementofbankingrisksincludingsecuritizationanddefeasance.Likewise,thesestatesarefortifiedto ensurepoliticalstability,whichisakeyfactorforbankingandfinancialstability.

©2020TheSocietyforPolicyModeling.PublishedbyElsevierInc.Allrightsreserved.

JELclassification: F3;G21;G28;O16;F36;O53

Keywords: Liquidityrisk;Creditrisk;Bankstability;PSTRmodel;MENAregion

∗Correspondingauthor.

E-mailaddresses:djbeli.nesrine@gmail.com(N.Djebali),k.zaghdoudi@yahoo.fr(K.Zaghdoudi).

https://doi.org/10.1016/j.jpolmod.2020.01.013

0161-8938/©2020TheSocietyforPolicyModeling.PublishedbyElsevierInc.Allrightsreserved.

1. Introduction

Bankstability isdifficult todefineandevenharder tomeasure.A bankingsystem canbe describedasunstableinthepresenceofexcessivevolatilityofassetsorcrises.Suchadefinition of banking stability is simpleto formulate, butdoes not capture the positive contributionto understandthestability of thebanking andfinancialsystem. Bankstabilityisacharacteristic offinancialstability.Ingeneral,abankisconsideredstableifitmeetstwobasicrequirements:

improvingeconomicperformanceandeliminatingimbalancescausedbyendogenousfactorsof unforeseenorunwantedeventsfromdifferentbankingrisks.

In most countries, especially developing ones, banks play a vital role in financing their economiesandpromotingtheireconomicgrowth.Hence,theneedtoensuretheirstability.Thisis why,inrecentyears,severalstudieshaveexaminedthefactorsthatensurethestabilityofbanksor thosewhodestabilizethem.Inparticular,creditriskandliquidityriskareretainedbythemajority ofauthorswhoconsideredthemasmajorrisks.

Thedebateontherelationshipbetweentheserisksandbankstabilityisnotconclusivesince theempiricalresultsfoundaredivergent.Thesefindingscanbedividedintothreetypes.Thefirst onesupportsthenegativeimpactofthesetworisksonthestabilityofbanks.Thesecondtype,on theotherhand,showsthepositiveeffectofbothrisksonthestabilityofbanks.Thethirdcurrent ofliteratureprovestheinsignificantimpactofliquidityandcreditrisksonbankstability.

Ontheonehand,thesestudieshavethemeritofadoptingdifferentempiricalapproaches,but ontheotherhand,theyassumealinearrelationshipbetweenrisksandbankingstability.This linearityhypothesis canlead toerroneousresults that canmislead decision-makers andbank managers,sincethesignofrisksonbankstabilitydoesnotchange;negativeorpositive.

Thispapertriestofillthisgapandcontributestotheexistingliteraturebystudyingthenon- linearrelationshipbetweenrisksandbankstabilitybyusingneweconometricapproachbasedon thePanelSmoothThresholdRegression(PSTR)modeldevelopedbyGonzalez,Terasvirta,and VanDijk(2005).

Unlikeotherworks,ourpaperseekstoshowthattherelationshipsbetweencreditandliquidity risksandbankstabilityarenon-linearandcharacterizedbythepresenceofthresholdeffects.Our paperhas theadvantagetoshow thatthe impactof bankingrisks onstabilityisnot thesame below andabove the thresholds.Asfar as weknow,thereare nopublished empiricalstudies whichapplythePSTRmodeltoinvestigatethenon-linearrelationshipbetweenrisksandbank stabilityforanimportantnumberofbanks.Thispaperisintendedtobeacontributionthatcan helpthebank’ssupervisorstomonitorandevaluatethestabilityofthebankingsystemandits determinants.Thispaperallowsustoidentifyanumberofdestabilizingfactorsthathavecreated instabilitythroughoutthesystem.

Todothis,weuseaneweconometricapproachbasedonthePSTRmodel,whichisthemost appropriateforendogenouslydeterminingtheoptimalthresholdbeyondwhichbankstabilityis affectedbyrisks.Weuseasampleof75conventionalbanksfrom11MENAcountriesobserved overtheperiod1999–2017.

Theremainderofthispaperisorganizedasfollows.Areviewofliteratureisgiveninsection2.

DataandPSTRmodelspecificationarepresentedinsection3.EmpiricalResultsandinterpretation aredevelopedinsection4,whilesection5concludes.

2. Literaturereview

IntheMENAcountries,themajorityofbankscontinuetodeveloptraditionalactivities(includ- ingtheprovisionofcreditandthecollectionofresourcesintheformofdeposits),despitereforms andtheexhaustionofmajorfactorsthathavesupportedtheseactivitiesforalongtime. These activitiescarryrisks,mainlycreditriskandliquidityrisk.

Theeffectsofcreditandliquidityrisksonthestabilityofbankshavebeenthesubjectofseveral academicworks(Acharya&Mora,2013;Adusei,2015;Amara&Mabrouki,2019;DeYoung&

Jang,2016;Ghenimi,Chaibi,&Omri,2017;Imbierowicz&Rauch,2014;Li&Zou,2014;Rajhi

&Hassairi,2013; Khemais,2019,etc.).Theirresults arenotconsensual. Thereisworks that haveshownthatthesetworiskshavedestabilizedthebanks.Ontheotherhand,asecondgroup ofauthorshighlightedthepositiveeffectofthesetworisksonthestabilityandsustainabilityof banks.Athirdgroupof studiesshowedtheneutral effectofcreditandliquidityrisksonbank stability,whichisfundamentallydependentonotherfactors.

Thenegativeeffectofcreditandliquidityrisksonbankstabilityhasbeenanalyzedinseveral studies.Ghenimietal.(2017)used49banksobservedovertheperiod2006–2013tostudythe effectsofcreditandliquidityrisksonbankstability.Thesebanksbelongtoeightcountriesinthe MENAregion:Bahrain,Jordan,Qatar,SaudiArabia,Turkey,UnitedArabEmirates,Kuwaitand Yemen.FollowingtheapproachproposedbyArellanoandStephen(1991),ArellanoandOlymia (1995)andBlundellandStephen(1998),theirresultsrevealedthattheinteractionbetweencredit andliquidityrisks(CR*LR)contributetobankinstability.

Adusei(2015)usedquarterlydatafrom2009to2013topickoutthemainfactorsdestabilizing theruralbankingsectorinGhana.Todothis,theauthorretainedthreemeasuresofbankstability whichareZ-score,risk-adjustedreturnonassets(RAROA)andrisk-adjustedequityonassets ratio(RAEA).Empiricalresultsindicatedthatcreditriskisharmfultothestabilityofbankswhen thelatterismeasuredbyRAEA.

ImbierowiczandRauch(2014)analyzedtherelationshipbetweenliquidityandcreditrisks andtheirjoint impactonbanks’probabilitiesofdefault forasampleof 4300UScommercial banks overthe period1998–2010.Their mainresults showedthat the tworisks influencethe probabilitiesofdefaultofbanksalthoughtheydonothaveareciprocalrelationship.Addedto that,theinfluenceoftheirinteractiondependsontheoveralllevelofbankrisksandcanworsens ormitigatestheriskofdefaultofbanks.

ForAcharyaandMora(2013),theroleofbanksasliquidityprovidersisastrongguarantor during the period of the 2008financial crisis. Their resultsproved that the banksthat failed duringtherecentfinancialcrisissufferedfromliquidityproblems.Thestudyshowsthatbanks thathavefailedattractdepositsbyofferinghighinterestrates.Indirectly,theresultsindicatethat thepresenceofbothliquidityandcreditriskscouldpushbankstodefault.

Inacomparativeanalysisduringtheperiod(2006–2009),RajhiandHassairi(2013)suggested thatcreditriskreducesthestabilityofbanksintheMENAregion.Usingameasureofdefault distance and Z-score, their results showed that credit risk and income diversityare the most commoncauseofinsolvencyforbanks.Creditriskmeasuredbytheratioofloanlossprovisions tonetinterestincome,decreasesZ-scoreinsmallbanksinMENAcountries.

Usingpaneldataanalysisfortheperiod2005–2015,Khemais(2019)examinedtheimpactof creditrisk,liquidityrisk,andoperationalriskonTunisianconventionalbankstabilityproxiedby bothZ-score(ROA)andZ-score(ROE).Empiricalresultsshowthatthecreditriskthreatensthe

stabilityofTunisianbankswhenthelatterismeasuredbyZ-score(ROA).Theinteractionofboth creditandliquidityrisksworsensalsotheirstability.

Otherauthorssupportthepositiveeffectofcreditandliquidityrisksonthestabilityofbanks, sinceprofitabilityandrisksaretightlylinked.Creditandliquidityrisksarenotdetrimentalfor bankstability.Contrarytothecreditrisk,Khemais(2019)revealsthattheliquidityriskimproves thestabilityofTunisianbanks.

Thefinancialcrisishasproventhatliquidityandcreditrisksaretwoimportantfactorsinthe bankingsectorthatcanaffectthesurvivalofbanks(DeYoung&Jang,2016).LiandZou(2014) studiedtheimpactof creditrisk measuredbythenon-performingloansratioonbankstability usingdatafrom47commercialbanksinEuropefrom2007to2012.Theirresultsshowedthat creditriskhasasignificanteffectonthe increaseinbankperformancemeasured byreturnon equity(ROE)andreturnonassets(ROA).

The thirdkindof worksretains theneutrality hypothesis.Creditandliquidityrisks donot affectsignificantlythestabilityofbanks.AmaraandMabrouki(2019)examinedtherelationship betweenliquidityandcreditriskandtheirimpactonthestabilityofTunisianbanksduringthe period2006–2015byusingtheZ-scorefunctionasanindicatorofstability.Theyfoundthatcredit riskandliquidityrisk donothaveaneconomicallysignificantreciprocalrelationshipandthat eachriskcategoryhasanon-significantimpactonbankstability.Inaddition,authorsreachedto theresultthattheinteractionbetweenthetworiskshasaninsignificanteffectonbankstability.

3. DataandPSTRmodelspecification

Themotivationofourpaperistoinvestigatetherelationshipsbetweenbothliquidityandcredit risksonbankstabilityfortheMENAregionwhichisaninterestingcasestudy.

IntheMENAregion,financialintermediationhasremaineddominatedbybankinginterme- diation.Banksarethemainfinancialintermediaries.Theycontinuetocollecttheirresourcesin theformofdepositthroughabranchnetwork,andgrantcredittotheirclients.Thesetraditional activities,notexclusiveofothers,carryrisks,whichcandestabilizebanks.

CountriesintheMENAregionhaveundergonewidespreadreformsinrecentdecades,affecting allsectorsofactivity.Thatiswhytheireconomieshavebecomehighlyopentotheoutsideand competitive.Inparticular,MENA’sbanksaremoreintegratedintotheglobalfinancialmarkets andexposedtotheshocksthataffectinternationalcapitalmarkets.

Furthermore,thebankingsectorintheMENAregionischaracterizedbythepresenceofIslamic banks.Infact,thepresenceoftheIslamicbankingsectorcanaffectthestabilityofconventional banks.Becauseoftheirstructure,Islamicbanksgenerallyhavehigherliquidityratioandlower creditriskthanconventionalbanks.

Finally,itisquitetruethatbanksoftheMENAcountrieshavebeenrecoveringfromtherecent financialcrisis,butrecentpoliticalturmoilandsuddenregimecandecreaseeconomicgrowthand destabilizebanks.

ThesemaincharacteristicspresentedaboveexplainthechoiceoftheMENAbankingsystem asaninterestingframeworkforanalyzingtherelationshipsbetweencreditrisk,liquidityriskand bankstabilityforapaneldatasetof75conventionalbanksbelongingto11countriesobserved duringtheperiod1999–2017.Table1displaysthenumberofconventionalbankspercountry.

Tohighlightthepossiblenon-linearrelationshipbetweencreditrisk,liquidityriskandbank stability,wereliedononeof theeconometrictechniquesusedtoestimatenon-linearrelations, namelythe Panel SmoothThresholdRegression (PSTR)model developedbyGonzalez etal.

Table1

Numberofconventionalbankspercountry.

Countries Numberofconventional

bankspercountry

Algeria 6

Bahrain 8

Egypt 8

Jordan 8

Kuwait 3

Lebanon 8

Morocco 4

Qatar 4

SaudiArabia 8

Tunisia 10

UnitedArabEmirates 8

Total 75

Source:Authors.

(2005),whoextendedHansen’sPTR (PanelThresholdRegression)model (1999).Theoretical formofthePSTRmodelispresentedasfollows:

yit=μi+β⁰xit+β¹xitg(qit,γ,c)+εit (1) where(i)and(t)representrespectivelycross-sectionandtimedimensionsofthepanel.μiindicates thevectoroftheindividualfixedeffects.εitistheerrorterm.β⁰andβ¹indicaterespectivelythe parametervectorsoflinearandnon-linear models.yit andxit representrespectivelydependent andindependentvariables.g(qit,γ,c)isthefunctionoftransitionwhichdependsonthetransition variableqit,thetransitionparameter(γ)andtheparameterofthreshold(c).Thistransitionfunction ofthePSTRmodeliscontinuousandnormalizedtakingvaluesbetween0and1.Itallowssystem totransitfromoneregimetoanother.Inorderforthisfunctionoftransitiontobeoperational, GrangerandTeräsvirta(1993),Teräsvirta(1994),Eliev andTimo(1996),andGonzalez etal.

(2005)proposedthefollowinglogisticformoforderm:

g(q_it,γ,c)=

⎛

⎝1+exp

⎛

⎝−γ m j=1

(q_it−c_j)

⎞

⎠

⎞

⎠

−1

(2)

With c=(c1,...,cj,...,cm) isavector ofthresholdparameterswithc1<···<cm andγ>0.

Whenγ→0,thetransitionfunctionapproachesaconstantandthePSTRmodelbecomeshomoge- nouslinearpanelwithfixedeffects.However,whenγ→+∞,thefunctionoftransitiong(q_it,γ,c) tendstoanindicatorfunctiong(q_it,c_j)whichtakes1ifqit>cj.IbarraandTrupkin(2011)showed thatifγisveryhigh,thePSTRmodelcanbeconfusedwithatwo-regimemodel(oronethreshold).

Giventhisfunctionoftransitiong(q_it,γ,c),Eq.(1)canbewrittenasfollows:

y_it=μ_i+β⁰x_it+ m j=1

β_jx_itg(q^j_ij,γ_j,c_j)+ε_it (3)

Inthispaper,weusedanunbalancedannualdataof604observationsfor75banksbelonging to11countriesobservedduringtheperiod1999–2017toinvestigatetheeffectsofcreditriskand liquidityriskonbankstabilitybyusingthePSTRmodelwhichcanbewrittenasfollows:

BSTAB_it=μ_i+β⁰x_it+β¹x_itg(q_it,γ,c)+ε_it (4) whereireferstobanks(i=1,...,75)andtrepresentstimeperiodinyears(t=1999,...,2017).

μ_i,ε_it,β⁰andβ¹keepthesamedefinitionsmentionedabove.BSTABisbankstability,which representsthedependentvariable.g(q_it,γ,c)representsthefunctionoftransition.Inourmodel, wetestthethresholdeffectoftwovariableswhicharecreditrisk(CR)andliquidityrisk(LR).

Consequently,wehavetwotransitionfunctionsg(CR_it,γ,c)andg(LR_it,γ,c).

Eq.(4)canbedividedintotwoequations:

BSTAB_it=μ_i+β⁰x_it+β¹x_itg(CR_it,γ,c)+ε_it (4-1) BSTABit=μi+β⁰xit+β¹xitg(LRit,γ,c)+εit (4-2) InEq.(4-1),creditrisk(CR)isthevariableoftransition,whileinEq.(4-2)liquidityrisk(LR) isthevariableoftransition.

Inbothequation,wekeptthesameexplanatoryvariables(whicharethesize(SIZE),thecapital adequacyratio(CAP),theperformanceofbank(PERF)andtheLernerindex(LERNER))and thesameexogenousvariables(whichareinflationrate(INF)andpoliticalstabilityandabsence ofviolenceandterrorism(POLIS)).

Replacingthevectorofindependentvariablesxit withitscomponents,we gettheempirical modeltobeestimatedwhichispresentedasfollows:

BSTAB_it =μ_i+β⁰₀CR_it+β⁰₁LR_it+β⁰₂SIZE_it+β⁰₃CAP_it+β⁰₄PERF_it +β⁰₅LERNER_it+β⁰₆INF_it+β⁰₇POLIS_it

+[β¹₀CRit+β₁¹LRit+β₂¹SIZEit+β¹₃CAPit+β¹₄PERFit+β¹₅LERNERit

+β¹₆INF_it+β₇¹POLIS_it]g(CR_it,γ,c)+ε_it

(5)

BSTABit =μi+β⁰₀LRit+β⁰₁CRit+β⁰₂SIZEit+β⁰₃CAPit+β⁰₄PERFit

+β⁰₅LERNERit+β⁰₆INFit+β⁰₇POLISit

+[β¹₀LR_it+β₁¹CR_it+β₂¹SIZE_it+β¹₃CAP_it+β¹₄PERF_it+β¹₅LERNER_it +β¹₆INFit+β₇¹POLISit]g(LRit,γ,c)+εit

(6)

Table2displaysdefinitions,measuresandsourcesofallvariables.

The dataset coversannual data relative toasample of 75banks for 11 countries,namely Algeria,Bahrain,Egypt,Jordan,Kuwait,Lebanon,Morocco,Qatar,SaudiArabia,Tunisiaand UnitedArabEmiratesovertheperiod1999–2017.Accountingandfinancialdatawerecollected directlyfromBankscopedatabaseandbank’sfinancialstatements,whiledataonmacroeconomic environmentwasextractedfromWorldBankDevelopmentIndicators(WDI)onlinedatabase.

4. Empiricalresultsandinterpretation

WefirstdisplaythedescriptivestatisticsofthevariablesusedinthePSTRmodel.Inthesecond stage,wepresentthecorrelationmatrixbetweenvariables.Wemoveinthethirdstagetolinearity

Table2

Definitions,measuresandsourcesofvariables.

Variables Definitions Measures Sources

BSTAB Bankstability Z-Score(roe)=

[E(ROE)+CAP]/σ(ROE)

LaevenandLevine(2008), Demirgüc¸-KuntandHuizinga (2010),Hakimi,Zaghdoudi, Zaghdoudi,andDjebali(2017), andAmaraandMabrouki(2019).

CR Creditrisk Non-performingloans/total

loans

CaiandZhang(2017),Mpofu andNikolaidou(2018),Natsir etal.(2019),andButhiena (2019).

LR Liquidityrisk Liquidassets/totalassets Ghenimietal.(2017)andAmara andMabrouki(2019).

SIZE Banksize ln(totalassets) Anginer,Demirguc-kunt,and

Zhu(2014)andHakimietal.

(2017).

CAP Capitaladequacyratio Totalequity/totalassets Pathan(2009),Hakimietal.

(2017).

PERF Bankperformance ROA=netincome/totalassets RashidandJabeen(2016), HakimiandZaghdoudi(2017), Ghenimietal.(2017),andAmara andMabrouki(2019).

LERNER INDEX

Marketpowermeasure Ameasureofmarketpower inthebankingmarket^a

Natsiretal.(2019).

INFL Inflationrate Consumerpriceindex ImbierowiczandRauch(2014), Adusei(2015),andKharabsheh (2019).

POLIS Politicalstability Variablethattakesvalues between−2.5and+2.5

Hakimietal.(2017).

Source:Authorsfromliteraturereview.

aItisdefinedasthedifferencebetweenoutputpricesandmarginalcosts(relativetoprices).Pricesarecalculatedas totalbankrevenueoverassets,whereasmarginalcostsareobtainedfromanestimatedtranslogcostfunctionwithrespect tooutput.https://datacatalog.worldbank.org/lerner-index.

testbetweencreditrisk,liquidityandbankstability.Oncethenon-linearityisverified,wefind outinthefourthstepthenumberofregimesinthePSTRmodel.Infine,weestimatethePSTR modelanddiscusstheresults.

4.1. Descriptivestatistics

Descriptivestatisticsarepresentedtorevealthemaincharacteristicsofdatausedinthisstudy.

Foreachvariable,wedisplaymean,standarddeviation,minimumandmaximumvalues.Table3 summarizesvariabledescriptivestatisticsofthePSTRmodel.

Theaveragevalueofbankstability(bstab)measuredbyZ-score(ROE)isequalto4.835with amaximumvalueof24.721andaminimumvalueof−2.327.Theaverageofcreditrisk(CR)of conventionalbanksis0.087withamaximumvalueof1.993.Theaveragevalueofliquidityrisk (LR)is0.262withminimumandmaximumvaluesof0.017and2.795respectively.

Sizeofbankswhichisequalonaverageto8.703issmall.Ithasamaximumvalueof12.314 andaminimumvalueof4.002.ConventionalbanksintheMENAregionareundercapitalized, withequityaccountingforanaverageof11.6%oftheir assets.Inaddition,thebanksperform

Table3

VariableDescriptiveStatistics.

Variable Mean Std.Dev. Min Max

BSTAB 4.835 3.949 −2.327 24.721

CR 0.087 0.130 0.000 1.993

LR 0.262 0.159 0.017 2.795

Size 8.703 1.412 4.002 12.314

CAP 0.116 0.069 −0.314 0.699

PERF 0.016 0.032 −0.216 1.014

LERNER 0.342 0.212 −0.800 0.640

INF 0.037 0.040 −0.049 0.295

POLIS −0.358 0.797 −2.117 1.224

Table4

CorrelationMatrix.

BSTAB CR LR SIZE CAP PERF LERNER INF POLIS

BSTAB 1.000

CR −0.088 1.000

LR 0.014 0.225 1.000

SIZE −0.061 −0.396 −0.170 1.000

CAP 0.422 −0.197 0.045 −0.160 1.000

PERF 0.044 −0.251 0.042 0.038 0.118 1.000

LERNER −0.061 −0.145 −0.131 0.121 0.150 0.083 1.000

INF −0.152 −0.059 0.066 0.055 −0.139 0.057 −0.049 1.000

POLIS 0.099 −0.045 −0.071 −0.027 0.248 0.055 0.405 −0.223 1.000

poorly,sincetheeconomicprofitabilityisonaverageverylowanditisequalto1.6%overthe entireperiodselected.The averageLernerIndexisequal to34.2%, meaningthatthe banking market isnot stronglydominated bybanks.In themajorityof MENA countries,the banking markets,whichplayaleadingroleinfinancingtheireconomies,havestillbeencontrolledbythe state,despiteexcessivewavesoffinancialliberalizationmadebythesecountries.

Asfortheinflationrate,itwasequalonaverageto3.7%withaminimumvalueof−4.9%and amaximumvalueof29.5%.Theaveragevalueofpoliticalstability(Polis)isequalto−0.358, whichisnegativeshowingthedeteriorationofinstitutionalquality,whichcandestabilizebanking sector.

4.2. Correlationmatrix

The correlation matrix givesinformation on the level and the nature of linkages between variablesbydeterminingthecoefficientsoftheirlinearcorrelations.Table4belowpresentsthe correlationmatrixofallvariablesusedinthisstudy.

Creditrisk(CR),size(SIZE),Lernerindex(LERNER)andinflation(INF)arenegativelyasso- ciatedwithbankstability.Whileliquidityrisk(LR),capitaladequacyratio(CAP),performance andpoliticalstability(POLIS)arepositivelylinkedtobankstability.ResultsdisplayedinTable4 showaweakcorrelationbetweenallvariables,rejectinghencetheexistenceofmulti-colinearity problem.

4.3. Linearitytest

To showthe non-linearityrelationshipbetweenCredit Risk(CR),LiquidityRisk (LR)and BankStability(BSTAB),weapplylinearitytestbasedontwohypothesespresentedasfollows:

NullhypothesisisH₀:β¹=0 againstAlternativehypothesisH₁:β¹=/ 0. However,thistest of linearityisnotstandard,sinceunderthenullhypothesis,thePSTRmodelcontainsuniden- tifiednuisanceparameters(Hansen,1999).To solvethisproblem,Luukkonen,Saikkonen,and Teräsvirta (1988)proposed toreplace the transition function withTaylor’slimited first-order developmentaroundγ=0.NullhypothesisbecomesH0:γ=0.Eqs.(4-1)and(4-2)canbewritten asfollows:

BSTABit=μi+β^∗0xit+β^∗1xitg(CRit,γ,c)+ε^∗_it

BSTAB_it=μ_i+β^∗0x_it+β^∗1x_itg(LR_it,γ,c)+ε^∗_it

Afterreplacingthevectorxitwithitscomponents,wegetthefollowingequation:

BSTAB_it=μ_i+β^∗₀⁰CR_it+β^∗₁⁰LR_it+β^∗₂⁰SIZE_it+β^∗₃⁰CAP_it+β^∗₄⁰PERF_it +β^∗₅⁰LERNER_it+β^∗₆⁰INF_it+β^∗₇⁰POLIS_it

+[β^∗₀¹CRit+β^∗₁¹LRit+β^∗₂¹SIZEit+β^∗₃¹CAPit+β^∗₄¹PERFit

+β^∗₅¹LERNER_it+β^∗₆¹INF_it+β^∗₇¹POLIS_it]g(CR_it,γ,c)+ε^∗_it

BSTAB_it=μ_i+β^∗₀⁰LR_it+β^∗₁⁰CR_it+β^∗₂⁰SIZE_it+β^∗₃⁰CAP_it+β^∗₄⁰PERF_it +β^∗₅⁰LERNER_it+β^∗₆⁰INF_it+β^∗₇⁰POLIS_it

+[β^∗₀¹LRit+β^∗₁¹CRit+β^∗₂¹SIZEit+β^∗₃¹CAPit+β^∗₄¹PERFit

+β^∗₅¹LERNER_it+β^∗₆¹INF_it+β^∗₇¹POLIS_it]g(LR_it,γ,c)+ε^∗_it

Wheretheparametersβ^∗₀¹,β^∗₁¹,...,β^∗₇¹aremultiplesofγandε^∗_it =ε_it+residualofTaylor’s limitedfirst-orderdevelopment.ThenullhypothesisoflinearitytestbecomesH0:β₀^∗1=···= β^∗₇¹=0.Totestthisnullhypothesis,weusethreetestswhichareWaldtest(LMW),Fishertest (LMF)andlikelihoodratiotest(LRT).

TheWaldtest(LMW)iswrittenasfollows:LMw= ^NT(SSR_SSR⁰⁻₀^SSR1)WhereSSR0isthepanel sumofsquaredresidualsunderH0(linearpanelmodelwithindividualeffects)andSSR1isthe panel sum of squared residuals under H1 (PSTRmodel withm regimes).Nand Trepresent respectivelycross-sectionandtime.

TheFishertest(LMF)isdefinedas:LM_F = ^NT_SSR^(SSR_0/TN^0−SSR_−N−^1)/mk_mk withkandmarerespectively thenumbersofexplanatoryvariablesandregimes.

Thelikelihoodratiotest(LRT)isexpressedasfollows:LRT=−2[log(SSR1)−log(SSR0)].

Undernullhypothesis,WaldandlikelihoodstatisticsfollowChi(2)distributionwithkdegrees offreedom(χ²(k))whileFisherStatisticpursuesaFisherdistribution(F(mk,NT−N−mk)).

Foralogistictransitionfunctionoforderone(m=1),resultsofthesethreetestsarepresented inTable5.

Table5showsthatthenullhypothesis(linearmodel)isrejectedat1%levelofsignificancefor thethreetests.Consequently,therelationshipsbetween:

Table5 Linearitytests.

Test BSTAB CR BSTAB LR

Statistics P-value Statistics P-value

Waldtest(LMW) 63.817 0.000 45.639 0.000

Fishertest(LMF) 10.298 0.000 7.125 0.000

LikelihoodRatioTest(LRT) 67.447 0.000 47.456 0.000

Table6

Testsforthenumberofregimes.

Tests BSTAB CR BSTAB LR

Statistics P-value Statistics P-value

Fishertest(LMF) 153.673 0.000 135.523 0.000

LikelihoodRatioTest(LRT) 10.379 0.000 8.799 0.000

i) BankStability(BSTAB)andCreditRisk(CR) ii) BankStability(BSTAB)andLiquidityRisk(LR) arenon-linear.

4.4. Determinationofthenumberofregimes

Afterrejectinglinearmodelandverifyingnon-linearity,PSTRmodelhashenceatleastone threshold. To findout thenumberof thresholds (or regimes)inthe PSTRmodel, we testthe followinghypothesisH2:PSTRmodelhasatleasttwothresholds(r=2).Alternativehypothesis is the followingHa:PSTR model hasone threshold(r=1). To check hypothesisH2,we use twotestswhichareFishertest(LMF)andlikelihood ratiotest(LRT).IfFisherandlikelihood statisticsaresignificant,werejectthehypothesisH2andweconcludethatthePSTRmodelhas onethresholdandhasthentworegimes.ResultsofthesetwotestsarereportedinTable6.

ResultsfromTable6indicatethathypothesisH2isrejectedat1%levelofsignificanceforthe twotests.Forthetwotransitionvariablesusedinthisstudy(CreditriskCRandLiquidityrisk LR),thetwoPSTRmodelshaveonlyonethresholdandtheyarethereforeatwo-regimemodels.

4.5. PSTRmodelestimation

WeusehencePSTRmodels withtworegimes toestimatethe relationshipsbetweenBank Stability(BSTAB)andCreditRisk(CR),andBankStability(BSTAB)andLiquidityRisk(LR)for 75banksbyapplyingnon-linearleastsquarestechnique.ResultsofthePSTRmodelsestimations arepresentedinTable7.

EstimationresultsofPSTRmodelreportedinTable7showthatboththerelationshipbetween bankstability(BSTAB)andcreditrisk(CR),andbankstability(BSTAB)andliquidityrisk(LR) are characterizedby the presence of a thresholdeffects. The optimalthresholds are equal to 13.16%forcreditriskand19.03%forliquidityrisk.

EstimationresultsofEq.(5)showthatbelowthethresholdof13.16%,creditriskandliquidity riskexertapositiveandsignificanteffectonbankstability.Thispositiveeffectcanbeexplained

Table7

PSTRmodelsestimations.

Estimationresultsofequation(5) Estimationresultsofequation(6)

Coeff. T-Stat Coeff. T-Stat

CR 915.482*** 2.910 0.182 0.572

LR 2216.502** 2.426 1.348*** 7.082

SIZE 233.439** 2.019 0.152*** 3.687

CAP 2885.709*** 4.183 18.367*** 40.332

PERF 10440.466** 2.352 −2.746* −1.801

LERNER 751.004 0.749 −0.047 −0.440

INF 0.396 0.719 −0.126 −0.238

POLIS 0.202*** 3.265 0.094 1.560

CR*g(qit,γ,c) −1826.540*** −2.903 1.010*** 3.504

LR*g(qit,γ,c) −4456.932** −2.425 −1.173*** −2.786

SIZE*g(qit,γ,c) −469.228** −2.017 0.029 1.348

CAP*g(qit,γ,c) −5759.691*** −4.151 5.206*** 10.575

PERF*g(qit,γ,c) −21,013.899** −2.353 −7.900*** −3.760

LERNER*g(qit,γ,c) −1510.857 −0.749 −0.592 −1.442

γ 0.200 5.000

C 0.1316 0.1903

AIC −1.815 −1.892

BIC −1.698 −1.776

Obs 604 604

Note:***,**and*indicatelevelofsignificancerespectivelyat1%,5%and10%.

bythefactthatbanksintheMENAcountriescontinuetogiveprimacytocreditactivity,despite theexhaustionofthemainfactors,whichhavesupportedthebankintermediationforalongtime.

Also,theressourcesofthesebanksarecollectedfromtheircustomsinformofdepositswhichare lessexpensive.Howevre,beyondtheoptimalthresholdsof13.16%,bothcreditandliquidityrisks becomedetrimentaltothestabilityofbanks.ThisresultisinlinewiththoseofAdusei(2015), Khemais(2019),Ghenimietal.(2017),etc.Whennon-performingloansexceed13.16%oftotal credits,theriskofnon-recoveryincreasesandbanksfinditdifficulttorecovertheircredits,which shouldhavebeenallocatedtomoreproductiveactivities.Banks’rentabilitydecreasesaffecting hencetheirstability.Abovetheoptimalthresholdofcreditriskof13.16%,liquidityriskhasa negativeandsignificanteffectatthe5%levelonbankstability.Liquidityriskisaddedtocredit risktodestabilizebanksinselectedcountries.Excessivebankliquiditycouldpushbankstogrant alotofcredits(quantityeffect)theirprobabilityofreturnislow.Thisrisktakingcouldadversely affecttheirprofitabilityandsolvencyandthreatentheirlongevity.

EstimationfindingsofEq.(6)revealthatbelowthethresholdof19.03%,contrarytocredit risk whichhasapositivebutnon significant impact,liquidityrisk acts positivelyat 1%level ofsignificanceonbankstabilityintheMENAregion.Abovetheoptimalthresholdof19.03%, creditriskbecomessignificantandcontinuestoaffectpositivelybankstability.Overtheselected period 1999–2017,bankliquidity,whichequalsonaverageto26.2%oftotalassetsof banks, thatisabovethethresholdof19.03%,favorsthegrantingofloans,whichgeneratesintereststhat reinforcestheir netincomeandensurestheirstability.Asforthe liquidityrisk, econometrical resultsrevealthatitdestabilizesbanksoftheMENAregion.Thisnegativeeffectisexplainedby theshortfallforbanksthatholdasignificantportionoftheirassetsinliquidform.Beyondthis threshold,liquidityisexpensiveforbanks,whicharedeprivedofinvestmentopportunities.

Unliketheotherworks,ourpaperhasthemeritofshowingthattheeffectsoftheothervariables onbankstabilitydependontheoptimalthresholdsofthecreditandliquidityrisks.Infact,when thecreditriskandtheliquidityriskexceedthethresholdsof13.16%and19.03%respectively, theperformancedestabilizesbanks.Overtheselectedperiod,thereturnonassetsisequalonan averageto1.6%,whichislow.Theprofitsgeneratedbybankscomemainlyfromtheirbalance sheetintermediation (granting of loans, collection of deposits,provision andmanagement of meansof payment).Asaresult,MENA banksare encouragedtodevelopnewbusinessesand providenewpersonalizedandonlineservicestotheircustomersaroundtheworld.

The effect of the capital adequacy ratio (cap)on bank stability depends on the transition variable.Ontheoneside,beyondthecreditriskthresholdof13.16%,thisvariableisdetrimental tothestabilityofbanks.Thisnegativeanddestabilizingeffectcanbeexplainedbytheunder- capitalizationoftheselectedbanks,sinceequitysrepresentonaverage11.6%ofthetotalassets overtheselectedperiod1999–2017.Thisunder-capitalizationaddstotheirlowprofitabilityto destabilizethebankingsectorofMENAcountries.

On the otherside, above the liquidityrisk threshold of 19.03%,thisvariable (cap)favors the stabilityof banks.Bankliquidityplaysthesamerole of reassuringbanks’customersand shareholders.Eveniftheequityisnotenough,bankliquiditycomesinsupplementandindicates thegoodfinancialhealthofbanks.

Asfortheeffectofthesizevariable,ithasanegativeandsignificanteffectonbankstability onlybeyondthecreditriskthresholdof13.16%.Thisdamagingeffectisduetothesmallsizeofthe majorityofbanksintheMENAregionwhichpreventsthemfromrecoveringnonperformingloans.

Banksarefindingitdifficulttousenewcreditriskmanagementtechniquessuchassecuritization anddefeasance.Inthevariouscapitalmarkets,thissmallsizeisoverwhelmingbanksandlimiting theirmargin ofnegotiation.Thisunderminestheirstability.Forthisreason,MENA banksare requiredtomakeadequatebankingrestructuringstobecomeregionalchampions,abletonotonly strengthentheirmarketshare,butalsotoconquernewforeignmarkets.

Regardingtheexogenousvariables(INFandPOLIS),empiricalresultsshowthatonlypolitical stabilityisimportantforbankingstability.Asaresult,MENAcountriesareencouragedtoensure politicalstabilityandcombatviolenceandterrorism.

5. Conclusionandrecommendations

Manyresearchesexaminedtherelationshipbetweencreditrisk,liquidityriskandbankstability.

Theyassumedalinearrelationshipbetweenbankrisksandstability.Thepurposeofthispaperis threefold:

i) showingthenon-linearrelationshipbetweencreditrisk,liquidityriskandbankstability.

ii) determiningtheoptimalcreditriskandliquidityriskthresholds.

iii) studyingtheimpactofthisthresholdeffectonthestabilityofbanks.

Toachievethisgoal,weselectedasampleof75conventionalbanksfrom11MENAcountries overtheperiod1999–2017.WeperformedthePanelSmoothThresholdRegression(PSTR)model developedbyGonzalezetal.(2005).

Differentfromotherstudies,ourpaperhastheadvantageofendogenouslydeterminingtwo thresholdsabovewhichbankstabilityisaffected.Indeed,ourresultsshowhencethattherela- tionshipsbetweenbothbankstability-creditriskandbankstability-liquidityriskarenon-linear

andcharacterizedbythepresenceoftwooptimalthresholdswhichareequalto13.16%forcredit riskand19.03%forliquidityrisk.

Contrarytotheirpositiveeffectsbelowthethresholdof13.16%,creditriskandliquidityrisk becomedetrimentaltobankstabilityinhighregime.Forcreditrisk,thisnegativeeffectcanbe explainedbythedifficultiesoreventhe inabilityof bankstoreturnthe grantedcredits,which arecolossalandwhichshouldhavebeenallocatedtomoreprofitableactivitiesthatcanensure bankstability.BanksintheMENAregionarestillslowtoapprehendandfunctionalizefinancial innovations.Newbankingriskmanagementtechniques(suchassecuritizationanddefeasance)are notdevelopedbybanks,whichstillrelyonbankingpersonnelwhodonotknowhowmarketswork.

Asaresult,theactivitiesofbanksintheMENAregionremainedconfinedtotraditionalactivities.

Concerningliquidityrisk,itsnegativeeffectcanbeexplainedbythemultiformcompetitionand thesuperfluousbankliquiditywhichcanpushbankstotakerisksandgrantmorecreditstotheir customerswithouttakingintoaccounttheirsolvability.Thisrisktakingincreasesnonperforming loans,decreasestheirprofitabilityandthreatenstheirstability.

EstimationfindingsofEq.(6)revealthatbelowthethresholdof19.03%,contrarytocredit riskwhichhasapositivebutnonsignificantimpact,liquidityriskactspositivelyat1%levelof significanceonbankstabilityintheMENAregion.Abovetheoptimalthresholdof19.03%,credit riskbecomessignificantandcontinuestoaffectpositivelybankstability.Overtheselectedperiod 1999–2017,bankliquidity,whichisequalonaverageto26.2%oftotalassetsof banks,which issuperiorthanthethresholdof19.03%,favorsthegrantingof loans.Thisgeneratesinterests thatreinforcetheirnetincomeandensuretheirstability.Asfortheliquidityrisk,econometrical resultsrevealthatitdestabilizesbanksoftheMENAregion.Thisnegativeeffectisexplainedby theshortfallforbanksthatholdasignificantportionoftheirassetsinliquidform.Beyondthis threshold,liquidityisexpensiveforbanks,whicharedeprivedofinvestmentopportunities.

Fortheotherexplanatoryvariables,resultsreportedinTable7showthatabovethetwooptimal thresholdsof13.16%and19.03%,theprofitabilityofbanks,whichisveryweak,threatenstheir stability.Banksarecalledtorevisetheprimacygiventocreditactivityandfocusonnewactivities andservices.Statesinselectedcountriesareencouragedtofundamentallyreformtheirfinancial systemsanddevelopthelegalframeworkfornewexternalriskmanagementtechniques,including securitizationanddefeasance.

The effectof the capital adequacy ratio (cap) on bank stability depends on the transition variable.Ontheonehand,beyondthecreditriskthresholdof13.16%,thisvariableisdetrimental tothestabilityofbanks.Thisnegativeanddestabilizingeffectcanbeexplainedbythe under- capitalizationoftheselectedbanks,sinceequitiesrepresentonaverage11.6%ofthetotalassets overtheselectedperiod1999–2017.Thisunder-capitalizationaddstotheirlowprofitabilityto destabilizethebankingsectorofMENAcountries.

On the otherhand,abovethe liquidityrisk thresholdof19.03%, thisvariable(cap)favors the stability of banks. Bankingliquidity playsthe same role of reassuring banks’ customers andshareholders.Eveniftheequityisnotenough,thebankliquiditycomesinsupplementand indicatesthegoodfinancialhealthofthebanks.

Asfortheeffectofthesizevariable,ithasanegativeandsignificanteffectonbankstability onlybeyondthecreditriskthresholdof13.16%.Thisdamagingeffectisduetothesmallsizeof themajorityofbanksintheMENAregionwhichpreventsthemfromrecoveringnonperforming loans.Banksfinddifficultsinusingnewcreditriskmanagementtechniques.Inthevariouscap- italmarkets,thissmallsizeisoverwhelmingbanksandlimitingtheirnegotiationmargin.This underminestheirstability.Forthisreason,MENAbanksarerequiredtomakeadequatebanking

restructuringstocompetewithgiantforeignbanks,whichcansetupintheregionundersigned agreements.

Asfor theexogenousvariables, empiricalresultsshowthatonlypoliticalstability(POLIS) impactspositivelyandsignificantlybankstabilityoftheMENAregion,showingtheimportance ofpoliticalstabilityforbankandfinancialstability.ThatiswhyMENAcountriesareencouraged toguaranteepoliticalstabilityandcombatviolenceandterrorism.

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