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Journal of Banking and Finance
journalhomepage:www.elsevier.com/locate/jbf
Currency matching by non-financial corporations R
Péter Harasztosi, Gábor Kátay
∗European Commission, Joint Research Centre (JRC) Italy
a rt i c l e i n f o
Article history:
Received 12 October 2017 Accepted 7 January 2020 Available online 14 January 2020 JEL classification:
G01 G11 G32 F31 F34 Keywords:
Borrowing decisions Currency matching Carry trade Financial crisis
a b s t r a c t
Thepaperinvestigatesfirms’willingnesstomatchthecurrencycompositionoftheirassetsandliabilities.
UsingdetailedinformationattheloancontractlevelfortheHungariannon-financialcorporatesector,the paperprovidesstrongevidencetosupportthetheorythatcurrencymatchingplaysaroleinexporters’
debtcurrencychoices.However,naturalhedgingisnottheprimarymotiveforfirmstochooseaforeign currency:it explainsonly3.8percentofthe overallnew corporateforeigncurrencyloanscontracted byexportersand 2.9percentoftheaggregatenew foreigncurrencybank loans.Besideshedging,our resultssuggestthatbothcarrytradeanddiversificationstrategiesarerelevantfactorsinfirms’currency- of-denominationdecisions. Supply sidefactors arealso foundto beresponsible forthe prevalenceof foreigncurrencyloansamongHungariancorporateborrowers.
© 2020 The Authors. Published by Elsevier B.V.
ThisisanopenaccessarticleundertheCCBYlicense.(http://creativecommons.org/licenses/by/4.0/)
1. Introduction
Matchingdebtpaymentstoexpectedforeigncurrency(FX)rev- enues is a natural wayto mitigate theadverse effects offoreign exchangeriskexposureofexportingfirms.Currencymismatchoc- curs when firms’assets andliabilitiesare denominated in differ- ent currencies. Financial stability concerns typically arise when firms’netFX-denominatedliabilitiesaregreaterthantheirnetFX- denominatedcash flows,i.e.whenfirmsborrow“toomuch” inFX relative to their export revenues. As aresult, whenthe domestic
R We owe a special thank to Sanvi Avouyi-Dovi, Lloyd Blenman, Martin Brown, Nicolas Coeurdacier, Fabrizio Coricelli, Lionel Fontagné and Róbert Lieli for their careful reading of our manuscript and their many valuable comments and sugges- tions. We are grateful to Ádám Szeidl for giving us access to the structured dataset on bank accounts; Gy ˝oz ˝o Gyöngyösi and Marianna Endrész for their comments and insights regarding data construction; Dzsamila Vonnák for the excellent work on matching the databases; Anna Naszódi for sharing with us the real-time Consensus forecasts; and Mihály Szoboszlai for his outstanding research assistance. We also thank the participants of the IMF seminar and the US Census Bureau seminar in Washington, D.C.; the seminar at the Paris School of Economics; the seminar at the Banque de France; the internal seminar at the Joint Research Centre; the EEA- ESEM Congress in Mannheim; the Annual Conference of the Hungarian Society of Economics; and the 2016 Paris Financial Management Conference for the valuable ideas they raised in discussion. The views expressed in this paper are those of the authors and not necessarily those of the institutions the authors are affiliated with.
∗ Corresponding author.
E-mail addresses: [email protected] (P. Harasztosi), [email protected] (G. Kátay).
currency depreciates, firmswith currency mismatch are likely to experienceadverse balance sheeteffectsasthe negativeeffectof therise in FX-denominatedliabilitiesexpressed in localcurrency usually outweighs the traditional positive competitiveness effect (Eichengreenetal.,2007).
This paper explores corporate borrowers’ choice between the local currency and several possible FXs when the market inter- est rates in one or several FXs are lower than that of the local currency.In particular, we investigatein depth firms’willingness to matchthe currency composition of their assets andliabilities.
Firms’ incentives to deviate from the perfectly matched portfo- lio(suchastherole oftheinterest ratedifferentials)andvarious supply-side factors related to the currency denomination of the loan(suchascredit-supplyconstraintsin localcurrency)are also tested.
Tocomplementtheexisting literature,we adopta moredirect andfocused approach to capturefirms’matching motives and to isolatetheireffectonfirms’currencychoices.Indoingso,thispa- per is the first to provide direct evidence to support the role of matching incentives.However, the paper showsthat the popular andapparently robust explanation ofcurrency matching explains onlyarelatively smallpartoftheoverallnewcorporateFX loans contracted.Thisisalsotrueforexporters,whicharelargelyviewed asmorerisk-awarethantheaverage.
Using detailed information at the loan contract level for the Hungarian non-financial corporate sector, we first construct a theory-consistent matching measure and test its influence on https://doi.org/10.1016/j.jbankfin.2020.105739
0378-4266/© 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license. ( http://creativecommons.org/licenses/by/4.0/ )
firms’choice of currency denomination ofborrowings. Ouriden- tificationstrategyreliesontheintuitionthatifcurrency matching isarelevantfactor,thefirmismore(less) likelytoincurnewFX- denominateddebtwhenitsexpectedexportrevenuesexceed(are lowerthan) its FXdebt reimbursement obligation duringa given period of time. This yields a binary dependent variable model inwhich the probability of choosing FX is related to a currency matching measure defined as the difference between the firm’s exportrevenuesandits debtrepayment obligationsdenominated inFX.Usingbothfixedeffects(Chamberlain,1980)andcorrelated randomeffects(Mundlak,1978)logitmodels,ourestimationstrat- egytakesintoaccountbothsupply-anddemand-drivenfirm-level fixed effects by conditioning on (fixedeffects model) orcontrol- lingfor(correlatedrandomeffectsmodel)howmanytimesFXhas beenchosenby thefirm.Theparameters ofinterestareidentified by using information on the timing of the choice, i.e.when the FX orthe local currency ischosen. A large set of firm-level and bank-levelcharacteristics controlforother,time-varyingdemand- side and supply-side factors. As a second step, a more accurate matchingmeasureyields amixedlogitspecification(Train,2003) in which separate but correlated equations for two possible FX alternatives – corresponding essentially to euro (EUR) and Swiss francs(CHF)– areestimatedsimultaneously.
We thenusethe estimatedmodeltoperforma counterfactual analysis.Toisolatetheeffectsofthenaturalhedgingmotiveonthe aggregate corporateFX debtshare,we “switch off” theeffects of currencymatchingon firms’debtdenominationchoiceby setting thecoefficient ofourmatching measureto zeroforall firmsand forallchoiceoccasionsandwepredictthecounterfactualcurrency sharesofnewly contracted corporateloans inthe absenceofthe hedgingmotive.
The paper highlights and addresses two important shortcom- ingsoftherelatedliterature.First,althoughpreviousresultsinthe literatureareconsistentwithnaturalhedging,theydonotprovide conclusivesupportfortheroleofthematchingmotive.Second,the economicimportanceofcurrencymatchinginfirms’debtcurrency choicehasnotbeenquantifiedtoourknowledge.
Totestfortheexistenceofthematchingmotiveinfirms’debt currency choice, previous empirical studies usually follow one of twopossibleapproaches.AlargestrandofliteratureestimatesFX loandemandequationsbyrelatingtheshareofFXdebttoaproxy forthesensitivityoffirms’profitstoexchangeratefluctuationsand otherfirm-specificormacroeconomicvariables.The earlieststud- iesinvestigate thecurrency-of-denomination decisionof large US firms(AllayannisandOfek,2001;KediaandMozumdar,2003)and Finnishfirms (Keloharju andNiskanen, 2001) andfind that firms withhigher export shares (or a larger share of total assets held abroad) holdmore FX-denominated debtin order to hedge their increasedforeign exposure. Aconsiderable number ofstudies on emergingmarketspointtothesameconclusion.1Thesepapersfol- lowasimilarreducedformestimationmethodinwhichtheiden- tificationreliesontheexport sharevariableoron anindicator of tradability (dummy variable indicating whether the firm belongs tothetradable sector)toshow that exportersorfirmsproducing tradable goods are more likely to carry foreign debt. The coeffi- cientsofthesevariablesareusuallypositiveand– withtheexcep- tionoftheArgentinianandBrazilian results– significantlydiffer-
1Many of these papers are published in a special issue of the Emerging Market Review (vol. 4 no. 4). The issue starts with a summary by Galindo et al. (2003) that collects findings from existing literature. Existing studies on emerging markets cover Argentina ( Galiani et al., 2003 ), Brazil ( Bonomo et al. 2003 ; Janot et al. 2008 ), Chile ( Benavente et al. 2003 , Cowan et al. 2005 ; Fuentes 2009 ), Colombia ( Echeverry et al. 2003 ), Mexico ( Pratap et al. 2003 ; Gelos 2003 ; Martinez and Werner 2002 ) Peru ( Carranza et al. 2003 ), Lebanon ( Mora et al. 2013 ) and several East Asian ( Allayannis et al. 2003 ) and Latin-American economies ( Kamil 2012 ).
entfromzero.Theauthorsconcludethatfirmstendtomatchthe currencycomposition oftheirliabilitieswiththeex-antesensitiv- ityoftheirrevenuestotherealexchangerate.
However, the existing evidence that exporting firms tend to borrow more in FX does not provide direct proof for currency matchingforatleasttwo reasons.First,export revenuesare only half of the story, the appropriate measure to test the matching considerations between assets andliabilities should include both exportrevenuesdenominated inFX andFXdebtrepayments.For instance,a firmthat increasesits FXdebttoa point thattheex- pected exportrevenuesdo not fullycoverits debtrepayments is exposed toa similarexchange rateriskasnon-exporterswithFX liabilitiesintheirbalancesheets.Similarly,itissafeforafirmwith arelativelylowexportsharetoincurdebtentirelyinFXaslongas itsexportrevenuesarehigherthanorequaltoitsdebtrepayment obligations.
Second, unobserved firm heterogeneity is typically not taken intoaccount.Comparingtwo distinctgroupsoffirms,(larger)ex- porters and (smaller) non-exporters, which are probably differ- ent in many respects, and identifying firms’ matching incentives fromcross-sectionalvariationdoesnotprovidedirectevidencefor matching.Ifthechoiceofcurrencydenominationofthedebtisa result ofan optimisation process, the same firm in different cir- cumstances shouldmake differentchoices. Thatis,theidentifica- tionofmatchingincentivesispossibleonlyoncefirmfixedeffects arecontrolledfor.2
Another approach indirectly infers the relevance of natural hedging by assessing firms’ post-crisis performance. Using data on listed firms from five Latin American countries, Bleakley and Cowan (2008) relate the firm’s investment after the crisis to its laggedshareofdollar debtinteractedwithshifts intheexchange rate. Theyfind thatforfirmsholdinghigherlevels ofdollardebt, the negativebalance sheeteffect following a depreciation of the localcurrencyismorethanoffsetbythepositivecompetitiveness effectinall countries.The authorsindirectlyinferthat thisresult isduetothehighdegreetowhichfirmsintheirsamplematchthe currencycompositionoftheirassetsandliabilities.
By definition,the currency matching motive isonly indirectly assessed when firms’ex-post performance is analysed. Moreover, therelevance andtheimportance ofnaturalhedgingcanonly be properly assessed whenthe positive competitivenesseffect dom- inatesthe balance sheeteffect,asinBleakley andCowan(2008). However, inthe casewhen practicallyall firms withFXdebt are negativelyimpactedbythedepreciationofthelocalcurrency,such asinHungary,therelevanceofthenaturalhedgingmotiveisless obvious.Indeed,severalpapersfocusing onHungary demonstrate the negative net effect of exchange rate depreciation on firms’
post-crisisperformance,suggestingthatcurrencymismatchesplay animportantroleinHungary.EndrészandHarasztosi(2014)show that the post-crisis investment rate of firms with FX loans de- clined by about 4-5 percentage pointsmore because of the bal- ance sheeteffects triggered by the depreciation. The adverse ef- fect on investment is particularly strongfor liquidity-constrained firmsandlessimportant(but stillpresent) forlargerandtrading
2The papers by Galiani et al. (2003) and Fuentes (2009) include specifica- tions with firm-level fixed effects. However, their exchange rate exposure mea- sures (export-to-value added ratio) are defined only at the sectoral level. These results do not show significant relationship between dollarisation and tradability.
Benavente et al. (2003) also account for firm fixed effects, but their model investi- gating the determinants of dollarisation does not include any proxy for the match- ing motive. Carranza et al. (2003) and Brown et al. (2014) test the robustness of their results to accounting for unobserved firm heterogeneity using random effects (or GLS) estimation technique, but the main underlying identifying assumption of these models remains the same as in pooled models: in panel datasets with large cross-sectional and short time-series dimension, the identification ultimately comes from cross-sectional variation.
firms.Onlyfirmswithforeignownershipseemtohavebeeninsu- latedfromthenegativebalancesheeteffects.Vonnák(2018)sep- aratelyinvestigates theeffectsof EURandCHFdebtaccumulated duringthepre-crisisperiodonfirms’loandefaultprobability.The author finds that the balance sheeteffects of both FXs are size- able, but the effect of the CHF debt is considerably higher than that ofthe EURdebt.Undersuch circumstances,the relevanceof currency matchingcan only be indirectly inferred from therela- tive post-crisisperformance ofsome sub-groupof firms,such as exporters versus non-exporters– with thesame caveatsasprevi- ouslydescribed.
Besides hedging, the literature has established a number of other factors that have a bearing on firms’ currency-of- denomination decisions. The role of the interest rate differential and thus firms’ carry trade strategy is often put forward as an explanation for dollarisation (e.g. Keloharju and Niskanen, 2001; Allayannisetal., 2003;RosenbergandTirpak,2009).Alargebody ofempiricalevidenceshowsthatinterestratedifferentialsare bi- ased predictors offuture exchange ratechanges (equivalent to a failureofuncoveredinterestparity).3Consequently,thecarrytrade – astrategy ofcurrencyspeculationwherebyaninvestor borrows in a currency offering a low interest rate and uses the loan to generate revenues in a currency with higher interest rate – is profitable on average.4 The optimal deviation from the perfectly matchedportfolioisinfluencedbymanyfactors,suchasfirms’risk perceptions,riskattitudes,distresscostsortheexpectationsabout thefuturepathsofinterestratesandexchangerates.
Other studies stressthe importance ofsupply-sidefactors.For instance,theresultsofAllayannisetal.(2003)suggestthatcredit- supply constraints in local currency may force firms to seek FX financing. Similarly, Brown etal.(2014) argue that FX lendingin Bulgariaisatleastpartiallysupplydriven.Consistentlywiththese findings,Vonnák(2018)alsoshowsthatcorporateFXindebtedness inHungarycanbeatleastpartlyexplainedbysupply-sidefactors.5 However,thereisasyetnoclearunderstandingofwhicheffect dominatesinafirm’sdecisiontoincurFX-denominateddebt.The economicsignificanceofthevariousfactorsisaddressedonlyina few studies. Brown et al.(2011) find that FX borrowingis more strongly relatedto firm-level FX revenues than it is to country- levelinterestratedifferentials,whichleadsthemtoconcludethat speculation is not the key driver of firms’ currency choice and
“retail clients which do take FX loans are better equipped to bear thecorresponding currencyrisksthaniscommonlythought”
(Brown etal., 2011, p. 300). The previously mentioned paper by Bleakley andCowan (2008) also supports the idea that the cur- rencymatchingmotiveismoreimportantthanspeculation.
The recent Hungarian experience of FX indebtedness together with a newly available collection of matched administrative datasets provide a valuable opportunity to reassess the determi- nantsoffirms’borrowingdecisionsinthesituationthattheyhave accessto FX loans.In Hungary,asinthe majorityof Centraland EasternEuropean(CEE)countries,thepost-socialisteconomictran- sitionwasfuelledinpartbyeasyaccesstoFX-denominatedloans through localbranchesofWesternbanks. Forabouta decadebe-
3Although not exhaustive, the “original sin” is a commonly used explanation for this phenomenon. See e.g. Eichengreen et al. (2005) for details.
4We use the terms “carry trade” and “speculation” interchangeably throughout the paper.
5The literature also proposes other explanations unrelated to hedging and carry trade strategy, such as costly monitoring and signalling based on profitability (e.g.
Allayannis et al. 2003 ), tax incentives ( Shapiro, 1984; Hodder and Senbet, 1990 ), legal barriers ( Jorion and Schwartz 1986 ) or the costs of gathering information ( Hietala 1989 ). While we do not exclude the relevance of any other factor in ex- plaining firms’ FX indebtedness in Hungary, we argue in the paper that these al- ternative explanations are unlikely to explain the FX borrowing of many domestic firms from local commercial banks.
forethe crisis, FX-denominated loans becamewidespread among bothhouseholdsandfirms.Aslongagoas2005,morethan45per centof theoutstanding corporateloans were denominated in FX (seeSection3fordetails).
Weonlyconcentrateonbankloansasotherdebtfinancingin- strumentsthatcouldpotentiallybeusedfornaturalhedging,such ascorporate bondsandcommercialpapers,representonlya very smallpartofthecapitalmarketinHungary.6 Werelyonaunique contract-firm-bank matched administrative dataset combining (i) detailed monthly information on all new and outstanding cor- porate loans contractedfrom Hungarianfinancial institutions be- tween2005and2011,(ii)annualfinancialreportsofallHungarian firms,(iii)monthlyexport revenuesandimport expendituresand (iv)dataon thecreditprovider available fromregulatoryreports.
Inourbaseline specification,we restrictouranalysistoexporting firms,forwhichthenaturalhedgingmotivemightberelevant.
The coefficient of the matching variable being positive and highly significant across all specifications, our results provide strongevidencetosupporttheroleofthenaturalhedgingmotive infirms’currency choice.Tosome extent,the matchingmotiveis evenstrongerforlargerandforeign-ownedcompanies,whencon- tractsarelongtermandduringthepost-crisisperiod.
Even though matching is a robust determinant of firms’ cur- rencychoice,oursimulations indicatethat naturalhedgingisnot the primary motive for firms to choose FX. Even for exporters, which are largely viewed as more risk-aware and more capable ofmanaging exchange rate risks,matching explains only 3.8per centof the overall newcorporate FX loans. When non-exporters are also considered, our simulation results suggest that currency matchingisresponsible for2.9percent oftheaggregate newFX bank loans during the period considered. These results contrast withprevious studiessuggestingthat matchingisa key driverof firms’ currency choice (Bleakley andCowan, 2008; Brown et al., 2011).
Whileour modelcannot unambiguously discriminatebetween allfactorsinfluencingthecurrency-of-denominationoffirms’debt, the relevance of some other explanations is also tested. Consis- tentwithcarry-trade behaviour,ourresultsshow that firmstend to chooseFX when the interest ratedifferential betweenFX and domesticcurrency ishigherthan average.Inaddition, ourresults indicatethatfirmswithahigherprobabilityofchoosingEURalso haveahigherprobabilityofchoosingCHF.Thebenefitsofholding bothEUR andCHFdebt thusseem tooutweigh theadvantage of consistentlychoosingone (the preferred)FXrelative totheother, whichcanbeinterpretedasfirmsplacinghighervalueondiversifi- cationthanonfullyexploitingperceivedarbitrage(carrytrade)op- portunitiesbetweenFXs.However,similarlytoBrownetal.(2011), wealsofindthatthemagnitudeoftheimpactoftheinterestrate differential on the probability of choosing FX-denominated debt is rather small: a one standard deviation shock (3.17 percentage points)resultsina1.8percentagepointincreaseintheprobability ofchoosingaFX.Finally,ourfindingscorroboratepreviousrelated empiricalstudiesandsuggestthat credit-supplyconstraintsinlo- calcurrencymayhaveplayedasignificantroleinthecorporateFX indebtednessinHungary.
After presenting the data used for our analysis (Section 2), weprovide a briefhistoricalbackgroundon FXindebtedness and describe the characteristics of new corporate loans in Hungary (Section 3). Section 4 develops the empirical framework. The econometric analysis of firms’ currency choice is presented in Section5,then thecounterfactualsimulationresultsareshownin Section6.Thefinalsectionconcludes.
6See e.g. Fig 1. of Ong and Iorgova (2008) .
2. Data
Toassessthedeterminantsofcurrency-of-denominationchoice ofHungariannon-financialcorporations,weusefourmatchedad- ministrative datasets. We principally rely on the Credit Register databasecontaining the universe of all new and existing corpo- rateloancontractsfromHungarianfinancial institutions between 2005and2011.Thedatasetincludesfirms’identifiersandprovides informationoncontracts’startingdate,duration,value,denomina- tionandtype ofproviders.Providersare banks,savingbanksand otherfinancialcompanies.
For thedetaileddescription ofthe CreditRegisterdataset, see Endrész et al. (2012). We deviate from the construction of the datasetdescribedinEndrészetal.(2012)inoneimportantaspect.
Tofocus on currency choices, we collapse the loan contacts de- nominatedinthesamecurrencyandsignedbythesamecompany inthe samemonth. Thatis,if a firm takesout two loans in the samecurrencyinthesamemonth,wecombinethecorresponding contractstoformasinglecontractwiththesumofthetwoloans anda duration definedasthe weightedmean ofthe duration of thetwooriginal loans.Asa result,the averageannualnumberof newcontractsfallstoabout70percentoftheoriginal,whilethe totalamountofoutstandingdebtineachmonthandtheaggregate monthlyflowofdebtserviceexpensesremainunchanged.
Information onthe lenderis notavailable inthe CreditRegis- ter.However,theCentralBankofHungary(MNB)collectsfinancial informationon credit providersfor regulatory purposes.The two datasetsarelinked usingfirms’bankaccountsidentifiersavailable intheComplexfirmregistrydataset.Bankaccount numbersiden- tifybanksuniquelyfacilitating bank-firmmatches. Thisallows us tomerge the financial informationon credit providers – such as foreignownership,totalassets,capitalratio,liquidityratio,return on assets anddoubtful loan ratio– to each firm in our dataset.
Thelargemajorityofthefirmsdealwithonebankonly.Forthese firms,thelendercanbeunambiguouslyidentified.Inaboutaquar- terofthecases,whereonefirmismatchedtomorethanonebank, bankcharacteristicsareaveragedoverthebanksinquestion.
The resulting database is then merged with the yearly panel databaseofcorporatetaxreturns.Thedatabaseisprovidedbythe Hungarian tax authorities (NAV) and contains balance sheet and income statement information for all double-entry book-keeping firmsoperatinginHungary.Weusevariablesthatarelikelytoaf- fectfirms’demandforcreditandthechoiceofitscurrencydenom- ination,suchasemployment,foreignownership, capital,liquidity, totalassetsandprofitabilitymeasures.
Although the NAV dataset containsthe export share of sales, we collectadditional trade informationfromthe Hungarian Cen- tralStatistical Officeon trade behaviour. We mergethe statistics on exports and imports calculated from the monthly reports on commoditytradeto Extra-andIntrastatfortheuniverseofdirect trading firms in Hungary. The monthly frequencies enableus to calculate,forexample,theexportrevenuesduringthe12months precedingthesigningoftheloancontract.
The resulting dataset, which covers the years 2005-2011, in- cludes129,066firmsandeachyear,onaverage,about42,700firms takeoutloans.Overtheperiod,theaverageannualnumberofnew contractsstandsatabout65,000.It rises fromabout77,000 toa littleover82,000 justbeforethestart ofthecrisis. After2008, it dropstobelow50,000.Whenwefocusonexportingfirmsonly,we observe10,984firms inall, out ofwhich, each year,about3,000 takeoutloans.7 Onaverage,weobserve6,700newloancontracts each year in a declining trend starting from 8,300 in 2005 and
7Exporters are defined as companies with positive export revenues in the sample period.
endingin4,900in2011.Onaverage,68percentofthenewcon- tractstakenout byexporting firmsaredenominated inlocalcur- rency(HungarianForint,HUF).
3. ForeigncurrencydenominatedcorporateloansinHungary 3.1. Theriseandfallofforeigncurrencydenominatedcorporateloans
InHungary,likeinmanyCEEcountries,lendinginFXstoboth householdsandfirmshaslongbeenthenormratherthantheex- ception. As long ago as 2005, the first year in our dataset, FX- denominatedbankloansaccountedfornearlyhalfofthetotalout- standingcorporatedebt(seeFig.1).
Threekeyfactorsfacilitatedandcontributedtothepropagation ofFXlending.First,legalbarrierstobothholdingFX-denominated assets by corporations and freely offering FX-denominated loans foranypurposebylocalbanksweregraduallyremovedafter1995 (Vígh-Mikle and Zsámboki, 1999).8 Second, for an extended pe- riodtherewasasignificantinterestratedifferentialbetweenHUF andFX-denominatedloans,therebymakingthelattermoreattrac- tive(seeFig.1fortheinterestratedifferences).In2001,theMNB adoptedaninflationtargetingregimeandannouncedanambitious disinflationstrategy. To bring inflationdown fromaround 10 per centtothevicinityofpricestability– definedasannualconsumer priceinflation of3 per cent,witha ±1 per centtolerance range – by the end of 2007,the MNB maintained a wide interest rate differentialbetweentheHUFandtheEUR.
ThethirdfactorthatfacilitatedthespreadofFXlendingwasthe perceived stabilityofthe exchange rate. Between1995and 2001, a crawling peg exchange ratesystem ensured a manageddepre- ciation of the currency. By the end of the crawling peg regime, 34 per centof the outstandingcorporate loans were already de- nominatedin FX.Withthe introductionof theinflationtargeting framework,thecentralbankfirstwidenedtheexchangerateband to±15percentaroundthecentralparity,thenintroducedafloat- ingexchangerateregime,whilestillmaintainingthe±15percent exchange rateband, leadingto a similar regime to the Exchange RateMechanism(ERM)II.Theexchangeratebandwasabandoned andafree-floatingregimewasadoptedonlyinFebruary2008.
Theriskofdepreciationofthelocalcurrency wasperceivedas lowevenintheERMII-typeexchangerateregimeforseveralrea- sons.Firstly, thetight monetary policy and theinflow of foreign investmentstothecountrycontinuouslypushedtheexchangerate to theedge oftheband, whichresulted ina relativelystableex- changeratevis-à-vistheEURforaprolongedperiodoftime.9 Sec- ondly, Hungary joined the EU in 2004 and an early adoption of theEUR promisedby thesuccessive governmentsanchored long- termexchange rate expectations.The first target date wasset in 2001,whentherulingconservativegovernmentannouncedaplan to adopt the EUR in 2007. In 2003, the newly appointed social- ist Prime Minister announced the government’s objectiveto join the Eurozone in 2008.10 Thirdly, while reports from authorities (e.g. the MNB or the HungarianSupervisory Authority) acknowl- edged the possible exchange rate risks related to FX indebted-
8Prior to 1995, only selected firms were allowed to hold FX bank accounts and FX assets. Firms were legally obliged to exchange their FX revenues for HUF at the MNB.
9Before the crisis, the highest volatility was recorded in 2003: after a speculative attack against the strong edge of the band in January; and after the devaluation of the central parity by 2.26% in June. The central parity has not been changed since then.
10 “Medgyessy: euró 2008-ban; adóreform jöv ˝ore” [Medgyessy: euro in 2008; tax reform next year], economic and political weekly magazine HVG, 16 July 2003, https://hvg.hu/gazdasag/0 0 0 0 0 0 0 0 0 04C9BA8 (only in Hungarian).
Fig. 1. Outstanding corporate loans and monetary conditions Notes: Subfigure (a) shows the evolution of the stock of outstanding loans held by non-financial corporations.
(Source: MNB). Subfigure (b) shows the evolution of HUF/EUR and HUF/CHF exchange rates. Depreciation of HUF vis-à-vis other currencies shows an increase in the statistic. Daily exchange rates are averaged over a quarter and statistics are normalised such that 1996q1 equals to 1 (Source: MNB). Subfigure (c) shows the average quarterly rates of one-year Treasury Bills. For HUF we use 12-months “Diszkontkincstárjegy” (Source: MNB). The data is available from the first quarter of 1998. We use Swiss one-year Treasury Bills for the CHF and German one-year Treasury Bills for EUR (Source: investing.com).
ness, thesewarningseitherremainedunheard orignored.11 Wor- riesweremanytimescalmedbycontrastingthecertainbenefitsto unlikelycosts.Forexample,theHungarianBankingAssociationes- timated,in2006,thattheHUFwasunlikelytodepreciatetosuch anextentthatthelossfromincreaseddebtrepaymentswouldout- weightheinterestrateadvantageofFXloans.12
Survey evidenceon theFXindebtedness ofHungariancompa- niesconfirmsthat,before2008,asignificantshareofthefirmsei- therignored foreignexchange risk ordownplayedits importance.
A survey conducted in 2005 and analysed by Bodnár (2006) on smallandmedium-sizedenterprises(SMEs)findsthat (i)thema- jority ofcompanies used FX loansto minimisedebt repayments;
(ii) they believed that they had no, or only negligible, exchange rateexposure; (iii) andwere notpreparedforadverse changesin theexchangerate.Furthermore,70-80percentofcompanieswith FX debt claimed that exchange rateshifts would not affect their debtburden.Only4per centofthe SMEsinthesurvey usedany derivativeinstrumentandonlyone-quarterhadnaturalhedging.
Inafollow-upsurvey conductedin2007,inwhichlargerfirms werealsorepresented,Bodnár(2009)findssimilarresults.Onthe onehand,firmswereclearlymotivatedbytheinterestratediffer- ential.Findingsindicatethathalfofthesurveyedcompaniesfound FXfinancing cheaper,afourthofthem evenafterconsideringthe risks.This isalsotrue forlargecompanies (over250employees).
On theother hand, therewasa large variation inrisk awareness andriskmanagementpractices.Avaryingshareoffirms(between 10andabout50percent,increasinginfirmsize)statedthat rais- ing FX debt wasat least partlymotivated by having FX income.
While firms generally understood the risks associated with ex- changeratefluctuations,asignificantshareofthefirmssimplyig- nored it. Either they were unaware of possiblerisk management techniquesorsuch techniqueswere perceivedasexpensive, com- plicatedandineffective.Indeed,thestudyfindsthat20percentof the firmshad atone point used derivatives.However, interviews revealthatdecreasingprofitsdiscouragedmanyfromrepeateduse.
11See e.g. “Report on Financial Stability”, MNB, June 2004, https://www.
mnb.hu/letoltes/stab- jel- 0406- en.pdf ; or “Éves Jelentés 2006” [Annual Report 2006], Hungarian Financial Supervisory Authority, http://www.mnb.hu/letoltes/
pszafhu- publ- jelent- eves2006.pdf (only in Hungarian).
12See e.g. “Nem kell félni a devizahitelekt ˝ol” [You do not need to be afraid of FX loans], online newsportal Origo, 2006, http://www.origo.hu/gazdasag/
bank-archivum/20060131nemkell.html (only in Hungarian).
Moreover,one-third ofthefirmsexpectedthe introductionofthe EURtoeliminatetheexchangeraterisk.Inall,drivenbytheattrac- tiveforeigninterestrates,thesefirmsexposed themselves,unwit- tinglyornot,torisksassociatedwithexchangeratedepreciation.13 ThefinancialcrisisoriginatingfromtheUSsubprimemortgage meltdownrapidly escalated to a globalscale andbrought to the forethevulnerabilityofseveralCEEcountriesheavily indebtedin FX.Hungarywasoneofthemostaffectedeconomiesintheregion.
Thecountryentered thecrisiswithacombinationofahighbud- getdeficit,largecurrentaccountimbalancesandanover-leveraged private sector withasignificant exchange raterisk exposure.The crisisledtothedepreciationoftheHUFandquicklyturnedtheFX debtpreviously consideredasadvantageousintoaserious trouble fornumerousfirmsandhouseholdsheavilyindebtedinFX.
3.2.Characteristicsofnewcorporateloans
Breaking down the composition of debt currencies by matu- rity and firm characteristics reveals a considerable heterogeneity in the use ofFX financing. Smaller bank loans – expressed as a percentageoffirms’totalassets,presumablyforcontinuingopera- tions orforfinancing replacementinvestment – weremainlyde- nominatedin localcurrency, whileFX loans were primarily used forfinancing larger projects (Fig.2). We arrive at thesame con- clusionbycomparingshort-term (lessthana year)andlong-term contractsseparately: comparing sub Fig.2(a) and(b)shows that, inthecaseofshort-termcontracts,abouttwo-thirdsoftheoverall underwrittensumsareinHUF,comparedwithabouthalfforlong- termcontracts.Inlinewithpreviousempiricalfindings(Section1), Fig.2also showsthat export-orientedfirmsare morelikely than otherfirmstotakeoutFXloans.Moreover,exporterstendtopre- ferEUR-denominatedloans.Giventhattheeuroareaaccountsfor an overwhelming share of Hungarian exports, these figures sug- gestthat matchingmotives are likelyto playa role inexplaining firms’FXchoices.Atthesametime,theFXdebtexposureofnon- exportingfirmsandtherelativelylargeshareofCHFloansinfirms’
13 The risks related to exchange rate fluctuations probably gained higher weights after 2008. The households’ financial literacy survey conducted in 2011 and anal- ysed by Beckmann and Stix (2015) suggests that the risks related to exchange rate fluctuations gained higher weights following the large depreciation of the HUF in 2008-09.
Fig. 2. Characterisation of long- and short-term contracts Notes: The left-hand panel describes short-term loans and the right-hand panel describes long-term loans. The figure shows the shares of new loans by denomination. A panel consists of three main blocks. The first describes all loans, with separate bars for the periods 2005-2011 and 2009-2011.
The second differentiates between three loan sizes: below 30%, between 30 and 70% and above 70% relative to total assets. The third block differentiates loans by firms’ export shares.
Table 1
Number of firms by the currency denomination of their contracts (exporters only)
one contract more than one contract total HUF FX only HUF only FX both
total number of firms 892 312 3553 420 5807 10984
(8.1%) (2.8%) (32.3%) (3.8%) (52.9%) only contracts after the first HUF loan 834 212 3103 222 4758 9129
(9.1%) (2.3%) (34.0%) (2.4%) (52.1%) only contracts after the first FX loan 105 208 298 642 4748 6001
(1.7%) (3.5%) (5.0%) (10.7%) (79.1%)
Notes: The table shows the number of exporting firms with at least one loan contract underwritten between 2005 and 2011, distributed according to the currency denomination of their loans. The table differentiates between single-contract and multi-contract firms. The first row shows all contracts. The second and third rows show only contracts underwritten after the first HUF loan and after the first FX loan only, respectively.
Thus, the last two rows show data for multi-contract firms only.
debtportfolioalsosuggest that theshare ofunhedgedloans was (andstillis)substantialinHungary.14
Vonnák (2018) pointsto thesameconclusion. ComparingCHF andEURborrowersinthelendingboomandduringthecrisis,the authorfinds that thelatter aremore likely tobe bigger, foreign- ownedandexport-orientedfirms,whiletheformeraremorelikely tobe non-exportingfirmsand firmswith weaker balance sheets andahigherdefaultprobability evenduringthepre-crisisperiod.
ThedescriptivestudyinEndrészetal.(2012) alsoreportsthatFX debtismostlyconcentratedamonglargerandmoreproductiveand mostlikelymultinationalfirms,butasignificantshareofdomestic non-tradingfirmsalsotookoutFXloans.
In thispaper, we focuson exporters, asnaturalhedgingis ir- relevant(or lessrelevant) fornon-exportingfirms.Tables1and2
14Note that the share of exports to Switzerland in total exports was only 1.3 per cent in 2008 and less than 1 per cent in 2015 (source: Eurostat Comext database).
show whetherthesimultaneous presenceof severalcurrency de- nominationsamongexportingfirmsresultsfromtheaggregationof distinctindividualcurrencychoicesorfromfirmsholdingmultiple currencies intheir debt portfolio.The first rowofTable 1shows that abouthalf oftheexporterstook out bothHUFandFXloans.
When onlythe9780 firmswithmore thanone loancontract are taken into consideration, this figure climbs to 60 per cent.That is,alarge proportionoffirmsdonotstick toone singlecurrency but choose – presumablystrategically – the currency denomina- tionoftheirloanonallchoiceoccasions.Thesefirmsprovideuse- fulwithin-firmvariationforoureconometricanalysis.
Todetermine if thereis a clear patternin theorder offirms’
currency choice, the second and third rows of Table 1 display the distribution of firms by their choice of currency denomina- tionoftheirnewloanssubscribedaftertheirfirstdomesticorFX- denominated loancontract.The thirdrowis particularlyinterest- ing,asitdisproves theideathat limitedaccesstoFXcreditisthe major source of within-firm variation. In this case, (risk-neutral)
Table 2
Number of firms with more than one FX loan (exporters only)
One FX-denomination Two or more FX-denominations Only Only Only EUR & EUR & CHF & EUR, CHF EUR CHF other FX CHF other other & other
Two FX loans 485 376 20 248 25 8
(41.7%) (32.4%) (1.7%) (21.3%) (2.2%) (0.7%)
Three FX loans 315 170 12 259 13 9 7
(40.1%) (21.7%) (1.5%) (33.0%) (1.7%) (1.1%) (0.9%)
Four or more FX loans 922 220 24 1058 134 19 101
(37.2%) (8.9%) (1.0%) (42.7%) (5.4%) (0.8%) (4.1%) Notes: The table shows the numbers of firms with two (first row), three (second row) or four or more FX loans (third row) contracted between 2005 and 2011, distributed according to the currency denomination of their contracts.
firms previously indebted solely in HUF that accessFX loans for the firsttime duringoursampleperiodwouldneverswitch back tothelocalcurrency.However,about86percentoffirmswithan existing FXdebtintheir balancesheetwill latersignatleastone HUF-denominatedloancontract.15Intheempiricalpartofthispa- per,wewillexaminewhetherfirms’currencychoice,andinpartic- ularthetimingofaspecificchoice,ispurelyarbitraryorwhether itisgovernedbyexplicablerationality.
Table 2 focuses on exporting firms with more than one FX-denominated loan contract. When only firms with two FX- denominatedcontractsaretakenintoconsideration,24percentof thesefirmscontractedloansintwoFXs,mainlyEURandCHF(sum ofthelastfourcolumnsofTable2).Theshareoffirmswithseveral FXs intheirdebtportfolioincreaseswiththenumberofFXloans contracted:36.7percentoffirmswiththreeFXloansand52.9per centoffirmswithfourormoreFX loansprefer todiversify their FXliabilitiesratherthanalwayschoosethesameFX.
4. Identificationstrategy
4.1. Modellingfirms’currencychoice
Usingmonthlypaneldataatthecontract level,firms’currency choiceisstudiedusingdiscretechoice models.Ifcurrency match- ing isa relevantfactorthat firmsconsiderin their choiceofcur- rency denomination of their bank loans, the firm is more (less) likely to incurnew FX-denominated debt when its expected ex- port revenuesexceed(arelower than)its FXdebtreimbursement obligationduringagivenperiodoftime.Thisintuitiveassumption isconsistentwiththeminimumvarianceportfolio(MVP)theory,a framework often used in the literature to model firms’decisions about the currency-of-denomination of their debts (e.g. Thomas, 1985; IzeandYeyati,2003;Luca andPetrova,2008;Bleakley and Cowan,2009; Bassoetal., 2011).AccordingtotheMVPapproach, risk-averseinvestorsseek,ontheone hand,tominimisetheir ex- posure to exchange rates deterioration and, on the other hand, tomaximisetheirexpectedreturnsbyexploiting(perceived)arbi- trageopportunitiesbetweenfundingcurrencies.Theresultingop- timal debt portfolio can be represented as the sum of a stan- dardMarkowitzportfolio(thespeculativecomponent)andahedge termrepresentedbytheperfectlymatchedportfolio.Theperfectly matched portfolio is achieved if the firm’s expected export rev- enuesfullycoveritsFXdebtrepaymentobligations.16
Totestnaturalmatchingconsiderations,werelyon acurrency matchingmeasuredefinedasthedifferencebetweenthefirm’sex-
15The 86 per cent can be deducted from the last line of Table 1 : 105 + 298 + 4748 firms, out of the total number of 6001 firms, sign one or more HUF contracts after the first FX loan.
16See Appendix B for a simple MVP model that illustrates the effect of exchange rate fluctuations on firms’ choice between the local currency and several possible FXs.
port revenuesdenominated inFX c andits debtrepayment obli- gationsinthesame FX(without consideringtheactual loancon- tract).Moreformally,Mi jct=(Xict−Li jc,t+1)/Sit,whereMijct isthe matchingmeasureforfirmiandcontractjincurrencycsubscribed in time t, Xict denotes the firm’s past 12-months average export revenuesinvoicedincurrency candLi jc,t+1 isthefirm’smonthly averagedebtrepayment obligation overthe next12 months – as setbytheloancontractsandassumingfixedexchangerate– inthe samecurrencycstemmingfromallexistingcontractsjotherthan theactualloancontractj.Themismatchmeasureisnormalisedby totalsalesSit.
NotethatifLi jc,t+1=0,i.e.ifthefirmdoesnotholdFXdebt atthe time when the decisionon the currency denomination of thenewloanismade, ourmatchingmeasureissimilar tosimply usingexportshareasaproxyforthematchingincentive.However, previousFXdebtisanimportantfactorthatfirmsarelikelytotake intoaccount: onabout60per centofall choice occasionsinour sampleofexportingfirms,thefirmalreadyhasanexistingFXcon- tract.
SinceLi jc,t+1 includesonlyinformationavailable attime tand itsvalue isindependent ofthefirm’ sactualchoice attime t,the variable is pre-determined and does not give rise to endogene- ityconcerns. For all other explanatory variables – including, and mostimportantlyforXict–,weusepastvaluestoavoidsimultane- ity.Inpractice,thedecisiononthecurrency denominationofthe debt,theexportstrategyandtheinvoicingcurrencyofexportsales mightbetakensimultaneouslyby thefirm.First, firmsmightad- justtheirexportsattheintensivemarginorpossiblyreorienttheir exports towards a specific destination in response to having ac- cessto FX loans with lower interest rates. Second, the choice of thecurrency denominationofthedebtmightalsoimpact thein- voicingcurrency offirms’exportsales.Thecurrency invoicinglit- eratureargues thatthe choiceof invoicingcurrency isinfluenced byseveralfactors,suchastheprice-sensitivityofdemand,theex- changerateandrelativemonetaryvolatility(e.g.Giovannini,1988; DonnenfeldandZilcha,1991;Friberg,1998;Devereuxetal.,2004) orthe tradingpartners’ relative bargainingpower (Bacchetta and vanWincoop,2005;FribergandWilander,2008),butalsobyfirms’
desireto hedgemarginal costsduetothe useofimportedinputs orFXdebt(GoldbergandTille,2008;Chung,2016).Thatis,itmay beoptimalforfirmstoinvoicetheir exportsincurrencyc iftheir liabilities are in the same FX. Using past values for export rev- enues minimises thepotential simultaneitybias. Accordingly, the matchingmeasureisnegativeifandonlyifthefirmisalreadyin mismatchwithoutconsideringtheactualloancontract,simultane- ousorfuturedecisionsonexportorientationortheinvoicingcur- rency.17
17 See also Section 4.2 for a more detailed discussion on the invoicing currencies of Hungarian exports.
Our empiricalanalysis isbased ona reducedform probabilis- ticchoice model.Alongwithourkeyvariableofinterest,the cur- rencymatching measure,a large setof controlscaptures various demand-sideandsupply-sidefactorsinfluencingthecurrency de- nominationoftheloans.
In line with the theoretical predictions of optimal currency shares stemming from the MVP approach, the demand for FX- denominatedloansisinfluencedbythefirm’sriskperception,risk attitude and the expectation about the future paths of interest ratesandexchange rates,all ofwhich aresubjectiveassessments that investors believe in andwhich differ fromfirm to firm. To- gether with any other potential demand-side rationales behind choosing FX, these measures are captured by a large set of ob- servedtime varying firm-level variables,a currency-specific firm- level unobserved parameter that represents the effects of firms’
unobservedattributes,timefixedeffectscapturingtheevolutionof themacroeconomicenvironment– suchastheinterestratediffer- entialsinrisk-freerates,theexchangeratesorthevolatilityofthe exchangerates– andani.i.d.randomcomponent.
Notethatallfirmandcontractcharacteristics(includingtheun- observedfirm fixed effects) mayequally capturesupply-side fac- tors.Forexample,potential differencesinrisk aversion (andthus indemand forFX loans)betweensmall andlarge firms are cap- turedbythefirmsize variable,butthisvariablealsopicks uppo- tentialcreditconstraintsinFXborrowingbysmallfirms.Inother words,theunderlyingcausesbehindtheobservedrelationshipbe- tweenfirmsizeandFXindebtednessarenotidentified.Tocontrol forother supply-sidefactors relatedto banks, we alsoinclude in ourmodelasetofbankcharacteristics.Forthefulllistofcontrols, seeAppendixA.
Assuming that therandom componentfollowsan i.i.d.logistic distribution,the conditionalprobability that currency c is chosen isgivenby(seeMcFadden,1974):
P
yi jt=c
Zit−1,Mi jct,δ
ct,aic,∀
c= exp
aic+Zit−1
c+
φ
cMi jct+δ
ct1+C
c=1exp
aic+Zit−1
c+
φ
cMi jct+δ
ct (1)where yijt is the observed outcome, Zit−1 is a set of firm- level, bank-level and contract-level controls,
δ
ct are time dum-mies, aic is a currency-specific firm-level unobserved parameter and Mijct is the matching measure discussed above.18 Parame- ters
φ
c capturefirms’willingness to matchthe currency compo- sition of their incomes and liabilities to avoid exposure to ex- changeraterisk. If matching incentivesmatter, we expect that a firm is more (less) likely to take out an FX loan when Mijct is high (low), soφ
c is expected to be positive. The baseline cate-gory is the local currency (HUF) with the probability of it be- ing chosen given by P(yi jt=HUF
Zit−1,Mi jct,δ
ct,aic,∀
c)=1/(1+ Cc=1exp(aic+Zit−1c+
φ
cMi jct+δ
ct)). 4.2.LimitationsandempiricalconsiderationsAnumberofpracticalissuesneedtobeaddressed. First,toes- timatethediscrete choice modelwithall available alternatives,a
18McFadden derives the analytical expression for the selection probabilities in Eq. (1) using the axiom of independence of irrelevant alternatives (IIA) intro- duced by Luce (1959) , which states that the relative odds of one alternative be- ing chosen over a second one is independent of the presence or absence of any other alternatives. Under this assumption, the relative odds of choosing a spe- cific FX rather than the local currency can be determined as if no other FX al- ternative were available. Accordingly, the probability of choosing FX c is given by P[ a ic+ Z it−1c+ φcM i jct+ δct > εi jct] . With multiple FXs, the system of independent logit equations leads to the expression for the probability that firm i chooses cur- rency c given by Eq. (1) . As explained later, the strong assumption of IIA can be relaxed by specifying, for example, a mixed logit model.
separatemismatchindicatorhastobeconstructedforallFXs.Un- fortunately, export destinations(or, which would be even better, exportinvoicingcurrencies)arenotspecifiedinthedatabasethat we use. We therefore estimate several simplified versions of the general model in Eq. (1). In our first specification, we estimate the impact of the matching measure on the probability of sign- inganFXloancontractratherthanoneinlocalcurrency.Foreach month,allFXs arecollapsedtogether.Thesetofpossiblealterna- tivechoicesisthuslimitedtoc=
{
fx}
andHUFisthebaselinecat-egory.Thematchingindicatorisconstructedusingtotalexportrev- enues andtotal debt repayment obligationsall FXs combined.In thiscase, we implicitlyassume that debt denominated in anyof theFXscanbeusedasnaturalhedge.Thatis,companiesconsider hedgingthemselvesmainlyagainstidiosyncraticshocksto thelo- calcurrency.Eq.(1)thusreducestothebinomiallogisticfunction (andsubscriptsccanbedropped).
Basedontherelative importanceofthedifferentcurrenciesin theHungarianexternaltradeandtheaggregatecurrencycomposi- tionoftheloans, oursecondspecificationassumesthat themain
“matching currency” that firms may consider to hedge exchange raterisksonexportsisEUR,whileCHFistheprincipal“speculative currency” and irrelevant for hedging purposes. Indeed, the euro areaisHungary’smajortradingpartner:itaccountsfor57percent (in 2008) of thecountry’s total exports, whereas lessthan 2 per cent go to Switzerland.19 Moreover, the EUR is also widely used as vehicle currency: according to the mostrecent data available, for2004,84.8per centoftotalHungarianexportsare invoicedin EUR(Kamps,2006).Thesecondmostimportantinvoicingcurrency isthe US dollar,accounting for9.6per centof Hungarianexport revenues. The HUFaccounts for no more than 2.3 per cent and, consequently,allothercurrenciesaccountforonly3.3percent.In thisspecification,wethereforerelyonthesimplifyingassumption thatallexportrevenuesareinvoicedinEURinallfirms.Theother FXs are collapsed together and we estimate a three-alternative choice modelwith c=
{
eur;otherforeigncurrencies}
andHUFasthebaselinecategory.Thematchingindicatorisconstructedforthe EURonly.
The second issue is that due to lack of data, our matching measure capturing firms’ net financial exposure excludes some potentially relevant variables. For example, cash reserves in FX that can serve as a buffer against exchange rate movements (Allayannis etal., 2003) orpossible currency substitution fordo- mestictransactionswithmultinationalsBrownetal.(2011)should ideally be taken into account. Similarly, even though survey evi- denceofBodnár(2006)andBodnár(2009)suggeststhatithashad only a rather limited role in Hungary, the use derivative instru- ments forfinancialhedgingshouldalso beconsidered. Wethere- foreperform an extensive set ofrobustness checksusing various alternativematchingmeasure definitionsusingthedata available.
Inparticular,wealsochecktherobustnessofourresultstoinclud- ing imports in our matching measure, which – considering that Hungary is a remarkablyopen economy withan import shareof about80% – islikely to be the most importantelement missing fromthebaselinemeasure.
Third, the paper focuses on firms’ matching behaviour only, banks’ motive for matching the currency composition of their loans with the currency-of-denomination structure of their de- posits is also a relevant aspect of the aggregate currency mis- matchesintheeconomy.Duetolackofdataonthecurrencycom- positionofbanks’ deposits,we cannotconstructa matchingvari- able forbanks similar to theone we havefor firms.Instead, we includeinthemodelasadditionalcontrolthesamevariableasin
19 Source: Eurostat Comext database. The latest available corresponding trade shares are (in 2015): 61 per cent to the Eurozone and less than 1 per cent to Switzerland.
