The Spillovers of LSAPs on Banks in the Euro Area

Finance and Economics Discussion Series Federal Reserve Board, Washington, D.C. ISSN 1936-2854 (Print) ISSN 2767-3898 (Online) The Spillovers of LSAPs on Banks in the Euro Area Marco Graziano, Marius Koechlin, and Andreas Tischbirek 2026-005 Please cite this paper as: Graziano, Marco, Marius Koechlin, and Andreas Tischbirek (2026). “The Spillovers of LSAPs on Banks in the Euro Area,” Finance and Economics Discussion Series 2026-005. Washington: Board of Governors of the Federal Reserve System, https://doi.org/10.17016/FEDS.2026.005. NOTE: Staff working papers in the Finance and Economics Discussion Series (FEDS) are preliminary materials circulated to stimulate discussion and critical comment. The analysis and conclusions set forth are those of the authors and do not indicate concurrence by other members of the research staff or the Board of Governors. References in publications to the Finance and Economics Discussion Series (other than acknowledgement) should be cleared with the author(s) to protect the tentative character of these papers.

The Spillovers of LSAPs on Banks in the Euro Area* MarcoGraziano† MariusKoechlin‡ AndreasTischbirek§ January2026 Abstract We study the spillovers of large-scale asset purchases (LSAPs) in the U.S. on financial intermediationintheeuroareausingbank-levelsupervisorydataandhigh-frequencyidentified policy surprises. Our detailed panel data permit us to trace the impact of LSAPs through bank balance sheets. We find that the Federal Reserve affects credit provision in theeuroareathroughachannelthatwerefertoasthe“internationalbankcapitalchannel” of unconventional monetary policy. In response to an LSAP shock that leads to a steepening of the U.S. Treasury yield curve, the Treasury positions of euro area banks shrink, capitalratiosworsen,andbanksthatarelesswellcapitalizedcontracttheirlendingrelative to banks that are better capitalized. Our results are consistent with an important role of revaluationeffects,imperfectriskhedging,andcreditasanadjustmentmarginforbanksin theproximityofregulatorycapitalconstraints. Keywords: Large-Scale Asset Purchases, International Spillovers, Global Financial Cycle, CreditChannelofMonetaryPolicy,U.S.TreasuryYieldCurve,ExchangeRates JELClassification: E52,F42,F44,G21 *We thank Miguel Ampudia, Philippe Bacchetta, Gianluca Benigno, Luigi Bocola, Andrea Ferrero, Jordi Gal´ı, Marek Jarocin´ski, Friederike Niepmann, Galo Nun˜o, Pascal Paul, Alessandro Rebucci, Emiliano Santoro, Jesse Schreger, Skander Van den Heuvel, Emil Verner, and numerous seminar participants for helpful comments and discussions. TheanalysisandconclusionssetforthinthispaperarethoseoftheauthorsanddonotindicateconcurrencebyothermembersoftheresearchstaffortheBoardofGovernors. †UniversityofBasel(marco.graziano@unibas.ch) ‡UniversityofLausanne(marius.koechlin@unil.ch) §BoardofGovernorsoftheFederalReserveSystemandCESifo(andreas.j.tischbirek@frb.gov)

1 Introduction Asaresultoflarge-scaleassetpurchases(LSAPs),thebalancesheetoftheFederalReservemore than doubled in size over the course of the years 2020 and 2021. The increase in the volume ofsecuritiesheldoutrightwaslargerthanduringthethreeQuantitativeEasing(QE)programs implementedintheaftermathoftheGreatFinancialCrisiscombined. FollowingtheCOVID-19 pandemic, the Federal Reserve began to shrink its balance sheet through a process popularly referred to as Quantitative Tightening (QT). In addition, members of the Federal Open Market Committee proposed to shorten the average maturity of Treasuries held to levels closer in line with the average maturity of all Treasuries outstanding. Monetary policy in the U.S. has been identified as a key driver of the Global Financial Cycle, the high degree of comovement observedinfinancialmarketsaroundtheworld(Miranda-AgrippinoandRey,2020a,b). Given the central role of the U.S. and the dollar in the international monetary system, the historical magnitude of the Federal Reserve’s balance-sheet normalization process may be the cause of severe spillovers to other countries. Nonetheless, many aspects of the international transmissionofQEandQThaveremainednotwellunderstood. Inthispaper,wecombinehigh-frequencyidentifiedfinancialmarketsurprisesaboutLSAPs intheU.S.withbankbalancesheetdatafromtheeuroareatostudythemechanicsunderlying the spillovers of unconventional monetary policy between advanced economies. Our banklevel panel data allow us to track the effects of LSAPs in different parts of the balance sheets of banks located in the euro area. We uncover a transmission channel that we refer to as the “international bank capital channel” of unconventional monetary policy—LSAP shocks that steepen the U.S. Treasury yield curve lower the market value of banks’ Treasury holdings, diminish standard regulatory capital ratios, and lead banks in closer proximity to regulatory capitalconstraintstocontracttheirlendingrelativetobanksthatarebettercapitalized. Our analysis relies on bank-level supervisory reporting data collected by the European Banking Authority (EBA) in the process of its EU-wide transparency exercises, supplemented withdatafrombankstresstests. Thepaneldatasetthatweconstructincludesobservationson more than 150 banks under the direct supervision of the European Central Bank (ECB) typically sampled twice annually between 2010 and 2024. Our data have two distinct benefits. 1

First,theyprovideawidecoverageofoneoftheworld’slargestadvancedeconomicareaswith thebanksinthesampleaccountingforabout90percentoftheassetsheldbytheentirebanking sector in the euro area in 2024. Thus, they allow us to provide a complementary perspective toexistingstudiesofinternationalmonetarypolicyspilloversatthemicrolevel,whichrelyon loan-level data from emerging market economies including Mexico (Morais et al., 2019) and Turkey(Baskayaetal.,2017;diGiovannietal.,2022). Second, ourpaneldataincludedetailed information on a range of balance sheet items and regulatory metrics, permitting us to gain insightsintothetransmissionofLSAPswithintheboundariesofabank. We first illustrate six facts about euro area banks that have immediate implications for the channelthatwedescribe. Namely,i)banksintheeuroareahavesubstantialoutrightholdings ofU.S.Treasuries,ii)Treasuryholdingsareconcentratedamongthelargestbanks,iii)abouthalf oftheTreasuriesheldbythetopsizequartilehaveamaturityofmorethan5years,iv)thelarge majorityofTreasuryholdingsismarkedtomarket,v)largerbankstendtohavelargerratiosof Treasuryholdingstorisk-weightedassets,andvi)capitalconstraintsgenerallydonotbindfor thebanksholdingthemajorityofTreasuries. Inshort, aneconomicallymeaningfulvolumeof Treasuries is held at maturities targeted by LSAPs and listed predominantly at market prices, opening the door to revaluation effects. Treasuries are furthermore concentrated among large banks, which tend to operate at some distance to regulatory capital constraints and can hence affordsomefluctuationsintheirregulatorycapitalratiosbutmayalsowishtolimitthem. Our LSAP shocks are based on surprises extracted from financial market data in a short windowaroundpolicyannouncementsusingthemethodpioneeredbyGu¨rkaynaketal.(2005). DrawingonashockseriesfromSwanson(2021),weeliminateresidualpredictabilityfollowing therecommendationsofMiranda-AgrippinoandRicco(2021)andBauerandSwanson(2023a). Impulse responses estimated with the help of local projections show that the resulting shocks have intuitive effects. Following an expansionary LSAP shock, the slope of the U.S. Treasury yieldcurveflattensandthedollardepreciatesagainsttheeuro. Withtheseingredientsathand, weturntoestimationsatthebanklevel. Inafirststep, we estimatetheeffectoftheslopeoftheU.S.yieldcurve,instrumentedwiththeLSAPshocks,on banks’outrightholdingsofU.S.Treasuries. Inresponsetoanincreaseintheslopeoftheyield 2

curve,banks’Treasurypositionsthataremarkedtomarketshrink. Thesteepeningoftheyield curveaffectsbanksintheeuroareathroughtheassociatedfallinthepricesofTreasurieswith longer maturities and the ensuing appreciation of the dollar. By extracting the component of the total effect that is linked to the exchange rate, we show that the appreciation of the dollar inisolationputsupwardpressureonbanks’Treasuryholdings. Thesamepatternemergesfor Treasury positions that are listed at historical cost—The exchange rate effect is positive while thetotaleffectisnegative,albeitatamuchsmallerscale. Furthermore,holdingswithamaturity between 1 and 10 years are affected, while shorter and longer maturities show no significant response. Effectsonsecuritiesissuedbyothercountriespointtointernationalcomovementin sovereignbondyieldcurves. Takentogether,theestimatesareconsistentwithadominantrole ofrevaluationeffectsonnewandexistingpositions. Importantly,banksintheeuroareadonot activelyadjusttheirportfoliostofullystabilizetheirTreasurypositionsinresponsetoLSAPs. Changes in the value of Treasuries on the asset side must result in corresponding changes incapitalontheliabilitysideofbankbalancesheets,unlessbanksuseinstrumentsthathedge riskstothetotalvalueoftheirassetsorunrealizedcapitalgainsandlossesarepermittedtobe excluded from reporting. We find that standard regulatory metrics measuring the adequacy ofbankcapital,theTier1capitalratioandtheleverageratio,respondtoLSAPswiththesame signasbanks’Treasurypositions. AsteepeningoftheyieldcurveintheU.S.hasadverseeffects on capital ratios in the euro area, whichsuggests that banks allow LSAP-induced fluctuations inthevalueofTreasuryholdingstofeedintotheirregulatorycapital. Finally, we inspect the consequences of LSAPs in the U.S. for domestic credit provision in the euro area. The overall effect of a shock that raises the slope of the yield curve is positive in line with increased profitability of maturity transformation resulting from a higher spread between lending and funding rates. Due to the decline in the value of Treasuries and other sovereign debt described above in combination with increased lending, banks’ capital ratios fall toward the regulatory limit. Our key result is the coefficient estimate on an interaction term showing that banks that are less well capitalized contract their lending relative to banks that are better capitalized. This negative effect on lending in response to the looming threat of the regulatory capital constraint is the international bank capital channel of LSAPs. Banks 3

in the euro area generally have capital buffers of a substantial size in our sample. Our results showthatthedistancetoprudentiallimitsaffectstheirresponsetoLSAPsnonetheless. Banks withrelativelylowvaluesofcapitalexpandtheirlendingbyless,preventingtheircapitalratio fromfallingevenclosertotheregulatoryconstraint. Literature. Our analysis contributes to a growing literature concerned with the origins of the Global Financial Cycle and, more specifically, the channels underlying the international spilloversofmonetarypolicy. Seminal work by Rey (2013) and Miranda-Agrippino and Rey (2020b) uncovers strong globalcomovementinfinancialvariablesincludingriskyassetprices,capitalflows,andcredit, pointingtotheexistenceofaninternationalcycleinfinancialmarketswithcommondrivers,the GlobalFinancialCycle.1 Basedonfinancialaggregates,Miranda-AgrippinoandRey(2020a,b) demonstrate that U.S. monetary policy lies at the heart of this cycle, consistent with the U.S.’s central position in the global monetary and financial system (Gourinchas et al., 2019).2 In response to an unexpected tightening of conventional monetary policy in the U.S., major stock priceindicesfallintherestoftheworld, internationalcapitalflowssubside, andglobalcredit contracts. Miranda-Agrippino and Rey (2020b) show furthermore that U.S. monetary policy affectsameasureofaggregateriskaversionextractedfromtheVIX.Theyconcludethatmonetarypolicyspilloversmaybetransmittedthroughanopen-economyanalogueoftherisk-taking channel first described by Borio and Zhu (2012) and Bruno and Shin (2015). We contribute to theseinsightsbyfocusingontheFederalReserve’sLSAPsandprovidingevidenceforanother cross-bordertransmissionchannel,theinternationalbankcapitalchannel. A number of additional studies analyze the spillovers of monetary policy based on financial and macroeconomic aggregates. Mac´kowiak (2007) shows that U.S. monetary policy is an important source of macroeconomic fluctuations in a group of emerging-market economies. Using a larger panel of emerging and advanced countries, Dedola et al. (2017) estimate that U.S. monetary policy has similar effects on real variables and inflation across many countries, 1SeeMiranda-AgrippinoandRey(2022)foradetailedreviewoftheliteratureontheGlobalFinancialCycle. 2BoehmandKroner(2025)presentevidenceforspillovereffectsofU.S.newsreleasesbeyondnewsrelatedto monetarypolicy. 4

whiletheresponsesoffinancialvariablesareheterogeneous. Miranda-AgrippinoandNenova (2022)andGeorgiadisandJarocin´ski(2025)alsorelyonhigh-frequencyidentifiedsurprisesto study the global effects of monetary policy. Both find that the Federal Reserve’s LSAPs generate sizable spillovers that are largely transmitted through trade and financial channels and that the latter are mediated by shifts in aggregate risk aversion. In contrast to these papers, ours combines high-frequency identified monetary policy shocks with bank-level micro data, permitting us to trace the effects of policy spillovers through bank balance sheets and to shed lightontheinternationaltransmissionofLSAPsworkingthroughfinancialintermediation. Monetary policy spillovers have been studied based on micro data before, albeit with a focus on emerging-market economies. Morais et al. (2019) inspect the influence of conventional and unconventional monetary policy in the U.S, the U.K., and the euro area on bank lending in Mexico. A characteristic of the Mexican banking sector is that the majority of commercialcreditisextendedbyforeignbanks. Theyfindthatsoftermonetarypolicyinadvanced economies leads to an expansion of credit supplied in Mexico, because global banks reach for yieldbyshiftingtheirlendingactivitiesabroadwheninterestratesfallintheirhomemarkets. By contrast, Baskaya et al. (2017) and di Giovanni et al. (2022) conclude that the transmission mainly runs through domestic banks in Turkey, where global banks occupy a smaller share of the market. They further show that banks that rely more on funding from international capital markets increase their credit provision in response to capital inflows relative to banks that resort more to domestic funding sources. While these papers provide valuable case studies of monetary policy spillovers to emerging-market economies, we study spillovers between two regionsthatbothrepresentasizablefractionofworldGDP,theU.S.andtheeuroarea. More broadly, our paper is connected to a large body of micro-level evidence on the role of credit for the domestic transmission of conventional monetary policy. In a classic article, Kashyap and Stein (2000) present estimates consistent with the bank lending channel, which posits that monetary policy affects the supply of credit through changes in bank reserves and hence banks’ reservable liabilities such as deposits. Jime´nez et al. (2014) provide evidence in favor of the risk-taking channel of monetary policy using information on loan applications in Spain. Similartous,Jime´nezetal.(2012)showthattheresponseofcreditprovisiondependson 5

thestrengthofintermediarybalancesheetsinaccordancewiththebankbalance-sheetchannel of monetary policy. We find that euro area banks let valuation effects of LSAPs feed into their regulatorycapitalandthatbankswithlessregulatorycapitalcontracttheirlendingrelativeto banksthatarebettercapitalized. Werefertothismechanismasthe“internationalbankcapital channel”inanalogytoarelateddomesticchanneldescribedbyvandenHeuvel(2002). Recent complementary work by Greenwald et al. (2024) and Orame et al. (2025) also highlights the importanceofmonetarypolicytransmissionthroughsecuritiesthataremarkedtomarket. Overview. Theremainderofthepaperisorganizedasfollows. Section2introducesthebanklevel data and characterizes the banks in our sample based on descriptive statistics. Section 3 provides details on the series of high-frequency identified LSAP shocks. Section 4 explains our estimation strategy. Section 5 contains the results. Section 6 collects a large number of robustnessexercises. Afinalsectionconcludes. 2 Bank-Level Data Our analysis draws on an unbalanced panel of banks located in the euro area that contains detailedinformationonbalance-sheetpositionsandregulatorymetrics. 2.1 Sources Thebank-leveldataareobtainedfromtheEBA.Theyarebasedoninformationfromregulatory reports that are released in the context of the EBA’s EU-wide transparency exercises. Releases from the transparency exercises are available between 2013 and 2024 with each of the annual exercisescontainingbank-levelinformationattworeferencedates, theendsofthefirsthalfof the current year and the second half of the preceding year. We supplement these data with information from the EBA’s EU-wide stress tests containing the corresponding data for the second half of 2010, two years prior to the first observations from the transparency exercises, and the second half of 2013, observations that are missing because 2014 was the only year in which no transparency exercise was carried out after they had been introduced. Our dataset thusincludes24semestersbetweenthesecondhalfof2010andthefirsthalfof2024. 6

Wefocusourattentiononbanksthatarebasedintheeuroarea. Onlybanksqualifyingfor directsupervisionbytheEuropeanCentralBank(ECB)fallwithintheremitofthetransparency andstresstestexercisesandhenceintooursample. Asaresult,theytendtohavecomparably large balance sheets, as we discuss below in more detail.3 Our data contain information on a total of 157 banks over the entire sample period. On average, about 60 percent of them are observedpersemester. Thebanksobservedin2024jointlyaccountforabout90percentofthe assets held by the entire banking sector of the euro area. Figure A.1 in the Appendix shows the number of banks observed over time, and Appendix Figure A.2 provides details on the frequencyatwhichindividualbanksarecontainedinthesample. 2.2 AccountingStandards Inaccordancewithprevailingaccountingrequirements,banksclassifyU.S.Treasuriesas“heldto-maturity”, “trading”, or “available-for-sale” securities. Holdings of the first type are intendedtobehelduntiltheymature. Thesepositionsarevaluedatamortizedhistoricalcostin banks’balancesheets. Therefore, werefertothemasthe“bookvalueportfolio”ofTreasuries. Holdings of the latter two types are, respectively, earmarked for short-term trading or placed inadefaultcategorythatdoesnotplacerestrictionsontheirliquidity. Becausetheyhavetobe valuedatfairmarketvalue,werefertothemasthe“marketvalueportfolio.”4 Theclassificationofbanks’U.S.Treasurypositionsisobservedinmostperiods. Anexceptionaretheyears2016and2017,inwhichthetotalamountofeachbank’sTreasuryholdingsare disclosedthroughtheEU-widetransparencyexercisebutnottheirbreakdownintotheindividualaccountingcategories. However,ourdatasetincludesthecategorizationoftotalsovereign debtholdingsforeachbankduringthatperiod. Toimputethemarketandthebookvalueportfolio of U.S. Treasuries for these two years, we assume that the relative amount of Treasuries listed at market and at book value is equal to the same ratio in the total sovereign portfolio. Experimentingwiththeimputationprocedureshowedthatithaslittleeffectonourresults. 3ThecriteriafordirectsupervisionbytheECBarelaidoutinanarticleoftheEURegulationknownasthe“Single SupervisoryMechanismregulation.” Thesecriteriapertaintothebanks’size,economicimportancefortheirhome state,andsignificanceforcross-borderactivity. 4Thetwocategoriesdifferinotherwaysthough. Forexample,fortradingsecurities,unrealizedgainsorlosses arerecordedintheprofitandlossstatementofbankswhile,foravailable-for-salesecurities,onlyrealizedgainsor lossesarerecordedasnetincome. 7

2.3 U.S.TreasuryHoldingsbyBanksintheEuroArea WehighlightsixfactsabouttheU.S.Treasuryholdingsofbanksintheeuroareabeforeturning to a broader description of the estimation sample. To derive insights about differences in the composition of balance sheets by bank size, we first obtain the percentiles of the total-asset distributionineachperiodandthencomputetherelevantstatisticspoolingallobservationsin theresultingsizegroupsovertime. Fact1—BanksintheeuroareahavesubstantialoutrightholdingsofU.S.Treasuries. On aggregate, banks in the euro area hold an economically meaningful amount of U.S. Treasuries. Thebankssampledinthefirsthalfof2024,themostrecentperiodcoveredbyourdata, heldU.S.Treasurieswithatotalvalueofabout478billioneuros. Fact2—U.S.Treasuryholdingsareconcentratedamongthelargesteuroareabanks. Figure 1 illustrates banks’ average U.S. Treasury holdings by bank size, remaining maturity, and valuation method. Banks with total assets below the median and banks with total assets between the 50th and the 75th percentile hold little Treasuries compared to banks above the 75th percentile in our sample. As a result, any changes in the yields of Treasuries likely affect the banking sector of the euro area predominantly through the balance sheets of the largest institutions. Fact3—AbouthalfoftheU.S.Treasuriesheldbythetopquartilehaveamaturityofmorethan5years. FromtheTreasuryholdingsofthelargest25percentofbankswithanaveragetotalvalueof10.9 billioneuros,2.1billionand3.2billioneurosareheldintheformofsecuritieswithamaturity of 5 to 10 years and more than 10 years, respectively, as shown in the right panel of Figure 1. LSAPsareequallyfocusedonlongermaturities. Between2009and2024,theaveragematurity of the Federal Reserve’s System Open Market (SOMA) Treasury portfolio lay in the range of 6.1to10.4years.5 Thus,theassetclassesthatareofmostimportanceforlargebanksintheeuro areaaretheonesthataredirectlytargetedbyLSAPs. 5IntheaftermathoftheGreatFinancialCrisis,thefractionofsecuritiesintheSOMATreasuryportfoliowitha maturityabove5yearsreachedapeakof77percent. 8

3 3 3 2 2 2 1 1 1 0 0 0 <1 1-5 5-10 >10 <1 1-5 5-10 >10 <1 1-5 5-10 >10 Figure1: AverageU.S.TreasuryHoldingsbyBankSize,Maturity,andAccountingMethod Notes: ThefigureshowsU.S.Treasuryholdingsbytotalbankassetsandmaturity,dividedintopositionsthatare listedatmarketprices(marketvalue)andathistoricalcost(bookvalue).Eachpanelshowsaveragesforthesegment ofthetotalassetdistributiongiveninthetitle. 0.8 0.8 0.8 0.6 0.6 0.6 0.02 0.4 0.4 0.4 0.2 0.2 0.2 0 0 0 0 0.05 0.1 0.15 0.2 0 0.05 0.1 0.15 0.2 0 0.05 0.1 0.15 0.2 Figure2: U.S.TreasuryHoldingsRelativetoRWAsbyBankSize Notes: EachpanelofthefigureshowsthedistributionofU.S.TreasuryholdingsrelativetoRWAsforthesegment ofthetotalassetdistributiongiveninthetitle. Treasuryholdingsincludethemarketandthebookvalueportfolio. Thedashedverticallineintherightpanelmarksthegroupmedian. 0.4 0.4 0.4 0.3 0.16 0.3 0.16 0.3 0.15 0.2 0.2 0.2 0.1 0.1 0.1 0 0 0 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. Figure3: Tier1CapitalRatiobyBankSize Notes:EachpanelofthefigureshowsthedistributionofTier1capitalrelativetoRWAsforthesegmentofthetotal assetdistributiongiveninthetitle.Thedashedverticallinesmarktherespectivegroupmedian. 9

Fact4—ThelargemajorityofU.S.Treasuryholdingsiscontainedinbanks’marketvalueportfolios. The breakdown of average Treasury holdings into market-value and book-value positions is alsoillustratedinFigure1. Forthetop25percentofbanks,closeto85percentofthetotalholdings are contained in the market value portfolio and hence marked to market. Consequently, changesinTreasurypricesinducedbyU.S.monetarypolicymayhaveanimpactonthebalance sheetsofeuroareabanksthroughbothsubstitutionandvaluationeffects. Fact5—LargerbankstendtohavelargerratiosofU.S.Treasuryholdingsrelativetorisk-weightedassets. TheratioofU.S.Treasuryholdingstorisk-weightedassets(RWAs)bybanksizeisdepictedin Figure2. Thefigureillustratesthedistributionoftheratiowithineachbanksizegroupforthe pooledsample. Amongtheeuroareabankswhosetotalassetslieinthetwobottomquartiles, themedianTreasuryholdingsareclosetozero. AratioofTreasuriestoRWAsaround5percent or higher is rare. For banks in the third quartile of the size distribution, Treasuries play only a marginally larger role. In the top quartile of banks, the majority of banks hold a substantial amount of Treasuries, the median Treasuries-to-RWAs ratio is 2 percent, and values around and above 5 percent are frequently observed, which suggests that Treasury holdings are of disproportionatesignificanceforlargebanks.6 Fact6—CapitalconstraintsgenerallydonotbindforthebanksholdingthemajorityofU.S.Treasuries. Regulatory capital requirements are generally not immediately binding for the banks in the euroarea. Toillustratethispoint, weplottheTier1capitalratio, theratioofhigh-qualityregulatorycapitaltoRWAs,inFigure3. ThevastmajorityofTier1capitalfallsintotheCommon EquityTier1(CET1)subcategory. Overthesampleperiod,differentrequirementsforTier1and CET1 capital were in place. These bounds, which have a common and an idiosyncratic component, were progressively tightened, reaching an average of about 11 percent in 2024 for the CET1-to-RWAs ratio. The median Tier 1 capital ratio in our sample is about 15.5 percent, and values below 10 percent are rare, especially among the largest 25 percent of banks. However, thereisasizableamountofvariationincapitalratiosthatweexploitinourestimations. 6ThesamequalitativepictureariseswhentheratioofU.S.Treasuryholdingstototalassetsisconsidered. See FigureA.3intheAppendix. 10

2.4 EstimationSample We impose two sample restrictions to improve the efficiency of our estimates. First, we eliminate banks that do not hold U.S. Treasuries at any point over the entire sample period. This restriction reduces the total number of banks in the estimation sample to 102 and the number ofbank-semesterobservationsinthepooledsampleto1,654. Second,welimittheinfluenceof outliersbyapplyingamildamountoftrimmingtoallratios. Morespecifically,wedropobservationsfromthesamplethatarestrictlylowerthanavariable’s0.5th andhigherthanits99.5th percentileineachperiod. Theboundsarechosensuchthataverysmallnumberofobservations with negative Treasury holdings are excluded while zero holdings are retained, and extreme outliers at the top are eliminated. Banks were not required to report all variables sampled in everyperiodsothattheeffectivenumberofobservationsisloweredsomewhatfurther. Assets and Capitalization. Table 1 reports summary statistics of key variables based on the pooled estimation sample. Because the banks in our sample fulfill the criteria for ECB supervision,theyhavesizablebalancesheets. Themediantotalassetholdingsare107billioneuros, andthereisastrongskewinthedistributionoftotalassetswiththemeanexceedingthemedian by a factor of nearly three. Our sample spans a wide variety of bank-semester observations. Total assets range from 21 billion at the 10th to 954 billion euros at the 90th percentile. RWAs aresmallerinaccordancewiththeappropriateregulatoryriskweights. Ashighlightedabove, Treasury holdings are also skewed and of disproportionate significance for large banks. The 90th percentileoftheTreasuries-to-RWAsratioisasizable6.2percentwhilethemedianisonly 0.4percent. ThemedianTier1capitalratiois15.4percent,effectivelyunchangedbythesample resrictions. Credit makes up a significant fraction of total assets. On average, the majority of credit is extended domestically, which opens the possibility for changes in Treasury yields to spillintobanks’locallendingoperations. RiskHedging. ThestrengthofpotentialspilloversofLSAPsdependsonthedegreetowhich banks are hedged against balance sheet risks. Two regulatory risk exposure indicators, also shown in Table 1, suggest that banks may not perfectly offset the risks arising from govern- 11

Table1: SummaryStatistics Variable Median Mean SD P10 P90 n EUR(billions) Totalassets 106.5 292.6 436.4 20.6 954.4 1,513 RWAs 47.6 108.2 148.5 5.7 347.0 1,654 U.S.Treasuries 0.2 3.8 12.4 0.0 7.6 1,589 Totalcredit 28.1 67.6 96.3 3.0 214.6 1,602 Tier1capital 7.1 16.0 21.4 1.5 50.5 1,654 Debtexposure 0.2 1.0 3.3 0.0 2.1 1,537 Foreignexchangeexposure 0.1 0.6 1.3 0.0 1.6 1,537 Ratios(percent) U.S.Treasuries/RWAs 0.4 2.3 5.0 0.0 6.2 1,559 Tier1capital/RWAs 15.4 17.0 8.0 11.5 22.1 1,614 Totalcredit/totalassets 21.8 23.2 8.8 13.4 35.1 1,468 Domesticcredit/totalcredit 61.2 55.5 25.9 15.3 88.4 1,408 Notes:Thetableshowssummarystatisticsofthepooledestimationsample. ment debt holdings denominated in a foreign currency. The first aggregates the interest-rate and credit-spread risk stemming from debt securities and related derivatives, and the second providesameasureofexchange-raterisk.7 Bothtakeonsizablevaluesintherespectiveupper tailoftheirdistribution.8 WhiletheyarenotspecifictoU.S.Treasuryholdings,theseindicators are suggestive for a non-negligible net exposure to Treasuries among banks in the euro area. WereturntoadetailedassessmentoftheeffectsofLSAPsonTreasurypositionsinSection5.1. 3 LSAP Shocks The well-known endogeneity of monetary policy is likely to result in smaller estimation bias in the context of international spillovers than in assessments of domestic policy outcomes. Nonetheless, the Federal Reserve may adjust its policies in response to developments in the euro area that affect inflation or the labor market in the U.S. Our approach to addressing this concernbuildsonthehigh-frequencyidentificationprocedureofSwanson(2021)coupledwith 7Thetwoindicatorsaccountforcorrelatedandoffsettingpositionsaswellasriskhedgingthroughderivative instruments. See the Standards on the Minimum Capital Requirements for Market Risk, published by the Basel CommitteeonBankingSupervision,fordetails. 8EvidenceforheterogeneityintheexposuretoexchangeratesisalsocontainedinAbbassiandBra¨uning(2023). 12

insights from Bauer and Swanson (2023a,b) and Miranda-Agrippino and Ricco (2021). Local projectionsestimatedusingtheexogenously-identifiedLSAPshocksthatweobtainyieldintuitiveimpulseresponsesatthemacrolevel. 3.1 Derivation WedepartfromtheseriesofLSAPshocksatFederalOpenMarketCommittee(FOMC)meeting frequencyidentifiedbySwanson(2021). Westriptheseshocksfromvariationthatispredictable basedondatareleasespriortoFOMCannouncementsandautoregressiveterms. High-FrequencySurprises. TheidentificationprocedureofSwanson(2021)proceedsinthree steps. First, changes in a 30-minute window around FOMC announcements are obtained for the prices or yields of financial assets following Gu¨rkaynak et al. (2005). Specifically, highfrequencysurprisesarecalculatedforthecontractratesonfederalfundsfuturesforthemonths of the current and the next FOMC meeting, the contract rates on eurodollar futures for the threesubsequentquartersstartingwiththenext, andtheTreasuryyieldsat2-, 5-, and10-year maturity. Second, the first three principal components are extracted from the resulting series. Third,threelatentstructuralfactorscorrespondingtochangesinthefederalfundsrate,forward guidance, and LSAPs are estimated through a rotation of the matrix of principal components. Importantly,theidentificationofthelatentfactorsisachievedbyimposingtherestrictionsthat theloadingsoftheforwardguidanceandtheLSAPfactoronthesurprisesinthecurrent-month federalfundsfuturesratebezeroandthattheLSAPfactorbeassmallaspossiblepriortothe lower-boundperiodstartingin2008.9 OrthogonalizationtoPriorDataReleases. Underfullinformation,rationalexpectationsand theabsenceofarbitrageopportunities,economictheorypredictsthathigh-frequencysurprises and hence shock series derived from them in the way outlined above are orthogonal to information available prior to the FOMC announcement at which they are measured. However, a numberofarticlesargueforthepresenceofinformationfrictionsthatmayresultincorrelation 9WethankEricSwansonforsharinganextendedversionofhisshockserieswithus. 13

betweenhigh-frequencysurprisesandpriordatareleases.10 Withoutorthogonalityoftheshock series, standard methods used to estimate causal effects of monetary policy may yield biased results(StockandWatson,2018). Weaddressthisconcernbyadoptingthesolutionproposedby BauerandSwanson(2023a,b),whichinvolvesregressingthehigh-frequencyidentifiedshocks on time series with predictive power for them and calculating a series of robust shocks as the regressionresiduals. Thetimeseriesthatweconsiderinourregressionsarei)thethree-month changeintheslopeoftheyieldcurve,ii)thethree-monthgrowthrateintheBloombergCommodity Spot Price index, iii) the surprise component of the most recent nonfarm employment datarelease,iv)theone-yeargrowthrateinnonfarmemployment,v)thethree-monthgrowth rate in the Standard and Poor’s 500 stock price index, and vi) the Treasury skewness index fromBauerandChernov(2024)averagedoverthepreviousmonth,asproposedbyBauerand Swanson (2023a) in the context of standard monetary policy. All variables are calculated for thedaypriortoeachFOMCmeetingorthelatestdatareleasethatprecedesit. Theshocksand thecontrolsareavailablefortheFOMCmeetingsbetweenFebruary1988andDecember2023. TableB.1intheAppendixcontainstheresultsforOLSregressionsoftheLSAPshocksfrom Swanson (2021) on the variables described above. The change in the yield curve slope and the employment surprises have the strongest predictive power. Using all six variables, we find evidence for mild but statistically significant predictability with an R2 of 0.04 and joint significanceatthe5-percentlevel. Autocorrelation. AnadditionalconcernhighlightedbyMiranda-AgrippinoandRicco(2021) isshockpredictabilityresultingfromautocorrelation. Toaddressthisissue,wefirstaggregate theorthogonalizedshockstomonthlyfrequencyandtheneliminatetheautocorrelationinthe monthly series through a regression of the aggregated shocks on their first 12 lags. The only horizon that is individually significant is the third lag. With an R2 of 0.03, this regression uncovers further mild predictability that we remove by taking the residuals of the regression forwardas ourexternally-identifiedLSAP shocks. FigureB.1in theAppendixplots theshock seriesbeforeandafterthetwoorthogonalizationsteps. 10See, for example, Romer and Romer (2000), Melosi (2017), Cieslak (2018), Nakamura and Steinsson (2018), Miranda-AgrippinoandRicco(2021),KarnaukhandVokata(2022),BauerandSwanson(2023b),andSastry(2025). 14

3.2 ImpulseResponses Next, we compute the impulse responses of the slope of the yield curve and the euro-dollar exchangerateusinglocalprojectionstoconfirmthattheLSAPshockshavetheexpectedeffects. Setup. FollowingJorda` (2005),weestimatethelocalprojections L (cid:88) y t+h −y t−1 = αh+βhs t + x ′ t−l γ l h+e t+h (1) l=1 at monthly frequency, where y is some outcome variable of interest, s is the monetary policy t t shockderivedasdescribedabove,x isavectorofcontrols,andh = 0,1,2...,H istheimpulse t response horizon. The sequence of estimated coefficients (cid:8) βˆh (cid:9)H traces out the impulse reh=0 sponse function for each outcome variable. Note that we allow the shock to operate through thecontemporaneousvaluesofthecontrolsbutnottheirlags(Ramey,2016;Holmetal.,2021). The outcomes are the slope of the yield curve, calculated as the difference between the marketyieldofU.S.Treasuriesat10-yearandat1-yearmaturity,andtheeuro-dollarexchange rate, denominated in euros per dollar (i.e., an increase corresponds to a dollar appreciation). Our choice of controls closely follows Miranda-Agrippino and Ricco (2021). It comprises the 1-year Treasury yield, the unemployment rate, the Gilchrist and Zakrajs˘ek (2012) corporate bondcreditspread,monthlyCPIinflation,andthelogofindicesforindustrialproductionand commodity prices. The controls also include lags of the shock series to enhance the efficiency oftheestimates. Wechoose L = 12lagsinlinewiththeVARestimatedbyMiranda-Agrippino and Ricco (2021), and document confidence bands with 68-percent and 90-percent coverage probabilitycalculatedbasedonhomoskedasticity-andautocorrelation-robuststandarderrors. Estimates. Figure 4 illustrates impulse response estimates that are normalized such that the responseoftheyield-curveslopereaches25basispointsinabsolutevalue. Bothresponseshave theexpectedsign. Theyieldcurveflattensandthedollardepreciatesfollowingassetpurchases. The effect on the yield curve takes longer to build up than the response of the exchange rate. FigureB.2intheAppendixshowsthatLSAPsfurtherhaveanexpansionaryeffectoninflation, which in turn leads the federalfunds rate to gradually rise somewhat. Figure B.3, also shown 15

0.2 0.04 0.02 0 0 -0.2 -0.02 -0.4 -0.04 -0.6 -0.06 0 4 8 12 16 0 4 8 12 16 Figure4: ImpulseResponses Notes: ThefigureshowsimpulseresponsestoanLSAPshockestimatedbasedonequation(1)togetherwith68percentand90-percentconfidencebandsconstructedbasedonNeweyandWest(1987)standarderrors. intheAppendix,evaluatestheimportanceofthetwoorthogonalizationsteps. Theresponseof theexchangeratethatcorrespondstoa25-basis-pointdeclineintheslopeoftheyieldcurveis ofsimilarshapebutsomewhatlargerwithouttheorthogonalizationinlinewiththesmallbut significantamountofpredictabilityoftheuncorrectedshockseries. 4 Estimation Strategy We estimate the spillovers of LSAPs in the U.S. on several bank-level outcomes in the euro area using the orthogonalized high-frequency shocks described in the previous section as an instrumentalvariable(IV). 4.1 EmpiricalModel Ourbaselinespecificationisoftheform y = α+β·qLSAP+x ′ γ+w ′ δ+u +ε , (2) i,j,t t i,j,t j,t i i,j,t where y isabalance-sheetpositionorregulatorymetricofbanki locatedincountry j ofthe i,j,t euro area at time t, qLSAP is a financial-market outcome that is informative for the nature of t the spillovers of LSAPs, x is a vector of bank-level controls, w is a vector of country-level i,j,t j,t controls, and u is the bank-specific unobserved component of the error term. We consider i 16

different bank-level outcomes as the dependent variable in the following section such as the book value and the market value portfolios of U.S. Treasuries as well as the amount of credit (cid:110) (cid:111) extended,allnormalizedbytotalassets. Welet qLSAP ∈ Slope 10y−1y ,E$,€ beeithertheslope t t t of the nominal Treasury yield curve, defined again as the difference between the 10-year and the1-yearrate,orthespotexchangeratebetweentheeuroandthedollarwiththeeuroasthe quotecurrency,asabove. Moredetailsonthecontrolvariablesareprovidedinourdiscussion of potential identification concerns in Section 4.3. We estimate the above equation using twostage least squares (2SLS), instrumenting qLSAP with the contemporaneous value and lags of t the orthogonalized LSAP shocks. The coefficient of interest β gives the effect of LSAPs in the U.S. on bank-level outcomes in the euro area transmitted through changes in the slope of the yield curve or the exchange rate. Note that these effects are not required to be independent.11 As we discuss below in more detail, changes in the slope of the yield curve are permitted to affectbanksintheeuroareathroughanindirecteffectontheexchangerate. 4.2 Timing Themicroandthemacrodataareobservedatdifferentfrequencies. Recallthatourbank-level data are available semiannually with a reference date at the end of each half year. Thus, we let the periods t = 1,...,T correspond to semesters. The slope of the yield curve and the exchange rate are observed more frequently. While bank balance sheets mechanically depend on the prevailing asset valuations on the reference dates, accounting for asset price changes on only those days likely ignores effects of LSAPs that take longer to pass through the entire balance sheet. We therefore aggregate the financial-market outcome qLSAP, the slope of the t yieldcurveortheexchangerate,tomonthlyfrequencyandassignthevaluefromthelastmonth ofeachsemestertoanygivenperiod. Therespectivefinancial-marketoutcomeisinstrumented with 12 successive monthly values of the LSAP shock series beginning with the month of the reference date denoted s = (s0,s −1,...,s −11)′ to allow the effect of the shocks to build up t t t t gradually.12 For example, if the reference date for the bank-level data in semester t is at the 11Greenwoodetal.(2023)developatheorythatconnectslong-termbonddemandandsupplytoexchangerates. 12Thatis,s−m isthevalueoftheshockseriesinthemthmonthpriortothemonthcontainingthereferencedate, t whichimpliess−o =s−o−6foro=0,1,...,5. t t+1 17

end of December, then qLSAP is the December average of say the slope of the yield curve and t theinstrumentvectors includesthemonthlyLSAPshocksbetweenJanuaryandDecemberof t thatyear. Figure5illustratesthisexample. s −11 s −10 s −9 s −8 s −7 s −6 s −5 s −4 s −3 s −2 s −1 s0 t t t t t t t t t t t t qLSAP t y i,j,t Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure5: TimingofEmpiricalModel Notes: Thefigureillustratesthetimingoftheempiricalmodelfortheexampleofbank-levelobservationswiththe referencedateattheendofDecember. 4.3 Identification Anaiveregressionofy onqLSAPandcontrolsmayfailtoyieldanestimateofthecausaleffect i,j,t t ofLSAPsonbankbalancesheetsintheeuroareaforseveralreasons. First,theremaybeunobservedbank-specificfactorsthatarecorrelatedwiththeregressors, whichcouldresultinomittedvariablebias. Wehenceestimateequation(2)withbank-specific fixedeffectstoallowforarbitrarycorrelationbetween X = (1,qLSAP,x′ ,w′ )′ andu . i,j,t t i,j,t j,t i Second, the causation may run from the banking sector in the euro area to the exchange rate or the slope of the yield curve rather than in the other direction. In addition, shocks that havetheiroriginintheeuroareamayaffectthelocalbankingsectorandalsospillintotheU.S. economy. These shocks may lead to omitted variable bias if they affect the exchange rate or the yield curve. Our 2SLS estimator addresses these concerns provided that the requirements for instrument validity are satisfied. Specifically, let Z = (1,s′,x′ ,w′ )′ be the regressors i,j,t t i,j,t j,t presumed to be exogenous. Instrument relevance can then be expresses as a standard rank condition, and exogeneity requires E(ε |Z ,...,Z ,b ,...,b ) = 0 with b ∈ {0,1} i,j,t i,j,1 i,j,T i,j,1 i,j,T i,j,t indicatingwhetherabankiscontainedinthesampleinagivenperiod.13 Instrumentexogeneity is satisfied if the regression errors are mean independent of the instruments and selection 13Theasymptoticdistributionofthepanelfixed-effects2SLSestimatorisderivedundertheassumptionthatthe observationsarei.i.d.acrossindividuals,whichisviolatedwithcountry-levelregressors.Wethereforeverifiedthat ourresultsarenearlyunchangedifw iseliminatedandclusterthestandarderrorsaccordingly. j,t 18

intothesample. Theformerholdswithrespecttothesetupthatincludestheslopeoftheyield curve if LSAPs of Treasuries in the U.S. influence banks in the euro area only through effects set off by changes in the slope of the Treasury yield curve. Exogeneity of this type is plausible, in our view, since longer term yields serve as a target for the Federal Reserve’s Treasury purchasessimilartothewaythefederalfundsrateservesasatargetforthestandardtool. We regard changes in the exchange rate as a subcomponent of the transmission mechanism and estimate the setup that includes the exchange rate to gain insights into its importance for the overalleffect. Thekeyassumptioninthissetupisthattheslopeoftheyieldcurveaffectseuro areabanksthroughtheexchangeratebutalsothroughotherchannels. Byinstrumentingtheexchangerate,weseparatethepartofthevariationintheslopeoftheyieldcurverelatedtoLSAPs that works through the exchange rate from the variation that works through the other channels, permitting us to decompose the overall effect. Inspecting the latter mean-independence condition,wefoundnoindicationforsampleselectionbasedonbankcharacteristics. Third, LSAPs may affect banks in the euro area not only directly through changes in bond pricesortheexchangeratebutalsoindirectlythroughanexpansionaryeffectontheeconomy morebroadly. Whilesuchanindirecteffectdoesnotposeathreattotheidentification,itcomplicates the economic interpretation of the estimation results. To isolate the direct effects, we include country-level variables in the model that control for the state of the business cycle in the country in which a bank is based. These country-level controls w are the growth rate of j,t GDP, core CPI inflation, the change in the unemployment rate, and a diffusion index of loan demand by corporations from the euro area Bank Lending Survey (BLS). We further use the change in the 2-year rate on German sovereign debt as a common control variable to account forspilloversontheECB’spolicystance. Fourth,additionalbank-levelcontrolshelpassignestimatedeffectstospecificbalance-sheet positionsandimprovetheiroverallefficiency. Wereturntothispointinthefollowingsection. Below, we report standard errors that are two-way clustered at the bank level to allow for autocorrelation and heteroskedasticity of the residuals within each bank and at the semester leveltoallowforarbitrarycross-sectionaldependenceintroduced,forexample,bythecountrylevelcontrols. 19

5 Results In this section, we trace the implications of LSAP shocks through bank balance sheets in the euro area by successively turning to different balance-sheet items. Our analysis begins with the effectson theoutright holdings ofsovereign debt. Wethen inspect consequencesfor bank capitalandcreditprovision. Foreaseofexposition,weconsidertheeffectsofapositivechange intheslopeoftheyieldcurveandhencecontractionarypolicy,orQT,below. 5.1 SovereignBondPortfolio As a starting point, we clarify how banks’ outright holdings of U.S. Treasuries are affected by LSAP shocks. To do so, we let the dependent variable in equation (2) be either the market value portfolio or the book value portfolio of Treasuries. We also explore the implications for differentbondmaturitiesandfornon-U.S.sovereigndebtpositions. Inallcases,thedependent variableisnormalizedbytotalbankassets,ratiosareexpressedaspercentages,andtwobanklevel controls are included in the estimation. These bank-specific controls are the changes in the ratios of RWAs and non-equity liabilities to total assets. Because sovereign bonds carry a risk weight of zero, the former ratio helps control for indirect effects running through asset classes with a positive risk weight, such as loans. The latter ratio helps control for indirect effectsstemmingfromtheliabilitysideofthebalancesheet. ReducedForm. TablesC.1andC.2intheAppendixshowtheresultsofthefirst-stageregression for the slope of the yield curve and the exchange rate, respectively. The LSAP shocks are jointly highly significant. The F-statistic of the robust weak-instruments test proposed by KleibergenandPaap(2006)isabout14inbothcases,comfortablyexceedingthecorresponding critical value from Stock and Yogo (2005) at the standard 10-percent relative bias level. Note that joint significance of the 12 successive values of the shock series is a weaker requirement thansignificanceoftheimpulseresponsefunctiontoasinglesuchshockillustratedinFigure4. Furthermore,impulseresponsefunctionsareestimatedusingdifferenttimingassumptionsand withmacroeconomiccontrols,aswedescribeindetailinSection3.2. Allthatisrequiredhereis thattheinstruments plausiblyisolateexogenousvariation intheslopeof theyieldcurve. The 20

firststageissimilarforalloutcomesofinterestthatweconsiderwiththehelpoftheempirical modeloutlinebefore. Therefore,weonlyreturntoitinpassingbelow. Channels. Changes in the slope of the yield curve induced by Treasury purchases or sales in the U.S. may affect the Treasury holdings of banks in the euro area through three direct channels. First, a steepening of the yield curve, for example, is associated with a decline in thepriceoflong-termbonds,whichdecreasesthevalueofTreasurypositionsthataremarked to market. Second, in response to an increase in U.S. yields, the dollar appreciates relative to the euro, increasing the euro value of Treasury positions listed at market value. Third, banks mayactivelyrebalancetheirportfoliosinresponsetoachangeintherelativepricesoffinancial assets. A rebalancing might be expected to favor positions with temporarily lower relative prices so that a decrease in Treasury prices results in a reallocation toward those positions. Moresuccinctly,theeurovalueofabank’sTreasuryholdingswithagivenremainingmaturity can be expressed as V € = P$·E$,€ ·Q , where P$ is the unit bond price in dollars and Q i,j,t t t i,j,t t i,j,t is the quantity held. The three channels outlined above suggest that a steepening in the slope oftheyieldcurveleadstoadeclinein P$,arisein E$,€ ,andpossiblyanincreaseinQ . t t i,j,t RevaluationandRebalancing. Table2showstheresultsoftheIVregressionsforthemarket andthebookvalueportfolio. Theestimatedcoefficientscanbeinterpretedthroughthelensof thetheoreticalchannelsdiscussedbefore. Ariseintheslopeoftheyieldcurvelowersthesizeof themarketvalueportfolio,suggestingthattherevaluationeffectfromthedeclineintheprices of Treasuries dominates the exchange rate and the rebalancing effects. The slope coefficient is statisticallysignificantatthe1percentlevelandofmeaningfulsize. Itsinterpretationisthatan unanticipated one-standard deviation increase in the slope of the Treasury yield curve lowers the ratio of the market value portfolio to total assets by 0.135 percentage point. In the setup, in which the exchange rate is used as the independent variable and instrumented with the LSAP shocks, the estimated coefficient turns positive. The coefficient reflects revaluation and rebalancing effects stemming from changes in the exchange rate, while any effects resulting fromchangesinTreasurypricesthatdonotworkthroughtheexchangerateareeliminated,as laidoutabove. Thepositivesignofthecoefficientisconsistentwiththesepredictionsif,again, 21

Table2: U.S.TreasuryHoldings MarketValue BookValue (1) (2) (3) (4) 10y−1y Slope -0.135*** -0.031** t (0.036) (0.015) E$,€ 0.178* 0.079** t (0.101) (0.039) n 1186 1186 1207 1207 N 94 94 92 92 T 22 22 22 22 KPF-stat. 14.2 14.9 14.1 14.7 SY10% 11.5 11.5 11.5 11.5 BankFE Yes Yes Yes Yes Controls Yes Yes Yes Yes Notes: The table shows 2SLS estimates of equation (2). The respective dependent variable is the market value portfolio(1)-(2)andthebookvalueportfolio(3)-(4),allnormalizedbytotalassetsandexpressedinpercent. The second part of the table shows the total number of observations (n), the total number of Banks (N), the number ofsemesters(T),theKleibergen-PaapF-statistic(KPF-stat.),andtheStock-Yogo10%bias-basedcriticalvalue(SY 10%). Allregressionsapplybankfixedeffects. Thecontrolsincludethebank-levelandthecountry-levelvariables. Thestandarderrors(inparentheses)aretwo-wayclusteredatthebankandthesemesterlevel. Asterisksindicate significanceatthe1%,5%,and10%level(***p<0.01,**p<0.05,*p<0.1). therevaluationeffectdominates. Inprinciple,rebalancingcouldalsoruninthesamedirection asrevaluationeffectsifbanksactivelyadjustsecurityholdingsduetoavalue-at-riskconstraint, for example. However, a mechanism of this type would generally result in adjustments of credit rather than safe government bond positions and be associated with changes in nonequityliabilities,onwhichweconditionusingourbank-levelcontrols(AdrianandShin,2010). Since the book value portfolio is not listed at market prices, the results of the regressions involving the book value portfolio are free from revaluation effects on preexisting positions. A plausible initial conjecture is that the setup with the slope of the yield curve may yield a positive coefficient estimate, because the decline in the bond price may lead banks to actively scaleuptheirTreasurypositionsinthebookmarketportfolio. Ourestimatesuncoveraneffect of the opposite sign. If the quantity of new Treasuries purchased every period by banks is more or less unaffected by the steepening of the yield curve, the revaluation of new positions rather than a rebalancing determines the size of the estimated coefficient. Thus, the negative 22

Table3: UntargetedSovereignDebt U.S.Treasuries Non-U.S.Debt mat.≤1y 1y<mat.≤10y mat.>10y (1) (2) (3) (4) (5) (6) (7) (8) 10y−1y Slope -0.018 -0.111*** -0.021 -4.465*** t (0.011) (0.027) (0.013) (1.531) E$,€ 0.016 0.186** 0.046 7.276** t (0.027) (0.074) (0.036) (3.138) n 1196 1196 1194 1194 1210 1210 1211 1211 N 93 93 93 93 94 94 94 94 T 22 22 22 22 22 22 22 22 KPF-stat. 14.5 14.2 14.4 14.5 14.7 14.5 17.0 14.7 SY10% 11.5 11.5 11.5 11.5 11.5 11.5 11.5 11.5 BankFE Yes Yes Yes Yes Yes Yes Yes Yes Controls Yes Yes Yes Yes Yes Yes Yes Yes Notes: Thetableshows2SLSestimatesofequation(2). ThedependentvariableisTreasuryholdingswithmaturitybelow1year(1)-(2),Treasuryholdingswithmaturitybetween1and10years(3)-(4),Treasuryholdingswith maturityabove10years(5)-(6),andallnon-U.S.sovereigndebt(7)-(8),normalizedbytotalassetsandexpressedin percent. Thesecondpartofthetableshowsthetotalnumberofobservations(n),thetotalnumberofBanks(N), thenumberofsemesters(T),theKleibergen-PaapF-statistic(KPF-stat.),andtheStock-Yogo10%bias-basedcritical value(SY10%). Allregressionsapplybankfixedeffects. Thecontrolsincludethebank-levelandthecountry-level variables.Thestandarderrors(inparentheses)aretwo-wayclusteredatthebankandthesemesterlevel.Asterisks indicatesignificanceatthe1%,5%,and10%level(***p<0.01,**p<0.05,*p<0.1). coefficient suggests that active rebalancing plays little role.14 The size of the effect is much smaller for the book value portfolio than for the market value portfolio, because only newly entered positions are affected. The sign and size of the estimate in the setup that includes the exchangeratesupportthisexplanation. UntargetedSovereignBonds. ThefirstsixcolumnsofTable3displayresultsforregressions in which all outright holdings of U.S. Treasuries are first pooled and then divided into three maturity buckets. In line with the maturities targeted by LSAPs, we obtain significant coefficient estimates for remaining maturities between 1 and 10 years while there are no significant effectsonholdingswithremainingmaturitiesbelow1yearorabove10years. Thus,wedonot findevidenceforspilloversintomaturitybucketsthatlieoutsideofthefocusofLSAPs. 14AlikelyexplanationsisthattheFederalReserve’slarge-scaleassetpurchasesorsalesdonotmateriallyalter banks’planstorollovermaturingTreasurypositionsinthebookvalueportfolio. 23

By contrast, the columns (7) and (8) show that news about LSAPs have a significant effect onthenon-U.S.debtheldbyeuroareabanks. Thedependentvariableinthetwospecifications shown is the ratio of all non-U.S. sovereign debt holdings to total bank assets. The estimated coefficientsareofthesamesignasintheregressionsforthebookandthemarketvalueportfolio of U.S. Treasuries. This finding is consistent with the existence of spillovers on the yields of bonds issued by other sovereigns and the finding that the Federal Reserve is an important driver of the “global financial cycle” (Miranda-Agrippino and Rey, 2020a,b). In the presence of such spillovers, LSAP shocks have revaluation effects working through bond prices and exchange rates that are analogous to those described in the context of U.S. Treasuries. The coefficientsarelargerinabsolutevaluethanforU.S.debtinaccordancewiththesubstantially biggervolumeofthebalancesheetitemsaffectedbytherevaluations. 5.2 RegulatoryCapital Next, we explore whether unconventional monetary policy in the form of asset purchases or salesisassociatedwithchangesinstandardregulatorycapitalratiosandhenceinthedistance oftheseratiostoboundsimposedbysupervisorypolicy. Influences on the Tier 1 Capital Ratio. The Tier 1 capital ratio, the ratio of Tier 1 capital to RWAs, may be affected in two ways. First, all else equal, a steepening of the yield curve mayleadtoadeclineinthevalueofgovernmentbondpositions,asdescribedintheprevious section. ThecontractionontheassetsidemaybematchedbyanadjustmentofTier1capitalon theliabilitysideofbankbalancesheets. Thisadjustmentneednotbeofequalsizethoughand may even be entirely absent. The reasons are twofold. Banks may hold interest rate swaps or other instruments that allow them to smooth the effect of fluctuations in their sovereign bond portfolio on the total value of their assets. In addition, banks had some discretion over the degreetowhichunrealizedcapitalgainsorlossesonsecuritiesneededtobereportedasapart ofTier1capitalovermostofthesampleperiod.15 Second,theTier1capitalratiomaychangein 15Banksweregiventhepossibilitytoexcludecapitalgainsorlossesonsecuritiesheldintheavailable-for-sale categoryfromtheirCET1capital.Thisoption,referredtoasa“prudentialfilter,”wasphasedouttowardtheendof thesampleperiod. 24

responsetoanincreaseordecreaseinRWAs. Adeclineinthevalueofsovereignbondholdings broughtaboutbyasteepeningoftheyieldcurvemayfeedintoTier1capitalandinducebanks toreducetheirRWAsbyshrinkingtheirloanportfoliotostabilizetheTier1capitalratio.16 The steepening of the yield curve also raises the profits obtainable from maturity transformation, providinganincentivetoscaleuplendingandhenceRWAs. Insum,theresponseoftheTier1 capitalratiotoasteepeningoftheTreasuryyieldcurvedependsontheextenttowhichchanges inthesovereignportfolioarepermittedtofeedintoTier1capitalandtherelativestrengthsof themotivestoshrinkorexpandRWAsbyadjustingtheloanportfolio. Spillovers on Bank Capitalization. The columns (1) and (2) of Table 4 contain the results of 2SLS estimations of equation (2), in which the dependent variable is the Tier 1 capital ratio. WenowdroptheratiosofRWAsandnon-equityliabilitiestototalassetsascontrolstopermit the coefficient of interest to pick up a broad set of influences and avoid reverse-causality concerns. An unanticipated increase in the slope of the U.S. Treasury yield curve of one standard deviationisassociatedwithastatisticallysignificantdeclineintheTier1capitalratioofabout 1.9percentagepoints,signifyingthatbanksdonothedgetheirinterestrateriskoradjusttheir RWAs such that the Tier 1 capital ratio is fully stabilized. The coefficients on the slope of the yield curve and the exchange rate have the same signs as before, consistent with an overall declineintheratioduetothevaluationeffectsdiscussedaboveoranincreaseinRWAs. Thesizeoftheeffectallowsdrawingconclusionsabouttheunderlyingmechanism. Because Tier1capitalissmallrelativetoRWAs,thevolatilityinRWAswouldhavetobeunrealistically high to bring about an effect on the ratio of the size that we uncover.17 We conclude therefore that valuation effects on sovereign debt at least contribute to the decline in the Tier 1 capital ratio. The remaining columns repeat the same estimations with the Tier 1 leverage ratio, the ratioofTier1capitaltototalassets,asdependentvariable. ThesamepatternaswiththeTier1 capitalratioaredetectableinthissetup. 16SovereigndebtholdingshavenomechanicaleffectonRWAs,becausetheirriskweightiszero. 17Considerthefollowingback-of-the-envelopecalculation. ThemedianTier1capitalratioisabout15.5percent inoursample. Startingfromthatvalue, adeclineof1.9percentagepointsthatsolelyresultsfromanincreasein RWAswouldrequireRWAstorisebyabout14percent. 25

Table4: CapitalRatios Tier1/RWAs Tier1/Tot. Assets (1) (2) (3) (4) 10y−1y Slope -1.874*** -0.449*** t (0.285) (0.107) E$,€ 3.697*** 1.025*** t (0.555) (0.220) n 1535 1535 1417 1417 N 99 99 99 99 T 23 23 23 23 KPF-stat. 43.7 17.2 27.1 8.3 SY10% 11.5 11.5 11.5 11.5 BankFE Yes Yes Yes Yes Controls Yes Yes Yes Yes Notes:Thetableshows2SLSestimatesofequation(2).ThedependentvariableistheTier1capitalratio,theratioof Tier1capitaltoRWAs(1)-(2)andtheTier1leverageratio,theratioofTier1capitaltototalassets(3)-(4),expressed inpercent. Thesecondpartofthetableshowsthetotalnumberofobservations(n),thetotalnumberofBanks(N), thenumberofsemesters(T),theKleibergen-PaapF-statistic(KPF-stat.),andtheStock-Yogo10%bias-basedcritical value(SY10%).Allregressionsapplybankfixedeffects.Thecontrolsarethecountry-levelvariables.Thestandard errors(inparentheses)aretwo-wayclusteredatthebankandthesemesterlevel. Asterisksindicatesignificanceat the1%,5%,and10%level(***p<0.01,**p<0.05,*p<0.1). 5.3 Credit WhatdoestheimpactoftheFederalReserve’slarge-scaleoperationsonbanks’sovereignbond holdings and regulatory metrics imply for their lending activities in the euro area? To answer thisquestion, wefirstdrawonthesameempiricalapproachasbeforeandthenextenditwith aninteractiontermthatisinformativefortheinternationalbankcapitalchannel. Credit Provision in the Euro Area. We depart again from our baseline model where the dependent variable is now the amount of credit extended domestically, normalized by total assets. Domestic credit is defined as the total amount of lending by a bank in the country in whichitsheadquartersareregistered. Forconsistencywiththeregressionsinvolvingtheregulatory capital ratios discussed above, we do not condition on other balance sheet items again. The estimation results are presented in the first two columns of Table 5. The total effect of the slopeoftheTreasuryyieldcurveondomesticcreditispositive,whilethesubcomponentofthe 26

effectworkingthroughtheexchangerateisnegative. Botharestatisticallysignificantatleastat the 5-percent level. Compared with the analogous estimates for sovereign bond holdings and regulatorycapital,thecoefficientsareoftheoppositesign,pointingtotheexistenceofchannels beyondaresponseofcredittotheworseningofcapitalratios. We isolate the role of regulatory capital by comparing banks that have ample capital with banksthatareless-wellcapitalized. Morespecifically,weestimate Credit i,j,t = α+β·q t LSAP+ζ·q t LSAP·1 LRi,j,t <P33t +η·1 LRi,j,t <P33t +w ′ j,t δ+u i +ε i,j,t , (3) whereCredit i,j,t istheratioofdomesticcredittototalassetsofabankand 1 LRi,j,t <P33t isabinary variableindicatingwhetherabank’sleverageratiofallsintothebottomtercileatagiventime.18 WenowinstrumentthelinearandtheinteractionterminqLSAP withtheorthogonalizedLSAP t shocks. TheresultsareshowninthelasttwocolumnsofTable5. Thecoefficientsontheslopeof theyieldcurveandtheexchangeratecontinuetobepositiveandnegative,respectively. Banks in the bottom tercile of the leverage ratio distribution give significantly less credit relative to their total assets than banks with higher leverage ratios. Importantly, the coefficient on the interaction term is significant and has the opposite sign as the respective slope coefficient on theyieldcurveortheexchangerateinthetwospecifications—Belongingtothebottomtercile lowersthe totaleffectofthe slopeofthe yieldcurveoncredit andincreasesthe componentof thetotaleffectworkingthroughtheexchangerate. Discussion. OurresultsabouttheeffectsofLSAPshocksonthebook-valueandthemarketvalueU.S.Treasuryportfolio,regulatorycapitalratios,anddomesticcreditprovideaclearview onthemechanicsoftheinternationalbankcapitalchannelofunconventionalmonetarypolicy. Inresponsetoapolicy-inducedsteepeningoftheTreasuryyieldcurve,thevalueoflong-term Treasury holdings of banks in the euro area declines and capital ratios worsen, which leads banks with comparatively little regulatory capital to contract their lending relative to banks with large capital buffers. The isolated effects stemming from the reaction of the exchange 18Thechoiceofthecutoffvalueintheleverage-ratiodistributiontradesoffsufficientseparationbetweenthelowcapitalandthehigh-capitalgroupswithasufficientsizeofeachgroupandhencetheprecisionoftheestimates. 27

Table5: LeverageandCredit (1) (2) (3) (4) 10y−1y Slope 1.817** 2.482** t (0.768) (0.966) E$,€ -5.392*** -6.941*** t (1.428) (1.579) Slope 1 t 0y−1y×1 LRi,j,t <P33t -1.615** (0.630) E t $,€ ×1 LRi,j,t <P33t 4.028*** (1.139) 1 LRi,j,t <P33t -2.512*** -2.480*** (0.757) (0.733) n 1217 1217 1217 1217 N 89 89 89 89 T 22 22 22 22 KPF-stat. 15.1 13.5 13.2 10.2 SY10% 11.5 11.5 11.1 11.1 BankFE Yes Yes Yes Yes Controls Yes Yes Yes Yes Notes: Thetableshows2SLSestimatesofequations(2)and(3). Thedependentvariableiscreditextendeddomestically,normalizedbytotalassetsandexpressedinpercent. Thesecondpartofthetableshowthetotalnumberof observations(n),thetotalnumberofBanks(N),thenumberofsemesters(T),theKleibergen-Paap F-statistic(KP F-stat.),andtheStock-Yogo10%bias-basedcriticalvalue(SY10%). Allregressionsapplybankfixedeffects. The controlsarethecountry-levelvariables.Thestandarderrors(inparentheses)aretwo-wayclusteredatthebankand thesemesterlevel.Asterisksindicatesignificanceatthe1%,5%,and10%level(***p<0.01,**p<0.05,*p<0.1). rate run in the opposite direction. The dollar appreciation increases the euro value of Treasuryholdings,relaxescapitalconstraintsortargets,andexertsupwardpressureonthelending of worse-capitalized banks relative to better-capitalized banks. These findings show that the distance to capital constraints or targets affects the response to LSAP shocks even if generally banksintheeuroareaarewellcapitalizedandregulatoryconstraintsarenotbindingoverthe sample period. Falling to the supervisory limit is likely associated with sufficiently high costs for banks to maintain a buffer that influences their response to yield curve shocks away from but within some distance to the constraint. When the U.S. yield curve steepens and the value of their assets contracts, banks fall toward the regulatory limit. The banks in the lower tail 28

of the distribution therefore contract their lending relative to banks in the remainder of the distributiontoavoidapproachingtheconstrainttooclosely. The significance of the coefficients attached to the linear yield curve and exchange rate termsareindicativeofadditionalchannelsthathavepreviouslybeendescribedintheliterature. Theprofitabilityofmaturitytransformationincreaseswiththeslopeoftheyieldcurve.19 Since positiveshockstotheTreasuryyieldcurvetendtoresultinasteeperyieldcurvealsointheeuro area,thepositivetotaleffectcanbeexplainedbyanexpansionoflendinginresponsetoabigger spread between the lending and the funding rates of banks. Conversely, a depreciation of the euroagainstthedollarcausesthefundingcostsofbanksintheeuroareatoincrease,becausea significantpartofbankfundingiscontractedindollars.20 Thus,thenegativecoefficientonthe exchangerateisconsistentwithacontractionaryeffectresultingfromcurrencymismatch. 6 Robustness Weperformalargenumberofrobustnesschecksandderiveadditionalresultstofillindetails onsomeofourfindings. IdentificationandTiming. Ourresultsmaydependontheidentifyingassumptionsimposed bySwanson(2021)toextracttheLSAPshocksfromhigh-frequencysurprisesinassetprices. To inspect the role of the identifying assumptions, we repeat our analysis using as instruments a seriesofLSAPshockstakenfromJarocin´ski(2024)thatweorthogonalizetopriordatareleases and autocorrelation precisely as laid out in Section 3.1. A benefit of this series is that its identification from high-frequency surprises merely relies on the assumption of non-Gaussianity of the structural shocks.21 The tables C.3 to C.5 in the Appendix contain the estimates for the impact on Treasury holdings, capital ratios, and credit. Overall, the results are little changed by the modification of the instrument. We find that the coefficients tend to be slightly smaller inabsolutevaluethough,likelyasaresultofattenuationbias. 19SeeEnglishetal.(2018)foradiscussion. 20Inthesecondquarterof2024,17percentofthefundingofbanksintheeuroareawasdenominatedindollars accordingtotheECB’sFinancialStabilityReviewfromNovember2024 21Identificationbasedonnon-Gaussianityhasbeenexploitedinseveralinstances. See,forexample,Bonhomme andRobin(2009)andGourie´rouxetal.(2017,2020). 29

The timing of the empirical model, described in Section 4.2, may also affect our findings. Bymatchingthebank-leveldataonagivenreferencedatewiththeslopeoftheyieldcurveand the exchange rate aggregated over the month that precedes it, we may focus too narrowly on revaluations and rebalancing that occur close to the reference dates. To assess this possibility, weassignquarterlyratherthanmonthlyvaluesoftheyieldcurveslopeortheexchangerateto eachreferencedate. WetreattheLSAPshockseriesaccordinglybyaggregatingittoquarterly frequency and using the contemporaneous value and the first four lags of the quarterly series asinstruments. TableC.6intheAppendixshowstheresults,concentratingontheestimatesfor creditforconciseness. Theestimatesareverysimilartoourbaselinesetofresults. Inparticular, thetotaleffectoftheslopeoftheyieldcurveandtheinteractiontermwiththeindicatorforthe bottomtercileoftheleverage-ratiodistributionarenearlyunchanged. Treasury Holdings, Capital, and Credit. By including the slope of the Treasury yield curve and the exchange rate separately in our models, we aim to draw conclusions about the total effect of LSAP shocks and the partial effect working through the exchange rate, respectively. In a robustness exercise shown in Tables C.7 and C.8, we add both variables jointly to the regressionsforbanks’sovereigndebtholdings. Inthissetup,thecoefficientontheexchangerate becomesinsignificant,suggestingthatthecoefficientontheyieldcurveslopeindeedpicksup theexchange-raterevaluationchannelwhentheexchangerateisexcludedfromtheequation.22 We further confirm that the effect of LSAP shocks on the Tier 1 capital ratio and the leverage ratioaredrivenbybankcapitalratherthantherespectivedenominator. Theresultsfromusing the logarithm of Tier 1 capital, RWAs, and total assets separately as the dependent variable are shown in Table C.9. Finally, we revisit the results for domestic credit. Table C.10 contains estimatesofequation(3), inwhichthelow-capitalindicatorisreplacedwith abinaryvariable reflectingwhetherabank’sTreasuryholdingsinthemarketvalueportfoliowithamaturityof 1to10yearsrelativetototalassetsareinthetoptercile. Thecoefficientontheinteractionterm withtheyieldcurveslopeisnegativebutsomewhatlesssignificantthaninTable5. Theremay be two explanations for this finding, both consistent with the international bank capital channel. First, the capital of banks with the largest Treasury portfolios may be eroded the most by 22NotealsothattheKleibergen-PaapF-statisticdropsbelowtheStock-Yogocriticalvalueinthissetup. 30

revaluation effects, prompting them to reduce credit provision to stabilize their capital ratios. Second,thesourceoftheeffectmaybecompositional. Thedistributionofcapitalratiosismore concentrated and hence the distance to regulatory limits is lower for banks with the largest Treasury holdings, as can be seen from Figures 1 to 3. The estimated effect of large Treasury positionsmaythereforebearesultofcorrelationwithcomparativelylowregulatorycapital. 7 Conclusion LSAPscarriedoutintheU.S.aredetectableinbankbalancesheetsintheeuroarea. Ourmain findingsarethreefold. First,LSAPshaveasizableimpactongovernmentbondsheldoutright by euro area banks. A steepening of the U.S. Treasury yield curve induced by LSAP shocks decreases the ratio of U.S. Treasuries to total assets. Further evidence strongly suggests that thisrelationshipislargelyaresultofrevaluationeffects—ThetotaleffectonTreasuryholdings is negative consistent with the decline in the prices of long-term Treasuries, the isolated effect oftheexchangerateispositiveinlinewiththeappreciationofthedollar,theresponseislarger for the market value portfolio than for the book value portfolio, and only maturities that are directlytargetedbyLSAPsareaffected. Second,banksallowthesevaluationeffectstofeedinto theirregulatorycapitalratios. Thatis,banksdonothedgetheirinterestrateriskoradjusttheir RWAssufficientlytofullystabilizeregulatorymetrics. Third,theoveralleffectofasteeperU.S. yieldcurveontheamountofcreditextendeddomesticallyispositive,consistentwithspillovers on the domestic yield curve and higher profits from maturity transformation. Importantly, bankswithacomparablylowleverageratiocontracttheirlendingrelativetobanksthathavea biggercapitalbuffer,whichpreventstheircapitalfromfallingevenclosertoregulatorybounds. Inaglobalizedfinancialsystem,policiesadoptedtoimprovedomesticoutcomesmayhave sizable international spillovers and interact with policies implemented abroad. Our results showthattheFederalReserve’sLSAPsaretransmittednotonlytoemergingmarketbutalsoto advanced economies. The spillovers that we describe are triggered by monetary policy in the U.S.andmediatedbycapitalconstraintssetbymicro-andmacroprudentialpolicyintheeuro area,highlightingtheinternationalinterconnectednessofdifferentpolicytools. 31

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A Data 125 111 108 100 102 89 89 94 96 91 102101104 100 95 98 95 99 98 100 98 100 75 56 48 48 50 37 25 0 0 0 0 0 2010 2 0 D 1 e 1 2 c 0 J 1 u 1 n 2 0 D 1 e 2 2 c 0 J 1 u 2 n 2 0 D 1 e 3 2 c 0 J 1 u 3 n 2 0 D 1 e 4 2 c 0 J 1 u 4 n 2 0 D 1 e 5 2 c 0 J 1 u 5 n 2 0 D 1 e 6 2 c 0 J 1 u 6 n 2 0 D 1 e 7 2 c 0 J 1 u 7 n 2 0 D 1 e 8 2 c 0 J 1 u 8 n 2 0 D 1 e 9 2 c 0 J 1 u 9 n 2 0 D 2 e 0 2 c 0 J 2 u 0 n 2 0 D 2 e 1 2 c 0 J 2 u 1 n 2 0 D 2 e 2 2 c 0 J 2 u 2 n 2 0 D 2 e 3 2 c 0 J 2 u 3 n 2 0 D 2 e 4 c J un sknab fo rebmuN FigureA.1: NumberofBanksObserved Notes:Thefigureshowsthenumberofbankscontainedinthesampleinthesemesterendinginthemonthlisted. 22 20 15 13 12 10 10 10 9 8 8 7 7 7 7 6 6 6 5 4 3 3 2 2 2 1 1 1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Number of periods reporting sknab fo rebmuN FigureA.2: FrequencyofBankSampling Notes:Thefigurereportsthefrequencyatwhichindividualbanksarecontainedinthesample. 0.8 0.8 0.8 0.6 0.6 0.6 0.4 0.4 0.4 0.01 0.2 0.2 0.2 0 0 0 0 0.02 0.04 0.06 0 0.02 0.04 0.06 0 0.02 0.04 0.06 FigureA.3: U.S.TreasuryHoldingsRelativetoTotalAssetsbyBankSize Notes: Each panel of the figure shows the distribution of U.S. Treasury holdings relative to total assets for the segmentofthetotalassetdistributiongiveninthetitle. Treasuryholdingsincludethemarketandthebookvalue portfolio.Thedashedverticallineintherightpanelmarksthegroupmedian. 37

B Monetary Policy Identification TableB.1: LSAPShockOrthogonalization (1) (2) (3) Yieldcurveslope(3mchange) -0.22∗∗∗ -0.21∗∗ -0.21∗∗ (0.08) (0.10) (0.10) Employment(expectationalerror) 0.06∗∗∗ 0.07∗ 0.08∗∗ (0.02) (0.04) (0.04) Employment(1ygrowth) 0.01 0.01 (0.03) (0.03) Commodityprices(3mgrowth) -0.41 -0.39 (0.35) (0.38) Stockprices(3mgrowth) -0.31 (0.69) Treasuryskewness(3mgrowth) 0.10 (0.16) T 360 360 360 R2 0.03 0.03 0.04 F 7.0 3.6 2.4 (p-value) (0.00) (0.01) (0.03) Notes: The dependent variable is the series of LSAP shocks at FOMC meeting frequency from Swanson (2021), extendedtorangefromFebruary1988toDecember2023.Showninparenthesesareheteroskedasticity-robuststandarderrors. Allregressionsincludeaconstant. Asterisksindicatesignificancelevelswith∗∗∗p <0.01,∗∗p <0.05, and∗p<0.1. 38

6 5 4 3 2 1 0 -1 -2 1990 1995 2000 2005 2010 2015 2020 FigureB.1: LSAPShocksBeforeandAfterOrthogonalization Notes: ThefigureshowstheextendedLSAPshocksfromSwanson(2021)andthesameseriesafterorthogonalizationwithrespecttopriordatareleasesandautocorrelation.Bothseriesareshownatmonthlyfrequency.Apositive shockindicateslargerthanexpectedassetpurchases. 39

1 1 1 0.5 0.5 0.5 0 0 0 0 4 8 12 16 0 4 8 12 16 0 4 8 12 16 FigureB.2: AdditionalImpulseResponses Notes: ThefigureshowsimpulseresponsestoanLSAPshockestimatedbasedonequation(1)togetherwith68percentand90-percentconfidencebandsconstructedbasedonNeweyandWest(1987)standarderrors. 0.2 0.04 0.02 0 0 -0.2 -0.02 -0.4 -0.04 -0.6 -0.06 0 4 8 12 16 0 4 8 12 16 FigureB.3: NoShockOrthogonalization Notes: The figure shows impulse responses estimated based on the orthogonalized shock series (Baseline) and the unorthogonalized shocks (No orthogonalization) together with 68-percent and 90-percent confidence bands constructedwithNeweyandWest(1987)standarderrors. 40

C Additional Estimation Results TableC.1: FirstStage(SlopeoftheYieldCurve) (1) (2) (3) s0 -0.0752 -0.0325 0.0219 t (0.233) (0.141) (0.143) s −1 -1.516 -0.863 -1.048 t (0.992) (0.551) (0.639) s −2 0.194 0.222 0.213 t (0.528) (0.372) (0.414) s −3 0.196 -0.116 -0.0575 t (0.166) (0.150) (0.148) s −4 2.017*** 2.719*** 2.906*** t (0.544) (0.342) (0.794) s −5 -0.227 1.063** 0.914* t (0.693) (0.537) (0.524) s −6 -0.751*** -0.663*** -0.683*** t (0.199) (0.135) (0.131) s −7 -2.439*** -2.642*** -2.762*** t (0.838) (0.808) (0.880) s −8 0.287 1.948** 1.791** t (0.562) (0.819) (0.761) s −9 -0.393** -0.835*** -0.807*** t (0.163) (0.170) (0.166) s −10 1.787* 3.529*** 3.559*** t (0.936) (0.764) (0.860) s −11 1.119 1.910*** 2.066*** t (0.692) (0.516) (0.514) n 1371 1371 1207 N 97 97 92 T 23 23 22 KPF-stat. 28.2 28.5 14.1 SY10% 11.5 11.5 11.5 BankFE Yes Yes Yes Countrycontrols No Yes Yes Bankcontrols No No Yes 10y−1y Notes:ThedependentvariableisSlope .Thesecondpartofthetableshowsthetotalnumberofobservations t (n),thetotalnumberofBanks(N),thenumberofsemesters(T),theKleibergen-PaapF-statistic(KPF-stat.),andthe Stock-Yogo10%bias-basedcriticalvalue(SY10%). Allregressionsincludebankfixedeffects. Thestandarderrors (inparentheses)aretwo-wayclusteredatthebankandthesemesterlevel.Asterisksindicatesignificanceatthe1%, 5%,and10%level(***p<0.01,**p<0.05,*p<0.1). 41

TableC.2: FirstStage(ExchangeRate) (1) (2) (3) s0 0.234** 0.245*** 0.318*** t (0.107) (0.0884) (0.0970) s −1 0.879* 0.643 0.199 t (0.474) (0.479) (0.461) s −2 0.335 0.433* 0.569*** t (0.240) (0.224) (0.216) s −3 -0.116 -0.0248 -0.0117 t (0.0808) (0.106) (0.0828) s −4 -0.718* -0.833*** 0.117 t (0.430) (0.321) (0.618) s −5 0.109 -0.106 -0.320 t (0.284) (0.420) (0.396) s −6 0.249*** 0.235*** 0.232*** t (0.0691) (0.0575) (0.0483) s −7 0.902 0.851 0.295 t (0.613) (0.618) (0.519) s −8 0.446 0.145 0.199 t (0.316) (0.433) (0.384) s −9 -0.0347 0.0365 -0.00471 t (0.0701) (0.0870) (0.0809) s −10 0.108 -0.420 0.0479 t (0.422) (0.492) (0.535) s −11 -0.830 -0.977** -0.670 t (0.519) (0.497) (0.468) n 1371 1371 1207 N 97 97 92 T 23 23 22 KPF-stat. 15.8 8.8 14.7 SY10% 11.5 11.5 11.5 BankFE Yes Yes Yes Countrycontrols No Yes Yes Bankcontrols No No Yes Notes: The dependent variable is E$,€ . The second part of the table show the total number of observations (n), t thetotalnumberofBanks(N), thenumberofsemesters(T), theKleibergen-PaapF-statistic(KP F-stat.), andthe Stock-Yogo10%bias-basedcriticalvalue(SY10%). Allregressionsincludebankfixedeffects. Thestandarderrors (inparentheses)aretwo-wayclusteredatthebankandthesemesterlevel.Asterisksindicatesignificanceatthe1%, 5%,and10%level(***p<0.01,**p<0.05,*p<0.1). 42

TableC.3: U.S.TreasuryHoldingswithJarocin´ski(2024)Shocks MarketValue BookValue (1) (2) (3) (4) 10y−1y Slope -0.088** -0.024 t (0.038) (0.016) E$,€ 0.104 0.051* t (0.067) (0.031) n 1186 1186 1207 1207 N 94 94 92 92 T 22 22 22 22 KPF-stat. 17.8 57.3 17.5 61.4 SY10% 11.5 11.5 11.5 11.5 BankFE Yes Yes Yes Yes Controls Yes Yes Yes Yes Notes: The table shows 2SLS estimates of equation (2). The respective dependent variable is the market value portfolio(1)-(2)andthebookvalueportfolio(3)-(4),allnormalizedbytotalassetsandexpressedinpercent. The second part of the table shows the total number of observations (n), the total number of Banks (N), the number ofsemesters(T),theKleibergen-PaapF-statistic(KPF-stat.),andtheStock-Yogo10%bias-basedcriticalvalue(SY 10%). Allregressionsapplybankfixedeffects. Thecontrolsincludethebank-levelandthecountry-levelvariables. Thestandarderrors(inparentheses)aretwo-wayclusteredatthebankandthesemesterlevel. Asterisksindicate significanceatthe1%,5%,and10%level(***p<0.01,**p<0.05,*p<0.1). 43

TableC.4: CapitalRatioswithJarocin´ski(2024)Shocks Tier1/RWA Tier1/Tot. Assets (1) (2) (3) (4) 10y−1y Slope -1.745*** -0.414*** t (0.321) (0.110) E$,€ 2.427*** 0.575** t (0.705) (0.252) n 1535 1535 1417 1417 N 99 99 99 99 T 23 23 23 23 KPF-stat. 85.1 47.2 37.9 32.7 SY10% 11.5 11.5 11.5 11.5 BankFE Yes Yes Yes Yes Controls Yes Yes Yes Yes Notes:Thetableshows2SLSestimatesofequation(2).ThedependentvariableistheTier1capitalratio,theratioof Tier1capitaltoRWAs(1)-(2)andtheTier1leverageratio,theratioofTier1capitaltototalassets(3)-(4),expressed inpercent. Thesecondpartofthetableshowsthetotalnumberofobservations(n),thetotalnumberofBanks(N), thenumberofsemesters(T),theKleibergen-PaapF-statistic(KPF-stat.),andtheStock-Yogo10%bias-basedcritical value(SY10%).Allregressionsapplybankfixedeffects.Thecontrolsarethecountry-levelvariables.Thestandard errors(inparentheses)aretwo-wayclusteredatthebankandthesemesterlevel. Asterisksindicatesignificanceat the1%,5%,and10%level(***p<0.01,**p<0.05,*p<0.1). 44

TableC.5: LeverageandCreditwithJarocin´ski(2024)Shocks (1) (2) (3) (4) 10y−1y Slope 1.867** 2.513** t (0.815) (1.057) E$,€ -3.787*** -4.711*** t (1.354) (1.752) Slope 1 t 0y−1y×1 LRi,j,t <P33t -1.544** (0.707) E t $,€ ×1 LRi,j,t <P33t 2.432* (1.325) 1 LRi,j,t <P33t -2.484*** -2.141*** (0.780) (0.705) n 1217 1217 1217 1217 N 89 89 89 89 T 22 22 22 22 KPF-stat. 24.5 55.4 3.1 67.8 SY10% 11.5 11.5 11.1 11.1 BankFE Yes Yes Yes Yes Controls Yes Yes Yes Yes Notes: Thetableshows2SLSestimatesofequations(2)and(3). Thedependentvariableiscreditextendeddomestically,normalizedbytotalassetsandexpressedinpercent. Thesecondpartofthetableshowthetotalnumberof observations(n),thetotalnumberofBanks(N),thenumberofsemesters(T),theKleibergen-Paap F-statistic(KP F-stat.),andtheStock-Yogo10%bias-basedcriticalvalue(SY10%). Allregressionsapplybankfixedeffects. The controlsarethecountry-levelvariables.Thestandarderrors(inparentheses)aretwo-wayclusteredatthebankand thesemesterlevel.Asterisksindicatesignificanceatthe1%,5%,and10%level(***p<0.01,**p<0.05,*p<0.1). 45

TableC.6: LeverageandCreditwithQuarterlyInstruments (1) (2) (3) (4) 10y−1y Slope 1.756** 2.403** t (0.816) (1.011) E$,€ -2.731*** -3.512*** t (0.979) (1.138) Slope 1 t 0y−1y×1 LRi,j,t <P33t -1.603*** (0.615) E t $,€ ×1 LRi,j,t <P33t 1.979*** (0.743) 1 LRi,j,t <P33t -2.393*** -3.121*** (0.712) (0.835) n 1217 1217 1217 1217 N 89 89 89 89 T 22 22 22 22 KPF-stat. 18.3 14.0 10.6 7.0 SY10% 10.8 10.8 10.6 10.6 BankFE Yes Yes Yes Yes Controls Yes Yes Yes Yes Notes: Thetableshows2SLSestimatesofequations(2)and(3). Thedependentvariableiscreditextendeddomestically,normalizedbytotalassetsandexpressedinpercent. Thesecondpartofthetableshowthetotalnumberof observations(n),thetotalnumberofBanks(N),thenumberofsemesters(T),theKleibergen-Paap F-statistic(KP F-stat.),andtheStock-Yogo10%bias-basedcriticalvalue(SY10%). Allregressionsapplybankfixedeffects. The controlsarethecountry-levelvariables.Thestandarderrors(inparentheses)aretwo-wayclusteredatthebankand thesemesterlevel.Asterisksindicatesignificanceatthe1%,5%,and10%level(***p<0.01,**p<0.05,*p<0.1). 46

TableC.7: U.S.TreasuryHoldings(Slope 10y−1y and E$,€ ) t t MarketValue BookValue (1) (2) 10y−1y Slope -0.156*** -0.018 t (0.060) (0.022) E$,€ -0.079 0.050 t (0.132) (0.058) n 1186 1207 N 94 92 T 22 22 KPF-stat. 5.5 5.5 SY10% 10.8 10.8 BankFE Yes Yes Controls Yes Yes Notes:Thetableshows2SLSestimatesofequation(2).Therespectivedependentvariableisthemarketvalueportfolio(1)andthebookvalueportfolio(2),allnormalizedbytotalassetsandexpressedinpercent. Thesecondpart ofthetableshowsthetotalnumberofobservations(n),thetotalnumberofBanks(N),thenumberofsemesters(T), theKleibergen-PaapF-statistic(KPF-stat.),andtheStock-Yogo10%bias-basedcriticalvalue(SY10%). Allregressionsapplybankfixedeffects. Thecontrolsincludethebank-levelandthecountry-levelvariables. Thestandard errors(inparentheses)aretwo-wayclusteredatthebankandthesemesterlevel. Asterisksindicatesignificanceat the1%,5%,and10%level(***p<0.01,**p<0.05,*p<0.1). 47

TableC.8: UntargetedSovereignDebt(Slope 10y−1y and E$,€ ) t t U.S.Treasuries mat.≤1y 1y<mat.≤10y mat.>10y Non-U.S.govt. bonds (1) (2) (3) (4) 10y−1y Slope -0.025 -0.110*** -0.015 -4.426** t (0.016) (0.029) (0.019) (2.105) E$,€ -0.025 0.006 0.021 0.141 t (0.033) (0.047) (0.050) (3.641) n 1196 1194 1210 1211 N 93 93 94 94 T 22 22 22 22 KPF-stat 5.3 5.6 5.5 5.8 SY10% 10.8 10.8 10.8 10.8 BankFE Yes Yes Yes Yes Controls Yes Yes Yes Yes Notes:Thetableshows2SLSestimatesofequation(2).ThedependentvariableisTreasuryholdingswithmaturity below 1 year (1), Treasury holdings with maturity between 1 and 10 years (2), Treasury holdings with maturity above10years(3), andallnon-U.S.sovereigndebt(4), normalizedbytotalassetsandexpressedinpercent. The second part of the table shows the total number of observations (n), the total number of Banks (N), the number ofsemesters(T),theKleibergen-PaapF-statistic(KPF-stat.),andtheStock-Yogo10%bias-basedcriticalvalue(SY 10%). Allregressionsapplybankfixedeffects. Thecontrolsincludethebank-levelandthecountry-levelvariables. Thestandarderrors(inparentheses)aretwo-wayclusteredatthebankandthesemesterlevel. Asterisksindicate significanceatthe1%,5%,and10%level(***p<0.01,**p<0.05,*p<0.1). 48

TableC.9: CapitalRatios(Components) log(Tier1) log(RWA) log(Tot. Assets) (1) (2) (3) (4) (5) (6) 10y−1y Slope -0.113*** 0.022 -0.030 t (0.022) (0.020) (0.020) E$,€ 0.217*** -0.047 0.048 t (0.045) (0.038) (0.047) n 1574 1574 1574 1574 1433 1433 N 99 99 99 99 99 99 T 23 23 23 23 23 23 KPF-stat. 43.9 16.8 43.9 16.8 28.3 8.4 SY10% 11.5 11.5 11.5 11.5 11.5 11.5 BankFE Yes Yes Yes Yes Yes Yes Controls Yes Yes Yes Yes Yes Yes Notes: The table shows 2SLS estimates of equation (2). The dependent variable is Tier 1 capital (1), RWAs (2), andtotalassets(3),allinlogs. Thesecondpartofthetableshowsthetotalnumberofobservations(n),thetotal numberofBanks(N),thenumberofsemesters(T),theKleibergen-PaapF-statistic(KPF-stat.),andtheStock-Yogo 10%bias-basedcriticalvalue(SY10%). Allregressionsapplybankfixedeffects. Thecontrolsarethecountry-level variables.Thestandarderrors(inparentheses)aretwo-wayclusteredatthebankandthesemesterlevel.Asterisks indicatesignificanceatthe1%,5%,and10%level(***p<0.01,**p<0.05,*p<0.1). 49

TableC.10: U.S.TreasuryHoldingsandCredit (1) (2) (3) (4) 10y−1y Slope 1.817** 2.130** t (0.768) (0.873) E$,€ -5.392*** -5.884*** t (1.428) (1.477) Slope 1 t 0y−1y×1 1y−10yUSi,j,t >p66t -1.103* (0.574) E t $,€ ×1 1y−10yUSi,j,t >p66t 2.226** (1.018) 1 1y−10yUSi,j,t >p66t -0.402 -0.282 (0.434) (0.376) n 1217 1217 1217 1217 N 89 89 89 89 T 22 22 22 22 KPF-stat. 15.1 13.5 13.6 26.8 SY10% 11.5 11.5 11.1 11.1 BankFE Yes Yes Yes Yes Controls Yes Yes Yes Yes Notes: Thetableshows2SLSestimatesofequations(2)and(3). Thedependentvariableiscreditextendeddomestically,normalizedbytotalassetsandexpressedinpercent. Thesecondpartofthetableshowthetotalnumberof observations(n),thetotalnumberofBanks(N),thenumberofsemesters(T),theKleibergen-Paap F-statistic(KP F-stat.),andtheStock-Yogo10%bias-basedcriticalvalue(SY10%). Allregressionsapplybankfixedeffects. The controlsarethecountry-levelvariables.Thestandarderrors(inparentheses)aretwo-wayclusteredatthebankand thesemesterlevel.Asterisksindicatesignificanceatthe1%,5%,and10%level(***p<0.01,**p<0.05,*p<0.1). 50