欧洲央行-识别欧洲央行货币政策的统计方法 A statistical approach to identifying ECB monetary policy

EUROPEANCENTRALBANK

EUROSYSTEM

WorkingPaperSeries

YıldızAkkaya,LeaBitter,ClausBrand,LuísFonseca

AstatisticalapproachtoidentifyingECBmonetarypolicy

No2994

Disclaimer:ThispapershouldnotbereportedasrepresentingtheviewsoftheEuropeanCentralBank(ECB).TheviewsexpressedarethoseoftheauthorsanddonotnecessarilyreflectthoseoftheECB.

ECBWorkingPaperSeriesNo29941

Abstract

Weconstructmonetarypolicyindicatorsfromhigh-frequencyassetpricechanges

followingpolicyannouncements,emphasisingtheconcentrationofassetpricere-sponsesalongspecificdimensionsandtheirleptokurticdistribution.Traditionally,thesedimensionsareidentifiedbyrotatingprincipalcomponentsbasedoneconomicassumptionsthatoverlookinformationinexcesskurtosis.WeemployVarimaxro-tation,leveragingexcesskurtosiswithoutusingeconomicrestrictions.Withinasetofeuro-arearisk-freeassetsVarimaxvalidatespolicynewsalongdimensionsprevi-ouslyderivedfromstructuralidentificationapproachesandrejectsevidenceofmacro-informationshocks.Yet,onceaddingriskyassetsVarimaxidentifiesonlyonerisk-freefactorinmedium-tolong-termyieldsandinsteadpointstoadditionalrisk-shiftfac-tors.

JELcode:E43,E52,E58,C46,G14.

Keywords:Monetarypolicyinstruments,Varimax,fattails,eventstudy,high-frequencyidentification.

ECBWorkingPaperSeriesNo29942

Non-technicalsummary

Inthewakeoftheglobalfinancialcrisis(GFC)andtheeconomicchallengesarisingfromitcentralbankshavedeployednovelpolicytools,impactingassetpricesinwaysdifferentfromthetraditionalshort-terminterestrateinstrument.TheEuropeanCentralBank(ECB)hasemployedvariousstrategiessuchasforwardguidanceoninterestratesandas-setpurchasestolowerlong-terminterestratesandreducefragmentationinthesovereigndebtmarket.Thesemeasureshaveattenuatedriskaversionandeasedfinancingcondi-tionsacrosstheboard.Conversely,asinflationsurgedinthepost-pandemicenvironment,centralbankshavebeguntounwindassetpurchaseprogrammesandtightenedmonetarypolicy,whileatthesametimecontinuingtoguidefinancialmarketexpectationsaboutfuturepolicyaction.Thisapproachhashelpedmanageshort-termpolicyexpectationsbuthasalsoledtosignificantresponsesinlong-termyieldstopolicynews.Thesedevel-opmentshaveshownhowdifferentmonetarypolicyinstrumentscanaffectspecificassetpricesegments,suggestingthatmonetarypolicyoperatesalongmultipledimensions.

Thispaperintroducesanew,agnosticapproachtomeasurethemulti-dimensionaleffectsofmonetarypolicyusinghigh-frequencyassetpricemovementsaroundECBpolicyannouncements.Traditionalmethodsoftensolelyrelyoneconomicassumptions,butourapproachutilisesstatisticalpropertiesofthedatatoidentifydifferentmonetarypolicyfactorswithoutimposingeconomicrestrictions.ThisapproachisnamedVarimaxrotationofprincipalcomponents.

WhenapplyingVarimaxrotationtorisk-freeyields,weidentifythesamepolicyfac-tors(target,path,andQEi.e.quantitativeeasing)asthosefoundinpreviousstudiesandwedonotfindevidenceofmacro-economicinformationnewsinECBpolicyannounce-ments.Thisvalidationshowsthatourmethodcanstatisticallysupporttheconventionalapproachtoidentifyingthesefactors.Yet,addingriskyassetsblursthepreviouslyidenti-fiedseparationbetweentheforwardguidanceandtheQEdimensioninfavourofrisk-shiftfactors.Specifically,whenconsideringyieldsonvarioussovereignbonds,ourapproachconfirmsanadditionalsovereignriskfactors.Includingmoredatafromriskyassets,suchascorporatebondspreads,stockprices,stockmarketvolatility,interestrateuncer-tainty,andtheEUR/USDexchangerate,uncoverfurtherriskdimensionsthatsegmentintosovereignrisk,policyuncertainty,andcorporaterisk.Wesubsequentlymodelthefinancialpropagationofthesefactors.

ECBWorkingPaperSeriesNo29943

Thesampleperiod(spanningfrom2002untillate2023)coversdifferentphasesofmonetarypolicyincludingthequiescentpre-GFCperiod,theGFC,thesovereigndebtcrisis,thesubsequentperiodinwhichpolicyinterestrateswereconstrainedbytheireffectivelowerbound,theCovid-19pandemic,andthepost-pandemicinflationsurge.WefindthatdifferentECBpolicyinstrumentshaveconsistentlyimpactedmedium-to-long-termmaturities,bothbeforeandaftertheGFCandbeforetheformaladoptionofforwardguidancein2013.However,theinfluenceofmonetarypolicyonriskyassets,particularlysovereignbondyieldspreadsandriskappetite,becamemoreprominentsincetheGFC.

OurapproachdepartsfromtraditionalmethodsofusingeconomicassumptionsbyemployingtheVarimaxrotationtechnique.Thismethodleveragesexcesskurtosis,asta-tisticalpropertyindicatingthepresenceofstrongoutliersinthedistributionofassetpriceresponsestopolicyannouncements,andthateachpolicyinstrumentinfluencesadistinctsubsetofassets,thusensuringinterpretabilityandsparsity.Inthiscontext,outliersareafeature,notadrawback.Whilemostmonetarypolicysurprisesaresmallandcentredaroundzero,largeannouncementeffectsareespeciallyinformativeforidentification.

Thesefindingshavesignificantimplicationsforcentralbankpolicydecisions.Bydemonstratingthattraditionalmonetarypolicyfactorscanbeidentifiedusingapurelystatisticalapproach,weprovidearobustmethodforpolicymakerstogaindeeperinsightsintohowpolicyinstrumentsworkandhowtodeploythemmosteffectively.

Additionally,theprominenceofthedetectedrisk-shiftdimensionfortheeuroareaenrichestheunderstandingofhowmonetarypolicyinstrumentswork.Itsuggeststhatcentralbanksneedtoaccountforbroadermarketconditions,beyondtraditionalrisk-freeassets,tofullyunderstandthetransmissionofmonetarypolicy.

Weshowthatcommunication,evenifnotconsideredanexplicitelementofforwardguidance,hasapowerfulandpersistentfinancialimpact.Inaddition,communicationandassetpurchasestransmitstronglyalongariskdimension,achannelthatintheeuroareaappearstodominatea‘central-bankinformation’impact(astrongfinancialimpactfromthecentralbank’spublicassessmentofthestateoftheeconomy),ratherthancommunicationaboutpolicyinstruments.

Inconclusion,ournovelapproachoffersastatisticallyvalidated,comprehensiveviewofthemulti-dimensionaleffectsofmonetarypolicy.Itunderscorestheimportanceofcon-sideringawiderangeofassetpriceresponsesandprovidesvaluableinsightsfordesigningmonetarypolicyandmonetarypolicycommunication.

ECBWorkingPaperSeriesNo29944

1Introduction

Inthewakeoftheglobalfinancialcrisis(GFC),centralbankshavedeployednovelpolicyinstruments,whichhavebeenaffectingassetpricesinwaysdifferentfromthetraditionalshort-terminterestrateinstrument.Intheeuroarea,theEuropeanCentralBank(ECB)useddifferentformsofforwardguidanceoninterestratesandassetpurchasestolowerlong-terminterestratesandattenuatesovereignbondmarketfragmentation,therebyeas-ingfinancingconditionsmorebroadly.Conversely,centralbankstightenedmonetarypolicyinresponsetothepost-pandemicinflationsurge,whileseekingtoguideexpecta-tionsaboutthepaceandextentofincreasesinpolicyrates.Thiscommunicationefforthascontributedtocontainexpectationerrorsaboutthenear-termcourseofmonetarypolicydecisions,butatthesametimealsogeneratedhistoricallylargeadjustmentsinlonger-termyields.Theseexamplesshowthattheimpactofdifferentmonetarypolicyinstrumentscanbeconcentratedinspecificassetpricesegments,pointingtomonetarypolicyworkingalongmultipleanddistinctdimensions.

Measuringsuchmulti-dimensionaleffectsofmonetarypolicyatdifferentmaturityhorizonsfromhigh-frequencyassetpricemovementsaroundpolicyannouncementshasbeenprominentlyproposedby

G¨urkaynaketal.

(2005),followingtheseminalpaperby

Kuttner

(2001)whofocusedonsingle-dimensionmeasuresofmonetarypolicyusingshort

-termyields.

Inthispaperweadoptanovel,agnosticapproachconstructingmulti-dimensionalmon-etarypolicyindicatorsfromhigh-frequencyassetpricechangesfollowingECB’smonetarypolicyannouncements,relyingonstatisticalpropertiesforidentification.Asopposedtotheestablishedliterature,whichreliesonstructuralassumptionsinrotatingprincipalcomponentsincross-asset-priceadjustments,weemployVarimaxrotation.Thisapproachleveragesexcesskurtosisandsparsityintheimpactofpolicyinstrumentswithoutusingeconomicrestrictions.

UsingVarimaxtoidentifydifferentdimensionsofmonetarypolicyisanaturalchoice,giventhatmonetaryannouncementsinducehigh-frequencychangesinassetpriceschar-acterisedbytwokeyfeatures.First,theimpactofmonetarypolicyinstrumentsisusuallyconcentratedwithinspecificdimensions,meaningthatcertainassetsegmentsexperiencemorepronouncedresponsescomparedtoothers.Second,thesehigh-frequencychangesinassetpricesdonotfollowanormaldistribution.AscanbeseeninFigure

1

,inmostcases

ECBWorkingPaperSeriesNo29945

theresponsesaresmall,butininstancesofsignificantmonetarypolicyannouncements,assetpriceresponsesaresubstantial,makingtheirdistributionfat-tailed(see

Jaroci´nski,

2024

).

WeshowthatapplyingVarimaxrotationtorisk-freeyieldsuncoversthesamepolicyfactors–target,path,andQuantitativeeasing(QE)–aspreviouslyidentifiedin

Altavilla

etal.

(2019)andotherstudies,statisticallyvalidatingtheirstructuralidentificationap

-proachwithinthisspecificsetofassets.However,whenaddingfurtherinformationfromriskyassets,likesovereignbonds,corporatebondspreads,stockprices,stockmarketvolatility,interestrateuncertainty,andtheEUR/USDexchangerate,wefinditmorechallengingtodistinguishforwardguidanceandQEdimensionsandinsteadidentifyafurtherrisk-shiftdimensionthatcanbesegmentedintothesovereignriskfactorandinadditionapolicyuncertaintyandacorporateriskfactors.

Oursample,spanningfrom2002untillate2023,capturesdistinctperiodsintheuseofmonetarypolicyinstruments.WeshowthattheECB’smonetarypolicyaffectedmedium-to-longertermmaturitiesintheperiodbeforetheGFCasmuchasitdidsincetheformaladoptionofforwardguidanceasof2013,andalsomeasurablybeforethedeploymentofassetpurchaseprogrammes.Atthesametime,theimpactofmonetarypolicyinstrumentsonriskyassets,inparticularsovereignbondyields,hasgainedprominenceinthecontextoftheGFCanduntilveryrecently.Acrossallinstrumentdimensions,monetarypolicyeffectshavebeensignificantduringtherecentinflationsurge.Duringthisperiod,theECBtightenedmonetarypolicybyraisinginterestratesandgraduallyreducingitsassetportfoliothroughquantitativetightening.

Surprisingly,despitebeingaconspicuousaspectofthedata,excesskurtosishasbeenlargelyoverlookedintheextensiveliteratureidentifyingmonetarypolicyfromassetprices.Theliteratureextensivelyreliesonstructuralassumptionsinrotatingprincipalcom-ponentstoextractthekeydimensionsinsurprisesobservedfromfinancialassetpriceresponsessurroundingmonetarypolicyevents

.1

Principalcomponentsareeffectiveinexplainingmostofthevarianceinassetpricesaroundpolicyannouncements,buttheyareessentiallystatisticalanddonotdirectlyrepresenttheunderlyingstructuraleconomicshocksresponsibleforthevariationsinassetpricesaroundmonetarypolicyannounce-ments.Similartoreduced-formshocksinvectorautoregression(VAR)literature,they

1Seee.g.,

Brandetal.

(2010

);

Altavillaetal.

(2019

);

Mottoandzen

(2022

)fortheeuroarea,orig-

inatingfrom

G¨urkaynaketal.

(2005

)fortheUS.Thesameappliestothesingle-dimensionindicatororiginatingfrom

Kuttner

(2001

).

ECBWorkingPaperSeriesNo29946

Figure1:ValueofthehighfrequencychangeinbasispointsinselectedassetsaroundECBGoverningCouncilmeetings,basedondatafrom

Altavillaetal.

(2019)

.

OIS3M

OIS10Y

Italian10Y−German10Y

15

10

10

525

5

0

0

−50

−5

−10

−10

−25

200220062010201420182022200220062010201420182022200220062010201420182022

Sampleperiod:January2002−October2023.

embodyacombinationofunderlyingstructuralshocks.

Toprovideastructuralinterpretationoftheprincipalcomponents,studiesexploringthemultipledimensionsofmonetarypolicysurpriseshavetypicallyimposedidentifyingrestrictionsbasedoneconomictheoryforrotatingtheprincipalcomponents.However,sinceanyrotationoftheprincipalcomponentsisobservationallyequivalentinthedata,thecredibilityoftheresultsdependsfullyonhowbelievablethea-priorieconomicas-sumptionsare.

ByusingVarimax,weemployastraightforwardstatisticalapproach,capitalisingonexcesskurtosisinassetpricedatatoestimatemonetarypolicyindicatorswithoutrelyingona-priorieconomicassumptions.Asconventionalintheliterature,wefirstextractprincipalcomponentsfromhigh-frequencyassetpricechangestopolicynews.Inasecondstep,insteadofusingstructuralassumptionstorotateprincipalcomponents,weutilisetheVarimaxrotationofprincipalcomponents,atechniqueintroducedby

Kaiser

(1958)

andwidelyappliedacrossvariousacademicfields(withthepaperaccumulatingmorethantenthousandcitationsonGoogleScholar).Wereconstructstructuralfactors,basedoneconomicassumptions,todemonstratethatconventionalmonetarypolicyfactorsbasedoneconomicrestrictionscanemergefromanapproachthatsolelyconsidersthepresenceofsignificanttailsinthereactionsofnumerousassetprices,withoutimposinganyeconomicrestrictionslinkedtospecificpolicyinstruments.

TheVarimaxrotationdistinguishesitselfbyrotatingfactorstoachievesparsityandinterpretability.Ittakesadvantageoftheleptokurticdistributionandconcentrationof

ECBWorkingPaperSeriesNo29947

responsesinspecificassetsegments.Inourcontext,theobjectiveoftherotationistouncovermonetarypolicyfactorswithoutimposingeconomicassumptionsonitsstructure.Itaimstomaximisethevarianceofthesquaredloadingsoffactorsacrossassetswhilemaintainingorthogonality.Thegoalistoattributeeachfactortoassmallasubsetofassetsaspossible,havinginmindtheideaofsparsity,meaningthateachfactorprimarilyinfluencesasubsetofthevariables.Inourspecificsetting,thisobjectiveimpliesthateachpolicyinstrumentaffectsadistinctpartoftheassetpricespectrum.Thehigherkurtosisinthedata,thebetteritenhancestheidentificationofthemostcrucialandinterpretablefactors.

Jaroci´nski

(2024)wasthefirsttoexploitthesecrucialstatisticalfeatures,estimat

-ingindependentandinterpretablestudent-t-distributedfactorsthatdriveassetpricere-sponsestomonetarypolicyannouncementsbytheFederalReserveintheUS.

Jaroci´nski

(2024)showsthathisresultsalignwiththoseobtainedidentifyingfourfactorsbasedon

economicassumptions.Unliketheapproachtakenby

Jaroci´nski

(2024),Varimaxdoes

notdependondistributionalassumptions.Italignsmorecloselywiththetraditionalmethodofobtainingprincipalcomponentsfromalargesetofassetpricesandrotatingthem.However,thereisananalogybetween

Jaroci´nski

(2024)’sapproachandVarimax:

intheabsenceoffattails,asisthecasewhendataarenormallydistributed,thelikelihoodfunctionbecomesflat.Insuchcases,theVarimaxapproachalsolacksstatisticalpowertoidentifyunderlyingrotationoftheprincipalcomponentthatgeneratesthedata.

Themaincontributionofourpaperisthefollowing:First,focussingonhigh-frequencychangesinrisk-freeassetsouralternativestatisticalapproachsubstantiallyconfirmsthepresenceandcharacteristicsofmonetarypolicyindicatorscommonlyidentifiedthroughstructuralmethods.Intheeuroarea,usingabaselinemodelwithsevenrisk-freerates(1-monthto10-year)and10-yearsovereignyieldsfromthefourlargesteconomies,fourfactorsnaturallyemerge.ThesefactorssupportevidenceofECBpolicydimensionsviatheinterestrate‘target’,‘path’forwardguidance,‘QE’,and‘sovereignrisk’(similarto

Altavillaetal.,

2019;

Mottoandzen,

2022

).However,wedonotfindstatisticalsupport

forcentralbankmacro-informationshocksintheeuroarea(identifiedby

Nakamuraand

Steinsson,

2018;

Jaroci´nskiandKaradi,

2022;

Miranda-AgrippinoandRicco,

2021,among

others,fortheUS).Second,expandingthesetofassetpriceswithvariablescapturinguncertaintyaboutmonetarypolicyandriskappetiterevealsevidenceofarisk-shiftfactor(asrecentlydocumentedby

CieslakandSchrimpf,

2019;

CieslakandPang,

2021;

Kroencke

ECBWorkingPaperSeriesNo29948

etal.,

2021;

Baueretal.,

2023

,fortheUS).InthisdatasetVarimaxnolongerproducesevidenceofseparateforward-guidanceandQEdimensions,butonlyonecorrespondingfactorloadingintomedium-tolonger-termrisk-freeyields.Thirdly,weinvestigatethefinancialtransmissionofpolicyindicatorsidentifiedbothwiththebaselineandwitharisk-extendedsetoffactors.Weshowthatthereissignificantevidenceofmonetarypolicytransmittingthroughrisk-takingwhenconsideringtheextendedsetofassetpriceresponsestopolicyannouncements.

Thereminderofthepaperisorganisedasfollows.Section

2

providesanoverviewofthemethodologiesforinferringmulti-dimensionalmonetarypolicyindicatorsbyusinghigh-frequencyassetpricemovements.Section

3

outlinestheconventionalapproachintheliterature,whileSection

4

introducestheVarimaxapproachforidentifyingmonetarypolicyindicators.Section

5

introducesadditionaldimensionsofmonetarypolicysurprisesusingVarimaxbasedonanextendedsetofassets.Section

6

presentsevidenceonthetransmissionofbothbaselineandextendedmonetarypolicydimensionstoselectedassetclassesandthepersistenceoftheireffects.Finally,Section

7

concludes.

2Identifyingmulti-dimensionalindicatorsofmonetarypol-

icysurprisesfromhigh-frequencyassetpricemovements

Inthissection,weprovideanoverviewofthemethodologiesusedtoinferthedimensionsofmonetarypolicysurprisesembeddedinhigh-frequencyassetpricemovementsaroundpolicydecisions.Wecollecthigh-frequencychangesinnseriesofassetpricesaroundT

monetarypolicymeetingsoftheECB’sGoverningCouncilinamatrixX.Westan-T×n

dardiseeachcolumntohavemean0andstandarddeviation1

.2

Wethenuseprincipal

componentstodecomposeXintokfactorsasX=FΛ+η,whereηisaresidual,

T×nT×kk×nT×n

andthecolumnsofFareorthogonaltoeachother,aswellastherowsofΛ.Fornow,thisprocedureispurelystatistical,anditmaximiseshowmucheachprincipalcomponent

2Inthis,wealsodeviatefrompaperssuchas

Altavillaetal.

(2019

)and

Mottoandzen

(2022

)for

theeuroarea,butnotfrom

Swanson

(2021

)fortheUS.Choosingwhethertostandardisetheinputdataaffectstheresults.SincethefirststepistoextractprincipalcomponentsfromX,standardisingallthecolumnsisequivalenttogivingeachcolumnthesameimportance.Notstandardisingimpliesthattheprincipalcomponentsattempttoexplainmoreofthesystematicvariationintheassetswithmorevolatilityintheirunitofmeasure.Thisaspectbecomesmoreimportantonceabroadersetofassetsisconsidered.Forexample,in

Altavillaetal.

(2019

),onlyrisk-freeyieldsareincluded.Inourpaper,wealsoincludesovereignyields,someofwhicharesignificantlymorevolatilethanrisk-freerates(seeTable

2

),aswellasotherassetswhicharemeasuredindifferentscales(e.g.,equityreturnsandequitymarketvolatility).Inthiscase,standardisingthechangesbecomesanaturalapproachalsotoavoidcomparingmovementsinassetswithdifferentunits.

ECBWorkingPaperSeriesNo29949

canexplainofthevarianceofthecolumnsofmatrixX.

Beyondsimplyprovidingastatisticalsummaryoftheassetpriceresponsestomone-tarypolicynews,weareinterestedinrotatingfactorstomaketheminterpretableintermsofthetypeofnewsassociatedtospecificmonetarypolicyinstrumentsorkeydimensionsofmonetarypolicytransmission.Noticethat,foranyorthonormalmatrix(i.e.asquare

matrixwhereallthecolumnshaveunitlengthandareorthogonal)U,wecanrotatek×k

principalcomponentsbyrewritingFΛasFUU′Λ=whilemaintainingthesamefit

andresiduals

.3

MultiplyingtheprincipalcomponentsbyarotationmatrixUisobserva-tionallyequivalenttodoingitwithanyotherorthonormalmatrix,i.e.,thereisaninfinitenumberofdatageneratingprocessesthatareequallycompatiblewiththeobserveddata.Toidentifytheunderlyingstructuraldriversofthedataandtheireconomicinterpreta-tion,weneedtoimposeadditionalassumptionstorestrictor,morecommonly,uniquelyidentifyarotationmatrixthatcharacterisesthestructuraldatageneratingprocess.Thischallengeisanalogoustothedifficultyofidentifyingstructuralshocksfromreducedformresidualsinvectorautoregressions.

3Conventionalapproach:structuralidentificationbased

oneconomicassumptions

Sofar,theliteraturehaslargelymeasureddifferentdimensionsofmonetarypolicybyrelyingonidentifyingassumptionstoexplaincross-assetpricemovementsaroundmone-tarypolicyevents.Themostcommonapproachinthemonetarypolicyfactorsliterature(e.g.,

G¨urkaynaketal.,

2005;

Brandetal.,

2010;

Altavillaetal.,

2019;

Swanson,

2021;

Mottoandzen,

2022

)istofindamatrixUthatimposesidentifyingrestrictionsbased

oneconomictheory.Commonapproachesincludeimposingzerorestrictions(indicatingthatarotatedprincipalcomponent,representingastructuralshock,doesnotaffectaspe-cificasset),signrestrictions(e.g.,indicatingthatcertainassetsmustmoveinaspecificdirectioninresponsetoashock),andapplyingvarianceminimisation(e.g.,ensuringthatfactorsrepresentingtheeffectsofassetpurchaseshavelowvariancebeforetheirofficial 3NotethatifUisorthonormal,thenU−1=U′.Whenextractingprincipalcomponentsfromadataset,thesolutionyieldsasetoforthogonalprincipalcomponentsF,andasetoforthogonalloadings

Λ.However,atmostonlyoneofthesepropertiescanberetainedafterrotation,asexplainedby

Jolliffe

(1995

).Ineconomicterms,thisresultimplieswemustassumethateithertheunderlyingdriversofmonetarypolicysurpriseshaveorthogonalimpactsontheyieldcurve,buttheiractivationiscorrelated,orthattheyareactivatedindependentlybuthavecorrelatedimpactsonfinancialassets.Inthispaper,wehavechosenthelatter,inlinewiththeusualassumptionthatstructuralshocksshouldbeorthogonal.

ECBWorkingPaperSeriesNo299410

introduction).

Weintendtoextractandidentifymulti-dimensionalindicatorsofmonetarypolicysurprisesfortheeuroareabasedonhigh-frequencycross-assetpricemovements.Thereby,theidentificationstrategyfollowseconomicreasoninghowdifferentpolicyinstrumentsaffectspecificassetprices,takingintoconsiderationthespecificroleofsovereignriskin

acurrencyunion,asdiscussedin

Mottoandzen

(2022),

MiraGodinho

(2021),

Wright

(2019)

.

WeusetheEuroAreaMonetaryPolicyDatabase(EA-MPD)of

Altavillaetal.

(2019),

updateduntilOctober2023.Thedatabasecontainsthechangeinacross-sectionofassetpricesaroundECBGoverningCouncilmeetingsinthreewindows:aroundthepressrelease,aroundthepressconference,andafulleventwindowcoveringtheperiodfrombeforethepressreleasetoafterthepressconference.Whileuntil2016theECBwouldannouncenon-standardmeasuresonlyinthepressconference,itisnowacommonpracticetoannouncechangesinforwardguidanceandassetpurchasesalreadyinthepressrelease

.4

Forthisreason,wedepartfromotherpapersintheeuroareamonetarypolicysurprises

literature,suchas

Altavillaetal.

(2019)and

Mottoandzen

(2022),andusethefull

eventwindow.

Weuseabaselinesetofassetscoveringinterestratesfrom1