This paper presents preliminary fi ndings and is being distributed to economists and other interested readers solely to stimulate discussion and elicit comments. The views expressed in this paper are those of the authors and are not necessarily refl ective of views at the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.
Federal Reserve Bank of New YorkStaff Reports
Staff Report No. 596February 2013
Darrell Duffi eDavid Skeie
James Vickery
A Sampling-Window Approach to Transactions-Based Libor Fixing
REPORTS
FRBNY
Staff
Duffie: Stanford University (e-mail: [email protected]). Skeie, Vickery: Federal Reserve Bank of New York (e-mail: [email protected], [email protected]). The authors thank David Hou and Ali Palida for outstanding research assistance, as well as Spence Hilton, Antoine Martin, Jamie McAndrews, and Simon Potter for comments. The views expressed in this paper are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System.
Abstract
We examine the properties of a method for fixing Libor rates that is based on transactions data and multi-day sampling windows. The use of a sampling window may mitigate problems caused by thin transaction volumes in unsecured wholesale term funding markets. Using two partial data sets of loan transactions, we estimate how the use of different sampling windows could affect the statistical properties of Libor fixings at various maturities. Our methodology, which is based on a multiplicative estimate of sampling noise that avoids the need for interest rate data, uses only the timing and sizes of transactions. Limitations of this sampling-window approach are also discussed.
Key words: shadow banking, financial intermediation
A Sampling-Window Approach to Transactions-Based Libor FixingDarrell Duffie, David Skeie, and James VickeryFederal Reserve Bank of New York Staff Reports, no. 596February 2013JEL classification: G01, G10, G18, G28
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1.IntroductionThisnoteconsidersanapproachtoconstructingLiborfixingsusingtransactionsdataandmulti‐daysamplingwindows.1Forinstance,onecouldfixthe3‐monthLiborrateonagivendateastheaverageoftheactualinterestratesonall3‐monthloansintherelevanthistoricalsamplewhosetransactionsdatesarewithinthetrailing10businessdays.This“10‐daysamplingwindow”ismerelyforpurposesofillustratingtheconcept.Wewillexaminetheinfluenceofthesamplingwindowonsamplingnoiseandconsideradditionaltechniquesfor“fattening”thesampleandweightingthedatasoastoreducesamplingnoiseandmitigatebiases.Wealsoconsiderthepotentialrangeofapplicationsofthisapproach,andsomeofitsdisadvantages.LiborprovidesanestimateoftheinterestrateatwhichmajorbanksactiveinLondonmayborrowfromotherbanksonanunsecuredbasis.TheBritishBankersAssociation(BBA)currentlyreportsLiboronadailybasisfor10currenciesand15maturitiesbetweenovernightandoneyear.2Thesedailyinterestrate“fixings”areconstructedbasedonbanksubmissions.Eachofapanelofbanksself‐reportsitsownestimatedhypotheticalborrowingratesateachtenor.Notably,Liborisnotcurrentlycomputeddirectlyfromactualloantransactionrates.PublishedLiborratesarereferencedinthesettlementofmanyformsoffinancialcontracts,includingcorporatebondsandloans,mortgages,aswellasinterest‐ratefutures,swapsandoptions.AttentionhasrecentlyfocusedonthepotentialtoaddressshortcomingsofthesurveyapproachtoLiborwithafixingmethodthatissomehowbaseddirectlyonactualloantransactionsdata.Whileadvocatingfortheretentionofasubmission‐basedapproach,theWheatleyReviewofLibor(H.M.Treasury,2012)recommendsthatLiborsubmissionsshouldbe“explicitlyandtransparentlysupportedbytransactiondata.”ItalsooutlinesguidelinesforhowthisprincipleshouldbeimplementedinpracticebyLiborpanelbanks.3Thejudgmentandexpertiseofsubmittingbanksstillplaysaroleunderthisapproach.AnalternativewouldbetocomputeLibordirectlyasanaverageofindividualtransactionrates.Oneconcernoversuchanapproach,however,istherelativesparsenessofdailyinterbankunsecuredloantransactionsatcertainmaturities,
1Liborstandsfor“LondonInterbankOfferedRate”.2ThenumberofcurrenciesandmaturitiesisplannedtobereducedinthefutureinlinewiththerecommendationsoftheWheatleyReviewofLibor(H.M.Treasury,2012).Seesection2.3Theseguidelines(section4.8oftheWheatleyReview)layoutahierarchyoftransactiontypesthatbanksshouldusewhendeterminingtheirsubmissions.Highestpriorityisgiventotransactionsintheunsecuredinterbankdepositmarket,particularlythoseundertakenbythecontributingbank.Intheabsenceofrelevanttransactiondatatheguidelinessuggestthatexpertjudgmentshouldbeusedtodeterminethebank’ssubmission.Theyalsostatethat“submissionsmayalsoincludeadjustmentsinconsiderationofothervariables,toensurethesubmissionisrepresentativeofandconsistentwiththemarketforinter‐bankdeposits”,suchasplacinglessweightonnon‐representativetransactions.
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particularlyduringperiodsoffinancialstress.Afixingthatisbasedonrelativelyfewtransactionscouldhaveexcessivesamplingnoiseandcouldalsocreateaheightenedincentiveforsomemarketparticipantstotransactwiththepurposeofinfluencingthedailyfixing.(Inastock‐marketcontext,Carhart,Kaniel,Musto,andReed(2002)discussevidenceoftransactionsdesignedto“paintthetape.”)TheWheatleyReport(H.M.Treasury,2012)indicatesthattherearetoofewtransactionstosupportLiborinmanyofthecurrency‐maturitypairsforwhichLiboriscurrentlyreported.4Weshow,however,thatatleastforsomeofthemoreactiveU.S.dollarmaturities,theuseofasampling‐windowapproachwouldsignificantlyreducethenoisinessoftransactions‐basedaverageinterestrates.Thisapproachwouldalsoimproverobustnesstomisreportingincentives.Theapproachcouldbeexploitedeitherasthebasisforanewfixingrateforasubsetofcurrenciesandmaturities,orasasourceofadditionalinformationinjudgingthevalidityofotherfixingmethods.Weillustratethetransaction‐windowapproachusingtwopartialdatasetsmeasuringunsecuredwholesalelendingactivity.Thefirstisahistoricaldatasetofbrokeredinterbankloans.ThesecondisasetofputativeunsecuredloansinferredfromFedwirepaymentsusingastatisticalalgorithmdevelopedbystaffoftheResearchGroupoftheFederalReserveBankofNewYorkthatextendstheworkofFurfine(1999).(SeeKuo,Skeie,VickeryandYoule,2013foradetaileddescriptionofthisalgorithm.)Wenotethatwhilethesedatasetsareusefulforillustratingourapproach,neithercouldbeusedinpracticeasthebasisforconstructingatransaction‐basedindexofbankfundingcosts.Inparticular,weemphasizethattheKuoetal.statisticalalgorithmidentifiesterminterbankloanswitherror.Historically,algorithmsbasedontheworkofFurfinehavebeenusedasamethodofidentifyingovernightortermfederalfundstransactions.TheResearchGroupoftheFederalReserveBankofNewYorkhasrecentlyconcludedthattheoutputofitsalgorithmbasedontheworkofFurfine5maynotbeareliablemethodofidentifyingfederalfundstransactions.6ThispaperthereforereferstothetransactionsthatareidentifiedusingtheResearchGroup’salgorithmasovernightortermloansmadeorintermediatedbybanks.Useoftheterm“overnightortermloansmadeorintermediatedbybanks”inthispapertodescribetheoutputoftheResearchGroup’salgorithmisnotintendedtobeandshouldnotbeunderstoodtobeasubstituteforortorefertofederalfundstransactions.
4Forthisreason,andbecauseoftheirlowusage,theWheatleyReviewrecommendsdiscontinuingLiborfortenorsof4,5,7,8,10and11months,anddiscontinuingLiborentirelyforfivecurrencies.ReportingofLiboristocontinuefortheUSDollar,Euro,JapaneseYen,UKPoundandSwissFranc.5Itshouldbenotedthatforitscalculationoftheeffectivefederalfundsrate,theFederalReserveBankofNewYorkreliesondifferentsourcesofdata,notonthealgorithmoutput.6Theoutputofthealgorithmmayincludetransactionsthatarenotfedfundstradesandmaydiscardtransactionsthatarefedfundstrades.Someevidencesuggeststhatthesetypesoferrorsinidentifyingfedfundstradesbysomebanksmaybelarge.
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Giventhelimitationsofexistingdatasets,atransaction‐basedindexwouldrequireconstructingacentralizedandauditablerepositoryofrelevantinterbanktransactions.OnepossiblemethodologyforreportingthenecessarydataistheTradeReportingandComplianceEngine(TRACE),developedbyFINRAforthereportingofindividualtradesincertaintypesoffixed‐incomesecurities.Insection4ofthispaperwealsohighlightanumberofconceptuallimitationsofthesamplingwindowapproach,andconsiderpotentialsolutions.Oneimportantissueisthatafixingbasedonalaggedmovingwindowwillreflectstaleinformationduringperiodswhenmarketconditionschangerapidly,forexampleaftermonetarypolicyannouncements,orattheonsetofafinancialcrisis.Forapplicationsthatallowhindsight,suchasex‐postcorroborationofothermethodsforfixingLibor,atwo‐sidedsamplingwindowcouldbeused,incorporatingtransactiondatafromboththedaysbeforeandafterthefixingdate.Thiscouldmitigatethestaleness.Atwo‐sidedsamplingwindowisofcourseinfeasibleifthefixingneedstobepubliclyreleasedinrealtime.Asecondpotentialconcernisthattheavailablesampleofunderlyingwholesaleloantransactionsmaybesmallevenwithamulti‐daysamplingwindow,particularlyduringperiodsofmarketstress.Onewaytomitigatethisproblemcouldbetoconsiderawiderrangeofunsecuredfundinginstrumentswhenconstructingthetransaction‐basedindex.2.WiderSamplingWindowsSupposethereisasourceofactualtransactionsdataonlargeunsecuredloanstobanksinthedesiredborrowerclass.Incasethevolumeofinterbankloantransactionsisviewedasinsufficient,onemaywishtoconsiderawiderrangeofsourcesofunsecured“wholesale”fundingtomajorbanks,perhapsincludingcertificatesofdeposit,commercialpaperandsoon.EvenforaglobalcurrencysuchastheU.S.dollar,thereareextremelyfewlargeunsecuredloantransactionsatmanyofthematuritiesatwhichLiboriscurrentlyfixed.Evenasampling‐windowapproachwouldnotbereliableinsuchcases.Alternativesforthese“sparselypopulated”maturitiesincludeinterpolation,improvingthecurrentsurvey‐basedapproach,oracessationofLiborfixings,asrecommendedbytheWheatleyReviewofLibor.Fortunately,thematuritiesatwhichtherearefewtransactionssuitablefordeterminingareferenceratearealsolessimportantforapplications.Forexample,therearerelativelyfewderivatives,bonds,andotherinstrumentsthatreference9‐monthLibor.ThemostcommonlyreferencedLiborratesinmajorcurrenciesarethosewithmaturitiesofonemonth,threemonthsand,toalesserextent,sixmonths,asindicatedbyasurveyappearingintheWheatleyReviewofLibor.Wefocusoncurrenciesandmaturitiesforwhichtheaggregate‐sampletransactionsfrequencyispotentiallysufficienttoconsiderforafixing,orforvalidationofafixing.
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EvenforrelativelyactiveU.S.dollarloanmaturitiessuchas1monthand3months,wewillshowthatasubstantialproportionalreductioninthesamplingnoiseassociatedwithatransactions‐basedfixingcanbeachievedwiththeuseofarollingsamplingwindow.Thisisnotsurprising,buttheempiricalmagnitudeoftheeffectisnotable.Moreover,ourmethodologydoesnotrelyonaccesstotheinterest‐ratedatathemselves,butratheronthetimesandsizesoftransactions.Ourapproachisinthespiritofstatisticalfiltersthatattempttoextractlonger‐frequencymovementsintime‐seriesdata(suchastheHodrick‐PrescottfilterortheKalmanfilter).Thisapproachcouldbeemployedinatleasttwoways:1)toprovideareplacementtothecurrentquote‐basedapproachfordeterminingtheLiborfixing;2)incorroborationofaquote‐basedorpoll‐basedLiborfixing,forexampleaspartoftheprocessofstrengtheningoversightofLibor.Inthefirstapplication,itwouldbenecessarytouseaone‐sidedlaggingwindow,sincethefixingwouldneedtobeannouncedinrealtime.Forex‐postvalidationpurposes,however,itwouldbepossibletouseatwo‐sidedsamplingwindowtoconstructthefixing,incorporatingbothpastandfuturedata.7Ournumericalexamplesbelowfocusonaone‐sidedwindow.Fromastatisticalfilteringpointofview,atwo‐sidedsamplingwindowwouldloweraveragethedegreeofsamplingerror.Oursimpleillustrativeexampleisafixingofthe3‐monthrateonagivendateastheaverageoftheratesonall3‐monthloansintherelevanthistoricalsamplewhosetransactionsdateiswithinthetrailing10businessdays.Onemayalsowishtouseasamplingwindowbasedonmaturity.Weelaborateandgeneralizeasfollows.SupposeonewantstocreateanestimateR(t,m)ofa“representative”m‐monthmaturityloanrateondayt.LetS(t,m;w,d)bethesubsetofallloansintheentirerelevanthistoricalsampleavailableonthefixingdatetwhosetransactiondateiswithinthetrailingwdaysandwhosematurityiswithinddaysofm.OnecouldfixR(t,m)asthevolume‐weightedaverageoftheloanratesinthisfixingsampleS(t,m;w,d).Forexample,foralag(w)of10daysandamaturitywindow(d)of5days,thefixingsampleforthethree‐monthborrowingrate(thatis,m=3months)onagivenday(t),sayMarch15,2013,wouldincludealltransactionsintherelevantpoolwithloanoriginationdatesbetweenMarch1,2013andMarch15,2013,inclusive(thatis,laggingbynomorethan10businessdays),withloanmaturitiesofthreemonthsplusorminus5businessdays.Inchoosingthelaggingtransaction‐datewindowwandthecenteredmaturitywindowd,onecantradeoffthebenefitofincreasedsamplesizeagainstthecostofbiasesassociatedwithincreasinglystaleoroff‐maturitydata.Inthelastsection,weexplorethebenefitsandcostsofreducingtheweightsappliedtothetransactionsaccordingtothetimelag,inordertomitigatestalenessbias.Inpractice,therelevanttermloanmaturitiesappeartobetightlyconcentratedaroundthestandardmaturitiesof1month,3month,and6months.Itmaybe7WethankSimonPotterforalertingustothispoint.
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arguedthatitisrelativelypointlesstouseanon‐trivialmaturitywindow.Ontheotherhand,fatteningupthesamplebyincludingsimilar‐maturityloantransactionswouldlowersamplingnoisesomewhatandseemsunlikelytocreateimportantbiases.Theuseofamaturitywindowalsolowersthepotentialincentiveforloanmarketparticipantswhosetransactionsaresampledtocustomizetheirmaturitydatessoastoavoidenteringthefixingsample.3.EmpiricalMethodsandResultsInthissectionwepresentaproportionalsampling‐noisemeasureandourempiricalevidenceregardinghowvariationinthesamplingwindowandotherdatafiltersaffectsthe“thinness”ofthedataunderlyingapotentialtransaction‐basedLiborindex.3.1DatasourcesWedonothaveaccesstoacomprehensivetransaction‐leveldatabaseofunsecuredwholesaleloans.Intheabsenceofsuchdata,weillustrateourapproachusingtwopartialdatasources:
1. Adatasetofbrokeredinterbanktransactionsfromtheperiod2000‐04.2. Statisticallyinferredtransactionsbasedoninterbanktransfersoffederal
reservespassingoverFedwireFundsService(“Fedwire”),alarge‐valuepaymentsystemoperatedbytheFederalReserve,fromtheperiod2007‐12.
ThefirstofthesedatasourceswaspreviouslyusedbyBartolini,HiltonandMcAndrews(2010)andobtainedfromBGCBrokers,oneofthefourlargestU.S.interbankbrokeragefirms.Thesedatarepresentoneoftheonlydirecttransaction‐levelresearchdatasetsforUS‐dollar‐denominatedinterbankloansavailableforresearch.However,thisdatasethasanumberoflimitations.First,thedataareavailableonlyforahistoricaltimeperiodfromJanuary1,2000untilSeptember27,2004.Thissamplepre‐datesthe2007‐08financialcrisisandthepost‐crisisperiod.Second,thedatacoveronlybrokeredloans,whichrepresentonlyasubsetoftheinterbankmarket,andrepresentonlytradesnegotiatedthroughasinglebroker.Theidentitiesoftradecounterpartiesarenotprovided.Finally,thedatacoveronlyinterbankloans,andthusdonotincludeotherunsecuredfundinginstruments(suchaswholesaletimedeposits)thatmaybeusefulforconstructingatransaction‐basedLiborfixing.TheseconddatasourceisasetoftermloansmadeorintermediatedbybanksinferredfrompaymentspassingoverFedwireusingastatisticalalgorithmdevelopedinKuo,Skeie,Vickery,andYoule(2012)(KSVY).TheKSVYalgorithmisageneralizationofFurfine(1999),whoappliedthemethodtoidentifypotentialovernightloans,nottermloans.TheideabehindtheKSVYalgorithmisthatmostwholesaleinterbankloansaresettledoveralarge‐valuepaymentsystem.Inthe
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caseofUS‐dollarloans,thisislikelytobeeitherFedwireorClearingHouseInterbankPaymentsSystem(“CHIPS”).TheKSVYalgorithmsearchesfortransactionpairsconsistingofa“send”leg(frompartyAtopartyB)foralargeround‐lotamount,anda“return”leg(fromBtoA)onasubsequentdateforaslightlylargeramount,suchthattheimpliedannualizedinterestrateisawholenumberofbasispointsandsuchthatthetransactionpairmeetscertainothercharacteristics.Forthepurposesofthispaper,thealgorithmisusedtoidentifyputativeinterbanktransactionsforwhichboththesendingandreturnlegpassoverFedwirebetweenJanuary1,2007andMay1,2012.ThemostimportantdisadvantageoftheKSVYinferencesisthatthesetofidentifiedtransactionpairsareinferences,notdirectobservationsoftermloans.Itisdifficulttoverifyatthispointhowwellorpoorlythesepairscorrespondtoactualunsecuredtransactions.KSVYdohoweverpresentsometestssuggestingthattheresultsofthealgorithmareinformative.Forexample,KSVYshowthatpriortotheonsetofthefinancialcrisis,thedistributionofimpliedinterestratesoftheseputativeloansisclusteredtightlyaroundtheLiborfixingrate,implyingthattheresultsarenotstatisticalnoise.Aswediscussedintheintroductiontothispaper,itisimportanttoemphasizethatthismethodissubjecttobothType‐IandType‐IIclassificationerrors(failurestodiscoveractualloans,andinferredloansthatarenotactualloans).OneparticularconcernisthattheproximatecounterpartiesidentifiedintheFedwiredatamaybeactingonlyascorrespondents,ratherthanbeingtheultimateborrowerandlenderoffunds.Thisisespeciallyrelevantifauserofthedatawantstorestricttheirsampletoaparticularsubsetofborrowers.Notably,recentresearchbyArmantierandCopeland(2012)concludesthattherelatedovernightFurfinealgorithmperformspoorlyinidentifyingovernightfederalfundsloansconductedbytwolargebanks.8(Note:FederalfundsloansareasubcategoryofinterbankloanswhicharenotsubjecttoU.S.reserverequirements.)Giventheissuesdescribedabove,weemphasizethatneitherofthedatasourcesweconsidercouldreliablybeusedinpracticeasthebasisforcomputingatransaction‐basedreplacementforLibor.Inpractice,suchafixingwouldpresumablyrequirethecreationofarecordlogofactualwholesaleloans(whetherrestrictedtointerbankloans,orencompassingawidersetofunsecuredinstruments),whichcouldbeaggregatedorauditedbyregulatorsorotheroutsideparties.Inthemeantime,however,intheabsenceofasuitabledatabaseofactualterminterbankloans,ananalysisofthesetwodatasetsprovidesatleastaroughideaoftheeffectofthesizeofthesamplewindowandotherfiltersontherobustnessofthesampling‐windowapproach.Giventhelimitationsofthedatasources,wedonot
8Inpartbecauseoftheseconcernswedonotmakeuseofmeasuredinterestratesinthispaper,foreitherdatasource.Instead,werestrictouruseofthesedatasourcestotransactiontimes,maturities,andsizes.
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presentsampling‐windowestimatesoftheinterbankrateitself,insteadwefocusonhowasamplingwindowapproachwouldaffecttherelativesamplingnoiseassociatedwithatransaction‐basedinterbankindex.3.2ResultsBearinginmindtheimportantcaveatsdescribedabove,weusethesetwodatasourcestocomputeestimatesoftherelativesamplingnoiseassociatedwithanillustrativeUS‐dollarindexrate,forvariousdatafiltersandmaturities.Figure1andTable1illustratetheeffectofchangingthesamplingwindowfortheimpliedsample‐volatilitymultiplierV(t),aproportionalsamplingnoisemeasurethatisbasedonthenumberandrelativesizesofloansinthefixingsampleS(t,m;w,d).Specifically,V(t)isthesquarerootofthesumofthesquareddollar‐sizeweightsoftheloansinS(t,m;w;d).Forexample,ifthefixingsampleS(t,m;w,d)includestwoloans,ofamounts$40millionand$60million,thentherelativesizeweightsare0.4and0.6.Thesumofthesquaredweightsis0.16+0.36=0.52,soV(t)is0.72.Ifoneweretoassumethat,conditionalon“fundamental”loan‐marketinformation,theindividualloanratesinagivenday’sfixingsampleareuncorrelatedandhavethesamestandarddeviationD(t),thenthefixingR(t,m)hasaconditionalstandarddeviationofD(t)V(t).Undertheseconditions,intheaboveexampleofafixingsamplewithtwoloansofamounts$40millionand$60million,thesamplevolatilitymultiplierof0.72meansthattheassociatedsize‐weightedaverageinterestratehasastandarddeviationthatis72%ofthatforafixingratebasedonasingleloantransaction.Thesestatisticalassumptionsdonotapplyinpracticeandwedonotrelyonthem,butthesample‐volatilitymultiplierV(t)neverthelessgivesusagoodideaoftherelativeeffectofthelengthofthesamplingwindowontherobustnessofthesample.ArelativelyhighsamplingvolatilitymultiplierV(t)meansthattherearerelativelyfewloansdominatingthesample,andthereforelittleopportunityfor“diversification”ofthesamplingnoise.Atitsmaximum,forthecaseofasinglesampledloan,V(t)=1.Asthenumberofloansbecomeslargeandthefractionofanyoneloansizerelativetothetotalquantityofloansbecomessmall,V(t)approacheszero,bythelawoflargenumbers.WeemphasizethatV(t)saysnothingaboutthelevelsorvolatilitiesofinterestratesintheinferred‐loansample.Rather,V(t)isdeterminedentirelybythenumberandrelativesizesoftheloansinthefixingsamplefordatet.Withinterest‐ratedatafromactualtransactions,onecouldalsodirectlystudythesamplestandarddeviationsoftheratesinthefixingsamples,andtheeffectofthesamplingwindowonbiasesandrelativenoise.GiventhepotentialformisclassificationusingtheKSVYalgorithm,weavoidusingtheinferredloaninterestrates.
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Forthe3‐monthmaturity,Figure1belowplotsthetimeseriesofV(t)basedona10‐daysamplingwindowfromthetwotransaction‐leveldatasources.9
Figure1:Time‐seriesplotofV(t)
ThedailysamplevolatilitymultiplierV(t)for3‐monthmaturityloans.Thesampleisbasedonaminimumtransactionsizeof$25manda10‐daysamplingwindow.Thesampleperiodis2000‐2004forthebrokereddata,and2007‐2012fortheFedwireinferences.A.Brokeredinterbankdata
B.Fedwireinferences
9ThebrokereddatasampleusedtoconstructFigure1aswellassubsequentfiguresandtablesincludesbothEurodollarandtermFederalfundsinferredtransactions(asdiscussedinBartolinietal.,2010,thedatasetincludesaflagwhichindicatesthetransactiontype;weretainbothcategories).Similarly,fortheFedwireinferences,wepresentresultsbasedontheentiredatasetofinterbankloaninferences,ratherthanattemptingtorestrictthesampletoaparticularloantype.
0
.2
.4
.6
.8
1
SVM
01jan2000 01jul2001 01jan2003 01jul2004Date
0
.2
.4
.6
.8
1
SVM
01jan2007 01jan2008 01jan2009 01jan2010 01jan2011 01jan2012Date
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Figure1showssubstantialvariationovertimeinthedailysample‐volatilitymultiplierV(t),forbothdatasources.Thesample‐volatilitymultipliermeasuredfromthebrokereddataisconsistentlyhigherthanthatforFedwire‐inferreddata.Thisisnotsurprising,giventhatthebrokereddatacaptureonlyasmallsegmentofthemarket(thosebrokeredinterbankloansintermediatedbyasinglebroker).ThedifferenceinV(t)betweenthetwodatasourcescouldalsopartiallyreflectfalse“matches”intheFedwireinferences,differencesinthesampleperiod,andotherfactors.Table1showsthemedianacrosstheperiodofthesamplevolatilitymultiplierV(t),forvariousmaturitiesandsamplingwindowslags,normalizedbythemedianofV(t)for3‐monthmaturityloansandasamplewindowlagof10days.Wevariedthesamplingwindowfromtwodaysto20days,andconsideredmaturitiesof1,3,and6months.(Thenormalizingcellassociatedwitha10‐daysamplingwindowand3‐monthmaturitythusalwaysshowsavalueof1.)Thetablealsoreportssummarystatisticsfromthetwodatasources.Table1:RelativevaluesofV(t)fordifferentmaturitiesandsamplingwindows
MedianvaluesofthesamplevolatilitymultiplierV(t),forvariouscombinationsoflagwindowandmaturity,normalizedbythemedianvalueofV(t)foralagwindowof10daysandamaturityof3months.Thesampleperiodis2000‐2004forthebrokereddata,and2007‐2012fortheFedwireinferences.Brokeredinterbankloans Maturity
1 month 3 months 6 months
Lag window (days) 2 1.04 1.57 2.22
5 0.81 1.31 1.66
10 0.61 1.00 1.36
15 0.51 0.85 1.17
20 0.46 0.77 1.05
Fedwireinferences Maturity
1 month 3 months 6 months
Lag window (days) 2 1.16 1.68 2.50
5 0.88 1.33 2.13
10 0.67 1.00 1.63
15 0.56 0.84 1.37
20 0.50 0.76 1.23
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Table1showsthatinbothdatasources,thesamplingnoiseasmeasuredbyV(t)issignificantlygreateratlongermaturitiesandforshortersamplingwindows.Forbothdatasources,V(t)istwotothreetimeslargerforsix‐monthloansthanforone‐monthloans.Thisisnaturalinpartfromthefactthatlonger‐termloansrolloverlessoftenthanshorter‐termloans.(Thatis,theratiooftheflowofloanstothestockofloansislowerinsteadystateforlonger‐maturityloans.)Inanycase,ourpreliminaryresultssuggestcautionoverwhetheritwouldbepossibletoconstructarobustLiborfixingfromunderlyingloantransactionsforlonger‐termloanssuchassixmonths.Table2presentssummarystatisticsofthedatausedtoconstructthesampling‐windowLiborindex.Forbothdatasources,theaverageacrossthesampleperiodofthenumberofinferred3‐monthloantransactionswithina10‐daysamplingwindowislow,8and25transactionsrespectivelyforthebrokereddataandFedwireinferences.Again,careshouldbetakenininterpretingthesestatisticsgiventhatneitherdatasourceiscomprehensive.Table2:Summarystatistics(10daywindow,3monthmaturity)SummarystatisticsfortheestimatedsamplevolatilitymultiplierV(t),aswellasthenumberoftransactionswithinthe10daysamplingwindow,andtheaveragetransactionsize.Sampleperiodis2000‐04forthebrokereddata,and2007‐12fortheFedwireinferences.p10,p25etc.referstopercentilesoftherelevantdistribution.Brokereddata
Mean p10 p25 p50 p75 p90 StDev
SVM 0.48 0.29 0.35 0.45 0.56 0.71 0.17
# of Transactions in Window 8.13 2 4 7 11 16 5.65
Transaction Size ($mm) 78.69 25 40 50 100 150 59.47
Fedwireinferences
Mean p10 p25 p50 p75 p90 StDev
SVM 0.30 0.21 0.24 0.28 0.34 0.41 0.10
# of Transactions in Window 25.45 13 18 24 31 41 10.59
Transaction Size ($mm) 110.81 25 38 54 110 246 213.74
3.3AlternativespecificationsWehaveexperimentedwithvariousotherdatafilters.Intheappendix,wepresenttwovariations.Thefirstconsidersaminimumtransactionsizeof$100million,ratherthan$25million.Applyingthishighersizecutoffinevitablyreducesthenumberofeligibletransactionsatanypointintime,andthusraisesV(t).Onebearsinmind,however,thatthe“root‐mean‐squared”definitionofV(t)impliesthataloan
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ofsize$100millionhasarelativeimpactonV(t)thatis16timesthatofa$25millionloan,whenbothsizesarepresentinafixingsample.Secondly,wehaveexperimentedwithanapproachinwhichmoreweightisgiventotransactionsclosertodatet.Seesection4belowforadiscussion.Inunreportedcalculations,wealsoexperimentedwithexpandingthewidthofthematuritywindow(byfivedaysineachdirection).Wefoundthatthishasonlyasmalleffectonthenumberofeligibletransactions.4.SomeDisadvantagesofThisApproach,andTheirMitigationInthissectionwediscusssomeimportantpotentialdisadvantagesofafixingthatisbasedonasampling‐windowapproach:(i)theeffectofusinglaggeddataonthetimelinessoftheresultingLiborfixing,(ii)theriskofalackofunderlyingtransactionsdata,evenwithinasamplingwindow,and(iii)possiblecalendar‐dateeffects.Wealsoconsidersomemitigantsoftheseproblems.Afirstdisadvantageofthesampling‐windowapproachisthatthefixingannouncedonagivendaywouldbebasedinpartonlaggeddatathatmaynolongerberepresentativeofmarketconditions.Thatis,thefixingratecouldbesomewhatstaleduringperiodsofrapidchangesinmarketconditions,forexamplearoundthetimesofsignificantcentral‐bankmonetarypolicyannouncements,orattheonsetofafinancialcrisisorotherperiodinwhichbankfundingcostsareshiftingrapidly,suchasAugust9,2007andtheperiodfollowingit.Theinformationthatmarketparticipantsandregulatorslearnfromtheresulting“Libor”reportcouldthereforebestale.Thereisnosingle“true”interbankborrowingrate,andnosamplingmethodisperfect.Onemaywishtocomparethebiasandsamplingnoiseofthesampling‐windowtransactions‐basedapproachthatwehavedescribedwiththoseofotherfeasiblemethods,includingthecurrentmethodforfixingLibor.Forapplicationsinvolvingbondorswapcontracts,thestalenessintroducedbyasamplingwindowmeasuredindaysisrelativelyunimportant.Afterall,aninvestorholdingapositioninswapsorfloating‐ratenotesisconcernedwiththelevelof3‐monthloanratesthatisgenerallylikelytoprevailseveralyearsintothefuture,andisprobablynotsointerestedinvariationin3‐monthloanrateswithinasmalltimewindowthatbeginsinseveralyears.Apartfromitsroleinfinancialcontracting,Liborisalsousefulforassessingcurrentmarketconditions.However,evenduringtherecentfinancialcrisis,Kuo,SkeieandVickery(2012)showthatmovementsinLiboroverallcommovequitecloselywithanumberofotherpubliclyavailableindices(suchassecondary‐marketCDratesandEurodollaryieldsreportedintheFederalReserveH.15report).Thesealternative
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indices,whichwouldbemoresensitivetoshort‐termmarketshocks,wouldremainavailabletopolicymakersandmarketparticipants.Wealsonotethatintermsofrevealinginformationtomarketparticipants,asampling‐windowfixingapproachallowstherecoveryofmostofthe“fresh”marketinformationthatispresentintheunderlyingdata.Giventhatthedifferencebetweenthefixingrateondaytandthatonthepreviousdayt‐1iscausedbydroppingobservationsfromdatet‐w(foralagwindowofw)andaddingobservationsfromthelatestdatet,observerscanapproximatelyinvertthemoving‐averageproceduresoastoestimatetheimpliedaveragerateoftransactionsthatoccurredonthelatestavailabledate.Ofcourse,itwouldalsobepossibletosimplyreleasetheaveragetransactionrateforeachday,asdiscussedfurtherbelow.Onecouldreducethebiasassociatedwithstalenessbyweightingthedatawithinthefixingsamplebasedonthetimelag,usingweightsthatdecaywiththelag,sayexponentially.Inordertoillustratetheimpactonsamplingnoiseofde‐weightingstaledata,weexploredtheeffectofanexponentialdecayintransactionweightsthatgivesobservationswitha10‐daylagonly50%oftheweightappliedtoobservationsonthecurrentday.(Thiscorrespondstoaweightfactorof0.933raisedtothepowerofthenumberofdayslagging.)Thisdegreeofde‐weightingofstaletransactionscausesarelativelysmalldegradationinsamplingnoise.10Forexample,for3‐monthinferredtransactionsobtainedfromFedwiredatafor2007‐2012,wesawinTable2thatthemeansamplevolatilitymultiplieris0.30.Withaweightdecayfactorof0.933perdayoflag(50%de‐weightingof10‐dayoldobservations),thesamedataareassociatedwithameansamplevolatilitymultiplierof0.31,about3%higher.Theestimatedeffectsonsamplingnoiseofde‐weightingstaledataaresimilarlymutedinallofthecasesthatwehaveexamined,asdemonstratedinadditionalchartsandtablesfoundintheappendices.Itistobecautionedthattheseresultsarepreliminaryandonlyforillustrativepurposes.Inadditiontopublishingthesampling‐window‐basedfixingrate,onecouldalsopublishsomepropertiesoftheunderlyingdata,suchasthedailyaveragerate,thedailynumberoftransactions,orthesample‐volatilitymeasure.Whilefinancialcontractswouldpresumablybetiedtothefixingrate,otherpublishedinformationbasedonthesamplecouldprovideadditionalusefulinformationandcould
10Inordertogainsomeintuitionforthelimitedimpactofdecayingweightsonthesamplevolatilitymultiplier,considerarelativelyadversecaseinwhichthetransactionsareconcentratedatthefirstandlastdateofa10‐daysamplewindow.Twoequallysizedtransactionsateachendofthe10‐daysamplingwindow,withoutdecay,wouldhaveasamplevolatilitymultiplierofV(t)=(0.52+0.52)0.5=0.707.Withweightsdecayingproportionatelybyafactorof0.933perday,or50%over10days,wewouldhaveV(t)=[(0.5/k)2+(0.5×0.5/k)2]0.5,wherek=0.5+0.25=0.75,implyingV(t)=0.74.So,indeed,eveninthisrelativelyextremesituation,theelevationofthesamplevolatilitymultiplierV(t)duetodecayisonlyabout5%.
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potentiallybeusedincontracting,forexampleinordertoallowfinancialcontractstobetiedtomarketliquidityortothequalityofthefixingsample.Aseconddisadvantageofasampling‐windowapproachisthatitisnotguaranteedtoproducereliableresultsunderallmarketconditions.Iftherearetoofewtransactionsatagivenmaturitytoprovideevenareasonableestimateofmajor‐bankborrowingrates,marketparticipantswillneverthelessrequireareferencerateonwhichtobasethesettlementofderivativesandfloating‐rateloancontracts.FortheU.S.dollarmarket,ourresultsbasedonalimiteddatasetsuggestsomehopeforthefeasibilityoftransaction‐basedfixing,usingsamplingwindows,for1‐monthand3‐monthmaturities.Inanycase,onemaywishtointroducerobustnesssafeguardsinthedefinitionofthefixingsampleS(t,m;w,d),suchasexpandingthefixingsamplewheneverthereisinsufficientdataforareliablefixing.Forinstance,onecouldtakethesamplewindowtobeafixednumberofdaysortheminimumnumberofdaysnecessarytoincludeagivenvolumeoftransactions,whicheverisgreater.11AsanalternativetofixingLiborbasedonunsecuredborrowingrates,ithasbeensuggestedthatLibormightbereplacedwithabenchmarkratebasedonsecuredlendingtransactions.Prominentamongthesuggestedsecuredinterestratesis“GCFrepo,”whosemarketisdescribedbyFlemingandGarbade(2003).12Thisapproachwouldintroduceseveralpotentialcomplications,however.First,forGCFrepo,thereremainrobustnessconcernsoverwhetherthereisasufficientvolumeofGCFrepotransactionsattherelevantmaturities.Second,GCFreporatesareonlyindirectlyconnectedtobanks’unsecuredcostoffunds,whichreducestheusefulnessofGCFrepoasthebasisforanindexrateforfinancialcontracting.Forcommercialbanksandbankholdingcompanies,unsecuredborrowingisgenerallyamuchlargersourceofoverallfundingthansecuredborrowing.Unsecuredborrowingisalsotraditionallytheprimarysourceoffundingonthemargin.(Forsecuritiesdealers,securedborrowingisalargersourceoffundingandamoretypicalmarginalsourceoffunding,relativetobanks.)Further,Libor‐basedswapsareheavilyusedforrisk‐managementandpricediscoveryfortheunsecureddebtofnon‐financialcorporations.BasingLiboronasecuredborrowingratewouldreduceitsusefulnesshereaswell.Third,usingasecuredfinancingratesuchasGCFreporaisesthe
11ArelatedconcernisthataLiborfixingbasedonasamplingwindowapproachcouldbecomedistortedaroundkeycalendardates,suchastheendofaquarterorcalendaryear.Counterpartiesmayforexamplelengthenorshortenthematurityofotherwisestandardcontractstoinfluencewhethertheycoverparticularfinancialstatementdates,forwindow‐dressingpurposesorforotherreasons.Thiscouldaffectthesetofcontractswhosematuritiesliewithinagivenrange(d)aroundastandardmaturitysuchasonemonthorthreemonths.Inourexamples,wesetthisdaterangetobeconstant,butitmaybenecessarytoadjustdinsuchsituations.12TheDTCCpublishesanaverageovernightGCFreporateforthreetypesofcollateral:Treasuries,agencyMBS,andagencydebt.TradinginfutureslinkedtotheseindicesbeganinJuly2012.Seehttps://globalderivatives.nyx.com/nyse‐liffe‐us/dtcc‐gcf‐repo‐index‐futures/settlement‐procedures
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questionofhowtotreatlegacyLibor‐basedfinancialcontracts,ofwhichthereareenormousquantities.AcounterpartyreceivingLiboronalegacycontractwouldnotwillinglyreceiveinsteadtheGCFreporate,whichistypicallymuchlower.Replacing“legacyLibor”withanapproximationofunsecuredratesthatareestimatedfromsecuredfinancingrateswouldlikelyleadtoasubstantialamountofcontractualdispute.Thisalsoraisesthepossibilityoftwoparallelmarkets,atleastduringatransitionperiod,with“legacy”and“new”benchmarksbasedonunsecuredandsecured(repo)rates,respectively.Theassociatedtransitionwouldbeawkwardandlengthy,andinvolvesplittingliquidityacrossthetwomarketswithanattendantlossinmarketefficiency.Inanycase,asampling‐windowapproachcouldalsobeusedfortermreporates,providedtherearesufficientdata.TheWheatleyReport(H.M.Treasury,2012)reviewsotheralternativeapproachesandbenchmarks,suchastheovernightindexswaprate(OIS),andprovidesadescriptionoftheiradvantagesanddisadvantages.
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ReferencesArmantier,OlivierandAdamCopeland(2012).“AssessingtheQualityof“Furfine‐based”Algorithms,”FederalReserveBankofNewYorkStaffReports,No.575October.Bartolini,Leonardo,SpenceHiltonandJamesJ.McAndrews(2010).“SettlementDelaysintheMoneyMarket”,JournalofBankingandFinance34,934‐945.Carhart,Mark,RonKaniel,DavidMusto,andAdamReed(2002)“LeaningfortheTape:EvidenceofGamingBehaviorinEquityMutualFunds,”JournalofFinance57,661‐693.Fleming,MichaelandKennethGarbade(2003).“TheRepurchaseAgreementRefined:GCFRepo,”FederalReserveBankofNewYorkCurrentIssuesinEconomicsandFinance,9(June).Furfine,Craig(1999)“TheMicrostructureoftheFederalFundsMarket,”FinancialMarkets,Institutions&Instruments8,24‐44.Kuo,Dennis,DavidSkeie,JamesVickery(2012),“AComparisonofLibortoOtherMeasuresofBankBorrowingCosts,”WorkingPaper,FederalReserveBankofNewYork.Kuo,Dennis,DavidSkeie,JamesVickery,andThomasYoule(2012),“IdentifyingTermInterbankLoansfromFedwirePaymentsData,”WorkingPaper,FederalReserveBankofNewYork.H.M.Treasury(2012).“TheWheatleyReviewofLibor:FinalReport,”H.M.Treasury,London,September,2012.
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Appendix:OtherDataFiltersAppendixA1.Minimumtransactionsizeof$100m(ratherthan$25m)ThestatisticsshownherearecomputedforthesamedataasthoseunderlyingFigure1andTable1,withtheexceptionthatthetransactionssizeshaveaminimumof$100m,ratherthanaminimumof$25m.FigureA1.Time‐seriesplotofV(t)i.Brokereddata
ii.Fedwireinferences
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TableA1.StatisticsforV(t)MedianacrossthesampleperiodofthesamplevolatilitymultiplierV(t)forthematurityandsamplingwindowlengthshown,normalizedbythemedianofV(t)forasamplingwindowof10daysandmaturityof3months.i.Brokereddata Maturity
1 month 3 months 6 months
Lag window (days) 2 1.00 1.41 1.41
5 0.79 1.41 1.41
10 0.54 1.00 1.41
15 0.44 0.78 1.41
20 0.38 0.70 1.41
ii.Fedwireinferences Maturity
1 month 3 months 6 months
Lag window (days) 2 1.22 1.73 2.42
5 0.92 1.42 2.42
10 0.66 1.00 1.71
15 0.54 0.83 1.54
20 0.48 0.74 1.43
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AppendixA2.UsingexponentialdecayThestatisticsshowninFigureA2andTableA2arecalculatedusingthesamesamplesasthoseofFigure1andTable1,exceptthatweincorporateexponentialdecayoverthesamplingwindow.FigureA2.Time‐seriesplotofV(t)(i)Brokereddata
(ii)Fedwireinferences
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TableA2.StatisticsforV(t)MedianfortheperiodofthesamplevolatilitymultiplierV(t)fortheindicatedmaturityandsamplingwindowlengthshown,normalizedbythemedianforasamplingwindowof10daysandmaturityof3months.(i)Brokereddata Maturity
1 month 3 months 6 months
Lag window (days) 2 1.03 1.55 2.19
5 0.80 1.28 1.63
10 0.61 1.00 1.35
15 0.52 0.86 1.18
20 0.47 0.78 1.09
(ii)Fedwireinferences Maturity
1 month 3 months 6 months
Lag window (days) 2 1.15 1.67 2.48
5 0.88 1.31 2.10
10 0.67 1.00 1.63
15 0.57 0.86 1.38
20 0.53 0.79 1.27