dealing with dealers: cds comovements in europe · sergio mayordomo was at the spanish securities...

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Dealing with Dealers: Sovereign CDS Comovements in Europe * Miguel Antón IESE Business School Sergio Mayordomo + Universidad de Navarra María Rodríguez‐Moreno Universidad de Navarra First Draft: November 2013 * Antón: Department of Finance, IESE Business School, Av. Pearson 21, 08034 Barcelona, Spain. Email: [email protected]. Mayordomo and Rodríguez‐Moreno: Department of Economics and Business Administration, University of Navarra, Edificio Amigos, 31009 Pamplona, Spain. Email: smayor‐ [email protected] and [email protected] . This paper was partially drafted and circulated while Sergio Mayordomo was at the Spanish Securities and Exchange Commission under the title “Intraday credit risk spillovers in the European sovereign CDS market”. We are very grateful for comments from Ana Babus, Xavier Freixas, Mireia Giné, Antonio Moreno, Fulvio Pegoraro, Tano Santos, Neal Stoughton, Carles Vergara, Xavier Vives, Pierre‐Olivier Weill, Haoxiang Zhu, as well as conference participants at the 7th Financial Risks International Forum in Paris 2014, the Arne Ryde Workshop in Financial Economics in Lund 2014, and seminar participants at IESE Lunchtime workshop. Miguel Antón acknowledges the financial support of the European Commission, Marie Curie CIG (GA no. 303990), and the financial support of the Spanish Ministry of Economy and Competitiveness (Project ref: ECO2011‐ 29533) at Public‐Private Sector Research Center ‐ IESE Business School, University of Navarra, Spain. Sergio Mayordomo acknowledges financial support from PIUNA (University of Navarra) and the Spanish Ministry of Economy and Competitiveness (grant ECO2012‐32554). + Corresponding author.

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Page 1: Dealing with Dealers: CDS Comovements in Europe · Sergio Mayordomo was at the Spanish Securities and Exchange Commission under the title “Intraday credit risk spillovers in the

DealingwithDealers:SovereignCDSComovementsinEurope*

MiguelAntón

IESEBusinessSchool

SergioMayordomo+UniversidaddeNavarra

MaríaRodríguez‐MorenoUniversidaddeNavarra

FirstDraft:November2013

                                                            *Antón:DepartmentofFinance,IESEBusinessSchool,Av.Pearson21,08034Barcelona,Spain.Email:[email protected]. Mayordomo and Rodríguez‐Moreno: Department of Economics and BusinessAdministration, University of Navarra, Edificio Amigos, 31009 Pamplona, Spain. Email: smayor‐[email protected] and [email protected] . This paper was partially drafted and circulated whileSergioMayordomowasattheSpanishSecuritiesandExchangeCommissionunderthetitle“IntradaycreditriskspilloversintheEuropeansovereignCDSmarket”.WeareverygratefulforcommentsfromAnaBabus,XavierFreixas,MireiaGiné,AntonioMoreno,FulvioPegoraro,TanoSantos,NealStoughton,CarlesVergara,XavierVives,Pierre‐OlivierWeill,HaoxiangZhu,aswellasconferenceparticipantsatthe 7th Financial Risks International Forum in Paris 2014, the Arne Ryde Workshop in FinancialEconomics in Lund 2014, and seminar participants at IESE Lunchtime workshop. Miguel AntónacknowledgesthefinancialsupportoftheEuropeanCommission,MarieCurieCIG(GAno.303990),andthefinancialsupportoftheSpanishMinistryofEconomyandCompetitiveness(Projectref:ECO2011‐29533)atPublic‐PrivateSectorResearchCenter‐ IESEBusinessSchool,UniversityofNavarra,Spain.Sergio Mayordomo acknowledges financial support from PIUNA (University of Navarra) and theSpanishMinistryofEconomyandCompetitiveness(grantECO2012‐32554).

+Correspondingauthor.

Page 2: Dealing with Dealers: CDS Comovements in Europe · Sergio Mayordomo was at the Spanish Securities and Exchange Commission under the title “Intraday credit risk spillovers in the

DealingwithDealers:SovereignCDSComovementsinEurope

ABSTRACT

AsimplemeasureofcommonalityinthequotesthatdealersgiveforSovereignEuro‐

pean CDS is a powerful predictor of cross‐sectional variation in CDS return

correlation,controllingforliquidityanddefaultrisks,andothercountry‐paircharac‐

teristicsandmacrovariables.Weshowthattheseresultsareconsistentwithanon‐

fundamental price pressuremechanism in two different ways. First, the predicting

effectofthecommonalityisstrongerfordealersthatexperiencesellingpressure,and

secondly, we show that the effect comes primarily from uninformed dealers. An

instrumentalvariableanalysisandtheuseof the“CDSnaked”ban fromtheGerman

BaFin as exogenous shock confirm that our findings reflect indeed a causal relation

betweencommonalityinquotesandCDSreturncomovement.

JELClassification:G12,G14.

Keywords:SovereignCDS,comovements,commonalities,dealers.

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I. Introduction

CreditDefaultSwap(CDS)spreadshavebeenwidelyusedasameasureofcreditrisk,

becausetheyprovideinsuranceagainstthedefaultofanunderlyingsecurity.Regula‐

torsandacademicscontinuetousethemasakeytooltoassessthecreditworthiness

ofabondoranentity.Anincreasingbodyofliteraturehasshown,however,thatCDS

spreads not only convey information about credit risk, but also about liquidity and

counterparty risk.1Much less attention has been given, however, to the sources of

comovement in CDS spreads. Understanding the drivers of these correlations is

central to regulatory and policy work, as they are at the heart of systemic risk by

definition–strongerinterlinksbetweendifferententitiesrevealahighersystemicrisk.

Ifthisisimportantamongcorporatecontracts,itiscrucialwithsovereignCDS,espe‐

ciallyinEurope,asacrediteventinonecountrycanhaveastrongimpactinthewhole

Euroarea.

WefocusonconnectingcountriesthroughtheCDSdealerstheyhave incom‐

mon.Specifically,weshowthatasimplemeasureofcommonalityinthequotesthat

dealers give for Sovereign European CDS is a powerful predictor of cross‐sectional

variation in CDS return correlation, controlling for liquidity and default risks, and

othercountry‐paircharacteristicsandmacrovariables.

Theanalysisalsoexplorescross‐sectionalvariationinthestrengthoftheeffect.

In particular we show that the commonality in the quotes given by dealers has a

stronger effect on subsequent correlation when the common dealers have excess

inventoryriskandfacesellingpressure.Dealersaimingtosellprotectionforthetwo

countriesatthesametimeleadstoa largercomovement,duetotheeffectof forced

sellingat firesaleprices.Wealsofindthatcommonality inquotesfromdealersthat

are uninformed on the CDS prices leads to stronger comovements, given that they

tendtoreviewpricessimilarlyforthetwocountriesinthepairwhentheylackinfor‐

mation.

                                                            1SeeLongstaff,Pan,PedersenandSingleton(2005)forliquidityriskinCDS,andArora,Gandhi,andLongstaff(2012)forcounterpartyrisk.

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CDSmarketdealersplayacrucialroleinprovidingliquiditytothemarketby

disseminatingbidsandofferstopotentialclients,seekingtotradecreditprotection.2

AlthoughafewpapersanalyzetheeffectoftheliquidityprovisiononCDSprices,not

muchisknownabouttheeffectofdealers’liquidityprovisionsonCDSpricecomove‐

ments.Inthispaper,weoffernewinsightsonthatissuebyexploitingavailabledata

providedbyCMA that consistsof intradayquotes for11EuropeanMonetaryUnion

countries.Thereason for focusing in theEMUis threefold.First, the levelsofconta‐

gionamong these countriesduring the currentEuropeansovereigndebt crisishave

beenverystrongandhavepersistedforalongperiodoftime.Second,theactivityin

thesovereignCDShasincreasedsignificantly.Thegrossnotionalamountoutstanding

bytheendof2008was$405billion,whilebytheendofoursample(October2011)

the gross notional amount outstanding for the 11 European countries was $1,047

billion.Infact,accordingtodataprovidedbyDTCC,France,Italy,andGermanywhere

thetop3referenceentitiesintermsofthenetnotionalamountoutstandingbySep‐

tember 2011, including sovereign and corporate references, being Spain the 5th

referenceentity.Belgium,AustriaandPortugalwereinthe11th,12th,and14thplace,

respectively. Third, all the CDS have similar characteristics in terms of currency,

restructuringclause,andtiming.

CDSdatavendorsemploytheirmethodologiestoofferdailyquotesthatareob‐

tainedaftercombiningthequotesreceivedbydifferentdealers.Wetestwhetherthe

commonquotes reportedby thesamedealer foragivenpairof countriesaffect the

correlation of CDS spreads, and find that they do. The effect of the commonality in

quotesissignificantatanystandardsignificancelevelandhasaverystrongforecast‐

ing power on the future comovements among sovereign CDS spreads. In fact, the

economicimpactofthecommonalityvariableisstrongerthantheoneattributableto

theremainingexplanatoryvariables,includingthetraditionalfundamentalvariables.

                                                            2The importance of dealers’ activity in the CDS market is remarkable. At the end of 2011, dealersaccounted for 58% of notional amounts outstanding, and 64% of gross market values in the CDSmarket.AdditionalevidenceisprovidedbyRobertPickel,CEOofISDA,inhistestimonytoCongressinMarch10,2009,statingthat86%oftheDepositoryTrust&ClearingCorporation(DTCC)tradesweredealer‐to‐dealertrades.Finally,TangandYan(2010)alsosustainthatmostCDScontractsaretradedthroughdealerswhoeithertradewithotherdealersdirectlyortradethroughaninterdealerbroker. 

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The relationshipbetween commonality inquotes andCDS return correlation

could also go the otherway, so endogeneity is a key concern. For example, dealers

couldchoosetogivemorequotestocountrieswhoseCDSpricesaremorecorrelated.

To address this concernwe provide two pieces of evidence.We first implement an

instrumentalvariableapproach,andusetheleverageofbrokersanddealersasproxy

for the commonality inquotes givenbydealers. Themechanism throughwhich the

excess correlation is caused is the selling of CDS at fire sale prices, because some

dealersreachtheirrisk‐bearingcapacity.Inthissense,brokersanddealers’leverage

proxies well for the risk bearing appetite of dealers, as stated recently by Adrian,

Etula,andMuir(2013).Also,astandardtestshowsthat the instrument isvalid.Sec‐

ondly, we use an unexpected ban of naked CDS by the German Securities and

ExchangeCommission(BaFin)onMay18,2010.Thisunexpectedeventresultedinthe

willingnessofdealerstoreducetheirinventories,whichinturnresultsinexogenous

variation in commonality in quotes. These two pieces of evidence confirm that our

findingcomesindeedfromacausalrelationbetweencommonalityinquotesandCDS

returncomovement.

Finallywe show that our finding is robust to the exclusion of Greece in the

analysis,totheuseofpre‐crisisandcrisisperiods,totheuseoffilteredCDSreturnsto

amarketmodel,andtodifferentdefinitionsofcommonalityinquotes.Therestofthe

paperisorganizedasfollows.InSection2,wereviewtheliterature.InSection3,we

describeourmethodologyanddatasources.Section4presentsourresults.InSection

5wepresentourconclusions.

II. LiteratureReview

Ourwork links topapersstudying thecomovementsorcorrelationsbetween

the CDS spreads of the EMU countries during the current European sovereign debt

crisis. Independently of the econometricmethodology employed tomeasure conta‐

gion, thesepapers document an increasing trend in the comovements of credit risk

indicators in the EMU countries since the beginning of the crisis (see Andenmatten

andBrill (2011), Zhang, Schwaab, andLucas (2011),Alter and Schüler (2012),Kal‐

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baskaandGatkowski(2012),GündüzandKaya(2013),ManasseandZavalloni(2013),

amongothers). Inthisprocessofcontagion,peripheralcountrieshavebecomemore

vulnerable to the euro zone contagion and have exhibited stronger comovements

betweenthem.

InspiteofthegrowingliteratureonthedriversofcreditriskandCDSreturns

in European countries in the context of the European sovereign debt crisis; little is

knownaboutthedeterminantsoftheobservedcomovementsandinterlinkagesofthe

countries’levelsofcreditriskandinCDSreturns.Thepotentialsetofdriverscouldbe

relativelysimilartothedriversthatexplainandpredictcorporateCDSprices:default,

liquidity,riskpremium,andcounterpartyriskfactors(seeAnderson(2012)orPuand

Zhao(2010)).3Forexample,theroleofthedefaultfactorsisdocumentedbyManasse

and Zavalloni (2013), who find that the countrymacro fundamentals during crisis

periodexplain80%ofthevulnerabilityofagivencountrytocontagion.Besidesmacro

fundamentals, Alter and Schüler (2012) document that financial sector shocks also

affectsovereignCDSspreadsintheshort‐run.Inthesameline,DieckmannandPlank

(2012)showthatthehighcorrelationsobservedsincethebeginningofthefinancial

crisisareexplainedbythestateofacountry’sdomesticfinancialsystem(itaffectsthe

expectationsaboutbailoutsandtheburdenofgovernmentintervention).

Wesharesomeoftheobjectivespursuedbythesepreviouspapersbyanalyz‐

ingtheeffectofcountryspecificandglobalvariablesreferringtodefaultrisk,liquidity,

and risk appetite on the correlations among EMU sovereign CDS spread changes.

Given that previous literature has left a significant part of these correlations unex‐

plained,ouraim is tobuildonprevious literatureand testwhether theCDSmarket

structure and their dealers’ activity helps improve the explanatory power on such

correlations.TheimportanceofdealersintheCDSmarketdescribedabovehighlights

theroleofthedealers’activityintheCDSpricescomovements.

                                                            3ThesefactorshavealsobeenfoundtobesignificantdeterminantsoftheEMUsovereignbondandCDSspreads by Geyer, Kossmeier, and Pichler (2004), Beber, Brandt and Kavajecz (2009), Mayordomo,Peña, and Schwartz (2012), Favero, Pagano, and Von Thadden (2010), Bernoth, von Hagen, andSchuknecht(2012),orBadaoui,Cathcart,andEl‐Jahel(2013),amongothers.

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TheinabilityoffundamentalstofullyexplainthesecomovementslinkstoBar‐

beris and Schleifer’s (2003) theory. They develop a theory where assets commove

beyond fundamentals, because of market frictions or noise‐trader sentiment. They

provideevidenceinBarberis,Schleifer,andWurgler(2005)insupportofthealterna‐

tivefriction‐basedview,usingdataoninclusionsintotheS&P500.

Ouranalysis focusesontheeffectof twospecificmarket frictionsontheCDS

prices comovements. On the one hand,we consider the effect of asymmetric infor‐

mationfromthepriorthatnotalldealersareequallyinformedateverydate.Onthe

other hand,we consider the effect of the inventory risk that could cause that some

dealerssuffersellingpressureintheCDScontactsofagivensetofcountries.

Market liquidity can be provided by both informed and uninformed agents.

Empirical literature has documented that market players in the stock market are

asymmetrically informed about asset values and that financial market transactions

may conveyprivate informationabout fundamental values (seeHasbrouck (1991a),

Hasbrouck(1991b),orDufourandEngle (2000),amongothers).Thephenomenaof

asymmetric informationanditseffectontheassetpricesshouldbeevenmoreobvi‐

ousinOTCmarkets.DufourandNguyen(2012)analyzetheinformationalcontentof

tradingactivityintheeuroareasovereignbondsandfindastrongevidenceofinfor‐

mationasymmetry inthismarket thatexplainsthecross‐sectionalvariationofbond

yields.

Recent studies referred to the CDS market such as Acharya and Johnson

(2007), Berndt and Ostrovnaya (2008), and Angelopoulos and Giamouridis (2012)

document the existence of informed trading in the CDSmarket on the basis of the

equitymarketasabenchmarkforpublicinformation.Thebetterinformeddealersin

theCDSmarketcouldbemajorbanksthatmayhaveaccesstonon‐publicinformation

on CDS obligors, through their lending and investment banking activities, and can

potentiallytradeonthisinformation(AcharyaandJohnson(2007)).

The informationheterogeneity affects to theCDSprices (Gündüz,Nasev, and

Trapp (2013), and Tang and Yan (2010)) and their liquidity (Qiu and Yu (2012)).

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Gündüz, Nasev, and Trapp (2013) use a proprietary dataset of CDS transactions to

showthatCDSpremiacontainasignificantnon‐defaultrelatedcomponentwhichCDS

traders charge to protect themselves against informational and real frictions. Tang

andYan(2010)showthatCDSnetbuyinginteresthasinformationcontentforfuture

corporateCDSpricechanges.QiuandYu(2012)studythedeterminationofliquidity

provision in the single‐name CDS market as measured by the number of distinct

dealersprovidingquotesandsupporttheexistenceofendogenousliquidityprovision

by informed financial institutions. They conclude that the degree of information

heterogeneityplaysanimportantroleinhowliquidityisreflectedintheCDSpremi‐

um.TheymeasureinformationflowfromtheCDSmarkettothestockmarket.Inour

approach, however, we only use information exclusively from the CDSmarket, be‐

causethereisalackof“sovereignequityprices”.

Another friction affecting financial market is the existence of inventory risk

thatcouldderive insellingpressure.Theprobabilitythat intermediariesfaceselling

pressure increases remarkablyduring liquidity crisis as theongoing financial crisis.

AsexploredinPedersen(2009),inAugust2007therewasasignificantliquidityevent

in which some quants were forced to unwind and others also reduced positions

leadingtoastrongpricepressureandliquidityspirals,inwhichsellingleadstomore

sellingandeven forcedselling.AsstatedbyMitchell,Pedersen,andPulvino (2007),

thecontinuedsellingpressurefromtheproprietarytradingdesksiscausedbyinter‐

nalcapitalconstraintsthatwerelikelyimposedasaresultofthelargelosses.Inthis

vein, Brunnermeier and Pedersen (2009) provide a model that links a security’s

market liquidity and traders’ funding liquidity. When dealers hit their capital con‐

straintsthentheyareforcedtoreducetheirpositions;thisleadstoanexcesssupply

andsothepricedeclines,whichinturnleadstohighermargins,furthertighteningthe

dealers’fundingconstraint,andsoon.

Shachar (2013)documents a significant effectof inventory riskon corporate

CDS prices. Using a proprietary dataset on transactions of CDS contracts on North

Americanfinancialfirms,sheexaminestheroleofliquidityprovisionbydealersinthe

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CDS market, and finds that order imbalances of end‐users cause significant price

impact.

Shockstothefundingconstraintofthedealersectoraffectallsecuritiestraded

byagivendealerandso,theycouldpropitiatecross‐stockpricepressure.HoandStoll

(1983)showthatwhenthedealertradesmorethanonestock,shenotonlylowersthe

bidandaskquotesinthestockherecentlyboughttoreduceinventorylevelsbutalso

adjusts quotes in other stocks to reduce his total inventory risk. Themagnitude of

quote adjustments in the other stocks depends on underlying correlations. In the

same vein, Andrade, Chang, and Seasholes (2008) use data from the Taiwan Stock

Exchangetodocument thatan imbalance inonestockalsoaffects thepriceofother

stocks. They show that this cross‐stock price pressure is higher among stockswith

morecorrelatedcashflowsthanamongstockswithlesscorrelatedcashflows.

The effect of liquidity shocks affecting financially constrained intermediaries

on the comovementsof fixed income instruments’ returnshasbeendocumentedby

Acharya,Schaefer,andZhang(2008).Theydocumentanincreaseofliquidityriskfor

market‐makersaroundtheepisodeofGMandForddowngradesinMay2005duetoa

wide‐spreadsell‐offintheircorporatebonds.Thisisshownbyasignificantimbalance

inthedealerquotestowardssales.Theyshowthatthisimbalancetowardssalesinthe

volumeandfrequencyofquotesonGMandFordbondsexplainsasignificantportion

of the excess comovement in the bond and CDS prices of all industries, not just in

those of auto firms. The effectwas stronger for firmswith a sub‐investment grade

comparedtoinvestment‐gradefirms,consistentwiththehypothesisthatintermediar‐

ieswithdrew liquiditymore sharply frombondswithgreater inventory risk.4In the

same vein, Micu, Remolona, andWooldridge (2006) show that the price impact of

rating downgrades on CDS not only from their information content but also from

sellingpressurebyrestrictedinvestors.Thepricepressurefromrestrictedinvestors

                                                            4The stronger effect on sub‐investment grade firms suggests that regulation could also cause somesellingpressureondowngradedcountries.Ellul, Jotikasthira,andLundblad(2011)paperinvestigatesfire sales of downgraded corporate bonds induced by regulatory constraints imposed on insurancecompanies. Thus, insurance companies that are relatively more constrained by regulation are, onaverage,more likely toselldowngradedbondthatgeneratessellingpressurearound thedowngradewhichcausespricepressuressuchthatpricescoulddeviatefromfundamentalvalues.

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shouldhaveatemporaryimpactgiventhatinformedtraderwoulddrivepricebackto

the fundamental value. Nevertheless, this mean reversal could not occur in poor

liquidityconditions.

Asdiscussedabove,previousstudieshaveemployedtransactionpricestodoc‐

ument theeffectofmarket frictionson theCDSprices.Weprovideevidenceon the

effectofthesefrictionsonthecomovementsinCDSreturns.CDSpricesareconstruct‐

edthroughacompilationofquotescollectedfromtheparticipantsintheCDSmarket.

Nevertheless,thelackofinformationonquotesatthedealerlevelleavesagapinthe

literaturedocumentingtheroleofdealers’activityonCDScontracts.Theaccesstothe

quotesreportedforeachspecificdealerenablesustofillthisgapintheliteratureand

providenewevidence about the role of non‐fundamental factors in explaining such

comovementsinperiodsinwhichmarketfrictionsintheCDSemerge.Inafirststage,

we finda significant roleof thedealer commonalities inquotesacross countrieson

thecomovementsofthechangesintheirCDSspreads.Inasecondstage,wedocument

a significant effect of the information asymmetries and the inventory risk, which

derives insellingpressure, facedbytheCDSmarketdealersonthecomovementsof

CDSspreads.Theseresultsarerobusttoawidevarietyofalternativespecifications.

III. DataandMethodology

A. DataandSample

Intraday CDS quotes disaggregated at the contributor or dealer level come

froma dataset provided by CMA, for 11European countries (Austria, Belgium, Fin‐

land, France,Germany,Greece,Netherlands, Ireland, Italy,Portugal, and Spain), and

spanningfromJanuary2008toOctober2011.TheintradayCDSdealerquotes(both

executableandindicative)comefromover‐the‐countercommunicationbetweenCDS

dealers and buy‐side institutions, including hedge funds and investment banks’

proprietary tradingdesks.5ThedailydatareportedbyCMAcomes fromthese intra‐

                                                            5As explained in Qiu and Yu (2012), the process of trading in the CDSmarket usually begins withclients receiving indicative quotes from dealers through information providers such as Bloomberg.They then initiate a request‐for‐quote with a single dealer or multiple dealers by phone, email, orthroughanelectronictradingplatform.Dealerscanrespondwithcompetitivebindingquotesthatoften

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dayquotes.CMAcollects thebuy‐sidedata foreverycontractandaggregates it toa

daily frequency (requiring at least three distinct dealers). CMA implements then

severalprocedurestomitigatetheinfluenceofoutliers.

CDSquotesemployedinthisstudyare5‐yearmaturitycontracts(themostliq‐

uidone)denominated inUSDollars.For thoseobservations forwhichweonlyhave

informationontheCDSup‐frontpricesbutnotfortheCDSspreads,wecalculatethe

spread following the ISDA CDS Standard Model to convert upfront payments into

spreads.6Toguaranteeaminimumlevelofsynchronicity,weexcludequotesoutside

themainworking hours (7am to 8pmGMT+1) and quotes given on Saturdays and

Sundays.7Information related to control variables comes from other sources, ex‐

plainedinsubsequentsubsections.

TableIreportsthesummarystatisticsofthefinalsampleofCDSquotesandthe

shareofquotesbydealers.PanelAdisaggregatesatcountrylevelthetotalnumberof

quotesanddealers,aswellasthedailyaverage.Wehavemorethanhalfamillionof

quotesforeveryperipheralcountryoverthewholesampleperiod(i.e.,morethan572

quotes on daily average). The total number of quotes in the core countries ranges

from469,751(France)to331,887(Finland).Regardingthenumberofdealersgiving

quotes toacertaincountrywedonotobservesizeabledifferencesacrosscountries.

We observe that there are around 90 dealers providing quotes to the 11 European

countries. Nevertheless, Panel B shows that not all dealers are equally active. Con‐

cretely,the10mostactivedealersprovide45.9%ofthetotalnumberofquotesinour

sampleandthe30mostactivedealerscoverthe90.8%ofthetotalnumberofquotes.

<InsertTableIhere>

ComovementsinthesovereignCDSarecomputedasthemonthlycorrelationof

dailysovereignCDSreturns forcountries iand jinmonth, , , forthesampleof11

                                                                                                                                                                                     result in actual transactions. They can also respondwith non‐competitive quoteswithwide bid‐askspreadsorchoosenottoprovidequotesiftheydonotwishtotrade.6http://www.cdsmodel.com/7Quotesoutsidethesehours,andinweekends,arescarce.Infact,theyrepresent2.25%ofallobserva‐tions.Duetothelowpercentageofexcludedquotes,wefindsimilarresultswhenweincludetheminouranalysis. 

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Europeancountries(55differentcountry‐pairs)andfortheperiodofJanuary2008to

October2011.Figure1showsthemedianof , ,jointlywiththe5thand95thpercen‐

tiles.FromthebeginningofthesampletotheLehmanBrotherscollapseweobservea

widedispersionacrosscorrelationrangingfrom‐0.45to0.96.SinceSeptember2008,

themedianofthecorrelationsfluctuatessteadilybetween0.5and0.9.The5thand95th

bands show a small dispersion in March 2009, due to the implementation of the

economic stimulus package in the US, and in May 2010, due to the Greek bailout

request.However,weobserveagreaterdispersionsinceMay2010,wherethereisa

sizeabledecreaseofcorrelationsinthe5thpercentile.Thiscomesasaconsequenceof

thedisproportionatelylargeincreaseoftheGreekandotherperipheralCDSpremiain

comparisontothecorecountriesCDSpremia.TableIIreportsthedescriptivestatis‐

ticsofthemonthlycorrelationofdailysovereignCDSreturnsforcountries iand jin

month jointlywithalternativespecificationsofthiscorrelation.

<InsertFigure1andTableIIhere>

B. MeasuringCommonalityinQuotes

i.Commonquotesreportedbythesamedealerforagivenpairofcountries.

Ourmainvariableofinterestmeasurestheamountofcommonquotesgivenby

different dealers to each pair of countries eachmonth.We label this variableCom‐

monalityinQuotes,anddefineitas:

∑ min ,

∈ 0,0.5 (1)

where and are the number of quotes given to country a and country b

respectivelybydealerdinagivenmonthtwhile and arethetotalnumberof

quotesgivenbyalldealerstocountriesaandbat timet, respectively.Thisvariable

capturestheconnectivityofcountriesaandbduetocommonalityinquotesgivenby

CDSdealers. Ifadealergives1quotetoFrance,and10quotestoSpain,wesaythat

FranceandSpainonlyshare“1commonquote”fromthatdealer,theminimumofthe

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two.Wethenaggregatethatvalueforalldealersgivingquotestobothcountries,and

normalizeitdividingitbythesumoftotalquotesgiventoFranceandSpain.

Figure 2 shows themedian of the variable commonality in quotes, together

withits5thand95thpercentilebands.Byconstruction,thevariablerangesfrom0(no

commonalityinquotes)to0.5(strongcommonalityinquotes).Weobservethatfrom

the beginning of the sample to May 2009 the median performs an upward trend

increasing from0.22 to 0.47while the 5th and95th percentiles tighten reaching the

tightest point in May 2009. There is a clear and significant time‐series, and cross‐

sectionalvariationinthisvariable.

<InsertFigure2here>

ii.Commonality inquotesandsellingpressurereportedbythesamedealer foragiven

pairofcountriesdependingonhersellingpressure

Wenow turn toamoredisaggregatedversionof the commonality inquotes.

Dealers giving low ask prices aremore willing to sell than dealers giving high ask

prices.Therearedifferentreasonswhyadealercouldgivealowaskprice,andthus

bewillingtosell.Ifthedealergivesquotesbasedoninformation,thenshecouldgive

lowaskprices foronecountryandhighaskprices foranothercountry. If,however,

thedealerisgivinglowaskpricesinbothcountries,shemightbefacingsomeinven‐

tory‐relatedproblem,andmightbewillingorforcedtosellpartofit(Shachar(2013)).

Tocapturetheeffectofthissellingpressure,wedecomposethevariableCom‐

monalityinQuotesintwovariables.CommonalityfromSellingPressureisdefinedasin

Equation (1),butweonlyuse thequoteswhoseaskprice is in the firstquartile for

countriesaandbrelativetothetotalnumberofquotesofdealerd.Alowaskpricein

the two countries at the same timewould indicate that the dealer is aiming to sell

protectionforthetwocountriesandso,itcouldleadtoalargercomovement,dueto

the effect of forced selling at fire sale prices (see Antón and Polk (2013), and Lou

(2013)).Thesecondvariablecapturesthecombinationsthatdonotcomefromselling

pressure,i.e.,wheretheaskpriceisnotinthefirstquartileinanyofthetwocountries,

andwelabelitasCommonalitynotfromSellingPressure.

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iii.Commonalityinquotesandmarketinformation

To test the informationchannel inouranalysis,weestimate theeffectof the

commonquotes reportedbydealers to two countries dependingon their degreeof

informationontheCDSprice.

To define the dealers as informed/uninformed we employ the Gonzalo and

Granger’s (1995) model which is based on the following Vector Error Correction

Model (VECM) specification and is used to study the effectiveness of the different

dealersintermsofpricediscovery:

Δ Γ Δ (2)

where Equation (2) is a system of two equations constructed from a vector auto‐

regressive(VAR)andanerrorcorrectionterm(ECT).ThevectorXtincludesapairof

CDS quotes or prices of the same underlying country from a given dealer and the

averageCDSquoteoftheremainingdealersinthemarket.TheECTisdefinedbythe

product 1 tX where 1, , are estimated in an auxiliary cointegration

regression.TheseriesforthepairofCDSpricesincludedin 1tX mustbecointegrated

to develop this analysis and the cointegrating relation is defined by

)( 1,2321,11 tDEALERtDEALERt CDSCDSX which can be interpreted as the long‐

run equilibrium. The parameter vector ’ , contains the error correction

coefficientsmeasuringeachprice’sexpectedspeedineliminatingthepricedifference

anditisthebaseofthepricediscoverymetrics.Theparametervector i for 1, . . ,

withpindicatingthetotalnumberoflags,containsthecoefficientsoftheVARsystem

measuringtheeffectofthelaggedfirstdifferenceinthepairofCDSquotesonthefirst

difference of such quotes at time t.8Finally, denotes a white noise vector. The

percentagesofpricediscoveryoftheCDSquotei(where 1, 2)canbedefinedfrom

thefollowingmetrics , 1,2whicharebasedontheelementsofthevectorα’:

; (3)

                                                            8TheoptimalnumberoflagsisdeterminedbymeansoftheSchwarzinformationcriteria.

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Thevectorα’containsthecoefficientsthatdeterminecontributiontopricedis‐

coverybytheindividualdealerandtheaveragedealer.Thus,giventhatGG1+GG2=1we

conclude that dealer 1 leads theprocess of price discoverywith respect to average

contributionsoftheremainingdealerswheneverdealer1pricediscoverymetricGG1

ishigherthan0.5.Thecloserto1(0)thehigher(lower)theabilityofthedealeris.We

estimatethepricediscoverymetricforeachcountry,month,anddealerusingallthe

possiblepairsofCDSspreads.Thepricediscoverymetricsareestimatedusingintra‐

dayinformationovertheperiodJanuary2008–October2011.Inordertoguaranteea

minimum of synchronicity in quotes we group the quotes in ranges of two hours.

Thus,fortherange7.00AMto9.00AM(GMT+1)theCDSpricetobeemployedinthe

pricediscoveryanalysisistheaveragepricewithinsuchrange.Weestimatetheprice

discoverymetricsonamonthlybasisusingrollingwindowsofintradaypricesinthe

lastquarter.That is,weuseamaximumofaround460observationsperestimation.

Weestimatethepricediscoverymetricforagivendealerinagivenmonthwhenthe

dealerhasreportedquotesinatleast22workingdaysinthelastquarter.9Themiss‐

ing observations are replaced by the last available intraday quote in order to

guarantee an adequate number of observations for the estimation. We use infor‐

mation fora totalof33dealers thataccording toPanelBofTable I representmore

than91%ofthetotalquotesintheCDSmarket.

Commonalityinquotesdependingoninformationisdefinedthenas:

∑ min ,

, , (4)

whereidenotesthelevelofinformationofthedealerdintheCDSpricesofcountries

aandb.Weconsiderthreepossibilitiesaboutthedegreeofinformationofdealerd:(i)

heisinformedaboutbothcountriesaandb(I),(ii)heisnotinformedaboutanyofthe

twocountries(NI), (iii)he is informedaboutonecountrybut isnotabouttheother

country(R).Thus,wehaveacommonalityvariableforeachoneofthethreeprevious

                                                            9We coincidewith Longstaff (2010) andArce, Mayordomo, andPeña (2013) on the affirmation thatprice‐discovery process in financial markets may be state‐dependent and perform a dynamic pricediscoveryanalysisonamonthlybasis.

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possibilities.Thenotationissimilartotheoneemployedinthebaselinecommonality

variablebutnow and are thenumberofquotesgiventocountrya and

countryb respectively by dealerdwith a level of information iin a givenmonth t.

Regardingthedenominator, and arethetotalnumberofquotesgivenbyall

dealersinthecategoryofinformationitocountriesaandbattimet,respectively.

denotesthenumberofdealersineachofthethreecategoriesdenotingthedegreeof

informationthatwillbedeterminedonthebasisofthepricediscoverymethodology

fromGonzaloandGranger(1995).Thisvariablewillenableustoknowwhetherthe

comovementsareaffectedbytheactivityofthemoreinformeddealersorwhetherthe

non‐informeddealersarepushingthelevelsofcontagionwiththeirquotedprices.

C. ModelingCross‐SectionalVariationinSovereignCDSComovement

The following equation represents the panel estimate of monthly cross‐

sectional regressions forecasting the monthly correlation of daily sovereign CDS

returnsforcountriesiandjinmontht , forthesampleof11Europeancountries

(55differentcountry‐pairs)andfortheperiodofJanuary2008toOctober2011:

, ∗ ,∗ ∗ , ,

(5)

where ,∗ referstoaourmeasureofcommonalityinthequotesthatdealersgive

to both countries in the pair at month 1as defined in Section III.B and

, containsthesetofkcontrolsthatincludethedependentvariablelagged

onemonth, a group ofmacro controls, and a group of pair‐level controls. All these

controlsareexplainedinthenextsubsection.Theparameter referstothecountry‐

pairfixedeffectsthatareincludedintheestimation.Thestandarderrorsaredouble‐

clusteredatthecountry‐pairandmonthlevel.Inthreedifferentspecificationsweuse

thethreedifferentversionsofCC:thebaseline, thedisaggregated insellingpressure

facedbydealers,andthedisaggregatedinthedegreeofinformationofthedealers.

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D. Controls

D.1 Globalvariables

TheuseofglobalfactorsismotivatedbyLongstaff,Pan,Pedersen,andSingle‐

ton’s(2012)studyofthenatureofsovereigncreditriskonthebasisofCDSspreads.

Their results showthat themajorityof sovereigncredit risk canbe linked toglobal

factors(asingleprincipalcomponentaccountsfor64%ofthevariationinsovereign

credit spreads).We focuson threevariables thataim toproxy for funding liquidity,

counterpartyrisk,andtheEuropeanCentralBank(ECB)bondpurchasesthroughthe

SecuritiesMarketProgram(SMP).

Financingcostsoffinancialinstitutions:Weusethespreadbetweentheuncol‐

lateralized interbank3‐monthsborrowingrate (Euribor)and theOvernight Interest

Swap(OIS).TheEuriborrepresentstheunsecuredinterestrateatwhichbanks lend

money tootherbankswhichsatisfycertaincreditworthinesscriteria. Euriborrates

comprisetheexpectedriskfreeoveraspecificpremium:thetermpremium,thecredit

riskpremiumofunsecuredtrading,andtheliquidityriskpremiuminlieuofovernight

(McAndrews,Sarkar,andWang (2008)).On theotherhand,OIS isequivalent to the

average of the overnight interest rates expecteduntilmaturity. It is almost riskless

andhence it isnotsubject topressuresassociatedwith thoserisks.Therefore,Euri‐

bor‐OIS spread over the same term quantifies the premium that banks pay when

borrowing funds for a pre‐determined period relative to the expected interest cost

fromarepeatedlyrollingoverfundingintheovernightmarket(Aït‐Sahalia,Andritzky,

Jobst, Nowak, and Tamirisa (2012)). An adverse liquidity shock would reduce the

amountofcapitalavailableforfinancialintermediarieswhichinturnlowertheability

oftheirtradingdesktoprovideliquidity.Asliquidityinthemarketworsens,trading

fallsandtheshort‐termcashinflowsoftheseinstitutionsdroptoo,sincemostoftheir

profitarisefrommarket‐makingrevenues.TheincreaseintheEuribor‐OISandso,in

the cost of capital for financially constrained intermediaries is the common factor

which influences both liquidity risk and correlation risk and due to this common

factorthecorrelationbetweenreturnsincreases.Thisrelationbetweenliquidityand

correlationriskisjointlyaddressedinAcharyaandSchaefer(2006).Forthisreason,

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weexpectthathigherfinancingcostswouldgohandinhandwithstrongercomove‐

mentsinCDSspreads.

Counterparty risk: followingArora,Gandhi, andLongstaff (2012),weuse the

dealers’CDSspreadstoconstructthecounterpartyriskvariable,andwefollowArce,

Mayordomo,andPeña’s(2013)methodology.WeproxycounterpartyriskintheCDS

market through the firstprincipalcomponentobtained fromtheCDSspreadsof the

main14banksthatactasdealersinthatmarket.10Thehigherthecounterpartyrisk,

thelowertheconfidenceamonginstitutionalinvestorsintheCDSmarketandso,the

moredifficult to findacounterparty tobuyor sellprotection, the lower themarket

activity, and the higher should be the correlation risk. . Thus,we expect a positive

effectofthisvariableonthedependentvariable.

ECB bond purchases: Finally,we control by the purchases conducted by the

ECB in theopenmarket in thecontextof theSecuritiesMarketProgram(SMP) that

was launched inMay2010. Itwasdesignedtoconductoutright interventions in the

euroareapublicandprivatedebtsecuritiesmarketwiththeobjectiveof(a)address‐

ing the malfunctioning of securities markets; and (b) restoring an appropriate

monetarypolicy transmissionmechanism.Weconsider this interventionasaglobal

variableofinterestduetotheparticulardisclosureoftheprograminwhichtheECB

didnotdisclosethesetoftargetedsecurities/countries,thetimespanoftheprogram

or the amount to be spent. Preliminary studies suggest that SMP purchases had a

positivebutshort‐livedeffectonmarketfunctioningbyreducingliquiditypremiaand

lowering level and volatility of yields (Manganelli (2012)). Therefore, we expect a

commondecreaseinthestrainofthesovereigndebtcountriesandhenceanincrease,

atleasttemporary,inthecomovementsassociatedtothosepurchases.

Interactiontermswithperipheralcountriespairs:Totakeintoaccountpossi‐

ble asymmetries (i.e., differential increasing/decreasing effects in the comovements

                                                            10The14maindealersare:BankofAmerica,Barclays,BNPParibas,Citigroup,CreditSuisse,DeutscheBank,GoldmanSachs,HSBC,JPMorgan,MorganStanley,RoyalBankofScotland,SocietéGenerale,UBS,andWachovia/WellsFargo.Thesedealersarethemostactiveglobalderivativesdealersandareknownas the G14 (see, for instance, ISDA ResearchNotes (2010) on the Concentration of OTCDerivativesamongMajorDealers). 

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betweentheCDSpricesofcountriesindistress)weinteractthethreeglobalvariables

with a dummy variable that takes value 1 when the two countries in the pair are

peripheralcountries(Greece,Ireland,Italy,Portugal,andSpain).

D.2 Pair/CountrySpecificVariables

This set of variables accounts fordifferences/similaritiesbetween two coun‐

trieswhichpotentiallyaffecttothecomovementsintheCDSspreads.Wecontrolby

four groups of county specific variables: credit risk of financial institutions, risk

premium,CDSliquidity,andmacroeconomicvariables.Foreverypairofcountrieswe

introduce thesevariablesas themonthly correlationcomputedusingdailyobserva‐

tions.However,giventhelowerfrequencyofthemacroeconomicinformation,weuse

theabsolutevalueoftheirdifferencetoproxyforthedivergenceofthecountrieswith

respecttothecorrespondingvariables.

Credit risk of financial institutions: Acharya, Drechsler, and Schnabl (2011)

study the relationship between the financial and sovereign CDSs in the Eurozone

countriesfor2007‐2011andshowthattheannouncementoffinancialsectorbailouts

wasassociatedwithanimmediate,unprecedentedwideningofsovereignCDSspreads

andnarrowingofbankCDSspreads.However,post‐bailoutsthereemergedasignifi‐

cant comovement between bank CDS and sovereign CDS. Thus, the stronger the

relationshipbetween the financial sectorsof twogivencountries, theeasiest is that

theshockstofinancialinstitutionsinagivencountryaffectthesovereignsectorofthe

othercountry.Tocontrolforthiscomovementsweconsiderthecorrelationbetween

thelogreturnoftheCDSspreadsofthebankingsectorofthecorrespondingcountries.

Weexpectapositiveeffectforthiscontrolvariable.

Countryspecificriskpremium:Tocontrolforthesimilaritiesofthecountries

intermsoftheirriskpremium,weusethecorrelationbetweenthesquaredreturnsof

the country stock indexes. The country risk premiums have been found to have a

positiveeffectincreditriskbythepreviousliterature(DieckmannandPlank,2012).

We therefore expect that the higher the correlation between the stockmarkets log

returns,thehigherthecorrelationbetweenthesovereignCDSlogreturns.

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CDS liquidity:Toproxy for theeffectof liquidity in thecomovementsweuse

the correlationbetween the sovereignCDS liquidity,proxiedby the relativebid‐ask

spread(i.e.,bid‐askspreadrelativetothemid‐spread).Previousliteraturehasdocu‐

mentedtheexistenceofaliquiditypremiuminsovereignCDSprices.Wethusexpect

that thehigher the correlationbetween the liquiditypremiumof two countries, the

largerwouldbethecorrelationintheprices.

Macro variables: We consider two macro fundamentals in our analysis: the

government debt and the government net deficit/surplus relative to GDP. These

variablesenableustoproxyforthestockofdebtinthecountriesandtheaccumulated

deficitandhavebeenfoundtohavesignificanteffectsonthesovereigncreditspreads

inBernoth,vonHagen,andSchuknecht(2012)andMayordomo,Peña,andSchwartz

(2012)amongothers.Tomeasurethesimilaritiesofagivenpairofcountriesinterms

of their macro fundamentals, we take the absolute difference in the value of the

previous two variables. We expect a negative effect of the absolute differences in

relative debt anddeficit,meaning thathigher differences are associatedwith lower

correlationsbetweenthesovereignCDSlogreturns.

IV. Results

A. ForecastingComovements

PanelAofTableIIIreportsourbaselineresults.Weemploythreealternative

specifications forwhichwe report the resultswithandwithout theCommonality in

Quotes variable to emphasize its power in forecasting comovements. Both columns

(1) and (2) contain the dependent variable lagged one month to control for any

possibleautocorrelationorpersistenceinthatvariable.Theresultssuggestamoder‐

ate persistence that discards any type of unit root in the dependent variable.

Regardingthenewvariableincludedincolumn(2),weobserveapositiveandsignifi‐

cant effect of the Commonality inQuotes variable: increases in the commonquotes

significantly increase the correlation between the sovereign CDS log returns. Apart

frombeingstatisticallysignificant,theforecastingpowerofthisvariableseemstobe

sizeable because theR‐squared increases by a 31% from24.2% to 31.6% after the

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inclusion of this variable in the regression. In columns (3) and (4) we add global

controlvariablesandchecktheforecastingpowerofthecommonalitiesinquotes.

Anincreaseinthefinancingcostsoffinancialinstitutionsleadstoasignificant

increase in the comovements of CDS log returns in the pairs formed by peripheral

countries with respect to the core countries. As said before, and increase in this

variablewouldlimittheabilityofthefinancialintermediariestradingdesktoprovide

liquidityandthiseffectcouldbemoresevereintheCDSofperipheralcountries.

Asexpected,wefindanoverallpositiveandsignificanteffectoftheECBsover‐

eignbondpurchasesdue its effectiveness todiminish the levels of credit risk, their

volatility,andtheliquiditypremiumdocumentedbyManganelli(2012).Nevertheless,

we findanegative significantdifferentialeffect in thegroupofperipheral countries

thatwere targeted in theSMPwith respect to thenon‐peripheral countriesbut still

positive in aggregate terms (0.003‐0.002 = 0.001).11The lower magnitude of this

positiveeffectoftheECBSMPonthecomovementsamongperipheralcountriescould

beduetothepre‐existenthighlevelsbeforetheinterventionoftheECB.Thus,theECB

bondpurchasesmaketheperipheralcountriesmoresimilartothecorecountries in

termsofthevariationoftheircreditriskandweakenstheflight‐to‐qualityobserved

duringtheepisodeswiththehighestlevelsoffinancialstress.

Weobserve that the coefficient for theCommonalities inQuotes variable re‐

mains positive and strongly significant after controlling by global effects thatwere

simultaneouslyfacedbythetwocountriesineachpair.Theforecastingpowerofthis

variablewhenconsideringglobalvariablesdiminishesbutitisalsoremarkablegiven

thattheR‐squaredincreasesbymorethan21%from27.6%to33.5%.

Incolumns(5)and(6)weincludethepair/countryspecificvariables.Consist‐

entlywiththeexistenceofasignificantliquiditypremiuminCDSspreads;wefindthat

thestrongertherelationbetweentheliquidityoftheCDScontractsofagivenpairof

                                                            11The Governing Council of the ECB decided the 21st of February 2013 to publish the Eurosystem’sholdingsofsecuritiesacquiredundertheSecuritiesMarketsProgramwiththereportingthefollowingcountryby countrybreakdown: Italy (103billioneuros), Spain (44billion euros),Greece (34billioneuros),Portugal(23billioneuros)andIreland(14billioneuros).

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countries,thestrongerthecomovementsintheirprices.Resultsalsoshowthatamong

themacro fundamentals, the similarity in thedegreeof the countries’ indebtedness

play a significant role in comovements. Thus, if two given countries exhibit a high

ratioofdebtrelativetoGDPthemarkettendstopushtheirCDSinthesamedirection.

ThecoefficientfortheCommonalitiesinQuotesvariableremainspositiveandstrong‐

ly significant after adding both global and local control variables. The forecasting

power of commonalities also diminishes when we consider pair/country specific

variablesbutitstillexhibitsasizeableincrease(18%from29.2%to34.5%).

Finally,weanalyzetheeconomicsignificanceofthevariablesaccordingtothe

baselineresultsobtainedincolumn(6).Theeconomicsignificanceofthestatistically

significant variables is obtained from the ratio thatmeasures the change in the de‐

pendent variable relative to its average value given a change in an explanatory

variable equal to its standard deviation, ceteris paribus. The coefficient with the

largest economic significance is the one for the commonality in quotes such that a

change equal to its standard deviation would lead to a change in the dependent

variable equal to 12.4% of its average value. The economic effect of the remaining

significantvariablesinabsoluteterms,byorderofrelevance,is7.91%forthelagged

dependent variable, 5.16% theoverall effect of theECBbondpurchasesbeing such

effectlowerforthecaseoftheperipheralcountries(4.54%),3.62%forthecorrelation

intheCDSliquidityofeachpairofcountries,1.1%fortheabsolutedifferenceofdebt

toGDP,and1.1%thedifferentialeffectofEuribor‐OISontheperipheralcountries.All

theseeffectsarewellbelowtheeffectofthecommonalityinquotes.

<InsertTableIIIhere>

TherevisioninNovember2009ofthemisleadingstatisticsoffiscaldeficitsby

theGreek authoritieswas the immediate triggerof the current European sovereign

debtcrisis.Weanalyzethepotentialeffectofthemostinfluentialeventinthesample

by excluding Greece from our analysis and splitting the sample in two sub‐periods

using as the break point such event. These results are reported in Table IV. Inde‐

pendentlyontheexclusionofGreeceandthesampleperiodemployedinouranalysis

we find that the commonality in quotes has a positive and significant effect on the

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comovement of sovereign CDS spreads, being the economic impact of this variable

almost15%.Someresultsarealsoworthmentioning.Comparingcolumns(1)and(2)

ofthistable,weobservethattheexclusionofGreecefromouranalysisweakensthe

effectoftheECBbondpurchases,whichisnotsignificantat5%,suggestingthatthese

purchases were very effective in weakening the potential contagion originated in

Greece. In fact theprogram started to be effective inMay2010 coincidingwith the

rescueofGreeceandthefirstremarkableincreaseintheEMUsovereignCDSspreads.

TheeffectoftheEuribor‐OISvariableinteractedwiththeperipheralcountriesdummy

andthecorrelationintheCDSrelativebidaskspreadsarenotsignificantat5%after

excluding Greece from the estimation analysis. This result is consistentwith a pre‐

dominant role of Greece in the liquidity risk channel leading to higher levels of

contagion.

We find someworthmentioning differences between the pre‐crisis (column

(3)) and crisis (column (4)) periods. Although three of the country/pair specific

variableshadasignificanteffectontheCDScomovementsinthepre‐crisisperiod,no

significanteffectremainsduringthecrisisperiod.Theoppositeeffectisfoundforthe

globalvariables.Thesechanges in theroleofpairspecificandglobalvariablesafter

thebeginningofthecrisissuggeststhatthecountryfundamentalswerelessrelevant

toexplaincomovementsthanglobalriskormarketsentimentfactors.12

<InsertTableIVhere>

B. ComovementsandDealersInformation

Informedtraderscanbedefinedasthoseagentswhohavenon‐public,market‐

sensitiveinformation.Previousliteratureemploysthestockmarketasabenchmarkof

publicinformationtoanalysetheexistenceofinformedtraders.Nevertheless,ouraim

istoclassifytheagentsasbeingmoreorlessinformedthanthe“average”dealer(i.e.

theaverageCDSpricesobtainedthroughthecontributionofalldealers)onthebasis

ofpricediscoverymetricsthatareobtainedusingtheGonzaloandGranger’s(1995)

methodology.Giventhedatarequirementsforestimatingthedealer’spricediscovery                                                            12ThevariablesrelatedtotheECBbondpurchasescannotbeemployedinthefirstpartofthesample(column(3)).

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metrics, we can estimate suchmetrics in 67% of themonths in our sample. These

metricsareestimatedforatotalof33dealers.Theaveragepricediscoverymetricis

0.28anditindicatesthat,onaverage,eachindividualdealerislessinformedthanthe

averagedealer.TheaveragepricediscoverymetricbeforeNovember2009was0.17.

Afterthatdatetheaveragemetricincreasedto0.33.Althoughthedealers’information

quality could have improved due to the higher liquidity in the sovereign CDS con‐

tracts, dealers were on average less informed than the “average” dealer. The

thresholdof0.5,whichdetermineswhetheradealerisinformedornot,coincideswith

thethirdquartileofthedistributionofthepricediscoverymetrics(i.e.theprobability

thatagivendealerisinformedinbothcountriesinagivenmonthis25%).

The effects of the commonalties in quotes dependingon thedegree of infor‐

mationofthedealersarereportedinTableV.Column(1)reportstheresultsobtained

when we consider the commonality in quotes coming from dealers who are less

informed than the averagedealer (“uninformed”hereafter). Column (2) reports the

results forthecase inwhichtheexplanatoryvariablefor thecommonality inquotes

only considers information from dealers who aremore informed than the average

dealer(“informed”hereafter).Thecommonalityinquotesemployedincolumn(3)is

obtainedusinginformationfromdealerswhoareinformedaboutonecountrybutare

notabouttheothercountry.Weobservethatthethreecommonalityvariablesexhibit

asignificanteffectondependentvariable.Nevertheless,theexplanatorypowerofthe

commonality of uniformed dealers (34.2%) is higher than the one observed in col‐

umns(2)and(3)(31.2and31.5%,respectively).

Theindividualuseofthethreetypesofcommonalitiesexhibitsalwayssignifi‐

canteffectsonthecomovements.Infact,thecorrelationacrossthesevariablesishigh

(e.g., the correlation between the commonality in quotes for the “informed” and

“uninformed” dealers is 0.65; 0.44 between “informed” and “others”; and 0.74 be‐

tween “uninformed”and “others”) suggesting thatdealers tend to followa common

pattern in termsofprovidingquotes for specificpairsof countries.Nevertheless, to

compare the effect of the commonalities given by the dealers with different infor‐

mationweestimatetheireffectsjointlyincolumn(4).Wefindthattheonlysignificant

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effect at 5 and 1% levels comes for the uninformed dealers while the role of the

informed dealers is not significant and the effect of the remaining dealers is only

significant at 10%.Weperforma test to checkwhether the coefficient for thenon‐

informeddealers issignificantlyhigher thanthecoefficient for the informeddealers

and find that it is at 5% significance level. According to Dornbusch, Park, and

Claessens(2000),intheabsenceofbetterinformationtothecontrary,investorsmay

believe that a financial crisis in one country could lead to similar crises in other

countries.This channelpresumes that investors are imperfectly informedabout the

fair price of credit risk in a given country and thusmake decisions on the basis of

otherreasons,whichmayormaynotreflectthetruecreditriskofthecountry,suchas

the belonging to the same monetary union. This theory based on the existence of

informational asymmetries could explain the stronger comovements when the less

informeddealersgivequotestoagivenpairofcountriesatthesametime.

Thecoefficientforthenon‐informeddealersissignificantlyhigherthantheco‐

efficient for thedealers thatare informedonacountrybutnotontheotheronlyat

10%significancelevel.Infact,thislastresultconfirmstheprevioustheoryaboutwhy

thecommonalitiesinthenon‐informeddealerstendtoincreasecomovements.Thus,

whendealershaveinformationonagivencountrybutnotontheother,theytendto

inferthepriceof thecountryonwhichtheir information ispoorerfromthecountry

onwhichtheyhavebetterinformation.13

<InsertTableVhere>

C. ComovementsandDealersSellingPressures

Inthissection,weperformatesttocheckwhetherthecoefficientforthedeal‐

erswithsellingpressureissignificantlylargerthantheonefortherestofthedealers.

We consider common low ask prices for a given pair of countries to define selling

pressure because dealerswill attempt to hedge their exposure to inventory risk by

adjustingtheaskspread.Theeffectsofthecommonaltiesinquotesdependingonthe

degree of dealers selling pressure are reported in TableVI. Column (1) reports the                                                            13Similarresultsarefoundwhenweusedailyinformationinsteadofintradayinformationforestimat‐ingthepricediscoverymetrics.

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resultsobtainedwhenweconsider the commonality inquotes coming fromdealers

whofacesellingpressurewhilecolumn(2)containstheresultsobtainedusingcom‐

monalities from dealers who do not face such pressure. We observe that the two

commonalityvariablesexhibitasignificanteffectat5%levelondependentvariable.

Tocomparetheeffectofthecommonalitiesgivenbythedealersfacingdiffer‐

entdegreesofsellingpressureweestimatetheirjointeffectsincolumn(3).Wefind

thatwhenconsideredjointlybotheffectsaresignificantat1%level.Weperformatest

tocheckwhetherthecoefficientforthedealersfacingsellingpressureisstatistically

significantlyhigherat5%levelthanthecoefficientforthedealersthatdonotsuffer

sellingpressureandfindthatitis.14Thisresultsupportsthetheorythatshockstothe

fundingconstraintofagivendealerwouldforcethisdealertoreduceherpositionsin

certain securities. This forced selling of securities would affect to their prices and

propitiatecross‐CDScontractspricepressurethatwouldbereflectedinthecomove‐

mentsoftheirprices.

<InsertTableVIhere>

D. DealingwithEndogeneity

InouranalysiswehaveregressedmonthlyCDScomovementsonthedealers’

quotescommonalitieslaggedonemonth.Nevertheless,endogeneitymaybeaconcern

here because it is plausible thatCDS comovements’ innovationsmay also affect the

dealers’ commonquotesat thesametime, throughsomebehaviorobserved insuch

correlations that may persist for a given number of months. To conclude that the

commonality in quotes is indeed causing CDS comovements to increase; we re‐

estimate the regressions reported in Equation (5) using two differentmethods: an

instrumentalvariableapproachandanexogenousshocktocommonalityinquotes.

Wefirstlyconsidertheuseofinstrumentalvariables.Weneedaninstrumental

variable that affects exclusively all the participants in the CDSmarket. The channel

throughwhichweexplainthiseffectofcommonalityincorrelationsisthevariationin                                                            14Thesellingpressureisobtainedasthenumberofquotesbyeachdealerd inwhichtheaskpriceisthefirstquartileforcountriesaandbrelativetothetotalnumberofquotesofdealerd.Wehaveusedothercuttingpointssuchasthepercentile33%andresultsaresimilartoonesreportedinTableVI.

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therisk‐bearingcapacityofthedealers(aswehaveseenintheprevioussection).We

proxy this risk‐bearing capacity with the log changes in the leverage of securities

brokers‐dealers in the US (half of the most active participants in the CDS market,

whicharecommonlyknownastheG14,areAmericanbanks).FollowingAdrian,Etula,

andMuir(2013),theleverageforbrokersanddealers(BD)isdefinedastheratioof

thesecuritiesbroker‐dealerstotalfinancialassetstonetworthdefinedasthediffer‐

ence between total financial assets minus total liabilities. Thus, our instrument

capturesthetime‐varyingbalancesheetcapacityoffinancialintermediariesandso,as

funding costs tighten, balance sheet capacity falls and intermediaries are forced to

deleverage by selling assets. In fact, Adrian, Etula, and Muir (2013) find that the

exposures to thebroker‐dealer leverage factorcanaloneexplain theaverageexcess

returns on awide variety of assets including Treasury bonds. Although our instru‐

ment isalsorelatedtofinancingcostsandglobal liquidityrisk, itmainlyreflectsthe

securitiesbroker‐dealersactivityandimbalances.Thereasonforfocusingindealersis

obviousgiventheirroleintheCDSmarket(i.e.bytheendof2011dealersaccounted

for64%ofgrossmarketvaluesintheCDSmarket). Additionally,Acharya,Schaefer,

and Zhang (2008) document that a significant imbalance in the dealers quotes to‐

wardssalesexplainasignificantportionoftheexcesscomovementsinbondandCDS

pricesaround theGMandForddowngrades.Theseauthorsshowthat “theeffectof

bondimbalanceonCDSwasstrongerforfirmswithasub‐investmentgradecompared

to investment‐grade firms, consistentwith the hypothesis that intermediarieswith‐

drewliquiditymoresharplyfrombondswithgreaterinventoryrisk”.Thus,aliquidity

shockwouldmakeliquiditytodryupmoreseverelyforsub‐investment‐gradesecuri‐

tiesthanforinvestment‐gradesecurities.

We run an instrumental variable regressionwith fixed effects and robust to

heteroskedasticity in which the common quotes are instrumented through the log

change inthe leverageofmarketparticipants.Resultsarereported incolumn(1)of

Table VII.We perform theKleibergen‐PaapRank LM statistic to checkwhether the

equation is identified that is, whether the excluded instrument (the securities bro‐

kers‐dealers leverage) is “relevant” (correlated with the endogenous regressor).

Accordingto thisunder‐identificationtestwereject thenullhypothesis(equation is

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27 

under‐identified) and so, the instrument is relevant and themodel is identified.We

alsoperformaweakidentificationtesttoanalyzewhetherthebrokers‐dealerslever‐

ageiscorrelatedwiththecommonquotesbutonlyweakly.Forthistestandgiventhat

theestimationisrobusttoheteroskedasticity,weusetheKleibergen‐PaapWaldRank

Fstatistic.Thestatisticobtainedincomparisonwiththecorrespondingcriticalvalues

enablesustorejectthehypothesesthattheequationisweaklyidentified.Asitcanbe

inferred from the significant coefficient for the dealers’ commonalitieswe conclude

thatthepotentialendogeneityofthesecommonalitiesdoesnotbiasourresults.

The previous instrumental regression is exactly identified and so,we cannot

checkthevalidityoftheinstrument.Weconsideranewinstrumentalvariableregres‐

sion using the interaction of the log change in broker‐dealer’s leverage and the

financing costs (Euribor‐OIS) as an additional instrument. This new instrument

captures thedealers’balancesheetcapacitywhen fundingcosts tighten.Theresults

arereportedincolumn(2)ofTableVIIandconfirmthesignificanteffectofthedeal‐

ers’commonalityinquotesonthesovereignCDSreturnscomovements.Inviewofthe

Kleibergen‐PaapRankLMstatisticandp‐valueweconcludethattheinstrumentsare

relevant relevant. The use of the second instrument also enables us to employ the

overindentification test based on Hansen J statistic and to confirm that the instru‐

mentsarevalidgiventhattheyareuncorrelatedwiththeerrorterm

Inoursecondattempttoexamineifthepreviousresultsaredrivenbyendoge‐

neity concerns, we consider the ban of naked transactions in CDS on Eurozone

sovereignbondsimposedbytheGermanSecuritiesandExchangeCommission(BaF‐

in)onMay18,2010asanexogenousshock.AccordingtotheEuropeanCommission‐

MEMO/11/713,thebuyeroftheCDSis“naked”ifhedoesnothaveanexposurewhich

he is seeking to hedge either to the sovereign debt itself, or to assets or liabilities

whose value is correlated to the sovereign debt.15This constraint is of special rele‐

vancefortheCDSmarketgiventhatalthoughthereisnodataonwhatportionofthe

market constitutes “naked” positions, someestimates put thenumber at around80

                                                            15SeeEuropeanCommission‐MEMO/11/713“RegulationonShortSellingandCreditDefaultSwaps‐Frequentlyaskedquestions”.

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percentof themarket.16In fact, inmanycases thecontractsareused tohedgerisks

associatedwithacountrythatarenotdirectlyassociatedtoitsdebt.Additionally,the

use of this shock is motivated by the effect that it would have on the CDSmarket

participantsandactivitygiven that itwouldreduce the liquidityavailable for inves‐

tors lookinghedge theirpositions, aswell asmake itharder forparticipants toexit

existingtrades.AccordingtoDarrenFox,aregulatorlawyerwhoadviseshedgefunds

at Simmons&Simmons inLondon “theway it’sbeenannounced […] it’s sentmany

marketparticipantsintopanicmode”.17Insimilarterms,JohanKindermann,acapital

marketslawyeratSimmons&SimmonsinFrankfurtsaidinaninterview:“youcannot

imagine what broke lose here after BaFin’s announcement [..] this will lead to an

uproar in the markets tomorrow. Short‐sellers will now, even tonight, try to close

theirpositionsatmarketswheretheycanstilldoso‐‐iftheyfindanypossibilitiesleft

at all now”. For all these reasons the relation to the common quotes variables is

obvioussince thebancould limit thepotentialnumberofcounterpartiesandso the

frequencyofcommonquoteswoulddependona lowernumberof institutions.This

interventionalsocontributestofearinvestorsaboutfurtherregulatoryinterventions

inthemarket.

<InsertTableVIIhere>

We employ a difference‐in‐differences approach to analyzewhether pairs of

countrieswithdifferent levelsofcommonalities inquotessufferdifferentdegreesof

comovementswhen they face the unexpected shock of the BaFin’s ban onMay 18,

2010.Accordingly,pairsofcountrieswithhighlevelsofcommonalitiesinquotesare

definedasthetreatmentgroup,andpairswithlowercommonalitiesarethecontrolor

non‐treatedgroup.Werankallpairsofcountriesbasedontheiraveragecommonali‐

tiesinquotesfrom1Mayto12May,2010.ThedummyvariableofTop‐quintileisset

tounityiftheaveragelevelofcommonquotesisinthetop‐quintile(80‐percentileand

above),andzerofortheremainingpairsbelowthetop‐quintile(below80‐percentile).

Wethenobtaintheaveragecorrelation(dependentvariable)forthethreedaysafter

                                                            16Seehttp://www.reuters.com/article/2010/05/19/germany‐swaps‐idUSN196324520100519.17 See http://www.bloomberg.com/news/2010‐05‐18/germany‐to‐start‐temporary‐ban‐on‐naked‐short‐selling‐of‐euro‐bonds‐banks.html.

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29 

theestablishmentoftheban(19‐21May,2010)andthesameforthethreebanking

daysbeforetheban(13,14,and17May)toperformthediff‐in‐diffinwhichwewould

haveapre‐banobservationforthe55pairsandapost‐banobservationforthesame

pairs.18ThedummyvariableofPost‐BaFinBanissettounityifthedateis19‐21May,

2010,andzeroduringthethreebankingdaysbeforetheban.Athirddummyvariable

Post‐BaFinBanxTop‐quintile is the cross‐product of the previous two dummy varia‐

bles.

Thediff‐in‐diff regressionresultsare reported inTableVIII.Column(1)con‐

tainstheresultsobtainedwhenwedonotincludeotherexplanatoryvariablesbutthe

threepreviousdummieswhile column (2) contains the control variables at country

levelwithadailyfrequencygiventhattheeffectofthosevariablesthatdonotexhibit

anychangeinMay2010wouldbecollectedintheconstanttermwhiletheeffectofthe

global variables is collected in the BaFin dummy. The coefficient estimates of the

cross‐product dummy (Post‐BaFinBanxTop‐quintile) in the twodiff‐in‐diff specifica‐

tionsaresignificantlypositive,suggestingthatagivenpairofcountrieswithstronger

commonalitiesinquotesbeforethebanexhibitedhigherlevelsofcomovementsafter

theban.Notsurprisingly,wefindthattheBaFinbanincreasedthecomovementsby

itself.

<InsertTableVIIIhere>

E. ExtensionsandRobustnesstests

a. OthermeasuresofsovereignCDSreturncorrelation

Inthissectionwefirstanalyzetheeffectofthecommonalitiesinquotesonthe

correlations among sovereign CDS filtered returns. These correlations can be inter‐

preted as contagion according to Bekaert, Harvey, and Ng (2005) who define

contagionas“excesscorrelation,that is,correlationoverandabovewhatonewould

expect fromeconomic fundamentals.”LikeBekaert,Harvey,andNg (2005),we take

anassetpricingperspectivetomeasuringeconomicfundamentalsandidentifyconta‐

                                                            18Thereweremanyevents inMay2010relatedtotheEuropeansovereigndebtcrisis.To isolate theeffectofthebanwefocusintheabovementionedthreedaysaroundtheban.

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30 

gion by the correlation of an asset pricing model’s residuals. These residuals are

obtainedfromaregressioninwhichthedependentvariableistheCDSlogreturnofa

given country and the explanatory variable is a market variable. We employ two

differentspecificationsofthemarketvariabletoestimatethefilteredCDSreturnsfor

agivencountryi.Inthefirstspecification(column(2)ofTableIX),weusetheaverage

dailyCDSreturnofallcountriesinthesampleexceptcountryi.19Inthesecondspeci‐

fication(column(3)ofTableIX),weusethedailyreturnoftheiTraxxIndex,whichis

anindexthatconsistsofCDSonEuropeancorporations.Wealsocomputetheexcess

correlation in the sovereignCDS log‐returns as thedifferencebetween themonthly

correlationofthedailysovereignCDSlog‐returnsandthemonthlycorrelationofthe

dailypercentagechangesofthesovereignbondyields.Resultsarereportedincolumn

(4)ofTableIX.

WeobservethatwhenwefiltertheCDSreturnsofagivencountrywiththeav‐

erageCDSreturnoftheremainingEMUcountriesthere isa largedecreaseintheR‐

squaredfrom34.5%to25.2%.Thecoefficientofthecommonalityinquotesismuch

lowerthaninthebaselineanalysisbutstillstatisticallysignificantat5%level.Infact,

the commonality in liquidity is the only significant regressor, besides the lagged

dependentvariable.Thisresult isworthmentioninggiventhatweareeliminatinga

highportionof the comovementsacross countrieswhen filtering theCDS return. In

fact,theR‐squaredobtainedwiththemarketmodelregressioninthefirststageison

average 0.64.Whenwe filter the returns using the iTraxx returns, the explanatory

powerof theregressorsalsodiminishessignificantly(theR‐squareddecreases from

34.5% to 27%). Nevertheless, the coefficient for the commonalities in liquidity is

similartotheoneobtainedinthebaselineanalysisandalsosignificantat1%signifi‐

cancelevel.

Wenextmodel the excess correlation as the difference between the correla‐

tions in the CDS and bond markets. If the hypothesis that CDS and bond spreads

                                                            19The only traded index on European sovereign CDS (SovXWestern Europe) started trading on 28September2009.Thisindexconsistsof15countriesbutitsinitialdateisfarawayfromthebeginningofoursampleandforthisreasonwedecidedtocreateanequalityweightedindexthatisavailableforthewholeperiod.

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31 

represent twomeasuresof thesamecredit risk is true, then thedifferencebetween

bothcorrelationsshouldcanceloutthecomovementsdrivenbycountryfundamentals

thataresignificantdeterminantsofcreditrisk.Badaoui,CathcartandEl‐Jahel’s(2013)

find that sovereign bond spreads are less subject to liquidity frictions than CDS

spreadsandso,weexpectthattheCDSmarketfrictionshaveastrongereffectonsuch

comovements. We find that the commonalities in quotes significantly explain the

excesscorrelationwhile,asexpected,countryandglobal risk factorsarenotsignifi‐

cantnowgiventhattheyarealsocontainedinthebondyields.

<InsertTableIXhere>

b. Othermeasuresofcommonalityinquotes

Alternatively to the baseline commonality measure we consider three other

variationsofsuchmeasure.Thefirstvariationisdefinedas:

2∑ min NQ , NQ

∑ sum NQ ,NQ∈ 0,0.5 (6)

whereNQ andNQ are the number of quotes given to country a and country b

respectivelybydealerd,whoisamongthedealersgivingcommonquotestocountries

aandbinagivenmonth.Thenoveltyofthismeasureisthatitignoresthequotesthat

aregivenbydealersonlyreportingCDSpricesforasinglecountry.Theresultsforthis

measure are reported in column (2) of Table X and are qualitatively similar to the

onesobtained for thebaselinespecification(column(1))andso, theresultsarenot

biasedbyrestrictingouranalysistothedealersgivingpricestoagivenpairofcoun‐

tries.

Thesecondvariationforthemeasureofcommonalitiesisdefinedasfollows:

3min NQ , NQsum NQ , NQ

∈ 0,0.5 ∗ NCD (7)

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32 

whereNCDisthenumberofcommondealersgivingquotestoapairofcountries.The

differenceof thismeasurewith respect to 2is that it aggre‐

gatesthecommonalityratioatdealerlevel.Contraryto 2, in

whichdealersreportingahighernumberofquoteshaveahigherweightinthecom‐

putationofthemeasure,thisnewmeasureconsidersalldealersequally.Thevalueof

this measure depends on the number of dealers giving common quotes in a given

monthanditsvaluesspanfromzeroto0.5*NCD.Ontheonehand,thedependenceon

thenumberofdealersenablesustoassignhighervaluestothecommonalitymeasure

asthemarketparticipantsactivityincreases.Ontheotherhand,thisprocedurecould

makethismeasuredependentonmarketactivity.Thelowermagnitudeforthecoeffi‐

cient of this variable as reported in column (3) is due to the different scale that

depends on NCD. Nevertheless, the commonality measure is still significant at 1%

levelconfirmingthattheresultsarenotdrivenbutthemostactivedealersintheCDS

market. Regarding the coefficients for the remaindervariables, themaindifference

with respect to the baseline specification is that the correlation between the CDS

relativebid‐askspreads isnotsignificantnow.Thiseffect isdue to the fact that the

commonalitydefinedinthiswayisalsoameasureofliquidityasitdependsonmarket

activity(i.e.NCD).

Toavoidthedependenceofthecommonalitymeasureonmarketactivity,werescale

ittoconstructanewmeasurewithpotentialoutcomesspanningfrom0to1:

43

0.5 ∗∈ 0,1 (8)

Resultsaresimilartotheonesreportedincolumns(1)and(2)ofTableXsup‐

portingthestatementthattheresultsarenotdrivenbutthemostactivedealersinthe

CDSmarket.

<InsertTableXhere>

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33 

c. OtherfrequenciesfordefiningsovereignCDSreturncorrelation

Toshowthat theeffectof thecommonalitydoesnotdependon thedata fre‐

quency employed we repeat our analysis using daily data (964 trading days). To

implementthedailyanalysisweneedtodefinethecorrelationbetweensovereignCDS

onadailybasis.Forsuchaim,wetakeadvantageoftheintradayquotestocalculate

hourlyCDSreturns.Weaggregatethequotesperhourusingthosereportedfrom7.00

to19.00(GMT+1)suchthatweuse13observationstocomputethedailycorrelation.20

Weconsider fourdifferentspecifications todefine thedailycorrelationbetweenthe

hourlyCDSreturnsreportedinTableXI.First,weestimatethedailycorrelationusing

CDSquotessuchthatwhenthereisamissingvalueforagivendealerinagivenhour

we impute the previous quote available for this dealer (column (2)). The second

specification(column(3))issimilartothepreviousonebutweexcludetheobserva‐

tionsforwhichthehourlyCDSreturnis0todiscardanyarbitraryimputationbeyond

ourscope.Toavoidanybiasduetotheeffectoftheimputedobservationswecompute

thedailycorrelationleavingthemissingvalueswithoutreplacement(column(4)).In

the lastspecification(column(5))we leave themissingvalueswithoutreplacement

andadditionallyexcludeobservations forwhich thehourly return is0 for thesame

reason explained in specification contained in column (3). Regarding the remaining

independent variables, the absolute differences for a given pair of countries in the

ratiosofdebtanddeficit toGDParetheonesused inTable IIIandhaveaquarterly

frequency.ThevariablesreferredtotheECBBondPurchasesareupdatedweeklydue

totheinformationavailability.Theremainingexplanatoryvariablesareupdatedona

dailybasis.

<InsertTableXIhere>

V. Conclusion

CDSdatavendorsemploytheirmethodologiestoofferdailyquotesthatareob‐

tainedaftercombiningthequotesreceivedbydifferentdealers.Wetestwhetherthe

                                                            20The lower frequency of quotes for the years 2008 and 2009 impedes to increase the number ofintradayobservationsemployed toobtain theobservationsgiven thatweneed touse thesame timespanforalltheyearsinthesample.

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34 

commonquotesforagivenpairofcountries,reportedbythesamedealer,affectthe

correlationbetweenCDSspreadsandfindthattheydo.

Wefindthattheeffectofthecommonalityinquotesissignificantatanystand‐

ard significance level and has very strong forecasting power on the future

comovements between sovereign CDS spread. In fact, the economic impact of the

commonality variable is much stronger than the one attributable to the remaining

explanatoryvariablesconsideredinouranalysis,includingthetraditionalfundamen‐

talvariables.ThisresultisrobusttotheinclusionornotofGreeceintheanalysis,the

pre‐crisisandcrisisperiods,thedatafrequency,theuseofabnormalCDSreturnson

the basis of amarketmodel to compute the comovements, or considering different

specificationsforliquiditycommonalityinquotes.

The strong effect of this commonality in quotes is explained by the strategy

adoptedby thedealers todealwith twomarket frictions: inventory risk and asym‐

metric information.The comovements among sovereignCDSare strongerwhen the

dealers have excess inventory risk and face selling pressure. Dealers aiming to sell

protectionforthetwocountriesatthesametimecouldleadtoalargercomovement,

duetotheeffectofforcedsellingatfiresaleprices.Finally,wefindthatthosedealers

are not better informed than the “average dealer” on the CDS prices also lead to

strongercomovements,giventhatthelackofinformationleadthemtoreviewprices

similarlyforthetwocountriesformingthepair.

Besidescontributingtotheacademic literatureonCDSmarketcomovements,

our findingswill be informative to regulators and investors and present a series of

relevantpolicyimplications.

Theintensityofthetwomainchannelsincreasingthecomovementsoverand

abovetheeffectoffundamentals(sellingpressureandlackofinformationbydealers)

couldbeweakenedbyimprovingthetransparencyandinitiatingthecentralclearing.

Aproper level of transparencywould lead to a lower adverse selection, to improve

dealerrisksharing,andtobetterinformationforinvestors.Thus,highertransparency

wouldcontributetoabetterfunctioningof theCDSmarketandtoabetter informa‐

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35 

tional content inCDSprices about the real credit riskof a given country.Themore

realistic and fairer prices of credit risk would propitiate a proper measuring and

monitoringofcomovementsandcontagionacrosscountriesthroughtheCDSmarket.

On the other hand, higher level of transparency could diminish trading profits for

marketparticipantsandcoulddeter themore informeddealers fromplacingquotes

thatcouldreducetheir informationaladvantageandso, theiropportunities forarbi‐

trage.Asaconsequence,itcouldleadtoalowerliquidityortoachangeinthequoting

practicesofmarketparticipants. 

Finally,thenewevidenceonthedeterminantsofthecomovementamongsov‐

ereignCDSspreadshasimportantimplicationsforriskdiversificationoftheeurozone

debtportfoliosgiventhatinvestorsshouldunderstandthatan importantpartofthe

comovements intheirportfolios isnotdueto fundamentalsbut tocommonalities in

the dealers’ quotes. A proper understanding of comovements across fair prices of

creditriskwouldcontributetoimprovethehedginganddiversificationstrategies.

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TableI:SummaryStatistics

This table reports summarystatisticson thequotesanddealers reportingquotesper country.PanelAcontainsinformationonthetotalnumberofquotesandthedailyaveragenumberofquotespercountryaswellasthetotalandthedailyaveragenumberofdealersgivingquotesforeachcountry.PanelBsummarizesthetotalnumberofquotesperdealerandthedealer’smarketshare.

PANELA:Descriptivestatistics.Sample:January2008‐Oct2011

Country NumberofQuotes NumberofDealers

Aggregate DailyAverage Aggregate DailyAverage

Austria 448773 458 90 23.5

Greece 598638 593 93 24.5

Italy 581538 572 91 24.4

Portugal 624875 621 92 24.2

Spain 682032 673 93 24.6

Finland 331887 355 90 22.1

Germany 386181 391 89 22.5

Belgium 449147 446 90 23.3

Ireland 606506 604 92 24.1

France 469751 472 91 23.0

Netherlands 347764 365 89 22.6

PANELB:Shareofquotesbydealers

Dealer NumberofQuotes

Top15 Total Share

1 303851 5.5%

2 298052 5.4%

3 274772 5.0%

4 268195 4.9%

5 264496 4.8%

6 262095 4.7%

7 240076 4.3%

8 221383 4.0%

9 204477 3.7%

10 200736 3.6%

11 183192 3.3%12 183031 3.3%

13 182606 3.3%

14 166659 3.0%

15 164469 3.0%

1‐10 2538133 45.9%

11‐20 1609411 29.1%

21‐30 873379 15.8%

31‐40 335378 6.1%

41‐50 110119 2.0%

51‐95 60672 1.1%

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TableII:SummaryStatistics(cont’d)

This table reports summarystatisticson theCDS spread level (CDS), thehourly anddailyCDS spread log return(cCDS), thedaily(CORR_d)andmonthly(CORR_hmandCORR_dm)correlationbetweentheCDSlogreturnforallthe pairs of EMU countries, and the daily (CC_d) andmonthly (CC_m) commonality inquotes for such countries.CORR_d,CORR_hm,andCC_dareobtainedusinghour‐leveldatawhileCORR_dmiscalculatedfromdailydata.PanelAreportstheinformationforthe11EMUcountrieslistedinTable1whilePanelBexcludesGreece.PanelsCandDbreakdownthemeanandstd.dev.peryear.PanelCreferstothe11EMUcountriesandPanelDexcludesGreece.

PANELA:ALLCOUNTRIESVariable Freq mean Std pctl_0 pctl_1 pctl_5 pctl_50 pctl_95 pctl_99 pctl_100CDS Hour 203 421 5 9 23 85 800 1631 8533cCDS Hour 0.00 0.02 ‐0.48 ‐0.04 ‐0.02 0.00 0.02 0.04 0.40cCDS Daily 0.00 0.06 ‐0.63 ‐0.14 ‐0.08 0.00 0.09 0.18 0.64CORR_d Daily 0.34 0.47 ‐1.00 ‐1.00 ‐0.61 0.44 0.93 1.00 1.00CORR_hm Monthly 0.38 0.27 ‐1.00 ‐0.39 ‐0.10 0.41 0.77 0.88 1.00CORR_dm Monthly 0.65 0.26 ‐0.58 ‐0.29 0.10 0.72 0.91 0.95 0.98CC_d Daily 0.38 0.09 0.01 0.05 0.17 0.41 0.48 0.48 0.50CC_m Monthly 0.37 0.09 0.04 0.11 0.18 0.40 0.47 0.48 0.48

PANELB:ALLCOUNTRIESbutGREECEVariable Freq mean Std pctl_0 pctl_1 pctl_5 pctl_50 pctl_95 pctl_99 pctl_100CDS Hour 143 179 5 8 22 78 547 927 1251cCDS Hour 0.00 0.02 ‐0.48 ‐0.04 ‐0.02 0.00 0.02 0.04 0.39cCDS Daily 0.00 0.06 ‐0.63 ‐0.14 ‐0.08 0.00 0.09 0.18 0.64CORR_d Daily 0.35 0.47 ‐1.00 ‐1.00 ‐0.60 0.45 0.93 1.00 1.00CORR_hm Monthly 0.39 0.28 ‐1.00 ‐0.41 ‐0.09 0.41 0.78 0.88 1.00CORR_dm Monthly 0.66 0.26 ‐0.58 ‐0.29 0.11 0.73 0.91 0.95 0.98CC_d Daily 0.38 0.09 0.01 0.05 0.17 0.41 0.48 0.48 0.50CC_m Monthly 0.37 0.09 0.04 0.11 0.18 0.40 0.46 0.48 0.48

PANELC:SUBPERIODS 2008 2009 2010 2011Variable Freq Mean std mean std mean std mean stdCDS Hour 45 42 93 65 181 201 414 727cCDS Hour 0.00 0.03 0.00 0.01 0.00 0.01 0.00 0.01cCDS Daily 0.01 0.08 0.00 0.05 0.00 0.05 0.00 0.04CORR_d Daily 0.12 0.72 0.22 0.44 0.47 0.34 0.46 0.35CORR_hm Monthly 0.14 0.31 0.32 0.16 0.55 0.16 0.52 0.20CORR_dm Monthly 0.41 0.34 0.76 0.12 0.71 0.17 0.70 0.18CC_d Daily 0.28 0.12 0.41 0.05 0.41 0.06 0.40 0.07CC_m Monthly 0.27 0.09 0.41 0.03 0.41 0.05 0.39 0.06

PANELD:SUBPERIODS(AllbGr) 2008 2009 2010 2011Variable Freq Mean std mean std mean std mean stdCDS Hour 41 37 85 61 132 117 260 271cCDS Hour 0.00 0.04 0.00 0.01 0.00 0.01 0.00 0.01cCDS Daily 0.01 0.08 0.00 0.05 0.00 0.05 0.00 0.04CORR_d Daily 0.12 0.73 0.22 0.44 0.46 0.34 0.50 0.34CORR_hm Monthly 0.14 0.31 0.32 0.17 0.55 0.16 0.56 0.17CORR_dm Monthly 0.41 0.35 0.76 0.12 0.73 0.15 0.73 0.15CC_d Daily 0.28 0.12 0.41 0.05 0.42 0.06 0.41 0.06CC_m Monthly 0.27 0.08 0.41 0.02 0.41 0.04 0.40 0.06

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TableIII:CommonalityinQuotesPredictsCDSReturnCorrelation

ThistablereportstheestimatesofmonthlypanelregressionsforecastingthecorrelationofdailySovereignCDSlogReturns inmonth t for thesampleof11Europeancountries listed inTable I,PanelA.Theregressionsareat thecountry‐pairlevel(55differentcountry‐pairs),andfortheperiodofJanuary2008toNovember2011(47months).TheindependentvariablesincludetheCommonalityinQuotes,whichreferstothequotesgivenbydealerstobothcountriesinthepair,andasetofcontrols,allofthemint‐1. ∑ min , /

,whereNQadtandNQbdtarethenumberofquotesgiventocountryaandcountrybrespectivelybydealerdinagivenmonthtwhileTQatandTQbtarethetotalnumberofquotesgivenbyalldealerstocountriesaandbattimet,respectively.Thesetofcontrolsincludethedependentvariablelaggedonemonth,agroupofglobalcontrols,andagroupofpair‐levelcontrols.Theglobalcontrolsarethechangeinthelogofcounterpartyrisk,thechange in the log of Euribor‐OIS, and ECB Bond Purchases. These three variables are also interacted with thevariablePeripheral,adummythattakesvale1whenthetwocountriesinthepairareperipheralcountries(Greece,Ireland,Italy,Portugal,andSpain).Thefivepair‐levelcontrolsareself‐explanatoryinthewaytheyarelabeledinthetable,andinthebodyofthetext.Country‐pairfixedeffectsareincludedinthesixspecifications.Timeeffectsarenotincludedgiventhatthemajorityofspecifications(3to6)containmacrocontrols.Standarderrorsinbracketsaredouble‐clusteredatthecountry‐pairandmonthlevel.*,**,and***indicatestatisticalsignificanceatthe10%,5%,and1%levels,respectively.

PANELA DependentVariable:CorrelationofdailyCDSlog‐returnst (1) (2) (3) (4) (5) (6)

CommonalitiesinQuotest‐1 1.228*** 1.140*** 1.087***[0.197] [0.178] [0.180]

CorrelationofCDSLogRet.t‐1 0.400*** 0.227** 0.375*** 0.225** 0.343*** 0.205*[0.116] [0.108] [0.118] [0.110] [0.118] [0.109]

ΔLogCounterpartyRiskt‐1 ‐0.144 ‐0.077 ‐0.132 ‐0.071[0.133] [0.109] [0.137] [0.113]

ΔLogCounterpartyRiskt‐1xPeripheral ‐0.056 ‐0.109 ‐0.068 ‐0.114[0.070] [0.072] [0.071] [0.071]

ΔLogEuribor‐OISt‐1 ‐0.125 ‐0.084 ‐0.108 ‐0.072[0.085] [0.075] [0.084] [0.075]

ΔLogEuribor‐OISt‐1xPeripheral 0.171** 0.175** 0.156** 0.163**[0.070] [0.070] [0.074] [0.072]

ECBBondPurchasest‐1 0.004*** 0.003*** 0.004*** 0.003**[0.001] [0.001] [0.001] [0.001]

ECBBondPurchasest‐1xPeripheral ‐0.003*** ‐0.002** ‐0.003*** ‐0.002**[0.001] [0.001] [0.001] [0.001]

Corr.CountryBanksCDSLogRet.t‐1 ‐0.016 0.005[0.021] [0.016]

Corr.CountryStockIndexesVola.t‐1 ‐0.008 ‐0.012[0.030] [0.023]

Corr.CDSRelativeBid‐Askt‐1 0.086** 0.066**[0.035] [0.033]

Abs|DeficittoGDPi‐DeficittoGDPj|t‐1 ‐0.001 ‐0.001[0.001] [0.001]

Abs|DebttoGDPi‐DebttoGDPj|t‐1 ‐0.001*** ‐0.001**[0.001] [0.001]

Constant 0.369*** ‐0.021 0.373*** 0.006 0.475*** 0.113 [0.085] [0.106] [0.085] [0.102] [0.102] [0.116]

Observations 2,370 2,370 2,370 2,370 2,370 2,370R‐squared 0.242 0.316 0.276 0.335 0.292 0.345

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TableIV:CommonalityinQuotesPredictsCDSReturnCorrelation(cont’d)

PanelBof thisTableshowstheestimatesof theregressionssimilartocolumn(6)inPanelA,butwhenGreeceisexcludedfromthesample(column(2)),andtwodifferentsubperiodsareanalyzed,correspondingtothefirstandsecondpartofthesample:January2008toNovember2009incolumn(3),andDecember2009toOctober2011incolumn (4). Everything else remains as in Panel A. Column (1) replicates column (6) of Panel A for comparisonpurposes.

PANELB DependentVariable:CorrelationofdailyCDSlog‐returns

(1) (2) (3) (4) All NoGreece Jan08‐Nov09 Dec09‐Nov11

CommonalitiesinQuotest‐1 1.087*** 1.296*** 1.400*** 0.866***[0.180] [0.175] [0.199] [0.237]

CorrelationofCDSLogRet.t‐1 0.205* 0.179* 0.103 ‐0.069[0.109] [0.107] [0.125] [0.060]

ΔLogCounterpartyRiskt‐1 ‐0.071 ‐0.051 ‐0.071 0.487***[0.113] [0.107] [0.119] [0.101]

ΔLogCounterpartyRiskt‐1xPeripheral ‐0.114 ‐0.059 ‐0.075 ‐0.138[0.071] [0.065] [0.069] [0.090]

ΔLogEuribor‐OISt‐1 ‐0.072 ‐0.067 ‐0.025 ‐0.100[0.075] [0.068] [0.073] [0.084]

ΔLogEuribor‐OISt‐1xPeripheral 0.163** 0.165* 0.148 0.125**[0.072] [0.089] [0.131] [0.051]

ECBBondPurchasest‐1 0.003** 0.004*** ‐0.001[0.001] [0.001] [0.001]

ECBBondPurchasest‐1xPeripheral ‐0.002** ‐0.002* ‐0.001[0.001] [0.001] [0.001]

Corr.CountryBanksCDSLogRet.t‐1 0.005 0.001 ‐0.028 ‐0.004[0.016] [0.014] [0.023] [0.027]

Corr.CountryStockIndexesVola.t‐1 ‐0.012 ‐0.020 ‐0.040 ‐0.018[0.023] [0.035] [0.081] [0.031]

Corr.CDSRelativeBid‐Askt‐1 0.066** 0.057* 0.161*** 0.021[0.033] [0.033] [0.062] [0.034]

Abs|DeficittoGDPi‐DeficittoGDPj|t‐1 ‐0.001 ‐0.001 ‐0.007** ‐0.001[0.001] [0.001] [0.003] [0.001]

Abs|DebttoGDPi‐DebttoGDPj|t‐1 ‐0.001** ‐0.001** ‐0.003 ‐0.001[0.001] [0.001] [0.002] [0.001]

Constant 0.113 0.140 ‐0.028 0.436*** [0.116] [0.114] [0.135] [0.106]

Observations 2,370 1,935 864 1,071R‐squared 0.345 0.370 0.420 0.345

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TableV:CommonalityinQuotesfromUninformedTraders

This table reports panel estimates of monthly cross‐sectional regressions forecasting the correlation of dailySovereignCDSReturnsinmonth forthesampleof11EuropeancountrieslistedinTableI,PanelA.Theregressionsareatthecountry‐pair level(55differentcountry‐pairs),andfortheperiodofJanuary2008toOctober2011(47months).TheindependentvariablesinthistablearesimilartothoseinTableIII.ThenoveltyhereisthatwenowlookindependentlyatthecommonquotesfordealersdependingontheirdegreeofinformationthatisdeterminedonthebasisoftheGonzaloandGranger’s(1995)pricediscoverymethodology.Thus,CommonalityfromUninformedTradersmeasuresthevariableCommonalityinQuotesfordealerswhoarelessinformedthantheaverageontheCDSpricesof the twocountries forming thepair. Similarly,CommonalityfromInformedTradersmeasures thevariableCommonalityinQuotes for dealerswith price discoverymetrics indicating that they aremore informed than theaveragedealerontheCDSpricesofthetwocountriesinthepair.Finally,CommonalityfromOtherTradersmeasuresthecommonquotes fordealers informedonacountrybutnoton theother.Allothercontrolsareas inTable III.Country‐pair fixed effects are included in the three specifications. Time effects are not included given that allspecifications contain macro controls. Standard errors in brackets are double‐clustered at the country‐pair andmonthlevel.*,**,and***indicatestatisticalsignificanceatthe10%,5%,and1%levels,respectively.

DepVar:CorrofdailyCDSlog‐returnst

(1) (2) (3) (4)

CommonalityfromUninformedTraderst‐1 1.188*** 1.009***[0.278] [0.255]

CommonalityfromInformedTraderst‐1 1.000*** 0.279[0.297] [0.234]

CommonalityfromOtherTraderst‐1 0.788** 0.448*[0.314] [0.239]

CorrelationofCDSLogRet.t‐1 0.205* 0.290*** 0.254** 0.160[0.111] [0.111] [0.103] [0.103]

ΔLogCounterpartyRiskt‐1 ‐0.076 ‐0.104 ‐0.078 ‐0.046[0.111] [0.126] [0.120] [0.106]

ΔLogCounterpartyRiskt‐1xPeripheral ‐0.059 ‐0.062 ‐0.065 ‐0.057[0.068] [0.073] [0.069] [0.067]

ΔLogEuribor‐OISt‐1 ‐0.108 ‐0.109 ‐0.081 ‐0.093[0.074] [0.079] [0.077] [0.071]

ΔLogEuribor‐OISt‐1xPeripheral 0.146** 0.170** 0.147** 0.146**[0.064] [0.080] [0.070] [0.063]

ECBBondPurchasest‐1 0.004*** 0.003** 0.003** 0.003**[0.001] [0.001] [0.001] [0.001]

ECBBondPurchasest‐1xPeripheral ‐0.003** ‐0.004*** ‐0.003*** ‐0.003***[0.001] [0.001] [0.001] [0.001]

Corr.CountryBanksCDSLogRet.t‐1 ‐0.025 ‐0.016 ‐0.016 ‐0.024[0.021] [0.020] [0.021] [0.020]

Corr.CountryStockIndexesVola.t‐1 0.001 ‐0.006 0.000 0.005[0.019] [0.025] [0.024] [0.016]

Corr.CDSRelativeBid‐Askt‐1 0.085*** 0.097*** 0.094*** 0.093***[0.033] [0.035] [0.034] [0.033]

Abs|DeficittoGDPi‐DeficittoGDPj|t‐1 ‐0.002 ‐0.002 ‐0.002 ‐0.002[0.001] [0.001] [0.001] [0.001]

Abs|DebttoGDPi‐DebttoGDPj|t‐1 ‐0.001*** ‐0.001*** ‐0.001*** ‐0.001***[0.000] [0.001] [0.001] [0.000]

Constant 0.363*** 0.455*** 0.417*** 0.342*** [0.107] [0.104] [0.114] [0.115]

Observations 2,370 2,370 2,370 2,370R‐squared 0.342 0.312 0.315 0.353

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TableVI:CommonalityinQuotesfromSellingPressure

This table reports panel estimates of monthly cross‐sectional regressions forecasting the correlation of dailySovereignCDSReturnsinmonth forthesampleof11EuropeancountrieslistedinTableI,PanelA.Theregressionsareatthecountry‐pair level(55differentcountry‐pairs),andfortheperiodofJanuary2008toOctober2011(47months). The independent variables in this table are similar to those in Table III. The novelty here is that thevariableCommonalityinQuotesisdefined in adifferentway. Instead ofCommonalityinQuotes for all dealers,wenowlookindependentlyatthecommonquotesfordealersthatexperiencesellingpressureinbothcountriesofthepair,andthosethatdonotexperiencesellingpressure.Tocapturetheeffectofthissellingpressure,wedecomposethevariableCommonality inQuotes in twovariables.Toconstruct theCommonalityfromSellingPressureweonlyuse the quotes by a given dealerwhose ask price is in the first quartile for the two countries forming the pairrelative to the total number of quotes. Similarly, Commonality not from SellingPressuremeasures the variableCommonalityinQuotesfordealerswhoseofferoraskquotesarenotinthefirstquartileinanyofthetwocountries.Allothercontrols(macro,andcountry‐pair)areasinTableIII.Country‐pairfixedeffectsareincludedinthethreespecifications.Timeeffectsarenotincludedgiventhatallspecificationscontainmacrocontrols.Standarderrorsinbracketsaredouble‐clusteredat thecountry‐pairandmonth level.*, **,and*** indicatestatisticalsignificanceatthe10%,5%,and1%levels,respectively.

DepVar:CorrofdailyCDSlog‐returnst(1) (2) (3)

CommonalityfromSellingPressuret‐1 1.113** 1.724***[0.432] [0.415]

CommonalitynotfromSellingPressuret‐1 0.801*** 1.024***[0.202] [0.186]

CorrelationofCDSLogRet.t‐1 0.323*** 0.256** 0.201*[0.115] [0.116] [0.108]

ΔLogCounterpartyRiskt‐1 ‐0.115 ‐0.100 ‐0.064[0.131] [0.121] [0.111]

ΔLogCounterpartyRiskt‐1xPeripheral ‐0.063 ‐0.105 ‐0.108[0.073] [0.069] [0.071]

ΔLogEuribor‐OISt‐1 ‐0.087 ‐0.097 ‐0.061[0.085] [0.079] [0.076]

ΔLogEuribor‐OISt‐1xPeripheral 0.162** 0.157** 0.166**[0.073] [0.074] [0.072]

ECBBondPurchasest‐1 0.004*** 0.004*** 0.003**[0.001] [0.001] [0.001]

ECBBondPurchasest‐1xPeripheral ‐0.003*** ‐0.003** ‐0.002***[0.001] [0.001] [0.001]

Corr.CountryBanksCDSLogRet.t‐1 ‐0.016 ‐0.001 0.004[0.019] [0.019] [0.015]

Corr.CountryStockIndexesVola.t‐1 ‐0.021 ‐0.002 ‐0.019[0.028] [0.025] [0.022]

Corr.CDSRelativeBid‐Askt‐1 0.078** 0.076** 0.062*[0.034] [0.034] [0.032]

Abs|DeficittoGDPi‐DeficittoGDPj| t‐1 ‐0.001 ‐0.001 ‐0.001[0.001] [0.001] [0.001]

Abs|DebttoGDPi‐DebttoGDPj|t‐1 ‐0.001** ‐0.001*** ‐0.001**[0.001] [0.001] [0.001]

Constant 0.448*** 0.228** 0.117 [0.106] [0.105] [0.117]Observations 2,370 2,370 2,370R‐squared 0.305 0.322 0.350

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TableVII:CommonalityInstrumentedbyBroker‐Dealers’Leverage

This table reports panel estimates of monthly cross‐sectional regressions forecasting the correlation of dailySovereignCDSReturnsinmonth forthesampleof11EuropeancountrieslistedinTableI,PanelA.ThevariableCommonalityinQuotesis instrumentedwith the log changeofbroker‐dealer’s leverage in column (1) and the logchangeofbroker‐dealer’sleveragejointwiththeinteractionofthisleverageandthefinancingcostsproxy(Euribor‐OIS)incolumn(2).Theregressionsareatthecountry‐pairlevel(55differentcountry‐pairs),andfortheperiodofJanuary2008 toOctober2011(47months).Standarderrors inbracketsaredouble‐clusteredat thecountry‐pairandmonthlevel.*,**,and***indicatestatisticalsignificanceatthe10%,5%,and1%levels,respectively.

PanelA Dep.Variable:CorrelationofdailyCDSlog‐returns (1) (2)CommonalitiesinQuotest‐1 1.839*** 1.876***  [0.336] [0.222]

CorrelationCDSLogRet.t‐1 0.110*** 0.105***  [0.042] [0.033]

Corr.CountryBanksCDSLogRet.t‐1 0.023 0.024  [0.021] [0.019]

Corr.CountryStockIndexesVola.t‐1 ‐0.013 ‐0.013  [0.017] [0.017]

Corr.CDSRelativeBid‐Askt‐1 0.050*** 0.050***  [0.014] [0.013]

ΔLogCounterpartyRiskt‐1 ‐0.030 ‐0.028  [0.026] [0.027]

ΔLogCounterpartyRiskt‐1xPeripheral ‐0.146*** ‐0.147***  [0.048] [0.048]

ΔLogEuribor‐OISt‐1 ‐0.050** ‐0.049**  [0.025] [0.022]

ΔLogEuribor‐OISt‐1xPeripheral 0.165*** 0.165***  [0.040] [0.041]

ECBBondPurchasest‐1 0.003*** 0.003***  [0.000] [0.000]

ECBBondPurchasest‐1xPeripheral ‐0.002** ‐0.002**  [0.001] [0.001]

Abs|DeficittoGDPi‐DeficittoGDPj|t‐1 ‐0.000 ‐0.000  [0.001] [0.001]

Abs|DebttoGDPi‐DebttoGDPj|t‐1 ‐0.001* ‐0.001*   [0.001] [0.001]

Observations 2,380 2,380Numberofcountry‐pair 55 55R‐squared 0.261 0.258Underidentificationtest(Kleibergen‐PaaprkLMstatistic) 30.595 31.601

Chi‐sq(1)P‐val 0.006 0.000Overidentificationtest(HansenJstatistic) Equationexactlyidentified 0.030Chi‐sq(1)P‐val 0.863Instrumented: CommonalitiesinQuotest‐1Excludedinstruments: ΔLogLeverage ΔLogLeverage;

ΔLogLeveragexFinancingCost

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TableVIII:ExogenousShock:“NakedCDS”banned,BaFin

Thistablereportsadifference‐in‐differenceanalysisusingtheBaFin’sbanonMay18,2010asanexogenousshock.Weusetheinformationforthethreedaysbeforeandthethreedaysaftertheannouncementandanalysetheeffectofcommonalitiesontheaveragecorrelationsconsideringasthetreatmentgroupthosepairsofcountrieswithhighlevelsofcommonalities inquotesaccordingtotheaveragecommonalities inquotesfrom1Mayto12May,2010.Thedependentvariableconsistsofoneobservationperpairbeforeandaftertheestablishmentoftheman.Thus,wecalculatetheaveragecorrelationforthethreedaysaftertheban(19‐21May,2010)andforthethreebankingdaysbeforetheban(13,14,and17May).Thesampleconsistsof55observationsforthepre‐banperiodand55observa‐tions for the post‐ban period. The variableTop‐quintile is a dummy variable that takes 1 if the average level ofcommonquotes is in the top‐quintile (80‐percentileandabove),andzero for the remainingpairsbelow the top‐quintile(below80‐percentile).ThevariablePost‐BaFinBanisadummythattakes1ifthedateis19‐21May,2010,andzeroduringthethreebankingdaysbeforetheban.AthirddummyvariablePost‐BaFinBanxTop‐quintileisthecross‐productoftheprevioustwodummyvariables.Column(2)alsocontainsthecontrolvariablesatcountrylevelwithadailyfrequency.Standarderrorsinbracketsaredouble‐clusteredatthecountry‐pairandmonthlevel.*,**,and***indicatestatisticalsignificanceatthe10%,5%,and1%levels,respectively.

DependentVariable:AverageCorrelationofdailyCDS log‐returnst

(1) (2)Post‐Bafin'sBan 0.043 0.069*   [0.026] [0.040]Post‐Bafin'sBanxTop‐quintile 0.254*** 0.292***   [0.026] [0.037]Top‐quintile ‐0.001 ‐0.022   [0.025] [0.023]Corr.CountryBanksCDSLogRet.t‐1 0.403***      [0.110]Corr.CountryStockIndexesVola.t‐1    ‐0.201      [0.135]Corr.CDSRelativeBid‐Askt‐1    0.005      [0.217]Constant 0.598*** 0.442   [0.025] [0.272]Observations 110 110R‐squared 0.031 0.178

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TableIX:OthermeasuresofCDSReturnCorrelation

This table reports panel estimates of monthly cross‐sectional regressions forecasting the correlation of dailySovereignCDSReturns (in column (1)), aswell as correlationof residuals of Sovereign CDSReturns in differentspecifications, in month for the sample of 11 European countries listed in Table I, Panel A. Column (1) justreplicatescolumn(6)ofTableIIIforcomparisonpurposes.Column(2)showsthespecificationwherethedepend‐ent variable is the correlation of residuals from the regression: , whereAvgCDSRetistheaveragedailyCDSReturnofallcountriesinthesampleexceptiatt‐1.Column(3)showsasimilarspecification,butreplacingtheAvgCDSRetwithiTraxxRet,whichisthedailyreturnontheiTraxxIndex.Column(4)showsthespecificationinwhichthedependentvariableisthedifferencebetweenthemonthlycorrelationofdailysovereignCDS log‐returnsand themonthlycorrelationofdailypercentagechangesof sovereignbondyields.Theregressionsareatthecountry‐pairlevel(55differentcountry‐pairs),andfortheperiodofJanuary2008toOctober2011(47months).TheindependentvariablesinthistablearesimilartothoseinTableIII.Country‐pairfixedeffectsare included in all four specifications. Time effects are not included given that all specifications contain macrocontrols.Standarderrorsinbracketsaredouble‐clusteredatthecountry‐pairandmonthlevel.*,**,and***indicatestatisticalsignificanceatthe10%,5%,and1%levels,respectively.

DependentVariablet:Corrof

CDSLog‐returnsCorrof

ResidualMACorrof

ResidualMI

ExcessofCorrbtw.CDSLog‐returns

andBond

(1) (2) (3) (4)

CommonalitiesinQuotest‐1 1.089*** 0.180** 0.965*** 0.945***[0.179] [0.078] [0.185] [0.325]

DependentVariablet‐1 0.205* 0.122*** 0.128 0.567***[0.109] [0.033] [0.084] [0.080]

ΔLogCounterpartyRiskt‐1 ‐0.072 0.023 ‐0.099 ‐0.117[0.113] [0.039] [0.106] [0.155]

ΔLogCounterpartyRiskt‐1xPeripheral ‐0.114 ‐0.112 ‐0.119 0.069[0.071] [0.079] [0.081] [0.137]

ΔLogEuribor‐OISt‐1 ‐0.075 0.004 ‐0.115 ‐0.170[0.075] [0.031] [0.085] [0.152]

ΔLogEuribor‐OISt‐1xPeripheral 0.160** 0.119 0.211** 0.220[0.070] [0.074] [0.082] [0.134]

ECBBondPurchasest‐1 0.003** 0.000 0.002 0.008***[0.001] [0.001] [0.001] [0.003]

ECBBondPurchasest‐1xPeripheral ‐0.002** ‐0.002 ‐0.003** ‐0.002[0.001] [0.002] [0.002] [0.004]

Corr.CountryBanksCDSLogRet.t‐1 0.009 ‐0.018 0.010 ‐0.026[0.015] [0.027] [0.025] [0.058]

Corr.CountryStockIndexesVola.t‐1 ‐0.011 0.028 0.050 ‐0.021[0.023] [0.020] [0.032] [0.057]

Corr.CDSRelativeBid‐Askt‐1 0.064* ‐0.011 0.044 ‐0.070[0.033] [0.013] [0.037] [0.068]

Abs|DeficittoGDPi‐DeficittoGDPj|t‐1 ‐0.001 ‐0.000 ‐0.001 ‐0.001[0.001] [0.001] [0.001] [0.002]

Abs|DebttoGDPi‐DebttoGDPj|t‐1 ‐0.001** ‐0.002 ‐0.002*** 0.001[0.000] [0.001] [0.001] [0.001]

Constant 0.106 ‐0.056 0.155 ‐0.250 [0.114] [0.074] [0.108] [0.167]

Observations 2,380 2,390 2,349 2,380R‐squared 0.345 0.252 0.270 0.512

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TableX:RobustnesstoothermeasuresofCommonalityinQuotes

This table reports panel estimates of monthly cross‐sectional regressions forecasting the correlation of dailySovereignCDSReturnsinmonth forthesampleof11EuropeancountrieslistedinTableI,PanelA.Theregressionsareatthecountry‐pair level(55differentcountry‐pairs),andfortheperiodofJanuary2008toOctober2011(47months).The independentvariables in this tablearesimilar to those inTable III,buteachof the4specificationsusesadifferentdefinitionofthevariableCommonalityinQuotes.Thefirstcolumnissimilartocolumn(6)ofTableIII,ourbenchmarkdefinitionofCommonalityinQuotes.Columns(2)to(4)correspondtothefollowingdefinitionsofCommonalityinQuotes:Column(1), ∑ min , / ∈[0,0.5]Column(2), 2 ∑ min , /∑ sum , ∈ 0,0.5 Column(3), 3 ∑ min , / sum , ∈ 0,0.5 ∗ Column(4), 4 3/ 0.5 ∗ ∈ 0,1 where and are thenumber of quotes given to countryaand countryb respectivelybydealerd in agivenmonthtwhile and arethetotalnumberofquotesgivenbyalldealerstocountriesaandbattimet,respectively, andNCD is the number of common dealers giving quotes to a pair of countries. Country‐pair fixedeffects are included in the three specifications. Timeeffects arenot includedgiven that all specifications containmacrocontrols.Standarderrorsinbracketsaredouble‐clusteredatthecountry‐pairandmonthlevel.*,**,and***indicatestatisticalsignificanceatthe10%,5%,and1%levels,respectively.

DepVar:CorrofdailyCDSlog‐returnst

(1) (2) (3) (4)

CommonalityinQuotest‐1 1.087*** 1.032*** 0.031*** 0.586***(Differentvariableforeachcolumn) [0.180] [0.181] [0.005] [0.104]

CorrelationofCDSLogRet.t‐1 0.205* 0.220** 0.144 0.214**[0.109] [0.110] [0.107] [0.105]

ΔLogCounterpartyRiskt‐1 ‐0.071 ‐0.077 ‐0.052 ‐0.078[0.113] [0.117] [0.103] [0.114]

ΔLogCounterpartyRiskt‐1xPeripheral ‐0.114 ‐0.109 ‐0.122* ‐0.106[0.071] [0.072] [0.065] [0.074]

ΔLogEuribor‐OISt‐1 ‐0.072 ‐0.078 ‐0.012 ‐0.074[0.075] [0.077] [0.073] [0.076]

ΔLogEuribor‐OISt‐1xPeripheral 0.163** 0.165** 0.165** 0.165**[0.072] [0.074] [0.066] [0.078]

ECBBondPurchasest‐1 0.003** 0.004** 0.003* 0.003**[0.001] [0.001] [0.001] [0.001]

ECBBondPurchasest‐1xPeripheral ‐0.002** ‐0.002** ‐0.002*** ‐0.003**[0.001] [0.001] [0.001] [0.001]

Corr.CountryBanksCDSLogRet.t‐1 0.005 0.002 ‐0.025 0.002[0.016] [0.017] [0.018] [0.018]

Corr.CountryStockIndexesVola.t‐1 ‐0.012 ‐0.012 ‐0.002 ‐0.014[0.023] [0.024] [0.022] [0.023]

Corr.CDSRelativeBid‐Askt‐1 0.066** 0.067** 0.040 0.067**[0.033] [0.033] [0.031] [0.033]

Abs|DeficittoGDPi‐DeficittoGDPj|t‐1 ‐0.001 ‐0.001 ‐0.001 ‐0.001[0.001] [0.001] [0.001] [0.001]

Abs|DebttoGDPi‐DebttoGDPj|t‐1 ‐0.001** ‐0.001** ‐0.001** ‐0.001**[0.001] [0.001] [0.001] [0.001]

Constant 0.113 0.126 0.183 0.097 [0.116] [0.119] [0.112] [0.132]

Observations 2,370 2,370 2,370 2,370R‐squared 0.345 0.338 0.380 0.345

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TableXI:CommonalityinQuotesandCDSCorrelationusingDailyData

This table reports panel estimates of daily cross‐sectional regressions forecasting the correlation of intra‐dailySovereignCDSReturns. The specification is similar to column (6) inTable III (replicated here in column (1) forcomparisonpurposes).Theregressionsareatthecountry‐pairlevel(55differentcountry‐pairs),andfortheperiodofJanuary2008toOctober2011,butusingdailydata(964tradingdays).TheindependentvariablesinthistablearesimilartothoseinTableIII,butupdateddaily,exceptECBBondPurchases,Abs|DeficittoGDPi‐DeficittoGDPj|andAbs|DebttoGDPi‐DebttoGDPj|,thatareupdatedlessfrequently(ECBPurchasesweekly,andthoserelatedtoGDP,quarterly).The four specifications fordaily analysis correspond to fourdifferentwaysofdefining thedependentvariable(thedailycorrelationofSovereignCDSReturn)usinghourlychangesinCDS.Column(2)correspondstothespecificationwhere if there is amissing CDS quote for a dealer in a given hour,we impute the previous quote.Column (3) is as column (2), butwe exclude the observations forwhich the hourly CDS return is 0. Column (4)leavesthemissingvalueswithoutreplacementforthepreviousvalue,andcolumn(5)leavesthemissingvalues,andexcludes theobservations forwhich thehourlyCDSreturn is0.Country‐pair fixedeffectsare included inall fivespecifications.Timeeffectsarenotincludedgiventhatallspecificationscontainmacrocontrols.Standarderrorsinbracketsaredouble‐clusteredatthecountry‐pairanddaylevel.*,**,and***indicatestatisticalsignificanceatthe10%,5%,and1%levels,respectively.

Dep.Var:CorrofCDSLogReturnst Monthly DailyFill Fill,nozeros Nofill Nofill,nozeros

(1) (2) (3) (4) (5)

CommonalityinQuotest‐1 1.087*** 0.829*** 0.886*** 0.759*** 0.752***[0.180] [0.080] [0.096] [0.124] [0.129]

CorrelationofCDSLogReturnst‐1 0.205* 0.181*** 0.155*** 0.145*** 0.143***[0.109] [0.016] [0.014] [0.015] [0.015]

ΔLogCounterpartyRiskt‐1 ‐0.071 0.085 0.217* 0.138 0.158[0.113] [0.114] [0.117] [0.155] [0.159]

ΔLogCounterpartyRiskt‐1xPeripheral ‐0.114 ‐0.073 ‐0.142 ‐0.241*** ‐0.248***[0.071] [0.063] [0.100] [0.064] [0.087]

ΔLogEuribor‐OISt‐1 ‐0.072 0.231** 0.253*** 0.233** 0.235**[0.075] [0.090] [0.092] [0.093] [0.094]

ΔLogEuribor‐OISt‐1xPeripheral 0.163** 0.013 ‐0.002 ‐0.021 ‐0.044[0.072] [0.077] [0.080] [0.092] [0.096]

ECBBondPurchasest‐1 0.003** 0.012*** 0.012*** 0.008*** 0.008***[0.001] [0.002] [0.002] [0.002] [0.002]

ECBBondPurchasest‐1xPeripheral ‐0.002** ‐0.007*** ‐0.007*** ‐0.006** ‐0.006**[0.001] [0.002] [0.002] [0.003] [0.003]

Corr.CountryBanksCDSLogRet.t‐1 0.005 0.037*** 0.044*** 0.049*** 0.049***[0.016] [0.008] [0.009] [0.009] [0.009]

Corr.CountryStockIndexesVola.t‐1 ‐0.012 ‐0.005 ‐0.009 ‐0.013 ‐0.013[0.023] [0.014] [0.014] [0.014] [0.014]

Corr.CDSRelativeBid‐Askt‐1 0.066** ‐0.000 ‐0.003 ‐0.009 ‐0.009[0.033] [0.007] [0.007] [0.007] [0.007]

Abs|DeficittoGDPi‐DeficittoGDPj|t‐1 ‐0.001 ‐0.001 ‐0.001 ‐0.001 ‐0.001[0.001] [0.001] [0.001] [0.001] [0.001]

Abs|DebttoGDPi‐DebttoGDPj|t‐1 ‐0.001** ‐0.001 ‐0.001 ‐0.001 ‐0.001[0.001] [0.001] [0.001] [0.002] [0.002]

Constant 0.113 ‐0.013 ‐0.025 0.013 0.023 [0.116] [0.099] [0.111] [0.127] [0.131]

Observations 2,370 35,220 34,101 32,798 32,440R‐squared 0.345 0.134 0.108 0.094 0.091

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51 

Figure1:ComovementsinSovereignCDS

ThisfiguredepictsthecomovementsinsovereignCDSlog‐returns .Itrepresentsthemonthlycorrelationof

dailysovereignCDSlogreturnsforthe11EMUcountriesconsidered(i.e.,55differentcountry‐pairs)fortheperiodofJanuary2008toNovember2011.Thechartshowsthemediancorrelation(dashedline),togetherwiththeir5thand95thpercentile(shadedarea).

-.5

0.5

1

Jan2008 Jan2009 Jan2010 Jan2011 Jan2012

Co-Movements in Sovereign CDS

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52 

Figure2:CommonalitiesinQuotes

Thisfiguredepictsthecommonalitiesinquotes definedonthebasisofEquation(1).Itreferstothequotes

given by dealers to both countries in the pair. ∑ min , /,where and arethenumberofquotesgiventocountryaandcountrybrespectivelybydealerd

inagivenmonthtwhile and arethetotalnumberofquotesgivenbyalldealerstocountriesaandbattime t, respectively. The chart shows the median correlation (dashed line), together with their 5th and 95thpercentile(shadedarea).

0.1

.2.3

.4.5

Jan2008 Jan2009 Jan2010 Jan2011 Jan2012

Commonalities in Quotes