DealingwithDealers:SovereignCDSComovementsinEurope*

MiguelAntón

IESEBusinessSchool

SergioMayordomo+UniversidaddeNavarra

MaríaRodríguez‐MorenoUniversidaddeNavarra

FirstDraft:November2013

                                                            * Antón: Department of Finance, IESE Business School, Av. Pearson 21, 08034 Barcelona, Spain. Email:manton@iese.edu.MayordomoandRodríguez‐Moreno:DepartmentofEconomicsandBusinessAdministra‐tion, University of Navarra, Edificio Amigos, 31009 Pamplona, Spain. Email: smayordomo@unav.es andmrodriguezm@unav.es.ThispaperwaspartiallydraftedandcirculatedwhileSergioMayordomowasattheSpanishSecuritiesandExchangeCommissionunderthetitle“IntradaycreditriskspilloversintheEuropeansovereignCDSmarket”.Weareverygrateful forcomments fromAnaBabus,RobertEngle,XavierFreixas,MireiaGiné,SvenKlingler,AntonioMoreno,PaoloPasquariello,FulvioPegoraro,TanoSantos,NealStough‐ton,DavideTomio, CarlesVergara, XavierVives, Pierre‐OlivierWeill,HaoxiangZhu, aswell as conferenceparticipants at the 7th Financial Risk International Forum in Paris, the Arne RydeWorkshop in FinancialEconomicsinLund2014,theFirstInternationalConferenceonSovereignBondMarketsinTokyo2014,andseminar participants at IESE Lunchtime workshop and Leicester University. The authors acknowledgefinancial support of the Cátedra UAM‐Accenture in the form of the prize on financial innovation. MiguelAntónacknowledges the financialsupportof theEuropeanCommission,MarieCurieCIG(GAno.303990),and the financial supportof theSpanishMinistryofEconomyandCompetitiveness (Projectref:ECO2011‐29533)atPublic‐PrivateSectorResearchCenter‐IESEBusinessSchool,UniversityofNavarra,Spain.SergioMayordomoacknowledgesfinancialsupportfromPIUNA(UniversityofNavarra)andtheSpanishMinistryofEconomyandCompetitiveness(grantECO2012‐32554).

+Correspondingauthor.

1 

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)spreadsarewidelyusedbyregulatorsandacademicsasa

key tool toassess thecreditworthinessof abondoranentitybecause theyprovide

insuranceagainstthedefaultofsuchunderlyingsecurityorentity..Anincreasingbody

ofliteraturehasshown,however,thatCDSspreadsnotonlyconveyinformationabout

creditrisk,butalsoaboutliquidityandcounterpartyrisk,amongotherfactors.1Much

lessattentionhasbeengiven,however,tothesourcesofcomovementinCDSspreads.

Understanding the drivers of these correlations is central to regulatory and policy

work, as they are at the heart of systemic risk by definition –stronger interlinks

between different entities reveal a higher systemic risk. If this is important among

corporatecontracts, itiscrucialwithsovereignCDS,especiallyinEurope,asacredit

eventinonecountrycanhaveastrongimpactinthewholeEuroarea.

WefocusonconnectingcountriesthroughtheCDSdealerstheyhave incom‐

mon.Specifically,westudywhetherasimplemeasureofcommonality in thequotes

thatdealersgiveforSovereignEuropeanCDShelpsustopredictthecross‐sectional

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

othercountry‐paircharacteristicsandmacrovariables.

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‐

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

3 

gionamong these countriesduring the currentEuropeansovereigndebt crisishave

beenverystrongandhavepersistedforalongperiodoftime.Second,theactivityin

theEMUsovereignCDScontractshasincreasedsignificantly.3Third,alltheCDShave

similarcharacteristicsintermsofcurrency,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.

Our analysis also explores cross‐sectional variation in the strength of the previous

effect.Inparticularweshowthatthecommonalityinthequotesgivenbydealershasa

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.

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

                                                            3 The  gross  notional  amount  outstanding  by  the  end  of  2008 was  $405  billion, while  by  the  end  of  our sample  (October 2011)  the gross notional amount outstanding  for  the 11 European countries was $1,047 billion.  In fact, according to data provided by DTCC, France,  Italy, and Germany where the top 3 reference entities  in  terms  of  the  net  notional  amount  outstanding  by  September  2011,  including  sovereign  and corporate references, being Spain the 5th reference entity. Belgium, Austria and Portugal were in the 11th, 12th, and 14th place, respectively. 

4 

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.

Therestofthepaperisorganizedasfollows.InSection2,wereviewthelitera‐

ture.InSection3,wedescribeourmethodologyanddatasources.Section4presents

ourresults.InSection5wepresentourconclusions.

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 (seeAndenmatten

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

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

amongothers). Inthisprocessofcontagion,peripheralcountrieshavebecomemore

vulnerable to the euro zone contagion and have exhibited stronger comovements

betweenthem.

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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)).4Forexample,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.

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.

TheinabilityoffundamentalstofullyexplainthesecomovementslinkstoBar‐

beris and Schleifer’s (2003) theory according to which assets commove beyond

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

Schleifer, and Wurgler (2005) provide empirical evidence supporting this friction‐

basedview.

                                                            4ThesefactorshavealsobeenfoundtobesignificantdeterminantsoftheEMUsovereignbondandCDSspreads 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.

6 

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 (see Hasbrouck (1991a), Hasbrouck

(1991b),orDufourandEngle(2000),amongothers).Thephenomenaofasymmetric

information and its effect on the asset prices should be evenmore obvious in OTC

marketsasshowninDufourandNguyen(2012).Theseauthorsfindastrongevidence

of informationasymmetry intheeuroareasovereignbondmarket thatexplainsthe

cross‐sectional variationof bondyields. Recent studies referred to theCDSmarket

suchasAcharyaandJohnson(2007),BerndtandOstrovnaya(2008),andAngelopou‐

losandGiamouridis (2012)alsodocument theexistenceof informed trading in this

market..

This informationheterogeneityaffects to theCDSprices(Gündüz,Nasev,and

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

Gündüz,Nasev, and Trapp (2013) show that CDS premia contain a significant non‐

default related componentwhich CDS traders charge to protect themselves against

informational and real frictions. Tang and Yan (2010) show that CDS net buying

interesthas informationcontentfor futurecorporateCDSpricechanges.QiuandYu

(2012)studythedeterminationofliquidityprovisioninthesingle‐nameCDSmarket

as measured by the number of distinct dealers providing quotes and support the

existenceofendogenousliquidityprovisionbyinformedfinancialinstitutions.

Another friction affecting financialmarkets 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

7 

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)documentsasignificanteffect

of inventory risk on corporate CDS prices of North American financial firms. She

examinestheroleof liquidityprovisionbydealersintheCDSmarket,andfindsthat

orderimbalancesofend‐userscausesignificantpriceimpact.

Beside the effect on single securities, shocks to the funding constraint of the

dealersectoraffectallsecuritiestradedbyagivendealerandso,theycouldpropitiate

cross‐securitiespricepressure.HoandStoll(1983)showthatwhenthedealertrades

morethanonestock,shenotonlylowersthebidandaskquotesinthatstockbutalso

adjusts quotes in other stocks to reduce her total inventory risk. In the same vein,

Andrade,Chang, andSeasholes (2008)usedata from theTaiwanStockExchange to

document thatan imbalance inonestockalsoaffects thepriceofother stocks.This

cross‐stockpricepressureishigheramongstockswithmorecorrelatedfundamentals

suchascashflowsthanamongstockswithlesscorrelatedcashflows.

Thecross‐securitieseffectofliquidityshocksaffectingfinancialconstrainedin‐

termediaries has been also documented for fixed income instruments. Acharya,

Schaefer,andZhang(2008)documentanincreaseofliquidityriskformarket‐makers

around theepisodeofGMandForddowngrades inMay2005due toawide‐spread

sell‐offintheircorporatebonds.Thisisshownbyasignificantimbalanceinthedealer

quotestowardssalesonGMandFordbondsthatexplainsasignificantportionofthe

excesscomovement inthebondandCDSpricesofall industries,not just in thoseof

8 

autofirms.5Theeffectwasstrongerforfirmswithasub‐investmentgradecompared

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

drew liquidity more sharply from bonds with greater inventory risk. As discussed

above,previousstudiessuchasGündüz,Nasev,andTrapp(2013)andShachar(2013)

have employed transactionprices todocument the effect ofmarket frictions on the

CDSprices.Weprovideevidenceontheeffectofthesefrictionsonthecomovements

in CDS returns. The lack of information on quotes of the CDS market participants

leaves a gap in the literature documenting the role of dealers’ activity on CDS con‐

tracts.Theaccesstothequotesreportedforeachspecificdealerenablesustoprovide

newevidenceabouttheroleofnon‐fundamentalfactorssuchasdealercommonalities

inquotesacrosscountriesinexplainingthosecomovements.

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, andSpain), and

spanningfromJanuary2008toOctober2011.TheintradayCDSdealerquotes(both

executableandindicative)comefromover‐the‐countercommunicationbetweenCDS

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

proprietarytradingdesks.6Infact,thedailydatareportedbyCMAcomesfromthese

intradayquotes.CMAcollectsthebuy‐sidedataforeverycontractandaggregatesitto

adailyfrequency.

                                                            5 In the same vein, Micu, Remolona, and Wooldridge (2006) show that the price impact of ratingdowngradesonCDSnotonlyfromtheirinformationcontentbutalsofromsellingpressurebyrestrict‐edinvestors. 6As 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.Dealerscanrespondwithcompetitivebindingquotesthatoftenresult in actual transactions. They can also respondwith non‐competitive quoteswithwide bid‐askspreadsorchoosenottoprovidequotesiftheydonotwishtotrade.

9 

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.7Toguaranteeaminimumlevelofsynchronicity,weexcludequotesoutside

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

Sundays.8Information 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

Europeancountries(55differentcountry‐pairs)andfortheperiodofJanuary2008to

October2011.Figure1showsthemedianof , ,jointlywiththe5thand95thpercen‐

tiles.FromthebeginningofthesampletotheLehmanBrotherscollapseweobservea

widedispersionacrosscorrelationrangingfrom‐0.45to0.96.SinceSeptember2008,

themedianofthecorrelationsfluctuatessteadilybetween0.5and0.9.The5thand95th                                                            7http://www.cdsmodel.com/8Quotesoutsidethesehours,andinweekends,arescarce.Infact,theyrepresent2.25%ofallobserva‐tions.Duetothelowpercentageofexcludedquotes,wefindsimilarresultswhenweincludetheminouranalysis. 

10 

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

two.Wethenaggregatethatvalueforalldealersgivingquotestobothcountries,and

normalizeitdividingitbythesumoftotalquotesgiventoFranceandSpain.

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

withits5thand95thpercentilebands.Byconstruction,thevariablerangesfrom0(no

commonalityinquotes)to0.5(strongcommonalityinquotes).Weobservethatfrom

11 

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.

iii.Commonalityinquotesandmarketinformation

To test the informationchannel inouranalysis,weestimate theeffectof the

commonquotes reportedbydealers to two countries dependingon their degreeof

informationontheCDSprice.

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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 andwhere dealer 1 refers to and individual dealerwhile Dealer 2

referstothe“average”dealer.Theparametervector ’ , containstheerror

correctioncoefficientsmeasuringeachprice’sexpectedspeedineliminatingtheprice

differenceand it is thebaseof thepricediscoverymetrics.Theparametervector i

for 1, . . ,withpindicatingthetotalnumberoflags,containsthecoefficientsofthe

VAR system measuring the effect of the lagged first difference in the pair of CDS

quotes on the first difference of such quotes at time t.9Finally, denotes a white

noise vector.Thepercentagesofpricediscoveryof theCDSquote i (where 1, 2)

can be defined from the following metrics , 1,2which are based on the ele‐

mentsofthevectorα’:

; (3)

Thevectorα’containsthecoefficientsthatdeterminethecontributiontoprice

discoverybytheindividualdealerandtheaveragedealer.Thus,giventhatGG1+GG2=1

                                                            9TheoptimalnumberoflagsisdeterminedbymeansoftheSchwarzinformationcriteria.

13 

weconcludethatdealer1leadstheprocessofpricediscoverywithrespecttoaverage

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.10Thatis,weuseamaximumofaround460observationsperestimation.

Weestimatethepricediscoverymetricforagivendealerinagivenmonthwhenthe

dealerhasreportedquotesinatleast22workingdaysinthelastquarter.Themissing

observationsarereplacedbythelastavailableintradayquoteinordertoguaranteean

adequatenumberofobservationsfortheestimation.Weuseinformationforatotalof

33dealersthataccordingtoPanelBofTableIrepresentmorethan91%ofthetotal

quotesintheCDSmarket.

Commonalityinquotesdependingoninformationisdefinedthenas:

∑ min ,

, , (4)

whereidenotesthelevelofinformationofthedealerdintheCDSpricesofcountries

aandb.Weconsiderthreepossibilitiesaboutthedegreeofinformationofdealerd:(i)

heisinformedaboutbothcountriesaandb(I),(ii)heisnotinformedaboutanyofthe

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

country(R).Thus,wehaveacommonalityvariableforeachoneofthethreeprevious

possibilities.Thenotationissimilartotheoneemployedinthebaselinecommonality

variablebutnow and are thenumberofquotesgiventocountrya and

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

                                                            10WecoincidewithLongstaff (2010)andArce, Mayordomo,andPeña(2013)on theaffirmation thatprice‐discovery process in financial markets may be state‐dependent and perform a dynamic pricediscoveryanalysisonamonthlybasis.

14 

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.

D. Controls

D.1 Globalvariables

TheuseofglobalfactorsismotivatedbyLongstaff,Pan,Pedersen,andSingle‐

ton’s(2012)studyofthenatureofsovereigncreditriskonthebasisofCDSspreads.

15 

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). Euribor‐OIS spread over the same term quantifies the premium that

bankspaywhenborrowingfundsforapre‐determinedperiodrelativetotheexpected

interest cost from a repeatedly rolling over funding in the overnight market (Aït‐

Sahalia, Andritzky, Jobst, Nowak, and Tamirisa (2012)). An adverse liquidity shock

would reduce the amount of capital available for financial intermediaries which in

turn lower the ability of their trading desk to provide liquidity. As liquidity in the

market worsens, trading falls and the short‐term cash inflows of these institutions

droptoo,sincemostoftheirprofitarisefrommarket‐makingrevenues.Theincrease

intheEuribor‐OISandso,inthecostofcapitalforfinanciallyconstrainedintermedi‐

aries is the common factorwhich influencesboth liquidity risk and correlation risk

andduetothiscommonfactorthecorrelationbetweenreturns increases.Thisrela‐

tion between liquidity and correlation risk is jointly addressed in Acharya and

Schaefer(2006).Forthisreason,weexpectthathigherfinancingcostswouldgohand

inhandwithstrongercomovementsinCDSspreads.

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

dealers’ CDS spreads to construct the counterparty risk variable. We follow Arce,

Mayordomo,andPeña’s(2013)methodologyandproxycounterpartyriskintheCDS

market through the firstprincipalcomponentobtained fromtheCDSspreadsof the

main14banksthatactasdealersinthatmarket.11Thehigherthecounterpartyrisk,

thelowertheconfidenceamonginstitutionalinvestorsintheCDSmarketandso,the                                                            11The14maindealersare: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). 

16 

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

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,giventhelowerfrequencyofthemacroeconomicvariables,weusethe

17 

absolute value of their difference to proxy for the similarities in terms of macro

fundamentals.

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.

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

18 

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

(2012) among others. 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

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

19 

thatwere targeted in theSMPwith respect to thenon‐peripheral countriesbut still

positive in aggregate terms (0.003‐0.002 = 0.001).12The 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

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                                                            12The 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).

20 

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

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.

21 

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.13

<InsertTableIVhere>

B. ComovementsandDealersInformation

Informedtraderscanbedefinedasthoseagentswhohavenon‐public,market‐

sensitiveinformation.Previousliteratureemploysthestockmarketasabenchmarkof

public information to analyse the existence of informed traders in the CDSmarket.

Nevertheless,ouraimistoclassifytheagentsasbeingmoreorlessinformedthanthe

“average”dealer(i.e.theaverageCDSpricesobtainedthroughthecontributionofall

dealers)on thebasisofpricediscoverymetrics thatareobtainedusing theGonzalo

andGranger’s (1995)methodology. Given the data requirements for estimating the

dealer’spricediscoverymetrics,wecanestimatesuchmetricsin67%ofthemonths

inoursample.Thesemetricsareestimatedforatotalof33dealers.Theaverageprice

discoverymetricis0.28anditindicatesthat,onaverage,eachindividualdealerisless

informedthantheaveragedealer.TheaveragepricediscoverymetricbeforeNovem‐

ber2009was0.17.Afterthatdatetheaveragemetricincreasedto0.33.Althoughthe

dealers’ information quality could have improved due to the higher liquidity in the

sovereignCDScontracts, dealerswereonaverage less informedthan the “average”

dealer.The thresholdof0.5,whichdetermineswhetheradealer is informedornot,

coincideswiththethirdquartileofthedistributionofthepricediscoverymetrics(i.e.

theprobability thatagivendealer is informedinbothcountries inagivenmonthis

25%).

                                                            13ThevariablesrelatedtotheECBbondpurchasescannotbeemployedinthefirstpartofthesample(column(3)).

22 

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.

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

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),intheabsenceofreliableinformation,investorsmaybelievethata

financial crisis in one country could lead to similar crises in other countries. This

channel presumes that investors are imperfectly informed about the fair price of

creditriskinagivencountryandthusmakedecisionsonthebasisofotherreasons,

whichmayormaynotreflectthetruecreditriskofthecountry,suchasthebelonging

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

asymmetriescouldexplainthestrongercomovementswhenthelessinformeddealers

givequotestoagivenpairofcountriesatthesametime.

23 

Thecoefficientforthenon‐informeddealersissignificantlyhigherthantheco‐

efficient for thedealers thatare informedonacountrybutnotontheotheronlyat

10%significancelevel.Infact,thislastresultconfirmstheprevioustheoryaboutwhy

thecommonalitiesinthenon‐informeddealerstendtoincreasecomovements.Thus,

whendealershaveinformationonagivencountrybutnotontheother,theytendto

inferthepriceof thecountryonwhichtheir information ispoorerfromthecountry

onwhichtheyhavebetterinformation.14

<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

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.15Thisresultsupportsthetheorythatshockstothe

fundingconstraintofagivendealerwouldforcethisdealertoreduceherpositionsin

                                                            14Similarresultsarefoundwhenweusedailyinformationinsteadofintradayinformationforestimat‐ingthepricediscoverymetrics.15Thesellingpressureisobtainedasthenumberofquotesbyeachdealerd inwhichtheaskpriceisthefirstquartileforcountriesaandbrelativetothetotalnumberofquotesofdealerd.Wehaveusedothercuttingpointssuchasthepercentile33%andresultsaresimilartoonesreportedinTableVI.

24 

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

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

25 

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

pricesaroundtheGMandForddowngrades.

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

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.Theuseofthesecondinstrumentalsoenablesustoemploytheoverindenti‐

26 

ficationtestbasedonHansenJstatisticandtoconfirmthattheinstrumentsarevalid

giventhattheyareuncorrelatedwiththeerrorterm

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.16This constraint is of special rele‐

vancefortheCDSmarketgiventhatalthoughthereisnodataonwhatportionofthe

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

percentof themarket.17In 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”.18Insimilarterms,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

                                                            16SeeEuropeanCommission‐MEMO/11/713“RegulationonShortSellingandCreditDefaultSwaps‐Frequentlyaskedquestions”. 17Seehttp://www.reuters.com/article/2010/05/19/germany‐swaps‐idUSN196324520100519.18 See http://www.bloomberg.com/news/2010‐05‐18/germany‐to‐start‐temporary‐ban‐on‐naked‐short‐selling‐of‐euro‐bonds‐banks.html.

27 

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

theestablishmentoftheban(19‐21May,2010)andthesameforthethreebanking

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

haveapre‐banobservationforthe55pairsandapost‐banobservationforthesame

pairs.19ThedummyvariableofPost‐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

                                                            19Thereweremanyevents inMay2010relatedtotheEuropeansovereigndebtcrisis.To isolate theeffectofthebanwefocusintheabovementionedthreedaysaroundtheban.

28 

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‐

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.20Inthesecondspeci‐

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‐

                                                            20The only traded index on European sovereign CDS (SovXWestern Europe) started trading on 28September2009.Thisindexconsistsof15countriesbutitsinitialdateisfarawayfromthebeginningofoursampleandforthisreasonwedecidedtocreateanequalityweightedindexthatisavailableforthewholeperiod.

29 

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

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)

30 

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)

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).

31 

Toavoidthedependenceofthecommonalitymeasureonmarketactivity,werescale

ittoconstructanewmeasurewithpotentialoutcomesspanningfrom0to1:

43

0.5 ∗∈ 0,1 (8)

Resultsaresimilartotheonesreportedincolumns(1)and(2)ofTableXsup‐

portingthestatementthattheresultsarenotdrivenbutthemostactivedealersinthe

CDSmarket.

<InsertTableXhere>

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.21

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

                                                            21The lower frequency of quotes for the years 2008 and 2009 impedes to increase the number ofintradayobservationsemployed toobtain theobservationsgiven thatweneed touse thesame timespanforalltheyearsinthesample.

32 

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

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

thatarenotbetterinformedthanthe“averagedealer”ontheCDSpricesalsoleadto

strongercomovements,giventhatthelackofinformationleadthemtoreviewprices

similarlyforthetwocountriesformingthepair.

33 

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‐

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

thedealers’quotes.

34 

References

AcharyaV.V., I.DrechslerandSchnabl,P. (2011). “APyrrhicVictory?BankBailoutsandSovereignCreditRisk”,NBERWorkingPaperNo.17136.

Acharya,V.V.,andJohnson,T.C.(2007).“Insidertradingincreditderivatives”,JournalofFinancialEconomics84,110‐141.

Acharya,V.andSchaefer,S.(2006).“LiquidityRiskandCorrelationRisk:ImplicationsforRiskManagement”,LondonBusinessSchool,WorkingPaper.

Acharya, V., S. Schaefer, S. and Zhang, Y. (2007). “Liquidity risk and correlation risk: AclinicalstudyoftheGeneralMotorsandFordDowngradeofMay2005”,WorkingPaper.

Adrian,T.,Etula,E.,andMuir,T.(2013).“FinancialIntermediariesandtheCross‐SectionofAssetReturns”.FederalReserveBankofNewYork.StaffReportNo.464

Aït‐Sahalia,Y.,Andritzky,J.,Jobst,A.,Nowak,S.,andTamirisa,N.(2012).“Marketresponsetopolicyinitiativesduringtheglobalfinancialcrisis”,JournalofInternationalEconomics87,162‐177.

Alter,A.,andSchüler,Y.S.(2012).“CreditSpreadInterdependenciesofEuropeanStatesandBanksduringtheFinancialCrisis”,JournalofBankingandFinance,36,3444‐3468.

Andenmatten,S.,andBrill,F.(2011).“MeasuringComovementsofCDSPremiaduringtheGreekDebtCrisis”,UniversityofBern,DepartmentofEconomicsDiscussionPaper11‐04.

Anderson,M.(2012).“ContagionandExcessCorrelationinCreditDefaultSwaps”,Work‐ingPaperSSRN2023043.

Andrade,S.C.,Chang,C.,Seasholes,M.S.(2008).“Tradingimbalances,predictablerever‐sals,andcross‐stockpricepressure”,JournalofFinancialEconomics88,406–423.

Angelopoulos,G.,andGiamouridis,D.(2012).“Dissectinginsidertradingincreditderiva‐tives”,WorkingPaper.

Antón,M.andPolkC.(2013).“ConnectedStocks”,JournalofFinance,forthcoming.

Arce,O.,Mayordomo,S.,andPeña,J.I.(2013).“Credit‐riskvaluationinthesovereignCDSand bondmarkets: evidence from the euro area crisis”, Journal of InternationalMoneyandFinance,35,124‐145.

Arora,N.,Gandhi, P., andLongstaff, F. (2012). “CounterpartyCreditRiskand theCreditDefaultSwapMarket”,JournalofFinancialEconomics103,280‐293.

BadaouiS.,CathcartL.,andEl‐JahelL.(2013).“Dosovereigncreditdefaultswapsrepre‐sent a clean measure of sovereign default risk? A factor model approach”, Journal ofBanking&Finance,37,2392‐2407.

Barberis,N.andShleifer,A.(2003).“Style investing”, JournalofFinancialEconomics68,161‐199.

Barberis, N., Shleifer, A. and Wurgler, J. (2005). “Comovement”, Journal of FinancialEconomics75,283–317.

Beber,A.,Brandt,M.W.andKavajecz,K.A.(2009).“Flight‐to‐qualityorflight‐to‐liquidity?EvidencefromtheEuro‐areabondmarket”,ReviewofFinancialStudies,22,925‐957.

35 

BekaertG.,Harvey,C.R.,andNg,A.(2005).“Marketintegrationandcontagion”,JournalofBusiness,78,39‐70.

Berndt,A.,andOstrovnaya,A.(2008).“DoEquityMarketsFavorCreditMarketNewsOverOptionsMarketNews?”,WorkingPaperSSRN972806.

Bernoth, K., von Hagen J. and Schuknecht, L. (2012). “Sovereign Risk Premiums in theEuropean Government Bond Market”, Journal of International Money and Finance, 31,975–995.

Brunnermeier,M.K.,andPedersen,L.H.(2009).“MarketLiquidityandFundingLiquidi‐ty”,ReviewofFinancialStudies,22,2201‐2238.

Cathcart,L,El‐Jahel,L.,andEvans,L.(2010).“TheCorrelationStructureoftheCDSMar‐ket:AnEmpiricalInvestigation”,TheJournalofFixedIncome,22,53‐74.

Dieckmann,S.andPlank,T. (2012). “DefaultRiskofAdvancedEconomies:AnEmpiricalAnalysisofCreditDefaultSwapsduringtheFinancialCrisis”,ReviewofFinance,16,903‐934.

Dornbusch, R., Park, Y. C., and Claessens, S. (2000). “Contagion: Understanding how itspreads”,TheWorldBankresearchobserver,15(2),177‐198.

Dufour, A., and Engle, R. F. (2000). “Time and the price impact of a trade”, Journal ofFinance55,2467‐2498.

Dufour,A.,andNguyen,M.(2012).“PermanentTradingImpactsandBondYields”,Euro‐peanJournalofFinance,18,841‐864.

Favero,C.,Pagano,M.andVonThaddenE.‐L.(2010)“HowDoesLiquidityAffectGovern‐mentBondYields?”,JournalofFinancialandQuantitativeAnalysis,45,107‐134

Forbes, K., and Rigobon, R. (2001). “Contagion in Latin America: Definitions, measure‐ment,andpolicyimplications”,Economia,1,1‐46.

Forbes,K.,andRigobon,R.(2002)“Nocontagion,onlyinterdependence:Measuringstockmarketcomovements”,JournalofFinance,57,2223‐2261.

Geyer,A.Kossmeier, S. andPichler, S. (2004). “Measuring SystematicRisk inEMUGov‐ernmentYieldSpreads”,ReviewofFinance,8,171–197.

Gündüz, Y., andKaya,O. (2013). “LongMemory andCorrelationDynamics of EurozoneSovereignCDSs”,WorkingPaperSSRN2248559.

Gündüz,Y.,Lüdecke,T.,andUhrig‐Homburg,M.(2007).“TradingCreditDefaultSwapsviaInterdealerBrokers”,JournalofFinancialServicesResearch,32,141–159.

Gündüz,Y.,Nasev, J.,andTrapp,M.(2013).“ThePriceImpactofCDSTrading”,WorkingPaper.

Hasbrouck, J. (1991a). “Measuring the information content of stock trades”, Journal ofFinance46,179‐207.

Hasbrouck, J. (1991b). “The summary informativeness of stock trades: An econometricanalysis”,ReviewofFinancialStudies4,571‐595.

Ho,T.,Stoll,H., (1983). “Thedynamicsofdealermarketsundercompetition”, JournalofFinance,38,1053–1074.

36 

Kalbaska, A. and Gatkowski,M. (2012). “Eurozone Sovereign Contagion: Evidence fromtheCDSMarket(2005‐2010)”, JournalofEconomicBehaviorandOrganization,83,657‐673.

King,M.A.,andWadhwani,S.(1990).“Transmissionofvolatilitybetweenstockmarkets”,TheReviewofFinancialStudies,3,5‐33.

Levene,D.B.(2012).“CreditDefaultSwapsandInsiderTrading”,WorkingPaper.

Longstaff, F. A., Pan, J., Pedersen, J. P., and Singleton, K. J. (2005). “How Sovereign IsSovereignCreditRisk?”,AmericanEconomicJournal:Macroeconomics,3,75‐103.

Longstaff,F.A(2010).“TheSubprimeCreditCrisisandContagioninFinancialMarkets”,JournalofFinancialEconomics,97,436–450.

Manasse,P.andZavalloni,L.(2013).“SovereignContagioninEurope:EvidencefromtheCDSMarket”,TheRiminiCentreforEconomicAnalysis.WP13‐08.

Manganelli,S.(2012).“Theimpactofthesecuritiesmarketprogramme”,ResearchBulle‐tin17,2‐5.

Mayordomo,S.,Peña,J.I.,andSchwartz,E.(2012).“TowardsaCommonEuropeanMone‐taryUnionRiskFreeRate”,WorkingPaper.

Micu, M., Remolona, E., Wooldridge, P. (2006). “The price impact of rating announce‐ments:whichannouncementsmatter?”,BISWorkingPapersNo207.

Mitchell,M.,Pedersen,L.H.,Pulvino,T.(2007).“SlowMovingCapital”,AmericanEconom‐icReview,97,215‐220,

Pedersen, L. H. (2009). “When Everyone Runs for the Exit”, International Journal ofCentralBanking,5,177‐199.

Pu,X., andZhao,X. (2010). “Correlation inCreditRisk”,Officeof theComptrollerof theCurrencyEconomicsWorkingPaper2009‐5.

Qiu,J.,andYu,F.(2012).“EndogenousLiquidityinCreditDerivatives”,JournalofFinancialEconomics,103,611–631.

Rigobon,R.(2002).“Contagion:Howtomeasureit?”,InS.Edwards&J.A.Frankel(Eds.),Preventingcurrencycrisesinemergingmarkets(269‐334).UniversityofChicagoPress.

Shachar,O. (2013). “Exposingtheexposed: Intermediationcapacity in thecreditdefaultswapmarket”.WorkingPaper.

Tang, D. Y., and Yan, H. (2010). “Does the TailWag the Dog? The Price Impact of CDSTrading”,WorkingPaper.

Zhang, X., Schwaab, B., and Lucas, A. (2011). “Conditional Probabilities and ContagionMeasuresforEuroAreaSovereignDefaultRisk”,TinbergenInstituteDiscussionPaperNo.11‐176/2/DSF29.

37 

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%

38 

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

39 

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

40 

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

41 

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

42 

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

43 

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

44 

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

45 

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

46 

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

47 

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

48 

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

49 

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