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The determinants of liquidity commonality in the
Euro-area sovereign bond market
Panagiotis Panagiotou, Xu Jiang & Angel Gavilan
To cite this article: Panagiotis Panagiotou, Xu Jiang & Angel Gavilan (2022): The determinants of
liquidity commonality in the Euro-area sovereign bond market, The European Journal of Finance,
DOI: 10.1080/1351847X.2022.2100269
To link to this article: https://doi.org/10.1080/1351847X.2022.2100269
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THE EUROPEAN JOURNAL OF FINANCE
https://doi.org/10.1080/1351847X.2022.2100269
The determinants of liquidity commonality in the Euro-area sovereign
bond market
Panagiotis Panagiotoua,XuJiang
b,cand Angel Gaviland
aUniversity of Greenwich, London, UK; bASEAN+3 Macroeconomic Research Office, Singapore, Singapore; cVrije Universiteit
Amsterdam, Amsterdam, Netherlands; dBank of Spain, Madrid, Spain
ABSTRACT
We examine time-series variation in liquidity commonality across sovereign benchmark
bonds from 10 Euro-area countries, over a 7-year period using tick-by-tick data from
the inter-dealer market and study how it is driven by supply determinants (funding
constraints of financial intermediaries) and demand determinants (investor sentiment,
uncertainty, and cross-market linkages with the equity market) of liquidity. Common-
ality in liquidity does change over time, tends to intensify in stress periods as well as
around ECB policy meetings, and we find stronger evidence in favor of the supply side
determinants.
ARTICLE HISTORY
Received 29 April 2021
Accepted 28 June 2022
KEYWORDS
Liquidity commonality;
eurozone sovereign bonds;
MTS bond market
JEL CLASSIFICATIONS
G12; G14; G15
1. Introduction
How market liquidity behaves over time and spills across borders are important concerns of both investors and
policy makers. However, the nance literature has had little to say about liquidity co-movements in xed income
marketsandevenlessisknownaboutwhatdetermineshowitevolvesovertime.Thispaperprovidesastudy
of the determinants of liquidity co-movements in the Euro-area government bond market. We rst test for and
document a common component in liquidity variation across Euro-area government bonds, and then uncover
which economic forces explain the time-series variation of liquidity co-movements in the Euro-area sovereign
bond market by considering several supply and demand side determinants of commonality in liquidity as well
as by examining liquidity-co-movements around announcements of key macroeconomic indicators.
An in-depth understanding of commonality in liquidity and its determinants is important for at least four
reasons. First, commonality in liquidity implies a systematic and, non-diversiable, factor inuencing variation
in trading costs of a large cross-section of assets rather than individual securities. Second, it may imply asset
pricing eects, because investors need to be compensated for holding a security that becomes illiquid when the
market, in general, becomes illiquid (Pástor and Stambaugh 2003; Acharya and Pedersen 2005). Third, it has
important implications for market viability as an illiquidity shock in one market may aect liquidity conditions
in other asset classes. The 2007-2009 global nancial crisis and the 2011 European debt crisis underlined the
importance of such cross-market illiquidity eects as investors sought the safety of government securities and
market illiquidity amplied shocks originating elsewhere and led to a contagious propagation of shocks within,
as well as across, asset classes. Last, sovereign bond markets are important in ensuring arbitrage conditions
in other markets (Pasquariello 2014) and market liquidity in sovereign bond markets is closely connected to
central bank operations and interventions, either in the form of interest rate setting, or quantitative easing, and
their unwinding (Pelizzon et al. 2016). From a central bank’s perspective, an implication of sovereign bonds
liquidity co-movements is that providing liquidity to specic bonds may potentially lead to ight-to-liquidity
CONTACT Panagiotis Panagiotou p.panagiotou@greenwich.ac.uk
© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor& Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution License(http://creativecommons.org/licenses/by/4.0/), which permits
unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2P. PANAGIOTOU ET AL.
and spillover eects to other sovereign bonds as well (De Santis 2014; Clancy, Dunne, and Filiani 2019;O’Sullivan
and Papavassiliou 2020).
In this study, we examine the time-series determinants of liquidity co-movements of a large number of Euro-
area government bonds, issued by 10 large Euro-area economies, over a 7-year period of 2011–2018 using tick-
by-tickdatafromMTS(MercatoTelematicodeititolidiStato),thelargestEuro-areainter-dealerxedincome
market. We focus on benchmark bonds as they are the most liquid bonds and are the focus of price discovery
(Remolona and Yetman 2019).
Our study mainly relates to two strands of microstructure literature. The rst is the literature on the
microstructure of the European sovereign bond markets. A number of studies using high frequency data from
MTS have previously studied various aspects of market liquidity in Euro-area sovereign bond markets such
as the relationship between sovereign yield dynamics and order ows (Menkveld, Cheung, and De Jong 2004;
Cheung,Rindi,andDeJong2005), the identication of benchmark status (Dunne, Moore, and Portes 2007),
the determinants of yield dierentials between sovereign bonds (Favero, Pagano, and Von Thadden 2010), the
price impact of trades (Dufour and Nguyen 2012), price discovery (Caporale and Girardi 2013), the interre-
lation between market liquidity and credit risk (Beber, Brandt, and Kavajecz 2009; Favero, Pagano, and Von
Thadden 2010;Pelizzonetal.2016), the eect of macroeconomic announcements on market liquidity (Paiar-
dini 2014), informed trading (Paiardini 2015),liquidityspill-overeects(Clancy,Dunne,andFiliani2019),
the linkage between expected issuance fees in the primary market and bond liqudity in the secondary market
(Buis et al. 2020), and commonality in liquidity and its pricing implications (O’Sullivan and Papavassiliou 2020).
However, we know relatively little about the fundamental sources that drive co-movements in liquidity of xed-
income securities over time, i.e. the determinants of commonality in liquidity across Euro-area government
bonds has not been examined. Our study contributes to the literature by empirically examining which underly-
ing economic sources generate time-series variation on commonality in liquidity in European sovereign bond
markets.
The study which our paper is closest to is that of O’Sullivan and Papavassiliou (2020). O’Sullivan and Papavas-
siliou (2020) test for and document commonality in liquidity in European sovereign bond markets. In addition,
they examine liquidity spill-overs across dierent maturities over calm and crisis periods, and test for the
pricing implications of commonality in liquidity. Our study corroborates the results of O’Sullivan and Papavas-
siliou (2020) by documenting signicant liquidity co-movements in the Euro-area government bond market
(O’Sullivan and Papavassiliou 2020) and further contributes to the literature by providing the rst empirical
examination of the eect of macroeconomic announcements and of several supply- and demand-side variables
on time-series variation of liquidity co-movements in the Euro-area sovereign bond market.
The second strand of literature that our paper contributes to, is that of examining the determinants of
commonality in liquidity. Several studies in equity markets (Coughenour and Saad 2004; Hameed, Kang, and
Viswanathan 2010;HasbrouckandSeppi2001; Kamara, Lou, and Sadka 2008;HubermanandHalka2001;
Karolyi, Lee, and Van Dijk 2012;Koch,Ruenzi,andStarks2016)andahandfulofstudiesinrelationtoFX
markets(Karnaukh,Ranaldo,andSöderlind2015; Sensoy, Uzun, and Lucey 2021) have examined several vari-
ables that help explain time-series and cross-sectional variation on commonality in liquidity. To the best of
our knowledge, no previous study has examined the determinants of commonality in liquidity in xed-income
markets.1
TheinstitutionalstructureoftheEuro-areamarketforgovernmentbondsisuniqueinthesensethatitallows
us to test for co-movements in liquidity of sovereign bonds at the national as well as the international level. As
a comparison, the US Treasury market is a mature, large, and liquid market in which government securities
are issued from a single issuer, the US government, and for this reason are exposed to the same credit risk
and the same underlying macroeconomic fundamentals with liquidity provided by a set of common nancial
intermediaries. In contrast, the Euro-area market for government bonds is characterized by signicant market
fragmentation and liquidity is provided by a set of common nancial intermediaries for all Euro-area countries
as well as by country-specic liquidity providers. In addition, government securities have multiple sovereign
issuers, and share the same monetary policy within the monetary union. However, there are signicant dier-
ences among Euro-area debt issuers in terms of the level of public debt, sovereign credit risk, and macroeconomic
fundamentals.
THE EUROPEAN JOURNAL OF FINANCE 3
We initially test for commonality in liquidity along three dimensions. First, we test for commonality in liq-
uidity within countries. However, commonality in liquidity across government bonds issued from the same
country is likely to be strong given the securities share common underlying features (Fleming 2003). Sec-
ond, we test for commonality in liquidity across benchmark government bonds that belong to the same
maturity bracket but were issued from dierent countries. To this end, we consider benchmark bonds with
maturitiesovertheentiretermstructure.
2Third, we also test for commonality in liquidity across dier-
ent benchmark government bonds, irrespective of issuing country and maturity at issue, i.e. at the pan-
European level. By doing so, we attempt to uncover a pan-European liquidity common factor inuencing
liquidity variation of Euro-area government bonds as a whole, rather than individual securities, despite
the fragmented structure of the market, the heterogeneity of market participants, the dierences in debt
size and quality as well as the exposure of each individual issuing country to dierent macroeconomic
fundamentals.3
We test for commonality in liquidity by running market-model time-series regressions and we use the
methodology of both Chordia, Roll, and Subrahmanyam (2000) and Korajczyk and Sadka (2008)toextract
market-wide liquidity. In measuring liquidity, we use tick-by-tick data to construct daily estimates of liquidity
for each benchmark bond in our data set. We capture dierent facets of market liquidity by calculating quoted
spreads and depths, eective spreads and price impacts, as measured by the Amihud ratio. We use the adjusted
R2of regressions of the liquidity of individual bonds on market-wide liquidity as a measure of the extent to
which the liquidities of individual bonds move together.
We nd strong evidence of commonality in liquidity at the national level in all four liquidity measures we
consider, with quoted spreads and depths exhibiting strong co-movements. With respect to commonality in
liquidity at the national level, the average across countries of the adjusted R2is equal to 68% and 42% for
spreads and depths respectively, with these percentages dropping to approximately 47% and 26% when examin-
ing commonality across bonds that belong to specic maturity brackets but were issued from dierent countries.
Although there is signicant commonality in liquidity of benchmark bonds at the national level and the matu-
rities level, there is also some cross-sectional variation of the commonality in liquidity both among issuing
countries and among maturity brackets. Strikingly, the average across all bonds in our dataset (i.e. irrespec-
tive of issuing country or maturity at issue) of the adjusted R2remains relatively high. On average, 40% and
23% of the variation in quoted spreads and depths of each individual bond is explained by the changes in the
pan-European, market-wide liquidity. This last nding strongly supports the hypothesis that commonality in
liquidity spills across borders. Overall, these results support the view that commonality in liquidity is a perva-
sive phenomenon in Euro-area sovereign xed-income markets. Similar results are also reported by O’Sullivan
and Papavassiliou (2020).
However, a clear understanding of the fundamental sources of co-movements in liquidity of xed-income
securities is still missing. Commonality in liquidity can theoretically have two basic sources: co-variation in
liquidity supply or co-movement in liquidity demand. However, with respect to equity markets, the empirical
evidence provides heterogeneous results. Some empirical studies have found support for supply-side sources of
commonality in liquidity related to the funding costs of nancial intermediaries (Coughenour and Saad 2004;
Hameed, Kang, and Viswanathan 2010). Other studies have explored demand-side factors driven by corre-
latedtradingactivity(Karolyi,Lee,andVanDijk2012), the level of institutional ownership (Kamara, Lou,
and Sadka 2008;Koch,Ruenzi,andStarks2016), investor sentiment (Huberman and Halka 2001), and cul-
tural and behavioral factors (Moshirian et al. 2017). With respect to FX markets, Karnaukh, Ranaldo, and
Söderlind (2015) nd stronger co-movements of FX liquidity in distressed markets, especially when funding is
constrained and volatility is high. Sensoy, Uzun, and Lucey (2021) nd that commonality in FX markets increases
signicantly before (after) ECB (Fed) monetary policy announcements. Similarly, in a yet unpublished study,
Moinas, Nguyen, and Valente (2018) examine the link between funding liquidity and market liquidity of Euro-
pean government bonds. In particular, they found a two-way response occurring between funding and illiquidity
shocks of European government bonds suggesting that market illiquidity for individual bonds reacts dier-
ently to tightening funding conditions. Our main contribution to the literature diers from Moinas, Nguyen,
and Valente (2018) as we empirically examine which underlying economic forces generate time-series varia-
tion of the common, pan-European liquidity factor related to various supply- and demand-side explanations
4P. PANAGIOTOU ET AL.
of commonality in liquidity. Our focus is on the determinants of the co-movement of a large cross-section of
bonds, rather than the determinants of liquidity of individual bonds.
On the supply side, we investigate whether bond liquidity deteriorates with funding constraints and higher
volatility, as postulated by recent theoretical models (Gromb and Vayanos 2002; Morris and Shin 2004;Brun-
nermeier and Pedersen 2009). These theoretical models predict that when a volatility shock occurs, lenders may
tighten their terms of funding (for example, in terms of higher repo rates). Thus, nancial intermediaries, who
actasliquidityprovidersinnancialmarkets,facehigherfundingcostsandadicultyinobtainingleverage.
Financial intermediaries are then forced to liquidate positions or withdraw liquidity across many securities. This
reduces market-wide liquidity, a cross-section eect, and triggers price drops leading to higher price impact and
higher volatility. The liquidation of positions and the drop in market liquidity then aects individual bonds via
theirliquiditybetasandresultsinanincreaseincommonalityinliquidity.Thereisthereforeaself-reinforcing
feedback mechanism linking volatility shocks, funding costs and commonality in liquidity.
On the demand side, we consider three potential explanations for commonality in liquidity. First, we exam-
ine whether demand shocks, related to investor sentiment, can give rise to commonality in liquidity. Second,
commonality in liquidity can arise as uncertainty about a government’s economic policy may aect both asset
valuation and volatility, increase informationasymmetries and potentially lead to liquidity dry-ups. In addition,
investors care not only about the mean and variance of asset returns, but also about the uncertainty of events
over which the future return distribution occurs (Bali, Brown, and Tang 2017). Third, various studies suggest that
there are signicant cross-market linkages between equity and xed income markets, either through liquidity
spillovers between two markets in which arbitrageurs and specialized dealers coexist (Cespa and Foucault 2014)
or through shifting wealth between stock and bond markets, with the eect leading to liquidity co-movements
under both channels.
The results of time-series regressions of the supply-side hypotheses that evaluate the potential role of fund-
ing constraints of nancial intermediaries acting as liquidity providers are strong. After controlling for market
volatility, market liquidity and trading activity we nd evidence that changes in commonality in quoted spreads
are statistically signicantly correlated with shocks in four out of the ve variables we use as proxies of the
funding conditions. Specically, we nd that monthly changes in commonality in quoted spreads are positively
associated with shocks in both the TED and LOIS spread, suggesting that distress in money markets is correlated
with stronger commonality in quoted spreads in Euro-area xed income markets.
Furthermore, we nd evidence that changes in commonality in spreads is negatively associated with the
nancial health of liquidity providers, as measured by the returns on portfolios of their stocks, and positively
correlated with ECB’s excess liquidity. Both ndings are consistent with the supply-side explanation of com-
monality in liquidity which suggests that dealers lower market liquidity when they hit their funding constraints.
Importantly, the positive correlation between ECB’s excess liquidity and commonality in liquidity is consistent
with the nding by Pelizzon et al. (2016) that the Long-Term Renancing Operations of the ECB weakened the
sensitivity of market makers’ liquidity provision to credit risk highlighting the importance of funding liquidity
measures as determinants of market liquidity. However, we nd no evidence that changes in commonality in
spreadsordepthsisgreaterintimesofhigherinterestrates.Evidenceonchangesincommonalityindepths
and our proxies for the funding conditions of liquidity providers is weaker as compared to commonality in
spreads. Given the fragmented structure of the market, a possible explanation might be that traded quantities
are negotiated and agreed bilaterally.
Our demand-side proxies, on the other hand, do not help to explain time-series variation in commonality in
liquidity. In particular, we nd no association between any of our investor sentiment proxies and commonality in
liquidity (either in spreads or in depths). We nd that our proxy for US economic policy uncertainty is positively
associated with commonality in liquidity in quoted spreads but not in depths, whereas the proxy for economic
policy uncertainty in Europe does not help to explain the variation in commonality in either spreads or depths.
Last, we nd a strong relationshipbetween European as well as US equity market volatility shocks and changes
in commonality in liquidity in xed income markets. This last nding suggests that liquidity provision in these
markets is, to some extent, inter-linked and a liquidity shock in one of these markets can also aect liquidity
condition in the other market. Overall, our results suggest supply-side factors are more inuential than demand-
side factors in explaining time-series variation in commonality in liquidity.
THE EUROPEAN JOURNAL OF FINANCE 5
The evidence that our proxies for funding conditions can help better explain the dynamics of changes in
commonality in liquidity as compared to variables that proxy for demand-side explanations should not come as
a surprise. Given infrequent trading and the use of government bonds as safe assets during periods of market
stress as well as a source of immediate funding (i.e. when used as collateral to obtain nancing in repo markets)
it is natural that government bonds as an asset class tend to exhibit particularly strong links between volatility,
funding conditions, and market liquidity.
We further test whether commonality in liquidity intensies around announcements of key macroeconomic
indicators in the Euro-area, a period in time in which new information is released and incorporated into prices.
We employ as our measure of co-movement of liquidity in the event window the measure of synchronicity, orig-
inally proposed by Morck, Yeung, and Yu (2000), and as modied by Brockman, Chung, and Pérignon (2009).
We nd strong statistical evidence that some announcements materially increase commonality with the stronger
eect stemming from ECB policy meetings on rate decisions. The average co-movement in spreads is 63.55%
across all trading days and all benchmark bonds while this percentage increases to 67.87% during interest rate
setting announcements, the dierence in means being statistically signicant at the 1% condence level. This
nding highlights the important linkage between interest rate changes and secondary-market liquidity. Similar
results are reported by Sensoy, Uzun, and Lucey (2021) in relation to commonality in liquidity in FX markets
and monetary policy announcements.
Overall, the existence of a pan-European common liquidity factor and the identication of the sources gen-
erating time-series variation in this factor are important for academics, practitioners, and policy makers. For
academics, our results point to specic directions that are likely to be fruitful in improving our current under-
standing of xed-income market liquidity by providing better insights into time-series, cross-section as well
as the cross-market dynamics of market liquidity. For practitioners, understanding the implications of Euro-
area market-wide commonality in liquidity aids the decisions of xed income portfolio managers. A common
liquidity factor is undiversiable and implies pricing eects. Finally,policy markers may be able to draw policy-
relevant implications from this study and central banks may be able to minimize the risk of liquidity crises in
stress periods by timely improving funding conditions of nancial intermediaries. Our results suggest that com-
monality varies over time and intensies in stress periods corroborating the view that market liquidity can be a
driving force for nancial contagion.
The remainder of the study is organized as follows: Section 2discusses the Euro-area secondary sovereign
bonds market, Section 3describes the data set and measures of liquidity. Section 4presents our empirical
approach for testing for commonality in liquidity and the eect of macroeconomic announcements and of sev-
eral supply- and demsnd-side variables on commonality in liquidity. Section 5discusses our results and Section 6
concludes.
2. The European sovereign bond market
Euro area sovereign issuers operate in the markets through a primary dealership system, i.e. an appointed group
of nancial institutions, either domestic or foreign, usually referred to as Primary Dealers with the objective
to perform certain specialized operations in the government securities market.4These operations, which dif-
fer from country to country, mainly include participating in primary issues, placing the government securities
with nal investors, and maintaining a liquid secondary market subject to some regulatory requirements.5
The number of primary dealers per country varies over time and across countries, with some degree of cross-
country overlap. The existence of common primary dealers across countries is an important feature as it may
induce cross-country spillovers in liquidity in both the primary and secondary markets. On average, each
European country operates in the markets through 17 primary dealers with this number ranging between 5
and 39.6
Usually, primary dealers are incentivized to incur the additional risks associated with market making by the
prospect of the gains received by the preferential treatment oered by issuers. This preferential treatment gener-
ally involves the granting of some form of exclusivity such as the exclusive right to participate in some treasury
auctions, and/or the right to serve as a counterparty to the central bank when it conducts open market opera-
tions, and/or access to a line of credit or permission to borrow particular issues from the central bank. Dealers
6P. PANAGIOTOU ET AL.
with no market-making requirements can participate in the market as price-takers. Dealers (either primary or
not) can purchase securities for their own portfolio, on behalf of their customers, or for resale in the secondary
market. In terms of instruments, sovereign issuers mainly access markets through conventional instruments
(e.g. xed coupon bonds), despite some dierences across countries, mainly driven by overall borrowing needs
as well as dierences in investor base.7
In terms of size, the European sovereign debt market is one of the largest in the world with the total govern-
ment debt securities outstanding at the end of 2017 being equal to e9.79 tn and e7.44 tn8for the EU-28 (64%
of EU-28 GDP) and Euro-Area (67% of EA GDP) countries respectively.9Comparatively, the total government
debt securities outstanding of US at the end of 2017 was $20.5tn. Despite dierences in macroeconomic funda-
mentals, levels of debt and credit quality, government debt has some similar characteristics across countries: (i)
most government debt is issued by the central government (approx. 80% among Euro-area countries) (ii) most
of the debt consists of tradable securities (e.g. bonds and T-bills), with loans accounting for a small portion
(apart from bailout countries) (iii) the debt is predominately denominated in Euros (iv) debt is traded through
a fairly similar market structure across countries and (v) securities are typically regulated by domestic law (with
the exception of Greek debt which is governed by English law). Since the focus of this study is the liquidity of the
secondary market, we proceed by describing the structure and size of the secondary market without referring
to primary markets.
In general, the structure of the secondary market for sovereign bonds across European countries is fairly
similar in that trading is divided into two segments, the dealer-to-dealer segment and the dealer-to-customer
segment with the inter-dealer segment being seen as the core of the market. In the inter-dealer segment, trading
is taking place either through an opaque, quote-driven, OTC market or through an observable exchange-
traded (predominantly electronic) market. The market is predominantly an institutional market with retail
participation being largely indirect (through e.g. pension funds, insurance companies, mutual funds, asset
managers).
Themarkethasundergonesignicantstructuralchangeoverthelast40yearsdrivenbythewidespread
introduction of new trading technologies, the harmonization of debt management practices and by regulatory
reforms. Despite the share of electronic trading in the European bond market being below that observed for
other asset classes, recent estimates suggest that share to be approximately 60% for 2015 and expected to further
increase in the future.10 A key factor behind the slower adoption of electronic trading in European sovereign
bond markets has been the heterogeneity of the traded instruments and the resulting diculty in nding matches
in supply and demand. In addition, since the 2008–2009 nancial crisis and the post-crisis global regulatory
reforms, dealers are facing higher funding and capital adequacy costs.
Little information is publicly available regarding trading volumes. However, the market share of both market
segmentsdependsontheissuingcountryandisnotalways(publicly)known.Becausebondtradingisnotcen-
tralized in any particular location, information on actual traded volumes is not readily available despite DMOs
publishing some statistics on volumes but sizes are often not directly comparable. AFME11 estimates an aver-
agedailytradingvolume(excludingbills)ofapproximatelye72.2 bn across EU-28 countries for the period
2014–2017 with most of the trading activity in the secondary market concentrated in the period between 9:00
and 17:00 CET.
3. Hypotheses
Commonality in liquidity can theoretically have two basic sources: co-variation in liquidity supply or
co-movement in liquidity demand. In this section, we discuss and formulate the hypotheses for our
empirical test by considering various supply and demand-side explanations of commonality in liquidity.
Section 3.1 discuss the relevant literature on the eect of market volatility on aggregate market liquidity.
Sections 3.2 and 3.3 discuss, respectively, the relevant literature on the supply- and demand-side explana-
tions. Last, in Section 3.4 we also examine whether commonality in liquidity is intensied around macroe-
conomic announcements, a period of time in which new information is released and incorporated into
prices.
THE EUROPEAN JOURNAL OF FINANCE 7
3.1. Market volatility
Previous theoretical and empirical research indicates that market liquidity and volatility are linked. Traditionally,
theoretical market microstructure models examine liquidity provision through the lenses of information asym-
metries and inventory risk (although, these are not mutually exclusive). From the perspective of asymmetries in
information, higher volatility increases the likelihood that market makers will transact with informed traders
and suer larger losses (see, for example, Glosten and Milgrom 1985). By splitting orders, informed traders hide
their information and only partially reveal it to the market with each additional trade. New information is thus
disseminated sequentially to traders, with liquidity traders not being able to perfectly deduce the presence of
informed trading. The sequential arrival of new information to the market generates both trading volume and
price volatility, with both increasing by information shocks. To protect themselves, market makers widen the
spread and/or quote limited quantities when fearful of informed traders, thus lowering market liquidity.
From the perspective of inventory models, market makers are exposed to risk through the inventory of a
security.Themarketmakereitherhaslimitsonthequantityofinventoryheldoradesiredinventoryleveland
a cost of deviating from it (see, for example, Stoll 1978;HoandStoll1981). Best bid and ask prices determine
the stochastic arrival rate of sellers and buyers, and market makers adjust prices dynamically to optimize their
inventory levels. At a single point in time, the aggregate inventory position across market makers measures the
amount of risk market makers have taken on. Models with limited risk-bearing capacity (for example Gromb
and Vayanos 2002) suggest that when large dealers hold undesired inventories, whether long or short, they
face greater risk exposure and lower risk-bearing capacity and, consequently the higher the volatility the more
reluctant these dealers are to provide liquidity.
From a demand-side perspective, the high liquidation risk implied by volatile prices can keep prospective
investors out of the market (Pagano 1989) lowering market liquidity. Thus, when volatility increases, liquidity
will tend to evaporate.
However, the volatility eect may be asymmetric, i.e. commonality in liquidity might be much stronger when
the market experiences large declines as compared to large market increases. The argument is that during stress
periods, given the funding constraints of liquidity providers, a large negative market return reduces the amount
ofcapitalthatistiedtomarketablesecuritiesandhencereducesthesupplyofliquidity.Suchaneectisconsistent
with the theoretical predictions of Brunnermeier and Pedersen (2009) and Gromb and Vayanos (2002). Hameed,
Kang, and Viswanathan (2010) provide relevant empirical evidence pertaining to the US equity markets and
show that commonality in liquidity intensies during market declines, especially when funding costs are higher.
Similar results are reported by Karolyi, Lee, and Van Dijk (2012) in relation to international stock markets and
by Marshall, Nguyen, and Visaltanachoti (2013) in relation to commodities. Kamara, Lou, and Sadka (2008)
nd that commonality in liquidity change over time and that these changes are aected by market volatility as
well as market returns. Hameed, Kang, and Viswanathan (2010) argue that, in stress periods, a large negative
market return may lead to greater commonality in liquidity through an eect on the wealth and the collateral
of investors and liquidity providers, that commonality should increase during periods of large market declines
andtheeectofvolatilityshouldbeasymmetric.
In the cross-section, all of the above arguments imply a positive relationship between changes in volatility
and changes in commonality in liquidity. Moreover, they predict that commonality is higher during periods
of high market volatility, and, in particular, during large market declines. We investigate these conjectures by
studying the link between volatility and commonality in liquidity empirically before we turn our attention to
other variables.
3.2. Supply-side hypothesis: funding constraints
Recent theoretical (Brunnermeier and Pedersen 2009) and empirical (Moinas, Nguyen, and Valente 2018)stud-
ies suggest that there might be situations in which volatility may act as a catalyst in the market and exaggerate the
eect of shocks originating elsewhere. Brunnermeier and Pedersen (2009) show theoretically that commonality
in liquidity can arise as a result of forces related to the supply of liquidity and amplied by market volatility.
According to their model, in order for market makers to provide liquidity and to nance their inventories, they
8P. PANAGIOTOU ET AL.
need to obtain leverage by either posting margins or by pledging securities that they hold as collateral. For exam-
ple, to nance the purchase of a bond, a dealer may need to raise cash by pledging the same bond as collateral
in the repo market. When the dealers nd an interested buyer, they can simply stop rolling over the overnight
repo (or terminate open ones) so as to obtain the collateral (bond) back when the sale is settled and then deliver
the bond to the buyer. Thus, as government bonds are often used as high-quality collateral in repo transactions,
it is reasonable to conjecture that liquidity of the sovereign bond market would be directly aected by funding
liquidity shocks.
When, for example, interest rates rise or a volatility shock occurs, obtaining nancing may become more
expensive or generate losses in their collateral values, forcing dealers to become reluctant to take on positions,
especially capital intensive positions in high-margin securities. This can result in dealers withdrawing mar-
ket liquidity (either by lower market participation, by quoting less depth and/or wider spreads, by liquidating
their positions across many securities or a combination of these). In turn, lower market liquidity suggests that
asset prices are more sensitive to the impact of individual dealers’ demand, with shocks leading to higher price
impact and higher volatility leading to further losses and/or margin increases, thus creating an illiquidity spiral
that further restricts market makers ability to supply liquidity. In the cross-section, this feedback loop between
market liquidity, volatility and funding liquidity implies a positive relationship between the funding conditions
of market makers and commonality in liquidity.
We investigate the relevance of market makers’ funding conditions in explaining time-series variation in
commonality in liquidity of European government bonds over our sample period. This supply-side explanation
predicts that commonality in liquidity should be positively related to nancial market stress and to the level
of interest rates. Commonality in liquidity should also be negatively correlated to the stock returns of nancial
intermediarieswhoactasmarketmakers,whicharelikelytobeinverselyrelatedtothetightnessofcapitalin
the market.
3.3. Demand-side hypotheses
3.3.1. Investor sentiment
The rst demand-side explanation we consider links commonality in liquidity to investor sentiment. The
investor sentiment literature assumes that irrational investors generate sentiment-based demand shocks that
aect prices and may be important sources of commonality in liquidity. For example, Huberman and
Halka (2001) argue that commonality in liquidity arises because of the ‘presence and eects of noise traders’.
Baker and Stein (2004) show theoretically that when noise traders receive private signals regarding future cash
ows they tend to overweight them and underreact to the information contained in aggregate order ow (since
they consider others to be less well-informed) and be more active in the market. Thus, prevailing market-wide
sentiment makes investors move together, thereby causing increased correlated demand for liquidity. Baker and
Wurgler (2006) show that investor sentiment aects the cross-section of stock returns with sentiment traders
shifting from safe to speculative securities when sentiment increases, and from speculative to safe securities
when sentiment declines. Hameed, Kang, and Viswanathan (2010) recognize that panic selling by investors may
be a sentiment-based cause of commonality in liquidity. In relation to the xed-income market, Beirne and
Fratzscher (2013) report that there was herding contagion in advanced and emerging economies during the
European sovereign debt crisis with sharp and simultaneous increases in sovereign yields across countries.
Motivated by the ndings in the equity markets, we attempt to link investor sentiment to commonality in
liquidity in xed income markets. In order to test this sentiment based explanation, we include various Sentix
indices as proxies for variation in investor sentiment in our time-series regressions.12 Sentix Indices are used to
measure investor sentiment in equity (Schmeling 2007), foreign exchange (Heiden, Klein, and Zwergel 2013),
and xed income markets (Afonso et al. 2018).InouranalysisweusetheSentixEuroAreaAggregateIndex,
Sentix Euro Area Breakup Index, Sentix Contagion Index as well as regional (e.g. global, US, Asia) indices. The
sentiment hypothesis does not oer clear predictions on whether investor sentiment is positively or negatively
correlated with changes in commonality in liquidity, thus we are a priori agnostic about the direction of the
postulated relationship (Karolyi, Lee, and Van Dijk 2012).
THE EUROPEAN JOURNAL OF FINANCE 9
3.3.2. Economic policy uncertainty
The second demand-side hypothesis we consider proposes that uncertainty related to a government’s economic
policy may determine how correlated the demand for liquidity is across government bonds, and thus commonal-
ity in liquidity. On frictionless and complete markets, with information available to all market participants, and a
full set of state-contingent assets, there is no illiquidity. However, markets are not perfect. There are information
asymmetries between liquidity providers and informed traders. The larger these information asymmetries are,
thelesswillingareinvestorstoparticipateinthemarket.Krishnamurthy(2010) show theoretically that mar-
ket participants when faced with risks they do not fully understand, as in stress periods, may be less willing to
trade assets whose characteristics and/or behaviour are not well known. They choose to disengage from risks and
seek liquid investments. In stress periods, when uncertainty related to a government’s economic policy increases,
trading could become impossible causing market liquidity to dry up. A market may altogether disappear (the
most extreme form of illiquidity) if information is suciently asymmetric (Adrian and Shin 2008).
In addition, investors care not only about the mean and variance of asset returns, but also about the uncer-
tainty of events over which the future return distribution occurs (Bali, Brown, and Tang 2017). Since the return
distribution is aected by the state of the economy, an increase in uncertainty regarding a governments’ eco-
nomic policy makes investors concerned about future outcomes and it may lead to less than optimal levels of
consumption and investment. To hedge against unfavourable shifts in the economy, investors prefer to hold
assets that have higher covariance with economic uncertainty. Thus, anticipation of future liquidity dry-ups or
uncertainty regarding future macroeconomic fundamentals, may aect current investment decisions and lead
to increased correlated demand for liquidity of government bonds. The above hypotheses predict a positive cor-
relation between economic uncertainty and commonality in liquidity. In our time-series regressions, we use the
Economic Policy Uncertainty (EPU) index developed by Baker, Bloom, and Davis (2016)ofboththeUSand
Europe as a proxy for aggregate government policy uncertainty.
3.3.3. Cross-market illiquidity effects
The third demand-side explanation we consider is based on cross-market liquidity interdependence owing
from volatility to liquidity between equities and xed income markets. If illiquidity is a systematic risk factor
across markets, a liquidity shock to one of the markets will aect its relative attractiveness resulting in trading
activity aecting liquidity demand in both markets. Fleming, Kirby, and Ostdiek (1998) report strong volatility
linkages between equities and xed-income markets. Chordia, Sarkar, and Subrahmanyam (2005)andGoyenko
and Ukhov (2009) examine the time-series properties of stock and bond liquidity and they nd evidence of
cross-market dynamics and common inuences in both markets. In particular, they nd that innovations to
stock volatility forecast an increase in bond spreads. Mink and De Haan (2013) examined the impact of news
about a Greek bailout on bank stock prices using data for 48 European banks and found a signicant eect on
bank stock prices, even on stock prices of banks without any exposure to Greece or other highly indebted euro
area countries. There are two potential channels through which the state of the stock market may aect liquidity
conditions of xed income markets.
According to the rst channel, a negative stock volatility shock may aect liquidity provision in both markets
by impacting the inventory risk of market makers,13 thus increasing commonality in liquidity in the cross-
section. Kyle and Xiong (2001) and Cespa and Foucault (2014)showthatifnancialintermediariesproviding
liquidity in two markets are suering trading losses in one market or if they are hitting their funding constraints
in one asset, then they may reduce liquidity provision in both markets. This illiquidity spillover eect arises from
cross-asset learning when dealers specialising in dierent asset classes learn from each other’s prices. Prices are
informative because they reect information about fundamentals known to dealers specialising in each asset.
However, prices are also aected by temporary demand pressures and even more so when the cost of illiquidity
increases. Thus, when one asset becomes less liquid, its price becomes less informative for dealers specialised in
the other asset. These dealers then face more uncertainty and require larger spreads to absorb liquidity traders’
order imbalances generating price impact and volatility. Thus, an increase in illiquidity in one asset propagates
to the second asset, generating a feedback loop which amplies the initial shock. This mechanism relies on the
sensitivity of price informativeness of each asset price to illiquidity and culminates beyond some critical values
of dealers’ risk tolerance.
10 P. PANAGIOTOU ET AL.
Although this illiquidity propagation mechanism is mostly a supply-side eect, Cespa and Foucault (2014)
show that when arbitrageurs, who are uninformed but trade in both markets, and specialized dealers coexist,
cross-asset learning is a source of liquidity spillovers for exactly the same reasons as in the baseline model.
Arbitrageurs respond to liquidity demand in one asset by hedging their position in other assets and thus transmit
temporary demand shocks for one asset to the other asset. However, the model predicts that co-movements in
liquidity should decrease with the capital allocated to cross-market arbitrage as arbitrageurs increase the overall
risk-bearing capacity of liquidity providers.
The second channel is through portfolio rebalancing and cross-market hedging eects. A number of asset
allocation strategies shift wealth between stock and bond markets (see, for example, Fox 1999;Barberis2000).
In addition, in times of market uncertainty (i.e.increased market volatility), investors tend to rebalance their
portfolios and shift towards less risky (ight-to-quality) and more liquid assets (ight-to-liquidity), especially in
xed-income markets (Beber, Brandt, and Kavajecz 2009). Hartmann, Straetmans, and Vries (2004) empirically
study the linkages between stock and government bond markets with a focus on crisis periods and their results
suggest that stock-bond contagion is approximately as frequent as ight to quality from stocks into bonds with
cross-border linkages being approximately equal in magnitude to national linkages. Thus, investors by hedging
their positions in other assets, they transmit shocks to demand for one asset to the other asset.
In our time-series regressions, we use two volatility proxies for European equity markets: the monthly stan-
dard deviation of Stoxx50 daily returns as well as the realized volatility calculated from ve-minute intervals.14
We hypothesize a positive correlation between stock market volatility and commonality in liquidity in xed
income markets.
3.4. Macroeconomic announcements
In the last part of our analysis, we examine the impact of macroeconomic news announcements in the Euro-
area on the European market-wide commonality in liquidity. We assume two dierent channels through which
macroeconomic announcements may aect commonality in liquidity. The rst is through the eect of port-
folio rebalancing. As new information is released regarding underlying macroeconomic fundamentals, it may
lead investors to revalue their portfolios and trigger portfolio rebalances, thus intensifying the correlation in
demand for liquidity for government bonds, and thus also the co-movement in liquidity. The second channel
is through the eect of adverse selection. The release of new macroeconomic information is usually associated
with an increase in trading activity and the presence of informed traders which, in turn, may induce dealers to
protect themselves by withdrawing market liquidity, in terms of wider spreads and lower depth, for a large cross-
section of government bonds. Although we cannot eectively disentangle the eects of these two channels, we
test whether commonality in liquidity overall is intensied during the event window. In addition, unanticipated
surprises may potentially have a stronger eect on liquidity commonality of government bonds as compared to
anticipated surprises. Forward-looking investors are expected to instantly adapt their expectations, revalue, and
rebalance their portfolios and trading positions as unanticipated information about fundamental asset values
becomes available in the marketplace.
4. Data
In this section we describe the data sourceand our data set, the limitations of the data, the screening and ltering
procedures, the identication of the term structure of benchmark bonds as well as the liquidity variables we use
to construct our measures of commonality in liquidity.
4.1. The MTS trading platform
We gained access to a granular dataset from MTS that includes historical data for almost all Euro-area coun-
tries from June 2011 to June 2018. MTS is considered as the major interdealer market for trading Euro-area
government bonds and for that reason it provides a good, but not complete, picture of interdealer activity. It is
sourced from a trading community of approximately 500 unique counterparties and reports an average daily
THE EUROPEAN JOURNAL OF FINANCE 11
volume,acrossMTSplatformsandxed-incomesecuritiesofapproximatelye100bn (i.e. both sovereign and
non-sovereign).15
MTS is an entirely electronic and order-driven interdealer market. European sovereign bonds can be traded
on two parallel markets: EuroMTS and/or domestic MTS, with the former only trading Euro-area benchmark
sovereign bonds and the latter trading both benchmark and non-benchmark sovereign bonds. Market partici-
pants can also trade quasi-government bonds, corporate bonds and repurchase agreements. Both platforms are
electronic limit order markets in which mainly banks participate, who are either market makers with a two-sided
quoting obligation (primary dealers) or price takers (dealers). Most market makers are active on both platforms,
which ensures market liquidity in the domestic and EuroMTS platforms are closely connected, despite their
technical fragmentation (Cheung, Rindi, and De Jong 2005).
Financial institutions must satisfy strict requirements about traded volumes and net asset values to qualify as
marketmakers.Marketmakersareassignedasubsetofsecuritiesforwhichtheyhavetoposttwo-sidedquotes
called proposals. Market makers’ quotes can be hit or lifted by other market participants via market orders. Price
takers are also allowed to submit single-sided limit orders (either buy or sell). Regarding market structure and
trading protocol, see also Dufour and Skinner (2004). All MTS quotes are transactable. Before the 2007–2009
global nancial crisis, MTS imposed strict requirements on dealers. Market makers were required to provide
rm quotes for a minimum number of hours during the trading day, for a maximum spread, and for minimum
quantities ranging from e2.5 m to e10 m, depending on the maturity and benchmark status of the instrument.
With the onset of the 2007–2009 global nancial crisis, MTS relaxed dealers’ obligations (e.g. minimum quote
and trade size of e1 m) and introduced more exible requirements recognizing that market makers were facing
higher liquidity and credit risk. Instead of imposing xed obligations, MTS monitors average quoting times and
average spreads of each individual market maker, which must be in line with market averages computed across
all dealers.
4.2. Data set
The sample period for our study is June 2011 to June 2018. This time period is of particular interest to the study of
the behavior of Euro-area xed-income markets both unconditionally as well as because a number of signicant
market events took place in that period such as the peak and aftermath events of the 2011 European debt crisis,
the ECB’s quantitative easing program, the Brexit referendum, and the uncertainty period surrounding Italian
elections.
Our MTS data set includes tick-by-tick data for more than 2600 individual xed income securities with
approximately 2.65 million trades in the sample period. We select sovereign bonds issued from 10 Euro-area
countries, namely Austria, Belgium, Finland, France, Germany, Ireland, Italy, the Netherlands, Portugal and
Spain16 (536bondsweredropped),whichcaptureabout98percentofthemarketuniverseofmedium-and
long-term bonds with original maturities larger than two years.17
We further constrain our focus to standard, xed-coupon sovereign securities in order to avoid the confound-
ing eects related to specic bond characteristics. Particularly, we exclude non-sovereign issuers (109 bonds were
dropped), sovereign securities with original maturity less than two years (1095 bonds were dropped), with less
common coupon types (such as oating, step coupon), ination and index-linked bonds, with coupons and/or
principals stripped from a conventional bond, and under special types of transactions such as bond buyback
and exchanges (242 bonds were dropped). After the ltering, we are left with about 1.45 million trade records
from 625 unique euro-denominated sovereign bonds in the sample period.
The dataset includes trade and quote information as well as security identication information. For the trade
information, the dataset contains the ISIN18 code for the bond, the date and time of all trades, the price and size
of each trade as well as the direction of each trade. The quote information includes the ISIN code for the bond,
the type of the order, the bid and ask prices and corresponding depth at each price on the limit-order book.
NotethatallMTSquotesaretransactable,thuswedonothavetorelyonproxiesforanaccuratemeasurement
of liquidity. The security identication information includes various metadata such as the issuing country, issuer
type, issue and maturity date, coupon rate, benchmark status etc.
12 P. PANAGIOTOU ET AL.
As with any high-frequency data set, it is important to note that prior to using the raw data, one must rst
clean the data with the objective of discriminating between noisy and valid data entries. Trades and/or quotes
that are out of sequence, that are recorded outside normal trading hours (dened as CET 8:30am–17:30 pm),
or that have special settlement conditions are discarded. Observations during weekends and public holidays
are also removed. Negative bid-ask spreads, depths and trade prices are also eliminated from the data set. We
further use the Brownlees and Gallo (2006) algorithm to clean the MTS data. This ltering procedure removes
rare and obvious outliers. A similar approach is used by Mancini, Ranaldo, and Wrampelmeyer (2013), Marshall,
Nguyen, and Visaltanachoti (2013), and Karnaukh, Ranaldo, and Söderlind (2015).
As a nal step, we aggregate the irregular spaced raw data to a one minute sampling frequency. At the end of
each minute, we reconstruct the order book, and following Pelizzon et al. (2016)wecomputetheglobalbestbid
and ask prices, along with their associated depths, across the two platforms for each country (i.e. regardless of
whether the trading or quoting activity took place on the domestic or the European market), taking into con-
sideration only active quotes. In addition, for each minute of trading activity we also calculate the midquote, the
total trading volume as well as net order ow. The logarithmic return is calculated based on midquote changes.
These data allow us to construct liquidity measures for each security.
4.3. Liquidity measures
A secondary market is viewed as liquid when market participants can execute large-volume transactions at low
cost, quickly and with minimum impact on market prices. We thus break down our measures into three cate-
gories, namely, trading cost (price dimension), market depth (quantity dimension) and price impact (elasticity
dimension) in an attempt to capture all aspects of market liquidity. This section details the liquidity measures
used in our study.
4.3.1. Trading costs
Our rst measure attempts to capture the cost of executing a trade. A market can be regarded as liquid if trading
costs are low and as illiquid if trading costs are high. Measuring trading costs is not simple as they depend on the
size of a trade, its timing, the trading venue, and the counterparties (Fleming 2003). The proportional quoted
bid-ask spread, LPQS, is a commonly used measure of market liquidity. It directly measures the cost of executing
a small trade, with the cost typically calculated as:
LPQS =(PA−PB)/PM
where the superscripts A, B, and M indicate the best ask, the best bid, and mid quotes, respectively per e100 of
face value. The latter is dened as PM=(PA+PB)/2. We calculate the proportional quoted spread per bond at
the end of each minute and daily estimates are obtained by averaging across all minutes per day. A drawback of
the bid-ask spread is that bid and ask quotes are good only for limited quantities and periods of time. The spread
therefore only measures the cost of executing a single trade of limited size at a specic point in time.
The quoted bid-ask spread reects the liquidity available at a given point in time, although an alternative
would be to measure trading costs using the prices actually obtained by investors. In practice, trades are not
always executed at the posted bid or ask quotes.19 Instead, deals frequently transact at better prices. The eective
spread better captures the cost of a round-trip order by including both price movement (dealers coming in to
execute orders at a better price than previously quoted) and market impact (spread widening due to the size of
the order itself). Eective costs can be computed by comparing transaction prices with the quotes prevailing at
the time of execution. The percentage eective cost of a trade is dened as:
ŁPES =(P−PM)/PM,forbuyer-initiatedtrades,
(PM−P)/PM, for seller-initiated trades,
with P denoting the trade price. The wider the eective spread, the less liquid is the asset. Since our data includes
quotes and trades, we do not have to rely on proxies for the eective spread, but can compute it directly from
observed data. Daily estimates of illiquidity are obtained by averaging the eective cost of all trades that occurred
on each day.
THE EUROPEAN JOURNAL OF FINANCE 13
4.3.2. Market depth
Our second measure captures the quantity dimension of market liquidity. The quantity of securities that can be
traded at the bid and ask prices is the depth of the market and complements the bid-ask spread as a measure
of market liquidity. The average quoted depth, LAQD, is a commonly used measure of market depth, typically
calculated as:
LAQD =(DA+DB)/2
where DAdenotes the ask depth, i.e. the quantity that liquidity providers are willing to sell at the best ask price
and DBdenotes the bid depth, i.e. the quantity that liquidity providers are willing to buy at the best bid price in
millions of euros. Intuitively, this characterizes the average quantity that a trader can trade at the best prices. The
larger the average depth, the more liquid the market is considered to be and the lower the likely execution cost
for a large order. In other words, depth denotes the size of transaction that can be absorbed without aecting
prices. A drawback of this estimate, however, is that market participants often do not reveal the full quantities
they are willing to transact at a given price, so the observed quote sizes at the best prices may underestimate true
market depth. Concerns that sovereign depth is uninformative are mitigated by the fact that the ratio of hidden
todisplayedordersinourdatasetisextremelylow(e.g.lessthan2%).
4.3.3. Price impact
The fourth liquidity measure we calculate combines the cost and quantity dimensions and captures the marginal
cost of trading an extra unit of the asset. Conceptually related to Kyle’s (1985)λ,thepriceimpactofatrade
measures how much the bond price changes in response to a given order ow. The higher the price impact,
the more the bond price moves following a trade, reecting lower liquidity. Roşu (2009)developsadynamic
model that predicts that more liquid assets should exhibit narrower spreads and lower price impact. Despite the
granularity of our dataset, regressing net order ow on intraday bond returns to obtain an accurate estimation
of price impact is not feasible due to infrequent trading. An alternative is to gauge the sensitivity of returns to
trading volume.20 To this end, we calculate Amihud’s illiquidity ratio at the daily frequency dened as:
LAIR =|Ri,t|
Voli,t
where |Ri,t|denotes the absolute daily log return and Vo l i,tdenotes the daily trading volume in euros for bond iin
day t. It can be interpreted as the daily price response associated with one euro of trading volume. A higher value
of the measure indicates lower liquidity. We scale up daily LAIR by multiplying by 106as the original magnitude
of the measure is too small to be used in empirical analysis. Goyenko and Ukhov (2009) show empirically that
the Amihud ratio is a good proxy for price impact, as it is highly correlated with high-frequency measures of
price impact. Kamara, Lou, and Sadka (2008) and Karolyi, Lee, and Van Dijk (2012) use Amihud’s ratio to test
and document commonality in liquidity is US equity markets, Marshall, Nguyen, and Visaltanachoti (2013)in
commodity markets, and Benos, Payne, and Vasios (2020) to measure the liquidity of interest rate swap contracts.
4.3.4. Commonality measure
Price impact, together with the spread and depth measures, provides a fairly complete picture of market liquidity
(Fleming 2003). We use the adjusted R2of regressions of the liquidity of individual bonds on market-wide liquid-
ity as a measure of the extent to which the liquidity of individual bonds move together. There are two approaches
in the literature in extracting market-wide liquidity: averaging (Chordia, Roll, and Subrahmanyam 2000)and
principal component analysis (Korajczyk and Sadka 2008). For robustness, we use and report results separately
for both approaches in the next section of this study. To obtain a monthly time-series average of the adjusted
R2measures, for each month, we use the equally-weighted average adjusted R2of regressions of the liquidity of
each individual benchmark bond series on market-wide liquidity to obtain a measure of commonality in liquid-
ity in a given month. We require a minimum number of at least 15 daily observations to estimate the adjusted
R2ofabenchmarkbondseriesinagivenmonthaswellasaminimumnumberof50bondsforthecalculation
of these aggregate adjusted R2measures in calculating commonality in liquidity across all bonds in our sample
14 P. PANAGIOTOU ET AL.
(i.e. irrespective of issuing country or maturity at issue) in a given month. Our raw commonality measure is not
suitable to use as the dependent variable in regressions, because their values always fall within the interval [0,1].
Following Karolyi, Lee, and Van Dijk (2012) and Moshirian et al. (2017), we use the logistic transformation of
the R2measures, ln[R2/(1−R2)], in our time-series regressions.
4.4. Liquidity measures of benchmark bonds
We generate liquidity measures for benchmark sovereign bonds across time and countries at daily frequency.
Traditionally, an on-the-run bond is the most recently auctioned bond of a particular maturity. It attains the
‘benchmark’statuswhenitbecomesthemostheavilytradedbondatthatmaturityforanadequateperiodoftime,
and normally without government intervention (Remolona and Yetman 2019). However, the existing literature
(see, for example, Paiardini 2014;Pelizzonetal.2016; O’Sullivan and Papavassiliou 2020) typically considers as
abenchmarkbondataparticularmaturitytheonethatismostactivelyforthegreatestpartofthesampleperiod
under examination.
This approach, in the case in which a new benchmark bond is issued in the sample period and thus should
have become the new benchmark in the respective maturity, results in treating the ex-benchmark bond as still
being the benchmark bond. This misuse may bias the empirical results of the analysis if there is signicant
liquidity dierence between the on- and o-the-run bonds (Pasquariello and Vega 2009). Moreover, trading
volume of non-benchmark bonds can be temporarily more active than the benchmark peers if these are subject to
idiosyncratic factors such as at the dates of bond re-openings. Thus, we manually identify the switching dates for
each of the 2-year, 3-year, 5-year, 7-year, 10-year, 15-year, 20-year and 30-year maturities, when a new benchmark
bondreplacestheformeroneinBloomberg.
Missing observations are recorded when benchmark bonds do not exist in certain periods for some coun-
tries.21 Missing benchmark bonds are replaced with bonds that are issued by the same sovereign, that have the
closest remaining maturity, and that are not considered as benchmark bonds in other maturities.22 Remaining
missing observations are kept if no replacement bonds are found which satisfy these conditions. We end up with
73 balanced benchmark bond series.23 Lastly, using the ISINs, the panel of benchmark bonds across time and
maturities are matched with the liquidity measures.
We partition our dataset along three dimensions. Firstly, we separate the bonds in our dataset by issuing
country, i.e. we have eight time series each representing a dierent maturity within a country for each of the 10
countries. The second dimension involves separating the bonds in our dataset by maturity at issue, i.e. we have
10 time series each representing a dierent country for each of the eight maturity brackets. The nal partition is
an unconditional partition which includes all benchmark bond series, irrespective of issuing country or maturity
at issue. By partitioning our dataset with respect to these three dimensions, we test for commonality in liquidity
at the [1] national level, [2] across maturities, and at the [3] pan-European level.
4.5. Summary statistics
Using the large data set described above, we calculate the four liquidity measures (bid-ask spread, eective cost,
marketdepthandpriceimpact)foreachtradingdayandeachsovereignbond.Table1shows means and stan-
dard deviations for the four measures of liquidity across all bonds of the same maturity but issued from dierent
countries. For all sovereign bonds, the quoted spread and the Amihud illiquidity ratio increase as time to matu-
rity increases whereas the quoted depth decreases as the time to maturity increase. This observation suggests that
bonds with shorter maturities are more liquid than bonds with longer maturities. Similar results are reported by
Pasquariello and Vega (2009) for US Treasury bonds and by O’Sullivan and Papavassiliou (2020) for Euro-area
government bonds. Eective costs are less than half the bid-ask spread, implying within-quote trading.
5. Empirical approach
In Section 5.1, we present the empirical method in testing for commonality in liquidity. In Section 5.2 we
discuss our empirical approach in examining the eect of several supply- and demand-side determinants on
THE EUROPEAN JOURNAL OF FINANCE 15
Tab le 1 . Daily liquidity measures per maturity.
2Y 3Y 5Y 7Y 10Y 15Y 20Y 30Y
LPQS
Mean 31.30 36.13 43.28 49.78 51.61 51.10 59.49 61.00
Std. Dev. 105.16 114.86 124.42 130.89 130.02 39.74 54.88 57.07
LPES
Mean 2.06 3.41 4.80 4.68 8.28 7.27 6.01 12.59
Std. Dev. 8.50 20.07 13.12 16.50 16.67 13.83 16.13 20.91
LAQD
Mean 19.70 17.65 15.93 15.33 13.01 8.31 6.86 6.57
Std. Dev. 12.49 10.07 8.06 7.59 6.95 3.75 2.43 2.33
LAIR
Mean 0.004 0.006 0.010 0.017 0.026 0.028 0.045 0.069
Std. Dev. 0.044 0.066 0.074 0.094 0.108 0.099 0.192 0.209
Notes: This table shows summary statistics for various daily measures of liquidity across all bonds of the same maturity but issued from different
countries. The percentage quoted spread (LPQS) denotes the average relative bid-ask spread computed using intraday datafor each trading day.
The percentage effective spread (LPES) is the average relative difference between the transaction price and the bid/ask quote prevailing at the
time of the trade. The average quoted depth (LAQD) is the average depth quoted at the best bid and ask prices computed using intraday data
for each trading day. The Amihud illiquidity ratio (LAIR) is the ratio of absolute daily log return and daily trading volume. The sample period is
2011:06–2018:6 and includes 1,804 daily observations.
commonality in liquidity. In Section 5.3 we present the test for the eects of macroeconomic announcements
on commonality in liquidity.
5.1. Testing for commonality in liquidity
We use the a d j u s t e d R2of time-series regressions of the liquidity of individual bonds on market-wide liquidity
as a measure of the extent to which the liquidity of individual bonds move together. Specically, daily percentage
changes24 in liquidity variables for an individual benchmark bond series are regressed on the daily percentage
change in market-wide measures of liquidity, i.e.
L(·)
j,t=αj+β1jL(·)
M,t+β2jL(·)
M−1,t+β3jL(·)
M+1,t+Controls +j,t,(1)
where L(·)
j,tis, for benchmark bond series jon day t, the percentage change ()fromtradingdayt−1totin liq-
uidity measure L(·),L(·)
M,tis an estimate of market-wide liquidity of the same variable, L(·)
M,t−1and L(·)
M,t+1are
the lag and lead percentage changes in market-wide liquidity and are intended to capture any lagged adjustment
in commonality.
Following Chordia, Roll, and Subrahmanyam (2000), the contemporaneous, leading and lagged market
return and the contemporaneous change in the individual benchmark bond series absolute return are included
as control variables. The market return is intended to remove spurious dependence induced by an association
between returns and the bid-ask spread.25 Finally, the absolute return is induced to proxy for volatility, which
is positively correlated with liquidity. We use the Bloomberg Barclays Pan-European Aggregate Bond Index as a
proxy for the market.26
There are two approaches in the literature in estimating market-wide liquidity: averaging (Chordia, Roll,
and Subrahmanyam 2000) and principal component analysis (Korajczyk and Sadka 2008). For robustness we
implement both methods, but most of the analysis is based on the second approach.
5.1.1. Averaging
Intherstapproach,market-wideliquidityL(·)
M,tis computed as the equally-weighted cross-sectional average27
of liquidity at the individual benchmark bond series level, i.e.
L(·)
M,t=1
N
N
j=1
L(·)
j,t.(2)
16 P. PANAGIOTOU ET AL.
where Nis the number of benchmark bond series and L(·)
j,tis the liquidity of the benchmark bond series jon day
t. Chordia, Roll, and Subrahmanyam (2000)andPástorandStambaugh(2003) use this method for determining
market liquidity in equity markets and Marshall, Nguyen, and Visaltanachoti (2013)incommoditiesmarkets.
Note that in each individual regression when computing the market-wide liquidity measure, L(·)
M,t,benchmark
bond series jis excluded, so the explanatory variable in (1) is slightly dierent for each benchmark’s bond time
series regression. This removes a potential mechanical correlation.
5.1.2. PCA
The second approach for extracting a market-wide measure of liquidity is based on principle component anal-
ysis. Korajczyk and Sadka (2008) used this approach to document liquidity commonality in US equity markets
and Mancini, Ranaldo, and Wrampelmeyer (2013) in FX markets. As a rst step, we calculate the cross-sectional
average of a liquidity measure on a daily basis and we compute the time-series mean and standard deviation of
this series. We then standardize each of the time-series observations by subtracting from each observation the
cross-sectional average and dividing by the standard deviation of the cross-sectional mean series calculated in
the rst step. Following Korajczyk and Sadka (2008) and Mancini, Ranaldo, and Wrampelmeyer (2013), for each
liquidity measure we extract the rst three principal components across all benchmark bond series. Principal
components can be interpreted as liquidity factors for an individual bond. The rst principal factor is the one
that is most likely to capture systematic liquidity, and for this reason, is viewed as representing market-wide
liquidity. In order to test for the degree of commonality across benchmark bond series for each liquidity mea-
sure, we regress for each benchmark bond series jthe time-series of daily liquidity measure L(·)
j,ton the rst three
extracted factors and record the p-values of the factor loadings and the adjusted R2value. The size of these fac-
tors is measured as the cross-sectional average adjusted-R2. In addition, we perform market-type regressions
using as market-wide liquidity the rst principle component. The regression results are reported in Table 2.The
size of the liquidity factors is reported in Table 3.Weapplythesemethodsforeachliquiditymeasureseparately
and we test for liquidity commonality across the benchmark bond series of (i) each individual country (national
level) (ii) across dierent maturities as well as (iii) across countries and maturities (pan-European level).
Figure 1shows market-wide liquidity, for all sovereign bonds in our sample over time. Decreased market-
wide liquidity was observed surrounding the EU debt crisis time period, while being signicantly ameliorated
in more recent years. Finally, market-wide liquidity seems to be negatively aected around stress periods as, for
example, around the 2018 Italian elections.
5.2. Time-series regressions
We examine the eect of several supply- and demand-side determinants on commonality in liquidity by running
time-series regressions of the monthly average commonality in spreads and in depths on 73 benchmark bond
series - denoted by R2
COM,t– on various variables aimed to proxy for dierent demand and supply-side expla-
nations of commonality in liquidity. Thus, for commonality for a given liquidity measure we run the following
regression:
R2,(·)
COM,t=α+βProxyt+γControls +j,t,(3)
We regress in dierences, when necessary, to address the issue of the dependent variable being stationary and
some of the explanatory variables being non-stationary. A constant term in a time-series regression on rst-
dierences implies a linear time trend in levels. We include the constant term to examine whether commonality
in liquidity has increased or decreased over time. All equations are estimated using OLS with Newey–West
standard errors.
We complement the monthly measure of commonality in quoted spreads and depths with low-frequency
proxies for various demand and supply-side explanations of commonality in liquidity. These proxies are collected
from Haver Analytics and Bloomberg at the monthly frequency for the period 2011:06–2018:06. All monthly
observations are calculated as monthly averages. Following Karolyi, Lee, and Van Dijk (2012) and Moshirian
et al. (2017), we also include in our time-series regressions changes of the average monthly market return of
THE EUROPEAN JOURNAL OF FINANCE 17
Tab le 2 . Market-wide commonality in liquidity.
PCA Averaging
LPQS LPES LAQD LAIR LPQS LPES LAOD LAIR
Panel A: Across Countries and Maturities
LM,t0.408 0.129 0.143 0.205 0.854 0.471 0.680 0.267
(5.10) (5.31) (3.25) (4.39) (12.92) (3.66) (5.22) (2.32)
% positive 98.63 93.15 84.93 79.45 100.00 97.26 97.26 90.41
% significant 84.93 64.38 58.90 24.66 100.00 80.82 87.67 54.79
LM,t−10.192 0.028 0.015 0.001 0.018 0.152 0.020 0.176
(1.52) (1.97) (0.15) (0.76) (0.44) (1.74) (0.11) (0.92)
% positive 79.45 80.82 46.58 64.38 53.42 80.82 49.32 67.12
% significant 47.95 49.32 16.44 9.59 13.70 43.84 9.59 39.73
LM,t+10.053 0.031 0.009 0.001 0.012 0.134 0.018 0.146
(0.24) (2.08) (0.40) (1.61) (0.26) (1.57) (0.07) (1.25)
% positive 6.16 76.71 36.99 80.82 49.32 75.34 52.05 71.23
% significant 4.11 50.68 15.07 23.29 13.70 45.21 5.48 30.14
Adjusted R20.456 0.082 0.240 0.045 0.113 0.042 0.021 0.022
Panel B: Within a Country and across Maturities
LM,t0.537 0.402 0.395 0.100 0.638 0.268 0.458 0.159
(32.15) (26.90) (14.35) (7.34) (18.16) (4.86) (9.81) (2.85)
% positive 98.63 98.63 98.63 86.30 100.00 98.63 100.00 84.93
% significant 97.26 95.89 89.04 63.01 100.00 86.30 98.63 54.79
LM,t−10.240 0.100 0.038 0.010 0.014 0.102 0.039 0.083
(2.08) (2.07) (0.62) (1.06) (0.30) (2.09) (0.78) (1.36)
% positive 67.12 71.23 61.64 63.01 42.47 78.08 72.60 65.75
% significant 69.86 63.01 34.25 21.92 17.81 45.21 19.18 30.14
LM,t+10.116 0.081 0.027 0.002 0.007 0.099 0.037 0.065
(1.24) (2.01) (0.53) (1.53) (0.27) (1.80) (0.75) (1.47)
% positive 64.38 69.86 68.49 64.38 50.68 68.49 78.08 75.34
% significant 68.49 63.01 35.62 30.14 8.22 35.62 12.33 28.77
Adjusted R20.686 0.256 0.414 0.179 0.194 0.041 0.066 0.028
Panel C: Within a Maturity and across Countries
LM,t0.436 0.133 0.494 0.060 0.444 0.156 0.109 0.117
(24.04) (8.42) (11.96) (5.38) (8.89) (2.43) (1.83) (1.56)
% positive 100.00 68.49 75.34 72.60 100.00 90.41 93.15 80.82
% significant 86.30 53.42 60.27 32.88 100.00 61.64 42.47 32.88
LM,t−10.331 0.035 0.007 0.002 0.035 0.090 0.006 0.060
(1.50) (0.59) (0.36) (0.35) (0.63) (1.82) (0.19) (0.84)
% positive 71.23 56.16 43.84 54.79 58.90 82.19 56.16 64.38
% significant 47.95 32.88 28.77 12.33 20.55 46.58 9.59 20.55
LM,t+10.325 0.036 0.016 0.004 0.036 0.098 0.009 0.046
(1.58) (0.23) (0.47) (0.44) (0.71) (1.56) (0.17) (1.06)
% positive 71.23 56.16 39.73 56.16 65.75 84.93 58.90 72.60
% significant 58.90 36.99 28.77 19.18 17.81 34.25 5.48 16.44
Adjusted R20.508 0.164 0.274 0.132 0.069 0.026 0.003 0.016
Notes: Daily proportional changes in an individual benchmark bond series liquidity measure are regressed in time series on proportional changes
in market-wide liquidity, extracted either as [1] the equally-weighted average liquidity for all benchmark bond series (‘the market’, LM), as well as
[2] the first principle component. In each individual regression, the market average excludes the dependent variable benchmark bond series.All
equations are estimated using OLS, cross-sectional averages of time series slope coefficients are reported with t-statistics in parentheses below
coefficient estimates. % positive reports the percentage of positive slope coefficients, while % significant gives the percentage with p-values
lees than 0.05. There are 1,804 daily observation in our sample period. The lead, lag and contemporaneous values of the market return and the
proportional daily change in individual benchmark bond series absolute return (a measure of volatility) were additional regressors; coefficients
are not reported to conserve space.
the xed-income market, trading volume, market volatility, market liquidity, and credit risk as proxies for the
overall capital market conditions.28
For example, based on the inventory explanation for liquidity, increased trading activity should result in
narrower spreads because inventory balances and risks per trade can be attained at lower levels whereas when
informed traders are present in the market, spreads should widen with the number of transactions.29 These con-
ditions can also aect changes in commonality in liquidity through various supply-side channels – for example,
18 P. PANAGIOTOU ET AL.
Tab le 3 . Size of within-measure common factors.
Variable Statistic Factor 1 Factor 2 Factor 3
Panel A: Across Countries and Maturities
LPQS Adjusted R2-mean 40.65 45.84 58.42
LPES Adjusted R2-mean 3.52 6.04 14.69
LAQD Adjusted R2-mean 23.18 28.51 31.97
LAIR Adjusted R2-mean 2.53 4.06 5.96
Panel B: Within a country and across maturities
LPQS Adjusted R2-mean 68.08 78.57 82.04
LPES Adjusted R2-mean 22.08 36.17 50.16
LAQD Adjusted R2-mean 41.91 53.57 63.70
LAIR Adjusted R2-mean 15.84 29.66 44.45
Panel C: Within a maturity and across countries
LPQS Adjusted R2-mean 46.98 55.80 67.08
LPES Adjusted R2-mean 12.77 25.22 39.11
LAQD Adjusted R2-mean 25.87 36.97 46.59
LAIR Adjusted R2-mean 11.45 23.29 34.31
Notes: This table reports statistics of time-series regressions. Within-measure common factors are extracted separately for four different liquidity
measures. Then for each variable and each benchmark-bond series, a time-series regression of the variable on its common factors in executed.
The liquidity measures analyzed are: the percentage quoted spread (LPQS), the percentage effective spread (LPES), the average quoted depth
(LAQD) and the Amihud illiquidit y ratio(LAIR ). Prior to the extraction of common factors and regression analysis, for each benchmark bond series,
is normalized every month by its mean and standard deviation calculated up to the prior month (with at least three prior monthly observations).
The table reports the mean adjusted R2of these regressions using one, two and three factors.
Figure 1. Market-wide liquidity.
Market-wide liquidity. The figure plots daily estimates of market-wide liquidity. Market-wide liquidity, for each liquidity measure, is extracted as the first principal
component across all benchmark bond series. The upper left graph shows the first principal component extracted for percentage quoted spreads (LPQS) across all
benchmark bond series, the upper right shows the market-wide estimate of percentage effective spreads (LPES), the lower left graph shows market-wide estimated of
average quoted depths (LAQD) and the lower right graph shows the market-wide estimate of daily price impacts, as captured by the Amihud illiquidity ratio (LAIR). The
sample is June 2011 to June 2018.
by aecting the funding liquidity of nancial intermediaries – or demand-side channels, such as the extent of
correlated trading by institutional investors. Thus, we need to include these proxies to account for changes in the
capital market environment, in general, before we explore the explanatory power of other variables. We measure
the economic magnitude of the estimated coecients by the eect of an increase of the standard deviation in
the time-series variable of interest, expressed as a fraction of the time-series standard deviation of R2
COM,t.30
THE EUROPEAN JOURNAL OF FINANCE 19
5.3. Macroeconomic announcements
As we cannot use the monthly R2
COM,tvalues to measure liquidity at daily frequency, we elect to use a dierent
measure of liquidity co-movements that can capture daily co-movements in liquidity of government bonds. We
employ as our measure of co-movement in the event window the measure of synchronicity originally proposed
by Morck, Yeung, and Yu (2000) and as subsequently modied by Brockman, Chung, and Pérignon (2009).31 As
in Chordia, Roll, and Subrahmanyam (2001)andBrockman,Chung,andPérignon(2009), we use a three-day
event window with the announcement day being the last day in the event window (days −2to0)inorderto
capture the eects of pre-announcement portfolio rebalancing on market liquidity. We measure the daily co-
movement of liquidity by rst counting the number of benchmark bonds with positive and negative changes in
their liquidity measure for each trading day and then dividing the larger of these two numbers by their sum.
We delete trading days in which the number of benchmark bonds with unchanged liquidity measures exceeds
50% of total number of bonds.32 Then we average daily co-movement percentages (i) across all trading days
(column 1 in Table 9), and (ii) across only those trading days with Euro area macro news releases (columns
2–5). We report results for spread and depth co-movements in Panel A and in Panel B, respectively. We further
split our cross-section of issuing countries between core and periphery markets33 andweperformthesame
analysis and present separate results.
Moreover, in order to examine the eect of unanticipated announcement surprises, following Balduzzi, Elton,
and Green (2001)andPaiardini(2014), we calculate announcement surprises as the dierence between the
actual announcement (αt) and the market expectation of the announcement (E(αt)) scaled by the cross-sectional
standard deviation of the forecast error (αt−E(αt)). Our unanticipated news component is created from con-
sensus forecasts, thus the shocks we use are the average of shocks across investors, meaning that there is a
motive to trade as there may exist a positive shock for one investor and a negative shock for another. We extent
our event-window to include two days after the announcement day (i.e. days −2to0andto+2), in order
to additionally capture the eects of post-announcement portfolio rebalancing on market liquidity. For each
announcement we distinguish between large and low announcement surprises and recalculate the measure of
co-movement as dened above. We report results for large and low announcements surprises for spreads and
depths co-movements separately and across core and periphery markets in subpanels in Table 9.
6. Empirical results
In this section, we report the estimation results of the market-type regressions, with market-wide liquidity
extracted as the cross-sectional average and the rst principal component. We further report the estimation
results of monthly time-series regressions of commonality in liquidity (based on daily data) on various proxies
for volatility, time-variation in supply- and demand-side factors. These estimation results are estimated by using
the rst principal component as the preferred method to extract market liquidity.34 We al s o re p or t th e r e s u l t s
of the eect of macroeconomic announcements on commonality in liquidity.
6.1. Commonality in liquidity
Table 2reports the estimated βjcoecients, from Equation (1) for both approaches used in extracting market-
wide liquidity (i.e. averaging and PCA). Both sets of results provide strong evidence of commonality in liquidity.
However,wemainlyfocusonthePCAresultsasthePCAcanbettercapturethevariabilityoftheliquidity
measures as compared to the simple average. The PCA results indicate that there is strong positive relation
between the rst principle component derived for all four liquidity measures.35
Thechangeinthepercentagequotedspread,LPQS
j,t, in the left-hand side of Table 2in Panel A where we test for
commonality in liquidity across all benchmark bond series irrespective of issuing country or maturity at issue,
i.e. at the pan-European level, displays an average value of 0.408 for the contemporaneous βj’s in (1) and an
associated average t-statistic of 5.10, with 85% of these individual βj’s being positive and statistically signicant
at the 5% condence level. In addition, although the leading and lagged terms are usually positive and often
signicant, they are small in magnitude. The results for the remaining liquidity measures, i.e. percentage eective
20 P. PANAGIOTOU ET AL.
spread, the average quoted depth, and the Amihud illiquidity ratio are similar to those of the percentage quoted
spread and suggest that they also exhibit co-movement. These results provide strong evidence in support of the
hypothesis that there is commonality in liquidity across borders, irrespective of issuing country or maturity at
issue.
In terms of explanatory power, the strongest commonality is observed for quoted spreads and depths. The
average adjusted R2for percentage quoted spreads and average quoted depths is approximately 45% and 25%,
respectively, whereas for the eective spread and the Amihud ratio it is approximately 8% and 5%. As additional
support, the cross-sectional average of the adjusted R2,reportedinTable3, increases further when two or three
principle components are included as explanatory variables.
In relation to commonality in liquidity at the national level (left-hand side, Panel B) and across maturities
(left-hand side, Panel C), the results also provide evidence of liquidity co-movements. For example, for the
percentage quoted spread the average contemporaneous coecient is equal to 0.537 and 0.436 at the national
and maturities level respectively and an associated average t-statistic of 32.15 and 24.04. Approximately 97%
and 86% respectively of these individual βj’sarepositiveandstatisticallysignicantatthe5%condencelevel.
The results for the remaining liquidity measures are fairly similar to those of the percentage quoted spread and
suggestthattheyalsoexhibitco-movement.
These results suggest the existence of strong liquidity commonality at the national level as well as across
maturities. In terms of explanatory power, the average adjusted R2of the percentage quoted spread is approxi-
mately 69% and 51% when measuring commonality in liquidity within national markets and across maturities
respectively, suggesting the commonality is stronger at the national level. We obtain similar results for the market
depth, the eective spread, and the Amihud ratio. Overall, the PCA results suggest that there is signicant com-
monality across benchmark bond series for most liquidity measures for all three dimensions used to partition
our dataset.
Again, we obtain similar results when the cross-sectional average is used in calculating market-wide liquidity.
Forexample,forthepercentageeectivespreadatthepan-Europeanlevel,theaverageestimatedcontempora-
neous coecient is 0.854 with an average t-statistic of 12.92, with 100% of these individual contemporaneous
coecients being positive and statistically signicant at the 5% condence level. We obtain similar results for the
average quoted spread, the percentage eective spread, and the Amihud illiquidity ratio. In terms ofexplanatory
power, the average adjusted R2is low for both four liquidity measures considered, with the percentage quoted
spread exhibiting the higher value. The average adjusted R2of the percentage quoted spread is approximately
10%.
Although the explanatory power of the typical individual regression is not high, it is signicantly higher as
compared tothe reported average adjusted R2fromsimilarregressionsfromotherassetclasses.Forexample,
Chordia, Roll, and Subrahmanyam (2000) report an adjusted R2of 0.017 for US equity markets and Marshall,
Nguyen, and Visaltanachoti (2013)reportsanadjustedR2of 0.015 for commodity markets. Evidently, there is
either a large component of noise and/or other inuences on daily changes in individual benchmark bond series
liquidity.
In terms of the percentage eective spread and the average quoted depth, the average adjusted R2is roughly
4% and 2% and seems consistent to the results reported by Chordia, Roll, and Subrahmanyam (2000)andMar-
shall, Nguyen, and Visaltanachoti (2013). Again, commonality at the country level is stronger at the national
level as compared with commonality across maturities and the pan-European level.
These results imply that commonality in liquidity in the Euro-area sovereign bond market is stronger as
compared to commonality in equity markets and weaker as compared to commonality in FX and commod-
itymarkets.StudiesrelatedtoUSequitymarketsreportadjustedR2ranging between 2% and 30%. Marshall,
Nguyen, and Visaltanachoti (2013) report an average adjusted average equal to 46% and 40% with respect to
proportional quoted and eective spreads respectively in commodity futures markets, and Mancini, Ranaldo,
and Wrampelmeyer (2013) report a cross-sectional average of 80% and 90% in FX spot markets at the daily
frequency.
Overall, we nd strong evidence of commonality in liquidity at the national level, across maturities, and the
pan-European level. In terms of liquidity measures, all four liquidity measures we consider show signicant co-
movements, with percentage quoted spreads and average quoted depths exhibiting stronger correlation, across
THE EUROPEAN JOURNAL OF FINANCE 21
the three dierent dimensions we test for commonality. In terms of dimensions, as expected, commonality is
stronger at the national level, with some cross-sectional dierences, and lower at the pan-European level.
6.2. Time-series variation in commonality in liquidity
The regression results above document signicant commonality in quoted spreads and depths, irrespective of
issuing country and maturity at issuance, suggesting a Pan-European liquidity factor. We use the PCA as the
preferred methodology to extract market-wide liquidity as it better captures the variability of the liquidity mea-
sures as compared to the simple average. However, this result does not reveal anything regarding how volatile
thiscommonalityisovertime.Toaddressthisquestion,foreachmonth,weusetheequally-weightedaverage
adjusted R2of regressions of the liquidity of individual benchmark bonds on market-wide liquidity to obtain a
measure of commonality in liquidity in a given month. Figure 2plots the average adjusted R2
COM,t, for both the
commonality in quoted spreads and depth, in a given month. The gure shows that commonality is signicantly
dierent in some periods than in others for both series, but without any apparent trend, as well as that the two
series do not seem to behave in the same manner over time (ρ=0.41), with the commonality in quoted spreads
being more volatile as compared to the commonality in depth.
In an attempt to potentially relate the peaks and troughs of the R2
COM,tseries to market events, we then sort
according to the calculated R2
COM,tin a given month for both series and report the extreme observations in
Table 4. We do not report the months with lowest R2
COM,tin Table 4as they all appear to be unrelated to specic
market events. Nevertheless, we observe interesting patterns in the time-variation of R2
COM,tof both series.
The results suggest that commonality tends to intensify during stress periods. Nine out of the 12 highest
values of the series in quoted spreads can all be related, to some extent, to political and crisis events in the Euro-
pean Union. Commonality in depth exhibits a similar pattern. In addition, there are seven common months in
Figure 2. Time-series variation in commonality in liquidity.
Time-series variation in commonality in quoted spreads and depths at the pan-European level. This figure depicts the average commonality in liquidity(R2
avg,t)for
each month during the sample period 2011:06–2018:06. Commonality in liquidity of individual benchmark bond series is measured by the adjusted R2of monthly
regressions of the daily percentage changes of a liquidity variable on the lead, lag, and contemporaneous daily percentage changes in market liquidity. We measure
liquidity by calculating two liquidity variables, i.e. percentage quoted spreads (LPQS) and average quoted market depth (LAQD), for each individual benchmark bond
series. For each liquidity measure, the first principal component is extracted and interpreted as market liquidity.
22 P. PANAGIOTOU ET AL.
Tab le 4 . Ranked monthly commonality.
Panel A: Commonality in spreads (LPQS)
Rank Month R2
COM,tEvent
1 October 2014 0.385 US Treasuries Flash Crash
2 May 2014 0.378 Greek Elections
3 June 2016 0.375 UK EU Referendum
4 June 2013 0.362 US Taper Tantrum
5 June 2011 0.349 European Debt Crisis
6 September 2014 0.324 T-LTRO 1
7 December 2015 0.278 Spanish & French Elections
8 December 2013 0.270 FED Tapening Announcement
9 July 2011 0.269 Greek sovereign debt restructuring
10 January 2015 0.253 ECB’s QE Officially Announced
11 November 2011 0.248 EU/IMF announces bailout loan to Ireland
12 May 2018 0.244 Italian Elections
Panel B: Commonality in depth (LAQD)
1 June 2016 0.269 UK EU Referendum
2 May 2014 0.214 Greek Elections
3 May 2018 0.194 Italian Elections
4 May 2015 0.187 Bund Tantrum
5 December 2016 0.166
6 October 2014 0.137 US Treasuries Flash Crash
7 December 2015 0.131 Spanish & French Elections
8 August 2014 0.129
9 December 2012 0.128
10 December 2013 0.128 FED Tapering Announcement
11 January 2015 0.128 ECB’s QE Officially Announced
12 March 2012 0.121
Notes: Ranked average monthly commonality in quoted spreads and depths at the pan-European level. This
table reports the extreme observations of sorting, from largest to lowest, the average monthly commonality
in liquidity (R2
COM,t) for each month during the sample period 2011:06–2018:06. Commonality in liquidity of
individual benchmark bond series is measured by the adjusted R2of monthly regressions of the daily percent-
age changes of a liquidity variable on the lead, lag, and contemporaneous daily percentage changes in market
liquidity. Wemeasure liquidity by calculating two liquidity variables, i.e. percentage quoted spreads (LPQS) and
average quoted market depth (LAQD), for each individual benchmark bond series. For each liquidity measure,
the first principal component is extracted and interpreted as market liquidity. We do not report the months
with lowest commonality in a given month as they all appear to be unrelated to specific market events.
the two ranked series, suggesting that market events aect dierent aspects of market liquidity simultaneously
but to a dierent extent. Another observation is that both series seem to be aected by central bank announce-
ments in the Euro-area and the US related to their asset purchase programmes. The ECB’s announcement on
quantitative easing in January 2015, the rst allotment of TLTRO I in September 2014, and the Fed’s taper-
ing announcement in 2013 are all events coinciding with increased commonality in liquidity in the Euro-area
sovereign bond markets. Central banks injected a sizable amount of excess liquidity into the nancial system in
order to repair monetary policy transmission channels.
ThepatternsweobserveinFigure2could also just be a manifestation of statistical noise in our commonality
measures. Also, much of the variation in commonality in Figure 2cannot be directly linked to stress periods
suggesting that other forces may also contribute to the variation of these series. Thus, we proceed by adopting
a more systematic approach and considering several variables in an attempt to identify potential drivers of the
commonality in liquidity over time.
6.3. Market volatility
Our time-series regressions include ve specic volatility proxies: the equally-weighted absolute intraday log
returnofthebenchmarkbondsusedinthisstudy(notedasMTSVolatility),thestandarddeviationofthedaily
percentage changes of a bond index (in our case of the Bloomberg-Barclays Pan-European Aggregate Bond
THE EUROPEAN JOURNAL OF FINANCE 23
Index), the MOVE index (a measure of implied Treasury volatility) as well as the VIX36 and VSTOXX indices
(two measures of implied equity volatility related to the US and European markets respectively).
Table 5reports the results of time-series regressions of changes in monthly average commonality in spreads
and in depths among 73 benchmark bond series – denoted by R2
COM,t–onvariousproxiesforvolatility.Each
model specication adds one proxy for volatility to the base model of control variables. Model (1) in Table 5
shows the regression results of the eects of the general capital market environment on changes in commonality
in spreads and in depths, suggesting a signicant link between changes in market-wide liquidity and changes in
Tab le 5 . Changes in volatility and in commonality in liquidity.
Model (1) (2) (3) (4) (5) (6) (7)
Panel A: Changes in Commonality in Spreads
Market Return 0.018 0.034 0.028 0.047 0.020 0.021 0.063∗
(0.46) (0.99) (0.77) (1.17) (0.52) (0.55) (1.66)
Trading Volume 0.017∗∗∗ 0.016∗∗∗ 0.015∗∗∗ 0.015∗∗∗ 0.017∗∗∗ 0.016∗∗∗ 0.012∗∗∗
(3.63) (3.70) (3.13) (3.12) (3.55) (3.48) (2.67)
Market Liquidity 0.011∗∗∗ 0.010∗∗∗ 0.005∗0.010∗∗∗ 0.011∗∗∗ 0.011∗∗∗ 0.005∗
(4.34) (4.76) (1.75) (4.29) (4.02) (4.09) (1.83)
Credit Risk 0.002 0.001 0.002 −0.008 0.009 0.001 −0.001
(0.52) (0.33) (0.60) (−0.25) (0.25) (0.10) (−0.31)
Bond Index Volatility 0.015∗∗ 0.010
(2.34) (1.51)
MTS Volatility 0.069∗∗∗ 0.062∗∗∗
(2.84) (2.69)
MOVE 0.010∗∗ 0.004∗
(2.11) (1.67)
VIX 0.008 −0.012
(0.55) (−0.42)
VSTOXX 0.011 0.010
(1.04) (0.44)
Constant −0.016 −0.016 −0.016 −0.016 −0.016 −0.016 −0.016
(−0.33) (−0.35) (−0.34) (−0.36) (−0.33) (−0.34) (−0.38)
Observations 84 84 84 84 84 84 84
Adjusted R20.15 0.18 0.17 0.19 0.14 0.14 0.20
Panel B: Changes in Commonality in Depth
Market Return 0.011 0.015 0.022 0.043 0.011 0.014 0.054∗
(0.32) (0.46) (0.70) (1.30) (0.34) (0.40) (1.67)
Trad in g Vol ume −0.020 −0.023 −0.025 −0.043 −0.020 −0.025 −0.054
(−0.49) (−0.42) (−0.62) (−1.05) (−0.51) (−0.63) (−1.41)
Market Liquidity −0.179∗∗ −0.167∗∗ −0.122 −0.131∗−0.179∗∗ −0.172∗∗ −0.055
(−2.31) (−2.02) (−1.44) (−1.71) (−2.29) (−2.31) (−0.69)
Credit Risk 0.001 0.001 −0.009 −0.002 −0.001 −0.001 −0.004
(0.05) (0.02) (−0.22) (−0.57) (−0.12) (−0.28) (−0.79)
Bond Index Volatility 0.385 0.804
(0.69) (0.14)
MTS Volatility 0.030∗0.003∗
(1.76) (1.72)
MOVE 0.016∗∗∗ 0.017∗∗∗
(3.06) (2.63)
VIX 0.003 −0.040
(0.39) (−1.14)
VSTOXX 0.011 0.033
(0.83) (0.96)
Constant −0.024 −0.025 −0.025 −0.025 −0.024 −0.024 −0.025
(−0.69) (−0.70) (−0.74) (−0.65) (−0.68) (−0.69) (−0.64)
Observations 84 84 84 84 84 84 84
Adjusted R20.06 0.06 0.11 0.12 0.05 0.06 0.15
Notes: This table reports results of time-series regressions of the change in monthly average commonality in liquidity among 73 benchmark bond
series – denoted by R2
COM,t, computed as the logistic transformation of commonality in liquidity in month t– over the period 2011:06–2018:06
on changes of various aggregate volatility proxies. The reported regressionsare in monthly changes. All equations are estimated using OLS with
Newey-West standard errors, with lag length T1/3,whereTis the indicated sample size. t-statistics are given in parentheses below coefficient
estimates. ∗∗∗p<0.01, ∗∗p<0.05, ∗p<0.1.
24 P. PANAGIOTOU ET AL.
commonality in liquidity. The results suggest that negative shocks to market-wide liquidity (either in spreads or
depths)resultintheliquidityofallsovereignbenchmarkbondstostartmovingtogethermoreclosely.
Models (2)–(7) in Table 5overall suggest a signicant correlation between changes in market volatility and
changes in commonality in either quoted spreads or depths, even after controlling for market returns, changes in
market-wide liquidity, trading volume and credit risk. Three and two out of the ve proxies for volatility are sta-
tistically signicant and positively correlated to commonality in quoted spreads and depth respectively. Models
(2)–(6) in Table 5show that shocks to the MOVE Index, to bond index volatility, and to the MTS volatility each
help explain monthly changes in commonality in quoted spreads and depths. A positive and statistically signif-
icant relation between volatility and commonality in liquidity is also reported for US equity markets (Karolyi,
Lee, and Van Dijk 2012) as well as FX markets (Mancini, Ranaldo, and Wrampelmeyer 2013).
Theeconomicimpactofmarketvolatilityoncommonalityissubstantial.Anincreaseofonestandarddevia-
tioninvolatility,asmeasuredbytheMTSvolatilityvariable,relativetothemeanisassociatedwithanincreasein
commonality in quoted spreads of 2.62%, equal to 0.24 times the standard deviation of commonality in liquid-
ity in spreads, i.e. σ(R2,PQS
COM,t). Similarly, the economic magnitude of the bond index volatility and MOVE index
variables on commonality in spreads is 0.21 ×σ(R2,PQS
COM,t)and 0.24 ×σ(R2,PQS
COM,t)respectively. Similarly impor-
tant is the economic impact of market volatility in commonality in quoted spreads. An increase of one standard
deviation in MTS volatility and MOVE index is associated with an increase in commonality in quoted depths
of 0.26 ×σ(R2,AMD
COM,t)and 0.30 ×σ(R2,AMD
COM,t)respectively.
Interestingly, the estimated coecients of VIX and VSTOXX proxies, although positive, are found to be
not statistically signicant. One possible explanation might be that those two proxies measure equity implied
volatility and, for that reason, may not be suitable proxies for capturing volatility in xed income markets. The
largest adjusted-R2of 19% in terms of quoted spreads and of 12% in terms of quoted market depth are both
attained by the MOVE index. In Model (7) all volatility proxies are considered simultaneously. The results sug-
gestthatchangesinimpliedTreasuryvolatilityandinMTSvolatilityaremorestronglycorrelatedwithchanges
in commonality in both quoted spreads and depths over time as compared to the remaining volatility proxies
used.
However, the results do not suggest any signicant relation between trading volume and commonality in
depth. Given the fragmented structure of the market, a possible explanation might be that traded quantities are
negotiated and agreed bilaterally. Finally, all models in Table 5include a constant term (implying a linear time
trend in levels). The constant has a positive but insignicant coecient for both quoted spreads and depths.
This result suggests that R2
COM,thas been relatively constant over our sample period. These ndings show that
commonality in liquidity is high during periods of high market volatility and high market-wide trading activity.
These ndings show that commonality in liquidity is high during periods of high market volatility and high
market-wide trading activity. Motivated by the related literature, we examine whether commonality in liquidity
in xed income markets is stronger during periods of large market declines, as opposed to periods of large market
increases. We dene large negative (positive) market returns as returns that are in the bottom (top) quartile of
market returns.37 We also include MTS volatility as a control variable to proxy for market-wide volatility. A
shock in market volatility is associated with stronger commonality in liquidity. Regression results are reported
in Table 6.38
Hameed, Kang, and Viswanathan (2010) argue that, in stress periods, a large negative market return may lead
to greater commonality in liquidity through an eect on the wealth and the collateral of investors and liquidity
providers, that commonality should increase during periods of large market declines and the eect of volatility
should be asymmetric. Regression results in Table 6do not show any strong negative relation between R2
COM,tin
spreads and large negative market returns, which would imply that R2
COM,ttends to increase during large market
declines. However, we do nd a positive and statistically signicant relation, at the 10% condence level, between
changes in R2
COM,tin depths and large market increases. This result suggests that commonality in depth tends
to increase with sharp market upswings. A potential explanation for this nding may be that during our sample
period the ECB bought a large volume of government bonds with purchases carried out in several stages.
For both set of results, we nd that shocks to market volatility can aect monthly changes in commonality
in both spreads and depths. Overall, the results show that monthly changes in commonality in liquidity in both
THE EUROPEAN JOURNAL OF FINANCE 25
Tab le 6 . Changes in market returns and in commonality in liquidity.
Model (1) (2) (3) (4)
Panel A: Changes in commonality in spreads
Market Return 0.028 0.060 −0.001 −0.013
(0.77) (1.21) (−0.12) (−0.08)
Market Return∗DDown −0.080 0.074
(−0.60) (0.51)
Market Return∗DUp 0.072 0.005
(0.77) (0.02)
Tradring Volume 0.015∗∗∗ 0.014∗∗∗ 0.014∗∗∗ 0.014∗∗∗
(3.13) (3.11) (3.16) (3.13)
Market Liquidity 0.005∗0.005∗0.005∗0.005∗
(1.75) (1.77) (1.85) (1.83)
Credit Risk 0.002 0.002 0.001 0.002
(0.60) (0.50) (0.46) (0.47)
MTS Volatility 0.069∗∗∗ 0.067∗∗∗ 0.631∗∗∗ 0.630∗∗∗
(2.84) (2.60) (2.52) (2.55)
Constant −0.016 −0.015 −0.016 −0.016
(−0.34) (−0.23) (−0.32) (−0.18)
Observations 84 84 84 84
Adjusted R20.17 0.22 0.22 0.22
Panel B: Changes in commonality in depths
Market Return 0.022 −0.013 0.067 0.079
(0.70) (−0.25) (1.34) (1.84)∗
Market Return∗DDown 0.090 −0.018
(0.82) (−0.09)
Market Return∗DUp −0.083 0.021∗
(−1.28) (1.69)
Tradring Volume −0.025 −0.024 −0.023 0.023
(−0.62) (−0.56) (−0.55) (0.55)
Market Liquidity −0.122 −0.119 −0.117 −0.117
(−1.44) (−1.41) (−1.43) (1.42)
Credit Risk −0.009 −0.005 −0.003 −0.004
(−0.22) (−0.12) (−0.09) (−0.10)
MTS Volatility 0.030∗0.030∗0.033∗∗∗ 0.030∗∗∗
(1.76) (1.84) (1.97) (1.94)
Constant −0.025 −0.024 −0.024 −0.024
(−0.74) (−0.42) (−0.68) (−0.32)
Observations 84 84 84 84
Adjusted R20.11 0.17 0.17 0.17
Notes: This table reports results from regressions of the monthly changes in commonality in quoted
spreads and depths on monthly market returns. The dummy variable DDown is equal to 1 if the mar-
ket return is in the bottom quartile of market returns and 0 otherwise. Similarly, the dummy variable
DUp is equal to 1-D. The control variables include,changes in trading volume, market-wide liquidity,
credit risk, and market volatility. All equations are estimated using OLS with Newey-West standard
errors, with lag length T1/3,whereTisthe indicated sample size. t-statistics are given in parentheses
below coefficient estimates. ∗∗∗p<0.01, ∗∗p<0.05, ∗p<0.1.
spread and depths are driven by both changes in volatility and we nd no signicant evidence of the asymmetric
eect of market returns on monthly changes commonality in liquidity.
6.4. Supply-side: funding constraints
Our time-series regressions include ve specic proxies for the local and global funding constraints of market
makers: the EONIA rate (the interest rate at which banks lend funds in the overnight interbank money market
in the Euro-area), the TED spread (the dierence between the three-month Treasury bill and the three-month
LIBOR based on US dollars), the LOIS spread (the dierence between three-month LIBOR and the three-month
Overnight Indexed Swap rate), the stock-returns of nancial intermediaries (Euro Stoxx Banks Index) and ECB’s
excess liquidity (dened as the sum of excess reserves held by nancial institutions and the net deposit facility
26 P. PANAGIOTOU ET AL.
of the ECB).39 Nowadays, nancial intermediaries can easily obtain leverage internationally, and for that reason
we use a mix of proxies estimated based on European as well as US data.
The EONIA rate is the basis of the term structure of Euro interest rates and the underlying rate of various
derivativescontracts.Itiscalculatedonadailybasisasaweightedaverageofallovernightunsecuredlending
transactions in the interbank market in euro, initiated within the euro area by the contributing banks. Under
normal circumstances the ECB provides liquidity such that EONIA xes close to the re-nancing rate. Hence
theECBusesEONIAastheinstrumentforitsmonetarypolicystance.Wehypothesizeapositivecorrelation
betweenshockstotheEONIArateandmonthlychangesincommonalityinspreadsanddepths,astheyreect
more constrained credit conditions and higher costs of obtaining leverage.
The second proxy of the funding costs of the market makers, is the TED Spread, dened as the dierence
between the three-month Treasury bill and the three-month LIBOR based on US dollars. Because a 3-month
US Treasury bill is considered a risk-free security, the dierence between it and the interest rate on interbank
loans, which is a gauge of international banks’ condence in lending each other, is a good measure of credit risk in
bank funding markets.40 By comparing the risk-free rate to the interbank rate, one can determine the perceived
dierence in risk. In times of uncertainty, banks increase the interest rates on interbank loans, driving up the
LIBOR. A ight to quality would then manifest itself as a widening of the TED spread which, would suggest a
higher default risk of interbank loans and, thus be more costly for the nancial intermediaries to obtain leverage.
Another measure of distress in money markets is the dierence between three-month LIBOR and the three-
month Overnight Indexed Swap rate (OIS). A bank entering into the OIS exposes the bank to future uctuations
in the reference rate. However, the bank can guarantee itself longer-term funding while still paying close to the
overnight rate. Because the alternative would be rolling over the funds on a daily basis at changing overnight
rates, banks are willing to pay a premium. This is reected in the LIBOR-OIS spread (Sengupta and Tam 2008).
In times of stress, the LIBOR, referencing a cash instrument, reects both credit and liquidity risk, but the OIS
has little exposure to default risk because these contracts do not involve any initial cash ows. The OIS rate is
therefore an accurate measure of investor expectations of the eective interest rate (and hence a central bank’s
target) over the term of the swap, whereas LIBOR reects credit risk and the expectation on future over night
rates. When the LOIS spread increases it implies that banks are less willing to lend to each other, it is a signal of
shrinking liquidity, and of increased funding costs.
The fourth measure of funding costs is the stock returns of local and global nancial intermediaries who act
as funding agents. As the balance sheets are continuously marked to market, changes in asset prices show up
immediately on balance sheets and have an instant impact on the net worth of all constituents of the nancial
system. When asset prices increase, nancial intermediaries’ balance sheets generally become stronger, and -
without adjusting asset holdings – their leverage tends to be low. The nancial intermediaries then hold surplus
capital and it is easier for them to nance their inventories and provide liquidity to the market (Adrian and
Shin 2010). Thus, the stock returns can be interpreted as proxies for aggregate funding liquidity and are likely
to be inversely related to the tightness of capital in the market (Karolyi, Lee, and Van Dijk 2012)aswellasto
commonality in liquidity.
ThelastproxyforfundingliquidityistheECB’sexcessliquidity.Inessence,ECB’sexcessliquidityisameasure
ofthecashinexcessofbanks’immediateneedsthatisowinginthenancialsystemanditisviewedasameasure
of tightness in money markets.We hypothesize a positive relationship between excess liquidity and commonality
in liquidity. This assumption is mainly derived by the nature of increases in excess liquidity, which mainly arises
from the ECB’s longer-term liquidity operations and Asset Purchase Programme.
Christensen and Gillan (2018) show that quantitative easing has a very direct eect of reducing liquidity risk
premiums in markets where central banks are buying bonds. Pelizzon et al. (2016) nd that ECB liquidity injec-
tions attenuate the link between the credit risk and market liquidity of sovereign bonds. For the former, the ECB
lends money to nancial intermediaries which reportedly use the nancing to purchase government bonds with
similar maturity to the lending. For the latter, the ECB provides stable demand for Euro-area government bonds
via its Public Sector Purchase Programme (Eser and Schwaab 2016). Both operations from the ECB arguably
contribute to co-movement in Euro-area bond market liquidity.
Table 7presents the estimated results of time-series regressions relating monthly average R2
COM,ton the
supply-side factors. Our regressions are estimated with Newey-West standard errors with lag length T1/3(where
THE EUROPEAN JOURNAL OF FINANCE 27
Tab le 7 . What drives time-series variation in commonality? (Supply-side).
Model (1) (2) (3) (4) (5) (6) (7)
Panel A: Changes in commonality in spreads
Market Return 0.028 0.026 0.035 0.029 0.018 0.022 −0.007
(0.77) (0.64) (1.00) (0.83) (0.48) (0.65) (−0.12)
Trading Volume 0.015∗∗∗ 0.015∗∗∗ 0.016∗∗∗ 0.016∗∗∗ 0.016∗∗∗ 0.013∗∗∗ 0.024∗∗∗
(3.13) (3.36) (3.82) (3.58) (3.68) (2.61) (2.96)
Market Liquidity 0.005∗0.005∗0.006∗0.006∗∗ 0.006∗∗ 0.004 0.002
(1.75) (1.72) (1.86) (2.00) (2.10) (1.52) (0.32)
Credit Risk 0.002 0.002 0.005 −0.006 −0.003 −0.012 −0.022
(0.60) (0.62) (0.14) (−0.20) (−0.17) (−0.29) (−0.37)
Market Volatility 0.069∗∗∗ 0.066∗∗ 0.064∗∗ 0.061∗∗ 0.062∗∗ 0.070∗∗∗ 0.109∗∗
(2.84) (2.15) (2.51) (2.35) (2.45) (3.30) (2.39)
EONIA Rate 0.204 0.278∗
(1.18) (1.69)
TED Spread 0.003∗−0.001
(1.83) (−0.59)
LOIS 0.004∗∗ 0.005∗∗
(1.93) (1.99)
ECB’s Excess Liquidity 0.003∗∗∗ 0.006∗∗∗
(2.04) (2.99)
Dealer’s Stock Returns −0.012∗∗ −0.010∗
(−2.08) (−1.79)
Constant −0.016 −0.016 −0.016 −0.016 −0.015 −0.016 −0.013
(−0.33) (−0.31) (−0.34) (−0.34) (−0.29) (−0.35) (−0.18)
Observations 84 84 84 84 84 84 84
Adjusted R20.17 0.16 0.18 0.18 0.18 0.19 0.25
Panel B: Changes in commonality in depth
Market Return 0.022 0.036 0.022 0.022 0.021 0.017 0.029
(0.70) (1.03) (0.71) (0.68) (0.63) (0.54) (0.90)
Trad in g Vol ume −0.025 −0.036 −0.026 −0.028 −0.024 −0.037 −0.052
(−0.62) (−0.85) (−0.61) (−0.64) (−0.57) (−0.92) (−1.20)
Market Liquidity −0.122 −0.114 −0.121 −0.118 −0.121 −0.108 −0.094
(−1.44) (−1.36) (−1.43) (−1.36) (−1.42) (−1.39) (−1.14)
Credit Risk −0.009 −0.012 −0.009 −0.004 −0.011 −0.004 −0.004
(−0.22) (−0.31) (−0.21) (−0.09) (−0.24) (−0.73) (−0.75)
Market Volatility 0.030∗0.036∗∗ 0.032∗0.033∗0.032∗0.030∗∗ 0.037∗∗
(1.76) (2.21) (1.75) (1.78) (1.76) (1.77) (2.26)
EONIA Rate 0.121∗∗ 0.152∗∗
(2.10) (2.17)
TED Spread 0.001 −0.002
(0.04) (−0.08)
LOIS −0.001 −0.013
(−0.59) (−0.51)
ECB’s Excess Liquidity 0.003 0.009
(0.21) (0.48)
Dealer’s Stock Returns −0.010∗−0.011
(−1.70) (−1.46)
Constant −0.025 −0.024 −0.025 −0.025 −0.024 −0.027 −0.024
(−0.47) (−0.74) (−0.69) (−0.73) (−0.68) (−0.69) (−0.67)
Observations 84 84 84 84 84 84 84
Adjusted R20.11 0.12 0.10 0.10 0.10 0.12 0.11
Notes: This table reports results of time-series regressions of the change in monthly average commonality in liquidity among 73 benchmark bond
series – denoted by R2
avg,t, computed as the logistic transformation of the average commonality in liquidity in month t– over the period 2011:06-
2018:06 on changes of various proxies for funding conditions. All equations are estimated using OLS with Newey-West standard errors, with lag
length T1/3,whereTis the indicated sample size. t-statistics are given in parentheses below coefficient estimates. ∗∗∗p<0.01, ∗∗p<0.05,
∗p<0.1.
Tis the indicated sample size). To be consistent in our modeling across specications, we include the market
return, trading volume, aggregate liquidity, credit risk, and market volatility as control variables in all models as
well as a constant.
28 P. PANAGIOTOU ET AL.
Each model specication adds one variable related to the supply-side explanations to the base model of con-
trol variables and we perform these regressions for percentage quoted spreads and average quoted market depth
separately.
Model (1) for spreads and depth in Table 7is the same as the model (3) and (4) in Table 5andshowsasignif-
icant correlation between changes in market volatility and changes in commonality in liquidity. The empirical
evidence that tightness in funding conditions aects quoted spreads is fairly consistent across several dierent
proxies and the explanatory power of these variables is reasonably good. The economic magnitude of funding
tightness proxies is substantial. Relative to commonality in depth, explanatory power is low, some variables have
unexpected signs and thus the evidence is much weaker. A common point between commonality in spreads and
depthsistheeectofthestockreturnsofnancialintermediarieswhoactasfundingagents.
More specically, the results show that shocks to the MTS volatility help explain monthly changes in com-
monality in quoted spreads and depths. The estimated coecient has a positive sign that it is signicant at the
1% condence level relative to commonality in spreads and at the 10% condence level relative to commonality
in depths and of the same sign. The values of the adjusted-R2in the baseline model for spreads and depths are
17% and 11% respectively.
Models (2)–(6) expand model (1) by including more direct proxies for the funding liquidity of market mak-
ers while in model (7) we consider all proxy variables simultaneously. Monthly changes to the EONIA rate are
signicantly correlated to changes in commonality in depths but not to changes in commonality in spreads.
Shocks to the TED spread and ECB’s excess liquidity are positively correlated with R2
COM,tin spreads at the 10%
and 5% signicance level respectively, but they are not statistically signicant with respect to commonality in
depths.AnincreaseofonestandarddeviationintheTEDspreadandinECB’sexcessliquidityisassociatedwith
an increase in commonality in quoted spreads of 0.16 ×σ(R2,PQS
COM,t)and 0.17 ×σ(R2,PQS
COM,t)respectively. More-
over, the estimated coecient on LOIS spread is positive and statistically signicant at the 5% signicance level
in relation to R2
COM,tin spreads, but it is not found to be signicantly correlated with changes in commonal-
ity in quoted depths. The negative sign of the estimated coecient of LOIS goes against the prediction of the
supply-side hypothesis. An increase of one standard deviation in the LOIS spread is accompanied by a change
in commonality in quoted spreads of 0.14 ×σ(R2,PQS
COM,t).
The stock returns of a portfolio of European dealer-banks appear to have a signicant inuence on com-
monality in liquidity. The coecient on European dealer-banks returns is negative and statistically signicant
at the 5% and 10% condence level in relation to changes in commonality in quoted spreads and depths
respectively. The economic magnitude of these coecients is considerable and equal to −0.20 ×σ(R2,PQS
COM,t)
and −0.19 ×σ(R2,AMD
COM,t)respectively.
Model (7) indicates that the eect of four out of the ve proxies for the funding conditions does not change
even when these variables are considered simultaneously. Noticeable is the increase in the adjusted-R2from
17% in the baseline model (1) to 25% in model (7). However, the eects of these proxies in relation to the
commonality in depth disappear, with the exception of the EONIA rate and underline the role of changes in
market-wide volatility. In sharp contrast to the increase in the adjusted-R2values with regard to spreads, the
adjusted-R2values for models (1) to (7) in relation to depths remain constant.
Overall, the evidence that our proxies for funding liquidity can explain the dynamics of commonality in
quoted spreads in our sample is strong but weak in explaining the dynamics of commonality in depth.
6.5. Demand-side determinants
Table 8presents the estimation results of time-series regressions relating monthly average R2
COM,tto the demand-
side factors. Again, model (1) for spreads and depth in Table 8arethesameasthemodel(3)and(4)inTable5
and shows a signicant positive correlation between changes in market volatility and changes in commonality
in liquidity for both quoted spreads and depths.
Models (2) and (3) indicate that the sentiment indices used do not help to explain time-series variation in
monthly changes in commonality for both quoted spreads and depths. Models (4) and (5) suggest that overall
European government policy uncertainty shocks have no signicant eect on monthly changes in commonality
THE EUROPEAN JOURNAL OF FINANCE 29
Tab le 8 . What drives time-series variation in commonality? (Demand-side).
Model (1) (2) (3) (4) (5) (6) (7)
Panel A: Changes in commonality in spreads
Market Return 0.028 0.052 0.028 0.036 0.027 0.037 0.032
(0.77) (0.59) (0.77) (0.95) (0.69) (1.06) (0.82)
Trading Volume 0.015∗∗∗ 0.016∗∗∗ 0.015∗∗∗ 0.013∗∗∗ 0.013∗∗∗ 0.014∗∗∗ 0.014∗∗
(3.13) (3.13) (3.10) (2.49) (2.81) (3.03) (2.45)
Market Liquidity 0.005∗0.005∗0.005∗0.004 0.003 0.005∗0.004
(1.75) (1.76) (1.76) (1.57) (1.36) (1.68) (1.60)
Credit Risk 0.002 0.010 0.009 0.010 0.002 0.001 −0.007
(0.60) (0.25) (0.52) (0.27) (0.41) (0.13) (−0.16)
Market Volatility 0.069∗∗∗ 0.068∗∗∗ 0.071∗∗∗ 0.070∗∗∗ 0.070∗∗∗ 0.069∗∗∗ 0.063∗∗∗
(2.84) (2.85) (2.75) (2.83) (2.67) (2.98) (2.58)
Sentix Euro−Area −0.007 −0.006
(−0.67) (−0.52)
Sentix USA −0.003 0.007
(−0.22) (0.72)
EPU Europe 0.011 0.004
(1.36) (0.41)
EPU USA 0.003 0.008
(1.36) (0.41)
Stoxx50 RV 0.101∗∗∗ 0.093∗
(2.85) (1.85)
Constant −0.016 −0.016 −0.015 −0.015 −0.012 −0.016 −0.016
(−0.33) (−0.32) (−0.33) (−0.33) (−0.32) (−0.34) (−0.32)
Observations 84 84 84 84 84 84 84
Adjusted R20.17 0.16 0.16 0.17 0.17 0.20 0.16
Panel B: Changes in commonality in depth
Market Return 0.022 0.027 0.023 0.033 0.022 0.028 0.036
(0.70) (0.85) (0.69) (0.93) (0.65) (0.81) (0.90)
Trad in g Vol ume −0.025 −0.033 −0.026 −0.053 −0.035 −0.030 −0.060
(−0.62) (−0.81) (−0.63) (−1.45) (−0.87) (−0.72) (−1.42)
Market Liquidity −0.122 −0.125 −0.122 −0.085 −0.105 −0.113 −0.129∗
(−1.44) (−1.45) (−1.42) (−1.13) (−1.33) (−1.43) (−1.79)
Credit Risk −0.009 −0.001 −0.009 −0.002 −0.002 −0.002 −0.002
(−0.22) (−0.13) (−0.20) (−0.46) (−0.37) (−0.44) (−0.28)
Market Volatility 0.030∗0.032∗0.032∗0.041∗0.031∗0.032∗0.023
(1.76) (1.72) (1.74) (1.79) (1.73) (1.71) (1.64)
Sentix Euro−Area 0.006 0.008
(1.02) (1.25)
Sentix USA 0.0075 0.004
(0.06) (0.33)
EPU Europe 0.001 0.001
(1.02) (0.81)
EPU USA 0.002 0.001
(0.82) (0.02)
Stoxx50 RV 0.057 0.059
(0.95) (1.05)
Constant −0.025 −0.025 −0.025 −0.025 −0.025 −0.024 −0.024
(−0.47) (−0.75) (−0.73) (−0.67) (−0.72) (−0.74) (−0.66)
Observations 84 84 84 84 84 84 84
Adjusted R20.11 0.10 0.10 0.11 0.11 0.11 0.06
Notes: This table reports results of time-series regressions of the change in monthly average commonality in liquidity among 73 benchmark
bond series – denoted by R2
avg,t, computed as the logistic transformation of the average commonality in liquidity in month t– over the period
2011:06–2018:06 on changes of various proxies on demand-side determinants. All equations areestimated using OLS with Newey-West standard
errors, with lag length T1/3,whereTis the indicated sample size. t-statistics are given in parentheses below coefficient estimates. ∗∗∗p<0.01,
∗∗p<0.05, ∗p<0.1.
in quoted spreads and depths. The realized volatility of Stoxx50 variable in Model (6) seem to help to explain
monthly changes in commonality in spreads but not in depths. The estimated coecient is positive and statis-
tically signicant at the 1% condence level providing evidence on the cross-market liquidity interdependence
betweenstockandbondmarkets.AonestandarddeviationintheStoxx50realizedvolatilityisassociatedwith
an increase in commonality in quoted spreads of 0.11 ×σ(R2,PQS
COM,t).41
30 P. PANAGIOTOU ET AL.
In Model (7), where we consider all demand-side variables simultaneously, only shocks to trading volume
and market volatility accompanied by stock market volatility help to explain time-variation in commonality in
spreads. In relation to commonality in depth only bond market volatility retains its information content. Overall,
the evidence that our demand-side proxies can explain the behavior of commonality in liquidity in our sample
is weak.42
6.6. Macroeconomic announcements and commonality in liquidity
We collect macroeconomic news announcements that relate to interest rate setting, ination, unemployment
and GDP in the Euro area in the sample period from Bloomberg. For the announcements on ination and GDP,
wekeeponlythereleaseofashestimatesastheymovethemarketsthemostcomparedtothereleaseofnal
data prints and revisions. The nal dataset comprises 85 CPI announcements, 28 GDP announcements, 71 ECB
meetings on policy rate decisions, and 84 unemployment announcements in the Euro area.
In Table 9we report the results the average daily co-movement percentages i) across all trading days (column
1), and ii) across only those trading days with Euro area macro news releases (columns 2–5). We report results
for spread and depth co-movements in Panel A and in Panel B, respectively. We further split our cross-section of
issuing countries between core and periphery markets43 and we perform the same analysis and present separate
results. We report results for large and low announcements surprises for spreads and depths co-movements
separately and across core and periphery markets in subpanels in Table 9.
Chordia, Roll, and Subrahmanyam (2001) report signicant pre-announcement liquidity eects for US
unemployment and GDP releases while Brockman, Chung, and Pérignon (2009) nd that both domestic and
US macro news increase exchange-level and global liquidity commonality in equity markets. In line with previ-
ous literature, we nd a heterogeneous liquidity response to macroeconomic news announcements with some
announcements having a stronger impact on commonality in liquidity than others. For example, the results in
Panel A of Table 9suggest that macroeconomic announcements related to interest rates and GDP signicantly
aect co-movements in liquidity in proportional quoted spreads. The average co-movement in spreads is 63.55%
across all trading days and all benchmark bonds. This percentage increases to 67.87% during interest rate setting
announcements. Interestingly, it reduces to 61.57% during GDP announcements, while ination and unemploy-
ment news releases seem to have no signicant eect on liquidity co-movements. We nd similar results for both
periphery and core economies.
Turning to Panel B, the average co-movement in depths is 56.48% across all trading days and all benchmark
bonds. This percentage increases to 61.77% for interest rate announcements with ination, GDP and unemploy-
ment announcements having no signicant eect on liquidity co-movements. Interestingly, when examining the
division between periphery and core economies, the results suggest that ination and unemployment announce-
ments in periphery economies and GDP announcements in core economies are associated with a lower average
co-movement in depths. We nd similar results when examining the eect of announcements surprises on
liquidity commonality of government bonds. It should be noted however that the eects of interest rate pol-
icymeetingsandGDPannouncementsarestrongerondayswithlargeannouncementsurprisesinrelationto
spreads.
Overall, our ndings show that ECB meetings on rate decision clearly drive up co-movement in spreads and
depth and highlight the important link between interest rate changes and Euro-area secondary-market liquidity.
7. Alternative methodology and endogeneity
For a better understanding of the joint dynamics of commonality in liquidity and the various variables that
proxy for the supply and demand-side determinants we further explore these relationships by estimating a vector
autoregressive (VAR) model. In the previous sections of this paper we run simple OLS time-series regressions
of the monthly average commonality in spreads and in depths on various variables aimed to proxy for dierent
demand and supply-side explanations of commonality in liquidity. Although our objective is not to test for
causation, the possibility of endogeneity due to simultaneity should be taken under consideration. As we estimate
these eects contemporaneously at the monthly frequency, we implicitly assume that the causality runs from the
THE EUROPEAN JOURNAL OF FINANCE 31
Tab le 9 . Macroeconomic announcements and commonality in liquidity.
Market
Unconditional
sample average Inflation GDP
Interest
rates Unemployment
Panel A: Commonality in spread and Euro area macroeconomicnews
Whole market 63.55% 62.70% 61.57%∗67.87%∗∗∗ 62.68%
Periphery 67.09% 66.67% 65.56% 70.63%∗∗ 66.63%
Core 64.86% 64.31% 62.59%∗∗ 68.60%∗∗ 66.29%
Subpanel A.I: Large announcement surprises
Whole market 62.91% 62.14% 61.51%∗∗ 68.82%∗∗∗ 62.05%
Periphery 65.75% 65.47%∗64.25%∗71.48%∗∗∗ 65.30%
Core 62.91% 62.57% 61.28%∗∗∗ 69.22%∗∗∗ 64.30%
Subpanel A.II: Low announcement surprises
Whole market 61.64% 57.06% 61.02% 67.19%∗∗ 61.43%
Periphery 66.42% 60.67% 65.30% 69.22%∗∗ 64.63%
Core 64.21% 58.52% 62.09% 66.54%∗∗ 62.31%
Panel B: Commonality in depth and Euro area macroeconomic news
Whole market 56.48% 56.23% 56.29% 61.77%∗56.19%
Periphery 59.99% 58.89%∗∗ 59.60% 60.09% 58.72%∗∗∗
Core 57.63% 57.40% 56.42%∗∗ 57.58% 57.46%
Subpanel B.I: Large announcement surprises
Whole market 55.97% 55.89% 56.23% 62.63%∗55.63%
Periphery 58.97% 58.07%∗∗∗ 58.17% 60.81% 57.55%∗∗
Core 56.19% 56.14% 55.24%∗∗∗ 58.10% 55.74%
Subpanel B.II: Low announcement surprises
Whole market 54.84% 51.17% 55.78% 61.21% 55.07%
Periphery 59.51% 53.59% 59.36% 59.13% 56.96%∗
Core 57.23% 52.23% 55.97%∗56.26% 53.73%
Notes: This table reports results of the liquidity co-movement measure averaged across all trading days overthe whole s ample period (i.e. uncon-
ditional sample average) and across event days on which four separate types of macroeconomic news are released relative to: central bank
meetings on interest rate decisions, inflation, unemployment and the gross national product (GDP). We use a three-day event window with
the announcement day being the last day in the event window (days −2 to 0) in order to capture the effects of pre-announcement portfolio
rebalancing on market liquidity. We measure the daily liquidity co-movement by first counting the number of benchmark bonds with positive
and negative changes in their liquidity measure for each trading day, then dividing the larger of these two numbers by their sum. The liquidity
co-movement measures are averaged across all sample days and over the three-day event window with the announcement day being the last
day in the window. We present two panels in this table across the whole market, periphery markets, and core markets in order to evaluate the
differential impact of macroeconomic news announcement on commonality in liquidity. Panel A and B show the impact of Euro Area macroe-
conomic announcements on proportional quoted spreads and average quoted depth. We calculate announcement surprises as the difference
between the actual announcement (αt) and the market expectation of the announcement (E(αt)) scaled by the cross-sectional standard devi-
ation of the forecast error (αt−E(αt)). We extent our event-window to include two days after the announcement day (i.e. days −2 to 0 and to
+2), in order to additionally capture the effects of post-announcement portfolio rebalancing on market liquidity. Here, ∗,∗∗, and ∗∗∗ indicate
that the test statistic is significant at the 10%, 5%, and 1% confidence levels, respectively, in a Welch’s two-tailed, t-test for differences in means
between the unconditional sample average and the average of the co-movement measure on macroeconomicannouncement days for the Euro
Area.
explanatory variable to the dependent variable. For at least several of the explanatory variables used, we cannot
rule out the possibility that the causality may in fact run in the opposite direction. We need to be cautious with the
extent to which we can make strong statements about the causal direction of the relations among these variables.
However, simple OLS time-series regressions have strong statistical power. If the possibility of endogeneity due
to simultaneity does not have a signicant eect on our estimated coecients, the simple OLS regression model
may be preferable due to increased precision (Comerton-Forde and Putnin¸š 2015).
A potential solution to the problem of endogeneity due to simultaneity could be the use of two-stage least
squares (2SLS) instrumental variables regressions (see, for example, Hasbrouck and Saar 2013;Comerton-Forde
and Putnin¸š 2015;Ibikunle2018).However,therearenoclearorevidentvariablestobeusedasinstruments.
It would require an exogenous institutional change across sovereign bond markets from dierent countries and
such an exogenous market structure is hard to identify. In addition with weak instruments, especially with many
instruments, and even in large samples two-stage OLS inference is not ecient as estimates may be biased and
32 P. PANAGIOTOU ET AL.
condence intervals too narrow (Hahn and Hausman 2002;Hausman,Stock,andYogo2005). Thus, we proceed
by considering the best next alternative which is the use of a vector autoregressive model.
For a better understanding of the joint dynamics of commonality in liquidity and between the variables that
controlforthegeneralmarketconditionsaswellasthevariablesthatproxyforthesupplyanddemand-sidedeter-
minants we estimate a vector autoregressive (VAR) model. In this approach, commonality in liquidity responds
to innovations on lags of itself and on lags of all the other explanatory variables. For both commonality in spreads
and depths, we estimate an eleven-equation VAR model with commonality in liquidity, market returns, volatility,
Table 10. Granger causality wald tests (supply-side).
Market Trading Market Credit Market EONIA TED LOIS ECB’s Dealer’s
R2
COM,tReturn Volume Liquidity Risk Volatility Rate Spread Rate Excess Liq. Stock Returns
R2
COM,tin Spreads 5.64 6.33 5.47 5.33
(0.06) (0.04) (0.07) (0.07)
Market Return 8.43
(0.02)
Trading Volume 5.71 8.76
(0.06) (0.01)
Market Liquidity 11.24 12.46 33.82 11.64
(0.00) (0.00) (0.00) (0.00)
Credit Risk 7.44 27.78 6.14 9.98 13.98
(0.02) (0.00) (0.05) (0.01) (0.00)
Market Volatility 11.00 8.95
(0.00) (0.01)
EONIA Rate 12.30 4.84 11.18 8.04
(0.00) (0.09) (0.00) (0.02)
TED Spread 5.22 23.29
(0.07) (0.00)
LOIS S pre ad 15.17
(0.00)
ECB’s Excess Liquidity 10.46 7.15
(0.01) (0.03)
Dealer’s Stock Returns 11.92 5.11 9.24
(0.00) (0.08) (0.01)
R2
COM,tin Depths 6.06 6.52 8.33
(0.05) (0.04) (0.02)
Market Return 8.50
(0.01)
Trading Volume 4.92 6.77 6.10
(0.09) (0.03) (0.05)
Market Liquidity 5.76 5.38 5.93 23.34 10.44
(0.06) (0.07) (0.05) (0.00) (0.01)
Credit Risk 5.08 23.56 14.83 12.03
(0.08) (0.00) (0.00) (0.00)
Market Volatility 8.50 9.94
(0.01) (0.01)
EONIA Rate 4.96 8.75
(0.08) (0.01)
TED Spread 7.38 9.45 9.50 5.47 4.84
(0.03) (0.01) (0.01) (0.07) (0.09)
LOIS Spread 5.84 6.00
(0.05) (0.05)
ECB’s Excess Liquidity 5.20 5.02
(0.07) (0.08)
Dealer’s Stock Returns 5.14 7.15 5.67
(0.08) (0.03) (0.06)
Notes: This table presents χ2statistics and p-values (in parentheses) of pairwise Granger causality Wald tests between endogenous VARvariables,
estimated at monthly frequency. The null hypothesis is that the row variable does not Granger-cause the column variable. The sample is from
June 2011 to June 2018 (85 months). To facilitate interpretation, we leave the entry blank if p-values are statistically insignificant at the 10%
significance level.
THE EUROPEAN JOURNAL OF FINANCE 33
Table 11. Granger causality wald tests (demand-side).
Market Trading Market Credit Market Sentix Sentix EPU EPU Stoxx50
R2
COM,tReturn Volume Liquidity Risk Volatility EA US Europe US RV
R2
COM,tin Spreads 5.46
(0.07)
Market Return 4.93
(0.09)
Trading Volume 12.54 8.90 11.72 4.88
(0.00) (0.01) (0.00) (0.09)
Market Liquidity 8.86 13.58 14.63 5.06 11.53 25.99
(0.01) (0.00) (0.00) (0.08) (0.00) (0.00)
Credit Risk 8.28 7.97 5.58 5.44 7.46
(0.02) (0.02) (0.06) (0.07) (0.02)
Market Volatility 15.65 6.12 11.88
(0.00) (0.05) (0.00)
Sentix Euro-Area 5.25 8.04
(0.07) (0.02)
Sentix USA
EPU Europe 6.30 7.39
(0.04) (0.03)
EPU USA 6.49
(0.04)
Stoxx50 RV 6.36
(0.04)
R2
COM,tin Depths 3.14 2.72
(0.08) (0.10)
Market Return 4.33 4.40
(0.04) (0.04)
Trading Volume 4.65 5.15
(0.03) (0.02)
Market Liquidity
Credit Risk 3.97 4.73 6.48
(0.05) (0.03) (0.01)
Market Volatility 4.12 2.85
(0.04) (0.09)
Sentix Euro-Area
Sentix USA 3.38 3.10
(0.07) (0.08)
EPU Europe 7.47
(0.01)
EPU USA 7.88 10.23 3.22
(0.01) (0.00) (0.07)
Stoxx50 RV 2.99
(0.08)
Notes: This table presents χ2statistics and p-values (in parentheses) of pairwise Granger causality Wald tests between endogenous VARvariables,
estimated at monthly frequency. The null hypothesis is that the row variable does not Granger-cause the column variable. The sample is from
June 2011 to June 2018 (85 months). To facilitate interpretation, we leave the entry blank if p-values are statistically insignificant at the 10%
significance level.
and market-wide liquidity as endogenous variables. We further expand the model by adding each time a dier-
ent set of endogenous variables that proxy for various demand and supply-side determinants. We estimate the
model up to two monthly lags. We further estimate Granger causality Wald tests in an attempt to establish the
direction of causation. For the null hypothesis that variable idoes not Granger cause variable j,wetestwhether
the lag coecients of iare jointly zero when jis the dependent variable in the VAR. The results for the supply
and demand-side models are reported in Tables 10 and 11,respectively.
Relativetothesupply-side,theresultssuggestthatourmainconclusionsarestillvalidevenaftercontrol-
ling for the joint dynamics of the variables. Market volatility seems to Granger cause commonality in quoted
34 P. PANAGIOTOU ET AL.
spreads and, again, three out of the ve proxies for the funding conditions of nancial intermediaries are found
to Granger cause commonality in quoted spreads, and the reverse is not supported by the empirical results. It
is also important to note that commonality in quoted spreads is not found to Granger cause any of the remain-
ing explanatory variables we used in our regression and VAR models. Market liquidity, as opposed to market
volatility, is found to Granger cause commonality in depths and, again, two out of the ve proxies for funding
conditions are found to Granger cause commonality in depths. Regarding the demand-side, our VAR results are
very similar to the results we obtain from the OLS time-series regressions in the sense that they also suggest that
proxy variables used do not help to explain time-series variation of commonality in both quoted spreads and
depths. Overall, our key ndings survive even after an attempt to address the endogeneity of the variables we
use in this study.
8. Summary
In this study we test for a common component in liquidity variation across sovereign benchmark bonds, within
as well as across countries and maturities, issued from 10 large Euro-area economies, over a 7-year period of
2011–2018 using tick-by-tick data from MTS, the largest Euro-area inter-dealer xed income market. We nd
strong evidence of commonality in liquidity in quoted spreads and depths within countries or maturities and
across countries and maturities. Particularly, at the pan-European level, approximately 40% and 23% of the
variation of spreads and depths, respectively, is explained by the variation of market-wide liquidity.
We then empirically examine which underlying economic sources generate time-series variation in the pan-
European, common liquidity factor we extracted. We derive testable hypotheses that stem either from supply-
side forces related to the funding costs of nancial intermediaries or from demand-side forces related to investor
sentiment, a government’s economic policy uncertainty and the cross-market linkages with the equity market.
Our overall evidence is more reliably consistent with supply-side explanations for commonality in liquidity. Our
demand side proxies do not help explain time-variation of commonality in liquidity. We also examine whether
commonality intensies around days in which announcements of key macroeconomic indicators are taking
place. We nd that commonality in liquidity intensies around ECB policy meetings.
Finally, policy makers may be able to draw policy-relevant implications from this study. Central banks con-
cerned about potential liquidity dry-ups across many xed-income securities may be able to minimize the risk
of liquidity crises by lowering the funding cost of nancial intermediaries and/or increasing liquidity provision
in periods of market stress.
Notes
1. Studies on the US xed-income market have examined commonality in liquidity in either US Treasury markets in isolation
(Fleming 2003) or liquidity dynamics between US equities and bonds (Chordia, Sarkar, and Subrahmanyam 2005;Goyenko
and Ukhov 2009) and across the corporate bond and credit default swap markets (Pu 2009).
2. The maturities are 2-year, 3-year, 5-year, 7-year, 10-year, 15-year, 20-year and 30-year.
3. All sovereign benchmark bonds included in our data set are based on the actions of the European Central Bank and are denom-
inated in the same currency, thereby isolating the liquidity dierences across countries (Beber, Brandt, and Kavajecz 2009).
4. Germany does not operate any primary dealership system per se but limits access to the primary market to nancial institutions
domiciledinanEUmemberstateandfulllingcertainconditions.TheinstitutionsentitledtooperateinGermanprimary
marketsareknownastheBundIssuesAuctionGroup.
5. Primary dealers, by being willing to hold inventories of government bonds and allowing investors to swap between various
outstanding issues of government bonds on a continuous basis, help bring liquidity to primary and secondary markets.
6. AFME, Government Bond Data Report, 2018Q2.
7. The use of alternative funding instruments is largely a function of overall borrowing needs. For example, if public decits
increase suddenly, sovereignissuers initial lytend to diversify into other funding sources to avoid large uctuations in the volume
of conventional issuance.
8. Including debt issued by supranational institutions.
9. ECB Statistical Data Warehouse and Haver Analytics.
10. BIS (2016), Electronic Trading in Fixed Income Markets, Markets Committee Report.
11. AFME, Government Bond Data Report, 2017Q4.
THE EUROPEAN JOURNAL OF FINANCE 35
12. Sentix Indices is a comprehensive capital markets survey designed to identify sentiment and expectations of private and insti-
tutional investors. From the survey results, various indices and indicators are calculated such as Sentix Euro Break-up Index,
which shows the likelihood, from the perspective of investors, for a breakup of the Euro Area within the next 12 months. These
are collected by ‘Sentix - Behavioral Indices’ and are available on the internet via www.sentix.de to frequent participants in the
survey. They are also obtainable inter alia via Bloomberg or Thomson Reuters.
13. Across markets, inventory carrying costs should co-move as these costs depend on market interest rates.
14. Realized volatility variables were obtained from Oxford-Man Institute of Quantitative Finance.
15. Sovereign bond trading is not centralized in any particular location, thus information on aggregated, actual traded volumes and
market shares is not available even to the banks collecting price data with the various competing trading platforms not publicly
revealing their actual trading volumes for business reputation purposes. The incompleteness of the data can cause estimated
liquidity measures to be biased measures of liquidity in the inter-dealer market as a whole, and to become more biased over
time.Toalleviatesuchconcerns,wecomparethetimeseriesofendofdaypricesinBloombergandinMTS.Theresultsshow
that quoted prices (bid, ask and mid) nearly perfectly co-move (the correlation coecient ranges from 96.5% to 99.5%) between
two electronic platforms, suggesting prices in the MTS are representative of market activities. In addition, although the level of
the MTS coverage ratio in terms of trading volume is incomplete and varies across countries it is, however, fairly accurate in
capturing trends in the overall market size. By collecting information on traded volumes from various sources and for the main
trading platforms over our sample period, we attempted to most accurately calculate traded volumes per country as well as the
Euro-area. We compared the estimated traded volumes with the traded volumes in our MTS dataset. The correlation coecient
from this comparison ranges from 65% to 90% across countries.
16. While we also have information on Greek securities, given the loss of market access that Greece experienced during our sample
period the data are too sparse for inclusion in our analysis.
17. The market share is calculated based on the outstanding amounts of debt securities issued by central governments in August
2018 from the ECB’s Statistical Data Warehouse.
18. International Security Identication Number.
19. For instance, new traders may come in, executing orders inside the publicized spread, or the spread may widen if the size of
an order is large. Moreover, in some electronic markets traders may post hidden limit orders that are not reected in quoted
spreads.
20. While trading volume and order ow are certainly distinct concepts, they are likely to be correlated as days after larger order
ows may well be the days with high trading volume.
21. Germany, France, Spain and Italy maintained the benchmark yield curve consisting of the above-mentioned eight maturities in
thewholesampleperiodofourstudy.However,forexample,Portugaldoesnothavea20-yearbenchmarkbondduringJune
2011 and January 2014 and Ireland does not have a 30-year benchmark bond until February 2015 due to loss of market access.
22. For example, Austria does not have a benchmark 20-year bond during February 2012 and March 2013. We compensated for the
missing data by using observations from another Austrian bond (ISIN:AT0000A04967) which has about 25 years of remaining
maturityandthusliesinthebucketbetween20and30years.
23. Missing benchmark bond series are: 15-year for Ireland and Portugal, 20-year for Finland, Ireland and Portugal and 30-year
for Ireland and Portugal. Please note that the 73 balanced benchmark bond series per each liquidity measure are formed from
a set of 625 sovereign bonds and making use of all 625 bonds. In each benchmark bond series, an observation represents a
liquidity measurement from the respective benchmark bond at a specic day. Given that benchmark status is attained by the
most recently issued and traded sovereign bond in each maturity, at the cross-section, this implies liquidity measurements from
the respective benchmark bond from each country and maturity for every date in our sample period.
24. Following Chordia, Roll, and Subrahmanyam (2000), we examine percentage changes rather than levels for two reasons: rst,
our interest is in discovering whether liquidity co-moves, and second, time series of liquidity level are more likely to be non-
stationary.
25. This,however,ismorerelevantfortheeectivespreadwhichisafunctionoftradepricesandisthussignicantlycorrelated
with market returns.
26. BloombergBarclaysPan-EuropeanAggregateBondIndexisawidelyusedandcitedindexbymarketparticipantswhenreferring
to European government bonds. It is a subset of the Bloomberg Barclays Global Aggregate Bond Index and is calculated with
the same methodology. It includes only bonds issued in a European currency. In addition, we perform the same analysis using
as a proxy for the market the Bloomberg Barclays Euro Aggregate Bond Index which includes only Euro-denominated bonds.
The results are very similar in either case and thus not reported separately.
27. In order for market-wide liquidity to be less inuenced by extreme values, a common practice is to rely on a trimmed mean.
We also calculate market-wide liquidities using trimmed mean, rather than simple mean by excluding the benchmark bond
series with the highest and lowest value. As expected, these market-wide liquidities are somewhat less volatile but share the
same pattern as market-wide liquidities based on a simple mean.
28. The market return is computed as the average over the month of the daily logarithmic return of the Bloomberg-Barclays
Pan-European Aggregate Bond Index. Market liquidity for each liquidity measure, is dened as the rst extracted principal
component. Trading volume is computed as the Euro daily mean of traded volume of all benchmark bond series included in
ourdataset.Weusetwodierentproxiesformarketvolatility:theequally-weightedabsoluteintradaylogreturnofthebench-
mark bonds used in this study (noted as MTS Volatility), and the standard deviation of the daily percentage changes of the
Bloomberg-Barclays Pan-European Aggregate Bond Index. We use the iTraxx SovX Western Europe Index as proxy variable for
36 P. PANAGIOTOU ET AL.
credit risk. This index measures the credit default risk of European sovereign debt covering the sovereign CDS of 15 European
countries (Hui, Lo, and Lau 2013; Kallestrup, Lando, and Murgoci 2016). The results of unit-root tests can be found in Table A2
in the Appendix.
29. Previous work suggests that the number of trades, and not the volume of trading, is a better indicator of an individual’s security
asymmetric information. The propensity of traders to hide their information by order splitting, is viewed as a possible explana-
tion for this result. Given that the sovereign bonds market is characterized by infrequent trading, we consider the total trading
volumeandwedonotdierentiatebetweenthenumberandthesizeofthetrades.
30. The impact of one one-standard-deviation (σ)increaseinthevalueofatime-seriesvariable(relativetoitsmean)
on R2
COM,tcanbecomputedusingthefollowingexpression:R2
COM,t=expα+β×(μ+σ)+γ×λ/(1+expα+β×(μ+σ)+γ×λ)−
expα+β×μ+γ×λ/(1+expα+β×μ+γ×λ),whereα,β,andγare the intercept, the estimated coecient on the time-series variable
of interest, and the vector of coecients on the other time-series variables in the model, respectively; μand λare the mean of
the time-series variable of interest and the vector of means of the other time-series variables, respectively. For σ,wetakethe
time-series standard deviation of the variable of interest. To express the economic signicance as a fraction of one standard
deviation of the commonality measures, we compute the time-series standard deviation of R2
COM,t.
31. The correlation coecient between the R2
COM,tmeasure and the monthly average of the synchronicity measure is estimated to
be 91% for spreads and 83% for depths.
32. We did robustness checks with both 10% and 20%. The qualitative results do not change.
33. Periphery markets include Spain, Portugal, Ireland and Italy in our sample and core markets encompass Austria, Belgium,
Finland, France, Germany, and Netherlands in our sample.
34. As a robustness test, we have estimated the same regressions using the cross-sectional average as the measure of market liquidity.
The results are similar and reported in Tables A4, A5, A6 in the Appendix.
35. The cross-sectional t-statistic for the average βwhen using the PCA method to extract market-wide liquidity, is calculated
under the assumption that the estimation errors in βjare independent across regressions. To check whether the equations are
related through the correlation in the errors, we conduct a simple investigation of the residuals in (1). The results suggest little
cross-equation dependence. Test details and results can be found in Table A1 in the Appendix.
36. Here the VIX index is viewed as a volatility indicator, although it could also be considered as a proxy for investor sentiment.
37. We use the returns of the Bloomberg-Barclays Pan-European Aggregate Bond Index as market returns.
38. Note that the results for model (1) in Table 6areconsistentwiththeresultsinmodel(3)inTable5.
39. More specically, excess reserves are measured as the dierence between the current accounts held by nancial institutions at
thecentralbank(availableattheendofeachday)andtheirrequiredreserves(denedonamonthlybasis).Thenetdeposit
facility corresponds to the dierence between the deposit facility and the marginal lending facility of the ECB, both available at
a daily frequency. Source: Recent Developments in Excess Liquidity and Money Market Rates, ECB, Monthly Bulletin, January
2014.
40. ‘Actions to Restore Financial Stability’, Federal Reserve Bank of Minneapolis, December 2008.
41. Similarly, we obtain a positive and statistically signicant coecient when using the monthly standard deviation of Stoxx50
daily returns as proxy variable.
42. In unre ported regression results, since the FX mar ket is in the crossroad of any international portfol io allocation, we also include
inourtime-seriesregressionsexchangeratechanges.WeusethenominalaswellastherealeectiveexchangerateoftheEuro
against a group of 19 partner countries. We expect that commonality is greater when the local currency depreciates (as this may
attract foreign investors). The results are not statistically signicant and thus not reported here.
43. Periphery markets include Spain, Portugal, Ireland and Italy in our sample and core markets encompass Austria, Belgium,
Finland, France, Germany, and Netherlands in our sample.
Acknowledgements
We would like to thank Chris Adcock (the editor) and two anonymous referees for their valuable comments. We are also grateful for
helpful discussions with and comments from Paul Beaumont (discussant), Daragh Clancy, Gbenga Ibikunle, Markus Rodlauer, Ian
Marsh, Ana Ôlo, Richard Payne, Juan Rojas, Khaladdin Rzayev (discussant), Lucio Sarno, and Rolf Strauch as well as seminar and
conference participants at the European Stability Mechanism, the ‘37th International Conference of the French Finance Association’
(Audencia Business School), the ‘2nd Paris-Dauphine Finance PhD Workshop’ (Paris-Dauphine University), the ‘3rd European
Capital Markets Workshop’ (Dublin City University), the University of Greenwich, the ‘10th Annual Financial Market Liquidity
Conference’ (Corvinus University), and the ‘2019 PhD Research Days’ (Cass Business School). We also wish to thank Edmund
Moshammer and Jacques Netzer for excellent data assistance. The views expressed in this paper are those of the authors and do
not necessarily represent those of the Bank of Spain, the European Stability Mechanism and the AMRO. We thank the European
StabilityMechanismfordataaccess.NoresponsibilityorliabilityisacceptedbytheBankofSpain,theAMRO,andtheEuropean
Stability Mechanism in relation to the accuracy or completeness of the information, including any data sets, presented in this paper.
Disclosure statement
No potential conict of interest was reported by the author(s).
THE EUROPEAN JOURNAL OF FINANCE 37
Notes on contributors
Panagiotis Panagiotou isaLecturerinFinanceandBankingattheUniversityofGreenwich.HeholdsaPh.D.inFinancefromBayes
(formerly Cass) Business School.
Xu Jiang is an Economist at ASEAN+3 Macroeconomic Research Oce. He is a Doctoral candidate in Economics at Vrije
Universiteit Amsterdam.
Angel Gavilan istheDirectorofGeneralEconomics,Statistics,andResearchattheBankofSpain.HeholdsaPh.D.inEconomics
from the University of Chicago.
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Appendix. Additional empirical results
Table A1. Test for cross-equation dependence in estimation error.
Liquidity Average Mean Mean Median |t|>1.645 |t|>1.96
measure correlation γj,1 tt (%) (%)
LPQS −0.007 −0.011 −0.210 −0.242 15.45 9.80
LPES 0.002 −0.007 0.016 0.013 8.22 4.32
LAQD −0.004 −0.009 −0.123 −0.140 7.25 4.94
LAIR 0.002 −0.007 0.095 0.038 5.18 3.73
Notes: After estimating 73 time series regressions of individual liquidity measures on equal-weighted market liquidity, Equation (1), residuals for
benchmark bond series j+1 are compared with residuals for benchmark bond series j, after assigning to each ja unique number generated by
a random number generating function and subsequently assigned based on the value of this randomly assigned number, from the lowest to
the largest value. We run 72 time series regressions between adjacent residuals, i.e.
j+1,t=γj,0 +γj,1j,t+ξj,t,(A1)
where γj,0 and γj,1 are estimated coefficients and ξj,t, is an estimated disturbance. The t-statistics for γj,1 provide evidence about cross-equation
dependence. From these 72 pairs, the table reports the average correlationcoefficient after 1,000 repetitions of the exercise. Also reported from
pair-wise regressions, Equation (A1), are the average slope coefficient as well as the sample mean t-statistic of the regression slope coefficient
and the frequency of absolute t-statistics (for the slope) exceeding typical critical levels, 5% and 2.5%. Because there are two tails, double these
critical percentages (i.e. 10% and 5%, respectively), should be found just by chance if, in fact, there is no dependence. The results suggest little
cross-equation dependence as the mean and median slope coefficients from Equation (A1) as well as the correlation coefficients are virtually
zero on average with relative few observations concentrated at the tails of the distribution.
40 P. PANAGIOTOU ET AL.
Table A2. Unit root testing.
Levels First differences
Series ADF PP DF-GLS ADF PP DF-GLS
Commonality in Spreads −7.57 −9.06 −5.28 −10.81 −14.46 −9.35
Commonality in Depths −7.69 −9.51 −7.68 −11.21 −15.17 −9.39
Market Return −6.15 −9.25 −5.06 −10.11 −16.84 −5.51
Trad in g Vol ume −3.14 −3.77 −3.03 −8.85 −11.81 −6.64
Market Liquidity (Spreads) −2.37 −1.76 −2.05 −7.79 −9.32 −3.74
Market Liquidity (Depths) −2.07 −1.80 −2.02 −7.53 −8.94 −4.56
Bond Index Volatility −3.91 −5.25 −3.17 −9.37 −13.38 −8.91
MTS Volatility −2.33 −2.62 −1.94 −8.15 −12.43 −5.29
MOVE Index −2.75 −2.56 −2.18 −7.65 −8.77 −6.59
VIX Index −3.21 −3.15 −2.80 −8.63 −8.93 −8.61
VSTOXX Index −2.62 −2.68 −2.22 −8.87 −9.41 −8.52
EONIA −3.11 −4.16 −1.15 −5.96 −6.21 −5.20
TED Spread −2.53 −2.22 −2.62 −7.18 −7.59 −7.14
E−OIS Spread −2.53 −1.52 −2.14 −7.60 −5.02 −5.83
L−OIS Spread −3.16 −1.95 −3.06 −5.89 −5.49 −5.77
ECB’s Excess Liquidity −0.22 −0.73 −0.83 −3.57 −5.77 −3.54
Dealer’s Stock Returns −6.68 −8.64 −6.71 −12.37 −15.27 −8.21
Sentix Euro−Area −1.88 −1.55 −2.65 −5.59 −6.57 −5.55
Sentix USA −2.58 −2.49 −2.51 −6.36 −7.59 −6.67
EPU Europe −3.77 −4.54 −3.77 −9.08 −11.58 −8.93
EPU USA −3.39 −3.49 −3.90 −10.18 −11.13 −6.19
Stoxx50 StDev −3.39 −4.23 −3.91 −8.73 −12.18 −8.70
Stoxx50 RV −3.80 −4.37 −4.12 −12.52 −11.32 −10.65
S&P500 StDev −4.03 −5.14 −4.46 −10.76 −12.82 −10.49
S&P500 RV −4.01 −4.64 −4.40 −11.48 −11.39 −11.43
Notes: This table reports the results of three unit root tests on the liquidity variables and low-frequency proxies related to various demand- and
supply-side explanations of commonality in liquidity for the period from June 2011 to June 2018 at monthly frequency (i.e. 85 observation). ADF,
PP and DF-GLS are Augmented Dickey–Fuller, Phillips–Perron and Elliott–Rothenberg–Stock DF-GLS test statistics, respectively. In each test, the
null hypothesis is that the series contains a unit root, and the alternative is that the variable was generated by a stationary process. The critical
values for ADF and PP are −3.44 (1%), −2.87 (5%) and −2.57 (10%). Testcritical values for DF-GLS are −2.58 (1%), −1.95 (5%) and −1.62 (10%).
Table A3. Market-wide trading volume.
Daily Monthly
Mean 36.90 4,850
Std. Dev. 10.60 1,330
Min 8.33 2,010
Max 92.40 7,570
Skewness 0.91 0.09
Kurtosis 4.96 2.32
Observations 1,804 85
Notes: This table shows summary statistics for market-wide trading
volume at daily and monthly frequency. Market-wide trading vol-
ume is computed as the Euro (in millions) mean of traded volume
of all benchmark bond series included in our data set. The sample
period is 2011:06–2018:6 and includes 1804 daily observations
for 625 euro-denominated sovereign bonds.
THE EUROPEAN JOURNAL OF FINANCE 41
Table A4. Changes in volatility and in commonality in liquidity (averaging).
Model (1) (2) (3) (4) (5) (6) (7)
Panel A: Changes in Commonality in Spreads
Market Return 0.016 0.034 0.015 0.039 0.008 0.022 0.059∗
(0.39) (0.87) (0.64) (1.11) (0.47) (0.59) (1.67)
Trading Volume 0.016∗∗∗ 0.015∗∗∗ 0.014∗∗∗ 0.014∗∗∗ 0.016∗∗∗ 0.016∗∗∗ 0.011∗∗∗
(3.39) (3.57) (2.99) (2.97) (3.16) (3.29) (2.61)
Market Liquidity 0.010∗∗∗ 0.008∗∗∗ 0.005∗0.008∗∗∗ 0.008∗∗∗ 0.008∗∗∗ 0.003∗
(4.04) (4.39) (1.69) (3.99) (3.74) (3.82) (1.79)
Credit Risk 0.001 0.000 0.001 −0.010 0.007 0.001 −0.001
(0.43) (0.23) (0.49) (−0.24) (0.26) (0.11) (−0.29)
Bond Index Volatility 0.014∗∗ 0.010
(2.21) (1.47)
MTS Volatility 0.071∗∗∗ 0.064∗∗∗
(2.81) (2.70)
MOVE 0.009∗∗ 0.003∗
(2.01) (1.65)
VIX 0.007 −0.010
(0.59) (−0.49)
VSTOXX 0.010 0.010
(0.99) (0.33)
Constant −0.021 −0.021 −0.021 −0.021 −0.021 −0.021 −0.021
(−0.39) (−0.41) (−0.39) (−0.41) (−0.39) (−0.40) (−0.40)
Observations 84 84 84 84 84 84 84
Adjusted R20.09 0.12 0.11 0.12 0.10 0.10 0.14
Panel B: Changes in Commonality in Depth
Market Return 0.007 0.011 0.015 0.038 0.008 0.011 0.031∗
(0.33) (0.48) (0.73) (1.30) (0.35) (0.42) (1.66)
Trad in g Vol ume −0.021 −0.020 −0.026 −0.045 −0.021 −0.026 −0.055
(−0.47) (−0.39) (−0.65) (−1.09) (−0.69) (−0.70) (−1.20)
Market Liquidity −0.158∗∗ −0.157∗∗ −0.102∗−0.119∗−0.157∗∗ −0.149∗∗ −0.099
(−2.20) (−2.04) (−1.67) (−1.73) (−2.01) (−2.12) (−0.25)
Credit Risk 0.001 0.001 −0.007 −0.001 −0.000 −0.000 −0.002
(0.07) (0.03) (−0.12) (−0.39) (−0.31) (−0.30) (−0.87)
Bond Index Volatility 0.401 0.440
(0.71) (0.35)
MTS Volatility 0.037∗0.004∗
(1.78) (1.73)
MOVE 0.014∗∗∗ 0.014∗∗∗
(2.97) (2.65)
VIX −0.000 −0.001
(−1.10) (−0.81)
VSTOXX 0.005 0.003
(0.43) (0.66)
Constant −0.030 −0.031 −0.031 −0.031 −0.031 −0.031 −0.030
(−0.77) (−0.84) (−0.90) (−0.81) (−0.71) (−0.75) (−0.73)
Observations 84 84 84 84 84 84 84
Adjusted R20.02 0.02 0.09 0.10 0.02 0.03 0.10
Notes: This table reports results of time-series regressions of the change in monthly average commonality in liquidity among 73 benchmark bond
series – denoted by R2
COM,t, computed as the logistic transformation of commonality in liquidity in month t– over the period 2011:06–2018:06
on changes of various aggregate volatility proxies. The reported regressionsare in monthly changes. All equations are estimated using OLS with
Newey–West standard errors, with lag length T1/3,whereTis the indicated sample size. t-statistics are given in parentheses below coefficient
estimates. ∗∗∗p<0.01, ∗∗p<0.05, ∗p<0.1.
42 P. PANAGIOTOU ET AL.
Table A5. What drives time-series variation in commonality? (Supply-side, Averaging).
Model (1) (2) (3) (4) (5) (6) (7)
Panel A: Changes in Commonality in Spreads
Market Return 0.020 0.018 0.029 0.027 0.019 0.021 0.010
(0.69) (0.59) (0.78) (0.81) (0.36) (0.50) (0.44)
Trading Volume 0.016∗∗∗ 0.016∗∗∗ 0.015∗∗∗ 0.015∗∗∗ 0.015∗∗∗ 0.014∗∗∗ 0.026∗∗∗
(2.97) (3.20) (3.67) (3.29) (3.31) (2.31) (2.69)
Market Liquidity 0.005∗0.005∗0.006∗0.006∗∗ 0.006∗∗ 0.003 0.004
(1.67) (1.65) (1.70) (1.98) (1.98) (1.22) (0.46)
Credit Risk 0.002 0.002 0.004 −0.003 −0.000 −0.006 −0.022
(0.40) (0.43) (0.01) (−0.01) (−0.04) (−0.13) (−0.20)
Market Volatility 0.050∗∗∗ 0.051∗∗ 0.048∗∗ 0.049∗∗ 0.049∗∗ 0.055∗∗∗ 0.099∗∗
(2.73) (1.99) (2.13) (2.05) (2.09) (2.51) (2.23)
EONIA Rate 0.099 0.126
(1.11) (1.41)
TED Spread 0.005∗−0.001
(1.89) (−1.63)
LOIS 0.003∗∗ 0.004∗∗
(1.78) (1.83)
ECB’s Excess Liquidity 0.003∗∗∗ 0.005∗∗∗
(1.99) (2.25)
Dealer’s Stock Returns −0.013∗∗ −0.011∗
(−1.97) (−1.75)
Constant −0.021 −0.021 −0.021 −0.021 −0.021 −0.021 −0.020
(−0.44) (−0.41) (−0.47) (−0.47) (−0.33) (−0.39) (−0.29)
Observations 84 84 84 84 84 84 84
Adjusted R20.10 0.09 0.11 0.11 0.11 0.12 0.16
Panel B: Changes in Commonality in Depth
Market Return 0.019 0.027 0.018 0.015 0.017 0.016 0.031
(0.75) (1.11) (0.79) (0.70) (0.71) (0.68) (1.00)
Trad in g Vol ume −0.020 −0.030 −0.020 −0.021 −0.019 −0.029 −0.040
(−0.33) (−0.40) (−0.35) (−0.36) (−0.41) (−0.75) (−0.99)
Market Liquidity −0.121 −0.111 −0.101 −0.117 −0.101 −0.091 −0.109
(−1.14) (−1.06) (−1.13) (−1.09) (−1.12) (−1.11) (−0.88)
Credit Risk −0.009 −0.012 −0.009 −0.004 −0.011 −0.004 −0.004
(−0.01) (−0.01) (−0.11) (−0.11) (−0.36) (−0.43) (−0.45)
Market Volatility 0.037∗0.045∗∗ 0.0328 0.036∗0.037∗0.035∗∗ 0.039∗∗
(1.80) (2.19) (1.77) (1.81) (1.75) (1.81) (2.31)
EONIA Rate 0.099∗∗ 0.102∗∗
(1.99) (2.05)
TED Spread 0.000 −0.001
(0.01) (−0.01)
LOIS 0.001 0.003
0.10 0.51
ECB’s Excess Liquidity 0.011 0.013
(0.35) (0.53)
Dealer’s Stock Returns −0.034∗−0.029
(−1.69) (−1.61)
Constant −0.047 −0.046 −0.047 −0.045 −0.046 −0.047 −0.046
(−0.67) (−0.88) (−0.91) (−0.89) (−0.68) (−0.74) (−0.97)
Observations 84 84 84 84 84 84 84
Adjusted R20.05 0.08 0.09 0.09 0.09 0.10 0.09
Notes: This table reports results of time-series regressions of the change in monthly average commonality in liquidity among 73 benchmark
bond series – denoted by R2
avg,t, computed as the logistic transformation of the average commonality in liquidity in month t– over the period
2011:06–2018:06 on changes of various proxies for funding conditions. All equations are estimated using OLS with Newey-West standard
errors, with lag length T1/3,whereTis the indicated sample size. t-statistics are given in parentheses below coefficient estimates. ∗∗∗p<0.01,
∗∗p<0.05, ∗p<0.1.
THE EUROPEAN JOURNAL OF FINANCE 43
Table A6. What drives time-series variation in commonality? (Demand-side, Averaging).
Model (1) (2) (3) (4) (5) (6) (7)
Panel A: Changes in Commonality in Spreads
Market Return 0.047 0.049 0.048 0.039 0.055 0.060 0.050
(0.34) (0.67) (0.70) (0.81) (0.39) (0.89) (0.71)
Trading Volume 0.010∗∗∗ 0.011∗∗∗ 0.010∗∗∗ 0.008∗∗ 0.008∗∗∗ 0.009∗∗∗ 0.012∗∗
(2.99) (2.87) (3.01) (2.01) (2.20) (3.01) (2.15)
Market Liquidity 0.005∗0.005∗0.005∗0.004 0.003 0.005∗0.004
(1.75) (1.74) (1.76) (1.57) (1.36) (1.68) (1.60)
Credit Risk 0.001 0.001 0.000 0.010 0.001 0.000 −0.001
(0.14) (0.20) (0.30) (0.21) (0.20) (0.08) (−0.37)
Market Volatility 0.075∗∗∗ 0.074∗∗∗ 0.080∗∗∗ 0.075∗∗∗ 0.074∗∗∗ 0.073∗∗∗ 0.075∗∗∗
(2.89) (2.87) (2.92) (2.86) (2.71) (2.87) (2.40)
Sentix Euro−Area 0.010 0.011
0.90 0.81
Sentix USA −0.021 −0.019
(−0.69) (−0.87)
EPU Europe 0.036 0.041
(1.49) (1.11)
EPU USA −0.003 −0.007
(−1.31) (−0.89)
Stoxx50 RV 0.236∗∗∗ 0.0192∗
(2.51) (1.90)
Constant −0.040 −0.040 −0.038 −0.039 −0.020 −0.029 −0.033
(−0.53) (−0.52) (−0.53) (−0.51) (−0.52) (−0.50) (−0.51)
Observations 84 84 84 84 84 84 84
Adjusted R20.09 0.10 0.10 0.11 0.11 0.14 0.11
Panel B: Changes in Commonality in Depth
Market Return 0.022 0.022 0.023 0.028 0.022 0.026 0.020
(0.49) (0.65) (0.71) (0.79) (0.57) (0.59) (0.61)
Trad in g Vol ume −0.025 −0.033 −0.026 −0.053 −0.035 −0.030 −0.060
(−0.62) (−0.81) (−0.63) (−1.45) (−0.87) (−0.72) (−1.42)
Market Liquidity −0.122 −0.125 −0.122 −0.085 −0.105 −0.113 −0.129∗
(−1.44) (−1.45) (−1.42) (−1.13) (−1.33) (−1.43) (−1.79)
Credit Risk −0.009 −0.001 −0.009 −0.002 −0.002 −0.002 −0.002
(−0.22) (−0.13) (−0.20) (−0.46) (−0.37) (−0.44) (−0.28)
Market Volatility 0.055∗0.052∗0.052∗0.069∗0.057∗0.059∗0.045∗
(1.78) (1.77) (1.77) (1.91) (1.79) (1.82) (1.68)
Sentix Euro−Area 0.001 0.001
(0.77) (0.77)
Sentix USA −0.001 −0.001
(0.46) (0.40)
EPU Europe 0.017 0.015
(1.02) (0.99)
EPU USA −0.011 −0.007
(0.60) (0.11)
Stoxx50 RV 0.141 0.0119
(1.03) (0.79)
Constant −0.047 −0.047 −0.047 −0.047 −0.047 −0.045 −0.041
(−0.61) (−0.89) (−0.85) (−0.70) (−0.72) (−0.73) (−0.69)
Observations 84 84 84 84 84 84 84
Adjusted R20.05 0.09 0.09 0.10 0.10 0.10 0.05
Notes: This table reports results of time-series regressions of the change in monthly average commonality in liquidity among 73 benchmark
bond series – denoted by R2
avg,t, computed as the logistic transformation of the average commonality in liquidity in month t–overthe
period 2011:06–2018:06 on changes of various proxies on demand-side determinants. All equations are estimated using OLS with Newey–West
standard errors, with lag length T1/3,whereTis the indicated sample size. t-statistics are given in parentheses below coefficient estimates.
∗∗∗p<0.01, ∗∗p<0.05, ∗p<0.1.
