Preliminary, please do not quote
Yield Spreads on Government Bonds Before and After EMU
Lorenzo Codogno (Bank of America)
Carlo Favero (IGIER, Bocconi University and CEPR)
Alessandro Missale (Florence University and IGIER)
January 21, 2002
Abstract
Contrary to market perceptions, this paper provides evidence that yield
differentials on euro-zone bonds are mostly due to risk-related domestic and
international factors, as opposed to liquidity factors. Monthly series point to
the existence of different, slowly evolving means, as well as to the importance
of common international factors in the determination of fluctuations around
the means. Slowly evolving means are related to the ratio of government
securities to the total outstanding securities. At higher frequencies, yield
differentials appear to be explained by common (credit-) risk related
international factors, such as the US asset swap spreads and US and Euro-zone
policy rates. Only the yield spread between on-the-run and off-the-run
benchmark bonds (of the same issuer) appears to significantly affect yield
spreads across member States; traditional liquidity indicators, such as bid-ask
spreads, trading volumes and outstanding amounts have no effect.
1
1. INTRODUCTION
Euro-zone government bond markets have rapidly changed as a result of global trends in the
financial industry and the process of European Monetary integration. The introduction of the euro in
January 1999 eliminated exchange risk between the currencies of participating member States,
thereby creating the conditions for a substantially more integrated public debt market in the euro
area. The government bond market may be considered as the "building block" that allows the nongovernment sector to grow. Therefore, even in a situation of shrinking government borrowing
requirements that reduce supply of paper, the government bond market plays a key reference role.
In terms of size and issuance volume, the euro-area government bond market is comparable to the
US Treasuries market. However, it appears still segmented, with public debt management
decentralized under the responsibilities of 12 separate agencies, and characterized by different
issuing techniques (see Favero, Missale and Piga 1999). The persistence of yield differentials and
their behavior over time may be perceived as the evidence of this segmentation. Understanding the
factors that explain current yield differentials and their behavior over time has important
implications for financial markets in general and may trigger policy responses with the aim to
achieve higher financial markets integration.
The aim of this paper is to study the determinants of observed yield spreads in the euro-area
government bond markets. To this end we attempt to decompose yield spreads into the components
that are expected to explain spread levels and movements, i.e. credit risk and liquidity (while in a
future version of the paper we plan to also look at deliverability into futures contracts). In
particular, we examine the relative role of international and liquidity factors that may be important
in determining the yields on government bonds, by looking at the impact of US asset swap spreads
and policy rate, European policy rate and liquidity factors such as trading volumes, outstanding
amounts, spreads between on-the-run and off-the-run benchmarks, etc. Finally, we examine the
effect of institutional changes regarding the functioning of primary and secondary markets.
Yield spreads provides a simple measure of the market's assessment of the risk of default and the
extent of financial integration between EMU member States. Therefore, a study of the determinants
of yield spreads can shed light on a number of issues. A first issue is whether the risk of default has
actually increased with the Monetary Union as countries have lost, with monetary independence,
the option of printing money to pay for their debts. This can be evaluated by looking at the
component of yield spreads, which does not depend on expected devaluation, before and after the
EMU. Secondly, yield spreads capture the degree of financial integration. A main issue is whether
a market for European public and private bonds as large and liquid as the US bond market will ever
develop. In fact, to the extent that bonds issued by different member States are perceived as
imperfect substitutes, the goal of creating such a market is frustrated.
While persisting differences between the expected return on government bonds issued by different
member States is a cause of concern independently of the source of segmentation, distinguishing
between the credit-risk component and the liquidity premium is crucial for policymaking. Yield
spreads reflecting persisting differences in credit ratings suggest that fiscal rectitude and
coordination imposed by the Stability and Growth Pact is either ineffective or lack credibility. On
the other hand, yield spreads reflecting differences in the liquidity of government bonds indicate
that fiscal efforts may not be enough to deliver a "same-bond" market. More importantly,
differences due to liquidity premia are likely to stay and prevent the creation of a European bond
market unless member States agree on a more coordinated approach to the choice of issuing
procedures, market-making requirements, futures markets, repo facilities etc.
2
To address these issues we rely on asset swap spreads (the difference between the yield on
government bonds and the interest rates from the asset-swap curve) that allow to separate the
liquidity and credit-risk components from expected exchange-rate depreciation so as to examine
such components over a long horizon. Then, a decomposition between the liquidity premium and
the credit-risk component is attempted by modeling their behavior in relation to a number of factors
potentially affecting the creditworthiness of the government but not the liquidity of its debt.
By focusing on potential determinants of liquidity and the credit-risk separately, we try to identify
and measure the most important factors that have affected the yield spreads over the last decade
(with monthly data) and, in particular, in the EMU period (with daily data).
We find, contrary to market perceptions, that yield differentials on euro-zone bonds are mostly due
to risk-related domestic and international factors, as opposed to liquidity factors. Monthly series
point to the existence of different, slowly evolving means, as well as to the importance of common
international factors in the determination of fluctuations around the means. Slowly evolving means
are related to the ratio of government securities to the total outstanding securities. At higher
frequencies, yield differentials appear to be explained by common credit-risk related international
factors, such as the US asset swap spreads and US and Euro-zone policy rates. Only the yield
spread between on-the-run and off-the-run benchmark bonds (of the same issuer) appears to
significantly affect yield spreads across member States; traditional liquidity indicators, such as bidask spreads, trading volumes and outstanding amounts have no effect.
2. MEASUREMENT
Before the introduction of the Euro, yield differentials within Europe had been determined by four
main factors: expectations of exchange rate fluctuations, different tax treatment of bonds issued by
different countries, credit risk and liquidity. Different tax treatments were eliminated or reduced to a
negligible level during the course of the 90s. The introduction of the Euro in January 1999
eliminated the first factor (i.e. expectations on exchange rate fluctuations) creating the conditions
for a substantially more integrated public debt market in the Euro area. Hence, yield differentials
are now mainly determined by two remaining factors: credit risk and liquidity.
Measuring the impact of credit risk and liquidity on Euro-area government bond yield differentials
requires the comparative analysis of two types of interest rates. We consider the yield on
government bonds and interest rate swaps of the same term to maturity. Using monthly data over
the sample period January 1992 to August 2001, we report these variables for Austria, Belgium,
Finland, France, Germany, the Netherlands, Ireland, Italy, Portugal and Spain respectively in
Figures 1 and 2. The figures show that fixed interest rates on asset swaps have converged fully
given that there is only one reference swap market since the launch of the Euro. Although smaller
than in the past, spreads between yields on government bonds still persist. This supports the
interpretation that differentials between fixed interest rates on swaps before Monetary Union are a
direct measure of expectations on exchange rate devaluation. In principle, a “pure” measure of
expected exchange rate changes could also be computed by comparing the yields on long-term
bonds issued by the same supranational institution (such as the World Bank or the European
Investment Bank) in different currencies. In fact, supranational issues are by definition free of any
country-specific default risk. However, we prefer to concentrate on asset swaps, as they are
homogenous and readily available for almost all EMU countries. Supranational issues in individual
currencies are not comparable for all the different countries and are not issued on a continued basis,
while yield differentials on government bonds are computed on the latest issues of the benchmarks
(the so-called "on the run” issues).
3
Two approaches can be taken to measure the importance of liquidity and credit-risk factors in
determining yield differentials. Considering Germany as the reference country, if we want to
remove the component due to expected exchange-rate depreciation we can either focus on:

 
RAS ti,T  Rti,T  RtGER
 RSWt i,T  RSWt GER
,T
,T

or, alternatively

ASti,T  Rti,T  RSWt i,T

The first approach looks at the relative asset swap spread, RAS, which is obtained by subtracting,
from the difference in yields, R, between bonds of country i and Germany, the difference between
fixed rates on asset swaps, RSW. This measures the combined importance of liquidity and default
risk factors. It is worth noting that the second part of the formula directly measures the expected
depreciation of the exchange rate. From 1999 onwards, as swaps differentials converged to zero,
RAS converged to the differentials between bond yields.
The second approach, represented by the asset swap spread, AS, provides a more detailed
disaggregation by taking the swap curve as the reference point. This does not necessarily imply that
the swap curve is a totally "neutral" reference point nor that it is the benchmark for government
bonds. In fact, the asset swap spread also depends on the credit standing of the banking system and
benchmark issues of major countries are in general more liquid and thus allow more accurate
pricing. Nevertheless, interest rate swaps provide a well-arbitraged homogeneous curve and asset
swap spreads a measure that is shielded from the effect of expected fluctuations in exchange rates.
This allows us to use long-time series, which includes the pre-EMU period. Moreover, this
approach clearly shows the contribution to yield spread developments due to each country as
opposed to a combination of the two.
The choice between the asset swap spread, AS, and the relative asset swap spread, RAS, can be seen
as a choice on the best level of aggregation at which our empirical investigation on the determinants
of yield differentials is to be conducted. In fact, given the identity:
RAS ti,T  ASti,T  AStGER
,T
ASt,T reflects two factors, i.e. liquidity of government bonds and credit risk relative to the banking
sector in country i. RASt,T reflects four factors, i.e. liquidity and credit risk of government issues in
country i relative to the country i banking sector, along with liquidity and credit risk of government
issues in Germany relative to the German banking sector.
It is instructive to compare the behaviour of these two measures over time and across different
regimes. Figure 3 reports the time-series behaviour of the two series for the non-German countries
in our sample, while Figure 4 visualises the inter-country correlation before and after the EMU
regime.
Figures 3 and 4 clearly show that the high positive correlation between the two measures in the preEMU period disappears in the EMU. In principle, this evidence could be explained by a higher
volatility of the credit risk (and the liquidity premium) in the pre-EMU period. In fact, as shown in
Figure 3 and, more clearly in Figures 5a, 5b, 5c and 5d , a stronger correlation between the domestic
asset swap spread and the German spread is clearly the driving force behind the absence of
correlation in the EMU period. Indeed, it appears that government bonds have been viewed as
4
closer substitutes, a sign of increased integration. In particular, as shown in Favero, Giavazzi,
Spaventa (1997), since fluctuations in the expectations of exchange rate devaluation were also
usually resulting in fluctuations of the credit-risk premia, it is conceivable that the EMU has
reduced this source of segmentation between the credit-risk premium of domestic and German
bonds.
Naturally, such evidence requires us to choose between the two different measures. We favour asset
swap spreads, AS, because identifying the significance of movements in a country’s asset swap
spread separately is easier than identifying the significance of the combined effect encapsulated in
the differential.1 For example, consider the Russian Crisis of Autumn 1998. The asset swap spread
in Germany jumped to reach a peak of 70 basis points below Libor from an historical average of 25,
as a consequence of spreading fears on the stability of the German banking system. The asset swap
spread in Italy, Ireland, Portugal and Spain did not show any such deviation from their historical
means in that period. Greece was not perceived as close to reaching EMU entry, and thus suffered
dramatically, with the Greek asset swap spread recording the impact. This example makes also clear
that the choice of the asset swap as the informational variable is to be taken cautiously in presence
of fears of instability of the domestic banking sectors.
To conclude, we favour the more disaggregated asset swap spread measure. This means that we
model total yield differentials in the Euro area by investigating separately the behaviour of the two
asset swap spreads which determine them.
3. UNDERSTANDING MOVEMENTS IN YIELD SPREADS
Asset swap spreads tend to fluctuate around a long-run slow-moving component. We report the
behaviour over time of asset swap spreads in the Euro area measured at monthly frequencies in
Figures 5a, 5b, 5c and 5d. Asset swap spreads of EMU countries had been rather volatile before
1998, as can be seen in Figure 5a. We believe the erratic behaviour is related to: i) market
segmentation; ii) tax distortions; iii) inefficient pricing and, more interestingly; iv) interaction
between credit risk and exchange-rate risk. Some sovereign borrowers used to have captive
domestic markets, and domestic banks/companies were induced to buy domestic bonds either by
regulatory constraints or by their assets/liabilities matching preferences. This probably explains the
behaviour of spreads in several countries, and among others in France, during the pre-1998 period.
The first and second factors are key in explaining asset swap behaviour in Italy. The latter factor
was at play in Portugal and Ireland, at least before 1998.
Since 1998, the behaviour of asset swap spreads has been less erratic and more homogeneous across
countries. At the beginning of 1998, EMU was almost completely discounted by financial markets,
which probably explains the stronger correlation. The correlation can be seen in Figure 5b, where
only Greek asset swap spreads behave as outliers. Figure 5e shows daily observations. In Figures 5c
and 5d, we have grouped countries according to the level of their asset swap spread and plotted
these variables for the two groups along with the German asset swap spread. All these figures
strongly suggest the existence of common fluctuations around different, slowly evolving, means.
1
Moreover, we have worked so far on the assumption that benchmark bonds have the same duration. Different
maturity/duration of benchmark issues causes differences in yield spreads that are not related to either credit or liquidity
considerations, but simply to the shape of the yield curve. To avoid this distortion and any bond-specific factor, we took
yields on constant maturity bonds (see Appendix 1).
5
To provide further insights to this observation we have considered a more international perspective
by considering the US asset swap spreads. Figure 6a reports the level of asset swap spreads for the
US and Germany, while Figure 6b also includes Italy. Asset swap spreads of German 10-year
benchmarks have shown a high correlation with US spreads since the beginning of the 1990s, i.e.
0.71 between January 1990 and August 2001 on monthly data. This correlation has become stronger
over time (0.84 between January 1998 and August 2001). The “sensitivity” of US asset swap
spreads to this common international factor has been roughly twice as much as that of German asset
swap spreads and the series appear to be increasingly correlated (Figure 6a).
To conclude, the international evidence confirms the existence of different, slowly evolving means,
as well as the importance of common international factors in the determination of fluctuations
around the means. Slowly evolving means are likely to be related to economic fundamentals, such
as debt and deficit to GDP ratios, and the ratio of the stock of outstanding government securities to
the total outstanding securities.
4. IDENTIFYING CREDIT RISK
Credit risk in the Euro area can be identified by looking at the role of slowly evolving variables and
a common international factor; i.e. variables not related to liquidity. Our strategy for extracting
information from asset swap spreads is based on separate modeling of evolving means and common
model of fluctuations. We relate means to economic fundamentals and we claim that different
means in the asset swap spread, AS, reflect different credit risk across countries. We circumstantiate
this claim by considering as a monthly available measure of the economic fundamentals the ratio of
the stock of outstanding government securities to the total outstanding securities.
Figure 7 shows the high correlation of the 10-year US asset spread with the ratio of US outstanding
government securities over total outstanding securities. Figure 8 reports the ratio of government
securities to total securities for Germany, Italy, Europe and the US. The graphical evidence suggests
not only a correspondence between the levels of such ratio and the levels of asset swaps but also the
possibility of a common long-run trend. Interestingly, also deviations from the long-run trends seem
to be correlated across countries. Such evidence suggests the possibility that, over the long-run, the
equilibrium value of the asset-swap spread is determined by the credit risk and that the relative
supply of government to total securities captures long-term fluctuations in this variable.
Furthermore, the correlation of deviations from long-run equilibria suggests the possibility of an
international determinant of short-run movements in asset swap spreads that are not related to
structural factors. Such common fluctuations might instead be referred to phenomena often quoted
by market participants such as “appetite for risk”, which have not so far received attention in the
academic literature.
Our interpretation of the graphical evidence suggests naturally a dynamic specification of the
following type:
AStUS  a0 1  a1   a1 AS US
 a2 1  a1 RATIOtUS  u1t
t 1



AStGER  b0 1  b1   b1 AS tGER
 b2 1  b1 RATIOtGER  b3 1  b1  AStUS  a2 RATIOtUS  u 2t
1

ASti  c0 1  c1   c1 AS ti1  c2 1  c1 RATIOti  c3 1  c1  AStUS  a2 1  a1 RATIOtUS

 

 c4 1  c1  AStGER  b2 RATIOtGER  AStUS  a2 RATIOtUS  u3t
This Vector Error Correction model has the following features:
6
1. In the steady state the asset swap spreads are determined only by the domestic economic
fundamentals as captured by the ratios variables. The steady state solution of the model is:
AStUS  a0  a2 RATIOtUS
AStGER  b0  b2 RATIOtGER
ASti  c0  c2 RATIOti
2. In the short-run international, German and local factors might have an impact on the
determination of the fluctuations of the asset swap spread. We propose a recursive structure in
which deviations from equilibrium in the US have a spill-over effect to Europe. We identify
such an international factor by assuming that the US asset swap spread does not respond to
fluctuations in European asset swap spreads. We then consider German data to extract a
potentially common European factor, by assuming that Germany plays within Europe the role
played by the US in the world and that the European factor is orthogonal in the long-run to the
US factor. Lastly, we consider the other member States of the Union to evaluate the residual
role of country specific factors in the determination of asset swaps.
3. This general specification will allow, in Section 5.3, an evaluation of the importance of liquidity
factors by augmenting the system with liquidity-related variables such as bid-ask spreads,
trading volumes, outstanding amounts, and yield spreads between on-the-run and off-the-run
benchmark bonds.
To illustrate the potential of the dynamic model let us start by concentrating on the US-German
block only. Given the recursive structure of the system this is done at no cost in terms of
consistency of estimators. The estimation of the model for US and Germany over the sample
1995:1-2001:8 delivers the following results:
a0 = -2.35(0.78), a1 = 0.91(0.06), a2 = 7.00(3.38)
b0 = 0.51(0.45),
b1 = 0.76(0.06), b2 = 1.86(0.61), b3 = 0.70(0.17)
We report in Figures 9-11 the actual asset swap spreads with their fitted long-run component along
with the deviations from the long-run component.
Estimation of the US-German block clearly shows that long-run trends in asset swap spreads are
determined by the economic fundamentals as captured by our ratio variable. The short-run
dynamics of the US spread is significantly determined by the disequilibrium with respect to the
long-run trend. Interestingly, deviations from long-run trend in the US are an important
determinant of the short-run dynamics in the German AS: such variable is significant in our
specification along with disequilibium with respect to local fundamentals.
As shown in Table 1, the extension of the model to other countries confirms the importance of local
fundamentals in the determination of the long-run trends. However, it is also clear that international
factors (such as deviation from long-run trends of US and German asset swaps) are much more
important than local factors in the determination of short-run fluctuations of asset swap spreads in
the euro-area. Interestingly, results are rather robust to the inclusion of the variable EXCH (the
difference of fixed interest rates on swaps between each country and Germany) which capture
expected exchange rate depreciation and allows to separate the regime of convergence to EMU
from the properly defined EMU regime.
7
4.2 Long-run trends
The interesting question on the long-run determinants is what is measured by the ratio of public
bond outstanding to total bonds. Are we proxying the default risk related to the size of government
debt or are we capturing relative supply effects between private and public bonds? We shall answer
these questions by assessing how our results in the previous section are affected by considering
different variables to capture the long-run trend. We shall consider variables more directly related to
the relative supply effect, such as the ratio of government bonds outstanding to private bonds
outstanding. We shall then assess the importance of variables more directly related to the risk
premium on government bonds such as the government debt to GDP ratio and the deficit to GDP
ratio. Credit ratings by major rating agencies may also provide insight.
4.3 Short-run fluctuations
The strong international correlation among short-run fluctuations in differentials seems to point
toward the importance of fluctuations over-time of a variable labeled “appetite for risk” by market
participants. We plan to investigate the role of this variable by assessing the co-movement of our
measured deviations from long-run trends with variables commonly thought as proxies for the risk
appetite of investors. In particular we shall consider corporate spreads, the slope of the yield curve,
the level of long-term interest rates and some naïve direct measure of the appetite for risk such as
the rank correlation between returns on assets and their variance.
5. IDENTIFYING THE LIQUIDITY PREMIUM
The econometric evidence, based on monthly data, leaves very little role for liquidity: spreads seem
to be determined by economic fundamentals in the long-run and by common international factors in
the short-run. The role of liquidity-related factors has, however, to be tested explicitly. This is
impossible at monthly frequencies since data on volumes and bid-ask spreads are available only
from 1999 onwards. Therefore, in this and the following sections, we concentrate on daily data from
the EMU regime in order to test the relative importance of international and liquidity factors in the
determination of spreads. Of course the role of macroeconomic fundamentals cannot be assessed at
daily frequencies, but this should not be a source of econometric problems as the long-run trend are
slowly evolving and they are not too different from constants over a two-year period.
To identify the relative importance of international and liquidity related factors in explaining asset
swap spreads we first focus on international factors and, then, in the next section, we test for the
introduction of a number of liquidity-related factors.
5.1 International factors
In our baseline specification, we consider US asset swap spreads, and US short and long-term
interest rates as the variables depicting the international common trend. Equally, local factors are
captured by Euro-zone short and long-term interest rates. The structure of our baseline model is as
follows:
8
US
US
AS tUS  c11  c12 AS tUS
1  c13 Rt 1  c14 rt 1  u1t
US
GER
EU
AS tGER  c 21  c 22 AS tGER
 c 24 AS tUS
1  c 23 AS t
1  c 25 Rt 1  c 26 rt 1  u 2 t
GER
GER
EU
AS ti,T  ci1  ci 2 AS ti1  ci 3 AS tUS  ci 4 AS tUS
 ci 6 AS tGER
1  c i 5 AS t
1  c i 7 Rt 1  c i 8 rt 1  u it
R
i
t

 RtGER  AS ti  AS tGER
i  3( Italy ), 4( Spain), 5(Be lg ium), 6(Austria), 7(Finland), 8(The Netherlands), 9(France),
10(Portugal), 11(Ireland)
We apply our baseline model to 10-year benchmarks and report the results in Table 2. The
equations are estimated simultaneously by Seemingly Unrelated Regression Equations over the
sample period June 1999 to August 20012. The results clearly show the importance of international
factors in determining Euro-zone asset swap spreads3. This is strengthened by the pattern of the
correlation of residuals. The co-variation between residuals in the Euro area raises some doubts on
the local nature of possibly omitted variables. To evaluate the capability of the model to explain
yield differentials and assess the relevance of different factors we use dynamic simulations under
different scenarios. Dynamic simulation of asset swaps allows results to be presented in terms of
yield differentials as the two variables are linked by an identity.
The results of dynamic simulations are reported in Table 3. In the first simulation, we report spreads
obtained by a dynamic simulation of the model assuming no modification in the exogenous
variables. We take the evidence of zero mean in the difference between the averages of the actual
and simulated series as a sign of the correct specification of our model. Actual and dynamically
simulated series are reported in Figure 12. The reported results illustrate the capability of the model
to track the evolution of all spreads. The pattern of correlation of the difference between actual and
simulated series reflects that of the residuals of the econometric model. Such evidence strengthens
our point on the importance of common factors in the determination of the fluctuations in yield
differentials not explained by our model. Having assessed the model by simulation, we proceed to
experiment with it and evaluate the impact on yield differentials of an increase in US government
bond swap spreads (Scenario 1), a generalised tightening of monetary policies (Scenario 2), and an
increase of international long-term interest rates (Scenario 3). The results of these simulations
reveal that monetary policy affects yield differentials substantially, while the impact of a (one
standard deviation) change in US government bond spreads is more limited. They also show that
the levels of the yields on long-term government bonds have no significant effect.
5.2 Liquidity
Liquidity is claimed to have become the most priced element in spread valuation since the launch of
the Monetary Union, but it is not clear what is meant by it. The econometric evidence of our
baseline model points strongly towards the importance of international factors in determining
2 The initial point of the sample is determined by the availability of measures for liquidity, which we are going to discuss in the next
section.
3
Following our simple and intuitive definition of “international factors”, we have also tried to include an international measure of
appetite for risk in our model, and found it not significant. We have considered the measure described in Kumar M.S. and Persaud
A.(2001), based on the correlation between the rank of assets performance and the rank of their historic volatility (where a value of -1
implies perfect risk aversion and a value of +1 implies risk seeking behaviour by investors). The use of such measures is consistent
with the evidence that models capable of generating slow countercyclical variation in the risk premia have been successful in
explaining a wide variety of dynamic asset pricing phenomena (Campbell J.Y. and Cochrane,1999).
9
fluctuations in asset swap spreads in the Euro area. Can liquidity-related factors increase the
explanatory power of international factors for yield differentials in the Euro area?
To answer this question we must first find a measure of liquidity. While there is no generally
accepted definition of liquidity, according to academic literature, there are four dimensions of
liquidity4:
i. immediacy, speed with which a trade of a given size at a given width is completed;
ii. depth, maximum size of a trade for any given bid-ask spreads;
iii. width, bid-ask spread, the cost of providing liquidity;
iv. resiliency, i.e. how quickly price movements revert to “normal” levels after a large transaction
and how quickly the imbalances in transaction flows dissipate.
Market breadth is also referred to as the market ability to absorb large buy/sell orders without largescale price movements. All dimensions tend to interact, and there is no single measure of liquidity.
It has been argued that, in the context of government bond securities, liquidity may be best thought
of in terms of the cost of supplying immediacy. Liquidity is closely linked to secondary as well as
primary market efficiency, i.e. having transparent issuing procedures with a high degree of
predictability and a small number of large issues. Liquidity is influenced by the market structure as
well as security-specific factors, i.e. the amount outstanding etc.
These four dimensions of liquidity are difficult to record directly, and therefore other measures are
usually considered to gauge liquidity conditions. These are:
a) bid/ask spread;
b) trading volume;
c) turnover ratios (total trading volume divided by the stock of securities outstanding, i.e. the
number of times the market "turns over" in the period); and
d) trading intensity (number of transactions that take place over a set period).
To study liquidity effects we identify a number of quantitative and qualitative proxies of liquidity or
variables that may have affected liquidity premia according to the above classification and
depending on the availability of data. The variables we consider comply with the requirements of
our study, and are available across the Euro area (see Appendix 1 on the data set for more
information). Such variables are: i) trading volume; ii) outstanding amount of the specific
benchmark; iii) bid/ask spread, and; iv) yield spreads between off-the-run and on-the-run securities.
We measure yield differentials between on-the-run and off-the-run securities by taking the
difference between the benchmark on-the-run and the previous benchmark5. This measure has a
potential problem in that the off-the-run security has a shorter maturity implying that term-structure
effects might obscure the impact of liquidity on the differential. However, as we model spreads on
German long-term rates, we consider all liquidity measures relative to the German ones.
We consider yield differentials between on-the-run and off-the-run securities and differentials
between bid-ask spreads for each country and those for Germany. Consistently, we take ratios of
daily volumes and outstanding amounts of the benchmark in each country to those of the German
benchmark. This transformation reduces the term-structure effect as the non-German tends co-move
with that of the German term structure.
4
These definitions are taken from Gravelle (1999c).
5
An analogue measure for the US market has been proposed by Fleming (2001).
10
5.3 Testing for liquidity effects
We combine the international (credit-risk related) factors with the liquidity factors in an
econometric model of yield differentials in the Euro area (instead of asset swap spreads as in
Section 4). We estimate by Seemingly Unrelated Regressions a nine-equation model on a sample of
daily data covering the period between June 1999 and August 2001. We start from a baseline
specification in which only international factors are considered, obtained by regressing yield
differentials on a constant, on the lagged dependent variable, on contemporaneous and lagged US
asset swap spreads and on the lagged level of Euro-zone policy rates. We then extend the model by
adding our four measures of liquidity. The results from estimation are reported in Table 4.
Table 4: Modelling 10-year yield spreads in the Euro-area
NO liquidity
OUTSTANDING
VOLUME
ONOFF
BIDASK SPREAD
Coef.
S.E.
Coef.
S.E.
Coef.
S.E.
Coef.
S.E.
Coef.
S.E.
ITA
constant
lagged d.v.
ASSUSA
ASSUSA-1
SHORT
LIQUIDITY
0.0011
0.9324
-0.0257
0.0230
0.0048
0.0028
0.0087
0.0091
0.0091
0.0008
-0.0003
0.9308
-0.0255
0.0229
0.0050
0.0009
0.0040
0.0089
0.0091
0.0091
0.0009
0.0002
0.0004
0.9350
-0.0250
0.0210
0.0040
0.00003
0.0030
0.0114
0.0094
0.0095
0.0009
0.00003
-0.0048
0.9360
-0.0230
0.0165
0.0047
0.0400
0.0037
0.0086
0.0090
0.0091
0.0007
0.0145
0.0050
0.9317
-0.0260
0.0240
0.0042
0.0005
0.0107
0.0168
0.0116
0.0114
0.0022
0.0015
SPA
constant
lagged d.v.
ASSUSA
ASSUSA-1
SHORT
LIQUIDITY
0.0152
0.8931
0.0013
0.0017
0.0043
0.0045
0.0151
0.0126
0.0127
0.0009
0.0167
0.8930
0.0011
0.0016
0.0042
-0.0020
0.0061
0.0152
0.0126
0.0126
0.0010
0.0052
0.0091
0.8870
-0.0100
0.0140
0.0061
0.0000
0.0046
0.0210
0.0130
0.0131
0.0015
0.0003
0.0060
0.8770
0.0010
-0.0040
0.0050
0.0740
0.0049
0.0162
0.0123
0.0123
0.0009
0.0160
0.0137
0.9320
-0.0110
0.0140
0.0022
-0.0030
0.0110
0.0182
0.0113
0.0112
0.0019
0.0050
BEL
constant
lagged d.v.
ASSUSA
ASSUSA-1
SHORT
LIQUIDITY
0.0120
0.8687
-0.0448
0.0337
0.0047
0.0047
0.0160
0.0144
0.0145
0.0009
0.0076
0.8632
-0.0435
0.0343
0.0056
0.0047
0.0057
0.0164
0.0145
0.0146
0.0012
0.0033
0.0090
0.8630
-0.0530
0.0406
0.0052
-0.0007
0.0050
0.0219
0.0151
0.0152
0.0013
0.0003
0.0080
0.8650
-0.0370
0.0250
0.0055
0.0300
0.0057
0.0160
0.0143
0.0144
0.0010
0.0180
-0.0150
0.8410
-0.0470
0.0280
0.0110
-0.0015
0.0152
0.0329
0.0162
0.0163
0.0037
0.0085
AUS
constant
lagged d.v.
ASSUSA
ASSUSA-1
SHORT
LIQUIDITY
0.0013
0.8561
-0.0099
-0.0066
0.0052
0.0038
0.0208
0.0128
0.0129
0.0010
0.0005
0.8560
-0.0096
-0.0065
0.0052
0.0026
0.0047
0.0208
0.0129
0.0129
0.0010
0.0090
0.0070
0.8430
-0.0136
-0.0020
0.0050
-0.0009
0.0049
0.0252
0.0159
0.0160
0.0013
0.0007
-0.0070
0.8560
-0.0100
-0.0080
0.0060
0.0730
0.0050
0.0205
0.0128
0.0128
0.0011
0.0210
0.0110
0.8390
-0.0057
-0.0095
0.0044
-0.0060
0.0193
0.0314
0.0206
0.0206
0.0036
0.0100
FIN
constant
lagged d.v.
ASSUSA
ASSUSA-1
SHORT
LIQUIDITY
0.0350
0.8738
-0.0260
0.0327
-0.0002
0.0064
0.0155
0.0147
0.0147
0.0008
0.0351
0.8733
-0.0257
0.0327
-0.0002
0.0015
0.0078
0.0156
0.0148
0.0147
0.0009
0.0110
0.0130
0.8230
-0.0188
0.0250
0.0070
-0.0001
0.0110
0.0310
0.0178
0.0178
0.0021
0.0008
0.0350
0.8660
-0.0180
0.0241
0.0000
0.0044
0.0069
0.0159
0.0145
0.0146
0.0008
0.0190
0.0391
0.7910
-0.0200
0.0320
0.0037
-0.0030
0.0159
0.0308
0.0170
0.0168
0.0029
0.0070
NET
constant
lagged d.v.
ASSUSA
ASSUSA-1
SHORT
LIQUIDITY
0.0171
0.8575
-0.0910
0.0871
-0.0001
0.0060
0.0181
0.0183
0.0184
0.0010
0.0164
0.8555
-0.0910
0.0870
-0.0001
0.0010
0.0066
0.0185
0.0182
0.0183
0.0009
0.0030
0.0286
0.7990
-0.0910
0.0860
-0.0012
0.0000
0.0069
0.0273
0.0188
0.0189
0.0010
0.0040
0.0020
0.8661
-0.0810
0.0720
0.0018
0.0480
0.0073
0.0172
0.0186
0.0186
0.0011
0.0163
0.0830
0.7620
-0.0720
0.0810
-0.0080
0.0009
0.0203
0.0355
0.0189
0.0188
0.0033
0.0060
FRA
constant
lagged d.v.
ASSUSA
ASSUSA-1
SHORT
LIQUIDITY
0.0056
0.7535
0.0104
-0.0126
0.0073
0.0086
0.0259
0.0290
0.0291
0.0017
-0.0023
0.7510
0.0100
-0.0120
0.0080
0.0030
0.0169
0.0264
0.0290
0.0291
0.0026
0.0060
0.0140
0.7230
0.0580
-0.0142
0.0200
-4.4E-04
0.0119
0.0373
0.0454
0.0448
0.0053
0.00420
0.0100
0.7540
0.0190
-0.0152
0.0070
0.1000
0.0087
0.0257
0.0290
0.0291
0.0017
0.0370
0.0890
0.6700
-0.0300
0.0500
0.0410
0.0470
0.0138
0.0413
0.0507
0.0510
0.0055
0.0290
POR
constant
lagged d.v.
ASSUSA
ASSUSA-1
SHORT
LIQUIDITY
0.0361
0.8314
-0.0028
-0.0030
0.0040
0.0100
0.0196
0.0300
0.0301
0.0016
0.0379
0.8303
-0.0027
-0.0030
0.0039
-0.0020
0.0144
0.0197
0.0300
0.0301
0.0018
0.0300
0.0610
0.7880
0.0086
-0.0126
0.0018
-0.00450
0.0135
0.0317
0.0407
0.0410
0.0035
0.0001
0.0165
0.8100
-0.0170
-0.0060
0.0054
0.1573
0.0094
0.0192
0.0278
0.0278
0.0015
0.0156
-0.0700
0.8600
-0.0700
0.0500
0.0240
-0.0020
0.0600
0.0560
0.0470
0.0477
0.0044
0.0100
IRL
constant
lagged d.v.
ASSUSA
ASSUSA-1
SHORT
LIQUIDITY
0.0243
0.8314
-0.1005
0.0867
-0.0025
0.0066
0.0182
0.0205
0.0206
0.0011
0.0237
0.8312
-0.1014
0.0882
-0.0025
0.0020
0.0090
0.0186
0.0205
0.0206
0.0012
0.0120
0.0169
0.8299
-0.0860
0.0710
-0.0010
0.0200
0.0072
0.0173
0.0203
0.0205
0.0014
0.0100
VOLUMES are the ratios of trading volume of bond issued by country i to those issued by Germany recorded by MTS.
OUTSTANDING are the ratios of outstanding amounts on specific benchmark bonds in country i to the outstanding
amount of the German benchmark. ONOFF is the difference in yield spread between on-the-run and off-the-run bonds
issued by country i and yield spreads between on-the-run and off-the-run bonds issued by Germany. BIDASK is the
difference between the bid/ask spread in country i and that in Germany.
The extension of the model to the inclusion of liquidity indicators shows that the significance of
international factors is always robust of the inclusion of liquidity indicators and only the relative onthe-run/off-the-run yield differentials have a generalised significant effect in addition of the
international factors. The impact is positive everywhere, showing that an increase in the differential
between on-the-run and off-the-run securities in country i relative to that of Germany widens the
yield differentials between country i 10-year bonds and German 10-year bonds.
11
The US asset swap spread is significant for Italy, Belgium, Austria, Finland, the Netherlands,
Portugal and Ireland. This variable has a negative impact: an increase in 10-year US asset swap
spreads widens yield differentials between non-German and German Euro countries. France and
Spain are the only countries whose differentials on German bonds are insulated from this effect.
Higher short-term interest rates in the Euro area have also a significant impact widening yield
differentials, the only exception being Finland, the Netherlands and Ireland where, however, the
short rate is not significant.
The negative results on three out of four liquidity indicators may be due to the lack of
comprehensive data for the whole market (including over-the-counter activity). The only data
available on traded volumes and other liquidity indicators for a sufficiently long time period and on
a daily basis are from EuroMTS.6 This subset of information is not an unbiased sample of
underlying market activity. Moreover, measuring the liquidity premium is also difficult because
liquidity and credit risk interact with each other. The lack of liquidity amplifies the effect of risk.
This is because liquidity variables, such as the bid-ask spread, reflect the risk borne by market
makers in managing unbalanced positions. As credit risk increases so does the risk they face.
Indeed, there is substantial anecdotal evidence of bid-ask spreads opening during crisis periods with
larger increases for assets and bonds of lower quality. Obviously this argument can also be applied
to the only significant measure for liquidity. A (credit-related) flight-to-quality argument might also
be used to interpret the significance of our off-the-run/on-the-run yield differentials. To shed further
light on this issue we report in Table 5 the correlation matrix of the on-the-run/off-the-run yield
differentials for each country, and the international factors which we relate to credit risk.
Table 5: Correlation of the ONOFF liquidity measures with the international factors
ITA
ITA
IRE
FRA
FIN
BEL
AUS
NET
POR
SPA
ASS10USA
SHORT
IRE
1.00
0.24
0.14
0.68
0.48
0.53
0.60
0.35
0.48
0.69
-0.33
FRA
0.24
1.00
0.21
0.01
0.48
0.30
0.59
0.14
0.43
0.29
-0.78
FIN
0.14
0.21
1.00
0.04
0.28
0.12
0.09
0.08
0.15
-0.01
-0.03
BEL
0.68
0.01
0.04
1.00
0.43
0.46
0.52
0.29
0.54
0.61
-0.16
AUS
0.48
0.48
0.28
0.43
1.00
0.40
0.55
0.20
0.37
0.18
-0.53
NET
0.53
0.30
0.12
0.46
0.40
1.00
0.50
0.23
0.46
0.36
-0.44
POR
0.60
0.59
0.09
0.52
0.55
0.50
1.00
0.27
0.54
0.51
-0.75
SPA
0.35
0.14
0.08
0.29
0.20
0.23
0.27
1.00
0.24
0.30
-0.14
0.48
0.43
0.15
0.54
0.37
0.46
0.54
0.24
1.00
0.55
-0.44
ASS10USA SHORT
0.69
-0.33
0.29
-0.78
-0.01
-0.03
0.61
-0.16
0.18
-0.53
0.36
-0.44
0.51
-0.75
0.30
-0.14
0.55
-0.44
1.00
-0.33
-0.33
1.00
The liquidity measure for each country is the difference in yield spread between on-the-run and off-the-run bonds issued
by country i and yield spreads between on-the-run and off-the-run bonds issued by Germany, the international factors
are the US asset swap spread and the 1-month policy rate in Europe (short).
The reported correlation does not solve the issue. In fact, the correlation of liquidity indicators with
international factors is only moderate, and features a considerable amount of cross-country
variability. However, there is also a rather strong pattern of cross-country correlation for these
6
EuroMTS is the electronic trading platform that started operating in April 1999 on Euro-zone government bond markets and that
was the only available source of information for homogeneous indicators of liquidity. The rest of the market has been, and still is,
mainly over-the-counter and no reliable, consistent and sufficiently long series can be obtained on liquidity indicators. EuroMTS
provided time series long enough to perform statistical analysis, i.e. from spring 1998 (some countries only) to present. Other
electronic systems have not been taken into consideration as they started operating only very recently, and therefore time series long
enough to perform statistical analysis are not available. EuroMTS data are available for the 10-year maturity, although some data are
also available for 2/3-year, the 5-year, and the 30-year benchmarks in the main government bond markets.
12
measures, suggesting global factors, such as flight-to-quality phenomena, might be important in
explaining its variability.
6. IDENTIFYING LIQUIDITY FROM CASE STUDIES
While there is no generally accepted definition of liquidity, we can broadly define liquidity risk
premia as the additional yield required by market makers for the risks and costs in managing their
bond positions.7 The less liquid a bond, the higher the yield it must offer. Relevant factors that
affect the return and risk of such activity include:
i. the features of the market where bonds are traded, from their infrastructures, the admission and
trading rules, the clearing and settlement of transactions, to the regulatory frameworks, etc.;
ii. the trading volume, which is related to the outstanding amount of bonds with the same
characteristics, among other things;
iii. the efficiency of the repurchase agreement and futures markets available to manage the risks
of dealers' activity;
iv. the primary market to the extent that the issuing techniques affect dealers' incentives to trade
the particular government’s securities.
The first group of factors is an important area for policy reform, since the creation of a fully
integrated European bonds market probably hinges on the definition of common regulatory
frameworks, trading rules, clearing and settlement arrangements. We do not know how to test the
impact of these factors, but the market perception is that such factors are not too important.
With regard to the second source of liquidity differentials, in the previous section we have
investigated whether differences both in trading volumes and in the outstanding amounts of
benchmark bonds (relative to German benchmarks' volumes and amounts) affect yield spreads
without finding any significant effect.
In this section, we turn to the other sources of liquidity differentials. Besides trading volumes, the
risks and costs faced by market makers in managing their bond positions depend on: i) the
efficiency of the futures market and of the repurchase agreement market; ii) institutional aspects of
primary and secondary markets, and repo facilities of the last-resort type that may affect dealers’
incentives to trade the securities of particular governments.
6.1 Futures markets
A proper functioning of associated derivatives markets facilitates the active trading and
management of interest rate risk. Where a well-developed futures market exists, market makers can
manage their positions using futures, thereby enhancing their ability to carry out inventory-risk
management in the cash market, which, in turn, promotes better liquidity. Trading activity in the
futures markets may also increase activity in the cash markets due to arbitrage activity on the basis.8
Equally, a well-developed futures market depends on a deep underlying cash market. This mutually
reinforcing process results in large liquid issues, which are deliverable into an actively traded
futures contract, commanding a price premium. In the Euro area the Bund futures contract has
become predominant. German government bonds, which have become the de-facto benchmark in
7
Market makers, i.e. those players making secondary markets on bonds, may not be the same institutions being part of the placement
consortium (also called group of primary dealers or specialists in some countries), and may also be different from the more closer
group that gets mandates for international issues or for Treasury’s related activities (M&A etc.).
8
There appears to be different positions on this issue. Some believe a liquid future market can withdraw liquidity from the
underlying cash market, as speculative trades would find trading in the future market cheaper.
13
the 10-year sector, appear to command a sizeable premium versus other sovereign issues due to this
"derivative factor".
Although we planned to analyse the impact on the derivative market, so far we have not been able
to collect sufficient and reliable data on overall trading volumes in the cash markets on a daily basis
(including over-the-counter transactions) to compare with trading volumes in derivatives markets.9
6.2 Institutional innovations in primary and secondary markets
Institutional aspects concerning primary markets may affect dealers' incentives to trade the
securities of particular governments and thus impact on their liquidity. For example, the regularity
and predictability of the issuance process is valued by market makers since it allows for better
planning of their activities and for a more effective risk management. Sovereign borrowers who
announce in advance the types of bonds (with indicative amounts) that they are going to issue may
gain a liquidity premium for their bonds, as market makers prefer to trade such securities. It is
indicative that with the advent of EMU and the implied increase of competition, issuance calendars
have been introduced in all EMU member States (see Favero, Missale and Piga 1999).
The liquidity that market makers are willing to provide by actively trading the bonds of specific
issuers may also depend on institutional aspects not regarding the primary market. For instance,
market makers may positively value the existence of a repurchase agreement facility in the form of
a window at the Treasury, say, a repo facility of the last-resort type.
It must be realised, however, that measuring the extent of reduction in cost and risk that is provided
by specific features of calendars, issuing procedures and placement techniques is practically
impossible; at best we can deal with qualitative differences among sovereign borrowers. Since
institutional aspects of debt management rarely change over time and, when they do, such changes
are part of broader reforms involving several features of debt management, as it was the case with
the advent of EMU, it becomes difficult to identify their effects on liquidity. For instance, the
introduction of dummies to account for differences in the placement techniques across member
States would provide no useful information, since it would be equivalent to attributing any
unexplained differences in yield spread to country-specific factors. Furthermore, even if we took
for granted that differentials in liquidity premia depend on debt management, the exact source of
such differentials would be difficult to identify since countries differ along several dimensions of
debt management.
However, such difficulties can however be overcome if any specific aspect of debt management
changes during the sample period (and if such changes do not occur contemporaneously and in all
countries). Innovations regarding the placement technique, the issuing method, the introduction of
risk-management facilities, etc. all provide important events that possibly affect the liquidity of the
securities issued by the reforming government. The impact of such events on liquidity can be
studied with the introduction of dummy variables in the equation system for yield-spreads.
9
The only available source of information for homogeneous indicators of liquidity was EuroMTS, the electronic trading platform
that started operating in April 1999 on Euro-zone government bond markets. The rest of the market has been, and still is, mainly
over-the-counter and no reliable, consistent and sufficiently long series can be obtained on liquidity indicators. EuroMTS provided
time series long enough to perform statistical analysis, i.e. from spring 1998 (some countries only) to present. Other electronic
systems have not been taken into consideration as they started operating only very recently, and therefore time series long enough to
perform statistical analysis are not available.
14
Events characterising debt management during the sample period from June 1999 to August 2001
have been identified through the answers provided by the debt managers of EMU member States to
the questionnaire reported in Appendix 2. The questionnaires (when incomplete or lacking the
indication of the precise day of the innovation) have been integrated with the information in the
annual reports and other documents at the web sites of the debt offices. Unfortunately, a complete
account of innovations is not possible since answers have not yet been received from Ireland and
Spain.
The questionnaire allows us identification of innovations in the following areas:
1) innovations regarding the primary market, in particular, changes in the issue calendar, in the
issuing method or in its characteristics;
2) innovations regarding the secondary market, such as changes in admission or trading rules of
wholesale markets and changes in the requirements (market making obligations) of dealers;
3) institutional reforms of the repurchase agreement market or the introduction of a repurchase
agreement facility in the form of a window at the Treasury;
4) the delegation of debt management to a different institution; i.e. an independent agency;
The questionnaire asks for the date when the change in debt management over the above areas
became effective. This allows us to create step dummies; i.e. 0-1 "event variables", that take the
value of 1 after an institutional change. Including these variables into the model of yield-spreads
allows us to assess the importance of institutional aspects as determinants of yield-spreads across
member States.
6.3 Innovations in primary markets
Primary market policy varies widely across EMU member States. For instance, while most
countries issue bonds by auctions, the tap issuing technique is still in use in the Netherlands and
small issuers, like Austria, Belgium, Finland, Greece and Portugal use a bank syndicate for the
launch of new bond lines. Most countries adopt multiple price auctions and hold a second round
auction with non-competitive bidding which is restricted to primary dealers with higher status, but
in Italy and Finland the uniform-price technique is used and no second round auction is held in
Germany. Despite these differences very little has changed since the advent of EMU.
We have investigated whether:
1) the issuance calendar has been modified;
2) the issuing technique has changed, for example, from auction to a bank syndication;
3) the auction method has changed from multiple to uniform pricing or in the method to determine
the total amount allotted;
4) the auction framework has changed; i.e. with the introduction of an electronic auction system or
with a shortening of the time period before the notification of the results;
5) second-round non-competitive auctions have been modified with an extension of the time
available for placing bids and with an increase in the amount of bonds available in the secondround;
6) the requirements that primary dealers must meet to participate in the auctions; in particular,
whether they must be based locally.
6.3.1 Event study: calendars and auction cancelled
Issuance calendars, whose importance has been discussed above, have not been an area of reform.
Few countries introduced or extended the calendar in preparation for EMU but since 1999 no
15
changes have been made. In particular, we asked: i) whether there has been a move from a
quarterly (or semi-annual calendar) to a yearly calendar; ii) whether bond types and indicative issue
amounts have been more closely specified and; iii) whether the time period (in cases where an
exact date is not announced) has been more narrowly defined. By contrast, some countries have
reported instances where an auction (previously scheduled in the calendar) has not taken place.
These events are likely to take place because of lower deficits and decreasing debts with very long
average maturities. The problem clearly shows up at low levels of debt as pointed out by the answer
of Finnish debt managers that auctions were cancelled because of no need for financing,
unfortunately without providing the dates of such episodes. But, surprisingly, in December 2001 a
bond auction was not held in Italy for the same reason. The cancellation of a previously scheduled
auction directly affects liquidity, if it regards a reopening of an existing issue, and, more generally,
it may lead market makers to change their plans with possible effects on the incentive to trade the
securities of the issuing country.
Although the answer from Finland was imprecise and the Italian episode falls outside the sample
period, the Austrian and Belgian authorities provided the following useful information for an eventstudy:
Episodes of cancelled bond auctions:
Country
Date
AUSTRIA
11-11-2000
BELGIUM 25-01-1999 (out of sample)
27-09-1999
20-12-1999
31-01-2000
29-01-2001
28-05-2001
26-11-2001 (out of sample)
Reason
Fulfillment of funding needs
Syndication
Syndication
Fulfillment of funding needs
Syndication
Syndication
Syndication
Fulfillment of funding needs
To account for such episodes we have included (in the equation system for the 10-maturity spreads)
point dummies for the date when the cancelled auction was supposed to have taken place. We
choose a point dummy taking the value of 1 on the days of the auctions and 0 otherwise (instead of
a step dummy as for other event studies) because such episodes are expected to have transitory
effects as opposed to institutional changes of a permanent nature. The specification used for the
system of yield spreads is that based on the international factors and the on-off-differentials so as to
control for the other factors affecting credit risk and liquidity.
The point dummy for the cancellation episodes of Belgium is significant at the 10% level and has a
negative coefficient delivering a long-run impact of minus 2 basis points. This unexpected result
could be explained by the fact that we do not use, as would be more appropriate, the dates when the
cancellations were announced. It is however worth observing that 4 out 5 episodes in our sample
result in a substitution of a syndicated placement for the cancelled auction. Since the syndication
technique eliminates the auction risk to which primary dealers (and thus most market makers) are
exposed, it may lead to an increase in the demand for the bonds of the same line (if a reopening) as
those which are going to be placed through syndication.
6.3.2 Event study: change in the auction method and targeted issue size
The issuing techniques adopted by EMU member States have remained the same since 1999 when
smaller issuers, namely, Austria, Belgium, Finland and Portugal moved from auctions to syndicated
placements because of common currency denomination and visibility problems. In fact, the main
16
features of the issuing method have also remained the same; no country reports a change in the
pricing system and/or in the determination of the total amount allotted in first-round competitive
auctions.
The only relevant innovations which have taken place regard the targeted issue size and the auction
framework, namely, the introduction of an electronic auction system or with a shortening of the
time period before the announcement of the results. The advent of EMU and the resulting stronger
competition for raising funds have led countries to increase the size of their benchmark bonds in
order to achieve a greater liquidity. Large borrowers now have benchmarks around euro 15 billion,
while the target for smaller issuers is set at euro 5 billion, that is the requirement for admission at
trading on the Euro-MTS. Accordingly, the issue sizes are large, around euro 1.5 billion for small
borrowers and over euros 2 billion for large borrowers. However, many of these changes have
taken place before 1999.
Austria and Belgium are the only countries (among respondents) which report an increase in the
target for the size of new issues, both since January 2000. Quoting the Belgian answer: "The
number of auctions has been reduced. For the auctions up to January 2000, we used to have
monthly auctions, with 3 lines issued at every auction. Since January 2000, we have cut down the
number of auctions to 6 and we issued only 2 lines at each auction. The average size has doubled
from what it used to be."
This is an important innovation that provides a clear event study
Episodes of increased issue size:
AUSTRIA
1-01-2000
BELGIUM
1-01-2000
To account for such episodes we have included (in the equation system for the 10-maturity spreads)
step dummies taking the value of 1 since 1/1/2000. As usual we augment the specification that is
based on the international factors and the on-off-differentials.
The step dummy for the change in the issue size targeted by Belgium has a negative coefficient, as
expected, but is not significant. By contrast, in the case of Austria the coefficient is significant with
a t-stat of 1.9, but its sign is negative, indicating, if anything, a deterioration of the liquidity of
Austrian bonds relative to German Bunds. Though disappointing, this result confirms, once more,
the difficulties in finding any effect of liquidity on yield spreads.
The introduction of an electronic auction system or the shortening of the time period between the
end of the bidding period and the notification of the results can provide further evidence on liquidity
effects. Indeed, the costs of hedging the risk of unbalanced bond positions taken at times of bond
auctions may depend on the length of time between the end of bidding and the announcement of the
result. An electronic auction system has been used in Belgium since May 2000. In the same year in
France the time needed for the announcement of the auction results has been reduced from 10 to 5
minutes, the shortest in the euro area. More recently, in Italy the time for auction results has been
reduced from 20 to 15 minutes.
A faster release of auction results may reduce the risk faced by primary dealers (and thus by most
market makers) and may increase the incentive to trade the bonds of the most efficient country. This
suggests that episodes of improvements in the infrastructure of auction systems could be used as
event studies.
17
Episodes of reduction of time period before notification of auction results:
Country
Episode
Date
BELGIUM Introduction of electronic auction system
29-05-2000
FRANCE
Reduction from 10 to 5 minutes
6-01-2000
ITALY
Reduction from 20 to 15 minutes
14-06-2001
As usual, a step dummy for each country has been included in the equation system for yield spreads
to account for such episodes. The dummy for the introduction of an electronic auction system in
Belgium has a positive coefficient, though the impact on the yield spread is not significant. The
reduction in the time needed for the notification of the results in Italy also appears to be associated
with a higher yield spread. The impact is significant, though its magnitude is small with a long-run
impact of 4 basis points. Only in the case of France does a faster release of auction results reduce
the yield spread. The effect is significant at the 5% level and the long-run impact on the yield
spread is minus 4 basis points.
6.3.3 Event study: change in second-round non-competitive auctions
The advent of EMU has led sovereign borrowers to compete for the services of market makers since
they are crucial for the bonds to be placed, quoted and traded. More concessions have been given to
primary dealers with higher status (who are also market makers) because of their crucial role in
promoting national bonds abroad and attracting the demand of European investors. A few
countries, such as Italy for example, no longer require that primary dealers be locally based. With
the start of EMU, the time available for non-competitive bidding in second-round auctions (to
which only dealers with highest status can participate) has been extended de facto in countries like
France and Spain. In the Netherlands, where bonds are issued by tap, a Primary Dealer System was
created in January 1999 and dealers receive a fee depending on the volume of bonds taken. The
system is expected to change in 2002 with the use of non-competitive bids.
Since 1999 policies regarding the localization of primary dealers have not been changed (with the
exception of Greece where local residence is no longer required since January 2001). On the
contrary, the time available for non-competitive bidding in second-round auctions has been
extended in Austria, Greece and Italy. This provides an interesting case study of whether
concessions given to primary dealers may increase the incentive to trade and place the bonds of
generous governments with a positive impact on their liquidity.
Episodes of extended time for placing bids in second-round auctions:
AUSTRIA
13-2-2001
GREECE (not usable)
1-01-2001
ITALY
30-3-2000
To study the effect of such episodes on the yield spreads of Austria and Italy, we have augmented
with two step dummies the 10-year system based on the international factors and the on-offdifferentials.
The extension of the bidding period does not appear to have any significant effect on the yield
spread, both in Austria and in Italy. A longer period to place non-competitive bids appears to
reduce the yield spread in Austria but the coefficient for the step dummy is not significant. The
same holds true for Italy, where the yield spread is even wider after the institutional innovation.
18
6.4 Innovations in secondary markets and repo markets
Institutional changes regarding the functioning of secondary markets are expected to have the
strongest impact on liquidity. However, as we focus on bond yields recorded on the electronic MTS
markets (Euro-MTS and local MTS), it is unlikely that institutional changes which do not affect the
framework, the admission and trading rules of such markets, may have an impact on yield spreads.
We introduced in the questionnaire, sent to the debt managers, a number of questions regarding the
functioning of local secondary markets in order to identify events that could possibly affect
liquidity.
We asked debt managers whether:
1) a domestic electronic securities market exists and the date when it became operative;
2) admission or trading rules in domestic wholesale markets have changed;
3) requirements for becoming market-makers have been modified since 1999;
4) the number of market makers has changed;
5) a well established repo market for bonds exist and the date when it became operative;
6) a repo facility of the last resort type exist at the Treasury and the date of introduction.
The study did not allow us to identify significant innovations in the structure of secondary markets
except for the start of operations of domestic MTS markets. Indeed, episodes of changes in
admission and trading rules in wholesale markets coincide with the beginning of the operations of
domestic MTS markets.
6.4.1 Domestic MTS markets
The increasing automatisation of trading is also an important factor of liquidity. Since 1999,
following the example of Italy and the success of Euro-MTS, electronic trading has developed in
Belgium, France, the Netherlands and Portugal. Since national MTS markets are electronically
linked with the Euro-MTS, their start has increased overall MTS trading volumes on the country’s
benchmarks. The MTS system offers a simple interface with other MTS markets (including EuroMTS), allowing for the use of a common platform for the trading of debt instruments of EMU
member States, while simultaneously keeping local specificities such as local management and
supervision, market makers, etc. As a consequence of the development of local MTS, the number
of market-makers, trading the same bonds, has increased. This has probably led to higher trading
volumes and better pricing and has possibly reduced the yield on bonds issued in the country of the
national MTS.
Beginning of the operations of domestic MTS markets
MTS Amsterdam
9-1999
MTS Belgium
5-05-2000
MTS France
25-04-2000
MEDIP/MTS Portugal
24-07-2000
To evaluate the impact on yield spread of the introduction of national MTS electronic markets, we
have introduced three dummies in the 10-year maturity system based on the international factors
and the on-the-run/off-the-run differentials (in short ‘on-off-differentials’). Specifically, we have
augmented the equations for the Belgian, French and Portuguese yield spreads with three different
dummies, one for each equation, taking the value of 1 since the date of the beginning of the MTS
for the corresponding country. Instead, we could not include a dummy for the start of MTS
Amsterdam since it begun operating in September 1999, which also marks the start of the series for
the Dutch spread.
19
The regression results show that the step dummies for the domestic Belgian and Portuguese MTS
markets significantly affect the yield spreads of the corresponding countries while the start of the
MTS France has had no impact on the French yield spread. This finding is not surprising, since
most trade on French bonds occur on the Euro-MTS while the opposite is true in the case of
Belgium and Portugal where the trading volume (of the corresponding bonds) on the domestic MTS
is substantially higher than that on the Euro-MTS. The start of MTS Belgium appears to have
added liquidity to the market, leading to a reduction in the yield spread. The dummy is indeed
significant at the 10% level and has a negative coefficient delivering a long-run impact of 2 basis
points. Also the start of MEDIP/MTS Portugal significantly affects the Portuguese yield spread,
but contrary to expectations, the effect is a widening of the spread up to 6 basis points in the long
run. This result is surprising since the start of MEDIP has been associated with a considerable
increase in trading volume and thus of a greater liquidity. However, Portuguese data on yields are
very volatile until July 2000, the start of the domestic market, and this may be an indication of the
poor quality of recording. Unfortunately, this problem is likely to affect the estimates concerning
the other episodes of institutional changes in Portugal.
Focusing on the more reliable event-studies of Belgium and France we observe that when the
introduction of the domestic market significantly add liquidity as in the case of MTS Belgium the
yield spread is reduced. However, the effect of increased trading volumes do not appear substantial,
only 2 basis points, a result which implicitly confirms our previous estimates of the effect of trading
volumes on yield spreads.
Focusing on the more reliable event-studies of Belgium and France, we observe that the
introduction of the domestic market significantly adds liquidity as is the case of MTS Belgium
where the yield spread is reduced. However, the effect of increased trading volumes do not appear
substantial, only 2 basis points, a result which implicitly confirms our previous estimates of the
effect of trading volumes on yield spreads.
6.4.2 Market making requirements
Interestingly, with the notable exception of Greece, the requirements for becoming market makers
does not appear to have changed since the advent of EMU. The number of bond lines to be quoted,
the amounts to be traded or the minimum amounts of bidding in auction (if applicable) or
qualitative requirements have remained the same. Some countries have changed the requirements
in January 1999 but have since left such requirements unaltered. In Greece a number of quantitative
and qualitative requirements have been introduced but data on Greek yields are available only since
February 2001. This leaves us with no main event to study. Only in Finland has a narrower bid-ask
spread been introduced for market makers; since January 2001 the bid-ask spreads have been
reduced to 4, 6 and 8 cents depending on the maturity of the bond being traded.
Episode of narrower bid-ask spread requested to market makers:
FINLAND
1-01-2001
A step dummy has been included in the equation for the Finnish yield spread, taking the value of 1
since January 2001. The very low t-stat for the coefficient of the step dummy clearly shows that the
stricter requirement for market makers has had no effect on the liquidity and the yield spread of
Finnish bonds.
20
6.4.2 Event study: introduction of a repo window at the Treasury
In all EMU member States, with the exception of Greece, well functioning repurchase agreement
markets for bonds existed before 1999 and further facilities have been provided through access
contracts to Euro-MTS repo facilities. However, the latter cannot be considered as independent
events to be studied separately from the development of domestic MTS.
Interesting case studies are instead provided by the recent introduction of repo facilities of the last
resort type available to primary dealers at the Belgian and Portuguese Treasuries. These repo
windows should further reduce the risk faced by market makers and allow them to take larger bond
positions. This should increase the volume of trading with positive effects on liquidity.
The event studies correspond to the following episodes.
Introduction of a repo facility of last resort type:
BELGIUM
1-04-2001
PORTUGAL
7-08-2000
A step dummy for each country has been included in the 10-year system to evaluate the effect of
such episodes on the yield spreads.
The introduction of the repo facility in Belgium appears to have had a positive effect on the
liquidity of the Belgian benchmark. However, though the effect is significant at the 10% level, the
magnitude is negligible with a long-run impact of 2 basis points. The introduction of the same
facility by the Portuguese debt office is also significant (at the 5% level) but it displays the opposite
effect on the yield spread which rises with a long-run impact of 8 basis points. In fact, the widening
of the Portuguese yield spread after mid 2000 is surprising especially since it follows the beginning
of the operations on the domestic MEDIP/MTS. A possible explanation of this finding is the low
quality of data recording until mid 2000 (see above).
6.4.3 Event study: repurchase programs
Decreasing debts and very long maturities have led to a strong contraction of financing needs in the
euro area. It is becoming problematic to build large benchmark issues just at a time when the
desired volume of bond issues has increased as a result of pan European markets. Consequently, it
is becoming common practice to repurchase old illiquid bonds in exchange for new ones, so as to
top up more recent bond lines. By reducing the number of funding instruments and concentrating
the outstanding debt in few bond lines, such operations should increase the overall liquidity of the
debt as well as that of the remaining benchmark bonds.
Buy back operations have been carried out by countries like Belgium, Finland, Ireland, Italy and
Spain much before the start of the EMU. The Belgian debt agency was probably the first to
announce a proper exchange program with dates of operations and types of bonds to be repurchased
set in advance according to a specified calendar. After the start of EMU France and Portugal also
announced repurchase programs in July 2000 and in January 2001, respectively. The first reverse
auction took place in France the 5th of September 2000, while the first repurchase operation in
Portugal was the set up of a buyback window for Eurobonds on the 8th of February 2001. The first
reverse auction for domestic bonds was instead held the 11th of April 2001. While this date is too
close to the end of the sample period to conduct an event study (as our sample ends in June 2001),
the start of the French program provides an ideal case to examine the impact of buyback operations
on the overall liquidity of the debt.
21
Episodes of first reverse auctions:
FRANCE
PORTUGAL (not usable)
5-09-2000
11-04-2001
To study the how the launch of the French repurchase program affected the yield spread we have
included a step dummy in the system based on the international factors and the on-off-differentials.
The coefficient on the step dummy is negative and significant at the 5% level showing that the
program has indeed been perceived as a reduction of the instrument fragmentation of the French
debt with a positive effect on liquidity. Indeed the long-run impact on the yield spread of minus 5
basis point appears substantial.
6.4.4 Delegation of debt management to an independent agency
In many countries debt management is delegated to an agency that can be internal or external to the
Treasury. Although there is little evidence (unlike for monetary policy) on the effects of delegation
in debt management, it is believe that independent agencies subtract debt management from
government pressure, eliminating the temptation to use private information in order to reduce
funding costs at the expense of investors and make debt management more transparent.
Independent debt agencies, especially if external, operate under performance contracts (or are
assigned specific targets) and are closely monitored and must be accountable to the Treasury. As a
result of delegation, cost minimisation becomes the main goal of debt managers and this gives rise
to a more active but also more transparent debt management. The announcement of delegating debt
management to an independent agency may thus send a signal to the market of a new regime
characterised by a more transparent and more efficient, cost effective debt management.
Over our sample period there have been three episodes of delegation. In Greece, debt management
has been delegated to an external agency since July 1999. In Germany, an external debt agency was
founded in October 2000 and started operating in June 2001. In France debt management has been
delegated to an internal agency since February 2001. The change in responsibility has been
associated with the announcement of an interest-swap program that started in October 2001, and
with an intensification of the repurchase program.
Establishment of an independent agency:
FRANCE
8-02-2001
GERMANY
1-10-2000
GREECE (not usable)
1-07-1999
Since the shift in regime is expected to affect the German yield and thus all countries spreads, we
introduce a step dummy, which takes the value of 1 from the date in which the German debt agency
was established (October 2000). Actually, the agency became operational only in June 2001 but
this date could not be tested, since it is roughly the end of our sample period. The step dummy for
the introduction of the French agency has only been included in the equation for the French yield
spread.
The step dummy for the German agency is significant at the 5% level for all countries, with the only
exception of Belgium. The coefficient is always positive indicating a widening of the yield spread
as a consequence of the delegation of German debt management to the external agency. Consistent
with this evidence, the equation for France shows that the yield spread decreases following the
22
establishment of the internal debt agency, after controlling for the change in regime in Germany.
Indeed, the dummy for the French agency is negative and significant at the 5% level indicating a
long-run impact of about four basis points.
This evidence seems to suggest that the delegation of debt management to an external agency has
been perceived as a change in regime characterised by a more efficient and transparent debt policy.
This is consistent with the belief by many observers that, despite its benchmark status, the German
debt is not efficiently managed with too large a number of instruments and an uneven maturity
structure. The move to an independent agency may have been viewed as the first step in closing the
gap with the more modern management of other member States. This interpretation is however in
contrast with the political environment in which the decision to delegate debt management has been
taken by the German Finance Ministry. As held by many observers, the move appears to be
motivated by the aim to subtract the management of the German debt from the Bundesbank that,
though not formally, has long played the main role.
To conclude, there is strong evidence of an increase in yield spreads relative to Germany since the
last quarter of 2000 with the exception of the French and Belgian yields. In principle, this increase
can be attributed to the announcement of the external debt agency in Germany, but the debate that
surrounded its creation casts serious doubts about this interpretation. However, if the reason for the
increase in yield spreads must be found elsewhere, what can safely be concluded is that liquidity
effects (with the important exception considered below) are hardly part of the explanation. We have
examined the most significant events that one would expect to have an impact on yield spreads and
found little evidence of their importance except for innovations occurring in Belgium and France.
The important exception is the impact on yield spreads of changes in trading volumes in futures
markets that we have not been able to examine because of data availability.
7. CONCLUDING REMARKS
In this paper we have shown that euro-zone yield on government bonds, more precisely, their
credit-risk (and liquidity) component have been increasingly correlated across issuers, a sign of
enhanced integration which is possibly explained by the elimination of the risk of exchange-rate
fluctuations. Interestingly, the risk and liquidity component appears to have been greater after the
EMU.
Our study suggests, contrary to market perceptions, that yield differentials on euro-zone bonds are
mostly due to credit risk as opposed to liquidity factors. The analysis of monthly series points to
the existence of different, slowly evolving means, as well as to the importance of common
international factors in the determination of fluctuations around the means. Slowly evolving means
are related to economic fundamentals such as the ratio of government securities to the total
outstanding securities and, most likely, to debt and deficit to GDP ratios.
At higher frequencies yield differentials appear to be almost completely explained by common
credit-risk related international factors such as the US and German asset swap spreads and US and
EU policy rates. The main source of yield differentials appears to be the different sensitivity to
common, credit risk-related, international factors. By contrast, only the yield spread between onthe-run and off-the-run benchmark bonds (of the same issuer) appears to significantly affect yield
spreads across member States: traditional liquidity indicators, such as bid-ask spreads, trading
volumes and outstanding amounts have no effect.
23
Attempts to introduce dummies, depicting structural changes in secondary and primary market
structure, that may have increased the liquidity of the bonds of the reforming country, were also not
particularly successful. Most of the times such changes are not significant and, when significant,
their impact appears small.
However, such evidence does not allow us to conclude that liquidity is irrelevant. “Structural”
liquidity factors could indeed explain the different sensitivities to international factors. For
instance, the hedging facilities provided by the Eurex futures market are expected to have an impact
on German yields relative to other member States, even though we have not been able to introduce
any variable capturing such effect. Indeed, the basis risk of managing a position is not present in the
case of German bonds, while it is non-negligible for the other members. This may well have
justified increasing liquidity premia over the past few years, and episodes of flight to quality may
have heightened the importance of the basis risk in hedging non-German bond positions with Eurex
contracts.
Furthermore, the fact that bid-ask spreads and trading volumes do not significantly affect yield
spreads may simply reflect a data availability problem. Indeed, the liquidity indicators that we have
used may not be representative of the overall market situation; we had to rely on indicators taken
from the MTS database instead of the more relevant but unavailable data on over-the-counter
activity.
On the other hand, evidence of little differences in liquidity premia could not be spurious; it may
reflect an equilibrium phenomenon. It is worth noting that the need to compete for funds in
European markets with instruments characterised by a common currency denomination has led
EMU sovereign borrowers to grant concessions to primary dealers. The bargaining power of
primary dealers has increased because of their crucial role in promoting national bonds abroad.
Most countries no longer require primary dealers to be locally based. Furthermore, the time
available for non-competitive bidding in second round auctions has been extended in a number of
countries. Syndication and even mandates for privatisation of public assets may all be perceived as
ways to ‘pay back’ some of the costs of doing business to institutions providing liquidity to the
government bond market. Other elements may come as part of tradeoffs. Although such
concessions may not show up in bond spreads, they may be considered as offsetting part of the
overall costs of market making10 and therefore be part of a liquidity premium that does not show up
in market data.
10
Market makers, i.e. those players making secondary markets on bonds, may not be the same institutions being part of the
placement consortium (also called group of primary dealers or specialists in some countries), and may also be different from the
more closer group that gets mandates for international issues or for Treasury’s related activities (M&A etc.).
24
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Campbell J.Y. and Cochrane (1999) “By force of habit: a consumption-based explanation of
aggregate stock market behaviour” Journal of Political Economy, 107, 205-251
Favero C.A, Giavazzi F. and L. Spaventa (1997) “High Yields: The Spread on German Interest
Rates” The Economic Journal, 107, 443, 956-986.
Favero C.A., Missale A. and G. Piga (1999) “EMU and Public Debt Management: One Money
One Debt?” CEPR Policy Paper No.3, December
Fleming M.J. (2001) “Measuring Treasury Market Liquidity”, paper available at
www.newyorkfed.org/rmaghome/economist/fleming/fleming.html
Fleming M.J. and E.M. Remolona (1999) “Price Formation and Liquidity in the U.S. Treasury
Market: The Response to Public Information”, Journal of Finance, 54, 5, 1901-15
Gravelle T. (1999a) “Liquidity of the Government of Canada Securities Market: Stylized Facts
and Some Market Microstructure Comparisons to the United States Treasury Market, Bank
of Canada Working Paper No. 99-11.
Gravelle T. (1999b) “The Market Microstructure of Dealership Equity and Government Securities
Markets: How They Differ” in Market Liquidity: Research Findings and selected Policy
Implications BIS-CGFS Study No. 11, May.
Gravelle T. (1999c), “Markets for Government of Canada Securities in the 1990s: Liquidity and
Cross-Country Comparisons,” Bank of Canada Review (Autumn) 9-18.
Kumar M.S. and A. Persaud (2001) “Pure Contagion and Investors’ Shifting Risk Appetite:
Analytical Issues and Empirical Evidence”, IMF Working Papers
25
Appendix 1: The data set
The daily data set we use runs from June 1, 1999 to August 30, 2001. We consider observations in
each main segment of the yield curve, i.e. 3, 5, and 10 years, for all EMU members except
Luxembourg and Greece. As for Luxembourg, data simply do no exist, while for Greece reliable
series are not available.
Our sources of data are Datastream and Prisma. We use mainly Prisma for daily asset swap and
yield data. As for monthly data we use Datastream/Thomson Financial. It provides the longest
series among the data providers we took into consideration, although not many maturities are
available.
All working days in which prices are recorded for several markets, either in London or
domestically, are included in our EMU-wide database. Week-ends and holidays shared across
Europe are therefore excluded. When it is a market holiday in a specific country while not in the
others and no prices are recorded in London, we consider the observation as missing value. Data
from Datastream are not subject to any filtering procedure while row data form Prisma have been e
filtered only to cut off clear outliers (3 standard deviations away from previous day’s observation).
Missing observations and outliers have been dropped in the estimation. Swap and bond rates are
mid-rates (average between bid and ask) recorded daily before market closing.
Key issues for our data set are:
Interbank credit standing affecting asset swap spreads
Government bond asset swap spreads are relative measures that reflect government-specific factors
as well as the credit standing of the interbank market or, more generally, that of the domestic
private sector. While the credit standing of the interbank market is generally perceived to have
remained stable during the 1990s in the Euro area, this element may not have been totally negligible
in specific sub-periods and may have played a role in the determination of Euro-area asset swaps.
This was probably the case during the Russian crisis in 1998. Moreover, before Monetary Union the
credit standing of domestic banking systems was not perceived to be totally homogeneous among
current EMU member states. This may have given rise to non-homogeneous changes in asset swap
spreads across EMU countries and therefore may have affected differentials for reasons not linked
to the variables considered in our study. We assume this ‘interbank’ factor to have remained largely
negligible on swap differentials during the period before the launch of the single currency, while for
the post-EMU period under examination, this problem has clearly disappeared.
Different approaches to calculating asset swap spreads
There are three main approaches that can be used to calculate asset swap spreads for benchmark
maturities: (1) what we call the ‘quick and dirty’ approach, (2) the ‘constant maturity bond’
approach, and (3) the ‘benchmark’ approach. There are trade-offs when choosing one approach over
the other.
1) Calculating ‘quick and dirty’ differentials between swap and bond rates can be considered
sufficiently reliable for most purposes. However, it causes several problems when trying to
estimate and compare asset swap spreads among countries and for high frequency data. First,
the maturity of benchmark bonds can significantly differ from the standard maturity of swaps
(this problem may be partly offset by interpolating swap rates). Second, comparing bonds with
26
different maturities and duration can produce differences in yield differentials, and therefore in
asset swap spread differentials, that are linked to the shape of the yield curve rather than to
credit, liquidity or other factors.
2) The second approach would call for an estimation of the term structure of interest rates from
swap rates, the zero coupon curve from bond data, and then ‘constant maturity bonds’ for each
of the main benchmark positions. This may give results that are too reliant on the model used.
The major problem relates to the estimation of zero coupon curves in the bond market, as a
certain degree of subjectivity must be introduced. For example, using models based on
exponential splines necessitates judgements on inflection points that would be subjective and
difficult to make retrospectively. Other methods that do not take into account the specifics of
each government bond curve risk losing a lot of information. Finally, matching liquidity
indicators specific to a single benchmark bond may not be appropriate. Despite these
drawbacks, this approach gives the opportunity to analyse asset swap data that always refer to
the same point along the term structure of interest rates. Prisma benchmark data that we choose
for our study are based on this approach. Benchmark bond yields are calculated by using a bond
model which uses least square fitting of a smooth curve through pre-defined maturities at 1.5,
4.5, 8.5 and 28.5 years. This gives constant maturity bond yields for each maturity. The values
have been estimated to be almost indifferent to the model specification. We find it appropriate
for the purpose of our study, while inevitable distortions are perceived as acceptable.
3) The ‘benchmark’ approach refers to the use of asset swap spreads for specific benchmark bonds.
This has the advantage of relying less on any model of term structure or any approximation of
benchmark yields (only modelling of the swap term structure is required), and allows matching
of specific indicators of liquidity to the bond to which they refer. Given a pricing curve, asset
swap spreads are defined as the constant spread we have to add on the curve in order to
correctly re-price a bond.
p  ai   cf i  df ti   e
 oast i
i
Where:
p
ai
cfi
df(ti)
ti
= bond spot price
= bond accrued interest
= i-th bond cash flow
= swap curve discount factor at time t
= time between the bond settlement date and the i-th bond cash flow payment date on
an ACT/365 basis.
4) Since there is no closed form solution, the equation is solved numerically. The drawbacks of
this approach are mainly related to the somewhat subjective definition of a benchmark bond and
to the bond-specific nature of the measure which may not be representative of the market
situation in that maturity range for the sovereign borrower. Data providers assign the benchmark
status to bonds according to different criteria, and therefore bonds are benchmarks in different
time intervals. Liquidity criteria used to decide whether the bond is ‘on-the-run’ or ‘off-the-run’
are inevitably subjective. In addition, the benchmark status also depends on the admission
criteria of electronic markets. Moreover, the distortion due to the shape of the curve is not
eliminated, although it only refers to the term structure of asset swap spreads. In our modelling
exercise we also tried to use Prisma bond-specific asset swap data calculated according to this
approach for the on-the-run/off-the-run liquidity indicator. However, there were missing data in
the series that suggested use of Datastream/Thomson Financial yield series for this indicator.
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3. Volumes data
Bond markets have been mainly over-the-counter in the past, and therefore few statistics are
available on a consistent basis and for a long period of time across EMU countries. In addition, the
market share of electronic markets substantially differs among EMU countries, and therefore even
the information contents of data are not the same. Settlement data on volumes are not useful for our
purposes as volumes on repurchase agreements, basis trading, refinancing operations with the
central bank, and primary market operations are all included, and it is almost impossible to net these
volumes out to obtain pure cash transactions on the secondary market.
Although other electronic initiatives were launched, up until very recently the EuroMTS 11 trading
platform was the only one with a significant presence across several EMU countries and therefore
our sole source of homogeneous data. The rest of the market was, and still is, mainly over-thecounter and no reliable, consistent, and sufficiently long series can be obtained on liquidity
indicators. Nevertheless, EuroMTS only started operating on March 30, 1999, and at the beginning
only the bonds of three countries were included. It was only by February 2000 that the sample
expanded to cover nine EMU countries. Therefore, we have no choice but to further shorten our
daily database in order to include liquidity indicators and narrow the maturities that have been
considered. Euro/MTS data are available for the 10-year maturity, although some data are also
available for 2/3-year, 5-year, and 30-year benchmarks in main government bond markets. A table
at the end of the Appendix summarises volumes traded by EuroMTS in EMU government bonds
and the date when electronic trading started.
Volumes for benchmark issues record big jumps in the series even when we allow for a shift in the
benchmark status only when there is a sustainable pick-up in trading volumes.12
EuroMTS has a rough idea of its market share in each country, although there is no official reliable
source of information on total daily trading volumes (the sum of electronic and over-the-counter
volumes). As can be seen in the table at the end of the Appendix, EuroMTS business is still
concentrated on the Italian market. In our liquidity indicator on volumes we take relative volumes,
and this is perceived to have offset the impact the larger volumes on the Italian market as the
relative trend in volumes is what is captured in the model.
4. Bid-ask spreads
EuroMTS was able to calculate and provide us with daily averages of market makers’ bid-ask
spread of ‘proposals’ inputted into the system for each benchmark security. This has involved
significant computational work, and is the best proxy of ‘market best’ bid-ask spreads. While this
information is extremely useful and important for the purpose of our study, considerable doubts
remain on the significance of these data in depicting liquidity conditions in the market.
Market makers are ‘forced’ to quote securities with a tight bid/ask spread according to specific
electronic market obligations, although these ranges are not really binding for benchmark bonds.
11
EuroMTS refers to the sum of the activity of MTS SpA, EuroMTS Ltd and other MTS companies in EMU countries.
12
Days in which volumes are higher than the previous benchmark and which mark the turn in relative volumes.
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However, ‘market best’ bid/offer spreads are generally much narrower than quoting obligations, and
therefore we perceive our indicator may not be biased for this reason.
A more serious distortion relates to the averaging process of bid/ask spreads of proposals during the
day. The introduction of unusually large bid/ask proposals into the system may affect the results,
and therefore data were filtered to cut off clear mistakes or abnormal spreads. Still, large bid/ask
spreads during periods when the market is not particularly active tend to have a disproportionate
effect on the daily averages.
To increase the significance of bid/ask spreads on proposals, we also tried to weigh them by means
of the number of proposals inputted into the system during the day. Unfortunately, there are
structural differences in domestic markets that allow for disproportionately strong activity (high
number of daily proposals) when basis trading is particularly active, as it is the case in Germany.
Therefore, this weighting did not work.
The bid/ask spread on ‘market best’ would be our preferred measure, but this information is not yet
available.
More fundamentally, we believe there is a problem of significance of EuroMTS trading for each
specific market. Trading executed on EuroMTS is perceived to be highly significant for capturing
overall market conditions in Italy and Belgium for instance. While we perceive the activity of
German government bonds is not fully representative of conditions in the market, It is believed that
in each specific market price discovery differs. In the German market, price discovery is probably
in the futures market. In Italy and Belgium it is in the electronic cash market. In other countries it is
still in the over-the-counter inter-dealer brokerage market. Moreover, these conditions appear to
change over time.
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