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S IN Economic Papers are written by the Staff of the Directorate-General for Economic and Financial
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© European Communities, 2009 Determinants of intra-euro area government
bond spreads during the financial crisis
Salvador Barrios, Per Iversen, Magdalena Lewandowska, Ralph Setzer
Directorate General for Economic and Financial Affairs Abstract
This paper provides an empirical analysis of the determinants of government bond yield
spreads in the euro area with a focus on developments during the global financial crisis that
started in 2007. In line with the previous literature, we find that international factors, in
particular general risk perception, play a major role in explaining governments bond yields
differentials. While domestic factors such as liquidity and sovereign risk appear to be smaller
but non-negligible drivers of yield spreads our results point to significant interaction of
general risk aversion and macroeconomic fundamentals. Moreover, the impact of domestic
factors on bond yield spreads increase significantly during the crisis, when international
investors started to discriminate more between countries. In particular, the combination of
high risk aversion and large current account deficits tend to magnify the incidence of
deteriorated public finances on government bond yield spreads. Overall, our results suggest
that an improvement in global risk perception will lead to a narrowing of intra-euro area bond
yield differentials. However, the differing impact of the crisis on Member States' public
finances and the expected higher risk awareness of investors after the crisis could keep
government bond yield spreads at a higher level then in the pre-crisis period. 1 This paper has benefited from valuable comments by Servaas Deroose, Massimo Suardi, Lucio Pench, Peter
Grasmann, Magnus Astberg, Jakob Ejsing, Wolfgang Lemke, Stefan Kuhnert, Sven Langedijk, Eric Ruscher,
Alexander Schulz, Jan In't Veld and Guntram Wolff. The authors are particularly thankful to Carlos San
Basilio (MTS group) who provided extensive comments and data on bid ask spreads. The authors also wish
to thank to Zbigniew Truchlewski for assistance with the data. 1 1. Introduction and summary of results
The aim of this paper is to study the determinants of sovereign bond yield differentials in the
euro area. Government bond spreads in the euro area have risen sharply since the beginning of
the financial crisis. While 10-year yield spreads to the German Bund averaged 18 basis points
in the period from 1999 to mid-2007, they averaged 56 basis points since August 2007 and 99
basis points since October 2008 (as of 30 July 2009). At the same time, differences between
euro area countries have become more pronounced, as the spreads of some countries
(especially Ireland and Greece) widened much more than those of other countries (such as
France and the Netherlands). Although financial conditions have been easing since spring
2009, and most of the spread widening since September 2008 has been reversed, spreads
remain elevated relative to the pre-crisis period.
In principle, government bond yield differentials within the euro area can be caused by three
main factors: First, spreads in government bond markets are indicators of fiscal vulnerabilities
and the risk of default. Second, the discrimination among sovereign bonds may reflect their
relative liquidity. Third, movements in yield differentials may also be affected by changes in
investors' preferences and an associated repricing of risk. In times of heightened financial and
economic uncertainty, investors typically have a higher preference for less risky and more
liquid assets. This then comes with a higher premium for more risky assets as portfolio
composition is adjusted to the desired new equilibrium (Favero et al. 2007, ECB 2009a).
To decompose empirically the driving factors of yield differentials is challenging. For
example, it is straightforward to argue that the strong rise in yield spreads in autumn 2008
stemmed from a combination of deteriorating market liquidity, increased pessimism about the
sustainability of public finances and "flight-to-safety" flows. The exact disentangling of these
factors is however no easy task as only the sum of the three components can be observed.
Decomposing empirically credit and liquidity risk is further complicated by the fact that
liquidity-related factors influence yields at high frequencies, while credit risk evaluations are
based on slow-moving macroeconomic fundamentals such as public debt and current account
imbalances (Codogno et al. 2003), which are only observed at lower frequencies. We deal
with these difficulties by pursuing two econometric approaches: First, we use the information
in credit default swaps spreads to obtain a "high frequency" measure of the size of the credit
default component. This allows us to restrict our estimation period and to look for changes in
the determinants of yield differentials compared to the pre-crisis period; Second, we use a
longer time span and more refined macroeconomic variables observed at quarterly frequency
to capture sovereign credit risk. The use of different estimation techniques, specifications and
time spans increases the robustness of the results.
Our main empirical findings are as follows:
• First, international factors such as general risk perception play a crucial role in
explaining euro area sovereign bond yield differentials. This suggests that an
improvement in general risk perception and global growth is likely to result in a
narrowing of spreads. • Second, the role played by domestic factors is smaller, but non-negligible. A
deteriorating domestic outlook for fiscal deficits, including the medium term
budgetary costs of financial support operations, is associated with higher bond yields.
The impact of deteriorated fiscal balance remains limited as our estimates show that,
on average, a deterioration by 1 percentage point in deficit (versus Germany) imply a
rise by 2.4 basis points in the government bond yield spread (versus Germany). As
2 regards the importance of liquidity considerations, our results are more mixed
depending on the specification and time frequency of the data used.
• Third, our results point to significant interaction of general risk aversion and
macroeconomic fundamentals. Domestic factors have become clearly more important
in times of financial stress, when international investors started to discriminate more
between countries. The combination of high risk aversion and large current account
deficits tend to magnify the incidence of deteriorated public finances on government
bond yield spreads. Countries with large current account deficits experience a 11 basis
points increase in government bond yield spread for each additional percentage point
deterioration in public deficit. 2 Our results are supportive of recent findings in the literature stressing the importance of
international factors (see Codogno et al. 2003, Longstaff et al. 2007). Other papers also find
country-specific factors to play a non-trivial role. Schuknecht et al. (2008) and ECB (2009a)
find an important role for credit risk both before and since the crisis. Haugh et al. (2009)
argue that while the degree of general risk aversion is an important factor on its own, it has
also magnified the importance of fiscal performance on yield spreads. A recent study by
Sgherri and Zoli (2009) find that the sensitivity of sovereign spreads to projected debt
changes has significantly increased after September 2008. In another recent study, the ECB
(2008) suggests that the broad-based rescue packages in the banking sector have brought
about an immediate transfer of risk from the private to the public sector. Furthermore,
differences in government bond market liquidity have also been found to be significant for
many euro area countries in some studies (Bernoth et al. 2006). Using data until 2004, Beber
et al. (2006) find that, while credit risk matters for bond valuation in normal times, liquidity
becomes more important in times of financial stress.
Our study provides novel evidence regarding the interaction between international and
domestic factors. Following the approach of Codogno et al. (2003), we consider whether
changes in fiscal conditions (i.e. projected deficits) have uneven effects across countries
depending on the state of general risk aversion and pre-crisis macroeconomic conditions,
namely the debt level and current account balance. In contrast with the aforementioned
authors, we find that general risk aversion and domestic fiscal conditions interact
significantly. In particular, high debt countries and, foremost, countries with large current
account deficits are found to experience the highest bond yield increases as consequences of
deteriorating public finances and increase in general risk aversion.
The rest of the paper proceeds as follows: First, we highlight the historical evolution of yield
differentials in the euro area and discuss the determinants of yield spreads in a monetary
union (Section 2). In Section 3, we provide descriptive statistics on intra-euro government
bond yields and test for the relative importance of common (versus country-specific) factors
of sovereign risk. Section 4 provides empirical evidence at the country-level on the
importance of the various determinants of bond yield differentials based on weekly data. In
Section 5, the analysis is extended by a panel estimation on quarterly data and adding further
macroeconomic indicators. Section 6 concludes. 2 Countries with high current account deficit are defined as those with current account deficit higher than the
euro area average by one-standard deviation. 3 2. Government bond yields in the euro in historical
2.1 Historical perspective
Euro area yield spreads have largely converged in a process that started well ahead of the
introduction of the euro in January 1999. Following some initial widening in the early-2000s
(related to the bursting of the dotcom bubble and the uncertainty following the terrorist
attacks on 11 September 2001), there was a phase of pronounced yield convergence until
2005. After 2005, a moderate reversal in yield differentials between the lower rated euro-area
government debt issuers Greece, Italy and Portugal and Germany (the benchmark country),
could be observed. At the same time, spreads for the higher-rated government debt issuers
remained relatively stable at a low level. Starting in October 2007, and especially after
September 2008, spreads between the German Bund and other euro-area government bonds
increased substantially. Much of this widening has been reversed in recent months (see Figure
Figure 1: Spreads of 10-year government benchmark bonds to German Bund
Spain 250 200 150 100 50 2009 2008 2007 2006 2005 2004 2003 0 -50 3 The main focus of the analysis are 10-year sovereign bond yields of Austria, Belgium, Finland, France,
Germany, Greece, Ireland, Italy, the Netherlands, Portugal, and Spain. For the remaining countries, a
comparative analysis is not suitable either due to the unavailability of reliable data or because these countries
only joined the euro area in recent years and thus the exchange rate risk is an additional component which
would have to be taken into account. 4 150
Netherlands 100 50 2009 2008 2007 2006 2005 2004 2003 0 -50 Figure 2 compares government bond yields since the beginning of the financial market
turbulences in mid-2007 to historical averages in the pre-crisis period (i.e. the period from 1
January 1999 to 15 July 2007). Three points are worth mentioning: First, countries with
higher spreads before the crisis have also exhibited higher relative financing costs since the
crisis. Second, differences across countries have been more pronounced since the crisis. This
is consistent with earlier research by Copeland and Jones (2001) who find that government
bond spreads between EMU member countries widened in periods of financial crises such as
the Russian crisis in 1998 or the Turkish currency crisis in 2001; Third, overall financing
costs in the crisis period have nevertheless been close to the historical average for most
countries as demand for safe assets classes as government bonds increased. The flight to
safety, however, clearly depressed German bond yields more than those of other countries.
Conversely, Ireland experienced a particularly pronounced increase in bond yields. 5 Figure 2: Government bond yields in historical comparison
Sovereign 10Y bond yields before and since
the financial crisis average government bond yield from
16 July 2007 - 31 July 2009 5.50
R2 = 0.74
4.50 GR IT
ES AT PT
FR 4.00 DE 3.50
3.50 4.00 4.50 5.00 5.50 average governm ent bond yield 1 Jan 1999 to 15 July 2007 2.2 Determinants of yield spreads
This section describes the three determinants of yield spreads in the euro area: credit risk,
liquidity consideration and changes in risk aversion.
a) Credit risk
There are three types of credit risk: (i) default risk, (ii) credit spread risk and (iii) downgrade
risk. The default risk is defined as the probability that the issuer fails to meet the obligations
either on coupon payments or repayment of principal at maturity. Credit spread risk is the risk
based on the price performance of the bond and is defined by the probability that the market
value of the bond will decline more than the value of other comparable quality bonds.
Downgrade risk reflects the possibility of a downgrade by the credit rating agency (Fabozzi
The financial crisis has had an impact on all these types of risks. The deterioration of fiscal
positions due to the high cost of financial rescue packages, discretionary fiscal stimulus and
the operation of automatic stabilisers raised questions about the sustainability of public
finances. In addition to the usual indicators of government debt and deficit, high current
account deficits in several euro area countries also heightened markets’ perception of default
as these countries were considered as particularly vulnerable to reversals in international
flows of funding. Moreover, credit rating agencies downgraded the debt of several euro-area
sovereign issuers. This may have had a direct impact on institutional investor portfolio
allocation decisions, as many portfolio managers have limits on investments depending on the
b) Liquidity risk
National bond markets in the euro area differ in terms of liquidity. A liquid market allows
participants to value and trade positions at any time. This means that there has to be a
6 sufficient volume of buy and sell orders (market depth) and that large-scale transactions do
not affect prices strongly (market breadth). The factors that determine liquidity include the
issuing volume and the national issuing policy 4 , as well as the existence of sufficiently liquid
futures markets that offer investors hedging possibilities. The German bond market is the only
one in Europe that has a liquid futures market, and this boosts demand for German Bunds also
on the cash side compared to other euro area debt.
Liquidity risk and credit risk are interconnected. On the one hand, an increase in the supply of
government bonds, as observed during 2009, should put downward pressure on liquidity
premia; on the other hand, high supply is also associated with increased public deficit and
debt and thus a higher credit risk premium.
c) Risk aversion
Risk aversion is associated with the willingness of investors to take risk. Investors
continuously adjust their risk-return preference function. As a result, even if the "amount of
risk" embedded in a security remains unchanged, the demanded risk premium may vary
depending on the "price of risk".
In times of financial uncertainty, investors rebalance their portfolio toward less risky
securities as their risk aversion increases. In principle, this should benefit all government
bonds as they are typically regarded as less risky than other asset classes such as corporate
bonds or equities. However, among euro-area sovereign issuers the German Bund is perceived
to be the "safest haven" both in terms of credit quality ("default-free") and liquidity.
Therefore, in times of high risk aversion, the "flight-to-safety" and "flight-to-liquidity" flows
to the German government bond market are more pronounced than for other sovereign bonds. 3. Descriptive analysis
3.1 Yield spreads and credit risk
As a first illustration to the importance of credit risk for yield spreads, we compare euro area
government bond yields to public debt, the budget balance and the current account balance as
these factors are mainly assumed to drive the markets’ beliefs about credit risk. In addition,
however, during the crisis governments have taken on large contingent liabilities that,
although they do not immediately impact on deficit and debt levels, are likely to affect their
Figure 3 and 4 suggest that higher government debt and higher fiscal deficits are associated
with rising bond yields. The correlation is however in both cases relatively weak. Taken
alone, a one percentage point increase in the government debt increases bond yields by around
one basis point. 5 4 5 The euro-area government bond market remains fragmented from the issuing side with distinctive
differences in the size of markets and the credit quality of bonds. Each Member State issues its own
government debt, with some harmonisation in key features such as interest-rate calculation, auction
calendars, trading days etc. Other features, such as coupons and maturity dates are not standardised and
remain specific to national issuers and thus make euro-denominated government bonds not fungible.
The correlation becomes somewhat stronger when government bond yield spreads are compared to the
expected fiscal balance for 2009 and 2010. 7 Figure 3: 10-year sovereign bond yields and government debt 6.00
1 0Y gov bond yield, average 2009q2 R2 = 0.07
5.50 IE GR 5.00 4.50 PT ES IT AT BE 4.00
NL FI FR 3.50 DE 3.00
0 20 40 60 80 120 100 government debt in 2009q2 (in % of GDP)
Source: Ecow in and Eurostat. Figure 4: 10-year sovereign bond yields and expected fiscal deficit 10Y gov bond yield, average 2009q2 6.00 R2 = 0.45
5.50 IE GR 5.00 4.50 IT PT
ES AT BE 4.00
FR NL 3.50 FI DE 3.00
-16 -14 -12 -10 -8 -6 -4 -2 0 fiscal balance/GDP in 2009 (in %, Commission forecast spring 2009)
Source: Ecow in and Eurostat. 8 A one percentage point rise in the expected fiscal deficit for 2009 increases, ceteris paribus,
government bond yields by around 10 basis points. The positive link between the two
variables is however almost exclusively driven by Ireland. 6
Credit risk may have been further influenced by the existence of large current account deficits
in some euro area countries. The occurrence of the financial crisis heightened the risk for
these countries to experience sudden stops in external financing and protracted period of low
growth. External imbalances have grown rapidly in the pre-crisis period. Countries such as
Spain, Greece, Portugal or Ireland experienced fast current account deterioration since the
adoption of the euro while benefiting from exceptionally low interest rates and lenient
external debt financing conditions. Other, in particular Germany, built up large current
account surpluses (Figure 5).
Figure 5: Current account balance, euro area countries
Current account balance (in bn euro)
1999 2000 Germany
Netherlands 2001 2002
Greece 2003 2004 2005 Spain
Other surplus 2006 2007 2008 Italy
Other deficit Source: Eurostat. Current account deficits and surpluses mostly reflect private lending and borrowing across
borders. Nevertheless, the adjustment of a current account deficit may lead to negative
implications for the government budget (Deutsche Bank Research 2009, Goldman Sachs
2009). First, in the EMU countries facing large external deficits can face added difficulties to
finance rising debt levels as these can no longer rely on exchange rate adjustments to restore
competitiveness and promote export-led recoveries. Current account deficits have to be
adjusted through a period of disinflation which, with sluggish price adjustment, implies lower
growth and falling tax revenues. Second, the distinction between private and public debt
becomes blurred if the government is forced to take over private debt. As the current crisis has
shown, once domestic banks encounter severe difficulties, nationalising banks or guaranteeing
their debt may be the only option for a government. Moreover, the government might have to
6 The figure does not change substantially when government bond yield spreads are compared to the expected
accumulated fiscal balance in 2009 and 2010. 9 take on part of household mortgages to avoid foreclosures. Investors may take these
considerations into account when analysing a country's fiscal conditions. Countries with
higher current account deficits have experienced sharper increases in bond yield spreads
versus Germany (Figure 6). 7 Again, Ireland is a clear outsider as yields spreads look elevated
compared to the current account deficit. This may be due to its high banking sector exposure.
Figure 6: 10-year sovereign bond yields and expected current account deficit 10Y gov bond yield average 2009q2 6.00 R2 = 0.33
5.50 IE GR
4.50 IT PT AT ES
FR BE NL FI 3.50 DE 3.00
-15 -10 -5 0 5 10 current account/GDP in 2009 (in %, Commission forecast spring 2009)
Source: Ecow in and Eurostat. An alternative way to assess the default risk of a sovereign issuer is to look at credit default
swaps (CDS). A sovereign CDS spread entails a transfer of sovereign credit risk between two
parties as it provides the buyer of the contract with protection against a negative "credit event"
(such as outright default, a rating downgrade, or delayed coupon payments). Figure 7
compares 10-year sovereign bond yields spreads to the German Bund with 5-year CDS
premia (in relation to Germany), which is the most standard and most liquid maturity on the
CDS market. 8 Not surprisingly, there is a very high correlation between government bond
yield spreads and CDS spreads, and there seem to be only small arbitrage opportunities for
some countries such as Austria, Finland and Spain. 7
8 Again, the picture does hardly change if government bond yield spreads are compared to the expected
accumulated current account balance in 2009 and 2010.
A premium of 100 basis points on the CDS market means it costs about 100,000 euro to buy protection on 10
million euro in government debt. 10 Figure 7: Government bond yields and CDS premia
10Y sovereign bond yields and 5Y CDS premia Difference to Germany, 30 July 2009
180 2 R = 0.92 spread CDS premia 160 IE 140
80 AT 60 IT ES 40 PT
0 20 40 BE 60 80 100 120 140 160 180 gov bond spread
Source: Ecow in and Bloomberg. 3.2 Euro area sovereign bond yields and risk aversion
In this section, intra-euro area sovereign bond spreads are related to a time series of general
risk aversion. The analysis is carried out in two steps: First, government bond yield
differentials (versus Germany) are decomposed into a common factor and a component which
is specific to each country. In the second step, the common sovereign risk factor is related to a
time series of general risk aversion. We use weekly data from 1 January 2005 to 30 July 2009.
To separate sovereign bond spreads into common and country-specific components, we apply
principal component analysis (Stock and Watson 2002). This involves extracting a linear
combination, which captures the common variation in the sovereign bond spreads of
individual countries. Government bond spreads in all euro area countries are normalised by
subtracting their respective sample mean and dividing by their sample standard deviation. It
turns out that the "common sovereign risk factor" (i.e. the first principal component) explains
95 percent of the total variation in the correlation matrix. It can thus be interpreted as a
"parallel shift" factor in euro area sovereign bond spreads vis-à-vis Germany. This finding
confirms the results of the visual inspection presented above (Figure 1) which suggests a clear
tendency of co-movement across sovereign yield spreads of different countries. It also
confirms earlier studies which find a high cross-country correlation of spreads movements
(Codogno et al. 2003, Favero et al. 2005, Manganelli and Wolswijk 2007, or Longstaff et al.
The common sovereign risk factor consists of a nearly uniform weighting of the sovereign
bond spreads of all countries in our sample. However, the second principal component places
significant positive weights on Ireland and Greece, a slight positive weight on Austria and
negative weight on all other countries. It could thus be viewed as an additional spread on
those countries that have been perceived to be especially vulnerable during the crisis (because
11 of the expected cost of banking sector rescues (Ireland and Austria) or unfavourable and debt
dynamics under current policies).
The sovereign risk factor is then compared to an indicator of general risk aversion. The latter
is the first principal component of spreads on AAA- and BBB-corporate bonds (CB_AAA,
CB_BBB), a measure of stock market volatility (VSTOXX), and exchange rate volatility in
the euro-yen exchange rate (XRVOLA). Corporate bond spreads reflect corporate default
probabilities, the VSTOXX index is a measure of economic volatility embedded in stock price
movements and the euro-yen volatility is a common indicator of risk perception in foreign
exchange markets. The common factor can thus be interpreted as an "overall risk aversion
Again, all four variables are normalised. Together those indicators form the vector of
⎛ CB _ AAAt ⎞
⎜ CB _ BBBt ⎟
(1) xt = ⎜
VSTOXX t ⎟
⎜ XRVOLA ⎟
⎝ This results in the extraction of one principal component which explains 89 percent of the
variance in the full data set, illustrating that our risk indicators are highly correlated. It is also
interesting to note that all four risk indicators contribute to the common factor to a very
similar extent as can be seen by the vector of factor loadings:
⎛ 0.51 ⎞
⎜ 0.50 ⎟
(2) Ft = ⎜
⎜ 0.51 ⎟
As displayed in Figure 8, general risk aversion and euro area government bond spreads
closely moved in tandem for most of the time until the end of 2008q3. Thus, a generalized rise
in risk aversion translated into higher sovereign bond risk premia. Since the intensification of
the financial turmoil in September 2008, the joint movement between the risk aversion factor
and sovereign bond spreads has deviated from the previously observed pattern. This change in
behaviour might be related to the transfer of risk from the banking sector to the public sector
(see also ECB 2009b for this argument). With the emergence of systemic risk in the banking
sector, many governments were called on to support their banking system through direct
capital injections (sometimes with state ownership) and indirect balance-sheet support in the
form of guarantees. 9 As such, the announcements of the national bank rescue packages in
autumn 2008 led to a downward shift in the level of the overall risk aversion factor, while
contributing to a significant rise in sovereign risk. The diverging behaviour may also be due
to non-linearity in the assessment and pricing of risk, with the impact of domestic
fundamentals on spreads assumingly becoming higher in crisis periods (see Sections 4 and 5).
Interestingly, the gap between the sovereign risk and the risk aversion indicator has narrowed
over recent months but still remains large. Thus, the risk transfer to the government sector
seems to be considered as permanent. This suggests that even with a further improvement in 9 This effect has been particularly strong for Ireland that has a large financial sector compared to the size of its
economy and Austria where many banks have relatively large exposure to Central and Eastern European
countries. 12 general risk perception government bond yield spreads could remain higher than in the precrisis period.
Figure 8: General risk aversion and sovereign risk indicator (1 January 2005 – 30 July
2009, weekly data)
16 12 8 4 0 -4
2006 2007 2008 2009 Sovereign risk
General risk aversion 4. Evidence at the country-level from weekly data
4.1 Data and econometric methodology
In this section we develop an econometric approach to distinguish between liquidity risk,
credit risk and risk aversion in the sovereign bond market. We use weekly data from March
2003 onwards, which allows us to evaluate the effect of the financial crisis on yield
differentials at high-frequency.
To measure risk aversion, we rely on our indicator of general risk aversion calculated in the
previous section. As regards credit risk, we use 5-year CDS spreads relative to Germany.
Although this indicator may be an imperfect proxy for credit risk as CDS spreads are also
affected by other factors (such as liquidity), it is seen as the best measure of credit risk
available at high frequency (see for example, Longstaff et al. (2005), Blanco, Brennan and
Marsh (2005) or ECB (2009)). Alternative measures such as fiscal deficits forecast are only
available at monthly or quarterly frequency.
To assess liquidity risk, we follow the literature and use bid-ask spreads (see, e.g. BIS 1999,
Brandner et al. 2007, De Nicolo and Ivaschenko 2008). The theoretical justification for this
procedure is that the size of the bid-ask spread is influenced by the depth of the market. A
deep market is generally considered to have low bid-ask spreads. Bid-ask spreads are better
indicators for gauging liquidity conditions in bond markets than traded volumes (as used e.g.
13 by IMF (2009)) since data on volume can be affected by multiple trading operations between
bank's affiliates to meet balance sheet requirements. Thus, big variations in volumes might
have little bearing on actual liquidity. 10 We use quoted bid-ask spreads (in relation to
Germany) based on electronic-trading data from the MTS Group’s European Benchmark
Market trading platform (see http://www.euromts-ltd.com), the principal e-trading platform
for secondary market trading of European government bonds. 11 Figure 9 illustrates the
evolution of liquidity conditions in government bond markets based on our bid-ask measure.
It shows a generalised worsening in liquidity from the third quarter of 2007 onwards, with a
further pronounced deterioration in the post-Lehman period (i.e. post-September 2008),
followed by some improvement during the second quarter of 2009. The worsening was
particularly pronounced for Austria, Belgium, Portugal, Spain and Ireland.
Figure 9: Liquidity conditions in 10-year government bond markets The following seven countries are included in the analysis: Austria, Belgium, Spain, France,
Greece, Italy, and Portugal (with Germany as a benchmark). For the remaining countries,
sufficiently long enough time series on CDS spreads are not available. All variables are
expressed in relation to Germany. To avoid spurious regressions, the basic model is estimated
in first differences and set as follows: 12
(3) Δsov _ spread t = α + β ΔCDS t + γΔb _ a t + ηΔrisk _ avt + λcrisis t + u t , 10 We thank Carlos San Basilio (MTS group) for a useful discussion on that point.
11 According to Persaud (2006), MTS maintains a market share of 72% of the electronic European cash
government bonds trading. Cheung et al. (2005) report that bid-ask spreads, quoted on EuroMTS and the
national platforms do not differ much for most bonds. Quoted spreads on national platforms may, however,
include periods where there is little trading and thus may give an inaccurate indication of actually incurred
12 In principle, given that all our variables are deviations to Germany, they can be assumed as stationary. 14 where the dependent variable is the change (Δ) in the 10-year sovereign bond yield spread to
the German Bund from week t-1 to week t; ΔCDS is the change in the 5-year sovereign CDS
spreads , Δb _ a is the change in the bid-ask spread, Δrisk _ av is the change in the general
risk aversion indicator, and crisis is a variable denoted one in the period from mid-September
2008 until early March 2009 and zero otherwise. It is intended to capture those effects of the
financial crisis on sovereign bond spreads which are not captured by our credit risk, liquidity
and risk aversion indicators. As noted in the previous section, an extraordinary increase in
sovereign spreads in the crisis period could be due to non-linear effects and to the risk transfer
from the private sector to the public sector in the aftermath of the announcement of the bank
rescue packages and guarantee schemes. Also, differences in size of the financial sector in
each country, where a relatively large financial sector is expected to enhance the negative
effects from the financial crisis, could have played a role.
Estimation is conducted via OLS separately for each country with robust standard errors
adjusted for clustering. In our benchmark specification we use the full data set available from
March 2003 to April 2009 (though for some countries the estimation starts somewhat later
due to data availability). While in principle daily data could be used, we measure all changes
over a weekly horizon in order to reduce noise. The 5-year CDS spreads are based on
instrumental variables to avoid endogeneity. 14
4.2 Econometric results
The results for the seven countries in our sample are presented in Table 1. In terms of the Rsquared, the model explains between 0.11 and 0.42 of the variance of the dependent variable,
which is satisfactory given that the regressions are in differences and at high frequency.
Nearly all the variables have the expected sign (among them all significant variables). The
Durbin-Watson tests show no serial correlation. However, further residual diagnostic tests
revealed signs of serial correlation and heteroskedasticity in the error terms in periods of high
volatility from August 2007 onwards. We therefore base our significance tests on
autocorrelation and heteroskedasticity consistent standard errors and t-statistics.
Looking at the country-specific results, CDS spreads (our credit risk indicators) are significant
for Austria, Spain, Greece, Italy and Portugal, but not for Belgium and France. To get an idea
about the economic implications of the results, the coefficient of 0.53 for Greece means, e.g.,
that an increase of 1 basis point in the CDS spread (i.e. a relative rise in the "insurance costs"
of 1,000 euro per 10 million euro of government debt compared to Germany) leads to an
increase of 0.53 basis points in the 10-year government bond yield spread. The credit risk
effect is strongest in countries such as Portugal, Greece and Spain with large current account
deficits in the pre-crisis period and large increases in public debt during the crisis.
Liquidity seems to have played a role in explaining the evolution of yield spreads in France,
Greece and Italy. In Italy, the coefficient is 55, meaning that an increase of 1 percentage point
in the bid-ask spread leads to an increase of 55 basis points in the yield spread. The large
explanatory power of the bid-ask spread in Greece is in line with the fact that the Greek
sovereign bond market has been sharply negatively affected by the crisis. In spite of a strong
deterioration in liquidity conditions in the Austrian and Portuguese government bond markets
in the crisis period, the bid-ask spread is not significant for these two countries.
13 The use of the 5-year CDS premia results in a maturity mismatch to the government bond market, where we
use 10-year maturities; however, the higher liquidity should more than outweigh the differences in risk
premia due to different maturities.
14 Based on the method of Generalised Instrumental Variables Estimator, four lags of the CDS spread together
with the remaining regressors in equation (3) were used as instruments. 15 Table 1: Determinants of sovereign bond yield spreads in the euro area: evidence from
weekly data (March 2003 – April 2009)
313 Note: *, ** means significance at the 0.1, 0.05 percent level.
The risk aversion component is significant for Belgium, France, Italy and Portugal.
Apparently, the general degree of risk aversion has played an important role in driving yield
spreads up, thus confirming the results of the previous section. There is also evidence that
some high-debt countries such as Belgium, Portugal and Italy are affected more strongly by
the risk aversion indicator.
The results also reveal a significant break in the relationship due to the financial crisis. The
crisis-effect variable is significant for all countries but Spain and Italy, indicating that factors
additional to those included in the model have had an impact on bond spreads during the
crisis. Alternatively, investors' risk assessment could have changed after 2008, leading to a
change in the coefficient. Credit rating downgrades may have played a role for Greece and
Portugal as the coefficient is particularly high and significant for these countries. 15
The finding of a structural break is also confirmed by further analyses in Table 2 and Table 3 which split the sample in two periods: before (March 2003-July 2007)
and during (August 2007-April 2009) the financial crisis. 16 Interestingly, before the crisis,
country-specific factors appear to have hardly played an important role. In some cases, our
proxies for credit and liquidity risk even have the wrong sign. Sovereign bond yields spreads
were largely driven by the degree of general risk aversion during that period. Since August
2007, however, investors strongly differentiated between countries and took macroeconomic
fundamentals and liquidity considerations into greater account. The coefficients have in all
cases the expected sign and are often significant. As such, the results confirm the basic picture
from the full-sample regression, with changes in credit risk and liquidity risk having played
significant roles for yield spreads. At the same time, general risk aversion remained an
important determinant of yield spreads. 15 We also tried to relate the extent of the crisis effect to a country's contingent liabilities and external
liabilities (both of the total economy and the banking sector). However, no clear conclusions could be drawn
from such a comparison.
16 This analysis allows for a more broad-based structural break than the break captured by the crisis-effect
variable in the full-sample regression, as the crisis period is now assumed to start in August 2007 (as
opposed to September 2008 for the crisis-effect variable) and as the coefficients on all variables are allowed
to change. The crisis-effect variable is not included in the sub-sample regressions. 16 Table 2: Determinants of sovereign bonds yields spreads in the euro area: weekly data
(March 2003 – July 2007)
222 Table 3: Determinants of sovereign bonds yields spreads in the euro area: weekly data
(August 2007 – April 2009)
91 In addition to the results reported above, several robustness checks have been performed.
First, we replaced the CDS spreads with the projected fiscal deficits for the current and next
year as a measure of credit risk. In doing so, we avoided the drawbacks of the CDS-spread as
a measure of credit risk. Data for the fiscal variable came from the Economist Intelligence
Unit and were converted from monthly to weekly frequency by interpolation. Overall, our
results were not affected by this substitution. The fiscal variable was however hardly
significant which may be due to the low frequency of the raw data or due to the importance of
other variables with a longer-term impact on fiscal sustainability such as current account
imbalances. Second, country-specific intercept dummies for the period following the
announcement of the national bank rescue packages were included but were generally not
significant. Finally, interaction terms of the risk aversion indicator with the bid-ask spreads
and the CDS spreads were added to the regressions to see if the importance of the domestic
fundamentals increased when the overall risk aversion increased. The interaction terms were
significant in several cases providing some evidence for the view that the impact of domestic
variables on yield spreads depends on the general level of risks aversion. This issue will be
analyzed in more detail in the next section. 17 5. The role of public debt and macroeconomic imbalances:
evidence from quarterly data
5.1 Econometric evidence from quarterly data
While the use of weekly data in the previous section was best suited to measure how liquidityrelated variables have affected bond yields, this approach came at the cost of not fully
appropriately capturing the effect of risk-related macroeconomic variables which are only
observed at low frequency. In order to have a closer look at the importance of these variables
for yield spreads we extend our analysis by estimating the determinants of yield spreads at
quarterly frequency. This procedure allows us to have a closer look at fiscal variables such as
fiscal deficit, public debt and the weight of interest payments on public debt. Moreover, we
analyze the role of current account imbalances in exacerbating risk premia on sovereign bond
yields. As argued in section 3, the distinction between private and public debt becomes
blurred if the government may be expected to take over private debt.
Financing conditions for euro area governments started to deteriorate already before the
global economic downturn (see Figure 10). This is evidenced by the rising ratio of interest
payment on government debt to total government revenues—an indicator reflecting borrower
quality (see Bernoth et al. 2004). In addition, the snowball effect, which provides an
indication of the risk of incurring into ever increasing debt burden due to high interest rates
payment and/or low GDP growth rates, also clearly signals the worsening of euro area
governments' financing conditions since 2007.
Figure 10: Interest payment on public debt and snowball effect in the euro area, 2000-2009
6 snowball effect, % GDP (lhs) 9 5 interest payment, % of government
revenues (rhs) 8.5 % of GDP 4 7.5
7 3 6.5
5.5 1 % of government revenues 8 5
2000 2001 2002 -1 2003 2004 2005 2006 2007 2008 2009 4.5
4 Source: Commission services
Note: Snowball effect = D t − 1 * i t − y t , where D is the stock of government debt, y is the level of GDP,
Y t −1
1 + yt
both measured in year t-1, i represents the average interest payment on debt and y is the nominal GDP growth
rate. 18 To capture the role of domestic macroeconomic fundamentals, we estimate the following
(4) sov _ spread it = c + λ1 fiscal _ conditionsit + λ 2 current _ account it + λ3 xit + u it ,
where the dependent variable is the 10-year government bond spread versus Germany, and the
set of explanatory variables includes fiscal conditions (i.e. fiscal balance and the debt level)
and the current account balance (all as a percentage of GDP). Data on budget balances are
provided by the Economist Intelligence Unit. We use the fiscal balance forecast for the
current year t as indicator of countries' fiscal position. In addition, we include the bid-ask
spread and the general risk aversion indicator represented by the vector x. Risk aversion is
represented by the summary variable obtained using principal components (see Section 3). 17
The country-specific variables are expressed relative to Germany. In view of the low number
of observations available, panel regressions rather than country-level regressions are
pursued. 18 The error term uit thus comprises a random term εt and a component ωi which
captures (unobservable) time-invariant country i-specific effects.
Table 4 reports the results of estimating equation (4). The findings indicate that the
deterioration in the fiscal position significantly increases the bond yield spread. The
quantitative effect of deteriorated fiscal balance remains relatively modest, however, as the
coefficient estimate shows that a one percentage point increase in the deficit implies, on
average, an increase of 2.4 basis points in the spread versus Germany. Such estimate is
somewhat similar in magnitude to those of Bernoth et al. (2004) and Schuknecht et al. (2008)
for EMU countries, who report coefficient estimates of 3.4 and 3.8 basis points respectively.
As regards the remaining variables, the results show that the bid-ask spread and the risk
aversion variable exert a positive and significant influence on government bond interest
spreads. The result concerning the influence of liquidity conditions (i.e. the bid ask spread)
thus seem more robust in contrast to results reported in Section 4. Column (2) extends the
empirical specification by considering the role played by the current account balance and
shows that a deterioration by one percentage point in the current account balance leads to a
rise by 1.3 basis points in the yield of a given country with respect to Germany. This result
suggests that current account evolutions might also influence significantly government bonds
yields in the medium term.
17 Using the deficit forecast for year t+2 provided very similar results.
18 Although the time series by country are too short to run simple OLS by country, panel regressions also suffer
from the fact that the set of countries considered (i.e. 11 euro area countries) is short compared to the length
of the time series (t=26) which entails estimation issues preventing the use of simple least square dummy
variable estimators in order to control for country-fixed-effect. There are two alternative ways to correct for
serial correlation in the dependent variable: the first one is to use lagged dependent variable; the second one
is to transform the variables to get rid of the serial correlation. The first solution would call for using a
dynamic panel data model (DPD) as the one described by Arellano and Bond (1991). One important reason
not to use a GMM estimator is that we avail of a short panel of countries observed over a relatively long time
period as noted above. If the time span considered is relatively large compared to the number of countries
available, dynamic panel bias becomes insignificant. Furthermore, the number of instruments in systemGMM tends to explode with T and the Arellano-Bond autocorrelation test may become unreliable. The
second solution proposed by Beck and Katz (1995) is to use a panel corrected standard errors (PCSE) or
Prais-Winsten model with panel-corrected standard errors, which is proposed as an alternative time
series/cross section model where the disturbances are assumed to be correlated across both panels and time.
With this model at hand, one can specifically correct for (and measure the degree of) serial correlation. The
advantage of the PCSE model is that one can assume the disturbances to be autocorrelated both within and
across individuals (i.e. countries) assuming an AR(1) process. In the following, therefore we will make use of
the PCSE model to estimate equations (4).
19 Bernoth et al. (2004) results concern the period 1991-2002 while Schuknecht et al. (2008) consider the period
1992-2003. 19 Columns (3) and (4) further extend the model to consider the role played by fiscal conditions
in more details while controlling for the current account balance. Column (3) shows that an
increase in interest payment (as a ratio of government revenues) acts to increase government
bond yields spreads versus Germany. This suggests that financial markets tend to penalise
countries with shrinking capacity to finance the interest burden of public debt, although the
magnitude of this effect appears relatively limited given that the coefficient estimate indicates
that an increase in interest payment on public debt as percentage of government total revenue
by 1 percentage point increases interest rate spread versus Germany by only 0.7 basis points.
Column (4) replaces the fiscal balance with the public debt. The influence of public debt on
government bond yield spreads must be gauged with caution, however, given that a high debt
level might also coincide with relatively liquid bond markets. The relationship between the
debt level and government bond yields could in fact be non-linear, i.e., countries with
historically high debt levels might benefit from liquid bond markets but might be penalised by
financial markets if debt rises above a given threshold. In order to investigate this possibility,
Column (4) includes, together with the initial debt position (i.e. the public debt level at t-1),
the square value of this variable. The deficit variable was omitted in order to avoid
collinearity. Results show that the non-linear relationship does hold as both the initial debt
level and its square term display positive and significant coefficients. In addition, the square
term of the debt level variable displays a much larger coefficient than the debt level
suggesting that acceleration in the public indebtedness exerts a further significant upward
pressure on government bond yields. This should provide an additional incentive for countries
with a high pre-crisis debt level to keep rising deficits at bay.
Table 4: Determinants of sovereign bonds yields spreads in the euro area: evidence from
quarterly data (2003q1-2009q2)
(0.017) Bid-ask 0.012***
(0.002) Fiscal balance -0.024***
(0.013) Risk aversion Current account -0.008***
(0.003) Debt 0.003***
(0.000) Debt2 0.007**
(0.000) Interest payment
Notes: Standard errors in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
Coefficients estimated using panel-corrected standard errors assuming first-order autocorrelation in
disturbance terms based on the Durbin Watson approach 20 Column (5) excludes the bid-ask spread variable from the specification in Column (4) to
investigate whether the positive influence of the debt level on spreads exceed the negative
impact of higher indebtedness on spreads which results from a reduction in liquidity premium.
Interestingly, the influence of the debt level on yield spreads remains positive and significant
(although in case of the square term only at the 10 percent level). As such, the credit risk
effect associated with higher debt clearly outweighs the opposing liquidity effect.
Overall these results show that domestic factors such as fiscal conditions and macroeconomic
imbalances together with market liquidity and general risk aversion have played a significant
role in determining government bond spreads in the euro area during the recent period. As
regards the economic significance, however, considerable differences between these
determinants of spreads can be observed. Based on the results of Column (2) in Table 4,
Figure 11 displays the predicted value of the government bond yield for each determinant. It
becomes evident that even after controlling for fiscal conditions and current account
imbalances, general risk aversion and bond market liquidity conditions explain most of the
surge in government bond spreads since the third quarter of 2008. This finding is in line with
the prominent role played by the surge in global risk aversion that took place in the aftermath
of the collapse of Lehman Brothers and the severe tightening of financial markets liquidity
conditions that followed during the fall of 2008. In our quarterly-data model, the tightening of
liquidity conditions since the third quarter of 2008 seems to have played an even more
important role compared to the country-level results reported earlier. 20 0 predicted bond yield basis points
1.5 Figure 11: Predicted value of government bond yield spreads and the economic
significance of explanatory variables 2003q1 2004q3 2006q1
time 2007q3 predicted bond yield spread (basis points)
Debt level & deficit forecast
current account 2009q1 bid/ask spread
global risk aversion Notes: Based on estimations reported in Column (2) of Table 4. Constant term deducted from fitted values.
20 It must be noted that the robustness of the bid/ask spread variable (compared to the results reported in Section
4 where this variable displayed mixed result) could be due to the fact that the CDS variable is not used here.
Given that the CDS spread might also capture part of the influence of liquidity conditions as suggested in
Section 4, not including the CDS spread variable in the quarterly data could thus indirectly increase the
explanatory power of the bid/ask spread variable.. 21 5.2 The interaction between global risk aversion, fiscal conditions and
The impact of domestic factors on government bond yields cannot be considered as
independent of that of global risk aversion. In particular the surge in government bond yield
spreads in the euro area was also driven by risk perception concerning the ability of some
countries to cater for the fiscal impact of the financial crisis. The role played by fiscal
conditions, including increases in public deficit as well as initial debt levels, might thus be
closely related to the global risk aversion as shown in particular by Codogno et al. (2003) and
Haugh et al. (2009).
In order to highlight the interaction between fiscal conditions, public debt and global risk
aversion we follow the simple approach described in Aiken and West (1991). 21 Two types of
interactions are considered here. The first one concerns the interplay between the fiscal deficit
and global risk aversion. It is represented in Figure 12 by the shift from the bottom line to the
line labelled "risk aversion effect". It shows that the additional premium on the interest rate to
finance new government bond issuance increases with the level of risk aversion. Specifically,
an increase of the general risk aversion by one standard deviation, which corresponds
approximately to the rise in risk aversion observed in 2008q3, significantly shifts upward the
impact on bond yield spreads of a deteriorated fiscal balance. Thus, an increase in the public
deficit by one percentage point leads to an additional premium of 1.9 basis points (i.e. to the
2.4 basis points increase reported in column (1) of Table 4) in times of high risk aversion.
The second interaction concerns the link between global risk aversion, fiscal deficits and the
debt level and is represented by the shift to the third line located at the upper right of Figure
12 ("risk aversion and high debt effect"). Interestingly, the shift in the relationship between
deteriorated fiscal deficit and bond yields spreads implied by the interaction between the
deficit forecast and the debt level appears to be nearly parallel to the situation where this
interaction is not considered. This result indicates that high debt countries tend to always
experience higher bond yield spreads in times of high risk aversion while the sensitivity of
their bond yield spread to the deficit forecast during periods of high risk aversion remains
similar to other countries. This result is especially relevant for countries such as Italy, Greece
and Belgium and also, to some extent, France and Portugal. 21 A simple interaction term between fiscal deficits and public debts would be of little help to consider the
differential impact of fiscal deficits on government bonds yields for high vs low-debt countries because these
two variables are continuous and have changing sign (in particular given that the change in the debt variable
can differ from the change in deficit due to stock flow adjustment, below the line operations and snow-ball
effect). Therefore there is no straightforward way to interpret their interaction. In order to remedy this issue
the simple approach described by Aiken and West (1991) is used in order to obtain correct coefficient and
standard errors. Accordingly, the coefficients estimated on interacted variables between (centered)
continuous variables can be decomposed into groups of estimated coefficient depending on the values taken
by the conditioning variable, i.e., in our case, the global risk aversion variable and the level of deb. This
simple method therefore allows us to estimate two different slope coefficients for the forecast deficit variable
according to whether the global risk aversion and debt variables are at the mean value (i.e. relatively close to
the German benchmark) or at one standard deviation above that level. 22 Figure 12: The impact of the budget balance on 10-year government bond spread at high
level of risk aversion and high debt level Figure 13: The impact of budgetary balance on 10-year government bond spread at high
level of risk aversion and large current account deficit In Figure 13 we conduct a similar exercise considering now the interaction between risk
aversion, current account balances and public deficits. The impact of these interactions on
government bond yield spreads becomes considerably larger as evidenced by the much wider
range of values in the y-axis. As a consequence, the interaction effect between the global risk
aversion and the public deficit variables now looks much more limited compared to the full
interaction between public deficit, risk aversion and current account imbalances. The
estimates obtained suggest that under a situation of relatively high general risk aversion (i.e.
with the risk aversion variable reaching values one standard deviation above its mean), the
23 additional risk premium for high current account deficit countries jumps from 2.4 basis points
to 11.2 basis points for each percentage point deterioration in the budget deficit. 6. Concluding remarks
The analysis presented in this paper has shown that euro area sovereign bond interest rates are
strongly influenced by conditions in global financial markets. Domestic factors like liquidity
and credit risk have become more important in the recent financial crisis to explain yield
differentials. More specifically, with respect to credit risk the role played by macroeconomic
fundamentals like fiscal and current account deficits is shown to increase with the level of
general risk aversion. In particular, high debt countries and countries with large current
account deficits are found to experience the highest bond yield increases as consequences of
deteriorated public finances. This may reflect market perceptions according to which
countries with high current account deficit might experiment added difficulties to restore
fiscal sustainability without compromising economic growth further. Thus our results are in
line with the 2009 Public Finance report (European Commission (2009)), which concluded
that post-crisis current account adjustment was instrumental to determine countries' room for
expansionary fiscal policy.
Compared to other asset classes like equities and corporate bonds, the adjustment in yield
differentials during the period of intense financial market turbulences appears to have been
proportionally stronger. This may be explained by a risk transfer effect from the private to the
public sector amid the announcement of the national financial rescue packages. These
comprehensive measures, though successful to the extent that they averted a financial
meltdown, came at the cost of increasing the level of government bond risk premia and their
sensitivity to further aggravations of the financial crisis.
Although conditions on government bond markets have been easing considerably since spring
2009, it seems unlikely that spreads will revert to pre-crisis levels in the near future. A
number of elements suggest this. First, the strong rise in financing costs by sovereign issuers
since September 2008 may, to a certain extent, be explained by the correction of abnormally
narrow spreads in the pre-crisis period, when domestic risk factors resulted in small yield
differentials. Second, it can be expected that government bond yield spreads will remain
elevated compared to the pre-crisis period as debt levels have increased significantly in a
number of countries (relative to the German benchmark) and the contingent liabilities
assumed by the public sector in rescuing the financial sector will continue to weigh on the
outlook for public finances.
Looking further ahead, greater market discrimination across countries may provide higher
incentives for governments to attain and maintain sustainable public finances. Since even
small changes in bond yields have a noticeable impact on government outlays, market
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