Sovereign and corporate credit risk:
Spillover effects in the Eurozone
Mascia Bedendo
Università Bocconi
Paolo Colla
Università Bocconi
This draft: January 2013
Abstract. We provide empirical evidence for the spillover from sovereign risk to non-financial
corporate credit risk using credit default swaps (CDS) data for Eurozone sovereign and corporate
entities over the period 2008-11. We show that an increase in sovereign risk is associated to a
robust increase in the credit risk (and, hence, borrowing costs) of non-financial firms. Consistent
with the idea that a government in financial distress is more likely to introduce restrictive
measures on the corporate sector, we further uncover that this association gets stronger as the
sovereign CDS widens. We also show that when sovereign default risk is sizeable, a further
deterioration in a country’s credit quality affects more adversely firms that are governmentcontrolled, those whose sales are more concentrated in the domestic market, and those that rely
more heavily on bank financing. Our findings suggest that government guarantees, domestic
demand, and credit markets are important transmission mechanisms.
Keywords: sovereign risk, corporate credit risk, credit default swaps, Eurozone.
JEL classification: G01, G15, G32.
1. Introduction
Recent shocks in European debt markets have raised concerns about the steadily growing amount
of sovereign credit risk in the aftermath of the 2007-9 financial crisis. As a result, academics, as
well as market participants and regulators, have devoted much of their attention to identifying the
determinants of sovereign risk, placing special emphasis on the decomposition between systemic
and country-specific risk (see, among others, Caceres, Guzzo and Segoviano (2010), Ang and
Longstaff (2011), and Dieckmann and Plank (2012)).
In this paper, we also put sovereign risk under the lens, but shift our attention to whether and how
changes in sovereign risk affect the credit risk of the non-financial corporate sector. Answering
these questions bears important consequences on firms’ access to financial markets and, in turn, on
corporate borrowing costs. The rationale behind the existence of a spillover from sovereign to
corporate credit risk is that a government in financial distress is likely to shift the burden onto the
corporate sector by increasing corporate taxation, imposing foreign exchange controls or, in
1 extreme circumstances, by expropriating private investments (so-called “transfer risk”).1 Our goal
is to gather evidence on the relationship between changes in sovereign and corporate credit risk in
developed markets. To this end, we exploit the recent turmoil in European debt markets, which led
to a sizable increase in sovereign risk for many Eurozone countries. The solid blue line in Figure 1
portrays the evolution of sovereign credit default swap (CDS) spreads over the period 2008-11 in
the main economies of the Euro area, and certifies such an increase. In all countries, sovereign CDS
spreads are essentially nil for the first three quarters of 2008, and ramp up after October 2008,
reaching levels between 100bps and 200bps at the beginning of 2009. After a tightening during the
second half of 2009, sovereign spreads steadily increase in countries facing fiscal strain (Belgium,
Italy, Portugal, and Spain), whose CDSs trade between 250bps (Belgium) and 1000bps (Portugal)
by the end of 2011. Sovereign risk raises in the second half of 2011 also in fiscally-virtuous
economies (Finland, France, Germany, and the Netherlands), although their CDS spreads remain
at much lower levels (between 50bps in Germany, and 150bps in France) at the end of 2011.
The generalized increase in sovereign risk experienced over the period under investigation questions
the plausibility of the common assumption of risk-free government debt issued by developed
countries, and it is precisely under these circumstances that we expect sovereign risk to affect
corporate credit risk. A preliminary look at corporate credit spreads reinforces our expectations in
this respect: The dashed red line in Figure 1 depicts the time series on non-financial corporate CDS
spreads, which evidently co-moves with the corresponding time series of sovereign CDS spreads,
particularly in countries facing more severe budget deficit’s issues.
In line with the recent literature (Pan and Singleton (2008), Acharya, Drechsler and Schnabl
(2011), and Longstaff et al. (2011)), we measure credit risk in both sovereign and corporate entities
with CDS spreads, in lieu of bond spreads, as they provide a more direct and timely measure of the
creditworthiness of the issuer. Our sample includes CDS data on 157 non-financial companies
headquartered in Eurozone countries, together with the corresponding sovereign CDS spreads,
between January 2008 and December 2011. We first show that an increase in sovereign risk is
associated to a robust increase in the credit risk (and, hence, in the borrowing costs) of nonfinancial firms, even after controlling for market-wide shocks to credit risk and volatility, and
common variation in corporate CDSs. We further uncover that the positive association between
changes in sovereign risk and changes in corporate credit risk gets stronger as the domestic
sovereign CDS spread widens, consistently with both the evidence reported in Figure 1 and
concerns that a government is more likely to introduce restrictive measures on the real economy as
its default probability increases.
1
Durbin and Ng (2005), and Aguiar, Amador and Gopinath (2009) investigate this issue within the context
of sovereign defaults in emerging markets. 2 Finally, we take advantage of the cross-sectional variation in firm characteristics to shed light on
the mechanisms through which sovereign risk spills over to corporate credit risk. Motivated by the
existing theoretical and empirical literature, we investigate three transmission channels:
1. Government guarantees. Concerns about sovereign creditworthiness reduce the value of the
debt guarantees enjoyed by firms that are under the influence of the government. As a
result, those firms should be more sensitive to changes in sovereign risk.
2. Domestic demand. Sovereign crises are frequently accompanied by a reduction in aggregate
domestic demand, which affects non-exporting firms more than exporting firms.
Consequently, firms whose output is placed primarily on the domestic market should be
more sensitive to an increase in sovereign risk.
3. Credit squeeze. Sovereign defaults often lead to a severe disruption in domestic credit
markets, given that banks typically hold disproportionately large amounts of bonds issued
by the domestic government. This, in turn, induces bank deleveraging that negatively
affects corporate lending. Hence, firms heavily exposed to bank debt should be more
sensitive to an increase in sovereign risk.
We find evidence supportive of the three mechanisms: When the sovereign default risk is sizeable, a
further deterioration in a country’s credit quality affects more adversely firms that benefit from
government guarantees, those with sales more concentrated in the domestic market, and those that
rely more on bank financing.
To the best of our knowledge, this is the first paper that investigates the extent to which an
increase in sovereign risk translates into higher funding costs for non-financial firms in the
Eurozone, and explores a number of potential firm-specific transmission channels. Our paper
contributes to the recent and fast-growing literature on the impact of sovereign risk on corporate
credit risk in developed economies, which has, so far, mainly focussed on financial firms. Acharya,
Drechsler and Schnabl (2011), and Ejsing and Lemke (2011) provide empirical evidence on the
effect of bank bailouts on sovereign credit risk and, more in general, on the relation between
sovereign risk and bank credit risk over the period 2007-10. Related studies that extend the
analysis of the spillover mechanism to non-financial firms are by Andrade and Chhaochharia
(2012), Augustin et al. (2012), and Bai and Wei (2012). Andrade and Chhaochharia (2012)
document a significant association between increases in sovereign credit spreads and decreases in
equity valuation levels of both financial and non-financial firms in the Eurozone. Through a
comparison of the cost of debt for governments and firms (instead of debt and equity), we
complement their analysis providing a direct assessment of the impact of an increase in sovereign
spreads on the borrowing costs of non-financial firms. Augustin et al. (2012) also study the effect of
an increase in sovereign CDS spreads on corporate CDS spreads, but limit the analysis to the
impact of the Greek sovereign credit risk shock on European firms over the period March 2010 June 2010. The paper most closely related to ours is Bai and Wei (2012), who investigate how a
3 widening in sovereign CDS spreads affects corporate CDS spreads in the period January 2008 February 2010. In accordance with their findings, we report a significant spillover of credit risk
from sovereign to corporates. However, our analysis differs from theirs under several aspects. First,
we concentrate on the credit risk transmission to non-financial firms in developed economies, while
they do not distinguish between: 1) banks and other firms, and 2) developed and emerging
markets. Since we expect the spillover to be stronger for financial institutions and within emerging
economies, their analysis does not provide conclusive evidence that such effect persists in nonfinancial firms in developed countries. Second, we differ in the proposed channels of transmission:
Bai and Wei (2012) explore the role of property rights institutions in determining the cross-country
variation in the intensity of the spillover, whereas we focus on firm-specific variables such as
government control, and the exposure to the domestic market and to the banking sector.2
The paper is structured as follows. Section 2 illustrates the steps we undertake to construct the
data set used in the analysis. Section 3 presents the empirical findings on the credit risk spillover
from sovereigns to corporates, and Section 4 investigates the transmission mechanisms. Section 5
concludes the paper.
2. Sample Construction and Summary Statistics
To perform our analysis we gather information from a variety of sources. As discussed, we use CDS
spreads as a measure of credit risk. A CDS contract essentially represents an insurance contract
against the risk that an entity (sovereign or firm) defaults on its debt. The key advantage in using
CDS data is that they provide a much more accurate measure of the credit risk of the issuer than
bond spreads (Longstaff, Mithal and Neis (2005), Pan and Singleton (2008), and Longstaff et al.
(2011)), given that bond spread dynamics are driven not only by credit risk, but also by interest
rate movements, illiquidity effects and other factors. Time series of CDS spreads on sovereign and
non-financial entities are from the MarkIt Group, which is a standard provider of CDS data largely
employed by both academics and practitioners.3 We select Euro-denominated4 CDS quotes for
senior unsecured debt with the modified-modified restructuring (MM) clause for firms and the
cumulative restructuring (CR) clause for sovereigns, which represent the conventional (and, hence,
most liquid) terms for CDS contracts on European reference entities. To ensure liquidity of the
2
The developed economies in our sample enjoy very similar property rights -according to the Bai and Wei
(2012) classification scheme, they display either ‘good’ or ‘mixed’ quality of property rights institutions-,
which makes it redundant to draw any inference on the role of property rights at the country level.
3
MarkIt provides composite prices based on quotes contributed by more than 30 major market participants
on a daily basis. The quotes are filtered to remove outliers and stale observations, and a daily composite
spread is computed only if two or more contributors have reported a valid quote. See Mayordomo, Peña, and
Schwartz (2010) for a list of papers that use MarkIt data.
4
We restrict our analysis to Euro-denominated CDS contracts since the Euro is the standard reference
currency for most CDSs on European corporate reference entities. For consistency, we use Euro-denominated
CDSs also on sovereigns, even though the most liquid contracts in this market segment are in USD. This is
unlikely to introduce a bias in our analysis, given that the correlation between weekly changes in Euro CDS
spreads and weekly changes in USD CDS spreads on the sovereign entities in our sample is equal to 94.4%.
4 CDS data, we only consider the 5-year maturity, which is the reference expiry in the corporate
CDS segment. We restrict our sample to the “old” members of the Euro area, as CDS data on
firms located in the new member states (Cyprus, Estonia, Malta, Slovakia, and Slovenia) are
scarce. To avoid confounding effects, we exclude Eurozone subsidiaries of companies headquartered
elsewhere. At this stage we have CDS data on 240 companies and 11 sovereigns.
Our data set includes daily CDS premia (in basis points) between January 1, 2008 and December
30, 2011.5 In investigating the effect of changes in sovereign risk on corporate risk, we carry out our
analyses at the weekly level, following Acharya, Drechsler and Schnabl (2011). Weekly CDS
spreads are obtained as simple averages of the daily spreads in the week. To avoid biases due to
missing or stale data, we apply a number of filters, in line with the existing literature (most
notably Schneider, Sögner, and Veza (2010), and Bai and Wei (2012)). First, we set as missing all
stale spreads, i.e. observations with zero changes in weekly corporate or sovereign CDS premia.
Second, we exclude CDS series where: (i) the percentage of missing spreads exceeds 15% of the
overall period —i.e. more than 31 missing weekly spreads-, and (ii) the length of the longest series of
consecutive missing spreads is more than 2 weeks. Finally, we remove small countries with
infrequent CDS transactions, by requiring valid data on a minimum of 5 companies per country.6
Our final sample includes 157 companies in 8 countries.
Table 1 reports the sample breakdown by country. Luxembourg is excluded given that its sovereign
CDS is not actively traded, while Austria, Greece and Ireland are removed because not enough
companies meet our thresholds on data quality. France and Germany are the most represented
countries, each one taking about 30% of the sample. It is worth noticing that the composition of
our sample is in line with that of widely traded CDS indexes, such as the iTraxx Europe for nonfinancial firms. According to the dynamics of sovereign CDS spreads, countries can be split into
two groups. The first is formed by countries (Finland, France, Germany, and the Netherlands)
characterized by a relatively low level of credit risk: sovereign CDS spreads are, on average, about
50 bps or less, and fairly stable over time. Countries in the second group (Belgium, Italy, Portugal,
and Spain) are instead riskier, as certified by average sovereign CDS spreads close to 100 bps or
higher, and a much larger time variation than the one observed for the countries in the first group.
In order to test the significance of the proposed transmission channels of sovereign risk into
corporate credit risk (which are described in detail in the next Section), we need to derive firmspecific measures of: 1) government control; 2) geographic segmentation of sales; 3) exposure to
bank lending.
An obvious candidate for the identification of government-controlled firms would be the proportion
of government-owned equity —either direct or indirect through government-owned agencies.
5
We discard earlier observations because sovereign CDS spreads in the Eurozone were very low and quite
stale prior to 2008, which would introduce distortions when computing log-changes.
6
Bai and Wei (2012) use a more restrictive criterion (more than 10 companies) for country coverage. At the
same time their filtering procedure for missing and stale CDS data is less demanding than ours. We therefore
choose to set a lower minimum number of companies per country.
5 However, this measure does not provide a realistic representation of the influence exercised by the
government on a firm: Bortolotti and Faccio (2009) document that governments tend to retain
substantial power in formerly state-owned enterprises in a number of ways, even after
privatization. For instance, a government can adopt ownership leveraging devices (pyramiding and
dual class shares), and remain the largest ultimate shareholder of a firm even without owning the
majority of its equity. Alternatively, governments may hold golden shares, which enable them to
outvote all other shareholders and significantly affect corporate decisions.7 Either way,
privatizations of state-owned firms often witness the sale of equity without a proportional transfer
of control. As a consequence, government ownership would underestimate the actual involvement
of governments in firms. We therefore resort to the FEEM-KPMG Privatization Barometer8 (PB)
database to identify firms that were entirely or partially privatized by the state, and may be still
de facto under the government’s influence through one of the mechanisms discussed above. We
create the (firm-year) indicator variable Govt which, for firm i and year t equals one if firm i is
listed in PB in any year between 1977 and t-1, and zero otherwise. We then use national stock
exchanges’ and regulatory bodies’ websites (see the data appendix in Bortolotti and Faccio (2009)
for a list of data sources) to augment the indicator Govt for those cases where a firm is stateowned and has never been privatized (and, hence, is not included in the PB database).
Finally, we retrieve information from the Bureau Van Dijk’s Orbis database on firms’ geographic
segmentation of sales and debt structure. We use the proportion of domestic sales (Sales),
computed as the ratio of sales in the country where the company is headquartered to total sales, as
a measure of exposure to the domestic market. The proportion of bank debt (Bank), computed as
the ratio of bank loans to total debt (i.e. the sum of long term debt plus long term debt in current
liabilities), is our proxy for the firm’s exposure to the banking sector. Orbis variables are available
at annual frequency, and we match CDS quotes in a given year t with Sales and Bank computed at
year-end t-1. Table 1 provides the coverage of the Orbis database for our companies. Overall, we
have information on Sales and Bank for about 60% of our sample firms, and the coverage differs
across countries. Geographic segmentation of sales and bank exposure are sourced from annual
reports, hence data availability is affected by both firms’ listing status, and country-specific
reporting requirements.9 Table 2 reports summary statistics on firm-specific characteristics. As of
7
A recent episode that gained vast press coverage was the veto imposed in June 2010 by the Portuguese
government (via its golden shares) to block Telefonica SA’s bid for Portugal Telecom’s stake in Brazil’s
largest phone company. Portugal Telecom had been entirely privatized between 1994 and 2000. Shortly
afterwards, the European Court of Justice declared the holding of golden shares by the Portuguese state
illegal. Similar judgements have been issued against France, Germany, the Netherlands, Italy, and Spain.
8
Privatization Barometer is a monthly updated database containing privatization transactions (defined as
“primary or secondary issues of shares on public equity markets by companies in which central or local
governments are shareholders”, see Bortolotti and Faccio (2009)) for 25 European countries from 1977 to
present. 9
We repeat the analysis of Table 1 using the number of weekly CDS observations instead of the number of
reference entities. We find that Sales and Bank are available for 59% and 60% of a total number of 27,625
observations, respectively.
6 December 31 2007, the beginning of our sample period,10 the median firm in our sample had
domestic sales which accounted for about 40% of the total sales, bank financing for about 20% of
the overall debt, and was not controlled by the government.
3. The relationship between sovereign risk and corporate credit risk.
Suggestive evidence of the relationship between sovereign risk and corporate credit risk in the
Eurozone comes from Figure 2, where we plot the correlation between the median of weekly
changes in corporate CDS spreads and weekly changes in sovereign CDS spreads (solid blue line,
left scale) against the sovereign CDS spread (dashed red line, right scale). Two empirical facts are
worth noticing. First, the correlation between changes in corporate and sovereign spreads is
generally fairly high, on average, for all countries in the sample. Second, it tends to increase as the
sovereign CDS spread widens: This trend is evident for riskier countries which are closer to the
default boundary such as Italy, Spain, and Portugal, over the entire sample period, but it can also
be observed in virtuous countries, especially during the second half of 2011.
We now proceed to formally investigate the effect of changes in sovereign risk on corporate credit
risk. We broadly follow the methodological design of Acharya, Drechsler and Schnabl (2011), and
regress changes in log weekly corporate CDS spreads on changes in log weekly sovereign CDS
spreads. This specification assumes that the direction of causality goes from sovereign to corporate
risk. Given that we only look at non-financial firms —which are less likely to be bailed out and less
subject to contagion than banks— the assumption seems reasonable, as the potential default of a
non-financial firm seems unlikely to have an impact on sovereign credit risk. A second concern with
this specification is that other (possibly unobserved) factors may affect the co-movement between
corporate and sovereign CDS spreads, without being evidence of a direct channel from sovereign to
corporate credit risk. To address this concern, we augment the baseline specification in a variety of
ways. First, we estimate the following model:
log(CDSijt) = + log(CDSjt) + Xt+ijt
(1)
where log(CDSijt) is the change in the log CDS spread in basis points of firm i headquartered in
country j from week t-1 to week t, log(CDSjt) is the change in the log CDS in basis points of
country j from week t-1 to week t, and Xt are the changes from week t-1 to week t for a set of
controls. The similarities in the dynamics of CDS spreads of both corporate and sovereign entities
across countries, as highlighted in Figure 1, suggest the existence of market factors that explain
most of the variation in credit risk in the Eurozone.11 Following Acharya, Drechsler, and Schnabl
(2011), we include the VDAX and the log weekly iTraxx Europe CDS index as our control
variables aimed at capturing market-wide changes that may affect both corporate and sovereign
Govt, Sales and Bank display limited time variation, hence we report summary statistics only for the cross
10
section of these variables at December 31, 2007. Related studies by Longstaff et al. (2011), and Dieckmann and Plank (2012) report that the first principal
component explains around 75% of the variation in sovereign CDS.
11
7 risk directly. The VDAX, which is the German counterpart to the VIX index, is a measure of
aggregate volatility in the Euro area, and is sourced from Bloomberg. The iTraxx Europe index,
which includes the most liquid 125 single-name investment grade CDS, is a proxy of CDS marketwide trends in credit risk, and is provided by the MarkIt Group. In order to account for potential
non-linearities in the dependence of firms on market variables, we further enrich specification (1)
by including firm fixed effects, as well as firm-specific coefficients for the market controls:
log(CDSijt) = i+ log(CDSjt) + iXt+ijt
(2)
The second strategy that we follow to purge the baseline specification from other (unobserved)
factors affecting both corporate and sovereign credit risk is to replace iTraxx and VDAX in (1)
with weekly fixed effects capturing common variation across all companies:
log(CDSijt) = +t +log(CDSjt) + ijt
(3)
Finally, we estimate a further specification which includes weekly fixed effects, and firm fixed
effects in lieu of the common intercept in (3):
log(CDSijt) = i+t +log(CDSjt) + ijt
(4)
Table 3 presents the regression results, and shows that the estimate of  is fairly stable irrespective
of the specification used to accommodate market-wide and firm-specific effects: An increase in
sovereign CDS spread translates into a highly statistically significant increase in corporate CDS
spread, with  ranging from 0.045 to 0.047. The economic magnitude of the effect may seem
limited, as a 10% increase in sovereign credit risk produces an estimated 0.46% increase in
corporate credit risk, on average. However, we argue that the effect is still substantial, as it
corresponds to about one fourth of the estimated impact of an increase in sovereign risk on the
credit risk of financial institutions in the aftermath of the bailouts episodes of 2008 (see Acharya,
Drechsler, and Schnabl (2011)) and, as discussed, banks are more directly and heavily exposed
through their holdings of sovereign bonds and other assets (see Kallestrup, Lando and Murgoci
(2012)).
Table 3-Column (1) shows that, as expected, the coefficients on the market variables are both
statistically and economically significant: A generalized increase in credit risk and volatility in
Europe leads to a widening of corporate credit spreads. The changes in iTraxx, VDAX and
sovereign CDS spreads together explain about 37% of the weekly variation in corporate CDS
spreads. The R-squared increases to 42% when we add firm fixed effects and their interaction with
market controls (see Table 3-Column (2)), and to 45% when we replace the market variables with
weekly fixed effects as in specification (3), or when we include both weekly and firm fixed effects as
in (4) (see Table 3-Columns (3) and (4)). We select specification (4) as our baseline for all the
following analyses.12
12
The introduction of firm fixed effects (see Table 3-Column (4)) does not contribute much in explaining the
variation in corporate spreads, compared to the specification with weekly fixed effects only (see Table 3Column (3)). However, we choose the model with both weekly and firm fixed effects as our reference
8 As discussed, the empirical evidence presented in Figure 2 suggests that sovereign and corporate
spreads tend to co-move more closely as the sovereign becomes riskier. To investigate this point, in
Column (5), we add to specification (4) the log of the sovereign CDS spread lagged by one week
log(CDSj,t-1), and its interaction with the weekly change in sovereign CDS between t-1 and t.13
log(CDSijt) = i+t +log(CDSjt) +log(CDSjt-1)+log(CDSjt)log(CDSjt-1)+ ijt
(5)
The direct effect of changes in sovereign risk, captured by the coefficient remains stable and
highly statistically significant. Moreover, in line with our preliminary evidence of Figure 2, the
coefficient of the interaction term indicates that the sensitivity of corporate credit risk to changes
in sovereign credit risk is significantly higher (at 5% level) when the cost of purchasing protection
against the sovereign default is higher.14 Intuitively, as a sovereign moves closer to default, it
becomes more likely to introduce restrictive measures aimed at avoiding default, which may impact
the real sector either directly (corporate taxes) and/or indirectly (personal taxes).
One potential issue with our results is that the two most represented countries, France and
Germany, may drive the estimate of  in Table 3, as they account for about 60% of our sample. To
investigate cross-country heterogeneity in the response of corporate to sovereign credit risk
changes, we re-estimate specifications (1)-(4) by letting the coefficient on log(CDSjt) to be
country-specific, and report results in Table 4. As for specification (5), we also allow for countryspecific coefficients on the lagged sovereign CDS spread, log(CDSj,t-1), as well as on its interaction
with log(CDSjt)). The findings in Table 4, Columns (1)-(4) confirm that our estimates are not
driven by the countries with the most observations in the sample and that, in fact, the relationship
between sovereign and corporate credit risk is positive and significant in most countries. Also, such
linkage seems to be indeed stronger for riskier countries: The sensitivity to changes in sovereign
risk is two to three times larger in countries facing fiscal strains such as Italy and Spain, than in
virtuous economies such as France and Germany. The estimates reported in Table 4-Column (5)
(which are in line with our previous results using specification (5) in Table 3) also confirm that
corporate credit risk is more affected by changes in sovereign risk the higher the level of the
government spread, and this holds true for both virtuous and strained countries.
4. Channels of Transmission
So far we have documented a significant transmission of credit risk from sovereign to corporate
entities, especially strong when sovereign spreads are relatively high. The next step is to analyze
the mechanisms through which such transmission takes place.
specification given that, in what follows, we will work with subsamples of companies, and firm-specific effects
may become more relevant.
13
The results (unreported, but available from the authors) remain essentially unchanged when using the log
of the sovereign CDS spread lagged by four weeks.
14
The interaction term is very highly correlated with log(CDSjt-1), with a linear correlation coefficient of
0.966. To reduce multicollinearity concerns, we demean log(CDSjt-1) before creating the interaction term. By
doing so, the correlation between the interaction term and log(CDSjt-1) drops to -0.199. 9 4.1. Government guarantees.
Government-controlled firms enjoy both deep credit lines and debt guarantees from the state.
Moreover, since governments usually control companies that are of strategic importance to the
country (e.g. utilities), it is unlikely that they would allow those firms to go bankrupt. Faccio,
Masulis and McConnell (2006) study 450 firms from 35 countries and document that politicallyconnected firms are more likely to be bailed-out than similar non-connected firms. Borisova and
Megginson (2011) find that a 1% decrease in government ownership is associated with a 0.75bps
increase in a firm’s credit spread. However, when concerns about the solvency of the government
arise, government guarantees quickly lose value, thus eroding the creditworthiness of governmentcontrolled companies. In addition, these firms are usually more likely to be the target of ad-hoc
measures should the government need to quickly raise funds to face budget concerns. As a result,
we expect firms under the influence of the government to be relatively more affected by an increase
in sovereign credit risk, particularly if the government is financially strained.
To empirically assess the relevance of the government guarantees channel, we add to the RHS of
specification (5): (1) the direct effect of the government-control dummy (Govt); (2) the interaction
between Govt and log(CDSjt); (3) the interaction between Govt and log(CDSjt-1); (4) the triple
interaction Govt x log(CDSjt) x log(CDSjt-1):
log(CDSijt) = i+t + log(CDSjt) + log(CDSjt-1) + log(CDSjt)log(CDSjt-1) + Govt +
Govtlog(CDSjt) + Govt log(CDSjt-1) + Govt log(CDSjt) log(CDSjt-1) + ijt
(6)
We report in Table 5-Column (3) coefficient estimates for , , and .15 The coefficient of the triple
interaction is positive and statistically significant, thus confirming that, when the sovereign default
risk is sizeable, a further deterioration in credit quality affects more adversely firms that enjoy
government guarantees.
4.2. Domestic demand
In normal circumstances, we do not necessarily expect domestic demand to be adversely affected by
an increase in sovereign credit risk. However, if the sovereign default risk is already sizeable, a
further increase in credit risk is likely to trigger the adoption of restrictive monetary or fiscal
measures aimed at restoring creditworthiness, which may lead to a significant contraction in
domestic demand. This, in turn, can result in an increase in default risk for those firms whose
business relies heavily on the domestic market: Non-exporting firms are more likely to experience a
decline in profits and net worth and, thus, to face tighter borrowing constraints (Arteta and Hale
(2008)). Consistently with this channel, Borensztein, Cowan and Valenzuela (2007) document a
larger impact of sovereign credit ratings on corporate credit ratings for firms in the non-tradable
sector relative to those in the tradable sector.
15
Again, we detect multicollinearity when creating the interaction terms including Govt. Therefore, we
demean Govt before including it in specification (6). We apply the same de-meaning to the other firm
characteristics (Sales and Bank) in the analyses that follow.
10 The empirical test of the domestic output channel is reported in Table 6, where the fraction of
domestic sales over total sales (Sales) is our proxy for a firm’s sensitivity to the domestic market.
Since, as discussed, the geographic composition of sales is only available for a subset of firms, we
first re-estimate models (4) and (5) on this subsample —Columns (1) and (2). The results are
consistent with the previous ones on the overall sample: The impact of a change in sovereign risk
on corporate risk is highly statistically significant, and larger for higher levels of sovereign default
risk. To test the relevance of the domestic sales channel, we repeat the methodological steps
followed above for the implicit government guarantees, and estimate specification in (6) by
replacing Govt with Sales, and report the results in Table 6—Column (3). In line with our
predictions, we document that corporate credit risk is more affected by changes in sovereign risk
for firms whose sales are more concentrated in the domestic market.
4.3. Credit squeeze
Recent theoretical models (see, among others, Gennaioli, Martin and Rossi (2011)) argue that
sovereign defaults lead to a severe disruption in domestic credit markets. Empirical evidence by
Borensztein and Panizza (2008) confirms that, indeed, sovereign defaults are frequently
accompanied by domestic banking crises that further depress investment and output. Recent
studies (see, among the others, Acharya, Drechsler, and Schnabl (2011), and Ejsing and Lemke
(2011)) document a significant increase in bank CDS spreads following an increase in sovereign
CDS spreads. This effect can be explained as follows. First, the government provides a series of
implicit and explicit guarantees to the financial system that become at risk as the sovereign
approaches default. Second, banks typically hold large amounts of government bonds in their
portfolios that lose value as sovereign credit risk widens. As a result, banks’ funding costs sharpen,
and fears of bank runs heighten. This induces a significant deleveraging of banks’ balance sheets
that has an immediate impact on non-financial firms in terms of reduced bank lending. Adrian,
Colla and Shin (2012) study the incremental financing choices of U.S. non-financial corporations
during the recent financial crisis of 2007-9 and document the substitution from loan to bond
borrowing together with the sudden increase in corporate financing cost. Hence, when sovereign
default risk is perceived to be substantial, we expect the cost of funding for companies that rely
more heavily on bank financing to be more severely affected by an increase in sovereign risk.
To test this hypothesis, we measure the exposure of a company to bank lending as the ratio of
bank debt to total debt (Bank), and follow the steps outlined above to assess the relevance of the
bank channel. We re-estimate models (4) and (5) on the subsample of firms for which information
on the proportion of bank debt is available, and present the results in Table 7, Columns (1) and
(2). Again, the findings are consistent in uncovering a significant impact of sovereign credit risk on
corporate credit risk, which becomes stronger as the sovereign moves closer to the default barrier.
We then estimate the model in (6) by replacing Govt with Bank, and report the results in Column
11 (3). In line with our prediction, we observe that a loss in sovereign creditworthiness affects bankdependent firms significantly more than other companies, thus confirming the existence of a
spillover from sovereign to corporate risk through the financial intermediation channel.
5. Concluding remarks
We explore the effect of changes in the creditworthiness of a developed sovereign entity on the
credit risk of non-financial firms headquartered in the same country. We measure credit risk with
CDS spreads on both sovereigns and corporates over the period January 2008 — December 2011 for
8 countries in the Eurozone area. We report the following findings. First, an increase in sovereign
risk translates into a significant increase in corporate credit risk, even after controlling for a set of
firm- and time-specific variables. Second, the impact is stronger the higher the sovereign default
risk. Third, conditional on the sovereign risk being sizeable, the spillover effect is significantly
higher for firms that enjoy government guarantees, place most of their output on the domestic
market, or rely heavily on bank funding.
Our findings are innovative and not trivial. While a rich empirical literature documents the
presence of transfer risk in emerging economies, a significant linkage between sovereign risk and
credit risk in the non-financial sector is not granted a priori for developed countries. This is
especially true in the context of the Eurozone, where two of the most important channels through
which risk is commonly transferred (i.e. currency controls and expropriation of private
investments) are ruled out. Moreover, detecting a significant spillover might be somehow
unexpected in our sample of companies with liquid CDS data: These companies are typically larger,
more internationally oriented, less financially constrained and less dependent on bank lending than
the average firm. As a result, we believe that our findings, in fact, may underestimate the impact
of an increase in sovereign default risk on the borrowing costs of the non-financial sector in the
Euro area.
12 References
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and sovereign credit risk”, Working paper, New York University.
Adrian, Tobias, Paolo Colla, and Hyun Song Shin, 2012, “Which financial frictions? Parsing the
evidence from the financial crisis of 2007-9”, NBER Macroeconomics Annual, forthcoming.
Aguiar, Mark, Manuel Amador, and Gita Gopinath, 2009, “Investment cycles and sovereign debt
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Andrade, Sandro C., and Vidhi Chhaochharia, 2012, “The Euro and European equity market
(dis)integration”, Working paper, University of Miami.
Ang, Andrew, and Francis A. Longstaff, 2011, “Systemic sovereign credit risk: Lessons from the
U.S. and Europe”, NBER Working Paper 16982.
Arteta, Oscar, and Galina Hale, 2008, “Sovereign debt crises and credit to the private sector”,
Journal of International Economics, 74, 53-69.
Augustin, Patrick, Hamid Boustanifarz, Johannes Breckenfelder, and Jan Schnitzler, 2012, “The
real effects of sovereign credit risk: Evidence from the European sovereign debt crisis”, Working
paper, Stockholm School of Economics.
Bai, Jennie, and Shang-Jin Wei, 2012, “When is there a strong transfer risk from the sovereigns to
the corporates? Property right gaps and CDS spreads”, Working paper, Federal Reserve Bank of
New York.
Borensztein, Eduardo, Kevin Cowan, and Patricio Valenzuela, 2007, “Sovereing ceilings “lite”? The
impact of sovereign ratings on corporate ratings in emerging market economies”, IMF Working
Paper 07/75.
Borensztein, Eduardo, and Ugo Panizza, 2008, “The costs of sovereign default”, IMF Working
Paper 08/238.
Borisova, Ginka, and William L. Megginson, 2011, “Does government ownership affect the cost of
debt? Evidence from privatization”, Review of Financial Studies, 24, 2693-2737.
Bortolotti, Bernardo, and Mara Faccio, 2009, “Government control of privatized firms”, Review of
Financial Studies, 22, 2907-2939.
Caceres, Carlos, Vincenzo Guzzo, and Miguel Segoviano, 2010, “Sovereign spreads: global risk
aversion, contagion or fundamentals?”, IMF Working Paper 10/120.
Dieckmann, Stephan, and Thomas Plank, 2012, “Default risk of advanced economies: An empirical
analysis of credit default swaps during the financial crisis”, Review of Finance, 16, 903-934.
Durbin, Erik, and David Ng, 2005, “The sovereign ceiling and emerging market corporate bond
spreads”, Journal of International Money and Finance, 24, 631-649.
Ejsing, Jacob W., and Wolfgang Lemke, 2011, “The Janus-headed salvation: Sovereign and bank
credit risk premia during 2008-2009”, Economics Letters, 110, 28-31.
13 Faccio, Mara, Ronald W. Masulis, and John J. McConnell, 2006, “Political connections and
corporate bailouts”, Journal of Finance, 61, 2597-2635.
Gennaioli, Nicola, Alberto Martin, and Stefano Rossi, 2011, “Sovereign default, domestic banks and
financial institutions”, Working Paper, Universitat Pompeu Fabra.
Kallestrup, Rene, David Lando, and Agatha Murgoci, 2012, “Financial sector linkages and
dynamics of bank and sovereign credit spreads”, Working paper, Copenhagen Business School.
Longstaff, Francis E., Sanjay Mithal, and Eris Neis, 2005, '”Corporate yield spreads: Default risk
or liquidity? New evidence from the credit-default swap”, Journal of Finance, 60, 2213-2253.
Longstaff, Francis E., Jun Pan, Lasse H. Pedersen, and Kenneth J. Singleton, 2011, “How
sovereign is sovereign credit risk?”, American Economic Journal: Macroeconomics, 3, 75-103.
Mayordomo, Sergio, Juan I. Peña, and Eduardo S. Schwartz, 2010, “Are all credit default swap
databases equal?”, NBER Working paper 16590.
Pan, Jun, and Kenneth J. Singleton. 2008. “Default and recovery implicit in the term structure of
sovereign CDS spreads”, Journal of Finance, 63, 2345-2384.
Schneider, Paul, Leopold Sögner, and Tanja Veza, 2010, “The economic role of jumps and recovery
rates in the market for corporate default risk”, Journal of Financial and Quantitative Analysis, 45,
1517-1547.
14 Tables
Table 1: Sample breakdown by country
This table shows summary statistics of CDS spreads included in the sample, after filtering as described in Section 2. For each country, the table contains the
number of firm-week observations, the number of firms, the mean, median and standard deviation of corporate as well as sovereign CDS spreads (in bps). The
last two columns show the percentage of reference entities with available data from Orbis on percentage of domestic sales (Sales), and proportion of bank debt
(Bank), respectively.
Country
Corporate CDS
Median
Std.dev.
Obs.
Firms
Mean
Belgium
Finland
France
Germany
Italy
Netherlands
Portugal
Spain
1,020
1,511
8,938
8,136
1,978
3,119
820
2,103
7
8
48
45
12
20
5
12
118.0
344.2
213.7
292.9
219.1
147.4
263.6
169.1
83.0
152.0
127.4
128.2
152.7
80.8
191.8
131.2
Overall
27,625
157
231.6
121.4
Mean
Sovereign CDS
Median
Std.dev.
91.8
622.8
355.3
548.2
207.2
173.6
222.3
139.8
97.5
32.1
54.2
34.1
140.4
44.8
332.0
142.4
91.6
27.2
51.2
32.4
116.6
37.8
144
125.8
65.3
18.6
37.2
18.1
100.8
27.7
357.3
90.8
57%
88%
60%
58%
100%
45%
80%
75%
57%
88%
48%
62%
100%
30%
80%
75%
399.1
68.8
43.8
96.8
64%
59%
15 Firm characteristics
Sales
Bank
Table 2: Summary statistics: Cross-section at December 31, 2007
This table shows summary statistics on percentage of domestic sales (Sales), proportion of bank debt (Bank),
and domestic government-control (Govt) for the cross-section of reference entities at December 2007.
Govt
Sales
Bank
Obs. (firms)
Mean
Std.Dev
Median
5th
percentile
95th
percentile
157
87
88
0.313
48.65%
38.01%
0.465
32.72%
37.82%
0
39.41%
19.64%
0
6.85%
0.05%
1
100.00%
100.00%
16 Table 3. Effect of changes in sovereign risk on corporate credit risk
This table shows the effect of changes in sovereign credit risk log(CDSjt) on corporate credit risk
log(CDSijt), both measured as changes in the natural log of weekly CDS spreads. The sample period goes
from January 1, 2008 to December 30, 2011, and includes 157 reference entities. Columns (1) and (2) use
weekly changes in the natural log of CDS market index spreads log(iTraxxt) and weekly changes in the
volatility index VDAXt as market control variables. Column (2) adds to Column (1) firm fixed effects and
interactions of firm fixed effects with the changes in the iTraxx and the VDAX. Column (3) includes weekly
fixed effects, and Column (4) includes both weekly and firm fixed effects. Column (5) adds to Column (4) the
log of the sovereign CDS lagged by one week log(CDSj,t-1) and its interaction with the current change in
sovereign CDS spreads. The standard errors are clustered at firm level. ***, **, and * denote significance at 1%,
5%, and 10% level, respectively.
log(CDSjt)
log(iTraxxt)
VDAXt
(1)
(2)
(3)
(4)
(5)
0.045***
(0.006)
0.642***
(0.020)
0.052**
(0.023)
0.045***
(0.006)
0.047***
(0.011)
0.046***
(0.010)
0.051***
(0.011)
log(CDSj,t-1)
0.004**
(0.002)
0.040**
(0.017)
log(CDSjt)x log(CDSj,t-1)
Week FE
Firm FE
Interactions
Observations
Adj R-squared
N
N
N
27,625
0.369
N
Y
Y
27,625
0.418
17 Y
N
N
27,625
0.446
Y
Y
N
27,625
0.446
Y
Y
N
27,492
0.447
Table 4. Effect of changes in sovereign risk on corporate credit risk: Country heterogeneity
This table shows the effect of changes in sovereign credit risk log(CDSjt) on corporate credit risk
log(CDSijt), both measured as changes in the natural log of weekly CDS spreads. The sample period goes
from January 1, 2008 to December 30, 2011, and includes 157 reference entities. Columns (1) and (2) use
weekly changes in the natural log of CDS market index spreads log(iTraxxt) and weekly changes in the
volatility index VDAXt as market control variables. Column (2) adds to Column (1) firm fixed effects and
interactions of firm fixed effects with the changes in the iTraxx and the VDAX. Column (3) includes weekly
fixed effects, and Column (4) includes both weekly and firm fixed effects. Column (5) adds to Column (4) the
log of the sovereign CDS lagged by one week log(CDSj,t-1) and its interaction with the current change in
sovereign CDS spreads. All specifications allow for country-specific coefficients on log(CDSjt). Column (5)
further allows coefficients on log(CDSj,t-1) and the interaction term log(CDSjt)x log(CDSj,t-1) to be countryspecific (coefficients on log(CDSj,t-1) are not reported). The standard errors are clustered at firm level. ***, **,
and * denote significance at 1%, 5%, and 10% level, respectively.
(1)
Belgium
Finland
France
Germany
Italy
Netherlands
Portugal
Spain
log(iTraxxt)
VDAXt
Week FE
Firm FE
Interactions
Observations
Adj R-squared
-0.111**
(0.056)
0.048*
(0.025)
0.043***
(0.012)
0.043***
(0.011)
0.123**
(0.049)
0.024
(0.018)
0.113
(0.101)
0.091*
(0.052)
0.641***
(0.019)
0.053**
(0.022)
N
N
N
27,625
0.372
(2)
(3)
log(CDSjt)
-0.007
-0.100*
(0.019)
(0.056)
0.060***
0.032
(0.014)
(0.026)
0.030*** 0.052***
(0.007)
(0.017)
0.030*** 0.047***
(0.008)
(0.013)
0.135***
0.117**
(0.028)
(0.049)
0.042***
0.008
(0.012)
(0.019)
0.154**
0.126
(0.074)
(0.102)
0.089**
0.105*
(0.037)
(0.054)
N
Y
Y
27,625
0.419
18 Y
N
N
27,625
0.450
(4)
-0.100*
(0.057)
0.032
(0.026)
0.052***
(0.017)
0.047***
(0.013)
0.118**
(0.049)
0.009
(0.019)
0.126
(0.102)
0.105*
(0.054)
(5)
log(CDSjt)x
log(CDSj,t-1)
log(CDSjt)
-0.143**
0.069**
(0.068)
(0.027)
0.069*
0.108***
(0.036)
(0.028)
0.041**
0.031**
(0.017)
(0.013)
0.050***
0.030**
(0.016)
(0.013)
-0.034
0.152***
(0.055)
(0.032)
-0.006
0.003
(0.025)
(0.019)
-0.099
0.184***
(0.082)
(0.030)
-0.019
0.120***
(0.036)
(0.046)
Y
Y
N
27,625
0.449
Y
Y
N
27,492
0.452
Table 5. Effect of changes in sovereign risk on corporate credit risk: Government guarantees
This table shows the effect of changes in sovereign credit risk log(CDSjt) on corporate credit risk
log(CDSijt), both measured as changes in the natural log of weekly CDS spreads. The sample period goes
from January 1, 2008 to December 30, 2011. Column (1) includes weekly and firm fixed effects. Column (2)
adds the log of the sovereign CDS lagged by one week (included in Other controls) and its interaction with
the current change in sovereign CDS spreads. Column (3) adds the government-control dummy Govt, as well
as its interactions with the other control variables (the direct effect of Govt and its double-interactions with
the controls are included in Other controls). The standard errors are clustered at firm level. ***, **, and *
denote significance at 1%, 5%, and 10% level, respectively.
log(CDSjt)
(1)
(2)
(3)
0.046***
(0.010)
0.051***
(0.011)
0.040**
(0.017)
0.043***
(0.009)
0.030*
(0.016)
0.057***
(0.019)
N
Y
Y
27,625
0.446
Y
Y
Y
27,492
0.447
Y
Y
Y
27,492
0.448
log(CDSjt)x log(CDSj,t-1)
log(CDSjt)x log(CDSj,t-1)x Govt
Other controls
Week Fixed Effects
Firm Fixed Effects
Observations
Adj R-squared
19 Table 6. Effect of changes in sovereign risk on corporate credit risk: Domestic demand
This table shows the effect of changes in sovereign credit risk log(CDSjt) on corporate credit risk
log(CDSijt), both measured as changes in the natural log of weekly CDS spreads. The sample period goes
from January 1, 2008 to December 30, 2011, and includes 100 reference entities, for which Orbis data on the
geographic composition of sales is available. Column (1) includes weekly and firm fixed effects. Column (2)
adds the log of the sovereign CDS lagged by one week (included in Other controls) and its interaction with
the current change in sovereign CDS spreads. Column (3) adds the proportion of domestic sales over total
sales Sales, as well as its interactions with the other control variables (the direct effect of Sales and its
double-interactions with the controls are included in Other controls). The standard errors are clustered at
firm level. ***, **, and * denote significance at 1%, 5%, and 10% level, respectively.
log(CDSjt)
(1)
(2)
(3)
0.061***
(0.015)
0.063***
(0.014)
0.046**
(0.021)
0.045***
(0.015)
0.060**
(0.023)
log(CDSjt)x log(CDSj,t-1)
log(CDSjt)x log(CDSj,t-1)x Sales
0.097**
(0.038)
Other controls
Week Fixed Effects
Firm Fixed Effects
Observations
Adj R-squared
N
Y
Y
16,355
0.478
20 Y
Y
Y
16,276
0.480
Y
Y
Y
16,276
0.482
Table 7. Effect of changes in sovereign risk on corporate credit risk: Credit squeeze
This table shows the effect of changes in sovereign credit risk log(CDSjt) on corporate credit risk
log(CDSijt), both measured as changes in the natural log of weekly CDS spreads. The sample period goes
from January 1, 2008 to December 30, 2011, and includes 93 reference entities, for which Orbis data on bank
debt is available. Column (1) includes weekly and firm fixed effects. Column (2) adds the log of the sovereign
CDS lagged by one week (included in Other controls) and its interaction with the current change in
sovereign CDS spreads. Column (3) adds the proportion of bank debt over total debt Bank, as well as its
interactions with the other control variables (the direct effect of Bank and its double-interactions with the
controls are included in Other controls). The standard errors are clustered at firm level. ***, **, and * denote
significance at 1%, 5%, and 10% level, respectively.
log(CDSjt)
(1)
(2)
(3)
0.061***
(0.014)
0.063***
(0.014)
0.047**
(0.021)
0.047***
(0.013)
0.035*
(0.018)
0.077***
(0.028)
N
Y
Y
16,522
0.481
Y
Y
Y
16,444
0.483
Y
Y
Y
16,444
0.484
log(CDSjt)x log(CDSj,t-1)
log(CDSjt)x log(CDSj,t-1)x Bank
Other controls
Week Fixed Effects
Firm Fixed Effects
Observations
Adj R-squared
21 Figure 1. Sovereign and corporate credit risk
The solid blue line represents the sovereign CDS spread, and the dashed red line is the median CDS spread
computed across non-financial reference entities.
Finland
0
0
100
100
CDS (bps)
200
CDS (bps)
200
300
400
300
Belgium
2008w1
2009w1
2010w1
2011w1
2012w1
2008w1
2010w1
2011w1
2012w1
2011w1
2012w1
2011w1
2012w1
300
France
0
0
100
100
CDS (bps)
CDS (bps)
200
300
200
400
500
Italy
2009w1
2008w1
2009w1
2010w1
2011w1
2012w1
2008w1
2009w1
Germany
0
0
200
100
400
CDS (bps)
200
CDS (bps)
600
800
1000
300
1200
Portugal
2010w1
2008w1
2009w1
2010w1
2011w1
2012w1
2008w1
22 2009w1
2010w1
200
150
CDS (bps)
100
0
0
2008w1
2009w1
2010w1
2011w1
2012w1
2008w1
23 Netherlands
50
100
CDS (bps)
200
300
400
Spain
2009w1
2010w1
2011w1
2012w1
Figure 2. Correlation between sovereign and corporate credit risk
Left axis: Moving correlation, computed over 52 weeks, between median weekly changes in corporate CDS
spreads and weekly changes in sovereign CDS (solid blue line). Right axis: Moving average, computed over
52 weeks, of sovereign CDS spread (dashed red line).
.7
.6
2011w1
30
(bps)
20
2012w1
2011w1
2012w1
2011w1
2012w1
60
(bps)
.6
.5
2012w1
.8
20
.4
2009w1
2010w1
2011w1
20
2012w1
2009w1
24 10
.3
.2
0
.3
.4
200
.5
400
(bps)
30
(bps)
.5
.6
40
600
.7
2010w1
Germany
800
Portugal
2009w1
50
2011w1
.7
2010w1
.6
2009w1
.4
.5
50
40
100
.6
150
(bps)
.7
80
200
.8
2010w1
France
250
Italy
2009w1
100
2010w1
.7
2009w1
10
.2
0
.2
.3
.3
50
.4
.4
100
(bps)
.5
40
150
.6
.5
50
Finland
200
Belgium
2010w1
2011w1
2012w1
Netherlands
2009w1
2010w1
2011w1
60
.7
.6
50
40
(bps)
2012w1
20
2009w1
25 30
.3
.2
.4
50
.5
100
.4
.6
150
(bps)
.5
.7
200
.8
250
Spain
2010w1
2011w1
2012w1