The Co-movement of Sovereign Credit Default Swaps, Sovereign

1

The Co-movement of Sovereign Credit Default Swaps, Sovereign Bonds and Stock Markets in Europe

M. Teresa Corzo1 Universidad Pontificia Comillas

Javier Gómez Universidad Pompeu Fabra and Barcelona GSE

Laura Lazcano Universidad Pontificia Comillas

This draft: March 2012

Abstract

We investigate the relationship between sovereign CDSs, sovereign bonds and equity markets for thirteen European countries during the period 2008-2011. We justify the connection between the sovereign debt market and the country’s stock market and using a VAR analysis we confirm the leading role of equity markets in incorporating new information during 2008-2009. We find, however, evidence supporting that during 2010 sovereign CDS markets took over this role and led the process. These results point to a private-to-public risk transfer during the Lehman period, and a reversal to a public-to-private risk transfer during the sovereign debt crisis. We also find that the role of CDSs with respect to the other two assets is state dependent, i.e., sovereign CDSs play a stronger role in economies with higher perceived credit risk. We finally perform a price discovery analysis between the sovereign CDS markets of the countries in our sample and show evidence that during the years 2007-2009 the Spanish CDSs led the price discovery process, while the Italian and French CDSs took over in 2011. During 2010, sovereign CDS premiums become “delinked” although there is some evidence that the German CDSs took a sort of leading role in market co-movements.

Keywords: sovereign credit risk, sovereign credit derivatives, price discovery, stock markets, lead-lag relationships.

JEL classification: G15, G14, G20

1 Corresponding author: Teresa Corzo, Universidad Pontificia Comillas, ICAI-ICADE. c/Alberto Aguilera 23, 28015 Madrid. Spain. [email protected]; Javier Gómez: [email protected]. Laura Lazcano: [email protected]. TC acknowledges financial support from the Spanish Ministerio de Ciencia e Innovación via project ECO2011-29144. JGB acknowledges the Barcelona GSE and financial support from the Spanish Ministerio de Economía e Innovación via project ECO2011-25607.

2

1. Introduction

The relationship between CDSs, bonds and equity prices has attracted increasing

attention, especially since CDS markets became liquid enough for these assets to serve

as hedging instruments. Both CDS premiums and bond spreads are measures of credit

quality: the co-movement of both markets, at least at a firm level, has been extensively

documented by, e.g., Norden and Weber (2004), Blanco et al (2005), Zhu (2006) and

Forte and Peña (2009), who find evidence that the CDS market leads the incorporation

of new information when considered along with the bond market. Furthermore, Byström

(2005) and Fung et al (2008), among others, have studied the relationship between

equity prices and CDS spreads. Financial theory posits that prices in an efficient stock

market reflect the default probability of firms and the general findings are in line with

this fact: firm specific information has been found to be embedded into stock prices

before it is embedded into CDS spreads. Fung et al (2008) also document that the lower

the credit quality of a company the stronger the feedback effect between the CDS

market and the stock market.

However, the relationship between the markets of sovereign CDSs, sovereign bonds and

stocks had been overlooked until very recently, probably because of the limited liquidity

of some markets for sovereign CDSs. During the Sovereign Debt Crisis of 2010

sovereign CDS markets started to receive increasing attention (Longstaff et al (2011),

Dieckmann and Plank (2011) or Günduz and Kaya (2011)) and, as a consequence, the

relationship between sovereign bonds and sovereign CDSs became the focus of many

analyses (see, e.g., the papers by Arce et al (2011), Coudert and Gex (2010), Fontana

and Scheicher (2010)). In this paper we take a step further and investigate the

relationships between the markets for sovereign bonds and CDSs and the stock markets

for a wide sample of countries: we extend the analysis in Coronado et al. (2011) and

analyze the most recent data (2007-2011) for thirteen European countries, some of

which have been affected quite substantially by the debt crisis2. Our results show that

the stock markets led the incorporation of information during 2008 and 2009 whereas

the CDS markets seemed to take the leading role during 2010. In 2011 the stock market

regains its leading role and, even though the relationships between the three markets

weaken, sovereign bonds gain in importance.

2 Coronado et al. (2011) carry out a more limited study on the lead-lag relationship between sovereign CDSs and stock markets.

3

We also attempt to assess if the relationship among the three markets is state dependent,

i.e, whether it depends on the level of perceived sovereign credit risk. To this end, we

perform an analysis where we pool together the different countries by dividing them

into two groups in terms of their risk level. As previously documented (Fontana and

Scheicher (2010)) we find that CDSs play a stronger leading role in economies with

higher perceived credit risk.

Finally, we study the relationship between the different European CDSs before and

during the crisis. For that purpose, we suggest the existence of an equilibrium

relationship between the CDSs of pairs of countries and estimate error correction

models for the period 2007-2009: we find evidence that the Spanish CDS led the price

discovery process during those years. During 2010, however, the different CDS markets

lost their equilibrium relationship (this is understandable, given the effects on the term

premiums induced by the European sovereign debt crisis) so we use VAR models

instead and test for Granger causality to examine whether a specific market exercises

some sort of price leadership. Our findings suggest that during 2010 the German CDS

market led the incorporation of risk information. In 2011 we observe changes in this

leadership position: many sovereign CDSs in our sample recovered a cointegrated path

and the Italian CDS, as well as the French, led the process of price discovery.

Although the Sovereign debt crisis is far from being solved, during this last year it has

become obvious that Greek debt restructuring will imply large losses for debt holders.

ISDA has not yet declared a Greek credit event, but the legitimacy of sovereign CDS

settlement has been called into question -given the ability of governments to influence

the “event of default”- though European sovereign CDSs continue to be traded. 3 For

example, during the third quarter of 2011, the Italian, Spanish, French and German’

CDSs have been within the top ten most liquid worldwide CDSs, the first three being, in

3 The question of why one would want to buy a CDS if there is never going to be a payout is relevant: investors can still profit from the rising and falling of the risk premium itself but the CDS contract would be stripped of its main purpose. Sovereign CDSs are used to hedge bond portfolios but also to hedge against corporates within that jurisdiction that are too small to have their own reference CDS. If sovereign CDSs become ineffective, this could inhibit an investor’s ability to take exposure to these foreign counterparties. Besides, the latter inevitably means investors will be under more pressure to reduce their exposure to sovereign debt and start selling said debt, thus pushing up the financing costs for governments. Observed volume data suggests that not everybody in the markets shares the same view about the future of sovereign CDSs. Even though some investors entertained the idea of a disappearing sovereign CDS market and forecasted declining volumes, the fact is that sovereign CDS continue to be traded, and European sovereign CDSs continue to be in the top-10 among the most liquid CDS.

4

fact, the top three in terms of average daily notional negotiated ($1,550,000,000;

$1,325,000,000; $1,075,000,000 respectively). As of January 2012, the four are still

within the top ten CDSs in terms of volume of trade.4

The rest of the article is structured as follows. In Section 2 we briefly justify the

existence of a relationship between the sovereign credit markets and the domestic stock

market. In section 3 we describe our data. In section 4 we estimate the models that relate

-at a country level, but also pooling groups of countries- the three markets of sovereign

CDSs, sovereign bonds and stocks. In section 5 we report the results of the price

discovery process among European CDSs. Finally, we conclude in section 6.

2. The link between sovereign CDSs and stock markets

Sovereign CDSs and bonds are quite different financial instruments but they constitute

the credit market of a country. Thus, the link between the markets for the two types of

assets has been vastly studied, both at the corporate level (e.g., Norden and Weber

(2004), Blanco et al (2005), Forte and Peña (2009)) and at the sovereign level (e.g.,

Arce et al (2011), Fontana and Scheicher (2010), Coudert and Gex (2010)). However,

the link between sovereign CDSs and a country’s stock market has been much less

explored (see Coronado et al, 2011), and only in indirect ways: Longstaff et al. (2011)

find that emerging sovereign credit spreads are strongly linked to global factors such as

US stock market returns and volatility indices, and Berndt and Obreja (2010), show that

European corporate CDS are significantly related to a factor which captures “economic

catastrophe risk” (Fontana and Scheicher, 2010, also find that common factors drive the

CDS market).

No study that we are aware of has linked directly the CDS and stock markets of a

certain country, though. One motivation for such an analysis starts from the empirical

correlation patterns between sovereign CDS spreads and stock market performance: as

the credit situation worsens, the stock market falls (we detail these findings in section 3).

The argument why we would expect to find a link between these two markets, however,

is not immediate.

4 Data from 2012-01-27 Weekly Activity Report and Market Activity Report June 20, 2011 through September 19, 2011 Depository Trust and Clearing Corporation.

5

An efficient stock market should incorporate in a timely manner the information

relevant to the default probability of firms. This link between the credit market and

stock market information was already present in Merton (1974): the value of any credit

derivative must be linked to the probability of the underlying reference entity being

exposed to a credit event at some point in the future. For entities with traded equity this

probability is, in fact, often estimated using information from the stock market. The link

between the country’s credit information and the country’s stock market is not as

obvious, though. Gapen et al (2005) develop a structural credit risk model where the

volatility of sovereign assets becomes a major factor in determining a country’s default

risk. To our knowledge, it has not been shown so far that the stock market could be a

proxy for a country’s “equity”. In other words, what constitutes the liability of a country

may be clear, but it is not so obvious how to measure the “equity” part. Some studies

have used GDP of a country as a proxy for the country’s equity: see, e.g., Boehmer and

Megginson (1990), Reinhart and Rogoff (2010) or Hilscher and Nosbusch (2010).

Alternatively, Dieckmann and Plank (2011) analyze the determinants of credit risk and

use the ratio of total debt outstanding to GDP as a measure of a country’s indebtedness.

Our approach, however, departs from these studies: we suggest that the stock market

can be taken as a valid proxy for the evolution of the “equity” of a country.

Corporate (not only financials) and sovereign credit instruments share exposure to

systematic risk. From basic asset pricing analysis, the price of any asset at time t can be

expressed as pt=Et (mt+1xt+1) where mt+1 is the stochastic discount factor (SDF) and xt+1

is the asset payoff at t+1. All risk corrections necessary for the pricing of assets should

be incorporated into a single SDF. The most frequently used SDF corresponds to the

basic consumption-based pricing model, where mt+1 is related to the growth in marginal

utility of consumption, but general linear factor pricing models have become a popular

alternative, where researchers attempt to model the SDF as a (linear) function of a set of

risk factors mt+1=a+b´ft+1. The challenge of these models is to find variables for ft+1 that

are good proxies for consumption risk or for other sources of aggregate marginal utility

growth (aggregate risks).

We use this rationale as a way to connect the sovereign credit market and the stock

market. Roll and Ross (1980), Chen et al. (1986) and Bansal et al (2005) have

documented the relationship of consumption, on broad-based portfolios, to returns,

interest rates, growth in GDP, and other macroeconomic variables, all of which measure

6

in one way or another the risks associated to the state of the economy. As Chen et al.

(1986) state, “any systematic variables that affect the economy’s pricing operator or

that influence dividends would also influence stock market returns”. This pricing

operator - the SDF mt+1 - “also depends on the risk premium; hence, unanticipated

changes in the premium will influence returns” and pricing. Thus, the SDF, which lies

at the core of any pricing model, becomes a key channel for the transmission of credit

risk to the country’s firms. News about the present and future credit quality of a country

will move the risk premium of the companies operating in this country (even though

some of them may not be listed in that country’s stock exchange and henceforth are not

accounted for in country-level data) and thus lead to an effect on stock prices. News

about deteriorating credit quality will raise the risk premium and lower prices. The

private sector will, thus, absorb the sovereign credit risk in what we could call a public-

to-private risk transfer.

Furthermore, as was pointed out in Coronado et al (2011), the credit risk problem can

also be transmitted into prices through xt+1, the asset’s payoff at t+1. If countries devote

increasing parts of their revenues towards external debt, governments will have little

money left over for investment, thus deteriorating future growth. Consumers and firms

become hard-pressed with taxes levied in order to pay the debt, so consumption and

investment drop, leading to declining profits and expected payoffs xt+1. The sovereign

credit risk problem, thus, becomes a generalized market risk. A vicious circle could be

generated since declining firms’ equity could affect solvency and reinforce the problem.

The above arguments work also in the opposite direction, i.e., a worsening situation in

the country’s companies must affect the credit quality of that country –we have

witnessed this risk transfer in the context of the 2008 subprime crisis-, while

improvements in the firms’ financial situation contribute to a better credit quality for the

country overall.

Our analysis in this paper is, therefore, in the spirit of Dieckmann and Plank (2010).

These authors analyzed 18 advanced economies from 2007 to 2010 and found that the

state of a country’s domestic financial system has strong explanatory power of the

behavior of CDS spreads, suggesting a private-to-public risk transfer. Ejsing and Lemke

(2010) document linkages between CDSs of Euro area banks and their governments’

CDSs, thus also suggesting a private-to-public risk transfer. Overall, these results point

at governments absorbing the risks of private sectors during the 2007-2008 crisis. We

7

extend these analyses and suggest the possibility of a two-way risk transfer. We

hypothesize a reverse channel in which a worsening public debt could lead to a lower

overall value of the firms, which could be reflected in both the debt and equity

components. We test this hypothesis in the 2010 sovereign debt crisis scenario but our

focus is on the firms’ equity part, proxied with stock market indexes, and not on

corporate CDSs. Stock markets should reflect more generally the spread of sovereign

risk into the private economy than the markets of liquid corporate CDSs, which are in

many cases very scarce and are mostly limited to the financial sector.

The following sections contain our analysis of the relationships between bond, CDS and

stock markets, although we place some emphasis on the latter two.

3. An exploratory look at the data

We use daily data of the closing prices of the 5-year sovereign CDS spreads, the 5-year

government bond spreads and of stock indexes.5 The benchmark maturity of sovereign

CDSs tends to be five years, though contracts of 10-year maturity are also available. We

use the mid-points between quoted bid and ask points for the 5-year maturity CDS

denominated in USD, which is the standard currency in this market. 6 Our sample

contains data for thirteen European countries: Spain, Portugal, Italy, France, Ireland,

United Kingdom, Greece, Germany, Austrian, Belgium, Netherlands, Finland and

Denmark. Our sample covers a period similar to those in Gündüz and Kaya (2012) and

Fontana and Scheicher (2011), who focus on smaller sets of countries.

Our choice of countries is determined by the need to have a set of risky countries (i.e.

countries with a high CDS premium), in order to document how the relationship

between stocks and CDSs has behaved during and before the 2010 turmoil, and a subset

of safer countries (i.e. countries with low risk premium) to use as a sort of control. The

two subsamples are described in Table 1.

As a proxy for the stock market in each economy we use daily closing prices of the

country’s main stock index: IBEX-35 (Spain), PSI-20 (Portugal), FTSEMIB (Italy),

CAC-40 (France), ISEQ-20 (Ireland), FTSE-100 (United Kingdom), FTSE Athex-20

5 Data come from Bloomberg; supplied by Credit Market Analytics (CMA) Data Vision.

6 According to anecdotal evidence the 5-Year CDS is more liquid and it is more often used as a reference in financial markets. Longstaff et al (2011), Coudert and Gex (2010), Arce et al (2011) use sovereign CDS of the same tenor. Dieckman and Plank (2011), Gündüz and Kaya (2012) and Fontana and Scheicher (2011) use 10 years CDS but their results are robust to using 5 years CDS spreads.

8

(Greece), DAX (Germany), WBI (Austrian), BEL-20 (Belgium), AEX (Netherlands),

HEX (Finland) and KFX (Denmark).

We are especially interested in looking at the stability of the co-movement between

bond, CDS and stock markets. Thus, although our sample coverage starts in January

2008 and ends in December 2011, we break the analysis into three different subperiods:

pre-sovereign debt crisis, 2010, and 2011. Our data sample covers a more recent period

that previous studies, which allow us to analyze the period post-2010, usually identified

with the peak of the turbulence and uncertainty in sovereign credit risk.

Table 1.a shows descriptive statistics in basis points for each country’ CDS, bond and

stock index. There is a wide dispersion within the sample: for the period 2010, the

lowest CDS average spread was 29.43 basis points (bp) for Finland and the highest one

was 690.25 bp for Greece. For the same period, the lowest average bond spread was

1.90 bp and 1.96 bp for Finland and Denmark, respectively, whereas the highest spread

was 9.44 bp for Greece.

Figures 1 and 2 show the co-movement during the sample subperiods of the CDS

premiums and the stock indexes for two countries that we take as an example, namely

Germany and Greece. The negative correlation between the two markets is apparent:

spreads on the CDS widen when deterioration in credit risk is perceived by the market.

As the CDS premiums widen, the stock indexes fall (market risk also increases).

[insert Figure 1 about here]

[insert Figure 2 about here]

Table 2 reports the Spearman pairwise rank correlation between the stock index, the

sovereign CDS and the bond spreads of daily time-series at a country-level. We look at

levels as well as percentage changes. Stock indexes and sovereign CDSs are negatively

correlated, consistent with the pattern observed in the previous figures. Despite this

result, we observe that the lower Spearman coefficient takes place in 2010 for all the

countries except for Spain, Italy, Ireland and Greece.

[insert Table 2 about here]

Finally, Table 3 shows the Spearman pairwise rank correlation between each pair of

sovereign CDS spreads. In line with Longstaff et al (2011), we find that many of the

pairwise correlations of sovereign credit spreads are large, positive and increase during

crisis periods. For example, the correlation between Italy and Belgium is 0.88 and the

9

correlation between Italy and Spain is 0.85 for 2011. The average correlation is about

0.51 for the 2007-2009 sample period and 0.61 in 2010 and 0.66 in 2011.


4. Relationship between Sovereign CDS, Sovereign Bonds and Stock Markets

4.1 Country-by-country analysis

The prices (levels) of these three markets cannot obey an equilibrium relationship

(cannot be cointegrated), so a Vector Error Correction Model (VECM) representation is

not appropriate and a price discovery process similar to those in, e.g., Forte and Peña

(2009) is not applicable.7 Instead, we use a simple VAR framework to study the co-

movement between the three markets in each country. We estimate the following three

dimensional VAR:

∆ ∆

∆ ∑ ∑ ∆ ∑ ∆ [1]

∆ ∆ ∆

where Rt is the stock index return in period t, ∆CDSt is the sovereign CDS spread

change in period t and ∆Bt is the sovereign bond yield change in period t. The lag order

of the VAR is p and εt is the innovation in period t. The lag structure and the maximum

lag order p have been determined using the AIC and SIC criteria. Besides, we

implement a Lagrange-Multiplier in order to test that there is no autocorrelation in the

residuals at the lag order selected.

7 We do not examine the price discovery process between CDSs and bonds in sovereign credit markets. This topic has already been addressed by Arce at al (2011) and Fontana and Scheicher (2010) among others. Our findings support and corroborate theirs, although from a different perspective.

10

VAR models have already been used in similar credit risk analyses, by, among others,

Longstaff (2010), Longstaff et al (2005), Blanco et al (2005) and Norden and Weber

(2009).8

Results for all thirteen countries are shown in Table 4. For the sake of brevity, we report

only the results for the first two lags. As said before, we split the sample into non-

overlapping subperiods in order to generate a more detailed picture of the relationship

between sovereign CDSs and Stock Indexes.


We observe that during 2008 stock markets took a leading role with respect to the bond

and CDS markets in most countries (Spain, Greece, Germany, Portugal, France,

Netherlands, Italy, Denmark and Belgium). There is little evidence that CDSs took a

leading role, although some evidence can be found for Italy, where CDSs led the stock

market, and the UK, where CDSs led the bond market. Overall, the bond market does

not seem to play an important role (Spain or Finland being the exception).

During 2009 the leading role of the stock market, with respect to the CDS market, is

confirmed, again, for most countries (Spain, Greece, Germany, Portugal, France,

Netherlands, Italy, Belgium, UK, Austria and Finland). The bond market seems to

provide, again, no relevant information (with the only exception of Denmark, where it

plays a leading role with respect to the CDS market).

The leading role of the stock market disappears during 2010. During this year sovereign

debt attracted all the attention and relegated the stock market to a secondary role. As the

results in Table 4 suggest, it was, however, the CDS markets that took on the leading

role with respect to both stock and bond markets: the CDS markets led bond and equity

markets in most countries during 2010 (Spain, Greece, Germany, Portugal, Italy,

Denmark and Finland).9 Note the negative sign relating the CDSs and the stock market

and the fact that the magnitude of the coefficients associated with CDSs also grows

noticeably during 2010.

8 Other examples in different areas are Engsted and Tangaard (2004), who study the co-movement of US and UK stock markets, or Gwilym and Mike (2001), who focus on the lead-lag relationship between the FTSE100 stock index and its derivative contracts. 9 We find a country, France, where bidirectional causality between the bond and the stock market appears and three countries with no obvious causal relationship (Netherlands, UK and Austria).

11

To sum up, stock markets were leading in incorporating new information during 2008

and 2009, but during 2010 the CDS market seemed to take over this role. As in Blanco

et al (2009), Forte and Peña (2009) bond markets seem to be lagging.

In relation to the link between CDS and bond markets, and following the discussion of

Blanco et al (2005), we suggest that CDS markets benefit from being the easiest venue

in which to trade credit risk. They do not suffer from short-sales constraints and trading

large quantities of credit risk is possible. Besides, participants hedging loan and

counterparty exposures are able to do so in the CDS market, whereas they cannot do so

in the bond market. Along this line, a report by ISDA (2010) points to this role of

sovereign CDS markets. 10 Sovereign CDSs provide effective hedges not only for

holders of government bonds but also for international banks that extend credit to that

particular country’s corporations and banks, for investors in stocks and for entities that

have significant real estate or corporate holdings in the country. For many of these

participants, the sovereign CDS acts as proxy hedge for credit risk in the country.11

Implicit in this reasoning is the fact that there is a broad set of investors using sovereign

CDSs, and it is this concentration of liquidity which gives CDS markets the lead over

bond markets.

However, the previous picture turned around during 2011. As the seriousness of the

situation in peripheral Europe, especially in Greece, was becoming obvious, sovereign

bonds regained attention –alternative approaches to hedging cash bonds are shorting

government bonds or buying protection on sovereign CDX indices-. In 2011 lead-lag

relationships lowered in intensity and we do not find much evidence of Granger

causality. The movement in the three markets becomes more synchronized than in

previous years and in many cases the information appears to be embedded into prices at

the same time.

We can observe a comeback of the leading role for the stock markets with respect to the

bond market in 6 countries (Spain, Italy, Greece, Ireland, UK and Denmark), and in the

case of Finland the stock market leads both the CDS and the bond market. We also

10 International Swaps and Derivatives Association, News Release, March 15, 2010. 11 Liquidity in the Sovereign CDS market altogether rose during 2009, 2010, 2011 and it is rising at the beginning of 2012. During the first half of 2011 the single-name sovereign CDS market, accounting for 8% of total CDS notional amounts, increased by 8%, after a 6% rise in the previous half-year, but representing less than 23% of the gain during the first half of 2010 (BIS, 2011).

12

observe bidirectional Granger causality between bonds and stocks in 3 countries, France,

Austria and Netherlands. In all, we find a role for the stock markets in 10 countries.

The CDS remains to be the principal road for incorporating information in the Italian

and Portugal cases, where it leads both the sovereign bond and stock market, and in the

Irish case, where it leads the bond market. Nevertheless, we find in 2011 an important

role for the bonds with respect to the CDS in four cases, namely Greece, France, UK

and Finland, a result we did not find in any of the previous years. These results are

consistent with a drying up of liquidity in the Greek CDS market during 2011.

4.2. Pooling of countries with comparable risks

To provide a more complete insight into the relationship of the sovereign CDS and

Stock Indexes we perform a complementary analysis. We estimate pooled VARs with

the following structure:

∆ ∆

∆ ∑ ∑ ∆ ∑ ∆ [2]

∆ ∆ ∆

where Rit is the stock index return of country i at time t, ∆CDSit is the change in the

sovereign CDS spread of country i at time t, ∆Bit is the change in the sovereign bond

yield for country i at time t, p is the lag order and εit is the innovation in country i at

time t. We pool the countries in our data dividing them into two groups -see Table 1.b-:

countries with CDS premiums above 100 b.p. during 2010 (Greece, Ireland, Portugal,

Spain, Italy and Belgium) and countries with risk premium below 100 b.p. (Austria,

France, the Netherlands, Germany and Finland). Given that our panels are “large-T”, we

use traditional time series methodologies to estimate the panels. In particular, we

perform a GLS estimation of the panel allowing for the error terms to be autocorrelated

across time, with panel specific autocorrelation.12

[insert Table 5 here]

12 We cannot allow the panels to be correlated contemporaneously given that our panels are unbalanced. The use of smaller samples where the panels can be balanced leads to comparable results, though, and the standard errors of the estimates are only marginally affected.

13

Panel data results confirm our previous findings. For countries with high risk premiums,

the stock market leads the other two markets during 2008 and 2009. There appears to be

a bi-directional Granger causality between the bond and the stock markets and between

the bond and CDS markets. As before, we find that this pattern changes during 2010

when the sovereign debt crisis comes into play. Bidirectional Granger causality occurs

between the CDS and the stock markets during 2010 and it is also present between the

bond and the stock market, but not between the CDS and bond markets (CDS markets

lead the bond market).

In 2010 the countries with low risk exhibit the CDSs leading the stock market and

displaying bidirectional Granger causality in relation with the bond market. In this

sample we find that the debt markets overall attracted the attention, thus outshining the

stock market. However, during 2008 the stock market led the bond market and during

2009, it lead the CDS market, so before the sovereign CDS crisis exploded, the stock

market was the main venue for incorporating new information, as suggested by the

results in section 4.1. This is consistent with a private-to-public risk transfer -using the

terminology in Dieckmann and Plank (2011) – during the period 2008-2009 and a

reversal (i.e. a public-to-private transfer) during 2010.

Again, the evidence from the pooling of countries suggests the prominent role of the

stock markets during 2008 and 2009, a role which was overshadowed by the CDS

markets during 2010.

The results for 2011 are revealing: we observe, for the six countries with more

sovereign credit risk, that the leading role of sovereign CDS remains (despite of the

Greek situation) in relation to both the stock and the bond markets –there appears a

feedback process with the bond market, consistent with the revival of this market in

2011-. On the other hand, the picture for safer countries is quite different and the bond

market leads the other two markets, displaying a strong bidirectional GC with the stock

market. No role for sovereign CDSs is evident anymore. Fontana and Scheicher (2010)

and Arce et al (2011) also find this change in the relationship between sovereign bonds

and CDSs although for samples that end in 2010, and October 2011 respectively.

We have not included the results obtained using data from the UK or Denmark, given

that these countries have a different currency. We did perform the analysis including

these two countries in the panel with lower credit risk and the results did not change at

14

all, suggesting that there is no significant currency risk effect in the relationship

between the three assets.13

We find that the role of sovereign CDSs with respect to the other two markets is state

dependent, i.e., the level of sovereign credit risk affects the importance of sovereign

CDSs. Similar results for corporate CDSs have been reported in the literature by Norden

and Weber (2009) and Fung et al (2008).

5. Price discovery among different European Sovereign CDS markets

One of the key functions of financial markets is price discovery, i.e, the efficient and

timely incorporation of the information implicit in investor trading into market prices.

(Lehman, 2002). Previous studies show evidence of major commonalities between

sovereign CDSs (Lonsgtaff et al (2011), Fontana and Scheicher (2010), Gündüz and

Kaya (2012) or Dieckmann and Plank (2010)), so we now explore possible price

discovery relationships between the CDSs of the different countries.14

Indeed, we find that during 2007-2009 the countries’ CDS price series were

cointegrated I(1) variables, a feature that also appears in 2011 for all the CDS, except

for those of Greece, Portugal and Ireland. Thus, CDSs seem to obey a long-run

equilibrium relationship, so we can apply price discovery methodologies in order to

assess if a specific country’s CDS was more important for the discovery of credit risk

during our period of study. During 2010 this equilibrium relationships among the

different CDSs disappear, so we limit ourselves to performing a reduced-form VAR

analysis to test the leading role of the changes in the CDS spreads.

Price discovery analysis has been applied in similar setups by Fontana and Scheicher

(2010), Blanco et al (2005), Mayordomo et al (2010), Chu et al (1999), and more

generally, in by Baillie et al (2002), De Jong (2002) and Yan and Zivot (2010). Our

methodology is based in the statistics developed by Gonzalo and Granger (1995).15 In

this model, two markets are assumed to be cointegrated, so they share a common trend

13 Estimations are available upon request.

14 This methodology cannot be applied to the different asset markets (stocks, bonds and CDS) in each country because the levels of the series are not cointegrated variables, that is, there is no long-run relationship(s) that links together the prices (levels) of the three assets.

15 Another popular methodology to investigate the dynamics of price discovery is due to Hasbrouck (1995), also based on vector error correction models of market prices.

15

(factor). The contribution of each market to the common factor is defined to be a

function of the error correction coefficient in the VECM that links the movements in the

markets. The common factor is, thus, decomposed and superior price discovery is

attributed to the market that adjusts the least to price movements in the other market.

Limiting our analysis to pairs of countries, we first test for cointegration using the

uniequation test by Phillips and Ouliaris (1990), where only one cointegration vector is

tested for (this is the maximum possible number of cointegration vectors, so a

multivariate test in the Johansen framework is not necessary). The existence of

cointegration means that at least the level of one market adjusts to an equilibrium

relationship. Then, in the cases where we can reject the null hypothesis of no-

cointegration we estimate, the following VECM:

∆ 1 1 2 1

∆ 2

∆ 2 1 2 1

∆ 2

[3]

With ∆CDSctry1(2)t being the change in the sovereign CDS spread of country 1(2) at

time t, CDSctry1(2)t is the sovereign CDS spread of country 1(2) at time t, p is the lag

order index and ε1(2)t are i.i.d. shocks.

The α coefficients determine each market’s contribution to the price discovery and

reveal which of the two CDSs leads the credit price discovery. If the CDS of country 2

is contributing significantly to price discovery, α1 will be negative and statistically

significant, as the CDS of market 1 adjusts to incorporate this information. If the CDS

of country 1 leads the price discovery, then α2 will be positive and statistically

significant. If both αi are significant, then both countries contribute to price discovery.

The relative importance of both countries in the price discovery is then defined from:

16

; [4]

Since GG1+GG2=1, we interpret that country i’s CDS leads the process of price

discovery if GGi is greater than 0.5. Additionally, [4] implies that price discovery

occurs entirely in market i if αi=0.

Results of the price discovery among countries in the euro area are displayed in Table 6.

For those countries where we find cointegrating relationships during the 2007-2009

period, the evidence points to a clear leadership role of the Spanish CDS and,

secondarily, of those of Fraance and Italy. The German CDS does not play a role in the

price discovery in relation to the PIIGS countries or to the other countries for which we

find cointegration (France, Austria, Belgium). Besides, it is noteworthy that we do not

find evidence of cointegration for the CDSs of Austria, Finland or Netherlands in the

euro area, or UK and Denmark, outside the euro area.

[insert table 6 about here]

During 2010, possibly due to the Sovereign Debt crisis, the cointegration relationships

disappear and CDSs seem not to obey to equilibrium relationships anymore (or,

alternatively, risk premiums become non-stationary, a fact that is also reported by

Dieckmann and Plank (2011)). To be able to infer which country, if any, played a

leading role we apply a reduced-form VAR in the changes in the spreads.

Dividing countries into groups of three we estimate the following VAR model:

∆ 1 ∆ 1 ∆ 2

∆ 3

∆ 2 ∆ 1 ∆ 2

∆ 3

17

∆ 3 ∆ 1 ∆ 2

∆ 3

[5]

Where ∆CDSctryit is the change in country i’ sovereign CDS spread at time t.

[insert table 7 about here]

In this case, (see table 7), the German CDS becomes the main player, leading the

incorporation of information in relation to Portugal, Italy, Greece, Spain, Belgium,

France, UK, Finland, Netherlands. We find bidirectional Granger causality with respect

to Denmark and no leadership in the case of Ireland.

After the German CDS, the Spanish one appears as the next most important participant,

before Italy or Greece. If we take a look at the average daily CDS volumes negotiated

during 2010, we can observe how both countries were in the top ten list. It is

noteworthy how the trading volume of the Spanish CDS increased during the year –

finishing 2010 with an average daily notional trade of $924,066,884, the most liquid out

of the 1000 references that DTCC publishes-, while the German CDS remained in the

seventh position, finishing the year with a daily average notional volume of

$288,624,653.16

Trade in the Greek CDS dropped during 2010 and 2011 as the inability of Greece to

face the due payments of the sovereign debt and the needs to reschedule and restructure

were becoming clear.17 The Greek CDS ranked 5th in trading volume by March 2010 –

with an average daily notional of $450,000,000-, while by December 2010 it ranked 16th

- with an average daily notional of $206,761,780- and by September 2011 it was in the

40th position – with an average daily notional of $150,000,000-. This highlights that CDS

instruments are useful while there is still uncertainty about the future credit condition of

16 These quantities have increased during 2011, more than doubling in the case of Germany. During this year, the Spanish CDS follows the Italian CDS, which is at the top of the most negotiated CDSs.

17 And continued to fall during 2012, by February 2012 it is near the 500th position.

18

the reference entity. Once the creditworthiness has been clarified (the underlying entity

becomes either bankrupt or reliable) the reference CDS stops attracting attention.

During 2011 many sovereign CDS markets returned to a cointegrated path, so we

perform a price discovery analysis (Table 6). Greece, Portugal and Ireland, the countries

with worst credit situation, do not show evidence of cointegration, which sets this group

apart from the other countries. Italy emerges now as the price discovery leader,

followed by France, a result which is consistent with market data: Italy is the European

country with higher amount of public debt issued and its credit situation has been

worsening during 2011. Its CDS was at the top of the 1000 more liquid CDSs reported

by DTCC, with a daily average notional traded of $ 1,550,000,000 as of June 2011. It

was followed by the French CDS. Spain and Germany do not play a significant role

anymore.18

6. Conclusions

We believe we contribute in three main ways. First, we justify the link between

sovereign debt markets and stock markets, an area where limited work has been done so

far. While at a firm level, finance theory suggests that an efficient stock market should

incorporate information on the default probability of firms in a timely manner, we

usually do not attempt to measure a “country’s equity”. We argue, however, that the

country’s firms proxy for this equity and, thus, the country’s credit information should

flow to stock markets (and viceversa), so a worsening credit situation of the companies

should be reflected in the country’s credit quality.

Second, we study the lead-lag relationships in the process of incorporation of

information into the credit and stock markets. The main role of CDSs in relation to

bonds when incorporating credit risk information has been well documented for

corporations, but it is relatively new in the context of sovereign credit risk. We use data

from a sample of 13 European countries during 2007-2011. Our findings are in line with

those of Arce et al (2011), Coudert and Gex (2010) and Fontana and Scheicher (2011).

A country-by-country analysis shows that debt markets lagged the stock markets during

2007-2009, while credit markets, CDS markets to be precise, led the incorporation of

new information during 2010. During 2011, stock markets recover the leading role in 18 We also performed a VAR analysis with 2011 data in order to include every country in the sample, and the results are robust, Italy is the main leader. Results are available upon request.

19

many economies and the sovereign bond market gains in importance, possibly due to a

more limited usefulness of European sovereign CDS as a hedge -once it is expected that

the Greek situation will not trigger a sovereign credit event. Additional results from

pooling countries into two groups -according to their risk levels- confirm these findings

and the leading role of CDSs during 2010. This leading role of CDSs is, in fact, state

dependent: sovereign CDSs play a stronger role in the economies with higher perceived

credit risk. Overall, our results are consistent with a private-to-public risk transfer

during the Lehman period, and a public-to-private risk transfer during the European

sovereign credit crisis.

Third, we analyze the relationship between the different European CDSs. During 2007-

2009, we find an equilibrium relationship between the CDSs of several countries.

Interestingly, the Spanish CDS turns out to be the market where price discovery takes

place and where informed traders transact most (a result which at first may seem

surprising, but which is aligned with the large observed trade volume in Spanish CDSs).

During 2010, the different CDS markets lost their equilibrium relationship –probably

because of the behavior of risk premiums- but we still find that movements in the

German CDS Granger-cause the movements in the rest of European CDSs, thus

confirming a sort of German price leadership. In 2011, CDS respond to equilibrium

relationships again, but during that period Italy becomes the country in which

information is embedded first, a finding which is also coherent with the behavior of

trading volume. Some countries, such as Greece, Portugal and Ireland –all of them

distressed economies- are not included in the 2011 price discovery analysis because of

the non-stationarity of their credit swap spreads.

20

7. References

Arce, O., S. Mayordomo and J.I. Peña, (2012) “Credit-Risk Valuation in the Sovereign CDS and Bonds Markets: Evidence from the Euro Area Crisis” Available at SSRN: http://ssrn.com/abstract=1896297

Baillie, R.T., G.G. Booth and Y. Tse (2002) “Price Discovery and Common Factor Models” Journal of Financial Markets 5, pp.309-321.

Bansal, R., R. F. Dittman and C. T. Lundblad (2005) “Consumption, Dividends, and the Cross Section of Equity Returns” The Journal of Finance, 60 (4), pp. 1639-1672.

Blanco, R., S. Brenan. and I.W. Marsh (2005) “An Empirical Analysis of the Dynamic Relationship Between Investment Grade Bonds and Credit Default Swaps” The Journal of Finance, 60, pp.2255-2281.

Boehmer E. and W.L. Megginson (1990) “Determinants of Secondary Market Prices for Developing Country Sindicated Loans” Journal of Finance, 45, pp. 1517-1540.

Bystrom, H. (2005) “Credit Default Swaps and Equity Prices: The iTraxx CDS Index Market” Working Paper, Department of Economics, Lund University.

Chen, N.F, R. Roll and S.A. Ross (1986) “Economic Forces and the Stock Market” Journal of Business, 59, pp. 383-403.

Chu, Q.C., W.G. Hsieh, and Y. Tse (1999) “Price Discovery on the S&P 500 Index Markets: an Analysis of Spot Index, Index Futures and SPDRs” International Review of Financial Analysis, 8, pp.21-34.

Cochrane J.H., 2001, Asset Pricing. Princeton University Press, Princeton, New Jersey.

Coronado, M., T. Corzo and L. Lazcano (2011) “A case for Europe: the Relationship between Sovereign CDS and Stock Indexes”, available at SSRN: http://ssrn.com/abstract=1889121.

Coudert, V. and M. Gex (2010) “Credit Default Swaps and Bond Markets: which leads the other?” Financial Stability Review, 14, Banque de France.

De Jong, F. (2002) “Measures of Contributions to Price Discovery: a Comparison” Journal of Financial Markets 5, pp.323-327.

Dieckmann, S. and T. Plank (2011) “Default Risk of Advanced Economies: An Empirical Analysis of Credit Default Swaps during the Financial Crisis” Review of Finance, 15 (3), 1-32.

Duffie D., L.H. Pedersen and K.J. Singleton (2003) “Modeling Sovereign Yield Spreads: A Case Study of Russian Debt” Journal of Finance 58, pp.119-159.

21

Ejsing, J. and W. Lemke (2010) “The Janus-headed Salvation: Sovereign and Bank Credit Risk Premia during 2008-2009” Working Paper Series 1127, European Central Bank.

Fontana, A. and M. Scheicher (2010) “An Analysis of Euro Area Sovereign CDS and their Relation with Government Bonds” Working Paper Series 1271, European Central Bank.

Forte, S. and J.I. Peña (2009) “Credit Spreads: An Empirical Analysis on the informational Content of Stocks, Bonds and CDS” Journal of Banking and Finance, 33, pp. 2013-2025.

Fung, H.G., G.E. Sierra, J. Yau and G. Zhang (2008) “Are the US Stock Market and Credit Default Swap Market Related? Evidence from the CDX Indices” The Journal of Alternative Investments, pp.43-61.

Ganpen, M., D.F. Gray, C.H. Lim and Y. Xiao (2005) “Measuring and Analyzing Sovereign Risk with Contingent Claims” IMF Working paper 2005/155.

Gündüz, Y. and O., Kaya (2012) “Sovereign Default Swap Market Efficiency and Country Risk in the Euro Area”, Available at SSRN: http://ssrn.com/abstract=1969131.

Hasbrouck, J. (1995) “One Security, Many Markets: Determining the Contributions to Price Discovery” The Journal of Finance, 50, pp. 1175-1199.

Hilscher, J. and Y. Nosbusch (2010) “Determinants of Sovereign Risk: Macroeconomics Fundamentals and the Pricing of Sovereign Debt” Review of Finance, 14, pp. 235-262.

Lehmann, B.N. (2002) “Some Desiderata for the Measurement of Price Discovery across Markets” Journal of Financial Markets 5, pp. 259-276.

Longstaff, F.A. (2010) “The Subprime Credit Crisis and Contagion in Financial Markets” Journal of Financial Economics, 97, pp.436-450.

Lonstaff, F.A., J. Pan, L.H. Pedersen and K.J. Singleton (2011) “How Sovereign is Sovereign Credit Risk?” American Economic Journal: Macroeconomics, 3, 75-103.

Mayordomo, S., J.I. Peña and E.S. Schwartz (2010) “Are all Credit Default Swaps Databases Equal?” Documento de trabajo nº 44, CNMV, Madrid.

Mayordomo S., Peña, J. I., and Romo J. (2011), “The Effect of Liquidity on the Price Discovery Process in Credit Derivatives Markets in Times of Financial Distress” European Journal of Finance, 17, pp. 851 – 881.

Merton, R. (1974) “On the Princing of Corporate Debt: the Risk Structure of Interest Rates” The Journal of Finance, 29, pp. 449-470.

22

Norden, L. and M. Weber (2009) “The Co-movement of Credit Default Swap, Bond and Stock Markets: an Empirical Analysis” European Financial Management, 15 (3), pp 529-562.

Phillips P.C.B. and S. Ouliaris (1990) “Asymptotic Properties of Residual based Tests for Cointegration” Econometrica, 58, pp. 165-193.

Reinhart, C.M. and K.S. Rogoff (2010) “Growth in a Time of Debt” American Economic Review, 100, pp. 573-578.

Roll R. and S.A. Ross (1980) “An Empirical Investigation of the Arbitrage Pricing Theory” The Journal of Finance, 35(5), pp. 1073-1103.

Yan, B. and E. Zivot (2010) “A Structural Analysis of Price Discovery Measures” Journal of Financial Markets, 13 (1), pp.1-19.

Zhu, H. (2006) “An Empirical Comparison of Credit Spreads between the Bond Market and the Credit Default Swap Market” Journal of Financial Services Research, 29, pp 211-235.

Zhang, F.X. (2008) “Market Expectations and Default Risk Premium in Credit Default Swap Prices: A Study of Argentine Default” Journal of Fixed Income 18 (1), pp.37-55.

Figur

Figur

re 1: Daily ti

re 2: Daily ti

me series fro

me series fro

om Germany

om Greece S

y Sovereign C

overeign CD

CDS spread v

DS spread vs

vs. DAX retu

. ASE return

DAX

Germany C

ASE

Greece CDS

urn.

n.

CDS

S

23

24

Table 1.a: Descriptive Statistics

We report the main descriptive statistics for the thirteen countries in our sample. Data included are only observations from 2010. Variables included are: CDS (sovereign CDS premium), ∆CDS (sovereign CDS premium daily changes), stock index, stock index daily returns, bond price and bond price daily changes.

SPAIN AUSTRIACDS ∆CDS IBEX ∆IBEX Bond ∆Bond CDS ∆CDS WBI ∆WBI Bond ∆Bond

N 256,00 256,00 256,00 256,00 256,00 256,00 N 256,00 256,00 248,00 240,00 256,00 256,00Mean 203,91 0,44% 10.421,52 -0,07% 3,29 0,19% Mean 77,55 0,07% 955,25 0,12% 2,19 -0,01%Median 211,67 0,38% 10.431,50 0,00% 3,05 0,22% Median 79,56 0,21% 944,78 0,18% 2,11 -0,15%Maximum 365,35 25,17% 12.222,50 13,48% 4,91 9,66% Maximum 113,17 0,33% 1.112,08 7,76% 2,94 16,11%Minimum 93,81 -37,04% 8.669,80 -6,87% 2,64 -23,06% Minimum 47,72 -13,65% 846,37 -3,88% 1,63 -5,32%Kurtosis -0,56 5,77 0,17 11,03 1,06 21,47 Kurtosis -0,04 12,70 -0,19 5,17 -1,06 8,20Skewness 0,21 -0,66 0,11 1,05 1,43 -2,19 Skewness -0,36 1,47 0,50 0,49 0,28 1,42

GERMANY BELGIUMCDS ∆CDS DAX ∆DAX Bond ∆Bond CDS ∆CDS BEL20 ∆BEL20 Bond ∆Bond

N 256,00 256,00 256,00 256,00 256,00 256,00 N 256,00 256,00 256,00 256,00 256,00 256,00Mean 39,94 0,30% 6.190,22 0,06% 1,78 -0,11% Mean 108,76 0,56% 2.555,69 0,01% 2,56 0,05%Median 40,46 0,26% 6.151,46 0,10% 1,70 -0,07% Median 113,76 0,49% 2.565,45 0,00% 2,57 -0,10%Maximum 57,97 13,73% 7.077,99 5,16% 2,42 8,16% Maximum 225,06 15,70% 2.716,70 6,15% 3,28 12,49%Minimum 25,18 -11,41% 5.434,34 -3,39% 1,20 -8,22% Minimum 46,35 -11,53% 2.329,60 -3,52% 1,90 -11,19%Kurtosis 0,00 1,67 -0,14 1,69 -1,26 0,50 Kurtosis 0,30 1,95 -0,69 2,88 0,28 6,54Skewness 0,26 0,12 0,57 -0,02 0,25 -0,14 Skewness 0,68 0,26 -0,29 0,21 0,26 0,50

FRANCE NETHERLANDSCDS ∆CDS CAC ∆CAC Bond ∆Bond CDS ∆CDS AEX ∆AEX Bond ∆Bond

N 256,00 256,00 256,00 256,00 256,00 256,00 N 256,00 256,00 256,00 256,00 256,00 256,00Mean 69,83 0,44% 3.745,93 -0,01% 2,02 -0,06% Mean 43,66 0,27% 334,59 0,02% 1,89 -0,10%Median 71,25 0,22% 3.756,17 0,00% 1,98 -0,06% Median 45,01 0,31% 335,88 0,04% 1,80 -0,14%Maximum 113,62 16,46% 4.065,65 9,22% 2,49 11,49% Maximum 61,37 14,92% 358,32 7,07% 2,46 7,41%Minimum 30,32 -15,39% 3.331,29 -4,71% 1,54 -6,42% Minimum 28,97 -22,20% 305,03 -4,34% 1,31 -6,81%Kurtosis -0,32 0,60 -0,64 6,03 -1,29 2,65 Kurtosis -0,23 6,60 -0,50 4,10 -1,27 0,26Skewness -0,07 0,09 -0,24 0,49 0,08 0,52 Skewness 0,10 -0,42 -0,17 0,32 0,21 0,02

UNITED KINGDOM IRELANDCDS ∆CDS FTSE100 ∆FTSE100 Bond ∆Bond CDS ∆CDS ISEQ ∆ISEQ Bond ∆Bond

N 256,00 256,00 251,00 247,00 256,00 256,00 N 256,00 256,00 252,00 249,00 11,00 11,00Mean 73,82 -0,03% 5.463,99 0,05% 2,25 -0,09% Mean 295,22 0,53% 2.925,35 0,01% 3,20 -0,01%Median 76,45 -0,02% 5.498,71 0,09% 2,21 -0,10% Median 250,95 0,42% 2.900,89 0,03% 3,19 -0,59%Maximum 94,74 14,76% 5.996,36 5,03% 2,99 9,72% Maximum 610,77 26,81% 3.497,17 7,57% 3,26 2,46%Minimum 53,84 -14,43% 4.805,75 -3,20% 1,45 -7,19% Minimum 110,53 -32,69% 2.620,12 -5,95% 3,11 -2,20%Kurtosis -0,69 3,34 -0,90 2,04 -1,32 1,93 Kurtosis -0,87 9,66 -0,18 3,00 -1,03 -1,12Skewness -0,42 0,22 -0,15 0,07 0,00 0,59 Skewness 0,69 -0,26 0,61 -0,06 -0,30 0,41

ITALY GREECECDS ∆CDS FTSEMIB ∆FTSEMIB Bond ∆Bond CDS ∆CDS ASE ∆ASE Bond ∆Bond

N 256,00 256,00 256,00 256,00 256,00 256,00 N 256,00 256,00 251,00 246,00 256,00 256,00Mean 165,55 0,31% 21.061,08 -0,06% 2,90 0,13% Mean 690,25 0,51% 1.701,46 -0,17% 9,44 0,39%Median 172,30 0,16% 20.831,51 0,00% 2,81 0,02% Median 762,32 0,19% 1.595,21 -0,16% 10,27 0,25%Maximum 269,00 19,71% 23.811,13 10,68% 3,87 10,89% Maximum 1.072,14 23,07% 2.327,57 8,74% 14,59 14,50%Minimum 89,74 -43,73% 18.382,71 -5,40% 2,56 -17,16% Minimum 247,01 -49,15% 1.403,92 -6,92% 4,52 -63,80%Kurtosis -0,99 13,53 -0,38 7,80 1,80 16,25 Kurtosis -1,22 25,00 -0,70 1,31 -1,28 107,01Skewness -0,08 -1,43 0,42 0,71 1,56 -1,19 Skewness -0,50 -2,26 0,79 0,16 -0,31 -8,08

PORTUGAL FINLANDCDS ∆CDS PSI20 ∆PSI20 Bond ∆Bond CDS ∆CDS HEX ∆HEX Bond ∆Bond

N 256,00 256,00 256,00 256,00 256,00 256,00 N 256,00 256,00 252,00 248,00 256,00 256,00Mean 289,79 0,68% 7.634,27 -0,04% 4,26 0,24% Mean 29,43 0,05% 6.894,43 0,08% 1,90 -0,10%Median 295,16 0,51% 7.575,53 0,02% 4,17 0,39% Median 29,34 -0,06% 6.808,51 0,13% 1,86 -0,06%Maximum 543,20 20,54% 8.839,75 10,20% 6,16 11,71% Maximum 37,04 10,34% 7.698,99 7,13% 2,49 17,82%Minimum 81,44 -47,38% 6.624,29 -5,51% 3,01 -45,42% Minimum 21,55 -12,43% 6.134,78 -4,39% 1,41 -6,74%Kurtosis -0,96 11,87 0,04 9,80 -0,85 46,84 Kurtosis -0,20 1,78 -1,11 4,41 -0,89 9,04Skewness 0,03 -1,27 0,35 0,83 0,41 -4,50 Skewness -0,22 0,05 0,15 0,20 0,30 1,26

DENMARKCDS ∆CDS KFX ∆KFX Bond ∆Bond

N 256,00 256,00 251,00 247,00 64,00 63,00Mean 36,96 0,15% 401,13 0,13% 1,96 0,28%Median 37,33 0,13% 406,98 0,14% 1,95 0,44%Maximum 48,02 16,24% 460,10 6,17% 2,31 5,23%Minimum 28,77 -9,70% 345,14 -4,23% 1,64 -4,79%Kurtosis -0,97 4,00 -0,60 2,47 -0,99 -0,53Skewness 0,13 0,66 -0,27 0,32 0,04 0,02

2010

2010

2010

2010

2010

2010

2010

2010

2010

2010

2010

2010

2010

25

Table 1b: Average CDS premium

We report CDS average premiums for each country in 2010. This justifies our pooling of the countries into two groups in the panel analysis: European countries with lower spreads (CDS average premium below 100 bp) and European countries with higher spreads (CDS average premium above 100 bp).

CountryGreece 690,25Ireland 295,22Portugal 289,79Spain 203,91Italy 165,55Belgium 108,76Austria 77,55United Kingdom 73,82France 69,83Netherlands 43,66Germany 39,94Denmark 36,96Finland 29,43

CDS Average 2010

26

Table 2: Contemporaneous correlation of country specific time-series

We report Spearman’s rank correlation coefficients ρ, calculated for pairs of country-specific time series (years 2008, 2009, 2010 and 2011; R: return on the stock index; ∆B and ∆CDS: changes in the bond price and CDS spread, respectively). ** denotes significant at a 5% level

ρs (R,CDS) ρs (∆R,∆CDS) ρs (R,B) ρs (∆R,∆B) ρs (CDS,B) ρs (∆CDS,∆B)Spain 2007-2008 -0,787** -0,163** 0,265** 0,450** -0,260** -0,127**

2009 -0,632** -0,350** -0,777** 0,273** 0,615** -0,0832010 -0,709** -0,493** -0,645** -0,236** 0,870** 0,483**2011 -0,905** -0,607** -0,216** -0,431** 0,387** 0,649**

Italy 2007-2008 -0,945** -0,191** 0,095* 0,360** -0,283** -0,092*2009 -0,799** -0,418** -0,826** 0,097 0,746** 0,0592010 -0,802** -0,482** -0,303** -0,182** 0,373** 0,440**2011 -0,856** -0,613** -0,816** -0,456** 0,848** 0,628**

Portugal 2007-2008 -0,817** -0,147** 0,245** 0,404** -0,318** -0,099*2009 -0,784** -0,309** -0,858** 0,176** 0,793** 0,0332010 -0,289** -0,512** -0,255** -0,294** 0,925** 0,468**2011 -0,903** -0,516** -0,801** -0,176** 0,873** 0,493**

Ireland 2007-2008 -0,498** -0,052 0,036 0,391** -0,001 -0,0722009 -0,632** -0,266** -0,860** 0,143* 0,714** 0,180**2010 -0,787** -0,285** 0,473 0,191 0,309 0,22011 -0,583** -0,375** 0,450** -0,106 0,141* 0,431**

Greece 2007-2008 -0,765** -0,161** -0,292** 0,306** 0,147** -0,099*2009 -0,712** -0,434** -0,880** -0,209** 0,876** 0,409**2010 -0,854** -0,463** -0,764** -0,400** 0,947** 0,678**2011 -0,956** -0,340** -0,960** -0,269** 0,971** 0,453**

Germany 2007-2008 -0,665** -0,128** 0,527** 0,478** -0,433** -0,0552009 -0,894** -0,229** 0,157* 0,426** -0,198** -0,127*2010 0,057 -0,332** -0,228** 0,483** -0,296** -0,336**2011 -0,797** -0,498** 0,781** 0,591** -0,845** -0,440**

France 2007-2008 -0,863** -0,069 0,458** 0,497** -0,448** -0,126**2009 -0,860** -0,270** -0,607** 0,419** 0,495** -0,0572010 -0,474** -0,353** 0,528** 0,421** -0,490** -0,184**2011 -0,816** -0,586** 0,768** 0,352** -0,731** -0,138*

United Kingdom 2007-2008 -0,775** -0,239* 0,724** 0,383** -0,919** -0,1332009 -0,780** -0,238** 0,280** 0,282** -0,290** -0,239**2010 -0,525** -0,373** -0,102 0,393** 0,740** -0,198**2011 -0,745** -0,510** -0,825** -0,392** -0,825** -0,392**

Austria 2007-2008 -0,852** -0,108* 0,485** 0,428** -0,324** 0,0042009 -0,867** -0,041 -0,791** 0,257** 0,796** 0,186**2010 0,186** -0,146* 0,054 0,270** -0,150* 0,0522011 -0,781** -0,540** 0,690** 0,338** -0,666** -0,233**

Belgium 2007-2008 -0,935** -0,042 0,265** 0,448** -0,475** -0,022009 -0,880** -0,383** -0,806** 0,285** 0,724** 0,0682010 -0,011 -0,484** 0,147* 0,079 -0,116 0,1142011 -0,870** -0,569** 0,118 -0,227** 0,015 0,419**

Netherlands 2007-2008 -0,834** -0,031 0,537** 0,480** -0,857** -0,0582009 -0,917** -0,274 -0,487** 0,370** 0,522** -0,0772010 -0,160* -0,421** 0,324** 0,442** -0,558** -0,2372011 -0,669** -0,505** 0,762** 0,482** -0,885** -0,317**

Finland 2007-2008 -0,920** -0,057 0,366** 0,477** -0,873** -0,1032009 -0,868** -0,195** -0,537** 0,324** 0,422** -0,0422010 -0,425** -0,343** 0,150* 0,432** -0,069 -0,236**2011 -0,817** -0,425** 0,815** 0,378** -0,868** -0,271**

Denmark 2007-2008 -0,877** -0,222 0,325** 0,396** -0,770** 0,0122009 -0,911** -0,279** -0,277** 0,438** 0,474** -0,132010 0,036 -0,266** 0,796** -0,003 0,702** -0,1992011 -0,747** -0,344** 0,801** 0,374** -0,869** -0,422**

27

Table 3: Correlation matrix of sovereign CDS Spread Changes

This table reports the pairwise correlation coefficients of daily changes in the sovereign CDS spreads for the indicated countries. ** mean significant at a 5% level

2007-2009Spain Germany France

United Kingdom Italy Portugal Austria Belgium Netherlands Ireland Greece Finland Denmark

Spain 1,000Germany .315** 1,000France .388** .375** 1,000

United Kingdom .614** .580** .562** 1,000Italy .543** .427** .470** .680** 1,000

Portugal .679** .353** .424** .595** .570** 1,000Austria .232** .156** .192** .312** .233** .202** 1,000Belgium .501** .434** .417** .644** .529** .480** .250** 1,000

Netherlands .642** .645** .616** .596** .705** .673** .371** .701** 1,000Ireland .405** .289** .340** .627** .411** .417** .245** .407** .686** 1,000Greece .565** .330** .416** .613** .589** .599** .195** .484** .663** .409** 1,000Finland .607** .612** .686** .573** .619** .622** .311** .657** .631** .585** .585** 1,000

Denmark .622** .635** .640** .600** .704** .635** .312** .667** .673** .603** .657** .580** 1,000

2010Spain Germany France




Portugal .810** .727** .699** .645** .791** 1,000Austria .146* .285** .236** .175** .217** .242** 1,000Belgium .739** .698** .692** .738** .762** .770** .257** 1,000

Netherlands .649** .719** .650** .640** .707** .698** .251** .717** 1,000Ireland .753** .597** .589** .625** .752** .790** .136* .731** .629** 1,000Greece .707** .602** .589** .562** .683** .719** .155* .673** .607** .702** 1,000Finland .651** .711** .631** .601** .660** .669** .244** .685** .674** .612** .621** 1,000

Denmark .686** .790** .684** .638** .733** .717** .265** .728** .779** .663** .635** .771** 1,000

2011Spain Germany France




Portugal .746** .551** .629** .616** .737** 1,000Austria .753** .801** .841** .798** .748** .601** 1,000Belgium .833** .745** .816** .787** .881** .731** .821** 1,000

Netherlands .655** .810** .790** .768** .737** .576** .767** .762** 1,000Ireland .753** .639** .675** .633** .715** .775** .643** .723** .646** 1,000Greece .462** .324** .381** .382** .476** .509** .357** .474** .370** .458** 1,000Finland .619** .688** .729** .664** .679** .528** .711** .704** .718** .576** .322** 1,000

Denmark .616** .627** .668** .646** .663** .504** .683** .684** .734** .568** .298** .703** 1,000

28

Table 4: Country-specific lead-lag analysis with three dimensional VAR model

The country-specific VAR model consists of three-equations with the log stock index return (Rt), the CDS sovereign spread change (∆CDS) and the bond spread change (∆Bond) as dependent variables respectively. We report coefficients (Coeff.) and p-values. We show the p-value for the Granger causality test (GC) only in the cases in which it is significant at a 5% level.

Spain

Dep.Var Rt ∆CDSt ∆Bondt Rt ∆CDSt ∆Bondt Rt ∆CDSt ∆Bondt

Coeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val.Rt-1 -0.053 0.450 -0.372 0.016 -0.141 0.007 0.063 0.348 -0.948 0.000 -0.104 0.191 -0.081 0.290 0.204 0.428 0.231 0.032Rt-2 -0.162 0.025 0.285 0.072 -0.079 0.133 0.032 0.646 -0.677 0.002 -0.027 0.741 -0.050 0.505 -0.083 0.742 -0.214 0.045∆CDSt-1 0.009 0.756 -0.104 0.110 -0.004 0.836 0.002 0.897 -0.052 0.411 0.019 0.435 -0.092 0.000 0.116 0.171 0.122 0.001∆CDSt-2 -0.045 0.127 -0.002 0.966 0.010 0.621 0.001 0.934 -0.157 0.009 0.034 0.136 0.034 0.185 -0.150 0.087 -0.048 0.189∆Bondt-1 -0.040 0.675 0.201 0.141 0.130 0.066 -0.019 0.725 -0.006 0.970 0.025 0.690 0.036 0.509 0.248 0.181 0.171 0.028∆Bondt-2 0.063 0.520 -0.876 0.000 0.084 0.239 -0.062 0.248 0.312 0.059 -0.176 0.005 0.047 0.394 -0.213 0.253 -0.163 0.037Obs. 254 254 254 254 254 254 256 256 256R2 0.1293 0.1352 0.1082 0.0093 0.1308 0.0616 0.1357 0.1407 0.1803GC test Rt --- 0.023 0.000 --- --- ---GC test ∆CDSt 0.016 0.001 --- --- 0.000 0.000GC test ∆Bondt 0.000 --- --- --- --- ---Optimal lags :

Portugal


Coeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val.Rt-1 -0.039 0.586 -0.382 0.097 -0.211 0.000 0.023 0.744 -1.451 0.000 -0.231 0.026 -0.128 0.106 1.073 0.001 0.596 0.007Rt-2 -0.097 0.193 0.240 0.315 -0.085 0.153 0.078 0.303 -0.753 0.041 0.038 0.721 -0.161 0.046 0.517 0.132 0.558 0.013∆CDSt-1 -0.013 0.488 -0.128 0.042 -0.003 0.826 -0.009 0.503 -0.135 0.057 0.036 0.085 -0.137 0.000 0.660 0.000 0.388 0.000∆CDSt-2 -0.014 0.456 -0.080 0.199 0.015 0.322 0.0008 0.953 -0.046 0.502 -0.001 0.943 0.017 0.445 -0.041 0.674 0.001 0.987∆Bondt-1 0.086 0.337 -0.156 0.588 0.145 0.043 0.021 0.666 0.126 0.605 0.230 0.001 0.099 0.000 -0.217 0.065 -0.010 0.894∆Bondt-2 0.164 0.061 -0.889 0.002 0.165 0.019 -0.126 0.011 0.161 0.500 -0.115 0.101 -0.070 0.014 0.034 0.774 -0.062 0.430Obs. 254 254 254 200 200 200 256 256 256R2 0.0264 0.0706 0.0765 0.0392 0.970 0.0885 0.2587 0.3133 0.3461GC test Rt --- 0.000 0.000 --- 0.005 0.000GC test ∆CDSt --- --- --- --- 0.000 0.000GC test ∆Bondt --- 0.004 --- --- 0.002 ---Optimal lags :

Italy


Coeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val.Rt-1 -0.092 0.168 0.155 0.460 -0.237 0.000 0.063 0.348 -0.379 0.001 -0.146 0.004 -0.072 0.351 0.424 0.116 0.141 0.166Rt-2 -0.260 0.000 0.037 0.862 -0.150 0.002 0.127 0.068 -0.022 0.852 0.006 0.902 -0.070 0.366 -0.019 0.944 0.019 0.848∆CDSt-1 -0.018 0.376 -0.195 0.004 -0.037 0.012 0.005 0.871 0.206 0.000 0.023 0.359 -0.052 0.041 0.330 0.000 0.114 0.001∆CDSt-2 -0.052 0.021 -0.024 0.726 -0.000 0.999 0.026 0.380 0.043 0.397 0.012 0.590 0.027 0.292 -0.065 0.469 -0.030 0.370∆Bondt-1 -0.035 0.709 0.161 0.587 0.068 0.300 -0.041 0.619 -0.049 0.735 0.139 0.027 -0.025 0.667 0.126 0.531 0.117 0.126∆Bondt-2 0.141 0.132 -0.772 0.009 0.103 0.117 -0.148 0.075 0.371 0.010 -0.154 0.014 0.003 0.951 -0.440 0.029 -0.210 0.005Obs. 247 247 247 254 254 254 256 256 256R2 0.2269 0.1079 0.2237 0.0285 0.1584 0.0782 0.0440 0.1113 0.1444GC test Rt --- 0.000 0.005 0.017 --- ---GC test ∆CDSt 0.017 0.010 --- --- --- 0.003GC test ∆Bondt 0.003 --- --- --- --- ---Optimal lags :

2008 2009 2010

2008 2009 2010

2008 2009 2010

5 2 5

2 7

5 2 2

2

29

Table 4 continued.

France


Coeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val.Rt-1 -0.187 0.011 0.047 0.803 -0.219 0.000 -0.011 0.876 -0.685 0.001 -0.013 0.096 0.006 0.925 -0.729 0.001 -0.103 0.218Rt-2 -0.235 0.002 0.147 0.453 -0.165 0.003 0.162 0.025 -0.865 0.000 0.099 0.229∆CDSt-1 -0.009 0.697 -0.063 0.321 -0.036 0.050 0.003 0.862 0.092 0.140 0.020 0.397 0.006 0.759 0.124 0.044 0.028 0.220∆CDSt-2 -0.056 0.022 0.063 0.323 -0.013 0.472 0.030 0.122 -0.029 0.619 -0.003 0.870∆Bondt-1 -0.023 0.807 0.145 0.566 0.105 0.147 -0.035 0.551 -0.170 0.348 0.066 0.335 -0.045 0.455 -0.092 0.619 0.048 0.491∆Bondt-2 0.782 0.420 -0.345 0.174 0.156 0.032 -0.179 0.003 0.647 0.000 -0.288 0.000Obs. 254 254 254 254 254 254 254 254 254R2 0.0986 0.0337 0.1164 0.0475 0.1555 0.0814 0.0030 0.0897 0.0150GC test Rt --- 0.000 0.000 --- 0.001 ---GC test ∆CDSt --- --- --- --- --- ---GC test ∆Bondt --- --- 0.008 0.001 --- ---Optimal lags :

Dep.VarCoeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val.

Rt-1 -0.015 0.833 -0.451 0.023 -0.130 0.008 0.025 0.721 -0.421 0.005 -0.072 0.253 0.064 0.409 -0.045 0.825 0.201 0.287Rt-2 -0.100 0.189 0.212 0.307 -0.023 0.646 -0.068 0.328 -0.075 0.618 0.017 0.789 -0.216 0.005 0.081 0.693 -0.069 0.711∆CDSt-1 -0.034 0.192 -0.282 0.000 -0.017 0.338 -0.031 0.269 -0.027 0.651 0.039 0.123 -0.150 0.002 0.404 0.001 0.436 0.000∆CDSt-2 -0.008 0.762 0.001 0.986 0.153 0.415 0.019 0.461 -0.040 0.469 0.017 0.456 -0.062 0.192 -0.259 0.041 -0.140 0.225∆Bondt-1 0.079 0.439 0.045 0.870 0.126 0.069 -0.030 0.710 0.257 0.142 0.221 0.003 0.156 0.001 -0.177 0.170 -0.319 0.007∆Bondt-2 0.053 0.599 -0.766 0.006 0.123 0.074 -0.157 0.050 0.326 0.058 -0.075 0.298 0.036 0.467 0.171 0.192 0.052 0.659Obs. 225 225 225 229 229 229 236 236 236R2 0.0181 0.1079 0.0525 0.0340 0.0826 0.0993 0.0781 0.0868 0.0646GC test Rt 0.049 0.026 0.018 --- --- ---GC test ∆CDSt --- --- --- --- 0.003 0.000GC test ∆Bondt --- 0.022 --- 0.036 0.006 ---Optimal lags :


Rt-1 0.011 0.873 -0.366 0.030 -0.070 0.108 0.042 0.551 -0.409 0.002 -0.093 0.078 0.028 0.685 -0.264 0.047 -0.094 0.076Rt-2 0.039 0.591 -0.177 0.192 -0.056 0.301∆CDSt-1 0.035 0.177 -0.081 0.196 -0.005 0.742 -0.015 0.673 0.206 0.004 0.140 0.000 -0.009 0.790 0.274 0.000 0.138 0.000∆CDSt-2 0.020 0.609 -0.011 0.883 -0.026 0.362∆Bondt-1 0.088 0.435 -0.331 0.223 0.061 0.381 -0.022 0.639 -0.059 0.718 0.164 0.013 -0.036 0.639 -0.127 0.396 0.173 0.004∆Bondt-2 -0.040 0.641 -0.156 0.342 -0.094 0.149Obs. 248 248 248 239 239 239 227 227 227R2 0.0098 0.0435 0.0108 0.0216 0.1520 0.2553 0.0027 0.1131 0.2041GC test Rt 0.030 --- 0.005 --- 0.047 ---GC test ∆CDSt --- --- --- 0.000 --- 0.000GC test ∆Bondt --- --- --- --- --- ---Optimal lags :


Rt-1 0.016 0.882 -0.017 0.930 -0.066 0.430 -0.028 0.657 -0.619 0.000 0.228 0.060 0.018 0.800 -0.195 0.388 -0.025 0.873Rt-2 -0.263 0.018 0.018 0.924 -0.144 0.078 0.122 0.066 -0.371 0.034 0.067 0.586 -0.014 0.839 0.050 0.824 -0.191 0.236∆CDSt-1 0.085 0.153 0.063 0.533 0.103 0.019 0.006 0.776 0.087 0.172 -0.018 0.679 -0.000 0.980 0.010 0.877 -0.032 0.514∆CDSt-2 -0.061 0.319 0.023 0.821 0.023 0.595 0.023 0.338 0.094 0.136 0.017 0.700 0.012 0.577 0.044 0.529 0.077 0.123∆Bondt-1 -0.137 0.373 -0.099 0.707 0.220 0.053 -0.052 0.146 0.031 0.736 -0.073 0.273 -0.010 0.722 -0.006 0.949 0.144 0.035∆Bondt-2 0.091 0.554 -0.610 0.021 0.136 0.231 -0.108 0.002 -0.030 0.742 -0.120 0.072 -0.040 0.188 -0.040 0.672 -0.004 0.943Obs. 93 93 93 243 243 243 239 239 239R2 0.0911 0.0877 0.1369 0.0587 0.0951 0.0330 0.0148 0.0076 0.0463GC test Rt --- --- 0.000 --- --- ---GC test ∆CDSt --- 0.045 --- --- --- ---GC test ∆Bondt --- 0.037 0.004 --- --- ---Optimal lags :

1

2 2

3

∆CDSt ∆Bondt

1

Greece2008 2009

2

1

Rt

2008 2009

∆CDSt

2

∆Bondt

∆Bondt

2010Rt

2

∆BondtRt ∆CDSt

2

2010

∆CDSt ∆Bondt∆CDSt

United Kingdom

2

Rt

∆CDSt ∆BondtRtRt

1

∆Bondt

2010∆CDSt

2009∆CDSt

∆Bondt Rt Rt∆CDSt ∆Bondt

Rt

Ireland2008

2008 2009 2010

30

Table 4 continued.


Rt-1 -0.015 0.833 -0.451 0.023 -0.130 0.008 -0.0002 0.997 -0.944 0.000 -0.023 0.793 -0.037 0.606 -0.022 0.926 -0.129 0.474Rt-2 -0.100 0.189 0.212 0.307 -0.023 0.646 0.136 0.059 -0.335 0.126 0.140 0.132∆CDSt-1 -0.034 0.192 -0.282 0.000 -0.017 0.338 -0.008 0.684 0.070 0.267 0.010 0.691 -0.059 0.002 0.325 0.000 -0.095 0.047∆CDSt-2 -0.008 0.762 0.001 0.986 0.015 0.415 0.035 0.068 0.072 0.224 0.013 0.592∆Bondt-1 0.079 0.439 0.045 0.870 0.126 0.069 -0.021 0.684 -0.089 0.581 0.101 0.142 -0.008 0.765 -0.0001 0.999 0.018 0.806∆Bondt-2 0.053 0.599 -0.766 0.006 0.123 0.074 -0.155 0.003 0.264 0.100 -0.300 0.000Obs. 225 225 225 254 254 254 256 256 256R2 0.0181 0.1079 0.0525 0.0500 0.1359 0.0813 0.0364 0.1076 0.0169GC test Rt 0.049 0.026 0.018 --- --- ---GC test ∆CDSt --- --- --- --- 0.002 ---GC test ∆Bondt --- 0.022 --- 0.036 --- ---Optimal lags :


Rt-1 0.054 0.446 -0.723 0.027 -0.142 0.004 0.031 0.647 -1.007 0.005 -0.095 0.128 0.112 0.104 -1.259 0.000 -0.095 0.436Rt-2 -0.009 0.904 -1.102 0.000 -0.087 0.188∆CDSt-1 -0.006 0.661 -0.051 0.438 -0.024 0.015 0.008 0.664 -0.143 0.036 0.004 0.799 0.012 0.523 0.175 0.001 0.028 0.420∆CDSt-2 -0.020 0.264 0.151 0.019 0.031 0.060∆Bondt-1 0.183 0.070 -0.109 0.813 0.162 0.020 0.098 0.184 -0.040 0.881 0.083 0.216 0.028 0.466 0.018 0.860 0.036 0.596∆Bondt-2 -0.094 0.188 0.118 0.677 -0.223 0.001Obs. 242 242 242 224 224 224 232 232 232R2 0.026 0.027 0.053 0.0327 0.2148 0.1346 0.0189 0.2403 0.0079GC test Rt 0.027 0.004 0.000 --- 0.000 ---GC test ∆CDSt --- 0.015 --- 0.039 --- ---GC test ∆Bondt --- 0.813 --- --- --- ---Optimal lags :


Rt-1 0.030 0.722 -0.746 0.013 -0.087 0.139 0.044 0.517 -1.082 0.001 -0.146 0.060 -0.043 0.545 -0.021 0.923 -0.071 0.590Rt-2 0.160 0.018 -0.564 0.072 0.042 0.587∆CDSt-1 0.023 0.245 -0.214 0.002 0.002 0.882 0.001 0.942 -0.088 0.169 0.019 0.235 -0.056 0.015 0.029 0.000 0.022 0.601∆CDSt-2 0.024 0.066 0.137 0.026 0.023 0.133∆Bondt-1 -0.061 0.613 0.592 0.169 0.082 0.331 0.066 0.252 -0.152 0.565 0.107 0.105 0.057 0.096 -0.145 0.177 0.159 0.012∆Bondt-2 -0.108 0.057 0.366 0.160 -0.200 0.002Obs. 189 189 189 252 252 252 256 256 256R2 0.0084 0.0736 0.0120 0.0403 0.0994 0.0749 0.0289 0.0840 0.0328GC test Rt 0.013 --- 0.000 --- --- ---GC test ∆CDSt --- --- --- --- 0,015 ---GC test ∆Bondt --- --- --- --- --- ---Optimal lags :


Rt-1 0.138 0.261 -0.007 0.979 -0.015 0.817 0.174 0.109 -0.085 0.688 0.076 0.449 -0.257 0.041 -0.559 0.162 0.049 0.884Rt-2 -0.090 0.461 -0.494 0.086 -0.281 0.000 -0.145 0.217 -0.119 0.749 -0.806 0.010∆CDSt-1 -0.045 0.393 0.294 0.018 -0.035 0.217 0.044 0.333 0.203 0.025 -0.015 0.722 -0.064 0.119 0.257 0.050 -0.196 0.074∆CDSt-2 0.028 0.602 -0.142 0.266 0.014 0.639 0.111 0.009 -0.202 0.141 0.156 0.173∆Bondt-1 0.131 0.513 -0.239 0.613 0.080 0.470 -0.060 0.583 -0.577 0.008 -0.049 0.633 -0.080 0.093 0.026 0.861 0.054 0.666∆Bondt-2 -0.192 0.331 -0.011 0.980 -0.102 0.346 0.044 0.320 0.018 0.896 0.023 0.843Obs. 67 67 67 111 111 111 60 60 60R2 0.3092 0.2276 0.4610 0.0270 0.1480 0.0073 0.2728 0.1795 0.1878GC test Rt --- 0.000 --- --- --- ---GC test ∆CDSt --- 0.002 --- --- 0.005 ---GC test ∆Bondt --- --- --- 0.008 --- ---Optimal lags :

Rt

2008 2009Germany

1 2 1

5 1 3

Denmark2008 2009 2010∆CDSt ∆Bondt∆CDSt ∆Bondt Rt ∆CDStRt ∆Bondt

2 1

1 3 1

∆CDSt

2010

2010Rt ∆Bondt

∆CDSt

∆BondtRt ∆CDSt

Rt ∆Bondt

2010Austria

Rt ∆CDSt ∆Bondt

2008

Rt ∆CDSt ∆Bondt Rt

2009

∆CDSt ∆Bondt

2

∆CDSt ∆Bondt

∆CDSt

2009∆CDSt ∆Bondt Rt

∆Bondt

Belgium2008

Rt

Rt

31

Table 4 continued.


Rt-1 -0.235 0.185 -1.047 0.063 -0.106 0.227 -0.014 0.837 -0.619 0.000 -0.012 0.869 -0.039 0.585 -0.143 0.470 0.009 0.950Rt-2 0.100 0.690 -2.097 0.009 -0.266 0.033 0.113 0.117 -0.241 0.171 -0.040 0.615∆CDSt-1 -0.004 0.944 0.520 0.006 -0.140 0.000 -0.019 0.456 -0.015 0.808 0.040 0.177 -0.042 0.081 0.147 0.027 -0.047 0.340∆CDSt-2 0.222 0.005 -0.054 0.829 0.206 0.000 0.040 0.128 0.190 0.003 -0.008 0.762∆Bondt-1 0.789 0.000 -0.640 0.355 0.035 0.742 -0.007 0.906 -0.041 0.790 0.062 0.373 0.013 0.701 -0.035 0.709 -0.010 0.882∆Bondt-2 -0.742 0.087 1.457 0.290 -0.546 0.011 -0.141 0.021 0.105 0.480 -0.228 0.001Obs. 36 36 36 223 223 223 244 244 244R2 0.9173 0.8915 0.9608 0.0401 0.1158 0.0719 0.0137 0.0330 0.0042GC test Rt 0.000 0.000 0.001 --- --- ---GC test ∆CDSt 0.000 0.000 --- --- 0.015 ---GC test ∆Bondt 0.000 0.000 --- --- --- ---Optimal lags :


Rt-1 -0.157 0.241 -0.091 0.746 -0.202 0.009 -0.006 0.924 -0.738 0.000 -0.129 0.097 0.070 0.376 0.008 0.968 -0.058 0.700Rt-2 0.175 0.018 -0.219 0.272 0.095 0.248 -0.100 0.207 0.259 0.250 -0.001 0.992∆CDSt-1 0.054 0.313 -0.018 0.872 0.036 0.246 -0.004 0.855 -0.058 0.378 -0.002 0.950 0.013 0.591 0.090 0.215 -0.001 0.977∆CDSt-2 0.035 0.134 0.056 0.376 -0.022 0.403 -0.044 0.078 0.042 0.546 -0.047 0.319∆Bondt-1 0.270 0.227 -0.442 0.344 0.326 0.012 -0.010 0.873 -0.147 0.380 0.095 0.168 0.011 0.758 -0.178 0.090 0.049 0.485∆Bondt-2 -0.208 0.001 0.101 0.547 -0.245 0.000 -0.030 0.409 0.103 0.330 -0.074 0.294Obs. 77 77 77 238 238 238 256 256 256R2 0.0341 0.0245 0.1100 0.0578 0.0931 0.0612 0.0400 0.0804 0.0347GC test Rt --- 0.009 0.000 --- --- ---GC test ∆CDSt --- --- --- --- --- ---GC test ∆Bondt --- --- 0.004 --- --- ---Optimal lags :

Rt ∆CDStRt ∆CDSt ∆Bondt Rt

2010∆CDSt ∆Bondt ∆Bondt

2008 2009Finland

10 2 1

1 2 4

∆Bondt∆CDSt ∆Bondt Rt ∆CDStRt ∆CDSt ∆Bondt Rt

Netherlands2008 2009 2010

32

Table 5: Aggregate lead-lag analysis with fixed-effect large-T panel regressions

For each market (stock, CDS and bond) we estimate large-T (GLS with autocorrelated errors) panel regressions to study the aggregate lead lag relationship across markets. We report coefficients and p-values. We show two tables: the first refers to the six countries with higher risk (Greece, Ireland, Portugal, Spain, Italy and Belgium) and the second refers to European countries with lower spreads (Austria, France, Netherlands, Germany and Finland). We show the p-value for the Granger causality test (GC) only in the cases in which it is significant at a 5% level.

High premium countries


Rt-1 -0.016 0.589 -0.392 0.000 -0.138 0.000 0.028 0.284 -0.606 0.000 -0.129 0.000 -0.049 0.149 0.272 0.015 0.223 0.001Rt-2 -0.117 0.000 0.094 0.258 -0.079 0.000 0.058 0.035 -0.305 0.000 -0.007 0.771 -0.111 0.001 0.062 0.575 0.000 1.000∆CDSt-1 -0.002 0.794 -0.163 0.000 -0.008 0.186 -0.010 0.267 0.003 0.904 0.037 0.000 -0.092 0.000 0.377 0.000 0.201 0.000∆CDSt-2 -0.002 0.865 0.005 0.841 0.014 0.028 0.014 0.093 0.012 0.610 0.018 0.025 0.016 0.183 -0.139 0.000 -0.048 0.048∆Bondt-1 0.026 0.548 0.090 0.453 0.105 0.000 -0.006 0.818 -0.076 0.312 0.133 0.000 0.062 0.000 -0.055 0.327 0.027 0.429∆Bondt-2 0.125 0.003 -0.654 0.000 0.146 0.000 -0.093 0.000 0.223 0.002 -0.126 0.000 -0.025 0.141 -0.002 0.976 -0.061 0.080Obs. 1407 1411 1416 1486 1495 1495 1271 1276 1275GC test Rt 0.000 0.000 0.000 0.000 0.044 0.005GC test ∆CDSt --- 0.024 --- 0.000 0.000 0.000GC test ∆Bondt 0.007 0.000 0.001 0.008 0.000 ---

Other countries


Rt-1 -0.035 0.357 -0.214 0.085 -0.160 0.000 0.006 0.825 -0.815 0.000 -0.074 0.033 -0.019 0.535 -0.439 0.000 -0.068 0.275Rt-2 -0.227 0.000 -0.160 0.298 -0.149 0.000 0.114 0.000 -0.528 0.000 0.064 0.071 -0.049 0.128 0.161 0.098 -0.033 0.603∆CDSt-1 -0.003 0.718 -0.094 0.005 -0.017 0.004 0.005 0.589 -0.010 0.711 0.020 0.043 -0.040 0.000 0.171 0.000 -0.020 0.284∆CDSt-2 -0.022 0.010 -0.027 0.423 -0.013 0.032 0.015 0.073 0.112 0.000 0.007 0.477 -0.001 0.888 -0.036 0.197 -0.004 0.833∆Bondt-1 0.048 0.357 -0.062 0.764 0.143 0.000 0.003 0.896 -0.105 0.200 0.088 0.003 0.020 0.218 -0.041 0.400 0.032 0.312∆Bondt-2 0.191 0.000 -0.427 0.036 0.151 0.000 -0.155 0.000 0.270 0.007 -0.255 0.000 -0.039 0.014 0.081 0.093 -0.054 0.080Obs. 881 881 885 1202 1207 1211 1232 1244 1244GC test Rt --- --- 0.000 0.020 0.000 ---GC test ∆CDSt 0.037 --- --- --- 0.000 ---GC test ∆Bondt 0.000 --- 0.000 0.013 0.020 ---

Dep.VarCoeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val. Coeff. p-val.

Rt-1 0.005 0.849 0.033 0.617 -0.022 0.703 -0.536 0.147 -0.107 0.234 -0.396 0.000∆CDSt-1 -0.037 0.005 0.182 0.000 0.066 0.015 -0.005 0.744 0.105 0.001 0.017 0.575∆Bondt-1 0.014 0.317 0.085 0.005 0.102 0.000 0.058 0.000 -0.06 0.064 0.158 0.196Obs. 1504 1514 1514 1243 1256 1256GC test Rt --- --- --- 0.000GC test ∆CDSt 0.005 0.015 --- ---GC test ∆Bondt --- 0.005 0.000 0.064

High premium countries2011

Rt ∆CDSt ∆Bondt

Other countries2011

Rt ∆CDSt ∆Bondt

∆Bondt∆Bondt

Rt ∆CDSt

2010

∆Bondt

2010

Rt ∆CDSt

2009Rt ∆CDSt Rt ∆CDSt∆Bondt

2008

20092008Rt ∆CDSt Rt ∆CDSt∆Bondt ∆Bondt

33

Table 6: Price discovery metrics for 2007-2009 period by pairs of countries. We show the Gonzalo-Granger statistics (in [5]) for the pairs of countries where we find cointegration, but we only report the results for pairs once we have identified the main leader. We only include countries in the euro area.

Price discovery metrics for 2007-2009 (In the case of Belgium we only have data with enough liquidity from 2009).

Price discovery metrics for 2011.

ΔCDS1 α1 p-value GG1 GG2 α2 p-value ΔCDS2Leading country

Germany -0.038 0.000 -0.310 1.317 -0.009 0.151 Spain SpainSpain 0.002 0.820 0.919 0.081 -0.027 0.001 Italy SpainSpain 0.003 0.730 0.966 0.034 -0.089 0.000 France SpainSpain 0.006 0.728 0.891 0.109 -0.053 0.001 Portugal SpainSpain -0.001 0.907 0.970 0.030 0.042 0.000 Greece SpainSpain 0.006 0.171 1.089 -0.089 0.074 0.000 Ireland SpainSpain -0.005 0.869 0.959 0.041 0.123 0.003 Belgium SpainGermany -0.05 0.000 -0.169 1.169 -0.007 0.208 Italy ItalyGermany -0.053 0.000 0.344 0.656 0.028 0.058 France FranceGermany -0.04 0.000 -0.019 1.019 -0.007 0.193 Portugal PortugalGermany -0.047 0.000 -0.191 1.191 -0.007 0.317 Greece GreeceGermany -0.033 0.001 0.740 0.260 0.093 0.000 Ireland IrelandGermany ‐0.065 0.000 0.348 0.652 0.034 0.011 Austria AustriaGermany ‐0.114 0.001 0.209 0.791 0.030 0.462 Belgium BelgiumItaly -0.021 0.024 0.402 0.598 0.014 0.000 France FranceGreece 0.013 0.180 0.870 0.130 -0.090 0.000 France GreeceIreland 0.121 0.000 0.160 0.840 -0.023 0.051 France FranceFrancia -0.168 0.000 0.478 0.522 0.154 0.008 Belgium BothItaly 0.020 0.375 1.189 -0.189 0.129 0.002 Belgium Italy

ΔCDS1 α1 p-value GG1 GG2 α2 p-value ΔCDS2Leading country

Italy -0.058 0.033 2.634 -1.634 -0.094 0.000 Germany ItalyItaly -0.086 0.039 2.178 -1.178 -0.159 0.000 Spain ItalyItaly -0.062 0.040 1.811 -0.811 -0.139 0.000 France ItalyItaly 0.094 0.000 1.480 -0.480 0.102 0.000 Belgium ItalyItaly 0.011 0.609 1.266 -0.266 0.052 0.006 Finland ItalyItaly 0.034 0.063 2.404 -1.404 0.059 0.001 Austria ItalyFrance 0.039 0.224 2.026 -1.026 0.078 0.010 Germany FranceFrance -0.014 0.379 0.728 0.272 0.038 0.028 Spain FranceFrance 0.08 0.019 2.041 -1.041 0.158 0.000 Belgium FranceFrance -0.021 0.399 0.614 0.386 0.034 0.157 Finland FranceFrance 0.047 0.148 2.234 -1.234 0.085 0.009 Austria France

34

Table 7: CDS-specific lead-lag analysis with three dimensional VAR model

The CDS-specific VAR model consists of three-equations with CDS sovereign spread changes (∆CDS) of each country as dependent variables respectively. In this table, we report coefficients (Coeff.) and p-values. We show the p-value for the Granger causality test (GC) only in the cases in which it is significant at a 5% level.

Dep.VarCoeff. p-val. Coeff. p-val. Coeff. p-val.

∆CDS Germany t-1 0.207 0.009 0.335 0.006 0.237 0.053∆CDS Germany t-2 0.008 0.919 -0.165 0.184 -0.135 0.279∆CDS Spain t-1 0.005 0.940 -0.265 0.016 -0.156 0.157∆CDS Spain t-2 -0.066 0.354 -0.366 0.001 -0.320 0.004∆CDS Portugal t-1 0.122 0.091 0.292 0.008 0.427 0.000∆CDS Portugal t-2 0.044 0.541 0.172 0.126 0.153 0.174Obs. 256 256 256R2 0.1813 0.2292 0.2749GC test ∆CDS Germanyt 0.026 0.087GC test ∆CDS Spain t --- 0.002GC test ∆CDS Portugal t --- 0.001Optimal lags : 4Dep.Var

Coeff. p-val. Coeff. p-val. Coeff. p-val.∆CDS Germany t-1 0.257 0.001 0.350 0.003 0.277 0.014∆CDS Germany t-2 -0.015 0.849 -0.251 0.028 -0.158 0.156∆CDS Italy t-1 -0.0004 0.995 0.195 0.039 -0.077 0.399∆CDS Italy t-2 -0.050 0.440 -0.185 0.049 -0.190 0.038∆CDS Greece t-1 0.119 0.052 -0.057 0.523 0.218 0.012∆CDS Greece t-2 0.010 0.870 0.145 0.108 0.039 0.656Obs. 256 256 256R2 0.1331 0.1384 0.1252GC test ∆CDS Germany t 0.002 0.026GC test ∆CDS Italy t --- 0.049GC test ∆CDS Greece t --- ---Optimal lags : 2Dep.Var

Coeff. p-val. Coeff. p-val. Coeff. p-val.∆CDS Germany t-1 0.235 0.007 0.347 0.001 0.245 0.037∆CDS Germany t-2 -0.034 0.699 0.091 0.391 -0.059 0.620∆CDS France t-1 0.046 0.507 -0.189 0.028 -0.002 0.984∆CDS France t-2 0.032 0.653 -0.054 0.533 -0.135 0.164∆CDS Ireland t-1 0.087 0.102 0.156 0.017 0.065 0.374∆CDS Ireland t-2 0.023 0.656 -0.025 0.698 0.063 0.380Obs. 256 256 256R2 0.1694 0.1252 0.1831GC test ∆CDS Germany t 0.005 0.010GC test ∆CDS France t --- ---GC test ∆CDS Ireland t --- ---Optimal lags : 4

2010∆CDS Germany t ∆CDS Spain t ∆CDS Portugal t

∆CDS Germany t ∆CDS Italy t ∆CDS Greece t

∆CDS Germany t ∆CDS France t ∆CDS Ireland t

35

Table 7 continued.


∆CDS Germany t-1 0.272 0.002 0.312 0.000 0.494 0.000∆CDS Germany t-2 -0.062 0.506 -0.097 0.233 -0.068 0.420∆CDS United Kingdom t-1 0.083 0.321 -0.027 0.711 0.103 0.169∆CDS United Kingdom t-2 -0.019 0.813 -0.006 0.924 0.010 0.885∆CDS Netherlands t-1 0.049 0.627 -0.156 0.077 -0.293 0.001∆CDS Netherlands t-2 0.114 0.257 0.189 0.032 -0.058 0.526Obs. 256 256 256R2 0.1618 0.1438 0.1933GC test ∆CDS Germany t 0.000 0.000GC test ∆CDS United Kingdom t --- ---GC test ∆CDS Netherlands t --- 0.013Optimal lags : 3Dep.Var

Coeff. p-val. Coeff. p-val. Coeff. p-val.∆CDS Germany t-1 0.160 0.107 0.454 0.000 0.247 0.003∆CDS Germany t-2 -0.054 0.609 -0.024 0.774 -0.022 0.807∆CDS Austrian t-1 0.009 0.897 0.131 0.035 -0.008 0.897∆CDS Austrian t-2 -0.132 0.089 -0.091 0.146 0.001 0.981∆CDS Denmark t-1 0.308 0.008 0.531 0.000 0.045 0.648∆CDS Denmark t-2 0.058 0.646 -0.068 0.504 -0.018 0.866Obs. 257 257 257R2 0.2284 0.5934 0.1498GC test ∆CDS Germany t 0.000 ---GC test ∆CDS Austriat --- ---GC test ∆CDS Denmark t 0.058 0.000Optimal lags : 8Dep.Var

Coeff. p-val. Coeff. p-val. Coeff. p-val.∆CDS Germany t-1 0.251 0.009 0.345 0.000 0.272 0.001∆CDS Germany t-2 0.110 0.264 -0.082 0.385 -0.022 0.799∆CDS Belgium t-1 0.135 0.167 0.064 0.494 0.206 0.016∆CDS Belgium t-2 -0.014 0.884 -0.037 0.687 -0.0004 0.995∆CDS Finland t-1 0.025 0.823 -0.013 0.899 -0.207 0.034∆CDS Finland t-2 -0.154 0.168 0.050 0.639 -0.161 0.100Obs. 256 256 256R2 0.1946 0.2043 0.1800GC test ∆CDS Germany t 0.001 0.006GC test ∆CDS Belgium t --- 0.037GC test ∆CDS Finland t --- ---Optimal lags : 6Dep.Var

Coeff. p-val. Coeff. p-val. Coeff. p-val.∆CDS Spain t-1 0.004 0.967 0.251 0.019 0.242 0.016∆CDS Spain t-2 -0.260 0.028 -0.129 0.239 -0.119 0.243∆CDS Italy t-1 0.097 0.447 0.105 0.373 -0.183 0.098∆CDS Italy t-2 -0.065 0.614 -0.155 0.193 -0.127 0.254∆CDS Greece t-1 0.125 0.223 -0.041 0.665 0.225 0.011∆CDS Greece t-2 0.179 0.087 0.173 0.074 0.013 0.880Obs. 256 256 256R2 0.1692 0.1617 0.2158GC test ∆CDS Spain t 0.013 0.000GC test ∆CDS Italy t --- ---GC test ∆CDS Greece t --- ---Optimal lags : 5

∆CDS Spain t ∆CDS Italy t ∆CDS Greece t

∆CDS Denmark t

∆CDS Germany t ∆CDS United Kingdom t ∆CDS Netherlands t2010

∆CDS Germany t ∆CDS Belgium t ∆CDS Finland t

∆CDS Austria t∆CDS Germany t

36

Table 7 continued.


∆CDS Spain t-1 -0.054 0.606 0.150 0.067 0.028 0.758∆CDS Spain t-2 -0.274 0.009 -0.016 0.838 -0.215 0.018∆CDS France t-1 0.099 0.329 -0.084 0.288 0.105 0.233∆CDS France t-2 -0.097 0.336 0.024 0.757 -0.065 0.460∆CDS Ireland t-1 0.200 0.068 0.118 0.170 0.086 0.370∆CDS Ireland t-2 0.194 0.075 -0.015 0.858 0.188 0.047Obs. 256 256 256R2 0.1710 0.0984 0.1709GC test ∆CDS Spain t --- 0.052GC test ∆CDS France t --- ---GC test ∆CDS Ireland t --- ---Optimal lags : 4Dep.Var

Coeff. p-val. Coeff. p-val. Coeff. p-val.∆CDS Spain t-1 0.215 0.011 0.138 0.003 0.284 0.000∆CDS Spain t-2 -0.235 0.009 -0.121 0.015 -0.040 0.414∆CDS United Kingdom t-1 -0.144 0.295 -0.055 0.469 0.013 0.856∆CDS United Kingdom t-2 0.103 0.453 0.007 0.926 0.070 0.352∆CDS Netherlands t-1 0.043 0.774 0.030 0.719 -0.162 0.048∆CDS Netherlands t-2 0.174 0.244 0.265 0.001 0.029 0.716Obs. 256 256 256R2 0.2116 0.1773 0.3010GC test ∆CDS Spain t 0.007 0.000GC test ∆CDS United Kingdom t --- ---GC test ∆CDS Netherlands t --- 0.011Optimal lags : 10Dep.Var

Coeff. p-val. Coeff. p-val. Coeff. p-val.∆CDS Spain t-1 0.108 0.170 0.210 0.000 0.144 0.000∆CDS Austrian t-1 -0.201 0.024 0.102 0.016 -0.030 0.494∆CDS Finland t-1 0.221 0.164 0.679 0.000 0.082 0.296Obs. 257 257 257R2 0.0499 0.5645 0.1140GC test ∆CDS Spain t 0.000 0.000GC test ∆CDS Austria t 0.024 ---GC test ∆CDS Denmark t --- 0.000Optimal lags : 1Dep.Var

Coeff. p-val. Coeff. p-val. Coeff. p-val.∆CDS Spain t-1 0.044 0.603 0.114 0.027 0.126 0.007∆CDS Spain t-2 -0.175 0.042 -0.111 0.033 -0.030 0.512∆CDS Belgium t-1 0.062 0.709 0.088 0.382 0.193 0.035∆CDS Belgium t-2 0.033 0.838 0.002 0.980 -0.002 0.974∆CDS Denmark t-1 0.223 0.174 0.145 0.143 -0.107 0.231∆CDS Denmark t-2 -0.144 0.377 0.072 0.462 -0.129 0.145Obs. 256 256 256R2 0.1488 0.1580 0.1593GC test ∆CDS Spain t 0.005 0.003GC test ∆CDS Belgium t --- ---GC test ∆CDS Finland t --- ---Optimal lags : 4

∆CDS Spain t ∆CDS Austria t ∆CDS Denmark t

∆CDS Finland t

∆CDS Spain t

∆CDS Spain t ∆CDS Belgium t

∆CDS Spain t ∆CDS France t ∆CDS Ireland t

∆CDS United Kingdom t ∆CDS Netherlands t

2010

The Co-movement of Sovereign Credit Default Swaps, Sovereign

Documents

Transcript of The Co-movement of Sovereign Credit Default Swaps, Sovereign