the co-movement of sovereign credit default swaps, sovereign
TRANSCRIPT
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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.
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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.
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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.
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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.
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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
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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
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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.
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(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
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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.
[insert Table 3 about here]
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.
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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.
[insert Table 4 about here]
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).
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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).
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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.
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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
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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
United Kingdom Italy Portugal Austria Belgium Netherlands Ireland Greece Finland Denmark
Spain 1,000Germany .664** 1,000France .678** .715** 1,000
United Kingdom .622** .569** .550** 1,000Italy .787** .663** .637** .715** 1,000
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
United Kingdom Italy Portugal Austria Belgium Netherlands Ireland Greece Finland Denmark
Spain 1,000Germany .667** 1,000France .738** .818** 1,000
United Kingdom .696** .817** .801** 1,000Italy .852** .678** .770** .744** 1,000
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
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 -0.039 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
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 -0.092 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
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 -0.187 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 :
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 0.011 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 :
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 0.016 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.
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 -0.0002 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 :
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 0.054 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 :
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 0.030 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 :
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 0.138 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.
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 -0.235 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 :
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 -0.157 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
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 -0.016 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
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 -0.035 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.
Dep.VarCoeff. p-val. Coeff. p-val. Coeff. p-val.
∆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.
Dep.VarCoeff. p-val. Coeff. p-val. Coeff. p-val.
∆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