The Aegean Contagion: a Case Study on the GIIPS
Akatsuki Sukeda
Erik Wedershoven
Takashi Yoshida

The University of Tokyo, Graduate School of Public Policy
Case Study on International Financial Institutions, 2010 Summer
Lecturers: Takatoshi Ito and Junko Koeda
Abstract
This report examines high-frequency contagion effects among Greece, Ireland,
Italy, Portugal, Spain (GIIPS), France and Belgium during the European
sovereign debt crisis. Using daily data of each country‟s government bond yieldspread between January 2008 and July 2010, the origin and direction of negative
spillovers are defined and analyzed. Regression analysis furthermore shows that
Greece has been the main origin country of high-frequency contagion during thecrisis, affecting Ireland and Portugal greatly, but Italy and Spain to a lesser
extent. A shift of origins as occurred in the spring of 2009, from Greece to Irelandand back again after Greece revealed its real deficits in November 2009. We also
found weak but supportive evidence of a significant shift of contagion. Finally, we
examined the transmission channel of the contagion. We found that the contagion
effect has a high correlation with the fiscal situation in the affected countries,
implying that investors‟ reassessment of the risks in countries with similar
fundamentals as the origin country is the cause of the contagion.

-1-1. Introduction
On 5 November 2009, less than one month in office, George Papandreou‟ssocial democratic government revealed that Greece‟s budget deficit for 2009 had
to be readjusted from 6 to 12.7 percent of GDP. It came as a tremendous shockthat the previous administration had been able to conceal almost half of its
deficit and over the months that followed many more shocks would follow. Even
though budget cuts were presented within days and Greece formally announced
its plans to cut its deficit according to the Eurozone‟s Stability and Growth Pact
norms by 2012, it did not prevent investors from losing confidence and financial
institutions giving out grim outlooks over the following months.
After a series of austerity plans proposed by the Greek government itself,
more negative revelations and eventual downgrades of Greece‟s bonds to „junk
status‟ by the world‟s major rating agencies, Greece was forced to ask for help tothe International Monetary Fund (IMF) and European Financial Institutions. On
2 May 2010, after days of negotiations, intervention finally came for Greece
through a large multi-stakeholder rescue plan coordinated by the IMF, the ECB
and other EU member states. Within a week, the bold and ambitious European
Financial Stability Facility was installed, affirming rumours that the greatest
concern the financial institutions had when they agreed on a rescue plan for
Greece was the fear for contagion to other member states such as Portugal andSpain. Already at an early stage of the crisis, analysts noticed that CDS and yieldspread of the GIIPS seemed to be worsening almost simultaneously, particularly
in comparison to countries such as Germany. Fear of contagion between thesecountries with similar macroeconomic conditions could therefore not have comeas a surprise.
Contagion of crisis and its potentially devastating effects have become amajor interest of researchers and International Financial Institutions over the
past decades and have especially been receiving increasing attention since the
Asian crisis of 1997-1998. How contagion occurs, how it could be prevented andwhat should be done to contain it are all highly relevant and pressing issues,particularly in light of the current European sovereign debt crisis.

-2-

This paper is one of the first attempts to analyze the role of contagion in
the European sovereign debt crisis, now often called „the Aegean Contagion‟ after
its most apparent origin country. At the hand of the daily yield spread data,
spillovers are defined and analyzed from the ground-zero or origin country to theaffected countries. With modifications, the model that is used is Ito andHashimoto‟s (2005) analysis of high-frequency contagion in the Asian crisis. We
used it to answer the following questions: given a large shock in one country, does
high-frequency contagion occur to others in the European sovereign debt crisis,
between 1 January 2008 and July 2010? If so, does this transmission of shocks
change over time and are there shifts of origins? Finally, in what direction does
the contagion occur and what are its channels of transmission?
The rest of the paper is organized as follows: The introduction will befollowed by a brief overview of the European sovereign debt crisis so far, relevant
literature on concepts of contagion and the data that is used for this analysis.
Then, our methodology will be further explained, after which the origins will bedefined and contagion effects will be examined at the hand of the proposed
regression analysis. Furthermore channels of transmission will be investigatedand financial linkages plus shifting investors' sentiments will be analyzed inlight of the current events. Finally, some implications of the model that has beenused will be discussed and the final conclusions of this case study will bepresented.

2. Background of the European Sovereign Debt Crisis
The Eurozone came into existence with the official launch of the euro on 1
January 1999, after all aspiring member states had met the convergence criteria.
Greece qualified in 2000 and was admitted two years later, and physical coinsand banknotes were introduced on 1 January 2002 for all. The Eurozone is an
economic and monetary union (EMU) that today consists out of 16 European
Union (EU) member states. The monetary policy of the Eurozone is in hands ofthe ECB, but the Eurozone‟s finance ministers meet as the Euro Group and takepolitical decisions. Finally, there is no common representation or fiscal policy for-3-the currency union as a whole, which has often been pointed out as a great
vulnerability.
After years of convergence and stability, the Eurozone experienced
increased widening of the CDS and yield spreads between its member states fromthe end of 2008 on (figure 1 and 2). This coincides with rapidly growing fiscal
deficits (figure 3 and 4) at a time when governments started to adopt fiscal
stimulus packages following the Lehman shock and the further global spread offinancial and economic distress. Some governments had been borrowing morethan others, such as the GIIPS, facilitated by low bond yields and some of thehighest growth rates in the EU, mostly due to large foreign capital inflows overthe early 2000s.
However, the real onset of the European sovereign debt crisis should betraced to 5 November 2009, when Greece‟s new Prime Minister George
Papandreou‟s social democratic government revealed that Greece‟s budget deficit
for 2009 had to be readjusted from 6 to 12.7 percent of GDP. This came as atremendous shock to investors and financial institutions, especially because they
had been unaware, or even misled for so long and might be as well by many othercountries.
After dozens of proposals by the Greek government to cut deficits on its
own, months of social and political unrest following these cuts, considerable help
from European institutions that only improved the situation temporarily, andincreasingly growing lack of confidence among investors, all major rating
agencies downgraded Greek government bonds to „junk status‟ from 27 April.
CDS and yield spread of Greece, but also to a lesser extent of Portugal and theothers, skyrocketed (figure 1 and 2) to almost unimaginable levels. It became
clear that much greater intervention from outside was needed. After a mere few
days of negotiations, the Eurozone‟s finance ministers, the ECB and the IMF
came up with an unprecedented rescue package for Greece. The markets
regained some confidence, especially following the installation of the bold andambitious European Financial Stability Facility one week later on 9 May 2010.
This plan, worth almost a trillion dollars was clearly aimed at ensuring financial
stability across Europe and not only for Greece. Rumours were finally confirmed-4-that the greatest fear policymakers in Brussels and Washington had was that the
Greek crisis might spill over to other countries in Europe, perhaps even to themuch larger economies of Italy and Spain.
Already from a very early stage in the crisis, analysts had noticed thestrikingly similar movements of CDS and yield spreads among the GIIPS (figure
1 and 2), in positive and negative terms. Many warned as well that an
unanticipated crisis in Italy or Spain could have devastating effects for Europe as
a whole and might perhaps even mean the end of the Eurozone. The potential
effects of contagion had been imprinted in the heads of policymakers andresearchers at International Financial Institutions by most notably the Asiancrisis of 1997-1998. After this crisis, increasing attention has been given to thestudy of contagion, its prevention and containment. The current European
sovereign debt crisis can be used to draw parallels to previous crises or to shine
new light on fiscal deficits and financial stability. What is truly remarkable inthis crisis so far has been the degree to which some of the world‟s most advanced
economies have been involved and how great the widening of CDS and yieldspread occurred among them in such a short period of time. The current
European sovereign debt crisis will therefore undoubtedly be an inspiration forthese researchers and policymakers for many more years.

-5-Figure 1: Sovereign CDS Spreads (5 Year, bpts)
Source: Bloomberg
1000
900
800
700

600
500
400
300
200
100
0
2007/01/01

2008/01/01
GreeceIreland2009/01/01
Italy

2010/01/01

Portugal

Spain

Figure 2: Government Bond Yield (Generic 10 Year, Percentage)
Source: Bloomberg
14

12
10
8
6
4
2
0
2007/01/01

2008/01/01

2009/01/01

2010/01/01

Germany

GreeceIreland

Italy

Portugal

Spain

FranceBelgium-6-

Figure 3: Budget Deficits of the Eurozone Countries (Percentage of GDP)
Source: Eurostat
10%

5%

0%

-5%

-10%

-15%

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Germany

Portugal

Ireland

Italy

GreeceSpain

Figure 4: Government Debt of the Eurozone Countries (Percentage of GDP)
Source: Eurostat
140%
120%
100%
80%
60%
40%

20%
0%
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Germany

Portugal

Ireland

-7-

Italy

GreeceSpain

3. Literature Review
The number of studies on contagion has been increasing dramatically overthe past decades, particularly after the 1992 ERM crisis and 1997 Asian crisis. In
this vast body of literature, a myriad of definitions of contagion and its
transmission channels has been examined. In an attempt to draw some of themost important and relevant definitions together for this research, first theWorld Bank's definition of contagion will be discussed, after which a detailed
examination of its channels of transmission will follow. Finally, Ito andHashimoto (2005), whose model helped us to analyze the Aegean Contagion withcertain modifications, will be discussed in more detail.
Some of the most important research on contagion has been published byresearchers of International Financial Institutions such as the World Bank andthe IMF, particularly since the 1997-1998 Asian crisis. The World Bank has
recently presented three useful categories of contagion in which much of theestablished literature can be divided (World Bank, 2010).
Firstly, there is a so-called 'broad' definition of contagion. This definitionrefers to all cross-country transmission of shocks and spillovers and is sometimes
referred to as fundamentals-based contagion through financial or real links.
These types of spillovers might reflect normal interdependence, not particularly
in times of crisis, in contrast to much research on contagion over the past years.
The so-called 'restrictive' definition explains the transmission of shocks toother countries or the cross-country correlation, beyond any fundamental linkamong the countries and beyond common shocks. Sometimes this definition isreferred to as excess co-movement and excludes herding behavior or fundamental
links per se.
Finally, according to the 'very restrictive' definition contagion occurs
when cross-country correlations increase during 'crisis times', relative tocorrelations during „tranquil times.‟ Such studies usually consider transmissions
across borders for a particular asset market, such as the stock market. In themost common approach adopted using this definition no channel of transmission
is identified or examined in detail. Fundamental linkages are not acknowledged-8-

using this definition either, only increases in correlation are recognized as
contagion.
Despite the variations and differences, it must be clear from this that there
is a fairly broad consensus in the empirical literature on financial contagion that
this type of spillover refers to a transmission of shocks. Contagion should thus bedistinguished from „normal‟ interdependencies and spillovers across asset
markets. Probably the most important strand of the contagion research up to now
uses conditional correlation analysis to test for shifts in linkages across financial
markets during crisis periods (Beirne, 2008).
One of the most fundamental questions, addressed in most works on
contagion, is through which channels contagion occurs. Since this has also been acentral question in this research, a more detailed investigation of the relevant
literature to our research will now follow.
Empirical studies on transmission channels of contagion so far most often
identified four potential channels of contagion. The first one is the phenomenonof a common shock. For some definitions of contagion, including ours, a common
shock is not considered as a transmission channel. However, it is often the casethat common shocks can generate a similar situation as contagion, which cannot
be overlooked. Caramazza et al. (2004) lists up some examples of common shocks,
which include the increase in U.S. interest rates in the early 1980s as animportant factor in the Latin American crisis. Also, the large appreciation of the
U.S. dollar during 1995-97 and Japan's weak growth in the 1990s is said to be
attributable to the weakening of the external sector in several Asian countries
before the Asian crisis.
Second, trade linkages are often investigated in contagion research. Strong
trade linkages among countries could transmit a shock originating from one
country to another, especially during a currency crisis. Using daily data of stock
prices and exchange rates during the Asian crisis, Ito and Hashimoto (2005)
conclude that it is highly likely that the transmission channel of contagion can befound in trade linkages. According to their classification, this channel takes
several forms, including the devaluation of origin countries or appreciation of the
trade partner country, which could decrease the export of the affected countries-9-and worsen the trade balance of the affected country (i.e. competitive effect). Also,
countries with a large export share to the worst-hit country can experience a
decline in their trade balance and income, which makes investors withdraw funds
from the exporting country (i.e. income effect). Finally, depreciation of the origin
country can make import prices of the trading partners cheaper, raising economic
welfare, other things being constant (i.e. cheaper import effect). Since among
these trade linkage channels two out of three channels are related to currency
effects and only one effect, the income effect, can occur without currency
devaluation, we do not consider trade linkage channels in our analysis for theEuropean single currency zone.
Third, financial linkages can form another channel of transmission. A
crisis in one or more countries might induce investors to rebalance theirportfolios, to prevent further losses or to retain liquidity. Kaminsky and Reinhart(2000) introduced a useful notion of the „common creditor hypothesis‟. When acrisis breaks out in one country, investors who have positions in that country
usually tend to reduce their now increased risk exposure and will sell assets
whose returns are likely to be correlated with those of the origin country.
Therefore the larger positions the common lender has to both the origin andaffected countries, the larger the contagion should be. Also, when both the origin
and affected country have larger liabilities to the common lender, the larger thecontagion becomes. Kaminsky and Reinhart (2000) concluded that financial
sector links via common bank lenders are a powerful channel of contagion. Using
different measures of financial linkages, many empirical studies, such as Van
Rijckeghem and Weder (2001) and Caramazza et al. (2004), show that financial
linkages play a crucial role in transmitting contagion. In section 6 of this paper,
we examine the role of the financial linkages in the European sovereign debt
crisis.
Fourth, as a channel based on non-fundamentals, shifts in investors‟
sentiments could be a channel of transmission. A crisis in one country can serve
as a „wake-up call‟, inducing markets to reassess other countries' risks. Gande
and Parsley (2005) examine the effect of a sovereign credit rating change of one
country on the sovereign credit spreads of other countries and found evidence of- 10 -

spillover effects. A negative rating change in one country can have a significant
effect on the sovereign credit spreads of other countries in their research. Other
things remaining equal, a credit rating change in one country itself has nothing
to do with the fundamentals of other countries. However, their findings showthat this update of information serves as a source of shift in investors' mind andcan thus affect other countries' sovereign credit spreads. Although they did not
limit themselves to the analysis of crisis situations, this „news spillover‟ effect isworth considering for crisis contagion as well.
Finally, we would like to separately review Ito and Hashimoto (2005) once
more in detail, since in this paper basically we follow their method with somemodifications. They investigated a rather usefully restrictive definition ofcontagion, also distinguishing from common shocks: so-called high-frequency
contagion in which spillovers occur within days of shocks during crisis from one
entity to other interdependent ones. This is a very useful definition to investigate
contagion of crises, not only the Asian crisis of 1997-1998, explored through auniquely operational method of contagion analysis.
There are three outstanding characteristics in Ito and Hashimoto (2005).
First, it is the use of daily data, so its definition of high-frequency, capturing theeffect of negative shocks within days. This has often been overlooked in analyses
with weekly or monthly data. Second, they first identify the direction of contagion,
from origin countries to affected countries by defining that origin countries
experience the largest effect of a shock because it originates from them. Third,
they looked carefully into the transmission mechanisms of contagion and pointed
out the relevance of trade links during the Asian crisis.
In the present paper, their approach has been gratefully modified and used
to analyze the European sovereign debt crisis. The biggest advantage to follow
their approach is that it enables us to detect the origin country empiricallywithout ad hoc assumptions, and identify the direction of contagion. As we will
see in the following sections, the origin country is not always as clear as otherstudies have assumed, and the origin country is not necessarily a single country
but may change in the midst of a crisis. Therefore, identifying origin countries- 11 -

using high frequency data enables us to avoid these problems and investigate thecharacteristics of the crisis more in detail.
High-frequency contagion in this research is defined as negative spillovers
from an origin to other countries in terms of its yield spreads. The origin is theworst-hit country of that day to which investors respond, having effect on othercountries‟ spreads. Particular attention has been given to shifts of contagion as
well, resulting from shifts of origins. As Ito and Hashimoto (2005), we also checkthe consistency of our definition of origin, by cross-checking it against countryspecific financial news reports from Bloomberg and other relevant sources, anduse this to also investigate the transmission of the contagion. All in all, we
believe this method is useful as a first investigation into the European sovereign
debt crisis, in particular because its operational methods, clear identification of
directions and channels, and successfulness in showing the consistency of theirmodel at the hand of news analyses.

4. Methodology and Data Analysis
In this section, we explain our methodology to examine the contagion in
the European sovereign crisis. Also, we identify „origin countries‟, the concept
which we will cover in the rest of the section, and provide the basement for thefollowing analysis in section 5.
Though we obtained both sovereign CDS spreads and government bondyield, we use government bond yield in our analysis, largely due to its advantage
in availability for a large number of countries and the small number of missing
data compared to the CDS dataset. The sample period is 1 January 2008 to 22
July 2010 and countries are the GIIPS countries (Greece, Ireland, Italy, Portugal,
and Spain) and France and Belgium. Data source is Bloomberg.
In section 4 and 5, we examine two situations: one is „crisis‟ situation andthe other is „non-crisis‟ situation. Although our primary interest is the „crisis‟contagion, we use „non-crisis‟ contagion as supplementary evidence.
Origin countries and affected countries for both situations are identified asfollows: First, we calculate the daily change of government bond yield spread- 12 -

against German sovereign bond. A daily change of the government bond yieldspread is expressed as:

where

is government bond yield spread against Germany, of country j atdate t. Since we use yield spreads against German government bond, which isconsidered as a safe asset, a rise in the government bond yield spread, DR(t,j),can be thought to reflect the sovereign risk.
Next, we pick up the biggest DR(t,j) for each t, daily change of thegovernment bond yield spread of each date in the sample. Then if the biggest
daily change of spread is larger than 0.05%, i.e. DR(t,j)>0.05, the date is labeled“crisis” situation and if the biggest daily change is positive, i.e. DR(t,j)>0, thedatet is labeled “non-crisis” situation. In both cases, the biggest DR(t,j) isdenoted as “DOR(t,o)”, and the country with DOR(t,o), i.e. country o, is called“origin country” of date t. The only thing that make difference between “crisis”
and “non-crisis” situation is the threshold choice: crisis situation set thethreshold as DOR(t,o)>0.05 and non-crisis situation set it as DOR(t,o)>0. In thatsense, crisis situation is a subset of non-crisis situation. Of course, choosing thesethresholds cannot avoid arbitrariness and deserves careful consideration.
Whether it is crisis or non-crisis situation, we define these worst-hit
countries as “origins” of contagion. Similarly, daily spillover, the situation inwhich the worst-hit country affects other countries, is defined as “contagion”. The
underlying assumption here is that the origin country always experiences thelargest negative shock on the date t, and if the rise in the spread beyond thethreshold affects other countries‟ spread, then it is “contagion.”First, we examine the crisis situation, when the biggest daily change in the
spread is more than 0.05 percent. We obtained 200 dates of crisis days out oforiginal 667 samples. Table 1 lists the origin countries of each date, if any exists,
and table 2 shows the number of origins for each country, how many times thecountry experienced the date when the country is the origin. Figure 5 plots theorigin country and is virtually the same as table 1, though helpful as well to have

- 13 -

a closer look at some of the aspects of the origins. It is easily seen that Greece
and Ireland became origin countries in the end of 2008 and over the first half of2009 and later on, Greece became the only main origin country and continues tobe through July 2010.
In addition to the shift in origin countries, we can see a cutoff around
November 2009. After November 2009, there is a clear intensification of theGreece origins, probably triggered by the revelations on Greece‟s real deficit on 5
November 2010. Ireland became sparse after mid-2009, replaced by Greece.
Moreover, news events from this time, such as strikes and downgrades,
almost all originated from Greece. To check the consistency of our definition oforigin, we cross-checked against country-specific financial news reports from
Bloomberg and other relevant news sources, just like Ito and Hashimoto (2005)
have. Indeed, all outstanding six big negative events occurred after November
2009 and seem to be only dates of the largest nation-wide strikes and protests in
Greece over the past few months plus the major downgrade to „junk status‟ at theend of April.
Secondly, we also examine “non-crisis” situation where the threshold is set
at DOR(t,o)>0. Figure 6 plots the origin countries and table 2 lists the numbers oforigins for each country. From these analyses, we can identify Italy, Ireland andGreece as non-crisis origin countries. First, the timeline shows a clear
intensification of Italy throughout 2008, gradually getting sparse after the end of
the year. These Italy origin cases almost entirely disappear in crisis situation,
where the threshold is set at DOR(t,o)>0.05, implying that the daily changes in
spreads were less than 0.05 percent. Except for Italy, the timeline shows asimilar pattern as that of the crisis situation. During the mid-2008 Ireland andGreece became intense, and then after November 2009, the great intensificationof Greece can be observed.

- 14 -

Table 1. Origin Countries of Each Date
2008/1/23
2008/1/31
2008/3/4
2008/3/5
2008/3/6
2008/3/10
2008/3/13
2008/3/14
2008/4/24
2008/5/9
2008/8/19
2008/9/15
2008/9/18
2008/9/29
2008/10/22
2008/10/23
2008/10/24
2008/10/27
2008/10/29
2008/10/30
2008/10/31
2008/11/3
2008/11/12
2008/11/13
2008/11/17
2008/11/25
2008/12/1
2008/12/2
2008/12/4
2008/12/5
2008/12/11
2008/12/12
2008/12/15
2008/12/29
2009/1/6
2009/1/9
2009/1/12
2009/1/13
2009/1/14
2009/1/15
2009/1/16
2009/1/19
2009/1/20
2009/1/21
2009/1/22
2009/1/23
2009/2/4
2009/2/5
2009/2/10
2009/2/11

Italy
Belgium
Belgium
Italy
Greece
Italy
Portugal
Spain
Ireland
Greece
Italy
GreecePortugal
Italy
GreeceGreeceGreeceIreland
Greece
Italy
Ireland
Ireland
Italy
FranceGreeceGreeceGreeceIreland
Italy
Ireland
GreeceGreeceGreece
Italy
Ireland
GreeceIrelandSpain
IrelandSpain
Belgium
Ireland
Ireland
Ireland
GreeceGreeceSpain
Italy
GreeceGreece2009/2/12
2009/2/13
2009/2/16
2009/2/17
2009/2/24
2009/2/25
2009/2/27
2009/3/2
2009/3/3
2009/3/4
2009/3/6
2009/3/11
2009/3/12
2009/3/19
2009/3/30
2009/3/31
2009/4/8
2009/4/15
2009/4/20
2009/4/21
2009/4/27
2009/5/11
2009/5/12
2009/5/13
2009/5/14
2009/5/21
2009/5/28
2009/5/29
2009/6/2
2009/6/3
2009/6/4
2009/6/8
2009/6/19
2009/6/22
2009/6/23
2009/6/24
2009/7/7
2009/7/8
2009/7/24
2009/8/12
2009/8/14
2009/8/17
2009/8/19
2009/8/26
2009/8/31
2009/9/1
2009/9/3
2009/9/25
2009/9/30
2009/10/1

Ireland
Ireland
Portugal
GreeceGreece
Italy
GreeceIreland
Portugal
Ireland
Ireland
Ireland
GreeceIreland
GreeceIreland
Ireland
Ireland
Italy
GreeceIreland
GreeceGreecePortugal
Ireland
Portugal
GreeceIrelandSpain
Ireland
Portugal
Italy
Ireland
GreeceIreland
IrelandFranceGreeceGreecePortugal
Belgium
GreeceGreeceIreland
GreeceGreeceGreeceGreeceGreeceItaly- 15 -

2009/11/13
2009/11/16
2009/11/18
2009/11/19
2009/11/24
2009/11/25
2009/11/26
2009/12/4
2009/12/7
2009/12/8
2009/12/9
2009/12/14
2009/12/15
2009/12/17
2009/12/18
2009/12/21
2010/1/6
2010/1/12
2010/1/13
2010/1/14
2010/1/20
2010/1/22
2010/1/26
2010/1/27
2010/1/28
2010/2/2
2010/2/3
2010/2/4
2010/2/5
2010/2/8
2010/2/12
2010/2/15
2010/2/16
2010/2/18
2010/2/23
2010/2/24
2010/2/25
2010/3/4
2010/3/8
2010/3/9
2010/3/11
2010/3/17
2010/3/18
2010/3/19
2010/3/22
2010/3/24
2010/3/29
2010/3/30
2010/3/31
2010/4/6

GreeceGreeceGreeceGreeceGreeceGreeceGreeceGreeceGreeceGreeceGreeceGreeceGreeceGreeceGreeceGreeceIreland
GreeceGreeceBelgium
GreeceGreeceSpain
GreeceGreeceGreecePortugalPortugal
FranceGreeceGreeceGreeceGreeceGreeceGreeceGreeceGreeceGreeceGreeceGreecePortugal
Ireland
GreeceGreeceGreeceIreland
GreeceGreece
Italy
Greece2010/4/7
2010/4/8
2010/4/13
2010/4/14
2010/4/15
2010/4/16
2010/4/19
2010/4/20
2010/4/21
2010/4/22
2010/4/26
2010/4/27
2010/4/28
2010/5/4
2010/5/5
2010/5/6
2010/5/7
2010/5/12
2010/5/13
2010/5/14
2010/5/17
2010/5/19
2010/5/20
2010/5/21
2010/5/25
2010/5/28
2010/5/31
2010/6/1
2010/6/2
2010/6/3
2010/6/4
2010/6/7
2010/6/8
2010/6/11
2010/6/14
2010/6/15
2010/6/16
2010/6/22
2010/6/23
2010/6/24
2010/6/25
2010/6/28
2010/6/29
2010/7/5
2010/7/8
2010/7/12
2010/7/14
2010/7/15
2010/7/19
2010/7/22

GreeceGreeceGreeceGreeceGreeceGreeceGreeceGreeceGreeceGreeceGreecePortugal
GreeceGreeceGreeceGreeceGreecePortugal
GreeceGreeceGreeceGreece
Italy
Greece
Spain
Italy
Spain
GreeceGreecePortugal
Ireland
Belgium
Portugal
GreeceSpain
GreeceGreeceGreeceGreeceGreeceBelgium
GreeceSpain
Spain
GreecePortugal
Ireland
GreeceGreecePortugalFigure 5. Crisis Contagion: Timeline of Origin Countries

Belgium
FranceSpain
Portugal
Italy
Ireland
Greece2008/01/01

2008/07/01

2008/12/31

2009/07/01

2009/12/31

2010/07/01

Figure 6. Non-Crisis Contagion: Timeline of Origin Countries

Belgium

FranceSpain
Portugal
Italy
Ireland
Greece2008/01/01

2008/07/01

2008/12/31- 16 -

2009/07/01

2009/12/31

2010/07/01

Table 2. Number of Origins for Each Country
Note: In non-crisis contagion, overlapping countries are counted individually. Total is thetotal number of days when DOR>0
GreeceIreland

Italy

Portugal

Spain

FranceBelgium

Total

Crisis

110

35

17

17

11

3

7

200

NonCrisis

175

88

83

57

62

37

42

527

5.

Regression Results
In this section, using regression analysis we examine whether there was acontagion during the European sovereign debt crisis. In the previous section, we
identified origin countries of both crisis and non-crisis situation. To repeat ourdefinition, if these origin countries affect other countries‟ change in spreads, it isidentified as “contagion.” This identification of contagion is what we do in thissection.
Before moving onto the regression analysis, we present graphicalinterpretation of contagion. Figure 7a and 7b plot the bilateral daily changes ofgovernment bond spreads, setting Greece as the origin, and Portugal (figure 7a)
and France (figure 7b) as the affected countries. From our definition of origin
country, all of the plots are below the 45-degree line (an origin country at date t
has larger daily change in spread than any other countries in the sample), andlocated in area above 0.05% points of x-axis. When comparing these two graphs,
one can see there seems to be a difference in how an origin country affects othercountries, suggesting that Greece may have huge impact on Portugal, and little
impact on France. In the rest of the section, we empirically show which origin
country affected which country.

- 17 -

Figure 7a. Daily Changes in Greece and Portugal Spreads (Greece Origin Case)
1.2
1Portugal DR

0.8
0.6
0.4
0.2
0
-0.2
0

0.2

0.4

0.05

0.6

0.8

1

1.2

Greece DOR

Figure 7b. Daily Changes in Greece and Spain Spreads (Greece Origin Case)
1.2
1France DR

0.8
0.6
0.4
0.2
0

-0.2
0

0.2
0.05

0.4

0.6
Greece DOR- 18 -

0.8

1

1.2

The equation estimated is as follows:

where affected(t,j) is the daily change of government bond yield spread of affected
countries, which is the same as DR(t,j); origin(t,o) is the daily change of thegovernment bond yield spread of the origin country and is equal to DOR(t,o)
We use 2SLS for the estimation, because there may be endogeneity in thiscase. The instrument variable for origin(t,o) is the one-day lagged stock priceindex of origin countries. As the stock price index, we use the Athens Stock
Exchange General Index (Greece), ISEQ Index (Ireland) and FTSE MIB Index(Italy). All the data are from Bloomberg. Since we also assume heteroskedasticity
in the error term, a heteroskedasticity robust standard error is used. We estimate
the model both for crisis contagion and non-crisis contagion separately.
First, we estimate the regressions for crisis situation. We set Greece andIreland as origin countries. Table 3 shows the list of results of the regression
analyses. One can see that some of the regressions of Greece as the origin casegive positive coefficients that are significantly different from zero. In two cases,
where Ireland and Portugal are the affected countries, p-values are close to 0.
Secondly, in Greece origin case, we found that the slopes vary across regressions.
The largest coefficient, 0.257, is the case where Portugal is the affected country;
Greece to Ireland comes next with 0.198. On the other hand, the coefficients ofGreece to France is slightly negative and Belgium is around 0.05, and the effectsof Greece to Italy and Spain are somewhere in-between. In cases in which Irelandis the origin we could not get reliable results, probably because of the lack ofsamples: the case in which Ireland is the origin has only 35 samples.
Next, we estimate the model for non-crisis situation. We set Greece,
Ireland and Italy as origin countries. Table 4 summarizes the results. The resultsof Greece origin case are not so different from those of crisis contagion: Estimatedslope is positive and significantly different from zero in the cases where Irelandand Portugal are the affected countries, then Italy and Spain come to next; and- 19 -

we found no sign of contagion in the cases where France and Belgium are theaffected countries.
What matters here is Ireland origin case. In the crisis situation, theestimated slopes are not significantly different from zero, though the point
estimates are positive in 4 out of 6 cases. In non-crisis case, things are opposite:
estimated slopes in all the regressions are positive and significantly different
from zero.
One potential reason is the increase in the sample size: the non-crisis
regressions have 88 samples (86 in the regressions, due to missing values inlagged stock price index). This explanation is probable because out of 88 samples,
35 still remain in crisis situation, implying that the results obtained from crisis
contagion regression is just a product of small sample size. Another potential
reason is the difference in characteristics of contagion between crisis and noncrisis situation. As the word “non-crisis” represents, the situation where thelargest daily change in spread is positive is common. A crisis situation is bydefinition different from that usual situation and it is natural to think that thecharacteristics of contagion can be different. It is not clear whether the increased
sample size or the difference in characteristics of contagion is attributable to theincrease in significance of regression. Nonetheless, the results in non-crisis
contagion suggest that Ireland might have affected other countries both in crisisand non-crisis situation, given the fact that large portion of Ireland origin casesin non-crisis situation still remain in crisis situation.
In Italy origin case, no regression results are significant and estimatedslopes are slightly negative. Since Italy origin cases disappear in crisis situation,
we can conclude that Italy did not affect other countries in both crisis and noncrisis situation.
To summarize the results of our origin analysis and regressions, we may
conclude that high-frequency contagion did occur from Greece to most notably
Ireland and Portugal and to a lesser extent to Italy and Spain. Although in crisis
contagion regression, we could not find evidence that Ireland affected othercountries, possibly due to the lack of sample, non-crisis case provides a weak but
supportive evidence to conclude that there was contagion from Ireland. Indeed,- 20 -

Greece has been the main origin country throughout the European sovereign debt
crisis, but during the spring of 2009 some of the largest shocks originated from
Ireland.
To put it in chronological order, in early 2008 Italy was the origin country
in non-crisis situation, but the negative shock was small and did not affect othercountries. From mid-2008 to mid-2009, Ireland and Greece were the origins,
where Greece affected Ireland and Portugal, and to lesser extent Italy and Spain.
Also, though the evidence is not so concrete, Ireland might have affected othercountries in this period. After November 2009, Greece experienced the intense
second wave of the crisis and became the only origin country, affecting Irelandand Portugal, to lesser extent Italy and Spain, but not France and Belgium.

- 21 -

Table 3. Crisis Contagion: Regression Result (Origin: Greece, Ireland)Note: *, ** and *** denote 10%, 5% and 1% statistical significance respectively. Numbers in parenthesis and brackets areheteroskedasticity-robust t-values and p-values respectively.
OriginGreeceAffected
Ireland

Italy

Portugal

Spain

FranceBelgium

Constant
Coefficient
R-squared# of obs
Constant
Coefficient
R-squared# of obs
Constant
Coefficient
R-squared# of obs
Constant
Coefficient
R-squared# of obs
Constant
Coefficient
R-squared# of obs
Constant
Coefficient
R-squared# of obs

0.006***0.198
0.51
109
0.021
0.053
0.13
109
0.004
***0.257
0.65
109
0.015
0.077
0.30
109
0.012
-0.014
NA
109
0.014
0.010
0.05
109

OriginIreland(3.66)

Affected
Greece[0.00]

Italy(0.85)[0.40](4.13)[0.00](1.20)[0.23]

Portugal

Spain

France(-0.51)[0.61]

Belgium(0.22)[0.83]- 22 -

Constant
Coefficient
R-squared# of obs
Constant
Coefficient
R-squared# of obs
Constant
Coefficient
R-squared# of obs
Constant
Coefficient
R-squared# of obs
Constant
Coefficient
R-squared# of obs
Constant
Coefficient
R-squared# of obs-0.0831.053
NA35-0.021
0.367
0.00
35
-0.003
0.367
0.13
35
0.005
0.129
0.09
35
0.016
-0.022NA
35
0.022-0.032NA35(0.82)[0.42](0.52)[0.60](0.31)[0.76](0.15)[0.88](-0.04)[0.97](-0.03)[0.97]

Table 4. Non-Crisis Contagion: Regression Result (Origin: Greece, Ireland and Italy)
Note: *, ** and *** denote 10%, 5% and 1% statistical significance respectively. Numbers in parenthesis and brackets areheteroskedasticity-robust t-values and p-values respectively.
OriginGreeceAffected
Ireland

Italy

Portugal

Spain

FranceBelgium

Constant
Coefficient
R-squared# of obs
Constant
Coefficient
R-squared# of obs
Constant
Coefficient
R-squared# of obs
Constant
Coefficient
R-squared# of obs
Constant
Coefficient
R-squared# of obs
Constant
Coefficient
R-squared# of obs

0.001***0.192
0.54
171
0.009
**0.088
0.21
171
0.001
***0.254
0.68
171
0.008
0.066
0.24
171
0.008
0.015
0.062
171
0.006
0.033
0.15
171

OriginIreland(5.76)

Affected
Greece[0.00]

Italy(2.43)[0.02](7.06)[0.00](1.46)[0.15]

Portugal

Spain

France(0.95)[0.34]

Belgium(1.22)[0.22]- 23 -

Constant
Coefficient
R-squared# of obs
Constant
Coefficient
R-squared# of obs
Constant
Coefficient
R-squared# of obs
Constant
Coefficient
R-squared# of obs
Constant
Coefficient
R-squared# of obs
Constant
Coefficient
R-squared# of obs-0.007*0.2170.26
86-0.011**0.2140.27
86-0.003***0.2910.35
86-0.005**0.1770.22
86-0.002***0.128
0.255686-0.001***0.1430.31
86(1.73)[0.09](2.05)[0.04](3.11)[0.00](2.15)[0.04](3.37)[0.00](2.51)[0.01]

Table 4. (Cont.) Non-Crisis Contagion: Regression Result (Origin: Greece, Ireland and Italy)
Note: *, ** and *** denote 10%, 5% and 1% statistical significance respectively. Numbers in parenthesis and brackets areheteroskedasticity-robust t-values and p-values respectively.
OriginItaly

Affected
GreeceIreland

Portugal

Spain

FranceBelgium

Constant
Coefficient
R-squared# of obs
Constant
Coefficient
R-squared# of obs
Constant
Coefficient
R-squared# of obs
Constant
Coefficient
R-squared# of obs
Constant
Coefficient
R-squared# of obs
Constant
Coefficient
R-squared# of obs

0.299-6.298NA83
0.143-3.040NA83
0.033-0.520NA83
0.038-0.671NA83
0.037-0.734NA83
0.043-0.829NA83(-0.18)[0.86](-0.17)[0.86](-0.13)[0.90](-0.15)[0.88](-0.17)[0.86](-0.17)[0.86]- 24 -

6. Transmission Channels of the Contagion
In the previous section, we empirically showed that contagion had occurredin the European sovereign debt crisis. However, questions remain: how can thecontagion occur? In what way can a sovereign crisis in one country spread into
other countries? What makes the difference between country pairs between
which contagion occurs and not? In this section, we approach these questions
and examine how the contagion took effect during the crisis. In this section, we
use the data of crisis situation, since the crisis contagion is our main interest.
As we saw in the literature review of the transmission channels of crises,
there are four hypotheses to explain contagion: the common shocks, trade links,
financial links, and shift in investors‟ mind. In this paper, we consider two ofthem, the financial links and the shift in investors‟ mind. We do not consider theother two hypotheses for the following reasons: A contagion caused by common
shocks does not fit into our definition, and the European sovereign debt crisis
does not involve currency movement among the member countries, which isnecessary for trade links to fully serve as a transmission mechanism.
Instead, financial linkages may be crucial factor in spreading crisis. Figure
8 is a chart in New York Times website article, titled “In and Out of Each Other‟sEuropean Wallets”, published May 1, 2010. The chart graphically shows how
much European countries lend and owe each other and how large the financial
interconnection between European countries is. Not only in media accounts but
many policy debates, it is often mentioned that in the European sovereign debt
crisis financial linkages may trigger contagion. Indeed, given the strong financial
interconnection among European countries, as the figure depicts, it is highly
likely that the financial linkages play a crucial role in transmitting a crisis in one
country to another.
Additionally, we examine the hypothesis of non-fundamental channels ofcontagion, i.e. the shift in investors‟ mind. As expressed in the word “the Aegeancontagion”, the already-worsened crisis situation was severely aggravated by thedisclosure of the real fiscal deficit of Greece, which had nothing to do with thefundamentals in other countries. It does not go too far to say that it was notchange in the fundamental per se, but the update of the information that- 25 -

triggered the second wave of the crisis. Therefore, the disclosure of the real fiscal
deficit in Greece might have served as a “wake-up call”, inducing the investors toreassess the risk of other countries with similar fundamentals.
First, we examine the financial links. The methodology is as follows: first,
for each contagion pair we develop two indices measuring the depth of contagion,
both for Greece and Ireland origin case. One is „the contagion coefficient‟ for both
Greece and Ireland origin case, the same index as the one used in Ito andHashimoto (2005). The contagion coefficient is calculated as the sample averageof DR(t,j)/DOR(t,o) over all the t in each origin case. This variable measures theratio of reaction of spreads in non-origin countries against the spreads of theorigin country, the biggest and more than 0.05 percentage change in sovereign
spreads. The other contagion index is the estimated slope of regressions. In theprevious section we estimated the following equation with 2SLS for each originaffected country pair:

Unless there are any factors generating bias in the estimation,and consistent estimator ofis an unbiased, how much affected(t,j) reacts to the rise inorigin(t,o). Thus the estimated slope,, measures the degree of contagion foreach i and o.
Next, following Ito and Hashimoto (2005) and Caramazza et al. (2004), weconstruct several indicators to measure the financial linkages of each contagion
pair, so that each country pair has both the contagion coefficient and thefinancial indicators. As the last step, we calculate the correlation coefficient
between the contagion indices and each financial indicator. The higher thecorrelation is, the financial indicators have greater effect in strengthening thecontagion.

- 26 -

Figure 8: Europe‟s Web of Debt
Source: Nelson, D. Schwartz “In and Out of Each Other‟s European Wallets” New YorkTimes, Retrieved on 31 August 2010 from:(http://www.nytimes.com/interactive/2010/05/02/weekinreview/02marsh.html)- 27 -

Table 5 shows the summary of the four financial indices. The first two
indices are to examine „the common lender hypothesis‟, originally presented in
Kaminsky and Reinhart (2000). The hypothesis assumes an indirect channel oftransmission: the common lender to both the origin and the affected country,
which is often assumed as banks, serves as a „financial intermediary‟ of the crisis.
When a crisis breaks out in one country, investors who have positions in thatcountry usually tend to reduce their now increased risk exposure and will sell
assets whose returns are likely to be correlated with those of the origin country.
Therefore, (1) the larger positions the common lender has to the origin andaffected countries, the larger the contagion should be. Also, (2) when both theorigin and affected country have larger liabilities to the common lender, thelarger the contagion becomes.
The first index, the Common Creditor Index (CCI) (Lender-based) is usedin Ito and Hashimoto (2005). This index is constructed to mainly capture thelender‟s aspect of the common lender hypothesis, that is, (1) in the explanation
above. On the other hand, the second index, the Common Creditor Index (CCI)(Borrower-based) is constructed to capture the borrower‟s aspects of the
hypothesis, which is (2) above. We identify France, Germany, the United
Kingdom, and the United States as the common creditors, given their large
amount of lending and borrowing share in the GIIPS countries.
The second two indices, the Composite Common Creditor variable, CCC(Lender: Origin) and CCC (Lender: Affected) are mainly used in Caramazza et al(2004). Unlike in the original usage, we use these indices to capture the direct
financial link between the origin and the affected country in our analysis.
Following their methodology, we calculate the share of the borrower‟s externalliabilities owed to the lender (CCA) and the share of the lender‟s portfolio lent tothe borrower (CCB). The composite common creditor variable (CCC) is thencalculated as the product of these two indices. CCC (Lender=Origin) sets theorigin country as the lender and the affected country as the borrower, while CCC(Lender=Affected) sets the affected country as the lender and the origin country
as the borrower.

- 28 -

Table 5. Summary of Financial IndicesNote: Arrows represent lending:Indexmeans the amount of lending from country i to j.

Definition

Common Creditor Index(CCI) (Lender-based)

Common Creditor Index(CCI) (Borrower-based)

Composite Common
Creditor variable (CCC)(Lender=Origin)

Composite Common
Creditor variable (CCC)(Lender=Affected)

‟

‟

‟

‟

- 29 -

Table 6 and figure 9a and 9b show the result of the analysis. We found noor even negative correlation between the contagion indices and financial indices.
Figure 9a and 9b plot the contagion indices (contagion coefficient in figure 9a andestimated slope in 9b) and four financial linkage indicators, showing that there isno clear relationship between the two variables. It is often mentioned that
financial linkages play a certain role in transmitting the crisis. Although it ishighly intuitive and straightforward to think so, we could not find any supportive
evidence.
Next, we consider the other possibility, shifts in investors‟ mind, using
fiscal data of the affected countries. Our hypothesis is that when the fiscal
situation in one country is in a severe situation, the country is vulnerable
contagion from the origin country. This can be interpreted in the following way:facing a crisis in one country, the investors may reassess the risks in othercountries with similar fundamentals. This can be a variety of financial linkages
hypothesis: While the financial linkages hypotheses analyzed above emphasize
the role of financial “links” in transmitting crisis, the interpretation here puts anemphasis on the “similar fundamentals” in the affected countries. A thought
experiment may make it easier to understand the mechanism: if you are an
investor facing losses in one country with fiscal crisis, which country would you
think become the next source of loss? You may reassess the risks in countries
with large or increasing government debt and think that these countries would
be the target.
We calculated the correlation coefficient between the contagion indicesused in the previous analysis and four fiscal indicators of the affected countries:1) consolidated government debt (percentage of GDP), 2) change in the GDP ratioof government debt, 3) fiscal deficit (percentage of GDP) and 4) change in the
GDP ratio of fiscal deficit. To categorize these four variable, 1) is stock variable,
2) and 3) are change in stock variable, or almost equivalently, flow variable, and
4) is the change in flow variable. All the data are annual data and as of 2009,
when the crisis, especially the second wave, was getting severe. We assign thesevariables to affected countries, whatever the origin country is.

- 30 -

Table 6 and figure 10a and 10b show the result. The correlation coefficientsare all positive and relatively high. The correlation between the contagion
coefficient and the fiscal deficits and the change in government debt is around 0.5.
The scatter plot of figure 10a and 10b also confirms this positive correlation.
The results also show that the flow variable (the fiscal deficits) andequivalently, the change in the stock variable (the change in the governmentdebt) have higher correlation with the contagion indices (around 0.5). On theother hand, the stock variable (the government debt) and the change in the flow
variable (the change in the fiscal deficit) are not highly correlated with the depthof contagion (the correlation coefficients vary from 0.1 to 0.4). This might imply
that the contagion was caused by investors‟ risk assessment, facing the now
worsening fiscal situation. The reason that stock variable itself does not havesuch a high correlation as flow variable might be that what investors care is the
direction of the fiscal situation, whether fiscal balance is getting worse or better.
To check the result in a different manner, we let the fiscal variables andcontagion coefficient vary across time. We calculate contagion coefficients foreach quarter and for each origin case (Greece and Ireland). In Greece origin case,
2008Q1-Q3 and 2010 Q2, in Ireland origin case 2008Q1-Q3, 2009Q3-Q4, and2010Q2 have only zero or just a few samples for each quarter, we could not
calculate contagion coefficient for these quarter. We treat data in these quarters
as missing values. Finally we have 60 samples (contagion coefficients for timevarying contagion pairs). As the last step, we calculated correlation coefficient
between contagion coefficients and lagged quarterly change in government debt
of the affected countries.
The results are summarized in table 7 and figure 11. The correlation
coefficient is 0.177, still positive, though it is not so high in the analysis above.
The depth of contagion is still positively correlated with the changes in fiscal
situation in the affected countries, even with time-varying contagion coefficientand fiscal variables. It is worth to note that the correlation almost disappears
when we use the non-lagged fiscal variable. The correlation coefficient is no morethan 0.07. This difference might be also supporting our analysis above, because it
is usual that government debt of a certain quarter is published during the next

- 31 -

quarter. It suggests that it might not be the fundamental per se but the update of
information that induces investors reassess the risks.
To summarize the results we obtained from this section, we found no
positive evidence for financial linkage hypothesis. On the other hand, we foundthat the contagion is highly correlated with the fiscal situation in the affected
countries, possibly suggesting that the contagion was transmitted by investors‟
reassessment of risks in countries with similar situations as the origin countries.
This correlation is robust to several fiscal and contagion indices and even to timevarying indices.
However, the analysis in this section has several limitations. First, fiscal
situation in affected countries cannot explain factors related to origin countries,
which is insufficient for a transmission channel analysis: it would not be the casethat countries with worsening fundamentals are hit by contagion whatever origin
country is. Ultimately, transmission channels of contagion should be explained by
the factors related to the links between origin and affected country. Moreover, it
is puzzling that while the investors‟ reassessment effect is a likely cause ofcontagion, the other side of a coin, financial linkage, does not seem to be playing
a role in contagion. For investors to face financial loss in an origin country andwithdraw the funds from the affected countries, there should be financial
linkages between countries, whether it is direct or indirect. Finally, since wehave only handful of samples (contagion pairs) and could not control for otherfactors, any results remain to be tentative. Further research is therefore anecessity to better understand the origins and spread of crises.

- 32 -

Table 6. Results on the Transmission Channel Analysis
Original Data Source: Eurostat, Bank for International Settlement (BIS), Consolidated International Claims of Reporting Banks on
Individual Countries, by Nationality of Reporting Banks, BIS Quarterly Review, March 2010
Note: The financial data are as of end-September 2009. Fiscal data are as of 2009 and transformed to percentage of GDP.
Contagion Pair

Contagion IndicesFinancial Indices

OriginAffected

contagioncoefficient

regression
slope

CCI lender

CCI
borrower

CCC origin

GreeceIreland

0.25

0.20

0.33

169.45

2.36

Greece

Italy

0.20

0.05

0.55

169.45

2.32

GreecePortugal

0.24

0.26

0.04

79.36

Greece

Spain

0.15

0.08

0.57

GreeceFrance

0.07

-0.01

GreeceBelguim

0.10

Ireland

GreeceIreland

Fiscal Indicesgov. debt

change ingov. debt

fiscal deficitchange infiscal deficit

0.64

0.20

0.14

0.07

0.10

1.16

0.10

0.05

0.03

20.14

0.09

0.77

0.11

0.09

0.07

196.43

0.03

0.04

0.53

0.14

0.11

0.07

1.44

189.64

51.05

0.16

0.78

0.10

0.08

0.04

0.01

0.39

160.15

2.15

0.10

0.97

0.07

0.06

0.05

0.30

1.05

0.33

169.45

1.15

0.16

0.14

0.06

Italy

0.16

0.37

12.20

122.41

5.59

1.16

0.10

0.05

0.03

Ireland

Portugal

0.34

0.37

0.83

57.32

29.04

1.30

0.77

0.11

0.09

0.07

Ireland

Spain

0.15

0.13

12.83

141.90

1.97

8.62

0.53

0.14

0.11

0.07

IrelandFrance

0.14

-0.02

32.25

136.99

17.70

4.50

0.78

0.10

0.08

0.04

Ireland

Belguim

0.14

-0.03

8.80

115.68

64.02

0.96

0.97

0.07

0.06

0.05

corr. coefficient withcontagion coefficient

-0.32

-0.47

-0.18

-0.06

0.10

0.47

0.52

0.39

corr. coefficient withregression slope

-0.26

-0.10

-0.28

0.01

0.38

0.45

0.52

0.20

- 33 -

CCC
affectedFigure 9a. Contagion Coefficient and Financial Indices
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0

5

10

15

20

25

30

35

Common Creditor Index (Lender-Based)

0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0

40

80

120

160

Common Creditor Index (Borrower-Based)- 34 -

200

Figure 9a. Contagion Coefficient and Financial Indices (Cont.)
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0

10

20

30

40

50

60

70

CCC (Lender=Origin)

0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0

2

4

6

CCC (Lender=Affected)- 35 -

810Figure 9b. Estimated Slope and Financial Indices
1.2
1
0.8
0.6
0.4
0.2
0
-0.2
0

5

10

15

20

25

30

35

Common Creditor Index (Lender-Based)

1.2
1
0.8
0.6
0.4
0.2
0
-0.2
0

40

80

120

160

Common Creditor Index (Borrower-Based)- 36 -

200

240

Figure 9b. Estimated Slope and Financial Indices (Cont.)
1.2
1
0.8
0.6
0.4
0.2
0
-0.2
0

10

20

30

40

50

60

70

CCC (Lender=Origin)

1.2
1
0.8
0.6
0.4
0.2
0
-0.2
0

2

4

6

CCC (Lender=Affected)- 37 -

810Figure 10a. Contagion Coefficient and Fiscal Indices
0.4
0.35
0.3
0.25
0.2
0.15
0.1

0.05
0
0%

20%

40%

60%

80%

100%

120%

Government Debt (% of GDP)

0.4
0.35
0.3
0.25
0.2
0.15

0.1
0.05
0
0%

2%

4%

6%

8%

10%

12%

14%

Change in Government Debt-GDP Ratio- 38 -

16%

18%

20%

22%

Figure 10a. Contagion Coefficient and Fiscal Indices (Cont.)
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0%

3%

6%

9%

12%

15%

Fisical Deficit (% of GDP)

0.4
0.35

0.3
0.25
0.2
0.15
0.1
0.05
0
0%

1%

2%

3%

4%

5%

Change in Fisical Deficit (% of GDP)- 39 -

6%

7%

8%

Figure 10b. Estimated Slope and Fiscal Indices
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0%

20%

40%

60%

80%

100%

120%

Government Debt (% of GDP)

0.4
0.35
0.3
0.25
0.2
0.15

0.1
0.05
0
0%

2%

4%

6%

8%

10%

12%

14%

Change in Government Debt-GDP Ratio- 40 -

16%

18%

20%

22%

Figure 10b. Estimated Slope and Fiscal Indices (Cont.)
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0%

3%

6%

9%

12%

15%

Fisical Deficit (% of GDP)

0.4
0.35

0.3
0.25
0.2
0.15
0.1
0.05
0
0%

1%

2%

3%

4%

5%

Change in Fisical Deficit (% of GDP)- 41 -

6%

7%

8%

Table 7. Results on the Transmission Channel Analysis Using Quarterly Data
Note: ⊿Debt is a quarterly change in government debt/GDP ratio of the affected country. The source of fiscal data is Eurostat.
Quarter
2008Q4

OriginGreeceIreland

2009Q1

GreeceIreland

2009Q2

GreeceAffected
Ireland
Italy
Portugal
Spain
FranceBelgium
Greece
Italy
Portugal
Spain
FranceBelgium
Ireland
Italy
Portugal
Spain
FranceBelgium
Greece
Italy
Portugal
Spain
FranceBelgium
Ireland
Italy
Portugal
Spain
FranceBelgium

Contagion coef.
0.34
0.54
0.36
0.38
0.14
0.24
0.37
0.24
0.39
0.20
0.34
0.23
0.34
0.42
0.29
0.10
0.08
0.16
0.38
0.15
0.30
0.13
0.09
0.10
0.37
0.57
0.19
0.14
0.14
0.17

⊿Debt
6.5
-0.5
0.4
0.9
0.8
0.7
1.0
-0.5
0.4
0.9
0.8
0.7
5.4
1.1
2.3
3.0
0.3
2.5
0.7
1.1
2.3
3.0
0.3
2.5
7.5
6.0
2.4
3.3
2.0
5.9

Quarter
2009Q2(cont.)

Affected
Greece
Italy
Portugal
Spain
FranceBelgium
2009Q3
GreeceIreland
Italy
Portugal
Spain
FranceBelgium
2009Q4
GreeceIreland
Italy
Portugal
Spain
FranceBelgium
2010Q1
GreeceIreland
Italy
Portugal
Spain
FranceBelgium
Ireland
Greece
Italy
Portugal
Spain
FranceBelgium
Correlation coefficient- 42 -

OriginIreland⊿Debt
Contagion coef.
0.18
0.17
0.31
0.13
0.10
0.11
0.30
0.33
0.25
0.39
0.22
0.26
0.28
0.07
0.18
0.13
0.00
0.05
0.17
0.11
0.23
0.12
0.04
0.07
0.12
0.05
0.36
0.15
0.17
0.09
0.177

8.0
6.0
2.4
3.3
2.0
5.9
8.2
2.0
5.4
4.1
4.7
1.3
2.4
2.9
-0.4
2.6
1.9
2.0
3.1
-1.2
3.5
3.5
2.0
-2.2
1.3
-1.2
3.5
3.5
2.0
-2.2

Figure 11. Quarterly Contagion Coefficient and Change in Government Debt
0.6
0.5
0.4
0.3
0.2
0.1
0
-3%

0%

3%

6%

Quarterly Change in Government-GDP Ratio- 43 -

9%

7. Conclusion
This paper has been one of the first attempts to analyze the role ofcontagion in the European sovereign debt crisis, today often referred to as the
Aegean Contagion after its most apparent origin country. With modifications, Itoand Hashimoto‟s (2005) analysis of high-frequency contagion in the Asian crisishas been used to empirically investigate the Aegean Contagion. At the hand ofdaily government bond yield spread data, negative spillovers have been definedand analyzed from their origin to the affected countries.
This paper has shown that Greece was the main origin country of highfrequency contagion during the crisis between January 2008 and June 2010,
affecting Ireland and Portugal greatly, and Italy and Spain to a lesser extent.
Although the evidence is not as convincing, Ireland might have affected othercountries from late 2008 to mid 2009. A shift of origins has occurred in the springof 2009, from Greece to Ireland and back again, after Greece revealed its real
deficits in November 2009. Finally, we found that the degree of the contagion ishighly correlated with the fiscal situations in the affected countries, suggestingthat the contagion was transmitted by investors‟ reassessment of risks incountries with similar fundamentals as the origin country.
However, massive and important, many aspects could not be covered inthis short research paper. Much more investigation is needed to determine thedifferences between for example the two main waves of contagion coming from
Greece during the crisis (before and after November 2009). Furthermore, thecrisis seems not to be over yet so renewed analysis at a later stage will be needed,which could also include countries like Belgium, Hungary and the UK that haveall shown striking GIIPS-like movements over the past few months as well.
Finally, the role of positive spillovers and especially the positive role IFIs seem tohave had in these in responding to this European sovereign debt crisis could
prove a very interesting follow-up topic on the mitigation of crisis and preventionof contagion. Academics and practitioners alike will be interested in this crisis
and its contagion for many more years – for now it proves to be a great case study
for what is still to come in a world with rising sovereign debts and everincreasing interdependence.

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References
Beirne, J. et al. (2008) Volatility Spillovers and Contagion from Mature to
Emerging Stock Markets. IMF Working Paper 09/05. International Monetary
Fund, Washington, DC.
Caramazza, F., Ricci, L., Salgado, R. (2004) International Financial
Contagion in Currency Crisis. Journal of International Money and Finance, 23,
51-79.
Gande, A., Parsley, D. (2005) News Spillovers in the Sovereign Debt
Market. Journal of Financial Economics, 75, 691-734.
Ito, T. & Hashimoto, Y. (2002) High-frequency contagion of currency crisis
in Asia. National Bureau Economic Research Working Paper 9376, Cambridge,
MA.
Ito, T. & Hashimoto, Y. (2005) High-Frequency Contagion of Currency
Crisis in Asia. Asian Economic Journal, 19 (4), 357-381.
Kaminsky, G. & Reinhart, C. (2000) On Crises, Contagion, and Confusion.

Journal of International Economics, 51 (1), 145–168.
Schwartz, Nelson, D. (2010) In and Out of Each Other‟s European Wallets.
New York Times, Retrieved on 31 August 2010 from:http://www.nytimes.com/interactive/2010/05/02/weekinreview/02marsh.html
Thesing, A. and Krause-Jackson, F. (2010) Greece Gets $146 Billion
Rescue in EU, IMF Package. Bloomberg news analysis, retrieved on 10 May
2010:
http://www.bloomberg.com/apps/news?pid=20601087&sid=a9f8X9yDMcdI&pos=1.
Van Rijckeghem, C., Weder, B. (2001) Sources of Contagion: Finance orTrade? Journal of International Economics, 54, 293-308.
World Bank portal on contagion, retrieved on 21 July 2010 from:http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/EXTPROGRAMS/EXTMACROECO/0,,contentMDK:20889756~pagePK:64168182~piP
K:64168060~theSitePK:477872,00.html.

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