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The European Sovereign Debt Crisis. A Correlation

Study of Sovereign CDS Spreads

Nathan Evans

Abstract

The current sovereign debt crisis is not the first event of its kind, but it is unique in the sense that it

affects a collection of heterogeneous countries within a monetary union. In this thesis I employ data

from the sovereign CDS markets to track contagion within the monetary union. The study

specifically addresses the spill over effects from the periphery, the so called PIIGS, to the larger

core economies of Germany and France. The first part of the study gives a market participant’sperspective on the similarities and differences between the sovereign bond market and the CDS

market. It reviews some of the major events of the crisis and the regulatory and market

developments these events have brought about. In the second part of the thesis statistical methods

are used to analyse the trends in the financial markets which were observed. Descriptive statistics

are presented to quantify these observations and subsequently a correlation study of 5yr sovereign

CDS rates of selected European sovereigns during a pre-crisis and sovereign crisis period is

performed. Finally the time varying correlation between Greek and German CDS returns is studiedin detail giving insight into the nature of the change in correlation over the entire study period from

2003 until 2010.



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Table of Contents

Abstract ................................................................................................................................................. i

1. Introduction ................................................................................................................................... 1

1.1. The Bond Market................................................................................................................... 1

1.1.1. The Make-Up ................................................................................................................. 1

1.1.2. Size and Scope ............................................................................................................... 2

1.1.3. Sovereign Bonds ............................................................................................................ 2

1.2. The European Sovereign Debt Crisis .................................................................................... 3

1.2.1 Historic Sovereign Defaults ........................................................................................... 5

1.2.2 From a Tiger to a PIIG ................................................................................................... 6

1.2.3 The Greek Crisis ............................................................................................................ 6

1.3. Credit Default Swaps............................................................................................................. 9

1.3.1. Size and Scope ............................................................................................................. 10

1.3.2. Reforms in the CDS Market......................................................................................... 12

2. Foundations................................................................................................................................. 16

2.1. Methodology ....................................................................................................................... 16

2.2. Theoretical Foundations ...................................................................................................... 19

2.2.1. Descriptive Statistics & Statistical Inference .................................................................. 19

2.2.2. Regression and Correlation Analysis ............................................................................... 20

2.3. Economic Theories and Financial Phenomena ................................................................... 21

2.3.1. Convergence .................................................................................................................... 21

2.3.2. Yield Compression and Decompression .......................................................................... 22

2.3.3. Flight To Quality ............................................................................................................. 22

2.3.4. Contagion ......................................................................................................................... 23

3. Data ............................................................................................................................................. 24

3.1. The Data Set ........................................................................................................................ 24

3.2. Limitations of the Data Set. ................................................................................................. 24

4. Results......................................................................................................................................... 25



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4.1. Descriptive Statistics ........................................................................................................... 25

4.2. Regression Analysis ............................................................................................................ 29

4.3. Correlation Analysis ............................................................................................................ 32

5. Conclusion .................................................................................................................................. 37

5.1. Further Studies .................................................................................................................... 39

5.2. Epilogue............................................................................................................................... 40

6. Appendix ..................................................................................................................................... 41

6.1. The CMA data set................................................................................................................ 41

6.2. Secondary Market GGB Volumes ....................................................................................... 41

7. List of Figures ............................................................................................................................. 42

8. Bibliography ............................................................................................................................... 43



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1. Introduction

‘ I used to think that if there was reincarnation, I wanted to come back as the president or the pope

or as a .400 baseball hitter. But now I would like to come back as the bond market. You can

intimidate everybody .’ – James Carville, Political Advisor to President Clinton

1.1. The Bond Market

The Bond Market (or Fixed Income Market) is a financial market where participants trade debt

securities. Bond Trading is a core investment bank activity. It is often grouped together with the

Commodity and Currency Markets and referred to by the acronym FICC (Fixed Income,

Commodity and Currency).

1.1.1. The Make-Up

The Securities and Financial Markets Association (SIFMA) classifies the bond market into the

following segments: 1

Government & Agency

Municipal, Corporate

Asset & Mortgage Backed Securities Money Market Instruments

The outstanding and new issuance of government bonds is so large compared to all other bond

issuance that the term ‘ The Bond Marke t’ is often used to denote the government bond market

itself.

1 http://www.investinginbonds.com/



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1.1.2. Size and Scope

The size of the global bond market is truly awe-inspiring. The outstanding global issuance increased

by 5% in 2010 to reach a record $95 trillion dwarfing the global equity market which by

comparison had a total market capitalisation of around $55 trillion. 2

Since 2007 and the onset of the World Financial Crisis sovereign issuance has become even more

pronounced due to the debt taken on by governments to bail out their banks and systemically

relevant financial institutions (SIFIs). In 2008 government bonds accounted for around 50% of

outstanding issuance; by 2010 this had increased to 57% of the total. 3

1.1.3. Sovereign Bonds

‘Sovereign b onds’ is the generic term for debt instruments issued by national governments. When

denominated in the countries own currency they are sometimes more specifically referred to as

government bonds. Government bonds have the longest history of any financial instrument in the

fixed income space, the first ever government bond being issued by the English government in

1693. The original purpose for which governments issued debt was usually to finance their warsagainst other powers. Today domestic government issuance serves many purposes. Government

debt offers an asset class in which banks can park liquidity. It also builds a reference yield curve,

often referred to as a ‘risk free curve’. This creates transparency in the market thereby lowering the

cost of capital for domestic companies. It is therefore usual that even wealthy countries with no net

national debt and who usually run a surplus to still issue in the sovereign bond market.

2 The City UK, Bond Markets, July 20113 The City UK, Bond Markets, July 2011



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1.2. The European Sovereign Debt Crisis

‘ Lieber ein End emit Schrecken als ein Schrecken ohne Ende .‘ – German Proverb

It is not a new phenomenon for countries to denominate some issuance in a currency they do not

control. The tendency of emerging market countries to issue in world reserve currencies reflects the

reality of the preferences of investors for assets in these currencies rather than the illiquid domestic

currencies of the emerging market issuers themselves. In the past this has led to problems for South

American and Asian issuers when the markets have speculatively attacked their currencies making

the servicing of foreign denominated liabilities particularly painful for these economies.

The European Monetary Union (EMU) has led to a rather special case. The Euro is indeed the

domestic currency for each of the 17 nations within the Eurozone, however the members of the

EMU face many of the same issues that in recent times have plagued emerging market countries

who denominated their bond issues is USD; namely that their primary source of funding and their

outstanding debt is in a currency over which they have no (or at the very best very limited)

monetary policy influence.

Sovereign debt is arguably the asset class that poses the greatest systematic risk to the world’sfinancial system today. Following the downgrade of the United States Credit Rating by S&P in

August 2011 4 the increasing interest in the sovereign CDS market has led to the market data

provider CMA adding sovereign CDS levels and the associated implied default probabilities to its

daily Marketflash 5. The extent of the risk posed to the financial system by Sovereign debt is due to

the sheer magnitude of the positions in this asset class held by institutions within the banking

system. The large size of these holdings is due to several reasons, the major ones of which are listed

here:

i. Until the onset of the European Sovereign Debt Crisis the sovereign issuance of advanced

economies asset was perceived as extremely low risk (if not risk free). This was not without

good cause. No government of an advanced economy has defaulted on, or restructured its

debt since World War II (cf. Glöckler 2011, p48).

4 http://www.standardandpoors.com/ratings/articles/en/us/?assetID=12453165295635 http://www.bobsguide.com/guide/news/2011/Aug/10/cma-adds-cds-data-on-key-sovereign-debts-to-its-free-of-charge-daily-marketflash.html



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1.2.1 Historic Sovereign Defaults

On a global scale sovereign defaults are nowhere near as unlikely as is the general perception today.

Since the 19th century there have been hundreds of cases where sovereign issuers have been unable,

or unwilling to service their obligations and have either directly defaulted, restructured or reached

an accord with a group of creditors (cf. Reinhart & Rogoff, 2008, p24-29).

The following table shows cumulative default rates for sovereign issuers by Moody’s Credit Rating

Agency over one to ten years for the period 1983-2007. Even over this recent period investment

grade rated sovereigns (≥Baa) have not been immune from de fault and amongst those with a

speculative g rade rating (≤Ba ) default is not an uncommon occurrence at all.

Figure 1-1 Historic sovereign cumulative default rates 1983-2007 ( Moody’s Global CreditResearch March 2008).

Historically the largest defaults measured in terms of nominal value of defaulted debt are Argentina

in 2001 which defaulted on $82.2 billion and Russia in 1998 which defaulted on $72.7 billion. The

seriousness of the European Sovereign Debt Crisis is evidenced by the fact that were any one of

Greece ($430 billion outstanding public debt and guarantees), Ireland ($145 billion) or Portugal



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($289 billion) to default, then the restructuring would prove to be approximately as large or larger,

and certainly far more complicated than the Argentinian and Russian defaults combined!

1.2.2 From a Tiger to a PIIG

‘ Remember the Celtic Tiger? Well, she turned out to be a pussycat with a shamrock .’ – Howard

Gold, Marketwatch.com 27 th September 2010

PIGS is an acronym used to refer to the Eurozone economies of southern Europe; specifically

Portugal, Italy, Greece and S pain. It’s has been in common usage by market practitioners,

academics and commentators since the days of the European Exchange Rate Mechanism (ERM) if

not before.

Between 1995 and 2007 Ireland was often referred to as the ‘Celtic Tiger ’ in reference to its then

impressive GDP growth. In 2008 this came to a stunning end when in the midst of the World

Financial crisis Irish GDP contracted by 14%. The subsequent Irish Banking Crisis during which

Ireland guaranteed the senior debt of its largest banks resulted in Ireland itself requiring an EU and

IMF funded Bailout in September 2010. In light of these events it became common practice

amongst practitioners and market commentators to add an additional ‘ I’ to PIGS rendering the

acronym PIIGS to include reference to the distressed economy of Ireland. 15 years after its golden

age had begun the Celtic Tiger had become a PIIG.

1.2.3 The Greek Crisis

Beyond a shadow of a doubt the main concern within the stricken economies of Europe is caused by

Greece. Like Ireland, Greece had one of the fastest growing economies in the period preceding the

financial crisis. All major world business news services have detailed the timeline of the events

leading up to the Greek bailout. Rather than reproducing one of these accounts here detailed

information can be referenced at any of the sources listed in the footnotes. 8, 9, 10

8 http://www.telegraph.co.uk/finance/economics/8580720/Timeline-of-a-crisis-how-Greeces-tragedy-unfolded.html 9 http://www.guardian.co.uk/business/2010/may/05/greece-debt-crisis-timeline

http://www.telegraph.co.uk/finance/economics/8580720/Timeline-of-a-crisis-how-Greeces-tragedy-unfolded.html



http://www.guardian.co.uk/business/2010/may/05/greece-debt-crisis-timeline







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The effects of the crisis in Greece have been truly catastrophic in both a social and economic sense.

Debt downgrades from investment grade to junk status and an EU and an IMF Bailout have gone

hand in hand with riots on the streets of Athens. The government has been obliged to push through

largely unpopular reforms resulting in a significant degradation of the quality of life of the average

Greek citizen.

Although the EU and IMF bailout has so far kept Greece from defaulting on its public debt the

impact to Greek debt instruments in the secondary market has been severe. As exhibited by Figure

1.2 liquidity has vanished from the market. Data from the Bank of Greece show a dramatic decrease

in secondary market activity as measured by volumes traded on their secondary market platform

HDAT. The volume in the second half of 2010 was just over €7 Billion , a factor of 80 less than the

~€560 Billion traded during the second half of 2004.

Figure 1-2 Greek bonds secondary market trading data on the Bank of Greece HDATplatform.

10 http://www.ft.com/intl/cms/s/0/003cbb92-4e2d-11df-b48d-00144feab49a.html#axzz1V0o8LUDo (Interactivetimeline: Greek debt crisis)

http://www.ft.com/intl/cms/s/0/003cbb92-4e2d-11df-b48d-00144feab49a.html#axzz1V0o8LUDo






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The decimation in the traded volumes has been accompanied by an equally impressive decline in

price (and related rise in yield to maturity) across the entire Greek yield curve. Figure 1.3 shows the

Greek sovereign bond curve sourced from the Stuttgart Stock Exchange. As can be observed many

of these debt instruments are currently being priced by the market at levels around 50% of their par

value. Any bank that has chosen not to impair their holdings in these instruments is in effectcarrying them at a value twice that at which the market values them in an environment where the

liquidity is so poor they would in any case find it difficult to liquidate anything but the smallest

position.

Figure 1-3: Greek sovereign debt secondary market prices. Stuttgart Stock Exchange 29 July2011.



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1.3. Credit Default Swaps

‘ Derivatives are financial weapons of mass destruction carrying dangers that, while now latent, are

potentially lethal ’ – Warren Buffet 11

Credit Default Swaps (CDS) are a relatively new financial derivative which enables both the

mitigation and transfer of credit risk between market participants. Essentially when a CDS is

traded the parties agree to terms of insurance for a designated reference obligation, or class of

obligation (senior or subordinated debt). The buyer of protection then pays an insurance

premium to the seller of protection to protect the value of his asset in the case of a broadly

defined ‘Credit Event’ . (Bankruptcy, restructuring, failure to pay, etc.)

One of the main arguments for CDS is the very fact that they enable the trading of specific risks

thereby helping make financial markets more efficient and transparent by increasing price

discovery and liquidity. (cf. Stulz 2009). The cost of capital of all companies is then reduced as

this enables specific risk to migrate to those most capable of pricing it.

Despite the obvious advantages of CDS when one read the press coverage of this previously

relatively unknown derivative from the time of the financial crisis, it is forgivable to think thatCDS as a product was a major cause of the crisis itself. This is probably due to the startling

moves in CDS rates which were often reported in news updates at that time. Indeed previous

studies have shown that in the sovereign space for high grade names (low bond yields and low

CDS risk premium) bond prices drive the CDS market, but that this relationship is then reversed

for high yield names (cf. Delatte et al 2010). During a bear market there will be a tipping point

where the driving relationship changes between the bond and the CDS market. This empirical

fact however does not justify a view that the existence of CDS as a product leads to wideningcredit spreads and bear markets themselves. An alternative view may rather be that the contract

was a victim of its own success. Like most interest rate derivatives (IRD) at the time of the

financial crisis CDS were traded in the unregulated and opaque Over the Counter (OTC)

market. The issues regarding the inefficiencies of OTC markets have been discussed in depth in

previous studies (cf. Duffie et al 2005). Furthermore the market measured by both notional

amount outstanding and by gross market value has seen unprecedented growth since its

inception in the 1990s. It is entirely plausible that the combination of exponential growth in the

11 Berkshire Hathaway 2002 Annual Report



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CDS market coupled with a slow to act regulatory regime are the real reasons for the systematic

danger to the financial system posed by banks CDS positions rather than intrinsic flaws in the

product itself. Indeed the observation has been made that even during the financial crisis the

credit markets functioned in terms of liquidity and due process better than the traditional bond

markets! An example is how well the CDS markets coped with the Lehman bankruptcy, whichwas the largest credit event ever to occur (cf. Stulz 2009). The Depository Trust & Clearing

Corporation (DTCC), the main clearing organisation for CDS, successfully closed out over $500

billion of market participants’ exposure from the Lehman Brothers Inc. bankruptcy which

occurred in September 2008. $72 billion of credit protection written on Lehman as a reference

entity itself was bilaterally netted resulting in a final transfer of ‘only’ $5.2 billion to protection

buyers from protection sellers. 12

1.3.1. Size and Scope

The CDS market is made up of two types of contract. Single-name contracts offer credit

protection on a single specifically named reference entity. An example would be a 5yr CDS on

HSBC or a 2yr contract on Poland. Multi-name contracts are often structured as Basket CDS.

They offer protection on a defined set of individual reference entities. Often these names will bethose that make up a tradable credit index (such as the ITRAXX index in Europe) and the

contracts will then be termed index CDS. In 2004 single-name deals made up 80% of the

market. By 2008 this had reduced to 58%. (cf. Stulz 2009).

In terms of total market size a previous study has identified the size of the CDS market to have

been just $180 billion by the end of 1998 (cf. Acharya et al 2009). The following decade

witnessed what can only be described as explosive growth in the number of CDS contracts

traded. According to reports from the Bank of International Settlements (BIS) by 2004 total

contracts measured by notional were over $6 trillion. Indeed from 2004 to 2005 the gross

market value of outstanding CDS contracts surpassed the total amount that had been traded just

7 years before. The size of the market measured in terms of outstanding notional peaked in 2007

at almost $58 trillion before falling back to $42 trillion by the end of 2008. By increasing

12 DTCC Press Release, DTCC Successfully Closes Out Lehman Brothers Bankruptcy, October 30 2008



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netting, product innovation (discussed later in this study) further reduced this figure to under

$30 trillion by the end of 2010. 13

It should be noted there is no industry standard when it comes to the reporting of CDS deals.

Estimates published by the International Swaps and Derivatives association (ISDA) quote

slightly higher figures than those obtained from BIS as can be seen in figure 1.4.

Figure 1-4 Differing estimates of the size of the CDS market from BIS and ISDA.

In terms of the sovereign CDS market there is less historical data. BIS do state that as of the end

of 2010 of the $15.5 trillion single-name CDS, $2.5 trillion were written on sovereign reference

entities. 14 In terms of the Greek sovereign CDS the market is surprisingly small. DTCC data

shows as of 22 July 2011 less than $5 billion in outstanding notional 15. This in itself is much

reduced from the $7 billion that was outstanding one year ago and pales in comparison to the

notional value of outstanding Greek debt of over $400 billion 16.

Figure 1.5 shows a snapshot of the Gross and Net amounts of Outstanding CDS contracts on the

Eurozone countries for which markets exist as of April 2011. From this table it is apparent why,

as serious as it may be, the major cause of concern to Eurozone politicians and economists is not

13

http://www.bis.org/statistics/otcder/dt1920a.pdf 14 http://www.bis.org/statistics/otcder/dt23.pdf 15 http://www.dtcc.com/products/derivserv/data/index.php16 http://www.ifre.com/derivatives-greek-cds-uncertainty-fuels-dumping/637574.article



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the effect of a CDS credit event on Greece per se, but rather the danger of contagion to some of

the larger Eurozone economies where significantly larger debt and CDS markets exist.

Figure 1-5 The size of Eurozone sovereign CDS markets. (NCB Irish Economy Monitor, May2011)

1.3.2. Reforms in the CDS Market

Since the onset of the financial crisis the CDS market has seen the most activity with regard to

implementing the reforms proposed by the Dodd-Frank Act 17. These reforms are aimed at both

increasing market transparency and reducing counterparty credit risk ending the concept of any

financial institute being ‘too big to fail’ . The implementation of these changes has been driven by

ISDA and has resulted in members of the dealing community signing up to two protocols which

aim to standardise the CDS product increasing their suitability for electronic trading on SEFs (Swap

Execution Facility) and settlement and clearing via a CCP (Central Counterparty) 18. Figure 1.6

shows a summary of the standardisation goals of the CDS market protocols.

17 http://www.gpo.gov/fdsys/pkg/PLAW-111publ203/pdf/PLAW-111publ203.pdf 18 http://economicsofcontempt.blogspot.com/2009/01/upfront-cds-with-fixed-coupons_28.html



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Figure 1-6 The standardisation goals of the ISDA CDS protocols. (Markit™ d ocument, TheCDS Big Bang, 2009)

It is noted none of the measures taken in the ISDA protocols would be strictly necessary for CDS to

be eligible for CCP clearing, but they do aid the market in terms of trade compression which makes

the attractiveness of a CCP model even greater. The benefits to the whole market of CCP models

are widely accepted and are detailed elsewhere 19 .

The CDS Big Bang & the Standard North American Contract (SNAC)

The deadline for the signing of the ISDA Big Bang Protocol was April 7th 2009 with trading under

the terms of the agreement beginning the next day on the 8th 20. The main difference to the dealer

community was that post protocol each deal trades with a 100bp or 500bp standardised coupon (as

opposed to a fixed rate equal to the deal spread - the agreed rate of the trade) and a cash settlement

is made from one counterparty to the other to reflect the present value of the difference between thedeal spread and the standardised coupon. This cash settlement is referred to as the ‘upfront

premium ’ of the trade.

By way of simplified example: Before the Big Bang Protocol i f asset manager ‘A’ had sold

protection on corporation ‘C’ to bank ‘B’ at an agreed percentage rate of r, he would receive r/4 of

19 CME Group Document 201020 Markit™ Document: The CDS Big Bang: Understanding the changes to the Global CDS contract and NorthAmerican Conventions



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the Notional every quarter for the lifetime of the trade. Under the new convention he would now

receive a fixed coupon f (of 100bp or 500bp) quarterly (f/4) and receive a cash payment equal to:

∑

Where DF i is the appropriate discount factor for each quarterly payment.

This is the present value of the difference between the agreed deal spread and the standardised

coupon on a quarterly basis. If the agreed deal spread is less than the standardised coupon (r<f) then

despite the asset manager having sold protection and effectively gone long the risky asset, ‘A’

would also make an initial compensatory payment to B to reflect the PV of the excess of the

standardised coupon payments he will receive from B above the fair risk premium over the lifetime

of the trade.

Although such a deal structure, especially when combined with the extra considerations of

standardised dates as detailed in the protocol would seem to make trading more complex the result

is a contract type with future cash flows that can be easily netted off against each other This enables

a great deal of trade compression (the eliminating of offsetting deals in the market) enabling

counterparties to reduce both counterparty credit risk and operational costs. Even in advance of the

ISDA protocol some dealers had already unilaterally switched to trading the vast majority of CDS

contracts on an upfront basis.

The CDS small Bang

The ISDA small bang became effective on 20th June 2009. It not only rolled out the changes made

in the North American market to the European and corporate segments, but also addressed the issue

of certain restructuring events which were relevant for the western European sovereign market.These differences are of interest to market participants and are explained in detail in other market

documentation and studies 21 but are too nuanced to be described in detail in this paper. Figure 1.7

lists the different measures as they were rolled out in the two protocols across North America,

Europe and Asia. As can be seen further standardisation measures remain to be addressed in the

Asian markets.

21 Markit™ Document: Small Bang for the Buckets.



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Figure 1-7 The blast radius of the bangs. The changes implemented per market segment in thebig and small bang. (Ma rkit™ Document, Small B ang for the Buckets, 2009)



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2. Foundations

2.1. Methodology

This thesis is based on the study of a data set of prices of financial instruments available for trading

by market participants. The instruments in question are the standard 5yr CDS contracts on a subset

of western European sovereign entities.

Descriptive statistics are employed to summarise the properties of the different data series. This

gives a basis for comparison by showing the similarities and differences between the CDS series

studied. It offers an overall macro picture of the market’s p erception of the sovereign risk associated

with the member states within the European Union. In a second phase of the study the relationship

between the sovereign CDS of Greece and Germany are examined in more detail as a proxy for the

best and worst of the credit risk in the European Union.

Although there do exist some academic studies of the CDS market the overwhelming majority of

studies within the sovereign space concern themselves with bond market data. Whilst choosing to

focus on the sovereign CDS market presents unique problems, it also has some advantages over

data taken from sovereign bonds

The main disadvantages of working with CDS levels are:

i. The shorter history of CDS as a financial product. The CDS market is when compared to

the bond market relatively new and the interest in sovereign CDS is newer still. Researchers

only have access to a few years of data, whereas in the bond market some form of reliable

price data has been available for decades if not longer.

ii. CDS as a product has historically been traded in an OTC manner. Market makers have

made two way prices at their discretion to profit fr om customer business (known as ‘flow

trading’ ) rather than due to a compulsion as is sometimes the case when a dealer takes a

designated market maker role on an exchange (cf. Cascino and Veglia 2007 p14-15). 22 With

the advent of the Dodd-Frank Act in the US and EMIR regulation in Europe and the

22 Citigroup famously took advantage of market makers obligati ons on MTS Bond Exchange in 2004 to sell €11.3

billion face of bonds in 18 seconds and later buy back €3.8 billion for a profit of €18.2 million on the cash short andcover. This action was viewed unfavourably by MTS itself, the banks various regulators and their counterparties leadingto a fine, a suspension a loss of reputation for Citigroup’s Government Bond Desk and even the dismissal of some of the traders responsible for the coup. http://www.fsa.gov.uk/pages/Library/Communication/PR/2005/072.shtml

http://www.fsa.gov.uk/pages/Library/Communication/PR/2005/072.shtml






17

widening scope of the MiFID II directive in Europe this looks set to change. Industry

participants believe most trading will be forced onto Swap Execution Facilities (SEFs),

however the data series in this study represent prices from the OTC era. The lack of

exchange traded quoting obligations of OTC products can disrupt the price discovery

process during periods of extreme volatility. Market makers tend then to withdraw from themarket totally or only engage with customers on a ‘request for quote ’ (RFQ) basis. This

leads to gaps in CDS time series data, which from an academic point of view

disappointingly often coincide with the periods of most interest to the researcher.

The main advantages of working with CDS levels are:

i. CDS turns out to be a better choice for assessing credit risk than the bond market. This has

been shown by previous studies to be true for investment grade companies (Blanco, Brennan

and Marsh 2005) and logically holds true for the sovereign market as well.

ii. The homogeneous nature of CDS contract specifications. CDS is a contract rather than a

specific instrument and hence remains uniform with time. With the exception of the

structural contract changes implemented as a consequence of the financial crisis, the terms

and conditions of a CDS contract today are identical to one traded a number of years ago. If

the bond market is used to investigate 5 year credit spreads over a period of years the data

will be derived from several different instruments. As each specific bond roles down the

curve a new reference bond becomes the appropriate instrument from which to measure the

5yr spread. Even in the well-established sovereign bond market these different bond issues

are often extremely heterogeneous. Covenants and documentation, coupon, initial maturity

and other factors can all change. Due to the investment preferences of market participants

this can affect pricing which can then act to mask the underlying risk based pricing effects

which are usually those of interest to the researcher.

iii. Liquidity effects act less to skew prices in the CDS market than in the bond market. These

liquidity effects include both those in the security itself and in the funding.

a. Security Liquidity: The liquidity in the security itself is affected by the time since the

security was initially issued. Packages of the security can migrate into buy-and-hold

portfolios, be asset swapped or be distributed amongst smaller retail investors

thereby reducing the amount available to the market for trading to significantly less

than the official outstanding size of the issue. Market participants observe this effect

in securities that then become ‘bid only’ ‘in the street’ with market makers only



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prepared to offer the security if they have an existing long position rather than go

short and look to cover. This effect can skew yields to the downside (because bids in

price terms can increase without being filled) making the bond trade expensive. The

security liquidity issue can also be affected by the actions of the REPO & securities

lending market23

. If a dealer wishes to sell a bond short they are obliged to borrowthe security to deliver to their buyer counterparty. Should they fail to do this they

will effectively be short the bond coupon to the counterparty and have to fund this as

it accrues. In the meantime as they have failed to deliver the security they will

receive no cash from the counterparty. Failing to deliver a bond (commonly termed

‘a settlement fail’ ) can be extremely expensive and is especially relevant to the

economics of the trade for short duration bonds. Extended settlement fails can also

have regulatory consequences resulting in fines or even exclusion from certainmarkets 24 . REPO markets themselves still trade predominantly OTC and are

amongst the easiest markets to manipulate. Short term REPO squeezes are common

in many securities. Should a bond become expensive to borrow in the REPO market

this can even affect the cash market, increasing the price and depressing the yield. As

a CDS is a contract rather than an asset it is not subject to any delivery and

theoretically can be traded in a limitless amount vastly reducing the role of the

security liquidity affect in the pricing.

b. Funding Liquidity: CDS is a derivative. Unlike a bond trader, if a CDS trader enters

into a long €100 Million position he does not need to have access to €100mio of cash

to buy the asset. If a bank enters into a long bond position it will of course attempt to

fund this in the REPO market by lending the bond out. This is cheaper than

borrowing unsecured funds, but this introduces another risk factor and REPO

funding on any given day is not guaranteed. A CDS deal is less subject to vagaries of

the liquidity in the interbank wholesale deposit market which is the back stop

funding level for bond trades.

The first part of this study which deals with the descriptive statistics of the seven different CDS

time series utilises daily data. This approach is taken to maximise the number of data points

available for calculation and to ensure the maximum and minimum values derived fully depict the

volatility of the data. During the second part of the study a regression analysis of the German (DE)

23 http://ftalphaville.ft.com/blog/2011/07/20/627896/the-romans-always-copy-the-greeks-including-the-repo-market/ 24 http://ftalphaville.ft.com/blog/2011/07/25/631676/settlement-failure-is-an-option/

http://ftalphaville.ft.com/blog/2011/07/20/627896/the-romans-always-copy-the-greeks-including-the-repo-market/



http://ftalphaville.ft.com/blog/2011/07/25/631676/settlement-failure-is-an-option/







19

and Greek (GR) CDS returns is performed. Here weekly data is taken from successive Wednesdays.

This approach is taken to exclude any possible liquidity effects associated with quotes either side of

the weekend when the focus of the analysis is purely to test for stationarity and persistence of the

data series. For the final part of the statistical analysis I return to the daily data and calculate static

correlation matrices for a pre-crisis and sovereign crisis period and finally an overlapping and timevarying monthly correlation for the German and Greek data series.

Many statistical studies of the returns on financial instruments concentrate on the relationship in the

excess return over a particular benchmark or aggregating index. For this study one possibility would

have been to reference the data to the Markit™ ITra xx SovX index. This index of CDS prices is

made up of equally weighted parts of the singles name CDS quotes of 15 western European

sovereigns. This approach has not been taken in this study due to the limited data available for this

index compared to the individual sovereigns themselves. The Markit™ ITraxx SovX index was

conceived in March 2009, launched in July 2009 and first started trading as late as September 2009.

2.2. Theoretical Foundations

A statistical analysis of a data series can expose trends in the statistical measures of that series.

These quantifiable properties taken together with a qualitative analysis can then be used to draw

conclusions, or raise questions, about the fundamental economic and market events that resulted in

the very nature of the data set itself.

2.2.1. Descriptive Statistics & Statistical Inference

When presenting the results of the analysis in descriptive data form, to the greatest extent possible

the raw data in graphical form will accompany the calculated values to avoid the possible pitfalls as

highlighted by Anscombe’s quartet (cf. Anscombe 1973). Outliers are excluded from the analysis

only if they can be shown to be spurious data points beyond all doubt, for which case both a

comparison to bond market data and the other CDS data sets is made. Should a CDS rate for a

particular date be excluded a Google news search is conducted to verify no extreme news events

occurred which could pertain to the idiosyncratic risk of a particular sovereign entity and qualify asuspected bad data point as extreme but justifiable.



20

2.2.2. Regression and Correlation Analysis

All regression tests are performed using KSTAT, the Kellogg School of Management Excelstatistical tool.

A simple linear regression analysis utilizes an ordinary least squares method (OLS) to estimate

regression coefficients. The regression tests in this thesis utilise a standard model of the form:

Yi = β0 + β1Xi + εi

Where Y is the dependent variable, X the in dependent variable, β 0 and β 1 are the regression

coefficients and εi is the error term.

Homoscedasticity is an assumption of a linear regression model. OLS relies on the data series being

studied being a stationary process; that is a stochastic process whose joint probability distribution

does not change when shifted in time and space. If the analysed process is not stationary the result

can be autocorrelated errors and a spurious regression with high R-squared values, high t-ratios and

no economic meaning (cf. Granger C.W.J. and Newbold P. 1974). In this thesis a simple data

regression vs. lagged data (a Dickey-Fuller test) is performed on the CDS data of Germany and

Greece the result of which determine whether CDS returns or absolute levels are then taken as the

data set for further analysis.

An often quoted maxim in statistical analysis is that correlation does not imply causation. The

logical fallacy of believing that correlation implies a causal relationship is referred to as cum hoc

propter hoc and can be expressed as follows:

i. The event of E 1 is correlated with the event E 2, therefore E 1 causes E 2.

This conclusion ignores the other possible explanations for this phenomenon. Namely:

ii. Event E 2 causes event E 1

iii. Event E 3 causes both event E 1 and event E 2

iv. A combination of i and ii and iii.

v. Coincidence

Covariation is a necessary but not sufficient condition for causality. Hence a researcher must

initially assume that coincidence is the real explanation for any correlation observed. Further



21

statistical tests can then be used to test the likelihood that the correlation is as strong (or stronger)

than that observed based on chance alone.

In this thesis possible explanations will be offered for the results of the correlation analysis

performed. The further calculations and study necessary to rule out coincidence and other possible

explanations in order to strengthen the arguments for the explanation offered are mostly beyond the

scope of this work.

The correlations are compared for all CDS time series for the pre-crisis period (March 2003-

September 2008) and sovereign crisis period (September 2008- August 2010). In the second phase

of the study when the Greek vs. German CDS time series are studied in more detail both a time

varying and an overlapping correlation are calculated and graphed. This is in order to present the

data in a way that both clearly shows the effect observed and preserves the granularity of the

correlation with time.

2.3. Economic Theories and Financial Phenomena

By studying the CDS spreads in the western European sovereign CDS market over the economic

cycle since 2003 several well-known economic theory and phenomena can hope to be observed.Some of these will be investigated in more depth whereas for others reference is made to previous

studies of European economies and bond markets for which data prior to 2003 is available and

where the economic phenomena is more evident.

2.3.1. Convergence

Economists use the term convergence to describe two distinct hypotheses; beta convergence

describes the mean reversion effect of an economies growth rate converging to its own long term

growth rate whereas sigma convergence (also termed the catch-up effect) describes the higher

growth rate experienced by poorer economies compared to richer ones which results in the

convergence of income per capita between all economies. Both beta and sigma convergence within

the European Union have been the subject of previous studies (cf. Desli 2009 and Kočenda et al

2008). These effects are relevant to the study of the European Sovereign Debt Crisis not only

because they have been observed and documented in the nation member states throughout the years



22

preceding the financial crisis, but also because fiscal convergence itself was the main criteria set out

in the Maastricht treaty for the adoption of the Euro. Fiscal convergence (a set of limits placed on

debt, inflation, and interest rates) of the European economies was designed to bring about sigma

convergence and guarantee a stable monetary union. The fact that after becoming a eurozone

member many countries ceased to adhere to these criteria is seen as a major reason for the EuropeanSovereign Debt Crisis.

2.3.2. Yield Compression and Decompression

Yield compression is the logical manifestation of convergence as observed in the market.

Specifically for the case of the Eurozone it is the decreasing differentials in longer dated sovereign

bond yields and (equivalently) in the corresponding CDS spreads of the different countries. Yield

compression represents the markets increased belief in the homogenization of sovereign risk in the

Eurozone.

Yield decompression is the context of the Euro area is the widening of the yield differentials of

sovereign bond yields and the corresponding CDS spreads. Even prior to the sovereign debt crisis

studies found some divergence in the relative bond yields of accession countries after euro

membership in the period January 2001 to January 2009 (cf. Gabrisch and Orlowski 2010).

2.3.3. Flight To Quality

A flight to quality is a phenomenon that occurrs in a bear market where investors seek safety in

assets which are perceived to have a higher credit worthiness or earnings quality. Investors give uppotential risk based excess returns in exchange for a perceived increase in the likelihood of capital

preservation. Typically this is a move from equities into government issued bonds of the highest

rating quality (US Treasuries, Bund, Gilts, etc.).



23

2.3.4. Contagion

Financial contagion is the effect where sharp increases in the volatility of the returns of certain

assets occur as a higher correlation between these assets is observed. Contagion can occur within an

asset class across different risk points (such as from country to country, or from company to

company within a particular industry) or across asset classes who are exposed to the same or related

risks (for example from subordinated bonds to senior bonds for a given issuer should profitability

concerns turn into worries about solvency). The concern of contagion to the banking industry and in

turn the economy at large is perhaps the greatest concern caused by the European Sovereign Debt

Crisis. The gross notional amount of Euro a rea sovereign CDS is €986 billion with a net notional

value of €120 billion. If CDS on European banks is included an additional €1.5 trillion gross

notional with net €108 billion outstanding n eeds to be considered 25. Figure 2.1 shows the empirical

relationship between the performances of European banks relative to the MSCI Europe Index with

the average European peripheral sovereign 5yr CDS spread.

Figure 2-1 Bank stock price returns vs. western European peripheral sovereign average 5yrCDS (Financial Times Lex Column, 18th August 2011)

25 http://www.ncbresearch.com/fixed_income/IrishEconomyMonitorMay2011.pdf

http://www.ncbresearch.com/fixed_income/IrishEconomyMonitorMay2011.pdf






24

3. Data

3.1. The Data Set

The data set upon which this study is based is taken from the CMA Datavision™ service which

provides a CDS pricing service for market participants. The data set consists of closing prices

on credit default swap contracts on the sovereign debt of Germany , France , Portugal , Italy ,

Ireland , Greece and Spain from March 2003 until August 2010. The prices taken are

exclusively for the 5yr CDS contract as this is the market standard CDS deal and is hence far

more liquid than any other CDS contract with a different tenor.

3.2. Limitations of the Data Set.

Due to the previously discussed OTC nature of the product the data set is prone to information gaps.

This effect is most pronounced in the data-set in Irish CDS spreads where only 28 observations are

available in the pre-crisis period.

A further limitation of the data set is the range which is covered by the sample. No quotes are

available which cover the period prior to (and also for a period after) the introduction of the euro on

1st January 2002. Prior studies of European sovereign bond yields over the period find a degree of

convergence within this time frame. (cf. Gabrisch and Orlowski 2010).



25

4. Results

4.1. Descriptive Statistics

Figure 4.1 shows the price development of the 5yr sovereign CDS contracts in par rate terms in the

pre-crisis period from March 2003 to September 2008.

Figure 4-1 Western European 5yr sovereign CDS spreads in the pre-crisis period.

Figure 4.2 shows the price development of the 5yr sovereign CDS contracts in par rate terms in the

sovereign crisis period.



26

Figure 4-2 Western European 5yr sovereign CDS spreads in the sovereign crisis period.

The table in Figure 4.3 shows descriptive statistics for the data set in both the pre-crisis and

sovereign crisis period. Numbers are par rates expressed in basis points in the same convention that

CDS spreads were quoted in the market during the study period. PIIGS figures are simple arithmetic

averages of the figures for the single countries with the exception of # Data Points which is the sum

total of data points for the PIIGS names.



27

Figure 4-3 Table of descriptive statistics of 5yr western European sovereign CDS rates (2003-2010).

The bar chart in Figure 4.4 shows a graphical representation of the statistical measures displayed in

table form in the previous figure. This representation gives an immediate visual impression of the

change in all measures from the pre-crisis to the sovereign crisis period.

Figure 4-4 Graphical depiction of descriptive statistics of 5yr western European sovereignCDS rates (2003-2010).



28

The entire data set is made up of 7 individual series containing a sum total of 11,163 data points.

7,775 data points are from the pre-crisis period which extends over a period of 78 months and 3,388

from the sovereign crisis period which extends over a period of 23 months. Although the pre-crisis

measurement period is 3 times as long, there are only twice as many data-points in that period

showing the increased liquidity in the sovereign CDS market that came about over the time frame of

the entire study.

Every single statistical measure for each of the seven data sets exhibits a dramatic increase from the

pre-crisis period to the sovereign crisis period. The increases in all measure are much more

pronounced in the PIIGS than in France or Germany. All of the data series experience some

widening before the start of the sovereign crisis period as can be seen from the graph of the 5yr

CDS par rates in the pre-crisis period in Figure 4.1 and also from the large differences between the

MIN and MAX values in the pre-crisis period. This is not surprising; the date chosen to represent

the start of the sovereign crisis period is that of the Lehman’s bankruptcy, which represents the

same approximate point in time as the bankruptcies of Iceland’s banks. Although this is commonly

accepted to be the start of the European Sovereign Debt Crisis the World Financial Crisis itself

which saw a sell-off in all risky asset classes is accepted to have begun a full year earlier in August

2007. Even though sovereign debt was not the focus of the crisis at this time the general poor

market sentiment also caused spread widening in this space from mid-2007 through 2008.

All series except for Ireland follow the trend that the minimum value of the 5yr CDS spread in the

sovereign crisis period is, in relative terms, significantly lower than the maximum value in the pre-

crisis period.

Amongst the PIIGS themselves we observe two distinct subsets; although the CDS quotes for the

larger economies of Spain and Italy certainly undergo dramatic widening from the pre-crisis to the

sovereign crisis period the magnitude of their CDS levels are significantly less than the other PIIGS.

At the same time the widening in these two names is significantly greater than that observed for

France and Germany. The same can be said of the volatility of the different series where both Italy

and Spain exhibit large increases in standard deviation, however these increases are relative benign

when compared to the rest of the PIIGS. Indeed, even compared to France and Germany the relative

increase in volatility of Italy and Spain is less pronounced. In the case of France the standard

deviation increases over seven fold to ~21 whereas for Italy (increase to ~44) and Spain (increase to

~53) the increasing factor is less than 5.



29

The stand out data series is that of Greece. In both of the periods of study the Greece 5yr CDS

series has the highest values of almost all measures as well as the greatest volatility. Ireland has the

dubious honour of being the only country to trade wider than Greece for a period of time during the

sovereign crisis period. Perhaps somewhat surprisingly, if we look at the pre-crisis period Italy for a

time underperformed Greece trading wider for much of 2007; however it is Greece that sees thegreatest shift in the crossover from the pre-crisis period to the sovereign crisis period, widening

more and exhibiting the largest increase in volatility (on both an absolute and relative measure).

Greece clearly stands out as the real dog amongst this drove of PIIGS.

In the pre-crisis period the price of the 5yr Greek Sovereign CDS contract peaks at ~ 65bp. This is

the annual cost expressed in basis points to insure the debt of the sovereign in question for 5 years.

To insure €10 million of debt at this time would cost €65,000 per year paid in 4 quarterly

instalments of €16,250. In the Sovereign Crisis period, Greek CDS widened to as high as 1037bp.

The equivalent annual insurance premium fo r €10mio of Debt for 5 years would then have been

€1,037,000 per year, although as previously stated in this study this payment is now rendered as a

reduced fixed coupon of 500bp ( €500,000 per year) and an upfront payment representing the

discounted value of the fair add-on premium over and above this coupon.

In the AAA space we observe differences in the development of the CDS levels of France and

Germany with Germany widening less and suffering less volatility in the sovereign crisis period

than France.

On the whole it is clear that Germany and Greece CDS levels represent the opposite extremes of the

data set.

4.2. Regression Analysis

The German and Greek data sets are now taken for further study as representing the best and the

worst of European sovereign risk. As the data set is sufficiently large weekly observations are taken

every Wednesday for the regression analysis in an attempt to exclude short term liquidity effects

from the sample and concentrate on underlying long term trends.

Regression analysis is used to test the stationarity and persistence of both data series. Stationarity is

tested by regressing both series vs. their own 1 week lagged values. Persistence by regressing each

series vs. the date of observation.



30

In a second test I then apply the difference operator to each series to transform the data from

absolute par rates into CDS returns and retest the data for stationarity and persistence by once again

using the same methods mentioned above.

Figure 4.5 and Figure 4.6 show the output from the Kellogg KSTAT Excel tool for each of the

regressions referred to above for Germany and Greece respectively. Both 99% and 95% confidence

intervals are calculated for the slope coefficient of the independent variable in each regression.

Figure 4-5 KSTAT output for regression tests on the 5yr German CDS spread series.



31

Figure 4-6 KSTAT output for regression tests on the 5yr Greek CDS spread series.

In both cases when the German and Greek time series are regressed against the lag (DE+1W vs. DE

and GR+1W vs. GR) we find a regression coefficient close to unity. In the case of Germany it is

only at the 83% confidence level that we can rule out that the true coefficient is 1. In the case of

Greece we can only rule out the true coefficient is 1 at a confidence level of 74%. Both the DE+1W

vs. DE and GR+1W vs. GR regressions (the non-differenced CDS rate vs. that of a week prior for

Germany and Greece respectively) exhibit high R-squared values (close to 1) indicating a

correlation between the observed CDS level and the previous weeks CDS level. The implication is

that the series themselves are not stationary and that a correlation study on the CDS rates

themselves will produce meaningless results. I thus run the regression vs. the Lag on the series oncedifferenced for Germany ( ΔDE+1W vs. ΔDE) and Greece ( ΔGR+1W vs. ΔGR). In both cases I find



32

much lower regression coefficients and values of R-squared which suggest the series once

differenced fulfils the stationarity criteria required for the subsequent correlation analysis.

A similar regression analysis for both Germany and Greece on their respective CDS levels vs. the

observation date is performed and compared to a regression of the once differenced data series vs.

the observation date. The results suggest the changes in CDS returns are less persistent than changes

in the par CDS rates themselves. Hence, for the subsequent analysis the difference operator will be

applied once to all data series transforming the series from par spreads to (daily) returns. The

subsequent correlation analysis will be performed on the daily returns of the series with respect to

each other.

4.3. Correlation Analysis

Figure 4.7 shows the correlation of daily returns for the 21 CDS pairs over the pre-crisis period.

Figure 4-7 Correlation matrix for the pre-crisis period (March 2003 - September 2009).

Figure 4.8 shows the correlation of daily returns for the 21 CDS pairs over the sovereign crisisperiod.



33

Figure 4-8 Correlation matrix for the sovereign crisis period (September 2008 - August 2010).

Figure 4.9 shows the correlation differences of daily returns for the 21 CDS pairs when moving

from the pre-crisis period to the sovereign crisis period.

Figure 4-9 Correlation development: Sovereign crisis period – pre-crisis period.



34

As evidenced in the correlation difference matrix in Figure 4.9 all correlations become considerably

more positive over the period from the pre-crisis to the sovereign crisis. In the pre-crisis period a

third of the 21 correlations between the different CDS series are negative and the other two thirds

are positive. In the sovereign crisis period all correlations become positive. Correlations in the pre-

crisis period are also generally weaker than in the sovereign crisis period. With the single exceptionof the Italy-Greek correlation all correlations become more positive in the sovereign crisis period.

Some interesting individual relationships can be observed in the data. In the pre-crisis period returns

on Ireland were positively correlated with returns on both Germany and France and negatively

correlated with returns with each of the PIGS. In the sovereign crisis period the correlations

between Ireland and each of the individual PIGS is more positive than that with either Germany or

France. Three of the four largest correlation movements occur in the Ireland data. The exception is

the Germany-France correlation which exhibits the third greatest move towards a positive

correlation increasing from ~0 in the pre-crisis time to ~0.8 in the sovereign crisis period.

Figures 4.10 and 4.11 show graphical depictions of the time development of the correlation of daily

returns on German CDS vs. those on Greek CDS. Both an overlapping and a time varying

correlation are shown. For the overlapping correlation the first date of the period for which the

correlation is calculated remains the first data point of the entire data series. For the time varying

correlation the correlation shown is that for the latest month of data.



35

Figure 4-10 Overlapping correlation of daily differenced CDS data for Germany vs Greeceover the entire study period.

Figure 4-11 Monthly rolling correlation of daily differenced CDS data for Germany vs Greeceover the entire study period.



36

The graphical depiction of the changing correlation between Germany and Greece shows the

immediacy, severity and sustained nature of the change in the c orrelation between the ‘best’ and the

‘worst’ of the risks in the European s overeign space as measured from CDS daily returns. The two

graphs expose the fact that although the effects of the bear market were reflected in a widening of

CDS levels from the start of the financial crisis in 2007, it is only in 2008 with the onset of the

sovereign debt crisis that the correlation of sovereign CDS daily returns becomes significantly more

positive. Indeed, from the graph of the monthly rolling correlation we see that in the pre-crisis

period the correlation between Germany and Greece CDS daily returns was at times positive and at

other times negative. From the start of the sovereign crisis period the correlation is observed to turn

positive and afterwards never again enters negative territory.



37

5. Conclusion

This thesis has investigated the development CDS spreads of countries who are members of the

EMU. The period of the study (2003-2010) contains some of the most interesting events from a

fiscal and economic perspective that the world has ever seen. The study commences in 2003 shortly

after the introduction of Euro coins and bank notes and continues through the Subprime mortgage

crisis (2006-2007), the ensuing Credit Crunch and Global Financial Crisis (2007-2010) and into the

Western European Sovereign Debt Crisis (2008-present) which persists today. The events from the

latter end of the study period plunged the world economy into the Great Recession (2007-2009) the

most severe period of economic contraction witnessed in developed nations since World War II.

The liquidity situation in the market becomes worse with the increasing volatility caused by theshocks to the system from each crisis of recent years. Despite this fact we observe the greater

frequency of observations of CDS data in the sovereign crisis period than in the pre-crisis period

and therefore conclude that CDS as a product has met with ever more acceptance over the time

period of this study.

The analysis performed in this study allows several observations to be made at both a level

pertinent to the EMU and relevant to the problems experienced by individual countries within the

union.

It is clear from the data that before the onset of the events leading to the Sovereign Debt Crisis the

market was increasingly pricing in only a very low level of risk in the western European sovereign

space. This state of affairs continued for a significant period of time and witnessed the convergence

of the CDS premia of the different countries into 2006. Both the absolute levels of risk premia as

measured by CDS levels, as well as the volatility of returns reached all-time lows. The observed

mixed negative and positive low levels of correlation during this period imply that the idiosyncratic

risk of each country dominated the systematic risk in the markets pricing. As we move into the

sovereign crisis period we see this drastically change.

As we enter the period of the financial crisis there is a global re-pricing of risk resulting in all CDS

data series experiencing widening, however the effect is far more pronounced in the PIIGS than the

AAA names. Decompression amongst the sovereign CDS levels occurs and the reduction in

correlation seen in the graphs of German vs. Greek CDS returns at this time indicates the initial

market reaction to the world’s economic wo es was a flight to quality. From the end of 2008 wewitness the correlation of Germany and Greece CDS daily returns become positive. Furthermore all



38

other correlations amongst the members of the Economic and Monetary union change from

negative to positive or become more positive in the transition from the pre-crisis period to the

sovereign crisis period. This indicates that systematic risk has started to dominate idiosyncratic risk

in the pricing in the western European sovereign CDS space. This result is somewhat expected

given the observed inverse relationship between European banks share price performance andwestern European Sovereign CDS prices and the escalation of commitment that has been witnessed

throughout the crisis with the Eurozone EMU members (in conjunction with the IMF and ECB)

bailing out first Greece and then later both Ireland and Portugal. The increasingly positive

correlations of CDS daily returns is an indication that the market may see an increasingly strong

link between the fate of the PIIGS and that of the rest of the Eurozone.

For all CDS series except for Ireland the minimum value of the 5yr CDS in the sovereign crisis

period is, in relative terms, significantly lower than the maximum value in the pre-crisis period.

This is due to the ‘eye of the storm’ effect which occurred in early 2009. Figure 5.1 shows world

equity markets over this period.

Figure 5-1 Leading European and US equity markets relative performance from the time of the Lehman’s b ankruptcy to the end of 2009 (http://finance.yahoo.com)

As equity markets began to once again find their feet a significant bull market correction occurred.

The Dow Jones Industrial Average added some 4,000 points for a 60% rally. During this period

western European sovereign CDS spreads retreated significantly from the highs seen in 2007.

At this time when the market began to price in the possibility of a strong recovery all sovereign

CDS levels declined. A possible explanation for the exceptional behaviour of Ireland at this time as

observed from the descriptive statistics would be the market placing greater emphasis on the

idiosyncratic risk of the Irish Banking crisis for the fate of Ireland than the general macro picturewhich was deemed to have improved significantly from the dark days just a few months before.



40

i. A deeper investigation of the time varying correlation of other CDS pairs beyond Germany-

Greece.

ii. A deeper study of the development of the price of risk as derived from CDS spreads before

and after the CDS market reforms of 2009.

iii. A simultaneous study of both Sovereign Bond and CDS markets investigating thedevelopment of the ‘negative basis’ 28 throughout the study period.

5.2. Epilogue

‘Schlimmer geht’s immer .’ – German saying and Headline of an article in ‘News’, a popular

Austrian Weekly News Magazine in July 2011 on the 3-notch Fitch downgrade of Greek Debtfrom B+ to CCC. 29

At the time of writing the last lines of this thesis in September 2011 the global economic picture has

once again darkened and the European Sovereign Debt Crisis has escalated to new heights.

European bank share prices are back around their all-time lows seen immediately after the Lehman

bankruptcy, some European stock markets have collapsed close to 30% in little over 2 months and

Greek CDS has widening suddenly and violently peaking at around 6 000bp; it now costs €6 million

per year to insure €10 million of Greek Bonds for 5 years. 30 Greek default is to a large extent priced

in, and the speculation is shifting from IF Greece will default to how much a bond holder will

receive (termed ‘recovery ’) WHEN Greece defaults. Some longer dated Greek debt instruments

have traded as low as 28 cents on the Euro despite the fact Greece, whilst pursuing private sector

participation for a proposed debt swap with a 21% haircut, is promising to honour its debt

commitments and not unilaterally default.

No one knows what the next years will bring, but it is certain European sovereign nations have been

shocked by the market forces. The bond market and its younger sibling the CDS market are as

stated by James Carville indeed capable of intimidating everybody.

28 The negative basis is the difference between the Z-Spread of a Bond and the matched maturity CDS rate.29 http://www.news.at/articles/1128/38/301744/griechenland-krise-schlimmer30 Although the sum total of the premiums being far greater than the amount insured is counter-intuitive this effect isoften observed in the market when the Probability of Default is considered so high that the market deeply discounts

premiums in later years because once a credit event occurs all CDS premium payments cease. In the new regime of trading upfront payments this then equates to an upfront premium much less than the non-discounted value of futurecash flows. The Markit CDS calculator shows a conventional spread of 6000bp is equivalent to a ~5.4mio upfrontpayment for a 10mio contract.



41

6. Appendix

‘ An investment in Knowledge always pays the best interest .’ – Benjamin Franklin (attributed)

6.1. The CMA data set

The data set used during this study is too large to be included in this appendix, but will be made

available on request via email. Please contact [email protected] .

6.2. Secondary Market GGB Volumes

Figure 6.1 shows the complete secondary trading volumes in Greek sovereign bonds on HDAT

Figure 6-1 Secondary market Greek sovereign bond trading volumes sourced from the Bankof Greece.

HDAT

Trading volume (EUR mil.)

Monthly data 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Jan 20,276 42,137 59,695 64,756 79,997 54,926 64,312 43,734 12,082 21,091 707 Feb 15,169 36,515 47,036 51,382 59,885 64,907 45,290 41,310 13,878 18,856 847 Mar 23,966 37,322 51,145 77,112 56,055 64,401 62,505 8,615 16,243 34,611 974 Apr 18,055 38,004 39,005 59,979 46,828 42,422 58,981 17,675 12,313 10,576 1,404 May 28,030 51,531 56,210 61,833 45,258 51,100 56,575 30,205 20,218 1,394 695 Jun 21,127 41,358 61,941 80,479 61,607 49,721 58,849 26,690 27,771 1,573 368 Jul 19,211 46,243 60,337 63,250 52,475 47,121 55,738 29,174 18,192 1,464 131

Aug 22,917 57,231 54,242 96,975 55,563 42,981 28,434 25,083 30,758 819Sep 26,311 66,400 65,202 135,749 78,730 52,453 34,350 26,300 51,794 1,819Oct 30,831 69,296 79,724 106,518 76,443 52,641 40,748 13,794 55,418 1,942Nov 56,691 46,886 70,069 123,507 74,677 64,557 40,353 7,639 50,211 926Dec 31,440 32,593 34,018 37,394 41,870 45,008 21,363 5,454 17,484 268

Total yearly 314,023 565,512 678,620 958,932 729,388 632,238 567,498 275,673 326,362 95,3395,126

mailto:[email protected]






42

7. List of Figures

Figure 1-1 Historic sovereign cumulative default rates 1983- 2007 (Moody’s Global Credit Research

March 2008). ........................................................................................................................................ 5

Figure 1-2 Greek bonds secondary market trading data on the Bank of Greece HDAT platform. ..... 7

Figure 1-3: Greek sovereign debt secondary market prices. Stuttgart Stock Exchange 29 July 2011. 8

Figure 1-4 Differing estimates of the size of the CDS market from BIS and ISDA. ........................ 11

Figure 1-5 The size of Eurozone sovereign CDS markets. (NCB Irish Economy Monitor, May

2011) .................................................................................................................................................. 12

Figure 1-6 The standardisation goals of the ISDA CDS protocols. (Markit™ document, The CDS

Big Bang, 2009) ................................................................................................................................. 13

Figure 1-7 The blast radius of the bangs. The changes implemented per market segment in the big

and small bang. (Markit™ Document, Small Bang for the Buckets, 2009) ...................................... 15 Figure 2-1 Bank stock price returns vs. western European peripheral sovereign average 5yr CDS

(Financial Times Lex Column, 18th August 2011) ........................................................................... 23

Figure 4-1 Western European 5yr sovereign CDS spreads in the pre-crisis period. ......................... 25

Figure 4-2 Western European 5yr sovereign CDS spreads in the sovereign crisis period. ............... 26

Figure 4-3 Table of descriptive statistics of 5yr western European sovereign CDS rates (2003-

2010). ................................................................................................................................................. 27

Figure 4-4 Graphical depiction of descriptive statistics of 5yr western European sovereign CDSrates (2003-2010). .............................................................................................................................. 27

Figure 4-5 KSTAT output for regression tests on the 5yr German CDS spread series. .................... 30

Figure 4-6 KSTAT output for regression tests on the 5yr Greek CDS spread series. ...................... 31

Figure 4-7 Correlation matrix for the pre-crisis period (March 2003 - September 2009). ................ 32

Figure 4-8 Correlation matrix for the sovereign crisis period (September 2008 - August 2010). ..... 33

Figure 4-9 Correlation development: Sovereign crisis period – pre-crisis period. ............................ 33

Figure 4-10 Overlapping correlation of daily differenced CDS data for Germany vs Greece over the

entire study period. ............................................................................................................................. 35

Figure 4-11 Monthly rolling correlation of daily differenced CDS data for Germany vs Greece over

the entire study period. ....................................................................................................................... 35

Figure 5-1 Leading European and US equity markets relative performance from the time of the

Lehman’s bankruptcy to the end of 2009 (http://finance.yahoo.com) ............................................... 38

Figure 6-1 Secondary market Greek sovereign bond trading volumes sourced from the Bank of

Greece. ............................................................................................................................................... 41



43

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