Sovereign Credit Default Swap Premia∗

Patrick Augustin† ‡

Stockholm School of Economics

This version: April 2012Preliminary, comments welcome

Abstract

This paper provides a comprehensive overview of the young, but rapipdly growing litera-

ture on sovereign credit default swap premia, describes key statistical and stylized facts about

prices, the market, its players and related trading activities and attempts to raise some thought-

provoking questions. We thereby hope to serve as a useful starting point for anyone interested

in the topic.

Keywords: Credit Default Swap Spreads, Default Risk, Descriptive Statistics, Literature Review,

Sovereign Debt, Term structure

JEL Classification: A31, D40, E02, F30, F34, G01, G12, G13, G14, G15, O57, Y10

∗This version is preliminary and incomplete. Comments are welcome and appreciated. We would like to thankRene Kallestrup for valuable feedback. All remaining errors are our own.†Stockholm School of Economics, Finance Department, Sveavagen 65, 6th floor, Box 6501, SE-113 83 Stockholm,

Sweden. Email: Patrick.Augustin@hhs.se.‡

1 Introduction

The late 1990s and early 2000s displayed a series of emerging market sovereign defaults. Nowadays,

the popular press is covered with news about developed countries in distress, in particular the

GIIPS countries: Greece, Italy, Ireland, Portugal and Spain. For most part of history, lenders to

national governments were unable to insure against sovereign default and had to trust creditors

that they honor their debt obligation in good faith. Our generation, on the other hand, has

witnessed the creation of insurance products to protect specifically against such contingencies.

These insurance products, so-called credit default swaps (henceforth CDS) have undergone two

decades of spectacular growth and, covering an underlying cash market of approximately 41 trillion

USD in 2011 represent an important part of the economy1. Policy makers, regulators and investors

increasingly look at sovereign CDS prices to gauge the health of our system2. While there have

been several treatments about the corporate CDS market, we feel that there is a void when it comes

to a comprehensive overview of the sovereign analogue3. The purpose of this document is to fill

this gap.

More specifically, the goal of this paper is to provide an integrated overview of the key stylized

facts of the sovereign CDS market, its players, frictions, the determinants of prices and trading

related information. This is executed by reviewing the very young but growing literature, and

discussing mainly the publicly available data from the Depository Trust and Clearing Corporation

(DTCC), the Bank for International Settlements (BIS) and a historical dataset provided by Markit.

As the focus of this review is the synthetic fixed income market, we mainly refer in citations to

academic work on sovereign CDS spreads, but refer at times to academic work on sovereign cash

bonds4.

We hope that this review provides a useful starting point for anyone interested in the topic and

that our exposition raises some thought-provoking questions. At the outset, we would like to note

some puzzling observations. Despite the huge press coverage of credit derivatives, they represent

1The Economist global debt clock displays an estimate of current global public debt. Source: www.economist.com.2Hart and Zingales (Fall 2011) suggest the use of financial CDS to evaluate the health of banks.3See for example Stulz (2010) for an overview of corporate CDS and their relation to the financial crisis or Das

and Hanouna (2006).4As our goal is specifically the treatment of sovereign CDS spreads, we apologize to any authors who feel that

their work is relevant and should have been included in this review.

1

only a tiny fraction (4.5%) of the overall ”over-the-counter” derivatives market. Moreover, among

all traded credit default swaps, sovereign contracts make up roughly 9%. Although the overall size

of the CDS market is valued at 32 trillion USD, the net economic exposure is only 1.3 trillion5.

Furthermore, why is it that contracts on emerging market economies tend to trade in high numbers

and small volumes, while developed economies tend to trade with a larger net economic exposure

per contract and have fewer contracts outstanding? Why are hedge funds so often blamed for

speculation in the sovereign CDS market, when in fact they represent only a small fraction of

all counterparties? Why is there such a surge in CDS trading on the US government in 2011?

What determines the shape of the sovereign yield curve when government term structures exhibit

similar patterns as those of corporations, but country-specific factors play relatively weaker roles in

explaining variation in CDS spread levels. And finally, why is it so important to avoid a trigger on

Greek CDS when the net reported economic exposure is below 4 billion at the end of 2011? These

facts and more are highlighted along the road.

The remainder of this document structures as follows. The second section explains the nature

and mechanism of CDS. Readers familiar with this may directly want to jump to section three,

which describes the market for sovereign CDS. Section four illustrates statistical stylized facts about

sovereign CDS prices. In section five, we focus on the determinants of these products and review

the literature. We follow up with a discussion on the relationship between sovereign CDS and cash

bonds, and CDS related frictions in section six. Finally, in section seven, we give an overview of the

trading in sovereign CDS markets based on the newly available data from the global data repository

DTCC, and provide some indication on how this relates to country specific fundamentals. Finally,

in section eight, we conclude.

2 What Are Credit Default Swaps

Engineered in the early nineties by JP Morgan to meet the growing demand to slice and dice credit

risk6, credit default swaps represent the most simple (”vanilla”) instrument among the class of credit

5The net economic exposure can be understood as an upper bound on insurance payments upon default. Actualpayments are expected to be lower, because recovery values of defaulted bonds are generally non-zero.

6The exact year of creation is not clear. Tett (2009) refers to the first CDS in 1994, when J.P. Morgan off-loadedits credit risk exposure on Exxon by paying a fee to the European Bank for Reconstruction and Development. More

2

derivatives. Nevertheless, they remain till this date highly controversial. While the proponents

would defend them as efficient vehicles to transfer and manage credit risk as well as means to

widen the investment opportunity set of return chasers, opponents denounce them as weapons

of mass destruction, time bombs7, financial hydrogen bombs8 or speculative bets on government

default. For the sake of this review, let us stick with the factual definition of what they really are:

insurance contracts offering protection against the default of a referenced sovereign government or

corporation.

Technically speaking, a credit default swap is a fixed income derivative instrument, which

allows a protection buyer to purchase insurance against a contingent credit event on an underlying

reference entity by paying an annuity premium to the protection seller, generally referred to as

the Credit Default Swap spread. The premium is usually defined as a percentage on the notional

amount insured and can be paid in quarterly or bi-annual installments. Readers not familiar with

this particular notion of insurance, should simply bear in mind the example of car insurance. But

whereas for car insurance, the contract would be written on a specific vehicle, credit insurance

could be contracted on a given brand, think of a Volkswagen for the sake of the argument. Thus,

any Volkswagen involved in an accident would trigger a payout to the holder of the contract. In the

language of credit derivatives, you would purchase a CDS on a company, the reference entity, and

if that company fails to meet its obligations for any of a predetermined set of its debt obligations,

the payout would occur. More specifically, the contract comprises usually a specific class of the

firm’s capital structure, such as the senior unsecured or the junior debt portion of the company,

and references a particular amount of insured debt, the notional amount.

Failure to meet the debt obligations is labeled a credit event. Hence, a credit event triggers a

payment by the protection seller to the insuree equal to the difference between the notional principal

and the loss upon default of the underlying reference obligation, also called the mid-market value.

In general, the occurrence of a credit event must be documented by public notice and notified to the

investor by the protection buyer. Amid the class of qualifying credit events are Bankruptcy, Failure

importantly for this review, the author also references a deal between J.P. Morgan and Citibank asset managementon the credit risk of Belgian, Italian and Swedish government bonds around the same time (p.48).

7See Warren Buffett, Berkshire Hathaway Annual Report for 2002, p.13, on line athttp://www.berkshirehathaway.com/2002ar/2002ar.pdf

8Felix Rohatyn, a Wall Street banker emplyed at Lazard Freres, quoted in Tett (2009).

3

to pay, Obligation default or acceleration, Repudiation or moratorium (for sovereign entities) and

Restructuring, and represent thus a broader definition of distress than the more general form of

Chapter 7 or Chapter 11 bankruptcy.

The settlement of insurance contracts comes in two forms, it may be either physically or cash

settled. In case of a cash settlement, the monetary exchange involves only the actual incurred losses

and the claimant holds on to the physical piece of paper providing him with a claim on the capital

structure of the sovereign balance sheet. On the other hand, if there is a physical settlement, the

claimant may hand any reference obligation referenced in the contractual agreement to the insurer

and obtains the full notional amount of the underlying contract in return. The insurer can then try

to maximize its resale value in the secondary market. This implies that the claimant literally holds

a Cheapest-to-Deliver option, as he may deliver the least valuable eligible reference obligation9.

This option is particularly relevant in the case of a corporate restructuring, which is why the

restructuring credit event is the most critical. As a consequence, it has been adapted numerous

times and comes in multiple colors and associated contractual clauses. In particular, apart from

Full Restructuring, which allows the delivery of any obligation with a remaining maturity up to

30 years, a contract may simply omit this definition, in which case it is labeled No Restructuring,

or it may qualify as Modified Restructuring or even Modified Modified Restructuring. The former

definition is more prevalent in the U.S. and limits the remaining maturity of the deliverable reference

obligation to a maximum of 30 months, whereas the latter is predominant in Europe, but restricts

the maximum maturity to 60 months. Even after default, prices of bonds usually suffer from

wide market fluctuations, so the question remains as to how the precise value of the insurance

settlement is determined. Markets have converged to a practice where the mid-market value is

obtained through a dealer poll. Whether this pricing mechanism is efficient, remains unclear.

Three recent papers have taken a closer look at this issue for corporate CDS: Helwege et al. (2009),

Chernov et al. (2011) and Gupta and Sundaram (2012).

Credit default swaps are part of the over-the-counter market (OTC). Thus they are not trad-

ing on an organized exchange and remain to a large extent unregulated. The reason for this is a

9See Ammer and Cai (2011) for evidence on sovereign CDS and Jankowitsch et al. (2008) for evidence on corporateCDS. Ammer and Cai (2011) also study the short-run dynamics between sovereign CDS spreads and their cashcounterparts, in a similar fashion as Blanco et al. (2005) did for the corporate market.

4

provision inserted in 2000 in the Commodity Futures Modernization Act by Senator Phil Gramm,

who from 1995 to 2000 presided over the Senate Banking Committee, exempting CDS from regu-

lation by the Commodity Futures Trading Commission (CFTC)10. Nevertheless, guidance on the

legal and institutional details of CDS contracts is given by the International Swaps and Derivatives

Association (ISDA)11. They also act as a non-voting secretary for the Credit Determination Com-

mittee, which deliberates over issues involving Reference Entities traded under Transaction Types

that relate to the relevant region, including Credit Events, CDS Auctions, Succession Events and

other issues. Moreover, ISDA has played a significant role in the growth of the CDS market by

providing a standardized contract in the 1998 (”the ISDA Master Agreement”), which was updated

in 2002, and supplemented with the 2003 Credit Derivatives Definitions (”The Definitions”) and

the July 2009 Supplement. The contractual details of these contracts are crucial, and as usual,

the devil lies in the details, as was recently proved in the restructuring case of Greek government

debt. European officials have heavily pushed towards a voluntary restructuring, which would not

be binding on all bondholders. In such a case, it would not be be likely that the agreed deal would

trigger payments under existing CDS contracts12. The landscape for CDS has further altered with

the implementation of the CDS Big Bang and Small Bang protocols on 8 April and 20 June 2009

for the American and European CDS markets respectively. The biggest changes relate to the stan-

dardization of the coupon structure and the settlement process. Going forward, the Dodd-Frank

Act in the U.S. will likewise have a significant impact on the way these products are traded. The

Inter-continental Exchange (ICE), a subsidiary of the NYSE, is already recording growing mar-

ket share in the clearing process and both academic and political voices call for a move towards

organized exchanges, more transparency and more orderly price dissemination13.

Before moving on to the statistical features of the market for sovereign CDS, we want to point

out that we don’t describe their pricing mechanism. The interested reader may however refer to

Duffie (1999), Hull and White (2000a) and Lando (2004), who provide excellent descriptions of

these details.

10See Nouriel Roubini and Stephen Mihm, Crisis Economics, pp.199.11See www.isda.org.12See Greek Sovereign Debt Q&A, October 31 2011, www.isda.org13Another growing clearing platform for CDS is provided by the CME.

5

3 The Market for Sovereign Credit Default Swaps

The last decade was accompanied with a spectacular growth in the market for credit default swaps,

nearing three-digit growth rates year by year. The Bank for International Settlements (BIS) pub-

lishes bi-annual statistics on the notional amounts outstanding and gross market values of OTC

derivatives14, and also includes credit default swaps in their reporting since 2004. As can be seen

in Table (1), the market grew from approximately 6 trillion USD of notional amount outstanding

to a peak of 58 trillion USD in the second term of 2007. Subsequent surveys show a significant

drop in trading with figures down to 32 trillion USD in June 2011, which is mostly linked due to

a netting of outstanding positions as regulators’ increasing concerns about counterparty risk and

calls for higher transparency have led to portfolio compressions, but also partly due to the fact

that credit derivatives were central to the 2007-2009 financial crisis15. While contracts written on

a single underlying reference made up for the bulk of the trading volume in 2004 with a market

share of 80%, multi-name instruments have increased more rapidly due to the practice of synthetic

securitization and represented roughly 44% of the market in 2011. While these numbers sound

impressive, the reader should bear in mind that credit default swaps still account for only a small

fraction of the overall OTC market, as is illustrated in Table (2). With a total trading volume

of roughly 32 trillion USD in June 2011, credit default swaps represent merely 4.58% of the total

amount of notional oustanding in all OTC derivatives, which has grown to 707.57 trillion USD.

The largest fraction comes from interest rate derivatives, which is estimated to have a size 553.88

trillion USD, or equivalently 78.28% of the market.

Nominal or notional amounts outstanding provide a measure of market size and a reference

from which contractual payments are determined in derivatives markets. However, such amounts

are generally not those truly at risk. The amounts at risk in derivatives contracts are a function of

the price level and/or volatility of the financial reference index used in the determination of contract

payments, the duration and liquidity of contracts, and the creditworthiness of counterparties. They

14The notional amounts are likely to slightly underestimate the total value of the market, as until the end of 2011,only 11 countries are reporting their OTC derivative statistics to the BIS. Belgium, Canada, France, Germany, Italy,Japan, the Netherlands, Sweden, Switzerland, the United Kingdom and the United States. From December 2011,Australia and Spain are expected to contribute to the semiannual survey, bringing to 13 the number of reportingcountries.

15Portfolio compression refers to the process by which two counterparties maintain the same risk profile, but reducethe number of contracts and gross notional value held by participants.

6

are also a function of whether an exchange of notional principal takes place between counterparties.

A measure commonly reported along the lines is gross market value, which provides a more accurate

measure of the scale of financial risk transfer taking place in derivatives markets. As is reported

in the BIS statistical notes, ”gross market values are defined as the sums of the absolute values of

all open contracts with either positive or negative replacement values evaluated at market prices

prevailing on the reporting date. Thus, the gross positive market value of a dealers outstanding

contracts is the sum of the replacement values of all contracts that are in a current gain position

to the reporter at current market prices (and therefore, if they were settled immediately, would

represent claims on counterparties). The gross negative market value is the sum of the values of

all contracts that have a negative value on the reporting date (i.e. those that are in a current loss

position and therefore, if they were settled immediately, would represent liabilities of the dealer to

its counterparties). The fact that these numbers are gross merely reflects the fact that contractual

positions of positive and negative values with the same counterparty are not netted out”. Taking

a look at the final column of Table (2), it is possible to see that the gross market value of credit

default swaps is 1.35 trillion USD during the first half of 2010, which is still a sizable market, but

this number is much less breathtaking than the total notional amount outstanding of 32 trillion

USD. Going a step further, gross credit exposure, which takes account of all legally enforceable

netting agreements, makes this number shrink further to a value of 375 billion USD, as can be seen

in the last column of Table (3).

This document is however about sovereign CDS, so we may wonder what these numbers look

like for insurance contracts written on government debt. Statistics on sovereign CDS have until

recently been much less detailed, but what is clear from Table (4) is that the amount of trading

in the sovereign CDS market with 2.91 trillion USD in 2011 represents approximately 9% of the

overall market for OTC credit derivatives. In contrast to corporate CDS, they are on the other

hand largely concentrated around single-name products, who reflect a trading volume of 2.75 trillion

USD, or respectively a fraction of 95%. While more detailed statistics for gross credit exposures

of sovereign CDS are not publicly available on the BIS website, we can get a noisy estimate if we

make the simplifying assumption that the fraction of gross credit exposure to total notional amount

traded is the same for sovereign reference entities and the overall market. Under this assumption,

7

the gross market value of all oustanding contracts is approximately 121 billion USD, while the total

net exposure reduces to roughly 33.6 billion USD. This is still an economically meaningful number,

but admittedly too small to justify interference in the market to avoid a payout trigger, unless it

is for other indirect reasons.

So who trades these products. Hedge funds are often blamed for their speculative one-sided bets,

but if we look at the counterparties involved in sovereign CDS trading in Table (5), we see that it is

to a large extent the reporting dealers, who operate in this market. Their reported trading volume

is approximately 1.8 trillion USD, reflecting a market share of 66.81%, followed second by Banks

and Security Firms with a market share of 21.54% or an underlying notional amount outstanding of

592 billion USD. Hedge funds are only third in line, with a total volume of 146 billion USD. While

this is no evidence, it suggests at least that sovereign CDS are used primarily for hedging purposes,

rather than for speculative bets16. We should however be cautious with such fast conclusions, as

their trading volume has almost doubled from 2010 to 2011, which reflect arbitrage opportunities

linked to the European sovereign debt crisis, wheras banks and security firms have reduced their

exposure from 828 billion USD to 592 billion USD over the same time period.

In contrast to cash bonds, CDS have the attractive feature that they are constant maturity

products. This means that on every day, a market participant can buy an insurance contract with

an identical remaining maturity. Bonds, on the other hand, are issued only seldomly, and their

maturity decreases as time goes by. In addition, an investor has access to a full set of maturities,

as CDS trade usually from maturities of 6 months up to about 30 years. However, liquidity is

still mostly concentrated around five years17. Table (6) shows that in 2011, the total volume of

notional amount outstanding with maturities above one and up to five years, was 23.19 trillion

USD, representing a total market share of 71.57%. This fraction has fluctuated closely around this

level since the BIS has started publishing their reports about CDS. The remaining contracts are

well balanced among maturities below one year, with a total volume 3.92 trillion USD or 12.11%,

or above five years, with a total volume of 5.29 trillion USD or 16.32%18.

16Bongaerts et al. (2011) also cite evidence that banks are primarily net buyers of (corporate) CDS, while insurancecompanies and funds are mainly net sellers.

17The following numbers are for the overall CDS market, and not specifically for sovereign CDS.18In fact, we should emphasize that for sovereign reference entities, trading is not as much concentraed in the 5-year

contract as it is the case for corporate reference entities. Evidence is also provided in Pan and Singleton (2008) andPacker and Suthiphongchai (2003).

8

Last, but not least, Table (7) illustrates that the CDS market is mostly a transnational market.

81.70% trade with a counterparty based in a foreign country, in contrast to only 18.30% of all

contracts that are handled with a counterparty of the home country. The international nature of

the trading raises questions about the usefulness of unilateral policy decisions affecting the market,

such as the ban of naked CDS by Germany in May 2010.

4 Statistical Facts about Sovereign CDS

Beyond knowing the size of the market, which parties trade and with whom, a comprehensive

overview should also provide some stylized statistical facts about the probability distributions

of sovereign CDS. The following statistics are based on a sample of daily mid composite USD

denominated CDS quotes for 38 sovereign countries taken from Markit over the sample period 9

May 2003 until 19 August 2010, where all contracts contain the full restructuring clause. The

sample covers prices for a wide selection of the term structure, including 1, 2, 3, 5, 7 and 10-year

contracts and spans 5 geographical regions, including the Americas, Europe, Africa, the Middle

East and Asia.

A first set of summary statistics is provided in Table (8), where we have grouped the countries in

rating categories according to their average credit rating on external debt over the sample period.

The average term structure is always upward sloping, going from 15 basis points at the 1-year

horizon to 26 basis points at the ten-year maturity for AAA rated entities, and from 433 to 599

basis points for the B rated countries. A mean upward sloping term structure was also shown

for a set of three emerging countries in Pan and Singleton (2008). As the countries become less

creditworthy, the volatility of their CDS spreads jumps up as well. For example, the average

standard deviation of the 5-year CDS spread is 34 basis points for the most creditworthy country,

and 328 basis points for the least creditworthy. Moreover, the level of CDS spreads exhibit positive

skewness, with a value usually around 2, and they have very fat tails. But the excess kurtosis

generally decreases with the asset horizon. Finally, CDS spreads are also very persistent. Among

all statistics, the lowest and highest first-order autocorrelation coefficients are 0.9901 and 0.9979

respectively for daily observations.

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While this measures provide a rough overview of the statistical stylized facts, a common theme

of interest among credit instruments is the slope of the term structure. A structural credit model

of Merton (1974) predicts that the credit curve is increasing for high-quality credit levels, hump-

shaped for intermediate credit quality and decreasing for low levels of creditworthiness. Lando

and Mortensen (2005) have confirmed these predictions for corporate CDS. For their analysis, they

rank their observations based on the average five-year spread level. We attempt to do the same

exercise here for our sovereign CDS sample. Hence for each country on each day, we look at the

5-year CDS spread and group the observations into ranges of 100 basis points and then report

within each category, the average spread, as well as the difference between the 10-year and the

1-year spread, the 10-year and the 3-year spread and the 3-year and the 1-year spread. The average

values are calculated across time and entities. The results, which are shown in Table (9), yield

similar conclusions as for corporate CDS. The overall slope, defined as the difference between the

10-year and 1-year spread, first increases as the credit quality weakens, and then decreases before

becoming negative at very high spread observations. Yet, if we look separately at the gap between

the 1 to 3-year, as well as the 3 to 10-year spread, it is evident that the former turns negative only

much later, confirming the humped shape curve for intermediate groups. Another way to look at

this is by computing the average slope levels in the same three segments, as well as the fraction of

negative values for sliding groups of 400 curves. This yields the outcome plotted in Figure (1), from

which we can draw the same conclusion. At first glance, such results make us feel comfortable that

statistical models of credit curves can be applied equally well to corporate and sovereign reference

contracts. From an empirical perspective, however, it raises the question about the determinants of

the shape of the sovereign credit curve, as country-specific factors seem to have little explanatory

power for the time-series variation of sovereign CDS, at least at higher sampling frequencies. This

contrasts with the predictions of a Merton (Merton 1974) type world. We will address this issue in

the following section.

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5 Determinants of Sovereign CDS spreads

Up to this point, it remains to a large extent unclear what is driving variations in sovereigns CDS

spreads. Let us again go back to the classical Merton model to frame our thoughts. According

to this set-up, the credit spread of a company should be determined by the asset volatility of the

firm’s balance sheet. Or in other terms, characteristics specific to each firm should influence its

likelihood of default. To make the analogy with sovereigns, we would also intuitively expect the

fluctuations in default probabilities to be driven by country-specific fundamentals. Yet, it turns

out that a major fraction of the variation in sovereign CDS spreads is determined by a few global

variables, at least at higher trading frequencies. Moreover, most studies reference some sort of risk

originating in the United States. Where the camps of thought are divided, is whether these global

factors are related to fundamental macroeconomic risk, or rather to financial risk.

There is a long strand of literature, which has tried to understand bond spreads. Here, apart a

few minor exceptions, we will only focus on derivative contracts written on government debt. On

average, sovereign CDS spreads generally have a downward trend, with sharp spikes every time

there is a run-up in risk aversion due to a global risk-related event. Figure (2b) illustrates this

more clearly by plotting the average five-year CDS spread for the sample of 38 countries, which

we have discussed in the previous section. Indeed, the average spread quickly rises each time the

global economy suffers a major shock, such as changes in U.S. monetary policy following falling

growth rates, which have led traders to unwind their carry trades, major corporate bankruptcies

of the usual suspects Bear Stearns and Lehman Brothers, or when the European governments

underwent major political bailouts. Of course, it might be that the risk premium embedded in

spreads, compensating investors for unpredictable variations in future default rates, is driven by

one set of determinants, while expected losses are a function of others. But even for this point, the

literature is at odds. It might be useful to review the papers who have analyzed this question.

Sovereign CDS spreads comove significantly over time, as can be seen in graph (2a) for the 38

countries studied previously. Performing a principal component analysis on a sample of sovereign

CDS spreads generally reveals that the first principal component explains a very large fraction of

the variation, much higher than is the case for equities. The precise figure depends on sample

11

frequency and countries studied, but can be as high as 96% at the daily level or 64% at a monthly

decision interval as shown by Pan and Singleton (2008) and Longstaff et al. (2010) respectively19.

This strong common component motivates the holy grail journey of what this common factor ought

to be.

Among those studies leaning more towards an explanation of global factors is for example

Longstaff et al. (2010). The authors carefully study monthly 5-year CDS of 26 countries over the

time period October 2000 to January 2010 and consider variables related to the local economy,

global financial variables, global risk premia, global investment flow measures and regional spreads.

They conclude that sovereign CDS can be explained to a large extent by U.S. equity, volatility,

and bond market risk premia. A tight link between sovereign CDS spreads is also reported by Pan

and Singleton (2008), who study the term-structure of daily CDS spreads of Korea, Mexico and

Turkey from March 2001 to August 2006. Their main interest lies in disentangling the risk neutral

default and recovery processes implicit in the term structure. With this goal in mind, they develop

a theoretical pricing model to decompose the spread into the part related to expected losses, and

the risk premium. Risk premia appear to comove strongly over time and are strongly related to

the CBOE VIX option volatility index, the spread between the 10-year return on US BB-rated

industrial corporate bonds and the 6-month US Treasury bill rate, and the volatility in the own-

currency options market. These findings corroborate the intuition of time-varying risk premia in

the sovereign CDS market.

Ang and Longstaff (2011) conduct a comparative analysis of CDS spreads written on sovereign

states in the United States and European countries. Rather than extracting the risk premium, they

extract the dependence of spreads on a common component and define this part as systemic risk.

The authors find evidence that this systemic risk component is influenced by global financial factors

and dismiss links with macroeconomic fundamentals. Related to the paper by Pan and Singleton

(2008) is the work of Zhang (2003), who develops a CDS pricing model and applies it to Argentina

to infer differences in expectations about expected recovery rates upon default and expected default

probabilities. In addition, the author also documents that the wedge between risk-adjusted and

historical default probabilities are associated with changes in the business cycle, both the U.S. and

19Pan and Singleton (2008) study 3 countries. Augustin and Tedongap (2011) report that the first principalcomponent explains 78% of the daily variation for a set of 38 countries.

12

Argentine credit conditions as well as the overall local economy. Augustin and Tedongap (2011)

provide empirical evidence that the first two common components among a set of 38 countries

spanning a wide geographical region are strongly associated with expected consumption growth

and macroeconomic uncertainty in the United States. The authors study daily quotes over the time

period April 2003 to August 2010 and document that this link is robust against the influence of

global financial market variables such as the CBOE volatility index or the Variance Risk Premium.

In addition, they rationalize this new finding in a structural asset pricing model with recursive

preferences and a long-run risk economy where the default rate process is driven by the global long-

run expectations of future consumption growth and macroeconomic uncertainty. Further evidence

of influence from the United States on sovereign CDS premia is provided by Dooley and Hutchison

(2009), who document how the subprime crisis channeled through to a sample of 14 geographically

dispersed countries, based on a series of both positive and negative real and financial news over

the sample period January 2007 to February 2009. While they document that a range of news

had statistically and economically significant effects, they find that in particular the Lehman event

and the expansion of Federal Reserve swap lines with the central banks of industrial and emerging

countries uniformly moved all country spreads (in the sample)20. Finally, Wang and Moore (2012)

study the dynamic correlations between the sovereign CDS spreads of 38 emerging and developed

economies with the U.S. from January 2007 to December 2009. Results indicate that in particular

developed economies have become much more integrated with the United States since the Lehman

shock. This tighter link with the U.S. seems to be driven mainly by the U.S. interest rate channel.

A good transition between the ”pro-global” and ”pro-local” camps is provided by Remolona

et al. (2008). Based on the intuition that financial asset prices are driven by country-specific funda-

mentals and investor’s appetite for risk, Remolona et al. (2008) decompose monthly 5-year emerging

markets sovereign CDS spreads over the period January 2002 to May 2006 into a Market-based

measure of expected loss and a risk premium to analyze how each of these two elements is linked

to measures of country-specific risk and measure of global risk aversion/risk appetite. Fundamen-

tal variables include inflation, industrial production, GDP growth consensus forecasts, and foreign

20On 13 October 2008, the Fed removed its USD swap limits to industrial countries, and on 29 October 2008, theFOMC established swap lines with the central banks of Brazil, Mexico, Korea and Singapore for up to $30 billioneach.

13

exchange reserves. Proxies for global risk aversion are taken as the Tsatsaronis, Karamptatos

(2003) effective risk appetite indicator, the VIX and a Risk Tolerance Index by JP Morgan Chase.

They find empirical evidence that global risk aversion is the dominant determinant of sovereign

risk premium, while country-specific fundamentals and market liquidity matter more for sovereign

risk. Both components thus behave differently. Carlos Caceres and Segoviano (2010) analyze how

much of the rising spreads can be explained by shifts in global risk aversion, country-specific risks,

directly from worsening fundamentals, or indirectly from spillovers originating in other sovereigns.

The authors argue that the widening of spreads during the early period of the crisis was essentially

driven by changes in a self-computed measure of risk aversion, but later in the crisis, country-

specific factors identified by each countrys stock of public debt and budget deficit as a share of

GDP played the dominant role.

Carr and Wu (2007) present a joint valuation framework for sovereign CDS and currency options

written on the same economy and execute an empirical test for Mexico and Brazil over the time

period January 2, 2002 to March 2, 2005. They document that CDS spreads show strong positive

contemporaneous correlations with both the currency option implied volatility and the slope of the

implied volatility curve in moneyness21. This analysis confirms their conjecture that economic or

political instability often leads to both worsened sovereign credit quality and aggravated currency

return volatility in a sovereign country. Interestingly, their results suggest that there are additional

systematic movements in the credit spreads that the estimated model fails to capture. Hui and Fong

(2011) reverses the analysis and documents information flow from the sovereign CDS market to the

dollar-yen currency option market during the sovereign debt crisis from September 2009 to August

2011. in a similar spirit, Pu (2012) exploits pricing differences between USD and EUR denominated

sovereign CDS spreads to show that the dual-currency spread significantly affects the bilateral

exchange rate for ten Eurozone countries from January 2008 to December 2010 and has predictive

power up to a perdio of ten days. Gray and Bodie (2007) apply the contingent claims analysis to

price sovereign credit risk by using the government’s balance sheet and exchange rate volatility as

inputs to their model and compare the results to observed CDS spreads. More recently, Plank (2010)

also suggest a structural model for sovereign credit risk based on macroeconomic fundamentals. In

21More specifically, the authors refer to the delta-neutral straddle implied volatilities and the risk reversals.

14

this model, the probability of default reflected in the CDS spread depends on a country’s foreign

exchange reserves, as well as its exports and imports. Ismailescu and Kazemi (2010) consider

the possibility of rating changes affecting the sovereign CDS spreads of 22 emerging economies

over the time period January 2, 2001 to April 22, 200922. Using classic event study analysis, the

authors conclude that the CDS of investment grade countries respond mainly to negative credit

rating announcements, while the spreads of speculative grade countries respond largely to positive

announcements. In addition, the information content of negative credit events is anticipated and

already reflected in CDS spreads by the time the credit rating change is announced. Alternatively,

CDS spreads do not fully anticipate upcoming positive credit rating events, which seem to contain

new information. Furthermore, the paper documents evidence of spillover effects arising mainly

from rating upgrades, with a one notch rise in the rating of an event country increasing the average

CDS spread of a non-event country by 1.18%. These effects are asymmetric, high grade countries

reacting stronger to rating upgrades and low-grade countries reacting stronger to rating downgrades.

Although controlling for previous rating downgrades reduces the magnitude of the spillover effect,

it is increased if countries have common a common creditor, which seems to be one channel of

transmission. However, competition effects arise if two countries have similar levels of trade flow

correlation with the U.S.

There is also a class papers documenting a link between sovereign credit risk implicit in the

CDS price and the health of the financial system. One such example is Acharya et al. (2011), who

illustrate the two-way feedback effect between sovereign risk and the financial sector, where gov-

ernment bailouts negatively impact the financial strength of the sovereign, which in turn reduces

the value of the government guarantees implicit in the financial institutions. As a consequence, the

CDS spreads of sovereign countries and financial companies co-move strongly once the government

has committed excessively to financial guarantees. Altman and Rijken (2011) propose a ”bottom-

up” approach to evaluate sovereign default probabilities by adapting the well-know credit-scoring

method to countries. This suggests that the local real economy significantly impacts default risk.

The outcomes are compared to implied CDS rankings provided by the financial market. Dieck-

mann and Plank (2011) study eighteen advanced western economies on a weekly basis during the

22For early evidence on the relationship between credit ratings and pricing information, see Cossin and Jung (2005),who show that ratings become more informative after a crisis event.

15

2007/2008 financial crisis23. They find that both the state of a country’s financial system and of

the world financial system have strong explanatory power for the behavior of CDS spreads, while

the magnitude of this impact seems to depend on the importance of a country’s financial system

pre-crisis. In addition, they provide evidence that Economic and Monetary Union member coun-

tries exhibit higher sensitivities to the health of the financial system. While Acharya et al. (2011)

stress the two-way feedback effect, the authors here underscore the unilateral private-to-public risk

transfer through which market participants incorporate their expectations about financial industry

bailouts. Evidence that contingent liabilities of sovereigns arising from implicit or explicit guar-

antees of the banking system influence sovereign CDS premia is also provided in Kallestrup et al.

(2011). Also Kallestrup (2011) shows that there are strong interactions between sovereign credit

risk and macrofinancial risk indicators calculated based on bank balance sheet variables. Sgherri

and Zoli (2009) conduct an analysis of the co-movement between sovereign CDS and those of the

financial sector for ten European countries from January 1999 to April 2009. While they confirm

the dominance of a common time-varying factor, they find that concerns about the solvency of the

national banking systems have become increasingly important. Finally, Brutti and Saure (2012)

show how financial shocks to Greece spill over to eleven other European economies. The magnitude

of contagion depends on the cross-country bank exposures to sovereign debt. All else being equal,

the transmission rate to the country with the greatest exposure to Greece (1.22 percent of GDP)

has been roughly 46 percent higher than the rate to the country with the least exposure (0.08

percent of GDP). 24.

6 The relationship between sovereign CDS and bonds and Fric-

tions

Duffie (1999) illustrates theoretically that the spread on a par floating rate note over a risk-free

benchmark should essentially be equal to the CDS spread25. As such, the CDS-Bond basis, defined

as the difference between the CDS and bond spread, should be zero. However, empirically observed

23The exact time period is January 2007 to 2010.24Further evidence on the relationship between sovereign and bank CDS is provided by Ejsing and Lemke (2011),

Alter and Schuler (2011) and Aktug and Vasconcellos (2010)25Other references are Lando (2004) and Hull and White (2000a).

16

imperfections of this theoretical arbitrage relationship has led researches to investigate whether the

CDS market is more informationally efficient than the cash bond market and whether it reflects

new information more quickly, or vice-versa. Alternatively, people have looked at the determinants

of the basis, which relates explicitly to limits of arbitrage and frictions in one of the two markets,

or both.

Regarding price discovery for the sovereign asset class, there has recently been a proliferation of

papers addressing the question of which market is more efficient because of the turbulences in the

sovereign debt market. Abstracting from technical details, these papers generally have the common

approach to test whether the two markets are co-integrated, that is whether they are characterized

by a long-run stationary relationship, and then to look at short-term deviations from equilibrium to

verify which market adapts to the other one. This is executed using the techniques of vector error

correction modeling, computing Hasbroucks’s and Gonazalo-Granger’s information measures, or by

analyzing statistical causality using Granger’s method. While for corporate reference entities, there

is more or less consent about the CDS market being more efficient, results for sovereign reference

entities are very mixed and ambiguous. These discrepancies are certainly related to the different

samples, time periods, sampling frequency and data sources. While some conclude in favor of the

bond market and others in favor of the CDS market, our interpretation of the literature is that

there is increasing price discovery in the credit derivative market. Arce et al. (2011) make the

sensible point that price discovery is state dependent and related to the relative liquidity in both

markets. As the CDS market has become more mature over time, this would also explain why its

relative informational efficiency has increased. A similar argument is also made in Ammer and Cai

(2011), who provide some evidence that the relatively more liquid market tends to lead the other,

as their measure of relative CDS price leadership is correlated positively with the ratio of the bond

bid-ask spread to the CDS bid-ask spread and negatively with the number of bonds outstanding.

Our further overall interpretation is that a positive basis is usually more persistent, due to the

difficulty of shorting bonds. Moreover, except for some minor exceptions, almost all studies focus

exclusively on the most liquid 5-year segment of the market. Without spending too much time on

the details, we provide a shopping list of the references in the following paragraph.

Palladini and Portes (2011) study 6 developed economies in Europe from 30 January 2004

17

through 11 March 2011 using CMA quote data and conclude that CDS prices lead in the price

discovery process26. A similar conclusion is drawn by Varga (2009), who takes a special look at

Hungary from 6 February 2004 to 18 June 2008 using the CMA database27. Using likewise the

CMA data, O’Kane (2012) studies cross-correlations and performs Granger causality tests for 6

European economies from 1 January 2008 to 1 September 201128. He finds that for Greece and

Spain, Granger causality flows from the CDS to to the bond market, wile the results is reversed

for Italy and France. For Portugal and Ireland, there seems to be two-way causality. Studying 18

sovereign developed and emerging economies from January 2007 to March 2010, Coudert and Gex

(2011) conclude that bonds seem to lead for developed European economies, while the derivative

market wins the horse race in emerging economies29. The authors also put forth a differential

liquidity argument and argue that the role of CDS has intensified during the crisis, as bond market

players with a bearish view will simply stay out of the markets, while CDS buyers will stay in

and purchase protection. This contrasts with Arce et al. (2011), who argue that the role of bond

markets has increased for European Economies during the financial crisis. They study 11 European

economies from January 2004 to September 2010 using CMA data30. Fontana and Scheicher (2010)

look at 10 Euro area countries from January 2006 to June 2010 using again CMA data31. In contrast

to most other studies, which use the 5-year spread, the authors study 10-year spreads. In line with

the previous mixed results, in half of the sample countries, price discovery takes place in the CDS

market and in the other half, price discovery is observed in the bond market. Further evidence is

provided by M. Kabir Hassan and Suk-Yu (2011), who study 7 emerging economies from January

2004 to October 2009 using data from JP Morgan Chase32. They find that bond markets lead

for Brazil and the Philippines, CDS markets lead for Argentina, and there is no clear dominance

for Mexico, China, and South Africa. The additional contribution is to link pricing efficiency to

26The 6 countries are Austria, Belgium, Greece, Ireland, Italy, and Portugal.27While Varga focuses on Hungary, some additional analysis is also provided for 14 emerging countries from 3

January 2005 to 30 May 2008. These countries are Brazil, Bulgaria, Czech Republic, South Africa, Estonia, Croatia,Poland, Latvia, Lituania, Russia, Romania, Slovkia, Turkey and Ukraine.

28The 6 countries are France, Greece, Italy, Ireland, Portugal and Spain.29See also Coudert and Gex (2010). The sample includes Austria, Belgium, Denmark, Finland, France, Netherlands,

Greece, Ireland, Italy, Portugal and Spain for the advanced European countries; Argentina, Brazil, Mexico, Lithuania,Poland, Turkey and Philippines for the emerging countries; in addition the authors study 17 financial companies.

30The 11 EMU countries are Austria, Belgium, Finland, France, Germany, Greece, Ireland, Italy, the Netherlands,Portugal and Spain.

31Austria, Belgium, France, Germany, Greece, Ireland, Italy, Netherlands, Portugal and Spain32The countries are Argentina, Brazil, China, Colombia, Mexico, Philippines and South Africa.

18

financial integration. It is argued that sovereigns, for which bond markets lead the price discovery

process, are more financially integrated with the world economy.

Li and Huang (2011) witness an increasing contribution of sovereign CDS rates to credit risk

discovery based on a sample of 22 emerging countries using Thomson reuters data from 1 January

2004 to 31 July 200833. C. Emre Alper and Gerard (2012), studying CDS premia in relation to

asset swap spreads, conclude in favor of price discovery in the derivative market given a sample

of 21 advanced economies from January 2008 to January 201134. Their data comes both from

Datastream and Markit. Aktug and Bae (2009) study 30 emerging markets at a monthly sampling

frequency from January 2001 to November 2007 using data from Markit35. They show that bond

markets lead CDS markets in general, but that they lag CDS spreads in some cases, and that the

markets have become more integrated over time. Support for the bond markets is also found in

Ammer and Cai (2011). The latter investigate 9 emerging economies from 26 February 2001 to 31

March 2005 using Markit data36. Overall, they find that the bond market leads the CDS market

more often. In an early paper, Chan-Lau and Kim (2004) have a hard time concluding in favor

of any market using CreditTrade data for 8 emerging economies from 19 March 2001 to 29 may

2003.37. Delis and Mylonidis (2011) look at Greece, Italy, Portugal and Spain using CMA data

from 9 July 2004 to 25 May 25 2010. Based on 10-year spreads, they argue that CDS almost

uniformly Granger cause bond spreads. Feedback causality is, however, detected during times of

intense financial and economic turbulences. Finally, additional elements of analysis can be found in

In et al. (2007), Boone et al. (2010), Li (2009), Bowe (2009), Carboni (2010) and Adler and Song

(2010).

As a conclusion, results are thus very inconclusive, but differential liquidity seems to matter.

Based on this intuition, Calice et al. (2011) investigate liquidity spillovers between the two markets

33The sample is composed of Argentina, Brazil, Chile, China, Colombia, Czech Republic, Egypt, Hungary, Indone-sia, Israel, Korea, Malaysia, Mexico, Morocco, Pakistan, Peru, Philippines, Poland, Russia, South Africa, Turkey,Venezuela.

34The 22 countries are Australia, Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy,Japan, the Netherlands, Norway, Portugal, Spain, Sweden, the UK and the US.

35They study Brazil, Bulgaria, Chile, China, Colombia, Croatia, Czech Republic, Dominican Republic, Ecuador,Egypt, El Salvador, Hungary, Korea, Lebanon, Malaysia, Mexico, Morocco, Pakistan, Panama, Peru, Philippines,Poland, Russia, South Africa, Thailand, Turkey, Ukraine, Venezuela and Vietnam.

36Brazil, China, Colombia, Mexico, the Philippines, Russia, Turkey, Uruguay, and Venezuela.37The following countries are reviewed: Brazil, Bulgaria, Colombia, Mexico, the Philippines, Russia, Turkey, and

Venezuela.

19

and find substantial variation in the patterns of the transmission effect between maturities and

across countries. For several countries, including Greece, Ireland and Portugal the liquidity of the

sovereign CDS market has a substantial time varying influence on sovereign bond credit spreads. In

et al. (2007) study the volatility transmission among the two markets across emerging economies.

While the dynamics between the cash and derivative market raise interesting questions, another

angle to analyze is the determinants of the short-term deviations from the equilibrium relationship.

Differential liquidity may naturally explain the seemingly arbitrage opportunity. Early studies on

this topic assumed that the credit derivative market was perfectly liquid, as the underlying traded

instruments are by nature simple contractual agreements, in contrast to the cash market, where

hard money is actually exchanged when the asset is purchased. In this spirit, Longstaff et al. (2005)

postulated CDS spreads to be pure indicators of default risk in order to infer liquidity characteristics

of the bond market. Academic work on liquidity effects in the CDS market is very young, even

for corporate reference contracts, but the literature is rapidly growing. Two papers that study

liquidity characteristics in the synthetic credit derivative market for corporate reference entities are

for example Tang and Yan (2007) and Bongaerts et al. (2011). The former show that both liquidity

characteristics and liquidity risk explain about 20% of the time series variation in CDS spreads,

while the latter provide strong evidence for an expected liquidity premium earned by the protection

seller. The premium is however economically small. For sovereign CDS, Pan and Singleton (2008),

who study the term structure of recovery rates of Mexico, Brazil and Turkey, report anecdotal

evidence of liquidity effects from discussion with practitioners. Regarding the determinants of the

CDS-bond basis, a liquidity story is supported by Arce et al. (2011), who show that differential

liquidity between the bond and the CDS partially explains the difference. Specifically, differential

liquidity is proxied by the ratio of the bid-ask to the mid spread in the CDS market divided by

the bid-ask to the mid spread in the bond market. In addition, the authors show evidence for the

role of counterparty risk, which is the risk that a CDS counterparty may not be able to honor its

obligations from the insurance contract. This risk becomes especially important when the default

of the counterparty occurs at the same time as the default event of the underlying reference entity.

Arora et al. (2012) show that counterparty risk is significantly priced in the corporate market, but

that it has economically a small magnitude. On average, counterparty credit risk needs to increase

20

by 646 basis points to reduce the insurance price by 1 basis point. This small effect is likely the

result of collateralization (and maybe overcollateralization) agreements. In a similar spirit, Levy

(2009) also links the pricing discrepancies to both counterparty risk and liquidity. Kucuk (2010)

finds that the basis is strongly associated with various bond and CDS liquidity variables such as

(for bonds and CDS) the average sample bid-ask spread and percentage bid-ask spread, and (for

bonds) lagged trading volume, notional amount outstanding, age and time to maturity. Similar to

the debate on global versus local factors for sovereign CDS spreads, Fontana and Scheicher (2010)

relate the basis mainly to common factors.

Another potential source of friction, which can create a wedge between the CDS and bond

spread is the Cheapest-to-Deliver option embedded in CDS contracts. As we have already ex-

plained, upon the occurrence of a credit event, the holder of the insurance contract has the right

to deliver the cheapest among a set of defaulted debt obligations to his counterparty in return

for the insurance settlement. This option makes the contract riskier from the perspective of the

CDS seller, and should be relatively more important, the closer a country is to default. Ammer

and Cai (2011) provide a treatment of the sovereign case, while Jankowitsch et al. (2008) provide

positive evidence for corporate CDS. For sovereigns, some early analysis is also available in Singh

(2003). Fisher (2010) studies theoretically how heterogenuous investors with different beliefs about

the probability of default of a sovereign borrower affects the sovereign CDS-Bond basis. The re-

sults of the model depend on the assumption that a fixed proportion of investors is unable to sell

CDS. The latter constraint becomes binding in bad times, when a relatively bigger proportion of

investors is pessimistic and wants to sell of credit risk (buying CDS). But as the supply is limited,

this constraint creates a wedge between the bond and CDS prices and induces a positive basis.

Thus, the time-varying equilibrium basis depends on the time-varying proportion of pessimistic

investors. A second mechanism, that induces the (positive basis) in his model is the prospect of

lending fees from the bonds in bad times. As supply in the CDS market is restricted, some pes-

simistic investors, who want to take a short position in sovereign credit risk need to short-sell the

physical bond. This allows those, who hold on to the bond to charge higher fees by lending it out

to short-sellers. This fee raises bond prices, lowers spreads, and creates a positive basis. Finally,

Adler and Song (2010) provide evidence that short-selling costs were partly responsible for persist-

21

ing positive bases for sovereign reference entities. In addition, the authors provide a more general

theoretical pricing framework for the basis, which corrects for biases arising from acrrued spread

or coupon payments and bond prices away from their par value38. In fact, the model illustrates

how accrued payments and bond prices below (above) par may mechanically introduce a negative

and positive (negative) basis respectively. It is exactly this latter effect, which may explain the

well documented basis smile. The average credit quality of a portfolio of bonds depends on the

statistical distribution of historical rating transitions. Hence, the joint consideration of price and

credit quality will determine the shape of the basis smile, which could very well be a ”frown”.

Another ”hot” topic about sovereign CDS is whether speculators manage to (negatively) impact

the market. In May 2010, BaFin, the German financial regulator went ahead with an outright

ban on naked sovereign CDS (and bond) positions, despite an official report failing to provide

conclusive evidence (See Criado et al. (2010).). The academic opinions don’t seem to agree on

this idea. Richard Portes (see Portes (2010)) has advocated a ban, arguing that naked CDS

buying (equivalent to shorting the bond) artificially drives up prices, thereby increasing a country’s

borrowing costs. This opinion seems to be shared in a study by Delatte et al. (2012). Darrell

Duffie on the other hand (Duffie (April 29 2010a, 2010b)) dismisses the argument and shows some

empirical evidence that there is no such evidence. He argues that a ban on naked sovereign CDS

trading may instead increase execution costs and lower the quality of price information. In contrast,

he argues that holders of both the derivative and the underlying cash bond have in fact no more

incentive to monitor the borrower, which may induce the borrower to invest less efficiently and

thus increases the probability of default. Bolton and Oehmke (2011) study this ”empty creditor”

problem theoretically for corporate CDS, while Subrahmanyam et al. (2012) provide empirical

evidence39. An early theoretical treatment of the effect of the existence of CDS on the incentives of

sovereign borrowers was also made available by Goderis and Wagner (2011). More specifically, the

authors show that without insurance, the sovereign country has no incentive to optimally invest (or

exert effort) as there is the possibility of offering a debt restructuring in the bad states of the world,

38The pricing framework considered in Adler and Song (2010) is a useful extension to the treatment in Duffie(1999).

39The concept of empty creditor refers to the separation of cash flow and voting rights through the use of creditderivatives. A debtholder, whose exposure is insured with a CDS, keeps an economic interest in the firm’s claims,but has no more risk alignment with other bondholders, who enjoy no such proection.

22

which the lender cannot credibly commit to reject. When the lender can purchase insurance on

the bond, the threat of rejecting the restructuring offer becomes credible and the debtor will have

to internalize more of the costs in the bad states. This provides incentive to invest more efficiently

ex-ante, which in turn reduces default. Strategic purchase by a (single) bondholder to increase

the bargaining power in debt renegotiation deal is thus beneficial from an ex-ante perspective.

However, in the presence of many bondholders, there may be coordination failures. This may

lead to a situation, where lenders buy more insurance than is socially optimal, which leads to an

increased probability of default.

Sambalaibat (2011) studies the issue of naked CDS theoretically and shows that the effect de-

pends on the infrastructure of the insurance market. The overall outcome depends on the parameter

values, and naked CDS buyers may either induce over-investment, leading to lower borrowing costs

or to under-investment with higher borrowing costs. For a legal treatment of how credit derivatives

may affect the sovereign debt restructuring process, see Verdier (2004).

7 Trading in the Sovereign CDS Market

Very little is known about the trading patterns in the sovereign CDS market. Based on a three-

month sample of detailed transaction-level data over the time period May 1 to July 31 2010 gener-

ously received from a set of dealers, the Federal Reserve Bank of New York spoiled us a with a more

detailed picture of the CDS trading behavior in their staff report No. 51740. Keeping in mind the

caveat that the data set stems from a period, where overall trading activity was below historical

average, the results indicate that CDS trade not that often despite their highly standardized nature.

For the 29,146 single name sovereign CDS transactions recorded for 74 reference entities, the most

actively traded reference entities traded on average 30 times per day, the less frequently traded 15

times per day on average and the infrequently traded reference entities traded only 2 times per day

on average. Another way to say this, is that a long tail of reference entities trade less than once a

day. In terms of size, the USD denominated trades were mostly traded in 5 million USD tickets,

with a median and mean trade of 10 and 16.74 million USD respectively. For EUR denominated

trades (which amounted to only 574 transactions), the mode was 10 million USD, with a median

40See Chen et al. (2011).

23

trade of 5 million and a mean trade of 12.53 million. For matters of comparison, sovereign single

name CDS trades were on average twice as large as their corporate counterparts.

Although the number of trades seems rather low, market participation for sovereign reference

entities was rather active. On average, 50 market participants are reported to have traded at least

once a day, 200 traded at least once a week and 340 traded at least once a month. In addition,

dealers were more likely to be sellers of protection and the four most active dealers were involved

in 45% of the buying and selling of all CDS trades and in 50% of the overall notional amount.

Yet, according to regulatory definitions of the Department of Justice, the Herfindahl Hirshman

concentration index with levels ranging between 885 and 965 is considered to be low41. Finally, the

market is confirmed to be highly standardized, as 92% of all single-name CDS contracts within the

sample had a fixed coupon and 97% had fixed quarterly payment dates42.

Another more exhaustive source of information on CDS trading behavior is the the Deposi-

tory Trust and Clearing Corporation (DTCC). In October 2008, the DTCC Trade Information

Warehouse started to publish detailed weekly reports on stocks and volumes in CDS trading. More

specifically, current and historical positions are shown on an aggregate level and for the 1,000 mostly

traded reference contracts. This move towards higher transparency is greatly welcome. Oehmke

and Zawadowski (2011) have used this data to study the determinants of corporate trading and

find that higher amounts of (corporate) CDS outstanding are associated with companies, which

have more assets on their balance sheet, more bonds outstanding, that are investment grade firms

or firms that have just recently lost their investment grade status, as well as with higher analysts’

forecast dispersion. Duffie (April 29 2010a) illustrates a few statistics on sovereign CDS from this

database in his testimony to the United House of Representatives, but we are not aware of any

formal treatment at this stage. Here, we provide a summarized overview of the main statistical

facts for government credit derivatives.

Tables (10) and (11) illustrate the average gross and net notional amounts outstanding in (mil-

41Note that Giglio (2011) emphasizes the high concentration of the CDS market, thereby referring to industryreports, which state that in 2006, the top 10 counterparties (all broker/dealers) accounted for about 89% of the totalprotection sold. See footnote 8.

42Using transactions and quote data from CreditTrade on 77 sovereign reference entities from January 1997 toJune 2003, Packer and Suthiphongchai (2003) emphasized the low trading volume by indicating that (in 2002), only6% of all quotes resulted in transactions, with a strong concentration in a few reference entities. The top five namesaccounted for more than 40% of all quotes. These countries were Brazil, Mexico, Japan, the Philippines and SouthAfrica.

24

lion) USD equivalents on all sovereign CDS contracts (excluding sovereign states in the US) among

the 1,000 mostly traded contracts over the time period 31 October 2008 through 11 November

201143. In addition, there is information on the average number of contracts over that period, the

ratios of gross to net notional amount outstanding and the net notional amount outstanding to the

number of contracts. The countries are classified into five geographical regions following the prac-

tice of Markit: Americas, Asia Ex-Japan, Australia and New Zealand, Europe-Middle East-Africa

(EMEA) and Japan. While the weekly total gross notional volume recorded for sovereign countries

in the data repository is approximately 2.1 trillion USD, the net exposure, which accounts for off-

setting effects between buyers and sellers and represents therefore a more economically meaningful

measure, reflects with 203.6 billion USD about 9.86% of that amount. Keeping in mind that this

number does not report the values for sovereign US states and other non-government supranational

bodies, this amount represents approximately 75% of the sovereign single name CDS outstanding

as reported by BIS in the first semester of 2011 (see Table (4)).

The total number over all country-specific averages of traded contracts is 147,258, the mean

ratio of gross to net notional amount is 10.74 and on average, the net credit exposure per contract

is 1.8 million USD. There is however a significant cross-sectional dispersion. Further analysis also

reveals that four of the five GIIPS countries rank among the top ten countries with the highest

amount of net notional exposure. Another striking feature we observe is that emerging economies

tend to have higher numbers of traded contracts, but smaller net exposures per contract on average,

while developed economies trade in bigger bulks, but have less contracts outstanding. The world’s

two biggest reference bond markets, the US and Germany, top the list with 5.16 and 6.66 million

USD per contract respectively. At the bottom of the list are the Philippines with an average of

360,000 USD per traded contract. Moreover, the top ten list of countries with the highest amount of

gross volume outstanding is dominated by emerging market economies, while developed distressed

economies cluster in the top ten list with the highest net notional exposure.

Tables (12) and (13) provide similar statistics at a yearly frequency since the data has been

made available by DTCC. From these numbers, it is clear to see that average weekly gross and net

volumes, as well as ratios of gross to net exposure have fluctuated only modestly, with the ratio

43Tables (10) and (11) provide essentially the same information, but Table (10) provides a sort in descending orderfor each column to help the exposition. The reader may find one or the other table more reader-friendly.

25

of gross to net exposure being roughly equal to a factor of 10 over time. The average number of

contracts reported in the warehouse has changed slightly more over time, which explains the bigger

fluctuations in the net notional exposure per contract, which has come down from 3.6 million USD

in 2008 to a mean 1.4 million in 2011.

Figures (3) and (4) provide some more intuition about how the CDS trading is related to

individual government characteristics for EMEA and Asian countries (ex-Japan). For these regions,

it is clear that within each year, there seems to be a statistical relationship between the net notional

amount of CDS outstanding and the nominal amount of government debt. This relationship appears

much weaker if debt levels are compared against gross exposures. Duffie (April 29 2010a, 2010b)

showed empirical evidence that the volume of CDS is unrelated to the level of spreads. This raises

once more interesting questions. We have seen that government characteristics and CDS volumes

are unrelated to to spreads, but characteristics are linked to volumes.

Table (14) separately reports statistics for the United States government and the sovereign US

states, which are registered in the DTCC database. In contrast to the statistics on other sovereign

governments, all yearly average values for CDS on US Treasuries exhibit a sharp increase in 2011.

In fact, the mean gross notional amount jumps by 90% from 2011 to 2011, going from 13.2 to

25 billion USD. That’s a huge surge. The same observation holds for net exposure, which rises

by 84% over the same time period, going from 2.4 to 4.5 billion USD, while the number of live

contracts increases from 479 million to approximately 1.1 billion contracts, which corresponds to

an increase of 120%. These spikes raise the provoking thought that investors may actually have

seriously considered the possibility of US government default during the lock-out period in summer

2011. In particular, as no such sharp changes are recorded for the individual states. There the

evidence is mixed. Gross exposures and number of traded contracts unilaterally increased, while

net notional exposure increased mainly for Illinois, while it dropped for instance for New York City,

Texas or Florida.

26

8 Conclusion

Sovereign credit risk is undoubtedly a topic of crucial importance. The supranational bailouts of

Greece, Ireland, Portugal and the vast amount of European Union summits ever since the start

of the sovereign debt crisis eliminate any doubt about the relevance of this topic44. As we speak,

trying to avoid information on sovereign debt issues is like walking through a mine field in North

Korea.

This paper tries to provide a comprehensive overview of the young, but steadily growing lit-

erature on sovereign CDS, describes key statistical facts and features of prices, about the market,

its players and related trading activities and attempts to raise some thought-provoking questions.

We thereby hope to provide a good starting point for anyone interested in the topic. We had fun

writing this document, we hope you enjoyed it too.

44We stopped counting at 16.

27

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32

Table 1: Credit Default Swaps: Notional Amount outstanding (’000,000)

This table reports the total notional amount outstanding in million USD of credit default swaps, its growth (in %)and the market share (in %) for the overall market, single-name and multi-name instruments. Data on total notionalamount outstanding are shown on a net basis, that is transactions between reporting dealers are reported only once.Source: BIS.

Period Total Growth Single-Name Growth Fraction Multi-Name Growth Fraction2004-H2 6 395 744 0.00 5 116 765 – 80.00 1 278 979 – 20.002005-H1 10 211 378 59.66 7 310 289 42.87 71.59 2 901 090 126.83 28.412005-H2 13 908 285 36.20 10 432 038 42.70 75.01 3 476 247 19.83 24.992006-H1 20 352 307 46.33 13 873 445 32.99 68.17 6 478 863 86.38 31.832006-H2 28 650 265 40.77 17 879 280 28.87 62.41 10 770 985 66.25 37.592007-H1 42 580 546 48.62 24 239 478 35.57 56.93 18 341 068 70.28 43.072007-H2 58 243 721 36.78 32 486 272 34.02 55.78 25 757 449 40.44 44.222008-H1 57 402 760 -1.44 33 412 115 2.85 58.21 23 990 644 -6.86 41.792008-H2 41 882 674 -27.04 25 739 929 -22.96 61.46 16 142 745 -32.71 38.542009-H1 36 098 169 -13.81 24 165 086 -6.12 66.94 11 933 083 -26.08 33.062009-H2 32 692 683 -9.43 21 917 050 -9.30 67.04 10 775 633 -9.70 32.962010-H1 30 260 923 -7.44 18 493 632 -15.62 61.11 11 767 291 9.20 38.892010-H2 29 897 578 -1.20 18 144 580 -1.89 60.69 11 752 998 -0.12 39.312011-H1 32 409 444 8.40 18 104 619 -0.22 55.86 14 304 825 21.71 44.14

33

Tab

le2:

OT

CD

eriv

ativ

es:

Not

ion

alA

mou

nt

outs

tan

din

g(’

000,

000,

000)

This

table

rep

ort

sth

eto

tal

noti

onal

am

ounts

outs

tandin

gin

billion

USD

of

Over

-the-

counte

rder

ivati

ves

,th

eir

gro

ssm

ark

etva

lues

and

the

mark

etsh

are

(in

%),

bro

ken

dow

nby

risk

cate

gory

and

typ

eof

inst

rum

ent.

Data

on

tota

lnoti

onal

am

ount

outs

tandin

gare

show

non

anet

basi

s,th

at

istr

ansa

ctio

ns

bet

wee

nre

port

ing

dea

lers

are

rep

ort

edonly

once

.Sourc

e:B

IS.

Noti

on

al

am

ou

nts

ou

tsta

nd

ing

(’000,0

00,0

00)

Gro

ssm

ark

etvalu

es(’

000,0

00,0

00)

2005-H

12006-H

12007-H

12008-H

12009-H

12010-H

12011-H

12007-H

12008-H

12009-H

12010-H

12011-H

1T

ota

lco

ntr

act

s282

665

372

513

507

907

672

558

594

553

582

685

707

569

11

118

20

340

25

298

24

697

19

518

Fore

ign

exch

an

ge

contr

act

s31

081

38

127

48

645

62

983

48

732

53

153

64

698

1345

2262

2470

2544

2336

(Fra

ctio

n)

11.0

010.2

49.5

89.3

68.2

09.1

29.1

412.1

011.1

29.7

710.3

11.9

7F

orw

ard

san

dfo

rex

swap

s15

801

19

407

24

530

31

966

23

105

25

624

31

113

492

802

870

930

777

Cu

rren

cysw

ap

s8

236

9696

12

312

16

307

15

072

16

360

22

228

619

1071

1211

1201

1227

Op

tion

s7

045

9024

11

804

14

710

10

555

11

170

11

358

235

388

389

413

332

Inte

rest

rate

contr

act

s204

795

262

526

347

312

458

304

437

228

451

831

553

880

6063

9263

15

478

17

533

13

244

(Fra

ctio

n)

72.4

570.4

768.3

868.1

473.5

477.5

478.2

854.5

345.5

461.1

870.9

967.8

5F

orw

ard

rate

agre

emen

ts13

973

18

117

22

809

39

370

46

812

56

242

55

842

43

88

130

81

60

Inte

rest

rate

swap

s163

749

207

588

272

216

356

772

341

903

347

508

441

615

5321

8056

13

934

15

951

11

864

Op

tion

s27

072

36

821

52

288

62

162

48

513

48

081

56

423

700

1120

1414

1501

1319

Equ

ity-l

inked

contr

act

s4

551

6782

8590

10

177

6584

6260

6841

1116

1146

879

706

708

(Fra

ctio

n)

1.6

11.8

21.6

91.5

11.1

11.0

70.9

710.0

35.6

33.4

82.8

63.6

3F

orw

ard

san

dsw

ap

s1

086

1430

2470

2657

1678

1754

2029

240

283

225

189

176

Op

tion

s3

465

5351

6119

7521

4906

4506

4813

876

863

654

518

532

Com

mod

ity

contr

act

s2

940

6394

7567

13

229

3619

2852

3197

636

2213

682

458

471

(Fra

ctio

n)

1.0

41.7

21.4

91.9

70.6

10.4

90.4

55.7

210.8

82.6

91.8

52.4

1G

old

288

456

426

649

425

417

468

47

72

43

45

50

Oth

erco

mm

od

itie

s2

652

5938

7141

12

580

3194

2434

2729

589

2141

638

413

421

Forw

ard

san

dsw

ap

s1

748

2188

3447

7561

1715

1551

1846

––

––

–O

pti

on

s904

3750

3694

5019

1479

883

883

––

––

–C

red

itd

efau

ltsw

ap

s10

211

20

352

42

581

57

403

36

098

30

261

32

409

721

3192

2973

1666

1345

(Fra

ctio

n)

3.6

15.4

68.3

88.5

36.0

75.1

94.5

86.4

815.6

911.7

56.7

56.8

9S

ingle

-nam

ein

stru

men

ts7

310

13

873

24

239

33

412

24

165

18

494

18

105

406

1901

1950

993

854

Mu

lti-

nam

ein

stru

men

ts2

901

6479

18

341

23

991

11

933

11

767

14

305

315

1291

1023

673

490

of

wh

ich

ind

exp

rod

uct

s–

––

––

7499

12

473

––

––

–U

nalloca

ted

29

086

38

332

53

213

70

463

62

291

38

329

46

543

1237

2264

2816

1789

1414

(Fra

ctio

n)

10.2

910.2

910.4

810.4

810.4

86.5

86.5

811.1

311.1

311.1

37.2

57.2

5

34

Table 3: Exposures of Credit Default Swaps: Notional Amount Outstanding (’000,000)

This table reports the total notional amount outstanding in million USD of credit default swaps as well as the grossand net market values. Data on total notional amount outstanding are shown on a net basis, that is transactionsbetween reporting dealers are reported only once. Source: BIS.

2005-H1 2006-H1 2007-H1 2008-H1 2009-H1 2010-H1 2011-H1Notional Amounts outstanding 10 211 378 20 352 307 42 580 546 57 402 759 36 098 169 30 260 923 32 409 444Gross Market Values 187 932 294 332 720 725 3 191 868 2 973 449 1 665 944 1 344 731Gross Credit Exposure – – – – – – 374 525

Table 4: Sovereign Credit Default Swaps: Notional Amount outstanding (’000,000)

This table reports the total notional amount outstanding in million USD of Sovereign credit default swaps and themarket share (in %) by type of product. Data on total notional amount outstanding are shown on a net basis, thatis transactions between reporting dealers are reported only once. Source: BIS.

Period Total Sov.Single-Name Sov.Multi-Name Sov.All2004-H2 6 395 744 – – – – – –2005-H1 10 211 378 – – – – – –2005-H2 13 908 285 1 258 139 ( 9.05 %) – – – –2006-H1 20 352 307 640 971 ( 3.15 %) – – – –2006-H2 28 650 265 1 100 990 ( 3.84 %) – – – –2007-H1 42 580 546 1 489 655 ( 3.50 %) – – – –2007-H2 58 243 721 1 837 873 ( 3.16 %) – – – –2008-H1 57 402 760 2 177 364 ( 3.79 %) – – – –2008-H2 41 882 674 1 650 743 ( 3.94 %) – – – –2009-H1 36 098 169 1 761 083 ( 4.88 %) – – – –2009-H2 32 692 683 1 943 005 ( 5.94 %) – – – –2010-H1 30 260 923 2 392 580 ( 7.91 %) – – – –2010-H2 29 897 578 2 542 331 ( 8.50 %) – – – –2011-H1 32 409 444 2 749 259 ( 8.48 %) 158 516 (0.49 %) 2 907 775 (8.97 %)

Table 5: Counterparties of Single-Name Sovereign Credit Default Swaps

This table reports the total notional amount outstanding in million USD of Single-Name sovereign credit default swapsby type of counterparty as well as their relative market share (in %). Data on total notional amount outstanding areshown on a net basis, that is transactions between reporting dealers are reported only once. Central Counterparty(CCP) is defined as an entity that interposes itself between counterparties to contracts traded in one or more financialmarkets, becoming the buyer to every seller and the seller to every buyer. Both contracts post-novation are captured.Insurance and Financial Guaranty firms include reinsurance and pension funds. Source: BIS.

2006-H1 2007-H1 2008-H1 2009-H1 2010-H1 2011-H1All Counterparties 640 971 1 489 655 2 177 364 1 761 083 2 392 580 2 749 259Reporting Dealers 301 320 781 382 1 123 078 877 496 1 320 039 1 836 889

(47.01) (52.45) (51.58) (49.83) (55.17) (66.81)Other Financial Institutions 292 907 644 922 985 117 813 241 1 017 122 891 483

(45.7) (43.29) (45.24) (46.18) (42.51) (32.43)Central Counterparties – – – – - 2 075Banks and Security Firms 168 174 216 677 569 346 647 135 828 023 592 194Insurance and Financial Guaranty Firms 2 110 5 293 8 356 4 371 8 858 14 688SPVs, SPCs and SPEs 712 2 375 323 418 7 905 18 106Hedge Funds 18 386 27 603 13 519 9 437 88 519 145 178Other Financial Customers 103 525 392 976 393 573 151 880 83 817 119 241

Non-Financial Institutions 46 745 63 347 69 169 70 357 55 429 20 885(7.29) (4.25) (3.18) (4,00) (2.32) (0.76)

35

Table 6: Maturity Structure of Credit Default Swaps: Notional Amount Outstanding (’000,000)

This table reports the total notional amount outstanding in million USD of credit default swaps by remaining maturityas well as their respective market shares. Data on total notional amount outstanding are shown on a net basis, thatis transactions between reporting dealers are reported only once. The remaining contract maturity is determined bythe difference between the reporting date and the expiry date of the contract and not by the date of execution of thedeal. Source: BIS.

Period Total 1y or less Fraction Over 1y up to 5y Fraction Over 5 years Fraction2004-H2 6 395 744 491 270 7.68 4 659 521 72.85 1 243 778 19.452005-H1 10 211 378 670 627 6.57 7 139 210 69.91 2 400 398 23.512005-H2 13 908 285 861 871 6.20 9 821 293 70.61 3 225 106 23.192006-H1 20 352 307 1 573 542 7.73 13 019 148 63.97 5 759 336 28.302006-H2 28 650 265 2 336 401 8.15 16 876 503 58.91 9 437 359 32.942007-H1 42 580 546 2 866 737 6.73 24 353 126 57.19 15 360 681 36.072007-H2 58 243 721 3 376 160 5.80 36 037 715 61.87 18 829 848 32.332008-H1 57 402 760 3 968 090 6.91 36 923 426 64.32 16 511 244 28.762008-H2 41 882 674 2 978 334 7.11 26 724 773 63.81 12 179 538 29.082009-H1 36 098 169 3 843 679 10.65 23 191 972 64.25 9 062 498 25.112009-H2 32 692 683 3 432 429 10.50 21 307 787 65.18 7 952 441 24.322010-H1 30 260 923 3 328 461 11.00 20 786 997 68.69 6 145 442 20.312010-H2 29 897 578 3 181 610 10.64 21 480 952 71.85 5 235 000 17.512011-H1 32 409 444 3 924 650 12.11 23 194 893 71.57 5 289 922 16.32

Table 7: Credit Default Swaps by location of counterparty

This table reports the total notional amount outstanding in million USD of credit default swaps by location ofcounterparty as of June 2011, as well as their respective market shares. Data on total notional amount outstandingare shown on a net basis, that is transactions between reporting dealers are reported only once. The notionalamounts outstanding is allocated to one of the locations listed in the table on an ultimate risk basis, ie accordingto the nationality of the counterparty. Home country means country of incorporation of the reporters head office.Based on the data reported by 10 countries. Source: BIS.

June 2011 Total Home Country Fraction Abroad FractionTotal 32 409 444 5 931 892 18.30 26 477 552 81.70

36

Tab

le8:

Su

mm

ary

Sta

tist

ics

The

table

rep

ort

ssu

mm

ary

stati

stic

sfo

rth

eC

DS

term

stru

cture

of

38

sover

eign

countr

ies

over

the

sam

ple

per

iod

May

9th

,2003

unti

lA

ugust

19th

,2010.

All

CD

Spri

ces

are

mid

com

posi

tequote

sand

USD

den

om

inate

d.

Rati

ng

class

ifica

tion

isach

ieved

by

ass

ignin

gan

inte

ger

valu

era

ngin

gfr

om

1(A

AA

)to

21

(C)

at

each

date

toea

chco

untr

y.T

he

equally

wei

ghte

dhis

tori

cal

aver

age

isth

enro

unded

toth

enea

rest

inte

ger

,w

hic

his

use

das

the

final

rati

ng

cate

gori

zati

on.

The

Mea

n(M

edia

n)

spre

ad

isca

lcula

ted

as

the

his

tori

cal

mea

n(m

edia

n)

spre

ad,

wher

eat

each

date

,all

obse

rvati

ons

wit

hin

agiv

enra

ting

cate

gory

are

aggre

gate

d

by

takin

gth

em

ean.

Sim

ilarl

y,th

est

andard

dev

iati

on

isca

lcula

ted

as

the

standard

dev

iati

on

of

the

data

seri

esaggre

gate

dat

each

date

wit

hin

agiv

enra

ting

cate

gory

.A

C1

isth

efirs

t-ord

erauto

corr

elati

on

coeffi

cien

t.Sourc

e:M

ark

it

AAA

12

35

710

AA

12

35

710

Mean

15

17

19

23

24

26

Mean

24

27

31

38

41

45

Median

22

34

57

Median

34

58

12

16

Min

imum

00

11

22

Min

imum

01

12

23

Maxim

um

304

301

293

274

270

266

Maxim

um

557

536

499

461

433

410

Volatility

26

28

30

34

34

33

Volatility

36

38

40

44

44

43

Skewness

22

22

22

Skewness

22

22

22

Kurto

sis

66

55

44

Kurto

sis

66

55

44

AC1

0.9

940

0.9

949

0.9

970

0.9

975

0.9

975

0.9

974

AC1

0.9

969

0.9

972

0.9

975

0.9

978

0.9

978

0.9

977

A1

23

57

10

BBB

12

35

710

Mean

49

55

61

71

76

81

Mean

77

95

109

132

143

155

Median

12

18

24

34

41

49

Median

33

54

74

106

123

139

Min

imum

12

35

56

Min

imum

33

58

11

14

Maxim

um

1235

1172

1127

1015

952

893

Maxim

um

1190

1100

1110

1106

1096

1081

Volatility

69

72

74

77

75

73

Volatility

105

106

105

103

99

96

Skewness

22

22

22

Skewness

32

22

22

Kurto

sis

65

55

55

Kurto

sis

98

77

66

AC1

0.9

967

0.9

970

0.9

971

0.9

974

0.9

973

0.9

972

AC1

0.9

976

0.9

974

0.9

970

0.9

967

0.9

966

0.9

964

BB

12

35

710

B1

23

57

10

Mean

110

157

196

255

281

305

Mean

433

484

517

564

574

599

Median

78

115

146

197

225

250

Median

328

403

455

517

538

562

Min

imum

15

71

174

72

Min

imum

19

34

55

117

140

186

Maxim

um

822

845

903

1032

1036

1039

Maxim

um

3654

3504

3400

3234

3111

3053

Volatility

92

111

125

138

138

138

Volatility

371

362

350

328

303

286

Skewness

21

11

11

Skewness

22

22

22

Kurto

sis

74

33

33

Kurto

sis

76

66

76

AC1

0.9

979

0.9

977

0.9

976

0.9

974

0.9

972

0.9

971

AC1

0.9

901

0.9

916

0.9

926

0.9

927

0.9

889

0.9

928

37

Table 9: Slope of the CDS Spread Curve

Summary statistics by groups of the 5-year spread level. For each group and maturity, the average spread level andslope has been calculcated across time and entities. The groups are sorted according to the 5-year spread level.Source: Markit

Average CDS premium (bps) Average Slope (bps)Group 1y 2y 3y 5y 7y 10y 10y-1y 3y-1y 10y-3y #curves #entities

2 (101-200 bps) 66 88 107 143 160 177 111 41 69 14 012 353 (201-300 bps) 139 172 201 245 263 279 141 62 79 5 995 344 (301-400 bps) 209 256 295 347 367 386 176 86 91 3 711 275 (401-500 bps) 273 334 383 447 468 490 217 111 106 2 444 246 (501-600 bps) 362 433 484 542 557 572 210 122 88 1 137 217 (601-700 bps) 503 567 603 642 633 661 158 100 58 645 178 (701-800 bps) 617 693 725 745 713 741 125 108 16 346 109 (801-900 bps) 622 759 812 850 846 840 218 189 29 230 810 (901-1000 bps) 724 845 894 940 932 919 195 170 25 148 611 (1001-1100 bps) 1 008 1 051 1 052 1 043 1 002 964 -44 44 -87 96 412 (1101-1200 bps) 1 075 1 142 1 149 1 145 1 095 1 052 -23 74 -98 70 213 (1201-1300 bps) 1 207 1 256 1 257 1 251 1 195 1 148 -60 50 -109 48 114 (1301-1400 bps) 1 347 1 382 1 376 1 363 1 309 1 260 -87 29 -116 33 115 (1401-1500 bps) 1 357 1 410 1 425 1 444 1 395 1 349 -8 67 -75 69 116 (1501-3234 bps) 2 369 2 362 2 334 2 281 2 213 2 146 -223 -35 -188 155 1

38

Table 10: Trade Information Warehouse Data

This table reports the average gross (Column 3) and net (Column 4) notional amount (in million USD) on CDS contracts

outstanding in USD equivalents of the sovereign reference entities among the 1,000 mostly traded contracts over the time

period 31 October 2008 through 11 November 2011 (excluding sovereign US states). All numbers are reported in million USD.

Column 5 indicates the average number of contracts live in the Depository Trust & Clearing Corporation’s (DTCC) Trade

Information Warehouse (Warehouse) over the same time period. Column 6 reports the ratio of gross to net notional amount

outstanding and column 7 is the average ratio of net notional amount outstanding to number of contracts outstanding. The

notional values are represented as US dollar equivalents using the prevailing foreign exchange rates. The final row labeled Total

reports the sum over all rows for column 3, 4 and 5, and the average for columns 6 and 7. The values are sorted in descending

order.

(1) (2) (3) (4) (5) (6) (7) (8)Country Gross Notional Country Net Notional Country # Contracts Country Net/ContractItaly 232 651 Italy 23 287 Brazil 10 955 Germany 6.66Turkey 152 667 Spain 14 846 Turkey 10 087 United States 5.16Brazil 147 673 Germany 13 665 Mexico 8 760 HongKong 4.69Spain 111 477 Brazil 13 307 Russia 7 401 France 4.64Mexico 103 758 France 12 687 Philippines 7 364 Finland 4.58Russia 103 099 UK 7 740 Korea 6 156 Austria 4.11Germany 70 774 Austria 7 120 Italy 6 132 Norway 4.01Greece 66 072 Portugal 7 102 Argentina 5 413 Italy 3.8France 64 073 Greece 6 926 Venezuela 5 033 Netherlands 3.75Philippines 63 228 Mexico 6 845 Spain 4 606 Sweden 3.72Korea 57 731 Turkey 5 748 Hungary 4 603 Belgium 3.68Portugal 55 638 Belgium 5 642 Indonesia 4 294 Denmark 3.63Hungary 54 333 Russia 4 973 SouthAfrica 4 168 Spain 3.22Argentina 50 781 Japan 4 709 Ukraine 3 764 UK 2.89Venezuela 49 909 Ireland 4 594 China 3 341 Portugal 2.82Ukraine 46 002 Korea 4 054 Greece 3 215 Ireland 2.63UK 41 957 China 3 952 Colombia 3 107 Slovenia 2.41Austria 40 389 Hungary 3 702 France 2 736 Greece 2.15SouthAfrica 37 797 Sweden 2 907 Japan 2 693 Australia 2.05Indonesia 34 006 United States 2 804 UK 2 683 SaudiaArabia 1.83Ireland 34 003 Netherlands 2 762 Poland 2 627 Japan 1.75China 33 142 Philippines 2 677 Portugal 2 519 NewZealand 1.74Belgium 32 153 Indonesia 2 261 Thailand 2 437 Lebanon 1.42Colombia 29 762 Denmark 2 239 Malaysia 2 291 Slovak Rep. 1.37Poland 28 989 SouthAfrica 2 190 Peru 2 212 Czech Rep. 1.32Japan 28 935 Poland 2 144 Germany 2 051 Chile 1.29Kazakhstan 21 914 Colombia 2 108 Kazakhstan 1 938 Brazil 1.21Peru 21 441 Finland 2 031 Bulgaria 1 771 Lithuania 1.19Malaysia 18 575 Argentina 2 004 Ireland 1 749 China 1.18Thailand 18 148 Australia 1 979 Austria 1 733 Estonia 1.17Bulgaria 17 412 Venezuela 1 953 Romania 1 638 Egypt 1.01Netherlands 15 908 Peru 1 806 Belgium 1 533 Israel 0.97Romania 15 732 Ukraine 1 697 Vietnam 1 145 Tunisia 0.97United States 14 574 Romania 1 255 Iceland 1 127 Poland 0.82Sweden 14 560 Bulgaria 1 213 Latvia 1 020 Peru 0.82Finland 11 385 Malaysia 1 199 Australia 963 Hungary 0.8Denmark 10 718 Thailand 1 136 Panama 957 Iceland 0.8Australia 10 499 Kazakhstan 1 086 Croatia 918 Mexico 0.78Czech Rep. 9 070 Czech Rep. 1 002 Israel 831 Romania 0.77Slovak Rep. 8 881 Slovak Rep. 941 Sweden 781 Panama 0.73Latvia 8 241 Iceland 903 Czech Rep. 756 Croatia 0.72Iceland 8 186 Norway 881 Netherlands 737 Latvia 0.71Vietnam 7 855 Israel 808 Qatar 730 Qatar 0.7Israel 7 550 Slovenia 805 Egypt 718 Bulgaria 0.69Panama 6 912 Egypt 726 Slovak Rep. 689 Colombia 0.68Croatia 6 730 Latvia 726 Denmark 617 Russia 0.67Qatar 5 864 Lithuania 710 Lithuania 598 Korea 0.66Norway 5 275 Panama 699 United States 544 Turkey 0.57Lithuania 5 149 Croatia 663 Finland 443 Kazakhstan 0.56Slovenia 3 994 Vietnam 602 Chile 420 Indonesia 0.53Chile 3 974 HongKong 586 Estonia 391 SouthAfrica 0.53Egypt 3 205 Chile 539 Slovenia 334 Vietnam 0.53Estonia 2 968 NewZealand 517 Lebanon 324 Malaysia 0.52NewZealand 2 700 Qatar 514 Tunisia 300 Thailand 0.47SaudiaArabia 2 188 SaudiaArabia 483 NewZealand 297 Ukraine 0.45Lebanon 2 027 Lebanon 459 SaudiaArabia 264 Venezuela 0.39Tunisia 1 972 Estonia 457 Norway 220 Argentina 0.37HongKong 1 705 Tunisia 289 HongKong 125 Philippines 0.36Total 2 066 309 Total 203 661 Total 147 258 Average 1.8

Source: http://www.dtcc.com

39

Table 11: Trade Information Warehouse Data

This table reports the average gross (Column 3) and net (Column 4) notional amount (in million USD) on CDS contracts

outstanding in USD equivalents of the sovereign reference entities among the 1,000 mostly traded contracts over the time

period 31 October 2008 through 11 November 2011. All numbers are reported in million USD. Column 5 indicates the average

number of contracts live in the Depository Trust & Clearing Corporation’s (DTCC) Trade Information Warehouse (Warehouse)

over the same time period. Column 6 reports the ratio of gross to net notional amount outstanding and column 7 is the average

ratio of net notional amount outstanding to number of contracts outstanding. The notional values are represented as US dollar

equivalents using the prevailing foreign exchange rates. The final row labeled Total reports the sum over all rows for column 3,

4 and 5, and the average for columns 6 and 7. The values are reported in descending order according to the net notional amount

outstanding. The countries are grouped in five regions: Americas, Asia ex-Japan, Australia and New Zealand, Europe/Middle

East and Africa (EMEA) and Japan.

(1) (2) (3) (4) (5) (6) (7)Country DC Region Gross Notional Net Notional # Contracts Gross/Net Net/ContractItaly EMEA 232 651 23 287 6 132 9.99 3.80Spain EMEA 111 477 14 846 4 606 7.51 3.22

Germany EMEA 70 774 13 665 2 051 5.18 6.66Brazil Americas 147 673 13 307 10 955 11.10 1.21France EMEA 64 073 12 687 2 736 5.05 4.64

UnitedKingdom EMEA 41 957 7 740 2 683 5.42 2.89Austria EMEA 40 389 7 120 1 733 5.67 4.11Portugal EMEA 55 638 7 102 2 519 7.83 2.82Greece EMEA 66 072 6 926 3 215 9.54 2.15Mexico Americas 103 758 6 845 8 760 15.16 0.78Turkey EMEA 152 667 5 748 10 087 26.56 0.57Belgium EMEA 32 153 5 642 1 533 5.70 3.68

RussianFederation EMEA 103 099 4 973 7 401 20.73 0.67Japan Japan 28 935 4 709 2 693 6.14 1.75Ireland EMEA 34 003 4 594 1 749 7.40 2.63Korea Asia Ex-Japan 57 731 4 054 6 156 14.24 0.66China Asia Ex-Japan 33 142 3 952 3 341 8.39 1.18

Hungary EMEA 54 333 3 702 4 603 14.68 0.80Sweden EMEA 14 560 2 907 781 5.01 3.72

United States EMEA 14 574 2 804 544 5.20 5.16Netherlands EMEA 15 908 2 762 737 5.76 3.75Philippines Asia Ex-Japan 63 228 2 677 7 364 23.62 0.36Indonesia Asia Ex-Japan 34 006 2 261 4 294 15.04 0.53Denmark EMEA 10 718 2 239 617 4.79 3.63

SouthAfrica EMEA 37 797 2 190 4 168 17.26 0.53Poland EMEA 28 989 2 144 2 627 13.52 0.82

Colombia Americas 29 762 2 108 3 107 14.12 0.68Finland EMEA 11 385 2 031 443 5.60 4.58

Argentina Americas 50 781 2 004 5 413 25.33 0.37Australia Australia NZ 10 499 1 979 963 5.30 2.05Venezuela Americas 49 909 1 953 5 033 25.56 0.39

Peru Americas 21 441 1 806 2 212 11.87 0.82Ukraine EMEA 46 002 1 697 3 764 27.11 0.45Romania EMEA 15 732 1 255 1 638 12.53 0.77Bulgaria EMEA 17 412 1 213 1 771 14.35 0.69Malaysia Asia Ex-Japan 18 575 1 199 2 291 15.49 0.52Thailand Asia Ex-Japan 18 148 1 136 2 437 15.98 0.47

Kazakhstan EMEA 21 914 1 086 1 938 20.18 0.56CzechRepublic EMEA 9 070 1 002 756 9.05 1.32SlovakRepublic EMEA 8 881 941 689 9.44 1.37

Iceland EMEA 8 186 903 1 127 9.06 0.80Norway EMEA 5 275 881 220 5.99 4.01Israel EMEA 7 550 808 831 9.35 0.97

Slovenia EMEA 3 994 805 334 4.96 2.41Egypt EMEA 3 205 726 718 4.41 1.01Latvia EMEA 8 241 726 1 020 11.35 0.71

Lithuania EMEA 5 149 710 598 7.25 1.19Panama Americas 6 912 699 957 9.89 0.73Croatia EMEA 6 730 663 918 10.14 0.72Vietnam Asia Ex-Japan 7 855 602 1 145 13.04 0.53

HongKong Asia Ex-Japan 1 705 586 125 2.91 4.69Chile Americas 3 974 539 420 7.37 1.29

NewZealand Australia NZ 2 700 517 297 5.22 1.74Qatar EMEA 5 864 514 730 11.40 0.70

SaudiaArabia EMEA 2 188 483 264 4.53 1.83Lebanon EMEA 2 027 459 324 4.41 1.42Estonia EMEA 2 968 457 391 6.50 1.17Tunisia EMEA 1 972 289 300 6.82 0.97

Total/Average(∗) 2 066 309 203 661 147 258 10.74∗ 1.80∗Source: http://www.dtcc.com

40

Tab

le12

:T

rad

eIn

form

atio

nW

areh

ouse

Dat

a

Th

ista

ble

rep

ort

sth

eaver

age

yea

rly

gro

ss(P

an

elA

)an

dn

et(P

an

elB

)n

oti

on

al

am

ou

nt

on

CD

Sco

ntr

act

sou

tsta

nd

ing

inU

SD

equ

ivale

nts

of

the

sover

eign

refe

ren

ceen

titi

es

am

on

gth

e1,0

00

most

lytr

ad

edco

ntr

act

sas

of

11th

Novem

ber

2011.

All

nu

mb

ers

are

rep

ort

edin

milli

on

US

D.

Pan

elC

ind

icate

sth

era

tio

of

gro

ssto

net

noti

on

al

am

ou

nt

ou

tsta

nd

ing.

Th

en

oti

on

al

valu

esare

rep

rese

nte

das

US

dollar

equ

ivale

nts

usi

ng

the

pre

vailin

gfo

reig

nex

chan

ge

rate

s.T

he

sam

ple

per

iod

start

son

31

Oct

ob

er2008

an

d

end

son

11

Novem

ber

2011.

Th

eover

all

stati

stic

sin

the

fin

al

row

lab

eled

Average

incl

ud

ein

form

ati

on

for

the

U.S

.st

ate

s,w

hic

hare

rep

ort

edin

ase

para

teta

ble

(Tab

le

(14))

.T

he

valu

esare

rep

ort

edin

des

cen

din

gord

eracc

ord

ing

toth

eco

untr

yn

am

e.

PanelA:Gro

ssNotional(’000,000)

PanelB:NetNotional(’000,000)

PanelC:Ratio

Gro

ss/Net

Country

2008

2009

2010

2011

Avera

ge

2008

2009

2010

2011

Avera

ge

2008

2009

2010

2011

Avera

ge

AbuDhabi

.2

493

4032

6683

4403

.468

834

1379

894

.5.3

4.8

4.8

5.0

Arg

entina

48

945

45

810

51

213

56

383

50

588

2815

1817

1755

2353

2185

17.4

25.2

29.2

24.0

23.9

Austra

lia

.2

722

8025

19

634

10

127

.438

1427

3862

1909

.6.2

5.6

5.1

5.6

Austria

17

169

30

413

44

938

51

203

35

931

4883

6933

8325

6364

6626

3.5

4.4

5.4

8.0

5.3

Belgiu

m12

964

21

035

30

670

50

583

28

813

3871

4836

5670

6893

5317

3.3

4.3

5.4

7.3

5.1

Bra

zil

136

964

123

478

148

786

176

463

146

423

11

160

10

204

13

811

16

730

12

976

12.3

12.1

10.8

10.5

11.4

Bulgaria

15

442

15

647

17

676

19

535

17

075

1717

1331

1167

1031

1311

9.0

11.8

15.1

19.0

13.7

Chile

2897

3262

4134

4825

3780

670

482

548

568

567

4.3

6.8

7.5

8.5

6.8

Chin

a19

814

24

200

33

615

45

583

30

803

2099

1938

3723

6919

3670

9.4

12.5

9.0

6.6

9.4

Colombia

28

208

27

278

29

422

33

344

29

563

2442

2152

1983

2136

2178

11.6

12.7

14.8

15.6

13.7

Cro

atia

4326

5775

6880

8136

6279

680

611

674

709

668

6.4

9.5

10.2

11.5

9.4

CzechRepublic

4929

7389

10

047

10

691

8264

1139

1145

946

874

1026

4.3

6.5

10.6

12.2

8.4

Denmark

4866

7995

10

966

14

742

9642

1706

1887

2330

2646

2142

2.9

4.2

4.7

5.6

4.3

Dubai

..

5649

7264

6457

..

519

545

532

..

10.9

13.3

12.1

Egypt

..

2236

4174

3205

..

518

935

726

..

4.3

4.5

4.4

Estonia

2240

2762

3280

2984

2816

549

466

456

428

475

4.1

5.9

7.2

7.0

6.0

Fin

land

3736

6545

13

011

16

591

9971

1413

1648

2272

2314

1912

2.6

4.0

5.7

7.2

4.9

Fra

nce

22

091

37

464

62

020

105

638

56

803

5533

7587

11

744

21

123

11

497

4.0

4.9

5.3

5.0

4.8

Germ

any

38

051

49

896

74

005

97

639

64

898

9905

10

936

13

988

17

192

13

005

3.8

4.6

5.3

5.7

4.8

Gre

ece

35

990

48

128

78

189

78

552

60

215

7459

7959

7533

4911

6966

4.8

6.0

10.4

16.0

9.3

HongKong

..

1705

.1

705

..

586

.586

..

2.9

.2.9

Hungary

33

875

43

582

57

916

66

628

50

500

4388

4052

3569

3316

3831

7.7

10.8

16.2

20.1

13.7

Iceland

8864

8839

7974

7544

8305

1193

976

815

864

962

7.4

9.1

9.8

8.7

8.7

Indonesia

30

813

31

737

34

112

37

142

33

451

2194

1860

1974

3075

2276

14.0

17.1

17.3

12.1

15.1

Ireland

17

589

24

300

37

482

44

400

30

943

4485

4741

4868

4123

4554

3.9

5.1

7.7

10.8

6.9

Isra

el

5149

6115

7225

10

069

7140

595

576

662

1289

781

8.6

10.6

10.9

7.8

9.5

Italy

153

732

187

481

238

376

293

888

218

369

17

388

20

972

25

306

24

762

22

107

8.8

8.9

9.4

11.9

9.8

Japan

7296

12

034

29

874

51

688

25

223

1741

2061

4843

8206

4213

4.2

5.8

6.2

6.3

5.6

Kazakhstan

22

479

23

375

22

225

19

745

21

956

2471

1271

855

867

1366

9.1

18.4

26.0

22.8

19.1

Kore

a50

311

56

473

59

615

58

449

56

212

5326

3720

3772

4517

4334

9.4

15.2

15.8

12.9

13.3

Latv

ia6

440

7359

8724

9053

7894

891

792

720

623

757

7.2

9.3

12.1

14.5

10.8

Lebanon

.1

675

1949

2127

1917

.412

441

482

445

.4.1

4.4

4.4

4.3

Lithuania

3302

4476

5394

6006

4795

709

785

703

632

707

4.7

5.7

7.7

9.5

6.9

Malaysia

16

178

18

174

18

961

19

063

18

094

1485

1149

1016

1416

1266

10.9

15.8

18.7

13.5

14.7

Mexico

75

514

88

742

106

557

123

461

98

568

4529

5769

6727

8690

6429

16.7

15.4

15.8

14.2

15.5

Neth

erlands

4904

10

125

17

825

22

533

13

847

1588

2524

3117

2852

2520

3.1

4.0

5.7

7.9

5.2

NewZealand

.1

659

2393

3086

2379

.481

495

544

507

.3.4

4.8

5.7

4.7

Norw

ay

2313

2959

5435

8251

4740

651

684

975

1035

836

3.6

4.3

5.6

8.0

5.4

Panama

6475

6375

6792

7761

6851

708

646

649

818

705

9.1

9.9

10.5

9.5

9.7

Peru

19

707

18

658

19

760

26

984

21

277

2158

1705

1612

2083

1889

9.1

10.9

12.3

13.0

11.3

Philip

pin

es

66

345

67

258

63

381

57

767

63

688

2903

2569

2506

2957

2734

22.9

26.2

25.3

19.5

23.5

Poland

17

152

22

602

30

165

37

352

26

818

2083

1936

2237

2288

2136

8.2

11.7

13.5

16.3

12.4

Portugal

25

066

41

192

62

951

69

833

49

760

5030

6659

8355

6554

6649

5.0

6.2

7.5

10.7

7.3

Qata

r4

033

4789

5540

7855

5554

456

341

434

821

513

8.8

14.0

12.8

9.6

11.3

Romania

11

839

13

878

16

542

17

700

14

990

1561

1377

1189

1130

1314

7.6

10.1

13.9

15.7

11.8

RussianFedera

tion

105

708

101

719

103

458

103

749

103

659

7188

5951

4110

4415

5416

14.7

17.1

25.2

23.5

20.1

SaudiaAra

bia

..

.2

188

2188

..

.483

483

..

.4.5

4.5

SlovakRepublic

5581

7666

9561

10

145

8238

1017

1042

888

870

954

5.5

7.4

10.8

11.7

8.8

Slovenia

.2

945

4138

4943

4009

.687

815

918

807

.4.3

5.1

5.4

4.9

South

Africa

31

681

34

253

37

776

43

140

36

713

2484

2079

2106

2357

2256

12.8

16.5

17.9

18.3

16.4

Spain

64

649

77

492

111

699

159

851

103

423

13

895

12

095

15

087

17

932

14

752

4.7

6.4

7.4

8.9

6.8

Sweden

6245

11

093

15

493

19

132

12

991

1829

2842

3086

2987

2686

3.4

3.9

5.0

6.4

4.7

Thailand

16

620

17

873

19

266

17

456

17

804

1187

1051

1036

1341

1154

14.0

17.0

18.6

13.0

15.7

Tunisia

.1

932

1841

2141

1971

.281

268

317

289

.6.9

6.9

6.7

6.8

Turk

ey

179

469

156

877

151

600

143

699

157

911

5816

5396

5809

6067

5772

30.9

29.1

26.1

23.7

27.4

Ukra

ine

59

556

46

198

46

450

42

535

48

685

2777

2062

1541

1242

1905

21.4

22.4

30.1

34.2

27.1

United

Sta

tes

4537

8716

13

156

25

020

12

857

1296

1987

2441

4478

2550

3.5

4.4

5.4

5.6

4.7

UnitedKin

gdom

12

843

21

323

47

368

65

251

36

696

2368

3361

9185

12

174

6772

5.4

6.3

5.2

5.4

5.6

Venezuela

46

073

42

703

50

787

57

968

49

383

2175

1630

1884

2363

2013

21.2

26.2

27.0

24.5

24.7

Vietn

am

5997

6246

8281

9582

7527

627

423

602

806

614

9.6

14.8

13.8

11.9

12.5

Avera

ge

30

539

27

732

32

192

38

228

32

376

3305

2818

3145

3674

3239

8.8

10.0

11.2

11.6

10.5

Source:

htt

p:/

/w

ww

.dtc

c.co

m

41

Tab

le13

:T

rad

eIn

form

atio

nW

areh

ouse

Dat

a

Th

ista

ble

rep

ort

sth

eaver

age

yea

rly

nu

mb

erof

contr

act

slive

inth

eD

eposi

tory

Tru

st&

Cle

ari

ng

Corp

ora

tion

’s(D

TC

C)

Tra

de

Info

rmati

on

Ware

hou

se(W

are

hou

se)

(Pan

el

A)

an

dth

eaver

age

rati

o(P

an

elB

)of

net

noti

on

al

am

ou

nt

on

CD

Sco

ntr

act

sou

tsta

nd

ing

inm

illion

US

Deq

uiv

ale

nts

toth

eaver

age

yea

rly

nu

mb

erof

contr

act

sof

the

sover

eign

refe

ren

ceen

titi

esam

on

gth

e1,0

00

most

lytr

ad

edco

ntr

act

sas

of

11th

Novem

ber

2011.

Th

en

oti

on

al

valu

esare

rep

rese

nte

das

US

dollar

equ

ivale

nts

usi

ng

the

pre

vailin

gfo

reig

nex

chan

ge

rate

s.T

he

sam

ple

per

iod

start

son

31

Oct

ob

er2008

an

den

ds

on

11

Novem

ber

2011.

Th

eover

all

stati

stic

sin

the

fin

al

row

lab

eled

Average

incl

ud

ein

form

ati

on

for

the

U.S

.st

ate

s,w

hic

hare

rep

ort

edin

ase

para

teta

ble

.T

he

valu

esare

rep

ort

edin

des

cen

din

gord

eracc

ord

ing

toth

eco

untr

yn

am

e.

PanelA:#

Contracts

PanelB:Ratio

NetNotional/Contracts

(’000,000)

Country

2008

2009

2010

2011

Tota

l2008

2009

2010

2011

Tota

lAbuDhabi

.427

866

1510

934

.1.1

1.0

0.9

1.0

Arg

entina

5011

4916

5478

5990

5349

0.6

0.4

0.3

0.4

0.4

Austra

lia

.270

684

1847

934

.1.6

2.1

2.1

1.9

Austria

686

1338

1905

2196

1531

7.1

5.2

4.4

2.9

4.9

Belgiu

m437

793

1441

2715

1346

8.9

6.1

3.9

2.5

5.4

Bra

zil

11

097

9818

11

097

12

074

11

021

1.0

1.0

1.2

1.4

1.2

Bulgaria

1515

1618

1782

1985

1725

1.1

0.8

0.7

0.5

0.8

Chile

302

359

432

498

398

2.2

1.3

1.3

1.1

1.5

Chin

a1

736

2177

3458

4869

3060

1.2

0.9

1.1

1.4

1.1

Colombia

3168

3112

3024

3186

3123

0.8

0.7

0.7

0.7

0.7

Cro

atia

588

791

937

1109

856

1.2

0.8

0.7

0.6

0.8

CzechRepublic

415

649

823

870

689

2.7

1.8

1.1

1.0

1.7

Denmark

236

399

498

1087

555

7.2

4.7

4.7

2.4

4.8

Dubai

..

691

941

816

..

0.8

0.6

0.7

Egypt

..

424

1012

718

..

1.2

0.9

1.1

Estonia

304

375

424

389

373

1.8

1.2

1.1

1.1

1.3

Fin

land

92

242

503

675

378

15.3

6.8

4.5

3.4

7.5

Fra

nce

547

1005

2490

5464

2377

10.1

7.6

4.7

3.9

6.6

Germ

any

760

1185

2097

3254

1824

13.0

9.2

6.7

5.3

8.6

Gre

ece

1131

1701

3944

4524

2825

6.6

4.7

1.9

1.1

3.6

HongKong

..

125

.125

..

4.7

.4.7

Hungary

3126

3857

4788

5542

4328

1.4

1.1

0.7

0.6

0.9

Iceland

1209

1111

1149

1105

1143

1.0

0.9

0.7

0.8

0.8

Indonesia

3973

3967

4314

4713

4242

0.6

0.5

0.5

0.7

0.5

Ireland

660

1000

1858

2704

1556

6.8

4.7

2.6

1.5

3.9

Isra

el

546

665

743

1183

784

1.1

0.9

0.9

1.1

1.0

Italy

3390

4122

6401

8685

5649

5.1

5.1

4.0

2.9

4.3

Japan

295

655

2840

5354

2286

5.9

3.1

1.7

1.5

3.1

Kazakhstan

2097

2143

1854

1769

1966

1.2

0.6

0.5

0.5

0.7

Kore

a4

612

5630

6590

6561

5848

1.2

0.7

0.6

0.7

0.8

Latv

ia857

972

1068

1053

987

1.0

0.8

0.7

0.6

0.8

Lebanon

.244

305

349

299

.1.7

1.4

1.4

1.5

Lithuania

423

541

612

682

565

1.7

1.5

1.1

0.9

1.3

Malaysia

1929

2195

2367

2386

2219

0.8

0.5

0.4

0.6

0.6

Mexico

6560

8061

8979

9748

8337

0.7

0.7

0.7

0.9

0.8

Neth

erlands

157

413

818

1134

630

10.1

6.1

3.8

2.5

5.6

NewZealand

.145

256

348

250

.3.3

1.9

1.6

2.3

Norw

ay

58

90

228

387

191

11.2

7.6

4.3

2.7

6.5

Panama

895

896

924

1078

948

0.8

0.7

0.7

0.8

0.7

Peru

2168

2031

1969

2715

2221

1.0

0.8

0.8

0.8

0.9

Philip

pin

es

8037

8014

7378

6464

7473

0.4

0.3

0.3

0.5

0.4

Poland

1589

2136

2725

3285

2434

1.3

0.9

0.8

0.7

0.9

Portugal

797

1446

2834

3732

2202

6.3

4.6

2.9

1.8

3.9

Qata

r416

525

638

1139

680

1.1

0.6

0.7

0.7

0.8

Romania

1294

1463

1672

1869

1574

1.2

0.9

0.7

0.6

0.9

RussianFedera

tion

7483

7352

7394

7447

7419

1.0

0.8

0.6

0.6

0.7

SaudiaAra

bia

..

.264

264

..

.1.8

1.8

SlovakRepublic

451

618

734

765

642

2.3

1.7

1.2

1.1

1.6

Slovenia

.274

328

407

336

.2.5

2.5

2.3

2.4

South

Africa

3639

3916

4122

4620

4074

0.7

0.5

0.5

0.5

0.6

Spain

2008

2429

4699

7533

4167

6.9

5.0

3.2

2.4

4.4

Sweden

276

594

795

1083

687

6.6

4.8

3.9

2.8

4.5

Thailand

2061

2267

2569

2553

2363

0.6

0.5

0.4

0.5

0.5

Tunisia

.269

283

332

295

.1.0

0.9

1.0

1.0

Turk

ey

13

325

11

088

9580

8880

10

718

0.4

0.5

0.6

0.7

0.6

Ukra

ine

5402

3958

3587

3420

4092

0.5

0.5

0.4

0.4

0.5

United

Sta

tes

124

241

479

1054

475

10.4

8.2

5.1

4.2

7.0

UnitedKin

gdom

433

881

3120

4699

2283

5.5

3.8

2.9

2.6

3.7

Venezuela

4781

4464

5093

5669

5002

0.5

0.4

0.4

0.4

0.4

Vietn

am

866

900

1206

1413

1096

0.7

0.5

0.5

0.6

0.6

Avera

ge

2279

1994

2273

2736

2330

3.6

2.5

1.8

1.4

2.2

Source:

htt

p:/

/w

ww

.dtc

c.co

m

42

Tab

le14

:T

rad

eIn

form

atio

nW

areh

ouse

Dat

a

Th

ista

ble

rep

ort

sth

eaver

age

yea

rly

gro

ss(P

an

elA

)an

dn

et(P

an

elB

)n

oti

on

al

am

ou

nt

on

CD

Sco

ntr

act

sou

tsta

nd

ing

inU

SD

equ

ivale

nts

of

the

U.S

.so

ver

eign

refe

ren

ce

enti

ties

am

on

gth

e1,0

00

most

lytr

ad

edco

ntr

act

sas

of

11th

Novem

ber

2011.

All

nu

mb

ers

are

rep

ort

edin

million

US

D.

Pan

elC

ind

icate

sth

era

tio

of

gro

ssto

net

noti

on

al

am

ou

nt

ou

tsta

nd

ing

an

dP

an

elD

list

sth

eaver

age

yea

rly

nu

mb

erof

contr

act

slive

inth

eD

eposi

tory

Tru

st&

Cle

ari

ng

Corp

ora

tion

’s(D

TC

C)

Tra

de

Info

rmati

on

Ware

hou

se

(Ware

hou

se).

Th

en

oti

on

al

valu

esare

rep

rese

nte

das

US

dollar

equiv

ale

nts

usi

ng

the

pre

vailin

gfo

reig

nex

chan

ge

rate

s.T

he

sam

ple

per

iod

start

son

31

Oct

ob

er2008

an

d

end

son

11

Novem

ber

2011.

Th

evalu

esare

rep

ort

edin

des

cen

din

gord

eracc

ord

ing

toth

eso

ver

eign’s

nam

e.

PanelA:Gro

ssNotional(’000,000)

PanelB:NetNotional(’000,000)

PanelD:Ratio

Gro

ss/Net

PanelC:#

Contracts

Country

2008

2009

2010

2011

Avera

ge

2008

2009

2010

2011

Avera

ge

2008

2009

2010

2011

Tota

l2008

2009

2010

2011

Avera

ge

Californ

ia.

4253

7928

9981

7387

.698

885

986

856

.6.1

9.0

10.1

8.4

.232

599

989

607

Florida

.2

688

3855

4636

3726

.298

296

267

287

.9.0

13.0

17.4

13.2

.161

274

318

251

Illinois

..

2061

3088

2575

..

329

608

468

..

6.3

5.1

5.7

..

152

403

277

Masschuse

tts

..

1682

1707

1695

..

171

174

172

..

9.9

9.8

9.8

..

112

116

114

NewJersey

.1

989

3075

4358

3141

.516

440

475

477

.3.9

7.0

9.2

6.7

.120

215

401

245

NewYork

.2

333

2922

3318

2857

.282

348

437

355

.8.3

8.4

7.6

8.1

.104

194

266

188

NewYork

City

.3

004

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Fig

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06/2009

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0

200

400

600

800

1000

1200

Time

Spread

5-year Sovereign CDS Spreads (Source: Markit)

(b)

45

Figure 3: Trade Information Warehouse Data

The following graphs plot the average yearly net notional amount of CDS outstanding (in million USD) by the yearly

amount of Government debt (in million USD) for the EMEA and Asia ex-Japan countries among the 1,000 mostly

traded reference entities. The graphs are separated by year. Graph (3a), (3b) and (3c) refer to 2008, 2009 and 2010

respectively. The notional values are represented as US dollar equivalents using the prevailing foreign exchange rates.

Source: www.dtcc.com.

(a) (b)

(c)

46

Figure 4: Trade Information Warehouse Data

The following graphs plot the average yearly gross notional amount of CDS outstanding (in million USD) by the

yearly amount of Government debt (in million USD) for the EMEA and Asia ex-Japan countries among the 1,000

mostly traded reference entities. The graphs are separated by year. Graph (4a), (4b) and (4c) refer to 2008, 2009 and

2010 respectively. The notional values are represented as US dollar equivalents using the prevailing foreign exchange

rates. Source: www.dtcc.com.

(a) (b)

(c)

47