Corporate Credit Spreads and the Euro Area SovereignDebt Crisis∗

Alessandro Fontana †

Geneva Finance Research Institute and FINRISK

March 11, 2013

Abstract

This paper highlights that from the onset of the Sovereign debt crisis, corporations

in countries with debt troubles have often lower credit risk (as measured by CDS

Premia) than the government. These corporations have also higher credit risk with

respect to those in countries with strong public finances, suggesting that which country

a firm is located in is an important element in determining its credit risk. Results of

the analysis show that (i) the risk-free rate implied in Euro Area corporate debt yields

approximates the dynamics of the german-bund yield. (ii) Firms in peripheral countries

have a higher sensitivity (0.36%) to their respective home government CDS dynamics

than the average Euro Area firm (0.17%). This pattern is stable across the “Lehman

period” and the Sovereign debt crisis, but the variance explained by the Sovereign

CDS is substantial (i.e. 30%) only during the Sovereign debt crisis and for firms in

peripheral countries. Sectors characterized by higher ex-ante sensitivity are those for

which the explained variance by the Sovereign CDS is the highest during the Sovereign

debt crisis. (iii) Moreover, there are significant spillovers from Sovereign credit risk on

government related (partially owned) companies.

Keywords: Corporate Credit Risk, CDS, Sovereign Debt Crisis, Spillover effects;

∗I am very grateful to Rajna Gibson, Stephen Schaefer, Loriana Pelizzon and Pierre Collin-Dufresnefor many helpful comments and discussions. I also thank Alberto Plazzi and participants to the CREDITconference in Venice 27-28, September, 2012, Jan Benjamin Junge and participants to the FINRISK researchday at the Study center Gerzensee 11-12 June 2012. Financial support provided by the National Centreof Competence in Research “Financial Valuation and Risk Management” (NCCR FINRISK) is gratefullyacknowledged. IP C1 ‘Credit Risk and Non-Standard Sources of Risk in Finance”, Rajna Gibson. Researchtopic: AP, FE. First draft of the paper: October 2012.†Geneva Finance Research Institute, University of Geneva, Bd du Pont d’Arve 40, 1211 Geneva 4, Switzer-

land, e-mail: alessandro.fontana@unige.ch https : //sites.google.com/site/alessandrofontanagfri/home

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I Introduction

In this paper, I use credit default swap data (i.e. CDS Premia) to investigate what

happened to the perceived credit risk of Euro Area corporations during the period from

2007 up to 2011, focusing mainly on non-financial firms. The following two new “facts”

stand out markedly from a preliminary exploration of the data. First, as shown in Figure 1

for the case of Spain, corporations in countries with weak public finances had lower perceived

credit risk (CDS) than their home governments, during the recent Sovereign debt crisis. This

is in contrast to what is assumed in empirical studies on the term structure where national

government bonds are typically used as risk-free benchmarks to price corporate debt.

(FIGURE 1 HERE)

Second, as shown in Figure 2 for Germany and Spain, during the “Lehman shock”,

credit risk increased for all corporations. Afterwards, while credit risk for German firms

decreased, from May 2010 on CDS of companies in Spain increased up to 300 basis points.

The differential reached 130 basis points in November 2011.

(FIGURE 2 HERE)

This preliminary evidence suggests that which country a firm is located in is an important

element in determining its credit risk, hence also the cost of debt capital for these firms1.

The first objective of this paper is to examine what is the risk-free benchmark used to price

the debt of corporations around the Euro Area. The second objective is to relate the large

differences in corporate credit risk across countries to the credit risk of their governments.

1The article: “Loan rates point to eurozone fractures” on the Financial Times (September 2, 2012)reports the following information: “The interest rate charged by banks on a corporate loan of up to 1mlasting between one and five years, which would typically be taken out by a small business, was 6.5 per centin July in Spain, according to the ECB figures. That was the highest since late 2008, when central banksstarted cutting official interest rates after the collapse of Lehman Brothers investment bank. In Italy, thecomparable figure was 6.24 per cent. German counterparts paid just 4.04 per cent, the lowest since the ECBfigures started in 2003.”

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To investigate the first issue, I assume that, a firm’s CDS premium approximates the

bond yield spread over the risk-free rate (Hull, Predescu, and White 2004). This is because

hedging a defaultable bond (at par) buying default protection via CDS, is replicating a risk-

free position.2 Results of this analysis show that the risk-free rate implied in Euro Area

corporate debt prices lies between the Euro Swap rate and the german bund-yield, but its

variation is more strongly related to the one of the bund. Hence, firms debt yields differ

largely across countries, but are priced over the same risk-free benchmark, suggesting that

a firm’s CDS premium consists of a “firm-specific” and a “country-specific” component 3.

The second objective of the paper is try to explain why corporate credit risk in countries

with weak public finances increased during the Sovereign debt crisis, while corporate credit

risk in core countries was not affected very much. What is the link between corporate and

Sovereign credit risk? Does Sovereign credit risk play a role in exacerbating corporate credit

risk beyond what would be implied by economic fundamentals?

From a preliminary look at the average pattern of CDS premia it stands out markedly

that while in countries with strong public finances, corporate CDS reached their peak during

the “Lehman period” and subsequently decreased (average of 50, 240 and 175 basis points

respectively before the US subprime crisis (period 1), during the US Subprime crisis and

the “Lehman period” (period 2) and during the Euro Area Sovereign debt crisis (period

3)) in contrast, in countries with debt troubles, corporate CDS followed an increasing trend

(average of 60, 165 and 265 basis points respectively in period 1,2 and 3). Also, during the

Sovereign debt crisis, the average corporate CDS was 100 basis points higher in periphery

countries. In order to analyze the “country effect” further, I carry out a principal component

2This pricing relation holds for floating rate bonds and is only approximate for fixed coupon bonds. Infact, cash flows on a long default-free fixed coupon bond and a short defaultable fixed coupon bond are notprecisely those on a CDS, but they are nearly the same. The quality of the approximation depends on howmuch the bond is away from par, the coupon, the shape of the term structure and the shape of CDS curve.

3The idea is that with the recent Sovereign debt crisis the situation for most Euro Area countries hasbecome similar to that which typically characterizes emerging market countries. It is common practice bymarket participants to add a risk premium, related to the Sovereign credit spread, to the discount rate, whenevaluating a project to account for “country risk” (see Bekaert et al. (2012).

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analysis on CDS of Utility and Telecommunication firms.4 Before the Sovereign debt crisis

73% of the variation of CDS premia is explained mainly by a first PC which places equal

weight across “core” and “periphery” countries and the two sectors which can therefore

be defined as a “level factor”. 6% of the variation is explained by a second PC which

discriminates well across sectors, i.e a “sector factor”. Differently, during the Euro Area

Sovereign debt crisis 71% of the variation is explained by a first PC and 10% by a “country

factor”, with a “sector factor” playing only a minor role (4%). This analysis shows that

there is, in general, a lot of commonality of credit risk across firms and that during the Euro

Area Sovereign crisis a firm’s credit risk depends largely on the geographical location.

An analysis of the co-movements of corporate and their respective home government

CDS shows that firms in periphery countries have a higher sensitivity (0.36%) than the

average Euro Area firm (0.17%). This pattern is stable across the “Lehman period” and the

Sovereign debt crisis, but the variance explained by the Sovereign CDS is substantial (i.e.

30%) only during the Sovereign debt crisis and for firms in periphery countries. Also, sectors

characterized by higher ex-ante sensitivity, are those for which the explained variance by the

government CDS is the highest during the Sovereign debt crisis.

When studying the relation between corporate and Sovereign credit risk the empirical

challenges are relevant for the following three reasons. First, Sovereign and corporate credit

risk tend to correlate because of the common exposure to the local macroeconomic cycle.

Second, a deterioration of Sovereign credit risk affects the funding conditions of banks (see

Panetta (2011)) and in turn the availability of credit for firms. Moreover, the direction of

the causality might go the other way around. Sovereign credit risk could be the result of a

bailout of the banking sector (see Acharya, Drechsler, and Schnabl (2011)) or of a wave of

corporate failures5.

4The choice of these two sectors is determined by the fact that these firms are the only ones which are welldistributed across “core” and “periphery” countries. Firms belonging to other sectors are mainly located inthe “core” countries. Differently, Healthcare and Basic Material firms are located only in “core” countries.

5Bad firms fundamentals could translates into a dramatic tax-income reduction for the government orweaker banks balance sheets which both in turn might affect negatively, directly or indirectly, the credit riskof a government.

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In order to study whether there are spillover effects, i.e whether government credit risk

is exacerbating corporate credit risk beyond what would be implied by firm economic fun-

damentals, I investigate the behavior of some specific firm, namely government-related (par-

tially government owned) firms, which as described in section IV 2, are peculiar in at least

two aspects. First, these firms benefit from the potential support of the government in case

of debt troubles6, but at the same time as the value of the “debt guarantees” reduces, the

creditworthiness of these firms will reduce as well. Second, government related firms (mostly

utilities, but not only) are more likely to be hit by special ad-hoc taxes when the government

is in troubles with its public finances7. Studying how the credit risk of these firms reacts to

Sovereign credit risk shocks, with respect to other firms operating in the same sector, which

are not government related, is an identification mechanism that makes it possible to identify

spillover effects. I filter CDS log-changes of government related firms, from common risk

factors (by mean of “sector indexes”) to control for the exposure to the local economic cycle

and using the quantile-regression and the binomial Logit-regression approach I show that

sharp increases in the filtered-residuals (that are above the 80% percentile of the distribu-

tion), exhibit excess co-movements. Moreover, I show that this clustering tends to coincide

with spikes in Sovereign CDS. This means that the credit spreads of government related

firms react to Sovereign credit risk shocks more strongly than what would be implied by

underlying economic fundamentals. Bekaert, Campbell, and Ng (2005) define contagion as

“correlation over and above what expected by economic fundamentals”. With this definition

the clustering observed is contagion, i.e. spillover effects.

Concerning the issue of the direction of the causality, results of Granger causality tests

and of a Vector Error Correction Mechanism analysis, on daily CDS observations, show that

Sovereign CDS tend to lead in terms of movements, followed by bank CDS, which in turn

are followed by corporate CDS. This supports the view that in the time period 2007-2011,

6This issue is largely discussed in Section V.2.7See the article, by Reuters (Aug, 16, 2011): ”Italian utility shares hit by austerity plan tax”. Or the

announcement by Moody’s (Sep, 2012) that the fiscal reform in Spain is credit negative for electricity firms.

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the Euro Area events driving CDS premia were mainly related, at least in the first moments

of their realization, to issues specific to the Sovereign and banking rather than the corporate

sector. To my knowledge this is the first study that highlights the role played by government

credit risk in exacerbating corporate credit risk focusing specifically on Euro Area countries.

The paper is organized as follows. Section II reviews the related literature. Section III

presents the data. Section IV shows some preliminary evidence on the pattern of corporate

CDS spreads across countries, sectors and periods. Section V discusses the relationship

between Sovereign and corporate credit risk and tests for the existence of Sovereign credit

risk spillover effects. Section VI presents the robustness checks. Section VII concludes.

II Literature review

This paper is related to several strands of the financial economics literature. First,

it is related to the literature on corporate debt pricing. The simple application of the

Merton model is known to produce yield spreads that are “too small. Huang and Huang

(2003) provide evidence on the poor performance of a number of second generation structural

models. By implication, this suggest that credit spreads are partly determined by factors

other than credit risk. Collin-Dufresne, Goldstein, and Martin (2001) and Schaefer and

Strebulaev (2008) find results consistent with this view. Longstaff, Mithal, and Neis (2005)

show that corporate bond prices depend on the specific default risk of the issuing firm, on

bond-specific illiquidity and on macroeconomic measures of bond-market liquidity. Bao,

Pan, and Wang (2011) show that corporate bonds yields are largely affected by illiquidity

8 In these studies, when pricing corporate bonds, it is generally assumed that government

debt plays an indirect role to the extent that it affects the term structure of interest rates.

Second this paper is related to the literature on the relationship between CDS and bond

8Using transaction-level data for a broad cross-section of corporate bonds from 2003 through 2007, Bao,Pan, and Wang (2011) find the illiquidity in corporate bonds to be significant, and closely linked to cross-sectional bond characteristics. Moreover, there is a strong commonality in the time variation of bondilliquidity, which rises sharply during market crises and reaches an all-time high during the recent sub-primemortgage crisis.

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spreads. In principle, for a given firm, the CDS premium should approximate the bond

yield spread over the risk-free rate. Hull, Predescu, and White (2004) examine how well

this relation holds and estimate that in the period 1998-2002 the market was using a risk-

free rate slightly lower than the swap rate9. Whether an arbitrage relation holds or not

(i.e. frictions are negligible or not) is ultimately an empirical issue. Several studies have

shown that during the 2007 Subprime crisis and even more so during the Lehman crisis in

the end of 2008 the CDS-bond basis (defined as CDS minus the bond spread) was largely

and persistently negative. Mitchell and Pulvino (2011) stress the role of de-leveraging and

financing risk in driving the basis negative. Bai and Collin-Dufresne (2011) point towards

several drivers related to: funding risk, collateral quality and counter-party risk and show by

means of a Fama-McBeth style analysis that these factors explain the basis both in the time-

series and in the firm cross-section. Fontana (2012) highlights the role of “limits-to-arbitrage”

and “arbitrage trading activity” in driving basis beyond what implied by systematic factors.

Third, this paper is related to the literature on the relationship between corporate and

Sovereign credit risk, which has mainly investigated the case of emerging markets. Durbin

and Ng (2005) use the spreads of emerging market bonds to study investors perception of

“country risk” and find several cases where a firm’s bond trades at a lower spread than that

of the government. Bonds for which this is true tend to have substantial export earnings

and/or a close relationship with either a foreign firm or with the home government. Dittmar

and Yuan (2008) analyze the impact of sovereign bonds on corporate bonds (in emerging

markets) by examining their spanning enhancement, price discovery, and issuance effects.

They find that the effect of spanning enhancement is positive and large; over one-fifth of the

information in corporate yield spreads is traced to innovations in sovereign bonds; and most

of these effects are due to discovery and spanning of systematic risks. Bai and Wei (2012)

analyze developed and emerging-market countries in the period from 2008 to February 2010

and find evidence that the likelihood of sovereign risk transferring to corporates is mitigated

9Also Longstaff, Mithal, and Neis (2005) and Blanco, Brennan, and Marsh (2004) have tested this parityrelation, in the period 2001-2002, and have obtained similar results.

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by legal institutions that provide strong property rights protection. To my knowledge, this

is the first study which specifically focuses on the Euro Area Sovereign crisis.

Fourth, this paper is related to the literature on “country risk”10. The link between

corporate and Sovereign risk is recognized by the common practice by market participants

of adding a risk premium, approximated by the Sovereign credit spread, to the discount

rate, when evaluating a firm’ s project to account for “country risk”. 11. The main focus of

this literature has been on emerging markets. Instead, studies on the Euro Area Sovereign

debt crisis have investigated the relationship between Sovereign and banking sector credit

risk. Panetta (2011) describes the several channels toward which Sovereign risk affect the

funding conditions of banks12, Acharya, Drechsler, and Schnabl (2011) highlight the two-way

feedback relation between financial and Sovereign credit risk, in the Euro Area.

III Data

There are two main advantages of relying on CDS rather than on cash bonds when

studying the relationship between corporate and Sovereign credit risk. The first is that

CDS, being derivatives contracts, have constant and standard maturities and this makes the

comparison of credit spreads straightforward.13 The second is that CDS are less affected

by liquidity so that the comparison is more homogeneous. In fact, while government bonds

are generally very liquid, corporate bond yield spreads are largely affected by illiquidity (see

Bao, Pan, and Wang (2011)) and, therefore, tend to overstate credit risk.

10 See Damodaran (2003) for a broad discussion of “country risk” issues such as how to measure “countryrisk” and how to assess a company exposure to “country risk”.

11Bekaert et al. (2012) propose a new method to isolate the “political risk spread” implicit in sovereignyield spreads to use for augmenting the discount factor for the evaluation of investment projects. The ideais that while the sovereign spread reflects political risk it also reflects other risks that are already includedinto the discount rate, hence leading to the double counting of risk.

12(i) Losses on positions on government debt weaken local banks balance sheets, decreasing their cred-itworthiness and making funding more costly and difficult to obtain. (ii), Higher Sovereign risk reducesthe value of the collateral local banks can use to raise wholesale funding and central bank liquidity. (iii)Sovereign downgrades generally flow through to lower ratings and higher funding cost for domestic banks.

13Instead bonds are issued with a certain redemption date, hence the maturity is declining continuously.Therefore, it is more difficult to match a firm’s bond yield with the government’s bond yield having equalmaturity and study how this relation evolves in time time.

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III.1 Corporate and Sovereign CDS

CDS began to gain popularity in 2003 in the US and are now considered a benchmark

for credit risk. A CDS is a bilateral contract where one counterparty buys protection on the

default risk of a reference entity (corporation or Sovereign).14 The amount paid is quoted

in basis points, per annum, of the contract’s notional value and is called CDS premium (or

spread). From April 2009, with the so called “Big Bank protocol” the CDS contract as

well as how it is traded underwent some changes 15. CDS can be used as pure insurance

instruments, but they are often used for other purposes such as: (i) Taking an outright

positions on spreads depending on expectations over a short horizon, (ii) Relative-value

trading, i.e trading a portfolio with a short position on entity X and a long position on

entity Y, and (iii) Arbitrage trading, i.e exploiting temporary price differences between cash

bonds and CDS. Sovereign CDS were, initially, trading mainly on emerging market debt

and started trading on Euro Area names16 only after the “Lehman collapse”. Sovereign

CDS are a useful tool for hedging “country risk”. Moreover they can be used for hedging

counterparty risk. One example of this is hedging a bank’s risk exposure to a governmental

body in interest rate swap transactions, since these usually do not post collaterals.

CDS are traded in a decentralized and highly opaque, dealer based OTC market, mainly

by large institutional investors such as banks, brokerage firms, hedge funds and asset man-

agers.

14This contract terminates at maturity or default, whichever comes first. In case of default the protectionbuyer is compensated with the difference between the par value of the bond and its value after default. Theprotection seller, collects a periodic fee, and profits if the credit risk of the reference entity remains stableor improves while the swap is outstanding. See Longstaff, Mithal, and Neis (2005) for a detailed discussionon corporate CDS.

15The main innovations consist in the introduction of an auction mechanism for establishing the recov-ery value of bonds in order to determine CDS payments following a default, and the way the premiumis quoted. Concerning the latter issue, here, I use the old convention since relation between the up-front fee and the old zero fee premium is mechanical. Originally the CDS premium was determined sothat net value of contract was zero so that, as in most swaps, no initial fee passed between buyer andseller at inception. Since Big Bang, annual premium is fixed at either 100 bps or 500 bps (depending oncredit quality) and an upfront fee is paid by the buyer to the seller (or vice versa). For more info seehttp://www.markit.com/cds/announcements/resource/cds-big-bang.pdf

16For a discussion on Sovereign CDS see (Longstaff, Pedersen, and Singleton 2011), for a discussion onEuro Area Sovereign CDS and their relation with government bonds see (Fontana and Scheicher 2010).

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III.2 Description of the data

I use Markit CDS data and focus on 11 Euro Area countries for which there are at

least 10 corporate names with sufficient trade frequency: Austria, Belgium, Finland, France,

Germany, Greece, Italy, Ireland, Netherlands Portugal, Spain. France, Germany and the

Netherlands are usually referred to as the “core countries” which are characterized by strong

public finances while Greece, Italy, Portugal and Spain as “periphery countries” which had

severe debt troubles. The sample of the analysis covers the period from the beginning of

2007 to the end of November 2011 and can be divided into three periods. The first period,

which goes from January 2007 to July 2007, is characterized by stable markets. The second

period, which goes from August 2007 to November 2009, includes the US subprime crisis

and the Lehman collapse. The third period, includes the onset of the Euro Area Sovereign

crisis in late 2009 and the peak reached in the summer of 2011.

In the Markit dataset, CDS premia are quoted in different currencies

(USD/EUR/AUD/JPY) and have several restructuring clauses17 and maturities. As

restructuring clause, I choose Modified Restructuring for corporates, as it is the most

common for European corporate names and Cumulative Restructuring for Sovereign as it

is the most common for European Sovereign18. I focus on CDS premia quoted in Euro

with 5 year maturity as this typically has the most liquidity and target CDS contracts on

senior unsecured debt, noted as SNRFOR in the data, which are by far the most frequent

17The cumulative-restructuring (CR) clause allows the protection buyer to deliver bonds of any maturityafter restructuring of debt. This was the standard contract term in the 1999 ISDA definition. The modified-restructuring (MR) clause, which has become common practice in North America from 2001 on, limitsdeliverable obligations to bonds with a maturity of 30 months or less after a restructuring. The modified-modified-restructuring (MM) clause, has been introduced in 2003, and is a “modified” version of modifiedrestructuring. Under this rule, which is more popular in Europe, deliverable obligations can be maturing inup to 60 months after a restructuring. The no-restructuring (XR) clause excludes all restructuring eventsunder the CDS contract as “trigger events”, eliminating the possibility that the protection seller suffers a“soft” credit event that does not necessarily result in losses to the protection buyer. The “Seniority levels”of debt in CDS contracts include (a) secured debt (SECDOM), (b) senior unsecured debt for Corporate andFinancial, and Sovereign debt for Government (SNRFOR), (c) subordinated or lower tier 2 debt for banks(SUBLT2), (d) junior subordinated or upper tier 2 debt for banks (JRSUBUT2), and (e) preference sharesor tier 1 capital for banks (PREFT1).

18Results of the empirical analysis are not substantially different when using different restructuring clauses.

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ones. Markit adopts the Industry Classification Benchmark (ICB) to classify reference

entities. The ICB sector classification decomposes non-government entities into following ten

categories: (a) Basic Materials, (b) Consumer Goods, (c) Consumer Services, (d) Financials,

(e) Health Care, (f) Industrials, (g) Oil & Gas, (h) Technology, (i) Telecommunications and

(j) Utilities. Government entities are classified as Sovereign and local government.

The initial sample for the 11 countries considered in the analysis, includes approximately

400 names, but not all have liquid time-series. Also, since the analysis focuses on non-

financial corporates, for each country I include only the largest banks, which where considered

in “European bank stress test” 19; these are 48 in total in the countries considered in this

analysis. Table I reports the distribution of the remaining 219 CDS names by country and

sector. The countries with the most entities are Germany (52 names), France (50 names)

and the Netherlands (31 names). Italy and Spain have respectively 23 and 20 entities. Other

countries, such as Austria, Belgium, Finland, Greece, Ireland, Portugal have between 5 and

10 entities.

In Euro area countries only few companies have debt outstanding which is traded on the

secondary market (hence traded CDS)20 and these are located mostly in “core” countries.

As a result, the sample is biased towards large international companies.

IV A first look at the CDS Premia

This descriptive overview highlights two main facts that motivate the analysis offered in

the empirical section. First, with the onset of the US subprime crisis and all the subsequent

financial markets events, not only did a repricing of credit risk occur, but also the traditional

19http://www.eba.europa.eu/EuWideStressTesting.aspx. On the 23rd of July 2010, the Committee ofEuropean Banking Supervisors, a body within the European Union, has conducted a stress tests on Europeanbanks. The list of the banks can be found at: http://www.markit.com/assets/en/docs/commentary/credit-wrap/2010/StressTests.pdf.

20Before the introduction of the Euro currency, in the Euro Area, only large and creditworthy corporationswere able to issue debt traded on the secondary market. After the introduction of the Euro more firms havegained access to financing through the issuance of bonds. I do not dispose of these data but it is very likelythat only firms in “core” countries, such as Germany France and the Netherlands, have benefitted from this.As a result, in the “core” countries the distribution of firms is more homogeneous across the rating spectrum.

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credit spread hierarchy reversed. Before July 2007, corporate credit spreads were typically

larger than bank credit spreads, which in turn were larger than Sovereign credit spreads.

Interestingly, during the Lehman and Euro Area Sovereign debt crisis in periphery countries

corporate CDS became often lower than CDS on their home governments. Second, while

before and during the “Lehman shock” there appear to be no relation between the level

of corporate and Sovereign credit spreads across the Euro Area, afterwards with the onset

of the Sovereign debt crisis, countries with public debt troubles (i.e. large Sovereign credit

spreads) are also those in which corporations have higher credit spreads.

Let’s first have a look at the evolution of the “credit spreads hierarchy”. Figure 3 shows

the time-series dynamics of the Sovereign CDS, the median bank CDS and the median corpo-

rate (non-financial firm) CDS for some selected “core” (France, Germany and Netherlands)

and “periphery” (Italy, Portugal and Spain) Euro Area country.

(FIGURE 3 HERE)

For France, Germany and the Netherlands bank CDS were larger than their respective

government CDS, throughout the three periods. Sovereign and bank CDS started co-moving,

following an upward trend, only from October 2008, as a consequence of the risk transfer from

the financial to Sovereign sector as documented by Acharya, Drechsler, and Schnabl (2011).

Corporate CDS were larger than banks and Sovereign CDS in the pre-crisis period. Moreover,

corporate CDS were the most reactive to the “Lehman shock”. During the Sovereign crisis,

corporate CDS were similar in levels to bank CDS, while Sovereign CDS remained relatively

low. For these countries21, the traditional credit spread hierarchy was preserved. For Italy,

Portugal and Spain the observed pattern is different. Banks CDS were larger than their

respective governments CDS, in the pre-crisis period and during the “Lehman shock”, but

started co-moving and became similar (slightly higher) in levels during the Sovereign debt

crisis. Corporate CDS were the largest in the pre-crisis period and in the case of Italy and

21The pattern is similar for other Euro Area countries characterized by strong public finances such asAustria, Finland. I do not report these charts for brevity.

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Portugal, the most reactive to the “Lehman shock”. Differently, in Spain bank CDS were the

most reactive to the “Lehman shock”22. Interestingly, during the Sovereign crisis corporate

CDS were generally lower than Sovereign and bank CDS23. The fact that corporations in

periphery countries have lower perceived credit risk than their home government is in contrast

with what assumed in empirical studies on the term structure where government bonds are

typically used as risk-free benchmarks to price corporate debt.

Let’s now have a look at the relation between corporate and Sovereign credit risk. Figure

4 shows the average bank, corporate and Sovereign CDS by country in three sub-periods.

The first period goes from January to July 2007. The second period goes from July 2007,

the onset of the US subprime crisis, to November 2009 and includes the September/October

2008 “Lehman shock”. The third period goes from the onset to the Sovereign debt crisis in

December 2009 to November 2011. Countries are ordered from the left to the right so that

Sovereign credit risk (as measured by the CDS level) is increasing.

(FIGURE 4 HERE)

In period 1 (chart a), bank and Sovereign CDS were very low and similar in levels across

countries. Instead, corporate CDS premia were different. For example, firms in Germany,

France and the Netherlands were characterized by higher credit spreads with respect to

firms in Greece, Italy, Portugal and Spain. In period 2 (chart b), all credit spreads reacted

to the “Lehman shock” and increased with respect to period 1. Again, corporate CDS

premia of firms in “core” countries were characterized by the higher credit spreads. The

difference in the level of corporate credit risk across countries is due to the fact that in the

“core” countries (where financial markets are more developed) firms issuing debt are well

distributed across the entire rating spectrum, while in “periphery” countries only large and

well internationalized firms (which tend to be more creditworthy than others) issue debt

22This holds also for Greece and Ireland, which I don not reported in figure three for brevity. There is acommon agreement that troubles in Spain, as well as in Greece and Ireland, originated in the banking sector.

23The pattern looks similar for other Euro Area countries characterized by week public finances such asBelgium, Greece and Ireland. I do not report these charts for brevity.

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traded in the secondary market. Basically, when looking at Figure 4 across chart a) and

b) there appear to be no relation between the level of corporate and Sovereign credit risk.

Instead in period 3 chart c), it stands out markedly that, in countries with public debt

troubles (i.e. large Sovereign credit spreads) corporations are more credit risky.

In countries with strong public finances, corporate CDS reached their peak in period 2

and subsequently decreased (average of 50, 240 and 175 basis points respectively in period

1,2 and 3). In contrast, in countries with debt troubles, corporate CDS followed an increasing

trend (average of 60, 165 and 265 basis points respectively in period 1,2 and 3). Also, in

period 3, the average corporate CDS is 100 basis points higher in peripheral countries.

In order to analyze the “country effect” further, I select the largest Utility and Telecom

firms across the following selected “core” and “periphery” countries: Italy, Spain, Portugal,

Greece, France and Germany24 and I carry out a principal component analysis on the CDS

dynamics (log changes). The results for the two periods: pre-Sovereign crisis period (January

2007- December 2009) and Sovereign crisis period (January 2010 to November 2011), are

reported in Table II. In the first period, two PC explain approximately 80% of the CDS

variation. In particular, the first PC explains 73% of the CDS variations. This component

places equal weights (eigenvectors) on each of the firms across all the countries and across

both sectors and can therefore be defined as a “level factor”. The second PC explains 6% of

the variations and discriminates well, between Telecom (positive weights across countries, i.e

(0.22+0.29)/2 = 0.25) and Utility firms (negative weights across countries, i.e (-0.23-0.20)/2

= -0.22) and can therefore be defined as a “sector factor”. The third PC explains 2% of

the variation and does not seem to have a direct economic interpretation (also loadings are

close to zero). The observed pattern is different during the Sovereign debt crisis. Three PC

explain approximately 85% of the CDS dynamics. In particular, the first PC explains 71%

of the CDS variations. Again, this component places equal weights (eigenvectors) on each of

the firms across all the countries and across both sectors. The second PC explains 10% of the

24This choice is motivated by the fact that, in the sample, Telecom and Utilities are the only firms whichare well distributed across these countries.

13

variations and discriminates well, between “core” countries (positive weights across sectors,

i.e. (0.32+0.15)/2 = 0.23) and the “periphery” countries (negative weights across sectors,

i.e. (-0.23-0.23)/2 = -0.23). The third PC explains 4% of the variation and identifies the

sector to which firms belong, Telecom firms have positive weights across countries (0.17+0.22

= 0.19) and Utilities have negative weights across countries (-0.05-0.29 = -0.17).

In summary, CDS premia exhibit strong commonality (i.e. tend to move in level all

together) given that their variation is mainly explained by the first PC defined as the “level

factor”(approximately 70% of the variation). In the first period, beyond the “level factor”

also the “sector factor” (6%) plays a significant role. Instead, during the Euro Area Sovereign

debt crisis the variation of CDS premia is explained to a large extent by the second PC defined

as the “country factor” (10%), with the third PC, i.e. the “sector factor” playing a only

minor role (2%).

IV.1 Sovereign vs. corporate CDS lead-lag relationship

The purpose of this paragraph is to investigate the following question: which of the series,

between corporate, bank and Sovereign CDS, is leading in terms of price movements? For

this purpose, I use daily CDS observations and I check, one at a time, the three CDS pairwise

relations: Sovereign vs. corporate, Sovereign vs. bank and bank vs. corporate, within each

of the 11 countries in the sample.

A bivariate Vector Error Correction Model is specified (the example refers to the relation

“Sovereign vs. corporate” CDS):

∆CDSSov,t = λ1(Zt−1) +

q∑j=1

γ1j∆CDSSov,t−j +

q∑j=1

δ1j∆CDSCorp,t−j + ε1t (1)

∆CDSCorp,t = λ2(Zt−1) +

q∑j=1

γ2j∆CDSSov,t−j +

q∑j=1

δ2j∆CDSCorp,t−j + ε2t (2)

Zt−1 = CDSSovereign,t−1 − α0 − α1CDSCorp,t−1 (3)

14

Equation (1) and (2) express the short term dynamics of the Sovereign CDS and corporate

(median value) CDS changes, while Zt−1 is the error correction term given by the long run

equation, that describes deviations of the series from their equilibrium relation25. If corporate

CDS are leading in terms of price movements, then λ1 will be negative and statistically

significant as the corporate CDS moves first and the Sovereign CDS adjusts subsequently,

based on the disequilibrium error. Similarly, if the Sovereign CDS is leading, then λ2 will be

positive and statistically significant. The existence of cointegration means that at least one

market has to adjust by the Granger representation theorem (Engle and Granger 1987).

This analysis conducted on different sub-periods (pre-crisis, Subprime crisis and “Lehman

shock” and Sovereign debt crisis) yields similar qualitative results, hence table III reports

the estimates on the entire period that goes from January 2007 to November 2011. The

first column in table III shows the results of this analysis on the lead-lag relation between

Sovereign and corporate CDS. The sign of the two coefficients are consistent across countries

and for the three pairwise relations, what changes are the size and the significance. Therefore,

on the bottom the table reports the average (across all the countries) coefficient λ1 and λ2

and their significance. λ1 is negative and statistically not significant, while λ2 is positive

(0.009) and significant (t-stat 3.122), meaning Sovereign CDS are generally leading and that

the corporate CDS adjusts subsequently. The Granger-Gonzalo measure, is a measure of the

contribution of the two markets to price discovery and is defined as: λ2λ2−λ1 and is bounded

between 0 and 1. Given how the model is specified a GG measure closer to 1, in this case

0.853 confirms that Sovereign CDS are leading. The second column shows the results of the

analysis on the lead-lag relation between Sovereign and bank CDS. The average λ1 is negative

and statistically not significant, while the average λ2 is positive (0.028) and significant (t-

25The cointegration between the levels of two variables characterized by the presence of a unit root, meansthat a linear combination of these variables is stationary. Cointegrated variables move together in the longrun, but may deviate from each other in the short run, which means they follow an adjustment processtowards their equilibrium relation. The ADF test does not reject the null hypothesis of a unit root for allCDS series in their levels, but it does for all series in their first differences, i.e. all series are integrated once,I(1). The results of the Johansen cointagration analysis (Trace test), between each pair of CDS (e.g Sovereignand bank, Sovereign and corporate and corporate and banks) strongly rejects the absence of cointegration.

15

stat 3.593), meaning Sovereign CDS are generally leading and that the bank CDS adjusts

subsequently26 The GG measure is 0.891, confirming that Sovereign CDS are leading. The

third column shows the results for bank and corporate CDS. The average λ1 is negative

and statistically not significant, while the average λ2 is positive (0.004) and significant (t-

stat 2.491), meaning bank CDS are generally leading and that the corporate CDS adjusts

subsequently27. Results of a Granger causality test, (not reported for brevity) on daily CDS

changes, confirm that Sovereign CDS tend to lead, followed by bank CDS, which in turn are

followed by corporate CDS.

This analysis does not address the (economic) causality between corporate and Sovereign

credit risk, but the outcome suggests that, in the time period 2007-2011, the events driving

CDS premia were mainly related, at least in the first moments of their realization, to issues

specific to the Euro Area Sovereign and the banking rather than the corporate sector.

V Empirical Analysis

V.1 Pricing corporate debt and the risk-free benchmark

Corporate bonds are typically priced with a yield spread over a risk-free benchmark. The

absence of defaults among Euro Area countries has underpinned the widely used assumption

that national government bonds were a good proxy for the (default-) risk-free rate. Things

changed in fall 2008, when the collapse of Lehman led to a fundamental reassessment of

Sovereign credit risk (see Acharya, Drechsler, and Schnabl (2011))28.

This section investigates what is this risk-free benchmark used to price corporate debt of

26Greece and Germany are exceptions in the sense that also bank CDS lead in terms of price movementsvery often ( λ1 and λ2 are both significant and with the correct sign).

27The GG measure of 0.56 might suggest that Bank and corporate CDS both lead one at a time, but sinceλ1 is not significant the GG measure looses its meaning.

28Initially Irish banks declared they had suffered large losses, then after the government announced thebailout on September 30, 2008, the large part of European countries made similar announcements. Subse-quently, in September 2009 Greece disclosed its fiscal problems. Finally, debt troubles reached also Portugal,Italy and Spain. During this period credit spreads of governments across the Euro Area widened dramatically.

16

Euro Area corporations based on the assumption that, for a given firm, the CDS premium

should be approximately equal to the bond yield spread over the risk-free rate. This is

because hedging a defaultable bond (at par) buying default protection via CDS, is replicating

a risk-free position (see Hull, Predescu, and White (2004)).29 This means that, under the

assumption of “no-limits-to-arbitrage”, the following relation approximately holds:

CDSCorp,i,t = Y TMCorp,i,t −Rf (4)

If CDSCorp,i,t is greater than Y TMCorp,i,t − Rf it is possible to make an arbitrage profit by

shorting the corporate bond, shorting the CDS and buying the risk-free bond. If CDSCorp,i,t

is less than Y TMCorp,i,t −Rf it is profitable to buy the corporate bond, buy protection and

short the risk-free bond. The idea is that arbitrage forces keep these variables to parity.

The analysis requires to match CDS with bond yields with the same maturity, hence it is

conducted only on a sub-sample of 30 entities, well distributed across sectors in two selected

“core” countries: France and Germany and two selected “periphery” countries Italy, and

Spain. The “synthetic constant 5 years maturity bond yield” is obtained by interpolating

yields of bonds with different maturities30. Figure 5 shows the bond yield, the CDS, the

“implied risk free-rate” (i.e the difference between the bond yield and the CDS premium,

based on equation 4) for one selected entity: Telecom Italia. It shows also the 5 years Euro

swap rate. Throughout the period from September 2010 to the end of 2011 the “implied

risk-free rate”, in debt prices of Telecom Italia, tracks reasonably well the level of the Euro

Swap rate.

29This pricing relation is only approximate for fixed coupon bonds. In fact, the cash-flows of a CDScontract can be replicated by a portfolio made by shorting a risky floating-rate note. In general, even thoughcash flows on a long default-free fixed coupon bond and a short defaultable fixed coupon bond are notprecisely those on a CDS, they are nearly the same. The quality of the approximation depends on how muchthe bond is away from par, on the coupon level, on the shape of the term structure of risk-free interest rateand on the shape of CDS curve.

30For each day in the sample and each entity with available data, I search for a bond with less than fiveyears left to maturity, and another bond with more than five years to maturity. When I have the choice Iselect the closest to par bond. Only senior, straight coupon bonds are used. Bonds with any special featurethat would influence pricing, are not considered. The idea is to neutralize as much as possible technicalfactors such as contractual specifications that affect the parity relation between bond and CDS.

17

(FIGURE 5 HERE)

Figure 6 shows the time series of the “implied risk-free rate” averaged across firms domi-

ciled in Italy, Spain, France and Germany. Moreover, it shows the 5 year German-bund

yield and the Euro swap rate. In the first part of the sample the “implied risk-free rate” is

closer to the Euro swap rate, afterwards it follows a decreasing trend and in the end it lies

in between the Euro swap rate and the german bund yield.

(FIGURE 6 HERE)

In the case of existence of “limits-to-arbitrage” it is not possible to get the correct level

of the risk-free rate using the no-arbitrage argument since this implied rate would also incor-

porate market frictions driving the divergence from parity31. As shown by Bai and Collin-

Dufresne (2011) and Fontana (2012) the CDS-bond basis on corporate entities has deviated

persistently from parity during the 2007 US Subprime crisis and during the “Lehmann pe-

riod” mostly because of the inability of traders to fund the “negative-basis-trade” (i.e. little

funding available, high funding cost and high margin requirements). Instead, the Euro Area

Sovereign debt crisis seems mainly an issue of government solvency rather than a “funding

liquidity crisis”.

In any case, assuming frictions are relatively constant in time, the most appropriate

framework to study the link between two variables which have unit-root in their levels, but

are bounded by an no-arbitrage condition is cointegration technique. Table IV a) reports the

estimates of the first step of the Engle and Granger procedure (long-run dynamics), where

the “implied risk-free rate” is regressed on the German bund yield and the Euro swap rate

separately. Consistently with what previously observed in Figure 6, both for the bond yield

and swap rate regressions, the constant is found to be significant and the trend significant

and declining. The coefficients on the bund and the swap rate are respectively 0.728 and

0.815 and are both significant. The Augmented Dickey Fuller test on the residuals of this

31For example if the CDS-bond basis is not zero then a portfolio made by a corporate bond and a longCDS position would yield a return different than the risk-free rate.

18

regressions reject the hypothesis of the presence of a unit-root with approximately 1% level

of confidence, hence rejects the hypothesis of the absence of cointegration.

Table IV b) reports the estimates of the second step where weekly changes of the “implied

risk-free rate” are separately regressed on weekly changes of the German bund yield and the

Euro swap rate, with deviations from the no-arbitrage condition (ECM, i.e. the lagged Error

Correction term) feeding back into the short-run dynamics. The ecm(-1) coefficient has the

correct negative sign, it is significant and economically large (-0.260 and -0.435 respectively

for the bund and for the swap rate) meaning that after deviating the variables strongly revert

back to their equilibrium. The coefficient on the bund yield is statistically significant and

very close to 1, i.e 0.987 (for the Euro swap rate it is 1.124), moreover the fit is extremely

good, i.e. 85% of the variance is explained (swap rate 70%).

In summary, results of this analysis show that the risk-free rate used as a benchmark for

pricing corporate bond, across Euro Area countries, lies between the Euro swap rate and the

German bund-yield, but its variation is more strongly related to the variation of the bund.

V.2 The link between corporate and Sovereign credit risk

The link between corporate and Sovereign credit risk is recognized by the common prac-

tice by market participants of adding a risk premia, approximated by the Sovereign credit

spread, to the discount rate, when evaluating a project in emerging markets to account for

“country risk”. There are at least two justifications for “country risk”. First, Sovereign

and corporate credit risk tend to correlate positively because of the common exposure to

the macroeconomic cycle. During periods of recession, economic and business conditions are

hostile increasing the likelihood of a government repayment crisis and, at the same time,

reducing the profits of firms. Second, a government in trouble with its public finances, might

make efforts for staying solvent, taxing firms or households. (Durbin and Ng 2005) refer to

this as “transfer risk”, i.e the risk that a government transfers it’s repayment problem to

19

firms32. One additional channel through which Sovereign credit risk and corporate credit risk

might be related is the one of the debt guarantees. This channel works for companies which

are partially owned by the government. The idea is that when a government is in trouble

with its public finances the value of implicit and explicit guarantees reduces and with it the

creditworthiness of firm which were initially benefiting from this guarantees. These links are

consistent with the way rating agencies evaluate the creditworthiness of corporations. As I

discussed below, during the recent Euro Area events, Moody’s and S&P have assessed the

credit risk of firms in relation to the credit risk of their respective home governments.

On December 21, 2010, Moody’s placed a series of ratings, of firms located in Portugal,

on review for possible downgrade33. Among others, the A3 (A-) rating of “EDP” Energias

de Portugal the largest utility firm in the country. These actions followed Moody’s earlier

rating action to place on review for possible downgrade the A1(A+) rating of the government

of Portugal. Later on, after many other events, on February 12, 2012 34, the government of

Portugal was downgraded to Ba3 (BB-) with a negative outlook while EDP’s rating was at

Ba1 (BB+) two notches higher. Moody’s rating methodology to evaluate EDP, refers to the

practice applied to evaluate government-related companies (partially government owned).

According to Moody’s, EDP is a large and international well diversified firm which operates

in a regulated business and has a low exposure to the economic cycle (countercyclical sector),

but EDP cannot fully isolate itself from it, hence the value of its debt as well as the gov-

ernment’s credtiworthiness are negatively affected be the deterioration of national economic

cycle. Moreover, the fact that its debt is backed by the government improves its rating by

one notch with respect to its stand alone rating (Moody’s).

Standard & Poor’s evaluates the credit risk of a company in relation to its home govern-

ment credit risk in a similar way. For example, government-related issuers, local governments,

32Another option would be devaluating, in accordance with the central bank, the currency in order toreduce the value of debt. In any case, this would not be an option for an Euro Area countries.

33http : //www.moodys.com/research/Moodys − places − ratings − of − Portuguese − government −related− issuers− on− review −−PR− 211705

34http : //www.moodys.com/research/Moodys − announces − impact − on − Portuguese −infrastructure− utility − companies− following − Portugal −−PR− 237660

20

domestic banks or firms in sectors that have low resilience to “country risk” 35 are less likely

to be rated above the government. As explained in its documentation36, S&P establish the

conditions under which in the European Monetary Union issuers can be rated up to six

notches above their home government rating. The exposure to “country risk” of an issuer is

given by the combination of two factors. First, the proportion of revenues derived within the

country of domicile; this is supposed to capture the exposure to the local economic cycle.

Second, to the degree of “country risk” sensitivity of an issuer. This sensitivity is considered

to be similar within a sector and across countries. Utility firms have a low exposure to the

local economic cycle (defensive sector), but are characterized by high “country risk”, because

they are subject to “fiscal” and “regulatory” risk (which are aspect of “transfer risk”). As

an example of “fiscal” risk, in Italy, with the austerity plan applied in September 2011, a

specific tax has been introduced on Utilities37. “Regulatory” risk, instead, refers to a situa-

tion in which the government, in times of economic recession, might tighten the regulation

for companies so as to improve the cost of services for its consumers.

V.3 Corporate and Sovereign CDS co-movements

This section uses regression analysis to investigate the co-movements between corporate

and Sovereign CDS across countries, sectors and periods. As shown in section V 1, firms

debt yields differ largely across Euro area countries but are priced with a credit spread over

the same risk-free benchmark. This means that the following relation holds:

Y TMCorp,i,t = Rf + CDSCorp,i,t (5)

35 Damodaran (2003) discusses different ways of considering “country risk” in evaluation and ways ofassessing a firm’s exposure to country risk.

36See NonsovereignRatingsThatExceedEMUSovereignRatingsMethodologyAndAssumptions.pdf .37See the article, on August, 16, 2012, by Reuters: ”Italian utility shares hit by austerity plan tax.

Similarly, on September 2012 Moody’s announces (see http : //www.moodys.com/credit− rating) that thefiscal reforms in Spain is credit negative for electricity companies.

21

The corporate CDS premium is typically related to firms default risk, determined by the

prospects of the sector to which the firm belongs and to the firm’s idiosyncratic risk, but

preliminary results suggest that corporate CDS premia are characterized also by a “country-

specific” component. The following analysis is based on the conjecture that the national

government CDS approximates the dynamics of the “country-specific” component. To for-

mally test this the following model is proposed:38:

∆log(CDSCorp,i,j,t) = α + β∆log(CDSSectorIndex,j,t) + γ∆log(CDSNationalSov,i,t) + εi,t (6)

where log CDSCorp,i,t is the corporate i CDS premium, CDSSectorIndex,j,t is the index of

the sector j to which a firm belongs, calculated as the average of the CDS within each sector

in the country taken as benchmark, i.e Germany. CDSSov,i,t is the corresponding National

Sovereign CDS. Finally, εit id the idiosyncratic firm default risk.

The focus is on two different groups of countries: all Euro Area countries and the “periph-

ery countries”: Greece, Italy, Portugal and Spain. Moreover, the following two periods are

analyzed separately. The first goes from July 2007 to November 2009 and is characterized by

the onset of the US subprime crisis and the Lehman collapse. The second period goes from

December 2009 to November 2011 and covers the still ongoing Euro Area debt crisis. The γ

coefficient measures corporate CDS sensitivities to the National Sovereign CDS. Therefore,

the coefficient captures the joint effect of the exposure to the country economic conditions

(priced both in the corporate and in the Sovereign CDS) and the potential spillover effects of

a government’s fiscal troubles to the corporate sector. As described in paragraph V 2, both

these effects together contribute to determine “country risk”. Hence, firms for which CDS

38Typically studies on CDS with a time-series focus consider CDS in changes. The problem is that thisanalysis in implemented in a panel setting. Therefore, since the volatility of CDS changes varies (it increases)a lot with the level of the CDS, in order to have homogeneous volatilities in the cross-section (CDS havedifferent ratings and belong to different sectors) I standardize changes by the level considering log-changes.Volatilities of log changes are also more homogeneous across the two periods, both for Sovereigns andcorporates. The disadvantage of using CDS log-changes is that large percentage changes on low CDS levels,for example from 5 to 10 basis points (100% increase) but which are economically non significant appear toin the same way as changes from 50 to 100 basis points, which are economically large. In the light of this,care needs to be take when interpreting the sensitivity coefficients.

22

are characterized by a higher sensitivity have either a large part of their revenues exposed to

shocks to the local economy, or are more likely to be negatively affected by the deterioration

of the government’s credit risk, or a combination of the two.

Table V presents the results of the estimation of model 6, where the variable Sector-

Index and National-Sovereign CDS are also interacted with the group dummy so to capture

the difference in the sensitivity of firms in “periphery countries”. The first column shows

the estimates for the entire sample period. The sensitivity to Sector Index is 0.467 for the

average firm and is lower for firms in “periphery countries” (0.467-0.201=0.266). Firms in

“periphery countries” have a higher sensitivity (0.17+0.19=0.36%) to National Sovereign

than the average Euro Area firm (0.17%). The second and the third column show estimates

for the sub-sample period 1 and period 2. The coefficients β and γ are stable across the

Lehman period and the Sovereign debt crisis. Also the difference, between the average and

“periphery countries”, in the sensitivity both with respect to Sector Index and National

Sovereign CDS are very similar. To provide and economic meaning to the results obtained,

an increase in the Italian Sovereign CDS from 200 to 400 basis points implies that corporate

CDS, starting from an initial level of 200 basis points, would increase by approximately 72

basis points.

Table VI shows that the variance explained by the Sovereign CDS is substantial, i.e 30%

only during the Sovereign debt crisis and for firms in “periphery countries”. In fact, the

explained variance by the Sovereign CDS for firms in “periphery countries” is only of 13.2%

before the Sovereign crisis and is only 4.2 and 6.0% for “core countries” respectively before

and during the sovereign crisis.

It is interesting to study the behavior of the sensitivity coefficients of corporate CDS

to the National Sovereign CDS across sectors. For this purpose I focus on firms located in

“periphery countries” and I estimate Model 6 separately for each sector. Figure 7 shows

that the sensitivity coefficients in period 1 and 2 do not change much, i.e the fitting line

has a coefficient of 1.19, only slightly higher than the 45 degree line (with zero intercept).

23

Oil& Gas, Banks, Telecommunications and Utility firm are the most sensitive to the national

Sovereign CDS dynamics.

(FIGURE 7 HERE)

Figure 8 shows the sensitivity coefficients in period 1 and the explained variance by the

National Sovereign dynamics during the Sovereign debt crisis. It stands out markedly that

sectors characterized by higher ex-ante sensitivity are those for which the explained variance

by the government CDS is the highest during the Sovereign debt crisis. Again, Oil&Gas,

Banks, Telecommunications and Utility firm those for which the national Sovereign CDS

dynamics has the largest explanatory power during the Sovereign debt crisis. Although

these sectors are generally characterized by a different exposure to the local economic cycle

(for example Utility firms are considered counter-cyclical) they all have a tight link with the

national governments.

(FIGURE 8 HERE)

V.4 The case of the government related firms

As described in section IV 2, government related firms potentially benefit from the sup-

port of the government in case of debt troubles, but at the same time if the value of these

“debt guarantees” reduces, their creditworthiness decreases accordingly. Moreover, govern-

ment related firms (mostly utilities) firms are subject to “regulatory” and “fiscal” risk.

There are 7 government related firms (partially owned by the government), in the sample,

which are located in the “periphery countries”. These belong to different sectors: EDP (Util-

ity) and Portugal Telecom (Telecom) are located in Portugal, Enel (Utility), Eni (Oil&Gas)

and Finmeccanica (Industrials) in Italy, OTE (Telecom) is located in Greece and Repsol

(Oil&Gas) in Spain. Each home government holds a share of 20-30% of the equity capital of

these companies. The following three charts highlight quite markedly that the credit risk of

government related firms is strongly linked to the dynamics of Sovereign credit risk. Chart 9

24

a) shows the time-sereis of the CDS for two Italian Utilities: Enel, which is partially owned

by the government and Edison which is not. Chart b) shows the time-series of the CDS

for two Italian Industrial firms: Finmeccanica, which is partially owned by the government

and Atlantia which is not. From April 2010, government-related firms correlate with the

Sovereign CDS more strongly, compared to firms belonging to the same sector but which are

not government related. Finally, chart c) shows the time-sereis of the CDS for some Oil&Gas

firms. Notice that ENI which is an Italian company (the only one located in the periph-

ery among these) is deviating with respect to the common behaviour of the other Oil&Gas

companies at the time when troubles with public debt starts for the Italian government.

(FIGURE 9 HERE)

In order to identify the effect of government ownership, I compare how the credit risk

of these firms reacts to Sovereign credit risk shocks, relative to other firms operating in the

same sector that are not government related. The focus is on the Euro Area Sovereign debt

crisis period which goes from October 2009 to November 2011. The following two steps

are taken. First, to control for exposure to common risk factors CDS log-changes of each

firm are regressed individually on a portfolio made by equally weighted CDS log-changes of

firms belonging to the same sector. In this way the obtained residuals are cleaned from the

effect of the “common exposure to the local economic cycle”. For example, Finmeccanica

is the only industrial firm, in the sample of the italian industrial firms, which is partially

government owned, therefore I use as a control a portfolio made of the other industrial italian

firms.39 Second, I test whether there is clustering in the filtered CDS log-changes of these 7

government related firms.

39In the case of OTE and Portugal Telecom, I use the return of a portfolio made by all the peripheraltelecom companies excluding the one for which the return need to be filtered, the other firm are not par-ticipated by the government. In the case of Enel, I use the return of Edison which is the unique Italianutility company which is not partially owned by the italian government. For Repsol and Eni, which bothare Oil&Gas companies, I use a portfolio made by the remaining firms in the sample, even if they belong tocore countries. The justification is that Oil&Gas companies are very internationalized and their prospectsare common across countries, in fact CDS returns of these firms are highly correlated through out the entiresample.

25

A preliminary review of the filtered CDS log-changes (residuals) indicates that the first

principal component explains approximately 30% of the variations. Moreover, the average

correlation among residuals is of about 35% (when it is significant). This is suggestive of the

presence of correlation beyond what would be implied by economic fundamentals. To further

investigate this issue, I test whether filtered CDS log-chages exhibit excess correlation in the

tail of the distribution using two different standard methodologies: quantile regressions and

binomial Logit regressions.

A quantile regression estimates the conditional probability that a variable yt falls below

a given threshold, lets say the 10% percentile, conditionally on the fact that a different vari-

able xt also falls below the same percentile. In an unconditional setting the probability that

yt falls below the 10% percentile is by definition 10%, but when the two variables are not

independent this probability is different. The conditional probability is estimated through

OLS regression using percentile co-exceedance indicators; co-exceedance occurs when both

random variables exceed the pre specified percentile.40 In the following analysis, the de-

pendent variable represents the filtered log-changes on an individual firm’s CDS, while the

explanatory variable is the equally weighted average of the filtered log-changes of the other 6

firms CDS in the sample of the 7 government related firms. I focus on positive CDS filtered

log-changes (which indicate a deterioration of the perceived credit risk) that belongs to the

20% highest log-changes of each firm’s distribution (log-changes above the 80th percentile).

Results, reported in Table VII, show that ENEL has a probability of 43% of being in the top

20% percentile when the portfolio made by the other firms is also in the to 20% percentile.

Compared to the case in which there is no dependence, and the probability should be 20%,

the probability is now twice the size. The average probability across the 7 entities is 40%,

providing evidence of excess tail co-movement. It is difficult to understand the statistical

significance of this test, therefore, as a further test, I perform a binomial Logit analysis.

40This approach is in the style of Cappiello, Gerard, and Manganelli (2005). Advantages of the quintileregressions technique is that there are no distributional assumptions on the data and can be estimated formany different quintiles.

26

The Logit is a parametric model, and in this context, it allows to that addresses the issue

of excess co-movements by estimating whether a given firm’s CDS log-changes are more likely

to be high when also the log-changes of the CDS of the other firms are high.41. Again, I use

a cutoff of 80% of the overall distribution to identify tail realizations (top 20%) of each single

firm and of the overall portfolio. Since there are 117 week-observations in the sample, a this

cutoff produces 24 tail observations for each firm. In this analysis, the dependent variable

is an indicator variable equal to one if a firm’s CDS log-change falls in the tail and zero

otherwise. The independent variable, COUNT, is equal to the number of firms for which

CDS log-changes fall in the tail, in the same week. This set up allows to measure the extent

of clustering of spikes in CDS, in fact a positive and significant coefficient, of the variable

COUNT, tells that a spike in the CDS of a firm clusters with spikes the CDS of other firms.

Table VIII shows that coefficients of COUNT are always positive and significant, confirming

the results obtained by mean of the quintile regression analysis.

Figure 10, shows the number of firms, each week, that have simultaneous filtered CDS

log-changes tail realizations. The extent of clustering is very strong, i.e. one firm’s CDS tend

to increase when other firms tend to increase. Moreover, Figure 10, includes an indicator

variable which captures Italian Sovereign 42 tail realizations, obtained using a cutoff of 90%

of the distribution so to capture the most severe shocks (top 10%) on Sovereign CDS. The

clustering of corporate filtered CDS log-changes across government related firms tends to be

very high when also the Sovereign CDS spikes.

(FIGURE 10 HERE)

These results all together tell that the credit spreads of government related firms react

to Sovereign credit risk shocks more strongly than what would be implied by underlying

economic fundamentals.

41For more on this methodology see Boyson, Stahel, and Stultz (2010).42I arbitrarily chose the Italian Sovereign as the most representative among the peripheral countries.

Moreover, CDS premia of Euro Area countries are highly correlated and even more so CDS premia of“periphery countries”.

27

VI Robustness Checks

In Section V I price deviations, between the CDS the bond yield and the risk-free rate,

from parity might be attributed to the calculation methodology, which is a first order ap-

proximation, because it is assumed that the bond is always close to par, hence that interest

rates do not change in time or equivalently that the bond is a floating rate note. A more so-

phisticated methodology is the one in which the bond is priced according to the risk-neutral

valuation paradigm, by mean of the cash-flows replication argument, using a risk-free bench-

mark, risk-neutral default probabilities implied from the CDS curve and an assumed recovery

rate. The implied risk-free rate is the one obtained shifting the risk-free benchmark, in the

bond pricing model up or down, until the present value of the cash flows of the bond equal

the market price. An empirical application, which I do not report for brevity, on real world

CDS and bonds prices during the 2007-09 financial crisis, shows that this “sophisticated”

measure and the basis calculated as the difference between the CDS and the bond spread

exhibit a common behavior, i.e. they are approximately zero, in normal market conditions

and they became negative since the onset of the funding crisis in August 2007 and even

more so during the “Lehman” funding crisis. Most importantly, the discrepancies between

the simple and the more sophisticated measure price discrepancies were always negligible.

VII Conclusions

During the recent Sovereign debt crisis which Euro Area country a firm is located in is

an important element in determining its credit risk, hence its cost of debt capital. Firms

debt yields differ largely across countries, but are priced over the same risk-free benchmark,

i.e. the bund yield. Firms in peripheral countries have a higher sensitivity (0.36%) to their

respective home government CDS dynamics than the average Euro Area firm (0.17%). This

pattern is stable across the “Lehman period” and the Sovereign debt crisis, but the variance

explained by the Sovereign CDS is substantial (i.e. 30%) only during the Sovereign debt

28

crisis and for firms in peripheral countries. Over all these results support the view that

a firms CDS premium consists also of a “country-specific” component linked to Sovereign

credit risk. In other words, the situation for most Euro Area countries has become similar to

the case of emerging market countries where, as documented by Damodaran (2003), Durbin

and Ng (2005) and Bekaert et al. (2012) the common practice by market participants is

to add a risk premium, related to the Sovereign credit spread, to the discount rate, when

evaluating a project to account for “country risk”.

Moreover, ex-ante sensitivity of firms is positively related to explanatory power of

Sovereign CDS in the Sovereign debt crisis. Banks, Telecom and Utility firms (but more gen-

erally firms which are government related) are the most sensitive to Sovereign risk. Sovereign

and corporate credit spreads tend to co-move because of the common exposure to the eco-

nomic cycle, but this study shows that credit spreads of government related firms react to

Sovereign credit risk shocks more strongly than what would be implied by underlying eco-

nomic fundamentals, meaning there are spillover effects. The idea is that government related

firms benefit from the potential support of the government in case of debt troubles, but at

the same time as the value of these “debt guarantees” reduces the creditworthiness of these

firms will reduce as well. Also, government related firms are more likely to be hit by special

ad-hoc taxes when the government is in troubles with its public finance.

29

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31

Figure 1: Corporate and Sovereign CDS: Spain. This figure shows the time-seriesdynamics of the 5 year maturity median corporate (non-financial firm) CDS and the 5-yearmaturity Sovereign CDS for Spain. Observations are weekly.

Figure 2: Corporate CDS: Germany and Spain. This figure shows the time-seriesdynamics of the 5 year maturity median corporate (non-financial firm) CDS for Germanyand Spain. Observations are weekly.

32

Figure 3: Bank, corporate and Sovereign CDS, by country. This figure shows thetime-series dynamics of the 5 year maturity Sovereign CDS, the median bank CDS and themedian corporate (non-financial firm) CDS, for some selected “core” and “peripheral” EuroArea country. Observations are weekly.

33

Figure 4: Bank, Corporate and Sovereign CDS, by country and period. This figureshows the average 5 year-maturity bank, corporate and sovereign CDS premia by country.Chart a) shows the first period, i.e the pre-crisis period, which goes from January to July2007. Chart b) shows the second period, which goes from August 2007 to November 2009 andincludes the “Lehman shock”. Chart c) shows the third period, which goes from December2009 to November 2011 and covers the Euro Area Sovereign debt crisis.

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Figure 5: Telecom bond yield, CDS, the implied risk-free and the Euro Swap rate.This figure shows the time series of the 5 year maturity bond yield, CDS and the impliedrisk-free rate (i.e YTM-CDS) for one selected company: Telecom Italia. It shows also the 5year-maturity Euro swap rate. The sample goes from Jan 2010 to November 2011 and coversthe Euro Area Sovereign debt crisis. Observations are weekly.

Figure 6: Average implied risk-free rate from corporate debt yields. This figureshows the time series of the average implied risk-free rate (i.e YTM-CDS) for firms basedin Italy, Spain, France and Germany. It shows also the 5 year maturity German bund yieldand the 5 year maturity Euro Swap rate. The sample goes from Jan 2010 to November 2011and covers the Euro Area Sovereign debt crisis. Observations are weekly.

35

Figure 7: CDS sensitivity to the National Sovereign CDS by sector and by period..The first period goes from July 2007 to November 2009 and includes the “US Subprime crisis”and the “Lehman shock”. The second period goes from December 2009 to November 2011and covers the Euro Area Sovereign debt crisis.

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Figure 9: Government-related vs. non government-related firms and SovereignCredit risk. Chart 10 a) shows the CDS for two Italian Utilities: Enel, which is partiallyowned by the government and Edison which is not. Chart b) shows the CDS for two ItalianIndustrial firms: Finmeccanica, which is partially owned by the government and Atlantiawhich is not. Finally, chart c) shows the CDS for some Oil&Gas firms. ENI is the onlyfirms located in the periphery (Italy). The sample goes from Jan 2010 to November 2011and covers the Euro Area Sovereign debt crisis. Data are weekly.

37

Figure 10: Clustering of the filtered CDS log-changes of the 7 government-relatedfirms and tail log-changes of Sovereign CDS. The variable COUNT (blue) is equalto the number of firms for which CDS filtered log-changes are in the tail (top 20%) of thedistribution. The variable SOVEREIGN (red) is equal to one for weeks in which SovereignCDS log-changes are in the tail (top 10%) of the distribution.

38

Table I: Number of reference entities by country and by sector.

Sector / Country AT BE FI FR GR DE IE IT NL PT SP Total

Basic Materials 1 1 3 3 - 6 - - 2 - - 16

Consumer Goods - 1 - 9 - 10 - 2 4 - 1 27

Consumer Services - 1 - 8 - 9 - 1 4 - - 23

Banks 2 1 2 4 9 4 2 5 4 4 10 47

Government - - - 2 - 1 - 4 1 - 1 9

Health Care - - - 1 - 4 - - - - - 5

Industrials - - 1 13 0 6 2 3 5 2 1 33

Oil&Gas 1 - - 3 - - - 1 1 - 1 7

Technology - 1 1 2 - 2 - - 3 - - 9

Telecommunication 1 1 2 2 1 2 1 1 3 1 1 16

Utilities 1 2 1 3 1 3 0 6 4 1 5 27

Total 6 8 10 50 6 52 5 23 31 8 20 219

39

Table II: PCA on Telecom and Utilities CDS Premia. This table reports the estimatesof a Principal Component Analysis on CDS log-changes of the largest Utility and Telecomfirms across some selected “core” and “periphery” countries: Italy, Spain, Portugal, Greece,France and Germany. The focus in on two periods: pre-sovereign crisis period (Jan 2007-December 2009) and Sovereign crisis period (January 2010 to November 2011). On top,the chart reports the proportion of the variance explained by each of the three components.Then it reports the estimated eigenvectors (loadings) across all firms. On the bottom thechart reports average loadings across the two groups of countries, i.e “core” and “periphery”and across the two sectors. Observations are weekly.

Period 1 Period 2

PC 1 PC 2 PC 3 PC 1 PC 2 PC 3

Explained Variance 73.24% 6.03% 1.78% 71.30% 9.51% 3.96%

Total Explained Variance 81.05% 84.77%

Variable Eigenvectors (loadings): Eigenvectors (loadings):

ENEL - Italy - Utilities 0.24 -0.21 0.22 0.26 -0.22 -0.23GAS NATURAL - Spain - Utilities 0.24 -0.18 0.20 0.25 -0.20 -0.39IBERDDROLA - Spain - Utilities 0.26 -0.18 -0.04 0.26 -0.19 -0.38EDP - Portugal - Utilities 0.25 -0.22 -0.07 0.25 -0.31 -0.14EDF - FRANCE - Utilities 0.25 -0.23 -0.06 0.21 0.42 -0.01GDFS - France - Utilities 0.24 -0.31 -0.24 0.22 0.36 -0.02EON - Germany - Utilities 0.25 -0.20 0.06 0.25 0.24 -0.06RWE - Germany Utilties 0.24 -0.21 0.17 0.25 0.26 -0.15BAD - Germany - Utilities 0.26 -0.22 -0.03 0.24 0.32 -0.02DEUTSCHE-T - Germany -Telecom 0.25 0.29 -0.22 0.25 0.19 0.32TELECOM - France - Telecom 0.25 0.20 -0.09 0.25 0.15 0.28VIVENDI - France -Telecom 0.21 0.28 0.77 0.24 0.11 0.04VODAFON - Germany - Telecom 0.25 0.12 -0.22 0.24 0.15 0.05OTE - Greece - Telecom 0.23 0.32 -0.06 0.19 -0.25 0.62TELECOM - Portugal-Telecom 0.23 0.26 -0.22 0.24 -0.29 0.14TELEFONICA - Spain-Telecom 0.24 0.28 -0.21 0.26 -0.20 0.05TELECOM - Italiy- Telecom 0.24 0.31 0.12 0.26 -0.17 0.06

Average Eigenv.(loadings): Average Eigenv. (loadings): Telecom - "Core" 0.24 0.22 0.06 0.25 0.15 0.17Telecom - "Periphery" 0.23 0.29 -0.09 0.24 -0.23 0.22Utilitiy - "Core" 0.25 -0.23 -0.02 0.23 0.32 -0.05Utility - "Periphery" 0.25 -0.20 0.08 0.25 -0.23 -0.29

Jan 2007 -Dec 2009 Jan 2010-Nov 2011

40

Table III: Sovereign, Bank and Corporate CDS: Lead-leg relation. Estimates arebased on the following Vector Error Correction Model regressions:

∆CDSSov,t = λ1(CDSSov,t−1 − α0 − α1CDSCorp,t−1) +∑q

j=1 α1j∆CDSSov,t−j +∑qj=1 β1j∆CDSCorp,t−j + ε1t

∆CDSCorp,t = λ2(CDSSov,t−1 − α0 − α1CDSCorp,t−1) +∑q

j=1 α2j∆CDSSov,t−j +∑qj=1 β2j∆CDSCorp,t−j + ε2t

The final lines report the average coefficient and t-stat across all countries and theGranger-Gonzalo measure, which is a measure of the contribution of the two markets toprice discovery and is defined as: λ2

λ2−λ1 and is bounded between 0 and 1. Sample period1/1/2007- 11/24/2011.

Lambda 1 Lambda 2 Lambda 1 Lambda 2 Lambda 1 Lambda 2Austria (Coeff.) -0.013 0.001 0.000 0.034 -0.017 0.000 (t-stat.) -3.006 0.496 0.104 4.461 -3.658 0.006

Belgium 0.001 0.002 0.000 0.027 -0.024 0.0030.0636 2.353 0.245 3.938 -2.982 2.354

Finland -0.007 0.007 -0.006 0.037 0.000 0.000-1.721 0.605 -1.245 2.789 1.471 1.109

France 0.000 0.002 -0.001 0.040 0.000 0.0010.433 1.901 -0.202 3.601 0.068 1.839

Germany -0.003 0.014 -0.011 0.014 0.000 0.000-0.941 1.798 -2.281 1.593 1.951 1.478

Netherlands -0.007 0.010 0.002 0.035 -0.018 0.009-2.114 2.159 0.662 3.662 -2.936 2.8862

Greece 0.000 0.006 -0.018 0.002 0.000 0.005-0.133 9.481 -3.723 2.026 2.595 5.672

Ireland 0.000 0.001 -0.006 0.060 0.000 0.0000.795 2.594 -1.464 4.654 0.217 2.264

Italy 0.000 0.040 0.003 0.015 0.022 0.021-0.038 5.854 0.930 4.758 3.711 3.897

Portugal 0.007 0.010 -0.003 0.011 0.003 0.0041.758 3.939 -0.637 2.565 1.898 3.042

Spain 0.004 0.010 0.002 0.035 -0.004 0.0061.040 3.161 0.502 4.877 -1.269 2.850

Average. coeff. -0.002 0.009 -0.003 0.028 -0.003 0.004Average. t-stat. -0.351 3.122 -0.646 3.539 0.097 2.491

GG Measure 0.853 0.891 0.563

Sovereign vs. corporate Sovereing vs. Bank Bank vs. Corporate

41

Table IV: The implied risk-free rate: Error Correction Model. This Chart shows theresults of the Engle and Granger two step method. Chart a) reports the estimates of thelong run regressions of the “average implied risk-free rate” (from debt yields of firms locatedin France, Germany, Italy and Spain) on the 5 year-maturity German bund yield and the5 year-maturity Euro swap rate, with the constant and the trend. Chart b) reports theestimate of the short run regressions in changes, with 4 lags. Data are weekly observations.

Variable Coefficient Coefficient

C coeff. 193.537 193.642 t-stat. 19.960 21.591

Trend -0.417 -0.741-13.128 -25.869

Bund Yield 5Y 0.72837.913

0.81541.116

Adj R-squared 0.951 0.958ADF Res. Prob. 0.014 0.000

Variable Coefficient Coefficient

Ecm (-1) coeff. -0.260 -0.435 t-stat. -3.326 -4.881

! Bund Yield 5Y 0.98721.756

! Euro Swap Rate 5y 1.12415.057

Lag +4 Bund Yield & Euro swap rate Yes Yes

Adjusted R-squared 0.852 0.701Durbin-Watson stat. 2.015 2.019

Long-Run Regressions

Short-Run Regressions

Average Impl. Risk-free 5y

! Average Impl. Risk-Free 5y

Euro Swap Rate 5y

42

Table V: Co-movements of corporate CDS with the Sector Index and SovereignCDS. This table shows the estimates of model 6. The variables Sector Index and NationalSovereign are interacted and with a dummy which groups “peripheral” countries (Italy,Portugal, Spain and Greece). The first period goes from July 2007 to November 2009 andincludes the “US Subprime crisis” and “Lehman shock”. The second period goes fromDecember 2009 to November 2011 and covers the Euro Area Sovereign debt crisis. Whitecross-section standard errors & covariance. Observations are weekly.

D(CDS) All sample Period 1 Period 2

Constant (Coeff.) -0.001 0.000 0.001(t-stat) -5.558 0.353 0.610

D(CDS(-1)) 0.243 0.239 0.2114.087 5.883 4.200

d(CDS) Sector Index 0.467 0.492 0.35412.419 11.164 6.391

d(CDS) Sector Index*Perih -0.201 -0.190 -0.206-8.028 -6.699 -3.760

d(CDS) National Sov 0.171 0.183 0.1576.750 4.397 8.098

d(CDS) National Sov*Peiph 0.196 0.153 0.25310.361 5.865 10.106

Adj R-sq 0.240 0.239 0.256D-W Stat. 2.064 2.072 2.061

Table VI: Corporate CDS explained variance by the Sector Index and the NationalSovereign CDS. Countries are grouped into “periphery” countries (Italy, Portugal, Spainand Greece) and “core” countries (the Netherlands, France and Germany). The first periodgoes from July 2007 to Nov 2009 and includes the “US Subprime crisis” and “Lehman shock”.The second period goes from December 2009 to November 2011 and covers the Euro AreaSovereign debt crisis. White cross-section standard errors & covariance. Observations areweekly.

Countries Explanatory Variables Period 1 Period 2

Core Sector Index (Adj R-sq) 24.1% 18.4%

Sector Index+ National Sov 28.3% 24.4%ExpVar by National Sovereign 4.2% 6.0%

Perihperal Sector Index 5.7% 4.2%Sector Index+ National Sov 18.9% 34.3%

ExpVar by National Sovereign 13.2% 30.1%

43

Table VII: Excess tail co-movements: Quantile regression results. This chart showsthe estimated conditional probabilities for each of the 7 government related firms. In anunconditional setting the probability that a firm’s CDS log-change is in the top 20% percentileis by definition 20%, but when the two variables are not independent this probability isdifferent. The conditional probability is estimated through OLS regression using quintileco-exceedance indicators; co-exceedance occurs when both random variables exceed the prespecified percentile. The sample goes from Sept 2009 to November 2011 and covers the EuroArea Sovereign debt crisis. Observations are weekly.

EDP Enel Eni Finmecc. OTE Repsol Telecom PtConditional Prob. 0.342 0.427 0.492 0.356 0.291 0.513 0.385

Unconditional Prob. 0.200Average Cond. Prob. 0.401

Table VIII: Excess co-movements: Logit regression results. In this analysis, thedependent variable is an indicator variable equal to one if a firm’s CDS log-change falls inthe tail (the top 20% percentile of the distribution) and zero otherwise. The independentvariable, COUNT, is equal to the number of firms for which CDS log-changes fall in the tail,in the same week. The sample goes from Sept 2009 to November 2011 and covers the EuroArea Sovereign debt crisis. Observations are weekly.

EDP Enel Eni Finmecc. OTE Repsol Telecom PtCount (Coeff.) 1.000 0.950 1.178 0.685 0.620 1.495 0.790 (z-stat.) 4.471 4.466 4.652 3.761 3.515 4.734 4.096

Constant -3.350 -3.016 -0.371 -2.495 -2.375 -4.402 -2.690-6.072 -6.057 -6.007 -5.874 -5.786 -5.750 5.979

McFadden R-squ 0.258 0.233 0.317 0.406 0.405 0.385 0.205

Obs with Dep=0 93Obs with Dep=1 24

44