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Systemic risk and financial market contagion: Banks and sovereign credit
markets in Eurozone.
Theodoros Bratis
Department of Business Administration, Athens University of Economics and
Business, 76 Patission Street, Athens 10434, Greece,
Email: [email protected]
Nikiforos T. Laopodis
ALBA Graduate Business School at the American College of Greece
6-8 Xenias Street, Athens 11527, Greece, email: [email protected]
Georgios P. Kouretas*
IPAG Lab, IPAG Business School,
184 Boulevard Saint-Germain, FR-75006, Paris, France
and
Department of Business Administration, Athens University of Economics and
Business, 76 Patission Street, Athens 10434, Greece, email: [email protected]
(corresponding author)
This version March 3, 2015
Abstract
The global financial and the European debt crises categorized as Minsky’s
moments present the physical laboratory for studying contagion cross country and
cross market. Our research based on the twin sovereign-banking crisis evolution of
the euro debt crisis era, focuses on addressing the co-movement of credit risk
measured by Credit Default Swap (CDS) spreads in both banking and sovereign
sectors within EMU in conjunction with the UK/US. We evaluate and compare
contagion/interdependence cross-country and cross-market. Our results err on the side
of interdependence within EMU as expected; contagion has been found for limited
cases.
Keywords: credit default swap spreads, financial crises, systemic risk.
JEL classification: G01, G15.
*The paper has benefited from helpful comments and discussions by seminar participants at Athens
University of Economics and Business and University of Piraeus. Kouretas acknowledges financial support
from a Marie Curie Transfer of Knowledge Fellowship of the European Community's Sixth Framework
Programme under contract number MTKD-CT-014288, as well as from the Research Committee of the
University of Crete under research grant #2257. We thank Jonathan Batten, Stelios Bekiros, Sris
Chatterjee, Alex Cukierman, Manthos Delis, Bill Francis, Dimitris Georgoutsos, Iftekhar Hasan and
Alexandros Kontonikas, for many helpful comments and discussions. The usual caveat applies.
1 Department of Business Administration, Athens University of Economics and Business, 76 Patission
Street , GR110434, Athens, Greece. Email address: [email protected] 2 ALBA Graduate School at the American College of Greece Email address: [email protected]
3 IPAG Lab, IPAG Business School, 184 Boulevard Saint-Germain, FR-75006, Paris, France.
* Corresponding author : Tel: 00302108203277, 00302108226203, fax: 00302108226203. Email
address: [email protected].
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1. Introduction
The financial stability questioned by economic uncertainty, financial fragilities
and growing risks in the context of the ongoing turmoil in financial and sovereign
debt markets has gained focus since the start of the recent global financial crisis
(2007-today). The growing interdependence of economies worldwide due to
globalization and especially the continuous integration of the Eurozone countries
(cross border financial activity) present the theoretical justification for cross countries
and cross markets linkages. Shocks/crises are transmitted through the real sector of
economies (trade section) or through financial channels (transactions among financial
institutions and markets). We focus on the contagion and interdependence effects
during the European debt crisis with epicenter the relation among banking and the
sovereign sector risk. In other words the central feature of our research is focused in
the mitigation of systemic risk among financial markets.
The global financial crisis starting as banking crisis (due to the subprime
mortgage loaning event sequence following overconfidence bias in both loan
providers and receivers) in the US (2007-2009) contributed to the Euro debt crisis
(2009-today) in conjunction to the absence of fiscal backstops. The collapse of
Lehman Brothers as of other investment and commercial banks and the consequent
near fail of the American International Group drew academic interest in systemic
financial intermediaries (banking institutions) and particularly those with exposure to
global investments i.e. derivatives’ trading of credit default swaps (CDS)
(Chiaramonte and Casu, 2013).
The systemic (sovereign) risk depicted by CDS prices is argued to have roots
in financial markets rather than fundamentals. Systemic sovereign credit risk may also
provide a solution to the longstanding debate about the source of systemic risk in
financial crises (Ang and Longstaff, 2011). The systemic (bank) risk also depicted by
banks’ CDS prices is associated with the idiosyncratic bank risk. The former are
extensively researched in the literature regarding CDS determinants.
Historically, impact was driven via financial institutions contagion through
balance sheet decrease appreciation in assets as a result of holding toxic financial
tools (banks with risk exposure). The banking sector of Iceland, UK and Ireland
belonged to the first wave of European countries immediately affected by the crisis
transmission. That led eventually sovereign policy makers in many European
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countries to support them in terms of bailing them out (with equity injection and the
creation of bad banks1). Nevertheless, besides bail-out policies, bail-in policies were
issued initially employed for the Cyprus case (2013)2.
Additionally in parallel endogenous countries’ reasons i.e. long term public
finance imbalances and short term irregularities (fiscal balance budget bore the cost of
bailing out) worsened during the crisis leading to the deterioration of fiscal balance
pushing economies near to insolvency and major debt episodes. Real sector’s turmoil
following the financial one combined with the already fragile profile of many
countries (1999-2007) at the time financial crisis hit Europe (Backe et al., 2010).
Especially fiscal imprudent countries belonging to the periphery of Eurozone (South-
West Eurozone Periphery) either suffered a sovereign/banking crisis and initiated
participation in international lending agreements3 through ECB, IMF, EU (Greece,
Portugal, Ireland, Spain, Cyprus) or bore the cost of policies for calibrating their
banking and sovereign sector in a manner of “moral suasion” dictated by ECB/EU
(Italy). Additionally, international bail-outs (sovereign level) didn’t reduce the
systemic risk Eurozone faces but just dispersed it throughout Eurozone states’ balance
sheets (state lenders replaced private lenders). Also bank rescue packages mitigated
risk from private to sovereign sector signaling ineffectiveness in absorbing crisis
effects.
Furthermore banks experienced a weakening of their asset position taken as
granted that country risk for countries under stress had risen (the value of the former
“risk-free” sovereign bonds decreased). In other words there has been a bilateral
causal relationship between the banking and sovereign sector (Merler and Pisany-
Ferry, 2012). The latter is proven by sovereign and banking crisis episodes presenting
an endogenous feedback loop: sovereign crisis evolving to banking crisis and the
opposite (or differently put sovereign risk impacting banking risk and vice versa)
depending on per se countries’ and banks’ idiosyncratic features4. The reason is that
1 With the exception of Iceland who let all its major ailing banks i.e. Landsbanki, Kaupthing and Glitnir
go bankrupt at the period September-October 2008, devaluated national currency and consequently imposed capital restrictions and frozen external debt payments. 2 On the debate among bail-out vs. bail-ins regimes there is a growing literature (Goodheart and
Avgouleas, 2014). 3 Known as Memorandums of Understanding (MoU) and their amendments, the Mid-Term Fiscal
Strategy (MTFS) framework. Participation in EMU eliminated currency risk but not default credit risk. 4 For a detailed presentation on the feedback loop between sovereign and banking crises see also
Correa and Sapriza (2014).
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the role of banks as liquidity providers is crucial for the economy and the business
cycle in the structured Euro Area. The sequential character of the crisis led eventually
to liquidity crunch (loan minimization), interbank market illiquidity (enhanced by the
growing interbank industry distrust) and in the end to possible bank insolvency
triggering emergency public support policies.
Eurozone members reacted to the crisis by instituting European Financial
Stability Fund (EFSF) and European Stability Mechanism (and forwarded the
European Banking Union, effective from November 4, 2014) to address the issue of
sovereign insolvency and bank stress test (for capital adequacy in parallel to Basle III)
to restore confidence in the banking sector. ECB reacted by subsequently lowering
short term interest rates and by initiating outright monetary transactions (OMT)-
conditional on government’s efforts in binding domestic measures-beyond its
traditional function of collateralized borrowing on repo agreements, in an effort to
safeguard the monetary policy transmission and support EMU financial stability and
growth.
Hence the cycle of illiquidity risk, insolvency risk and credit risk for both
banks and countries is well established under the crisis and meta-crisis financial and
debt episodes in Eurozone. The vicious cycle of “twin crises” as banking and
sovereign crises are named, also depicts the financial interlinkages between the two
sectors and gave rise to a continuous feedback. Generally, systemic risk is related to
contagion or interdependence of the bank/sovereign sector5.
Nevertheless the Euro debt crisis exposed the fragmentation of the integrated
European financial markets, thus revealing financial sectors’ susceptibility to shocks.
While the US and UK succeeded in supporting their banking sector without
establishing credit moral hazard for the sovereign sector, Euro Area (as a non
consolidated bond market area) failed to do so. Turning to the banking sector, Basel II
and III capital framework have been targeted for the zero risk assigned to banks’
sovereign exposure (Bank of International Settlements, 2013). Minsky’s cycle in
terms of assets collapsing as part of a decaying credit cycle triggered a continuous
threat to EMU survivor when the largest economies of periphery (Italy, Spain)
signaled a rescue out of EMU’s possibilities at the last quarter of 2011. The Greek
5 According to Louzis and Vouldis (2013) the systemic risk following ECB’s definition is “the risk of
an extensive financial instability that causes the disfunctioning of a financial system to the point where
economic growth and welfare suffer materially”.
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debt episode in May 2010 (1st bailout) is considered as the milestone of the Euro Area
debt crisis. More or less EMU countries are following post crisis a mixture of fiscal
austerity and financial repression leading to the creation of different groups of
accelerating debt adjustment dynamics given country specific profile.
The line of research focuses on the co-movement between sovereign and bank
credit spreads (inter-EMU, intra-country) with a view of deriving useful results
especially for the post EMU debt crisis period6. The latter is expected to produce
results on the link between fiscal and financial distress based on the “twin (sovereign
&banking) crises” within EMU’s financial turmoil period. Analysis is based on the
Credit Default Swaps (CDS) market as the key credit financial market of interest.
CDS spreads are considered pure measures of credit risk (proxy of default or
bankruptcy probability) for both banks and sovereigns. CDS are thought to be a better
index for either sovereign or bank systemic risk (presented by bond and banks
spreads). They are directly observable than all kinds of spreads e.g. corporate or bond
spreads, given the risk free entity subtracted. Furthermore referring to sovereign credit
risks CDS due to their tendency to be more liquid than bond spreads, are preferable in
terms of information dissemination (Stolbov (2014), Forte and Pena (2009), Delis and
Mylonidis (2011))7. It is acknowledged that both sovereign CDS and sovereign bonds
depict the credit market of a country8.
We focus in the sovereign level (sovereign risk) for EMU core countries
represented by weighted sovereign and bank CDS series in Germany, France and
6 The start of EMU’s debt crisis originates in November of 2009 when Greece revised its government
data. From January 2014 only two EMU countries: Greece and Cyprus have active international
agreements. Euro debt crisis seems to have no official ending and it is also argued that the ongoing
disinflation and worse deflation dynamics incubates a larger crisis for EMU. Even if the idiosyncratic
risk coming from Greece and less from Cyprus is excluded as “stand-alone” cases EMU’s real sector
figures do not directly signal recovery rather “stagdisinflation” dynamics during 2014. 7 Generally indications for illiquidity in CDS markets are due to the contract nature of the transactions,
the OTC market (lacking in transparency) in which transactions take place and the following costs. The
latter (limited liquidity in sovereign CDS market) may offer an additional reason that academic
research interest is focused on CDS markets post financial crisis (see also Longstaff, 2010). Finally in
contrast to bonds (whom result coincides with CDS in long term period) CDS in the short term period
respond quicker to changes in credit conditions (Zhang et al., 2009). 8 According to Blanco et al. (2005), bond spreads and CDS should reflect approximately the same price
of risk by arbitrage. CDS market is likely more efficient in signaling creditworthiness of borrowers in
the short time period. Kapar and Olmo (2011) state that the study of CDS spreads for measuring credit
risk can be motivated theoretically and empirically: “CDS and bond spreads converge to each other in
the long run but there are significant differences between each other in the short run”. The latter is
attributed to CDS reflecting market condition quicker than bond spreads and due to the fact that they
are produced solely to represent the risk from the reference entity (p.2). In other words they are already
in “spread” form while bond spreads need to be subtracted by a reference free risk rate.
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periphery EMU: Italy, Spain, Ireland, Greece, Portugal9. We include in our analysis
the United Kingdom (UK) and the United States of America (US) to stress out
potential contagion depicted by credit risk among countries originating from different
financial systems and produce insight on cross-system credit risk vis-à-vis
comovements where possible. The pairwise or multivariate analysis is directed to
cases where possible contagion exists. We acknowledge that Eurozone, the US and
UK have increased their real and financial flows therefore non contingent theories in
terms of Forbes and Rigobon (2002) and interdependence may be the expected
turnover. Therefore we research on contagion in terms of crisis contingent theories
and expect to find adequate evidence. Our results (except limited cases) err on the
side of interdependence for EMU’s risk linkages in sovereign, bank or cross market
risk following a battery of different modeling applied.
Hence based on our data sample (in daily frequency for the period 3/11/2008-
30/4/2014) we aim to answer the following research questions: 1) First, which is the
extent to which banks CDS are related to sovereign CDS across selected countries and
regions (within EMU and to EMU vs. UK/USA) and domestic CDS markets
(sovereign vs. bank tier)? Which is the dynamic relation of the previous in terms of
market contagion (short term effect-after a shock) and interdependence (long term
effect)? 2) Secondly, post-crisis which are the determinants of the dynamic correlation
among sovereign/bank CDS (based on crisis-contingent theories) for which contagion
is found?
Our contribution is based on selecting different dataset (weighted risk pool) of
countries in advanced markets in order to investigate the lead-lag relations of credit
risk during the EMU debt turmoil: a) within Eurozone (core Eurozone-periphery
Eurozone), b) between Eurozone and USA/UK10
. Secondly we contribute in
contagion literature by comparing time varying conditional correlation of CDS
between “tranquil” (pre crisis period) and EMU debt crisis period and within crisis era
where tranquil periods are apparent. The research design provides a comparative
9 For the debate on sovereign risk being idiosyncratic (country specific risk) or systematic (driven by
external i.e. regional/global factors) see Longstaff et al. (2007). 10
Our focus lies within the Eurozone, hence we created two weighted “risk portfolios” one belonging
to core Eurozone (Germany, France) and the second to periphery (Greece, Ireland, Italy, Portugal,
Spain). The risk portfolios of each category are further categorized in addressing sovereign and bank
risk, hence in total we created 4 portfolios added to other 4 derived by control countries (US, UK) in a
similar way. For sensitivity analysis we utilize periphery portfolios by including/excluding Greece as
the ground zero country of the Euro debt crisis.
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context within insight on various combinations by categorizing cross country/cross
market co-movements as contagion or interdependence hence contributing in parallel
to the literature about financial stability for Eurozone.
2. Literature review
As a result of the recent financial and debt crisis there has been a growing
literature which aims to identify the channels through which the banking sector has on
the sovereign sector in terms of mitigating risk and vice versa. Afonso et al. (2011)
based on Acharya et al. (2011), Candelon and Palm (2010) and Gerlach et al. (2010)
for the role of the banking system on the widening of EMU sovereign spreads, argues
that the global banking risk appears to have been transformed into sovereign risk
through 3 channels (p. 7-8): 1) Compulsory banking recapitalization by governments
(public spending worsening fiscal data). 2) Banking credit (liquidity) crunch negative
impact on investments (therefore worsening recession and further production of fiscal
imbalances in second degree) and 3) banking bailouts impact negatively on the value
of government bonds (increased premia).
In parallel the Committee on the Global Financial System (2011) recognizes 4
risk transmission channels from which sovereign risk influences the cost and the
availability funding for banks: Firstly, the asset holding channel. Banks by
withholding sovereign debt from countries under distress will suffer losses (asset
devaluation) in their balance sheet. Secondly the collateral channel. Sovereign bonds
are used as collateral by banks in order to get funding from Central Banks, hence
increase in country’s risk pose a threat towards the eligibility of the collateral and its
value (leading even to the exclusion of governments’ securities as collateral). Possible
haircuts in sovereign bonds have the same negative effect. The latter can also present
a benchmark rate of “haircut” applicable to other assets (leading to decreased
lending). Thirdly, the rating channel. Sovereign ratings act as a benchmark for the
private sector, hence any downgrade reduce domestic banks’ ratings. Furthermore the
cost of banks’ debt and equity funding is affected since their funding opportunity will
be narrowed. Fourthly, the guarantee channel. Implicit and explicit guarantees given
by governments to the banking sector for funding will be narrowed also given fiscal
tightening.
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Concerning the first part the literature is extended on both theoretical and
empirical models. Definitions regarding contagion, interdependence and spillover
effects are still a debatable issue for literature. In this point it should be made clear
that especially the notion of contagion and spillover effect are not the same even if
literature tends to use them as substitute definitions. Allen and Gale (2000) categorize
contagion as amplification of spillover effects i.e. spillover effects are considered a
(not necessary) precondition of contagion. Persistence in spillovers after a threshold
(contagion boundary) is met following a negative incident evolves to contagion. That
is the reason why in many cases spillover effects enter analysis lagged when
contemporaneous shock transmission is researched. Dornbusch et al. (2000) refer to
“fundamentals based contagion” as spillovers resulting naturally given countries’ real
and financial linkages (based on macroeconomic variables or other fundamentals).
Those spillovers constitute contagion only if manifested in crisis eras and have
adverse effect (i.e. extreme effect). Contagion is also characterized as a case where
co-movements occur under no shock episode where also fundamentals or
macroeconomic indexes are not important. Karolyi (2003) refer to “fundamentals
based contagion” where co-movements in asset prices come as a result of the
interdependence and financial linkages between market economies. He also refers to a
second category namely “irrational contagion”, where co-movements are not
associated or can be explained by fundamentals but are attributed to investors’
behavior.
Pericoli and Sbracia (2003) give the following five definitions: “1) Contagion
is a significant increase in the probability of a price in one country, conditional on a
crisis occurring in one other market, 2) contagion occurs when volatility of asset
prices spills over from the crisis country to other countries, 3) contagion occurs when
cross-country movements of asset prices cannot be explained by fundamentals, 4)
contagion is a significant increase in co-movements of prices and quantities across
markets, conditional on a crisis occurring in one market or group of markets and 5)
(shift-contagion) occurs when the transmission channel intensifies or more generally
changes after a shock in one market (p. 574-575).
Chiang et al. (2007) study contagion in Asian markets and find evidence for
contagion effect. For the latter they summarize four types of transmission channels:
the correlated information channel or the wake-up call hypothesis, the liquidity
channel, the cross-market hedging channel, and the wealth effect channel. Constâncio
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(2012) analyze contagion from market perspective focusing on policy making
implications and interventions required following the EMU debt crisis. He argues that
“contagion is one of the mechanisms by which financial instability becomes so
widespread that a crisis reaches systemic dimensions” (p.1).
We follow Longstaff (2010) on the definition of financial contagion as: “an
episode in which there is significant increase in cross market linkages after a shock
occurs in one market” (p.438). Forbes (2012) explains that the notion of contagion is
treated different and that are numerous disagreements on the notion of contagion
versus interdependence. Nevertheless the main approach is that the idea of
interdependence is proven by correlation across markets (reflecting the same exposure
to similar macro shocks) and contagion (i.e. excess correlation: over and above from
the expected effect of macroeconomic fundamentals) as the spillover effect after a
major negative incident. The latter is also debatable on the context of controlling or
not for fundamentals during the contagion transmission.
Forbes and Rigobon (2002) develop the following definition of contagion: “a
significant increase in cross market linkages after a shock to one country (or a group
of countries)” (p.2223)11
. Hence pre-crisis (under normal or “tranquil” conditions)
high co-movements in markets followed by high post crisis (crash era) co-movements
indicate the physical linkages (interdependence) between markets. Only a significant
increase in co-movement post-crisis would be empirically categorized as contagion
(rare phenomenon). Of course no co-movement prior to crisis and high co-movement
post crisis may indicate pure contagion (shift-contagion) or spillover effect12
. Podlich
and Wedow (2011) referring to credit contagion between financial systems argue that
“contagion involves an initially idiosyncratic event spreading horizontally across the
11 Forbes and Rigobon (2002) argue that there are according to the theoretical literature on how
shocks are propagated internationally there are two theories: crisis-contingent and non-crisis-contingent theories. “Crisis-contingent theories are those that explain why transmission mechanisms change during a crisis and therefore why cross-market linkages increase after a shock. Non-crisis-contingent theories assume that transmission mechanisms are the same during a crisis as during more stable periods, and therefore cross-market linkages do not increase after a shock. As a result, evidence of shift-contagion would support the group of crisis-contingent theories, while no evidence of contagion would support the group of non-crisis-contingent theories”. 12
Flavin et al. (2008) disentangle definitions as follows: “Shift contagion implies that the diffusion of
common shocks changes between low- and high-volatility regimes; thereby causing the ‘normal’
relationship between market pairs to become unstable during episodes of financial turmoil. On the
other hand, pure contagion is suffered during a crisis period when a shock that is normally idiosyncratic
spills over to another market (becoming an additional common factor). The transmission of these
idiosyncratic shocks occurs through channels that are not identifiable during normal market
conditions”(p.2). As it is obvious the classification of the phenomena is elementary for the
methodology tested and for further recommended policy design.
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financial system” (p.1). They classify contagion in two categories: 1) the first relates
to spillover effects resulting from the interdependence of markets and financial
intermediaries, 2) and the second referring to transmission of shocks which are
unrelated to observed changes in the fundamentals of an economy.
Masson (1998) also disentangles the aforementioned categories: 1) contagion
for a crisis triggering a crisis to another country for reasons unexplained by its
fundamentals (attributed to shifts in market sentiment or to the interpretation of the
information). As he argues: “Pure contagion involves changes in expectations that are
self-fulfilling with financial markets subject to multiple equilibria, for given values of
a country’s macroeconomic fundamentals” (p. 3). 2) Spillover is the case when the
second country’s macroeconomic fundamentals are affected by the eruption of the
crisis and 3) “monsoonal effects” following policy decision making applicable to a
string of countries.
Beirne and Frantzscher (2013) define contagion as “the change in the way
countries’ own fundamentals or other factors are priced during a crisis period, i.e. a
change in the reaction of financial markets either in response to observable factors,
such as changes in sovereign risk among neighboring countries, or due to
unobservables, such as herding behavior of market participants”. Hence they produce
according to their definitions three types of contagion: fundamentals or “wake up”
contagion (due to a higher sensitivity of financial markets to existing fundamentals),
regional contagion (from an intensification of spillovers of sovereign risk across
countries), and herding contagion (due to a temporary overreaction of financial
markets that is clustered across countries) (p. 21). During the EMU debt crisis they
found strong evidence for the 1st and 3
rd type and weak (spillover decreased) for the
2nd
type.
Caporin et al. (2013) refer to contagion literature in two parts. The first relates
to co-movements under extreme conditions (or tail events i.e. measurement of
transmission following a negative event) and the second compares shocks differently
under normal and crisis eras (i.e. the difference of propagation mechanism).
Kaminsky et al. (2003) discriminate between contagion and spillover effects with
respect to time: “contagion is an episode in which there are significant immediate
effects in a number of countries following an event—that is, when the consequences
are fast and furious and evolve over a matter of hours or days. This “fast and furious”
reaction is a contrast to cases in which the initial international reaction to the news is
11
muted. The latter cases do not preclude the emergence of gradual and protracted
effects that may cumulatively have major economic consequences. We refer to these
gradual cases as spillovers” (p.55). Therefore according to them spillovers evolve
during pre-crisis period as cross market shock transmissions and contagion as high
co-movements among markets in post crisis in response to a financial event shock.
There are three significant channels through contagion propagates: 1) The
correlated-information channel 2) the liquidity channel and finally 3) the risk-
premium channel (Longstaff, 2010). Of course the realization of the channel is
understood ex post. An ex ante analysis for the core channel is considered optimistic
when no benchmark crisis hypothesis has been manifested. Kaminsky et al. (2003)
categorize models explaining theoretical contagion based on a) herding (investors
copy moves of other investors or follow rumors moving as a “herd”), b) trade
linkages, c) financial linkages, d) other explanation.
Secondly, focusing on previous CDS literature characterizing sovereigns and
banks we have the following: Lahmann (2012) examine the contagion effects
between sovereign and bank CDS spreads on the inter- and intra-regional levels and
on the inter-country level (October 2005-April 2011). Gross and Kok (2013) in their
study conclude that: “i) Spill-over potential in the CDS market was particularly
pronounced in 2008 and more recently in 2011-12; ii) while in 2008 contagion
primarily went from banks to sovereigns, the direction reversed in 2011-12 in the
course of the sovereign debt crisis; iii) the index of spill-over potential suggests that
the system of banks and sovereigns has become more densely connected over time.
Should large shocks of size similar to those experienced in the early phase of the
crisis hit the system in 2011/2012, consider-ably more pronounced and more
synchronized adverse responses across banks and sovereigns would have to be
expected”. De Bruyckere et al. (2013) measuring contagion (as excess correlation)
between banks and sovereigns found evidence for the period covering both the
banking and sovereign crisis in Europe.
Alter and Beyer (2013) empirically investigate spillover effects and perform a
construction for a spillover index. Alter and Schüler (2012) find the following: 1) in
the period prior to bank bailouts the contagion disperses from bank credit spreads into
the sovereign CDS market. In the post era of bailouts, 2) a financial sector shock
affects sovereign CDS spreads more strongly in the short run and 3) that government
CDS spreads become an important determinant of banks’ CDS series. Finally the
12
interdependence of government and bank credit risk is heterogeneous across
countries, but homogeneous within the same country. Acharya et al. (2011) indicate a
bilateral feedback among sovereign and banking risk, studying recent bailouts. The
aforementioned policies weaken public finance (increased sovereign credit risk). On
the other hand the eroded value of government debt weakens the financial sector. The
latter is depicted by co-movement between the CDS spreads of sovereign countries
and banks (post bailout era). Cotter and Avino (2014) focus on the price discovery
process for bank and sovereign CDS spreads. Sovereign CDS spreads appear to lead
bank CDS during crisis periods while in parallel their findings support the hypothesis
of the interconnection of CDS markets.
In the end we draw insight from literature concerning CDS determinants in
sovereign and bank level. Concerning bank CDS determinants we refer to Annaert et
al. (2012), Samaniego-Medina et al. (2013), Chiaramonte and Casu (2012) and Di
Cesare and Cuazzarrotti (2010). In general models use a combination of market and
firm specific variables for explaining bank CDS movements or market and country
specific variables for the case of sovereign CDS movements enriched with global
indexes in both cases.
3. Methodology
3.1. Theoretical and empirical model specification for sovereign/ bank CDS
contagion.
3.1.1 VAR modeling
Firstly we are based in the VAR methodology design, for inter-temporal co-
movements within the banking or sovereign sector (banks CDS to banks CDS and
sovereign CDS to sovereign CDS, i.e. intra-sectoral relations) and the relation
between banking and sovereign sector (banks’ CDS to sovereigns’ CDS and the
opposite i.e. inter-sectoral relations). The analysis follows domestic pattern (both
CDS markets within the same country) as well as regional one (aggregated/weighted
CDS spreads for Eurozone pooled to core and periphery tier as well as in UK, US).
We are interested in bidirectional causality (or feedback) in order intuitively
to accept interdependence over lead-lag relations. Cases where no unidirectional or
bidirectional causality exists (i.e. variables are exogenous) is not expected for the
CDS market due to markets’ financial openness. The former task will be addressed in
13
a bivariate setting (if VECM is utilized under the price-discovery process) while
causality directions in a multivariate VAR setting (Granger causality test) in order to
acquire results on lead-lag relations between variables.
Generally our intuition rests in the following: changes in the existence and
direction of causality through Granger causality (VAR setting) as a tool used in cross
country/market correlations (we produce at least solid result for interdependence in
case of bidirectional causality). Since there is no financial theory in choosing the
ordering of the CDS data for the given frequency we employ the generalized impulse
response function (GIRF) as in Pesaran and Shin (1998) -which is invariant to the
ordering of variables- in the case we use least trivariate class of VAR models.
Furthermore when cointegration relation rise between variables post crisis
(hence VEC model is used) on the contrary to pre crisis period (VAR model) we have
also intuitively evidence for contagion in terms of accepting a (weak or strong) long
term association between them.
Therefore two VAR settings (pre-crisis and post-crisis) are tested with daily
frequency. The first will investigate the intra-country level and the second the inter-
Eurozone lead-lag relations between Eurozone and the rest countries (US, UK). We
employed 4 models: 1) a 4-variate based on sovereign CDS (EMU: core-periphery,
UK,US) , 2) a 4-variate based on bank CDS (EMU: core-periphery, UK, US), 3) a 4-
variate on cross sector CDS (EMU sovereign-bank CDS) and finally 4) a 8-variate
cross sector sovereign-bank CDS (EMU, UK and US: sovereign-bank CDS). We
follow initially a bivariate approach in order to map the price discovery mechanism
for domestic analysis (if possible in the presence of VECM design) and secondly a
multivariate model approach13
.
Hence in light of the previous our general VAR specification model for daily
CDS is14
: 1 1 1 1
1 1
p p
t k t k k t k t
k k
BCDS a CDS SCDS
2 2 2 2
1 1
p p
t k t k k t k t
k k
SCDS a SCDS BSCDS
13
“One of the key functions of financial markets is price discovery i.e. the efficient and timely
incorporation of the information implicit in investor trading into market prices” (Corzo et al., 2012). 14 Finally we employed VARX models with UK and US sovereign/bank CDS as exogenous variables
which didn’t present any noticeable difference to the baseline models hence we do not report them.
14
The former equations are applied for each country separately or follow pair country
analysis (BCSD are averaged at country level). 1st difference of CDS prices is used,
where BCDS= bank CDS, SCDS=sovereign CDS, 1t and 2t errors are i.i.d. shocks.
The prices (in levels) of the two markets can follow an equilibrium relationship
(cointegration) hence in case VECM is used then the benchmark will be:
1 1 1 0 1 1 1 1 1
1 1
( )p p
t t t k t k k t k t
k k
BCDS a CDS SCDS CDS SCDS
2 2 1 0 1 1 2 2 2
1 1
( )p p
t t t k t k k t k t
k k
SCDS a CDS SCDS SCDS BSCDS
Incorporating the error correction term e.g. 2 1 0 1 1( )t tCDS SCDS , where
0 1, are estimated in an auxiliary cointegration regression and the parameter vector
2 contains the error correction coefficients measuring each price’s expected speed of
adjustment. In literature there are two measures of price discovery, Gonzalo and
Granger (1995) and Hasbrouck (1995), respectively. Given the VECM model we
employ the first one i.e. 2 2 1( / )BCDSGG where it represents the percentage of
price discovery in bank CDS spreads and the remaining (1-GGBCDS) represented by
1 2 1( / )SCDSGG the percentage of price discovery coming from sovereign
CDS. The intuition is that if i.e. BCDSGG is higher from 50% that bank CDS market
leads the price-discovery process.
3.1.2 Modeling time varying CDS volatility
Firstly, we relax the constant variance hypothesis under the context of a
multivariate approach following Dynamic Conditional Correlation GARCH (DCC-
GARCH) by Engle (2002), in order to investigate the dynamic correlation of CDS
between sovereign and banking sector. The problem of heteroscedasticity is solved
explicitly since the model estimates correlation coefficients of the standardized
residuals. Secondly, we produce robust results concerning dynamic correlation of
CDS series. The mean equation for CDS changes in percentage rates (ΔCDS%) in 1st
differences, is the following:
0 1 1t t tr r
where 1 1, 2 1 1 2( ), ( , )t t t t t tr r r and 1 (0, )t t tN H . The multivariate
conditional variance is t t t tH D R D (2) where tD is the 2x2 diagonal matrix of the
15
time varying standard deviations from univariate GARCH(p,q) processes with 11,th
on the ith diagonal and tR is the 2x2 time varying correlation matrix. The latter
contains the conditional correlation of the standardized residuals, / /t t t t ite r D r h .
Hence the elements of tD are given by the GARCH process:
2
, , ,i t i i i t p i i t qh c a e bh (2) for i=1,2
Where ia represents the short run persistence of a shock to CDS change i (or the
ARCH effect) and ib the contribution of a shock to CDS change i to the long
persistence (or the GARCH effect). The stationarity hypothesis holds if the sum of a
and b is <1. The evolution of the conditional correlation in the DCC model is the
following: __
'
1 1 1 1 1(1 ) t t t tQ a b ae e Q , where t is the unconditional
correlation between CDS rates, ,( )t ij tQ q is the 4x4 time varying covariance matrix
of te and 1 and 1b are the DCC parameters. For t the conditional covariance of
, , , ,ij t ij t ii t jj th h h where , , , ,/ij t ij t ii t jj tq q q and ,ij tq is the conditional covariance
between the standardized residuals. The model’s log-likehood function to be
maximized is given by:
2 ' 2 ' 1 '
1 1
(0.5) ( log(2 ) log( ) ) ( 0.5) (log( ) )T T
t t t t t t t t t t
i i
L k D r D r R e R e e e
where the first bracketed term represents the volatility component and the second term
the correlation component.
The aforementioned bivariate DCC analysis is employed in regional
(weighted) level for sovereign market CDS (SCDS), bank market CDS (BCDS) cases
and domestic cross market case. Graphs depict full sample DCC outputs followed by
pre vs. post DCC ARCH/GARCH term coefficients of univariate GARCH series and
DCC thetas (1,2). We are interested in proving statistical significance for DCC
parameters and high values post crisis since that signs for higher comovement among
CDS markets. Furthermore we expect volatility persistence to be near 1 (sum of
ARCH and GARCH effect). Of course since the end of US financial crisis almost
16
coincides with the start of EMU debt crisis we may have significance pre and post
crisis and focus on absolute values15
.
Secondly, in order to find if there is contagion we employ a one-sided test for
mean differences among subsamples primarily for an array of combinations in the
derived dynamic correlation series. We conduct tests in order to see whether means of
conditional correlations are different between tranquil (pre EMU debt crisis) and
crisis period (specifically whether they are higher during crisis periods to constitute
contagion effect). The two sample one sided t-test is defined as 0 1 2: and
1 1 2: , for n1=1153 obs. and n2=280 obs.
The t-statistic is 1 2
2 2
1 2
1 2
x xt
s s
n n
and ,0.05 1.6449t , hence when ,0.05t t we reject Ho
and assume that we have statistical proof for contagion. In parallel we employ a two
sided test with 0 1 2: , 1 1 2: and base intuition on interdependence (reject
Ho ,0.002 ,0.002,t t t t or 3.090, 3.090t t ).
We can also derive contagion by cross-comparing results from t-test to non
parametric Mann-Whitney-Wilcoxon or Mann-Whitney U test (for median differences
or Wilcoxon signed ranks test) hence to robust our results we also employ it16
. The
null hypothesis Ho: median difference between crisis and tranquil (pre crisis) sample
is zero 1 2m m (or differently the distribution of returns of both series are equal) as
15 Additionally we expanded our model taking into account possible asymmetric effects on conditional
second moments under the context of an asymmetric DCC (1,1) GARCH (AG-DCC) model focusing on core and periphery (full sample) in sovereign level (with and without Greece) to see whether asymmetries can be taken into consideration for model specification under crisis period. DCC-GARCH allows conditional variances/covariances to react differently to positive and negative innovations. The latter as covariance specification has an appealing economic justification as to capture asymmetry in volatility. As Cappiello, Engle and Sheppard (2006) argue: “conditional estimates of the second moments of equities often exhibit the so-called “asymmetric volatility” phenomenon, where volatility increases more after a negative shock than after a positive shock of the same magnitude; in fact, evidence has been proffered that volatility may fail to increase or even fall subsequent to a positive shock for certain assets. Asymmetric effects have also been recently found in conditional correlations, although the economic reasoning behind these effects has not been widely researched.”(p.1) As seen from the graphs (between core-periphery with and without Greece, insert figures 21-22, data appendix 2 about here) there is no potential comparison, series have almost identical trend. Symmetric DCC captures volatility effects hence there is no need for addressing the issue in asymmetric DCC model (proven by the fact that the asymmetric term (theta 3) in DCC output is not significant: p-value 0.3568>0.05 and 0.246>0.05 for the case including and excluding Greece, respectively). 16
Data are regarded as random sample from their population, observations within samples are independent of one another and also two samples are independent of one another.
17
opposed to H1: 1 2m m (or differently put the mean ranks of the two group are not
equal)17
. The U test statistic is defined as follows:
1 11 1 2 1
( 1)
2
n nU n n R
and 2 2
1 1 2 2
( 1)
2
n nU n n R
Where n1, n2 are the crisis and tranquil (pre crisis) samples, R1, R2 the sum of ranks of
1st and 2
nd samples respectively. For large samples, U is approximately normally
distributed18:
( )
( )
U E UZ
Var U
with 1 2( )
2
n nE U and 1 2 1 2( 1)
( )12
n n n nVar U
To support our argument since we use GARCH family model, we employ also
the test among variances for the sub samples. The null hypothesis is that variances in
all subgroups are equal against the alternative that at least one subgroup has a
different variance. The F-statistic used is: 2 2/L SF s s
where SL is the variance of the subgroup with the largest variance and SS is the
variance of the subgroup with the smaller variance. This F-statistic has an F-
distribution with 1Ln numerator degrees of freedom and 1Sn denominator degrees
of freedom under the null hypothesis of equal variance and independent normal
samples. The test though is sensitive to non-normal distribution.
17
To be able to compare medians i.e. the difference in medians and show shift in location (medians increase i.e. distribution post crisis is at the right of that belonging to pre crisis) we include the assumption that the shapes of the distributions of returns have the same shape (including dispersion). If significance is present in favor of our case by approximation we err on the side of contagion since the median post crisis is “larger” than pre crisis. To elaborate MWW is considered the alternative to two sided t-test where both compare conditional mean/medians in absolute values therefore a positive t or MWW statistic (significant) leads to contagion effect. One sided t test leads directly to contagion, two sided test err on the side of interdependence and MWW though two sided also provide evidence for contagion. 18 Furthermore from the descriptive statistics we observe that DCC series pre vs. post crisis have
different distribution characteristics (excess skewness, excess curtosis). Therefore beyond our approximation on MWW test (test on location and shape of samples’ distributions) we may also employ the Kolmogorov-Smirnov non parametric test (K-S test on any difference in samples’ distributions which makes it more sensitive to location and shape of samples’ distributions). The K-S statistic quantifies the distance between the empirical distribution function of the sample and the cumulative distribution of the reference distribution. The null hypothesis is that the empirical (observed) unknown cumulative distribution of conditional correlations is equal to another (true) cumulative distribution (benchmark is the Gaussian) against the alternative of not being equal. MWW are similar to K-S results. Lilliefors test statistic for assessing normality is employed instead K-S, if the mean or the standard deviation of the population is not known (estimated from sample). When p-values<0.05 then the null hypothesis (data come from a normally distributed population) is rejected.
18
4. Data and preliminary empirical results
We construct a VAR model by employing daily 5 year CDS from Datastream
(Thompson Reuters, CMA), since they are the most traded ones. CDS “premium mid”
category selected depicts the mid rate average of ‘CDS premium bid’ and ‘CDS
premium offered’ and reflects the spread (expressed in basis points) between the
entity and the relevant benchmark curve. Secondly we construct multivariate GARCH
models regarding CDS returns (in terms of volatility).
The Eurozone countries under analysis are: France, Germany (core Eurozone),
Portugal, Ireland, Italy, Greece, Spain (periphery Eurozone) and US/UK. Banks per
country are selected based on their systemic feature (as national/regional systemic
financial intermediaries) with respect to data availability on CDS premium mid (we
use a representative sample of 3 per country) (insert table 1, data appendix about
here)19
. Our study for the pool of countries incorporates the comparison between the
accumulated core Eurozone and periphery Eurozone countries’ CDS for both banking
and sovereign sector. Generally we employ diversified (weighted) risk portfolios for
sovereign and bank risk (in terms of CDS) in core-periphery EMU level as well as in
US and UK (control countries) hence we cross group analyze up to 8 different
portfolios20
.
The portfolio weighted series in sovereign level follow ratios based on 2012
GDP figures (insert table 2, data appendix about here) and in bank (country/regional
level) level on 2012 total assets (insert table 3, data appendix about here). We didn’t
calculate the ratio on the average GDP or total assets for the years 2008-2014 due to
misspecifications in availability for 2008 (2 months) and 2014 (4months). At the end
19
Our criteria on selecting sovereign states rest on the concept of involving the most indicative as possible for core Eurozone (Germany, France) and peripheral Eurozone suffering the ongoing or the aftermath of the financial and debt crisis (Portugal, Italy, Ireland, Spain, Greece) turning at the end to non Eurozone members (US, UK) for robustness purposes. The idea is to incorporate all systemic important countries and its important banking foundations. In some countries those foundations do happen in parallel to be categorized as Systemically Important Financial Institutions (SIFIs). We include Global SIFIs along with financial institutions given the value of total current assets in accordance to data availability. As long it concerns European banks selected they are among those participated in EU capital exercise (i.e. banks' re-capitalization needs) by the European Banking Authority (EBA) in December 2011. Selection bias is expected given data availability for bank institutions. 20
We employ two versions for periphery CDS portfolios for both sovereign and bank risk tier; one involving Greek CDS series and the second one excludes them, given that Greece is considered the ground zero country for the Eurozone’s debt crisis. In other words we allow for a sensitivity analysis for the pool of periphery CDS portfolios. For a visual depiction of series see figures 1-5, data appendix.
19
of 2011 the EMU reached its peak (on December 2011, following the threat of the
resolution plan for Italy and Spain to EMU financial coherence), while from
September 2012 (after the agreement on Spanish bailout programme) the crisis
appears to deescalate. Hence the year 2012 is used as the transition year from relevant
turmoil period to relevant tranquil period within post crisis era.
We conduct our daily CDS analysis considering the full sample and two sub-
periods: the pre-EMU debt crisis (November 3, 2008 to November 27, 2009) and
post-EMU debt crisis (November 30, 2009 to April 30, 2014) with breakpoint the date
that the Greek Government announced the review of its public finance data
(November 27, 2009)21
. The break date between unequal sub-periods (280 obs. vs.
1153 obs.) is permissible for conclusions and comparable, since: 1) our main concern
rests on the second period while the first one is used as reference period (being as far
as possible from the peak of global financial crisis 2007-2008 less spillover noise is
expected, given that the periods of financial crisis and Eurozone debt crisis are
consequent); 2) due to increased information quality (acknowledging expected
“noise” from high data frequency); 3) proposed models are robust/functional for both
sub-periods.
The issue concerning data availability on CDS is that Credit Market Analysis
(CMA) reports CDS in USD (type CR: complete restructuring) while Thompson
Reuters in USD/EUR (type CR and MM: modified-modified restructuring). Following
Thompson Reuters CDS analysis: “full restructuring clause (CR) was the standard
contract term in the 1999 ISDA credit derivatives definitions. Under this contract
option any restructuring event qualifies as a credit event. Modified- modified
restructuring (MM) clause introduced in 2003 further modified restructuring (MR)
clause (2001) which limited the scope of opportunistic behavior of sellers in the event
of restructuring agreements that did not cause loss). MM clause was introduced in
response to the perception on the part of some market participants that MR had been
to severe in its limitation of deliverable obligations”.
21 Formal announcement concerning deficit over the 3% barrier of Maastricht Pact, was made on October 30, 2009 by the Greek government which was certified by Eurostat on the 15
th of November.
Following the announcement by Dubai World (regarding its debt problems) we set the start of the euro debt crisis post November 27, 2011. The Dubai default was linked to a reassessment of sovereign debts worldwide (Grammatikos and Vermeulen, 2011) which fueled risk aversion and turn the attention to Greek case (Dellas and Tavlas, 2012).
20
Thompson Reuters does not produce series prior to January 2008 while CMA
post September 2010. The latter for both sovereign/bank entities cannot be remedied
even with merging time series given currency denomination. In order to include a
larger sample of countries/banks it is permissible to compare among CR type and use
MM type for proxy CR type. According to Dieckmann and Plank (2012) CR type
CDs is the most usual case for Western Europe States. Daily CDS data are derived
from Datastream Thompson Reuters.
At first we produce descriptive statistics between countries’, banks’ CDS for
the whole period and subsamples (insert table 4, data appendix about here). We start
our analysis for the basic sovereign CDS (SCDS) and country averaged bank CDS
(BCDS) variables22
. Pre-crisis the highest SCDS mean value belongs to Ireland
(1.909%) followed by Greece (1.645%), Italy (1.070%), Spain (0.884%), UK
(0.855%), Portugal (0.753%), US (0.456%), France (0.4355%) and Germany
(0.386%). Averaged periphery EMU SCDS (1.064%) has in average higher value than
core EMU (0.407%). Post crisis Greece as expected has the higher value (84.903%)
followed by Portugal (5.141%), Ireland (3.400%), Spain (2.187%), Italy (2.145%),
France (0.661%), UK (0.528%), US (0.386%) and Germany (0.296%). The latter
intuitively supports the “flight to quality phenomenon” for Germany as well as the
difference inside core EMU with France. Periphery SCDS (including Greece as a
member of the periphery also considered the “outlier” by most analysts) has a higher
average value (7.163%) than core EMU (0.450%) as expected.
With respect to the BCDS pre-crisis the highest mean value comes from
Greece (3.594%) providing an intuition on an already stressed domestic banking
sector followed by Ireland (3.170%), US (2.480%), Portugal (1.519%), UK (1.480%),
Spain (1.122%), Germany (1.155%), Italy (1.094%) and France (0.865%). We would
expect BCDS to follow SCDS in ranking but the latter so the results come as nearly
unexpected. Ireland had already a banking sector which faced bankruptcy due to the
sub-prime crisis hence it is not a surprise that we get the highest value for the Irish
case. US BCDS are higher on average which is the result of the aftermath of the
financial turmoil in the US. Averaged BCDS EMU has a higher average value
(1.370%) than core BCDS (0.959%). During the post-crisis period Greece again has
22 Since 1 bps=0.01% or 0.0001 we have scaled original mid premium series/100 for calculation
purposes, hence figures from basis points reflect directly percentage points. Furthermore we acknowledge that averaging CDS may mask idiosyncratic behavior by components.
21
the highest value (13.189%, thus providing evidence for a continuous trend of
increased banking risk) followed by Ireland (9.119%), Portugal (7.340%), Italy
(2.861%), Spain (2681%), UK (1.769%), France (1.672%), US (1.567%) and
Germany (1.383%). Periphery EMU has again the higher mean value (3.838%) than
core EMU (1.565%) (generally, see also graphs 1-3, data appendix). The skewness
and kurtosis measures indicate that all series are positively skewed (above average
spread variations from one day to another) and highly leptokurtic relative to the
normal distribution. Furthermore, based on the Jarque-Bera normality test we reject
the assumption of normality. Rejection of normality can be partially attributed to
intertemporal dependencies in the moments of the series.
We then examine the stochastic properties of the variables. We apply a battery
of tests as the Augmented Dickey-Fuller (ADF), Ng-Perron (2001), Elliot et al. (1996)
and the Kwiatkowski-Philips-Schmidt-Shin (KPSS) (following confirmatory
analysis). All variables in each sub-samples are proven I(1).
Turning to the conditional correlation model (regarding volatility) the CDS
returns are calculated by scaling series in level form (/100) and take their first
difference in percentage points (ΔCDS%) (insert figure1, data appendix 2 for all cases
under examination, about here). The simplest way to provide intuitively justification
for time varying volatility (i.e. standard deviation of variance) is to compare variance
in subsamples (descriptive statistics) which in our case is proven. If the variances are
larger in crisis period than pre crisis (with persistence) then we have solid evidence
and other modeling is applied (under GARCH framework). For the multivariate
GARCH model specification (DCC GARCH) we employ besides returns on CDS
additional variables i.e. bond spreads (among 10 year Treasury bond and Overnight
Interest Swap), as well we use the derived DCC series.
4.1 Empirical Results
4.1.1 VAR analysis
Our analysis produced interesting outputs concerning the bivariate (domestic)
and multivariate (regional) VAR setting23
. Consequently we employ cointegration
tests with the null of no cointegration against the alternative of cointegration (insight
23
All VAR models are checked 1) for stationarity in their optimal lag length structure by use of AR root graph and 2) autocorrelation (Portmanteau test). Hence we have consistency in our results.
22
on long run connections) for all country (and averaged region) pair combinations24
.
For the bivariate (country tier) case in the post-crisis period (November 30, 2009-
April 30, 2014). Based on the Johansen (1988, 1991)-Juseline (1990, 1992) test we
derived Vector Error Correction Models (VECM) only for Spain a country belonging
to the EMU periphery signing international bailout agreements as in the case of
Greece (May 2010, February 2012), Ireland (November 2010), Portugal (May 2011)
and Cyprus (March 2013)25
. The presence of long term relationship between the
sovereign and the bank credit risk sector unveils a hidden pattern of continuous
interdependence for both sectors domestically. Therefore we argue that there exists at
least strong interdependence (between sovereign-bank tiers in terms of risk) or
contagion is present given than pre crisis only VAR and not VEC model is
employed26
. The Spanish Granger-Gonzalo metric on price discovery for banks’
CDS>0.5 thus banks CDS appear to lead sovereign CDS.
For the multivariate cases we observe that only in the regional averaged cross-
sector analysis (averaged/weighted sovereign core and periphery CDS and
averaged/weighted core and periphery banks’ CDS) a VEC model is employed post
crisis. The latter shed light to the EMU debt crisis turmoil dynamics. Hence in
aggregate level there is evidence for comovement or weak contagion given that in pre
crisis only a VAR model was employed. In general the amplification of long trend post
crisis (only VAR models employed) provides justification for the integrity of (core)
Eurozone on the contrary to Spain which suffers persistence in the domestic
comovement between banking and sovereign sector credit risk.
As already mentioned we employ Granger causality tests in VAR setting
only for multivariate regional cases and not domestic bivariate ones. We report the
following cases pre vs. post crisis: 1) for the sovereign EMU/UK/US tier (insert table
24
To save time and space results on testing stationarity and cointergation are available upon request. Furthermore results with cointegrating variables are limited to our analysis since the majority of Johansen tests rejected cointegration pre and post crisis. 25
Spain though did receive an EFSF (under EU, ECB, EBA and IMF guidance) 100 bill. Euro loan support on June 2012 for its ailing banks (for recapitalization etc.) and expected excess turmoil by the forthcoming Greek elections and finally exited its programme in January 2014. 26
It is intuitively permissible to accept that contagion will be a result (post crisis after the negative effect of Greece reviewing its fiscal data) following a vector error correction model i.e. by accepting the presence of long trend between variables given pre crisis a VAR model (no long trend). The same intuition holds for positive impulse response functions (IFRs) presenting in parallel persistence (weak convergence). We acknowledge that for our arbitrary argument a number of co-integrating relations found above 1 following Johansen test, constitutes weak co-integration (hence we accept VAR model to be applied in those cases).
23
1 data appendix 1 about here): UK CDS-Core CDS; only post crisis a bidirectional
relation between them (weak); 2) for the bank EMU/UK/US tier (insert table 2 data
appendix 2 about here): Periphery banks-UK banks; unidirectional pre-crisis,
bidirectional post crisis (near robustness); 3) for the cross-section EMU (sovereign-
bank) tier (insert table 3 data appendix 1 about here): Core sovereign-Periphery
banks; bidirectional post crisis (weak); 4) for the cross-section EMU/UK/US
(sovereign-bank) tier (insert table 4 data appendix 1 about here): Core sovereign-
Periphery banks; unidirectional pre-crisis, bidirectional post crisis (near robustness).
VAR Granger causality approach delivers in general weak results for bidirectional
relations (interdependence) pre vs. post crisis.
4.2 DCC-GARCH analysis
In order to trace the correlations’ dynamic path and produce policy changes
we turn to bivariate DCC exhibits (insert figures and corresponding tables for panel I.
regional and panel II. domestic analysis, data appendix 2). Our main target is to draw
conclusions on the increase/decrease of the dynamic correlation graphs following the
start of EMU debt crisis. Overall we observe that the pairwise correlation paths seem
similar over time depicting a positive relation. The only exemptions have extreme
negative spikes attributed to news (excess noise due to daily data). We argue that the
main factors which have caused correlation coefficient to change over time are
attributed to news on the EMU debt crisis. We observe that the sum of the
coefficients of ARCH and GARCH terms in the post-crisis period are close to one,
which implies volatility persistence indeed as well that the GARCH terms are higher
than ARCH terms i.e. past variance impact more on current variance. DCC
parameters are significant post crisis and though we get a mix of results for all models
in the majority of cases absolute values are larger than pre crisis depicting thus
contagion in CDS markets (after a negative event).
With respect to regional analysis (insert figures 1-7 and corresponding tables
1-7, data appendix 2 about here) for sovereign CDS (SCDS) between core and
periphery EMU (with Greece PER or without Greece PER1) , core EMU and US and
core EMU and UK seem to follow the same pattern. Periphery EMU to UK and
periphery EMU to US seem to oscillating more. The most erratic diagram is that
between US and UK SCDS which has the most negatives spikes. The last also depicts
intuitively the rigid connection between US and UK economy.
24
Leaving the regional bank CDS analysis outside analysis (insert figure 8 and
table 8, data appendix 2 about here), since it does not provide any practical meaning
we turn to selected regional cross market analysis (among sovereign-bank risk tier).
There we see an intense positive correlation especially post EMU crisis between
sovereign core CDS (CORE) and periphery banks CDS (BANKSPER). The case
(insert figure 9 and table 9, data appendix 2 about here) appears to be unique in
comparison to the rest figures. It is a surprise to see that the sovereign core risk
correlates more intense with the periphery banking risk and not with the periphery
sovereign risk. For the robustness of our result we select to present the correlation
between (CORE) and US bank CDS (BCDSUS) as control variable. The outcome is
that the correlation (insert figure 11, table 11 data appendix 2, about here) is not that
profound as in the first case.
The figure between sovereign periphery CDS (PER) and periphery banks
(BANKSPER) depicts also a positive trend but it’s not intensive (insert figure 10,
table 10 data appendix 2 about here). We would expect the same graph as in the first
case. The explanation is that core and periphery EMU are strongly interconnected in
sovereign level (as shown above) with sovereign core EMU risk to be linked more to
periphery EMU bank risk than sovereign periphery risk itself. Hence we derive
intuition on twin crises and argue that periphery EMU banking risk is a more serious
threat to core than periphery sovereign EMU risk.
Turning to domestic cross CDS market analysis (insert figures 12-20 data
appendix 2 about here27
) we see that figures again depict a positive trend which leads
us to conclude that there is at least strong interdependence between the sovereign and
banking CDS market i.e. risks are positively correlated. Country specific reasons are
the causes of different graphs and spikes as well news on EMU mainly those referring
to MoU for countries under international agreements or future to be involved in
international lending. The common observation is that EMU graphs (figures 12-18
data appendix 2) pairwise correlation seems to increase between the end of 2009 and
the start of 2010 which appeals to logic given that economies throughout EMU were
affected according to their dynamics and due to the fact that daily data have noise.
France’s diagram (figure 13) is the most erratic with many spikes even if the mean
throughout the period seems stable. Germany’s diagram (figure 12) has the pattern
27
To save time and space we do not report the DCC-GARCH outputs. They are available upon request.
All models are robust.
25
followed by other countries; in pre crisis period it’s positive with declining trend
while post crisis it increases and drops till after the 2nd
bailout package for Greece
(February 2012), where again it increases in June 2012 (Cyprus petition for financial
aid).
For the periphery countries we observe that Italy’s diagram (figure 16) shows
a growing increase on the correlation post EMU crisis partially attributed to the fact
that Italy didn’t receive official support as other countries hence there is a continuous
feedback between sovereign and banking risk till the end of 2013. Spain’s diagram
(figure 18) has also a positive correlation. We observe though that after Spain’s exit
from MoU (January 2014) trend went up again after continuous decrease during 2013.
The latter may provide proof for the inconsistence of government policy results or
new bank peril looming after the end of international support to the bank sector of the
country. Greece’s diagram (figure 14) has been calculated in the same period as the
other countries only for presentation reasons since after February 23, 2012 returns on
sovereign CDS are zero, hence results of DCC analysis till the end of sample is
considered as outlier (data loss). It is the only case though where pre crisis delivered
negative correlation even for a small period of time depicting that at least one sector’s
risk was dropping following an increase in the other. We assume that the increased
risk sector was the banking one (in the aftermath of global financial crisis) and not the
sovereign one. Ireland’s diagram (figure 15) on the contrary to the rest countries
presents a steady decrease in correlation for the period after its MoU in November
2011 a sign that the Irish crisis is purely idiosyncratic with progressive improvement
from the financial crisis. Portugal (figure 17) follows a similar to the German pattern.
Correlation seems to increase after January 2012 since markets started to discount
Portugal as the next country to enter EMU debt domino following Greece and Ireland
(as it happened in May 2012).
For the control countries we see minimum turbulence in DCC graph for US
(figure 19) a mark that US economy had recovered from the aftermath of the
subprime crisis (2007-2009). UK’s diagram (figure 20) depicts a positive correlation
trend and it is similar to the EMU graphs. The latter also derive proof given prior
results for the interconnection of EMU-UK more than EMU-US.
In light of the previous the cases of sovereign US vs. sovereign UK CDS,
sovereign core vs. periphery bank CDS and sovereign French CDS vs. French bank
CDS indicates high volatility throughout the entire period. Furthermore from DCC
26
outputs it seems graphically that we have contagion. To accept our graphical findings
we need to employ parametric t test on the difference between means of subsamples
for selected DCC regional return series pairs given data availability for DCC models
(pre vs. post crisis since initial graphs reflect all sampling period. Insert table 12 data
appendix 2 about here). The focus is rather on the degree of co-movements between
two periods (interdependence under two sided test context) rather than contagion
(under one sided test context) under the prism of EMU as an integrated financial
area. We employ tests in 5% (one sided) and 1% level (two sided).
Additionally to robust our empirical findings we proceeded in pairwise
equality testing among series of sub samples (crisis vs. pre-crisis) by cross comparing
t-test (conditional means) and Mann-Whitney-Wilcoxon test (conditional medians),
on two sided test version (1% significance level). Results are presented in the
following table:
Pairs Mean
Post(pre) crisis
Median
Post (pre)
crisis
t-statistic MWW statistic Result
Sovereign core-
periphery
0.535
(0.711)
0.545
(0.710)
-25.512*** 24.301***
Sovereign core-
periphery banks
0.5124
(0.5129)
0.518
(0.510)
-0.067*** 0.699***
Sovereign core-
periphery1
0.573
(0.704)
0.603
(0.704)
-16.665*** 18.629***
Sovereign periphery-
periphery banks
0.698
(0.628)
0.723
(0.649)
10.250*** 11.895*** contagion
Sovereign core-US
banks
0.361
(0.265)
0.357
(0.264)
23.870*** 23.060*** contagion
Sovereign periphery-
US
0.348
(0.505)
0.347
(0.502)
-24.497*** 20.095***
Sovereign periphery-
UK
0.407
(0.754)
0.402
(0.755)
-34.264*** 25.925***
Core bank-periphery
bank
0.858
(0.792)
0.863
(0.794)
25.253*** 21.469*** contagion
Sovereign core-UK 0.413
(0.645)
0.427
(0.651)
-22.563*** 22.460***
Sovereign US-UK 0.393
(0.533)
0.392
(0.529)
-23.985*** 22.716***
*** p-value 1% significance level for two sided tests
Intuitively we would expect in terms of contagion for mean values of
correlation to increase post crisis. The same stands for median values but the safe
criterion is the mean over median test (the MWW statistic is positive for all cases p-
27
value=0.000<0.05). For majority of cases discussed we find evidence for
interdependence and contagion in limited cases.
Furthermore we also report the following results from the variance ratio (F)
test; for averaged series; banks core-banks periphery (2.195***), sovereign core-bank
US (7.145***), sovereign core-periphery (348.230), sovereign core-periphery1
(300.592), sovereign core- banks periphery (1.443***), sovereign core-UK
(12.720***), sovereign periphery- banks periphery (1.655***), sovereign periphery-
UK (191.020***), sovereign periphery-US (1.217***), sovereign UK-US
(12.395***). All results are significant at 1% significance level (p-value=0.000)
therefore we reject the null (equality of variances) for all cases, therefore the
difference in volatility pre vs. post crisis is justified.
We observe that only for 3 cases we get results towards the same direction i.e.
positive sign of t or MWW statistic and significance (p-value for all cases <0.05
hence Ho: equality in means or medians between sub samples is rejected for both
tests). Therefore from the rejection of the null hypothesis we base interdependence
and from the conjunction with the positive sign of the statistics we have evidence for
increased conditional mean and median respectively. The latter constitutes contagion
according to Forbes and Rigobon (2002).
Generally with the t test (one sided vs. two sided) we deliver results for
independence except two EMU cases of contagion the latter in combination with
former methodologies reveals 1) that interdependence is present in the majority of the
cases examined (as in literature) 2) (pure) contagion is difficult to be proven even in
terms of testing correlation coefficients from a DCC-GARCH model (even if it
remedies simple correlations methodology due to bias linked with the presence of
heteroskedasticity, endogeneity, and omitted variables).
We repeat the rationale this time within each country in order to investigate
more precisely the difference among core and periphery countries for (national)
conditional correlation mean and median. The following table depicts the cross results
also from MWW test, as before:
Country Mean
Post(pre) crisis
Median
Post (pre)
crisis
t-statistic MWW statistic Result
Germany 0.414
(0.473)
0.432
(0.473)
-7.625*** 7.879***
France 0.522 0.521 6.149*** 7.556*** contagion
28
(0.484) (0.478)
Greece* 0.348
(0.086)
0.331
(0.086)
33.309*** 23.322*** contagion
Ireland 0.206
(0.258)
0.180
(0.257)
-6.855*** 12.171***
Italy 0.705
(0.588)
0.713
(0.610)
31.253*** 22.949*** contagion
Portugal 0.476
(0.440)
0.514
(0.440)
2.966*** 6.675*** contagion
Spain 0.701
(0.569)
0.705
(0.569)
31.259*** 23.761*** contagion
UK 0.398
(0.542)
0.402
(0.543)
-18.519*** 19.0128**
US 0.207
(0.156)
0.207
(0.167)
23.883*** 19.730*** contagion
*post crisis sample 30/11/2009-27/2/2012
We also derive for country level the F ratio test statistic: Germany (7.84***),
France (1.904***), Greece (13.583***), Ireland (6.465***), Italy (1.050***),
Portugal (20.964***), Spain (2.116***), UK (3.921***). All results are again
significant at 1% significance level (p-value=0.000) therefore we reject the null
(equality of variances) for all cases, therefore the difference in volatility pre vs. post
crisis is justified.
Generally we derive contagion effect in all periphery EMU countries except
Ireland which has entered the financial crisis one year earlier (September 2008) so it
doesn’t come as a surprise. On the contrary coming as a surprise for a core EMU
country we reconfirm suspicion over France case (from the erratic graphical DCC
output) were the second economy of Eurozone seems to suffer contagion in terms of
increasing correlation among sovereign and banking risk post vs. pre EMU debt crisis.
The second surprise is that data question US’s recovery even though DCC graph
seems rather stable (series appear mean reverting) except two extreme spikes post
crisis. Besides that we are more interested in EMU cases, in this case we err on the
side of not contagion due to profound outliers affecting mean values.
4.4 Dererminants of CDS risk
We follow Alexander and Kaeck (2008) in order to design a causality
relationship (under OLS methodology) for the determinants of CDS changes in
EMU’s sovereign sector. Especially we take (post crisis) the dynamic conditional
correlation series for the two EMU regional cases we found contagion: sovereign
periphery-periphery banks; and core banks-periphery banks as the dependent variable
29
(considered a risk factor since DCC coefficients were derived by CDS return
volatilities or else standard deviations) and try to employ structural modeling for basic
economic fundamentals.
Concerning determinants for the dependent we assume those over illiquidity,
insolvency and credit risk through which interdependence/contagion propagates
and intuitively employ a variety of sovereign, banking and market indicators. CDS
risk should reflect information by fundamentals’ values therefore we examine in
parallel CDS market efficiency. The last two pairwise relations have been proven to
depict contagion hence we’ll use in accordance to Forbes and Rigobon (2002)
variables to proxy crisis contingent theories in terms of endogenous liquidity shocks,
political contagion and random global monetary shocks enriched by other critical
indexes.
Hence, we expect that both aforementioned cases will be explained by the
sovereign bond spreads of periphery EMU (instead the dsitraxx5y is a proxy instead
of a benchmark bond spread). Bonds spreads are taken in the basis of a policy rate or
monetary rate given that German bond rates used as risk free assets are risk
underestimated (flight to quality phenomenon) or in levels of bond yield.
Secondly following Gündüz and Kaya (2013) we use stock market (log) return
as indicator of country’s economic health and in parallel as domestic market
expectation index also applicable in Euro level (Eurostoxx 50). We also follow Corzo
et al. (2012) and Coronado et al. (2011) in using equity market as proxy for a
country’s “equity” (instead of employing traditional measures as GDP not applicable
for higher than monthly frequency i.e. daily).
A volatility indicator (as fear index) is also incorporated to cope with
investor’s sentiment. The latter can be represented by VSTOXX (as fear indicator
equivalent of VIX in US)28
. Relative volatility is used as an additional measure or
volatility risk premium “VRP” ((log)VSTOXX-realised volatility of
EUROSTOXX50)29
.
28
Additionally in order to see what is the effect of risk factors in the dependent variable or else how the correlation among countries/banks is impacted by classic risk measures we can also produce the st.dev. of GARCH model series (realized volatility). 29
“VRP is on average negative-expected volatility is higher than historical realized volatility, and since volatility is persistent, expected volatility is also generally higher than future realized volatility. In other words, the volatility risk premium represents compensation for providing volatility insurance”. (Della Corte et al. (2013). In our case we calculate the difference among the (log) implied volatility index (VSTOXX) and the realized volatility on the EUROSTOXX50.
30
Considering the bank proxy indicator (since we cannot average betas for
banking industry) we follow an indirect route by employing principal component
analysis for Euro Overnight Interest Swaps term structure or differently a proxy to
short term interbank risk30
. The disadvantage of the modeling approach is that it
requires a strong theoretical framework for the determinants and their form on
dynamic correlation series. We follow Fillipovic and Trolle (2013) and Morana
(2013) and Dubecq et al. (2014) to establish our argument on the interbank risk
variable who study OIS spreads in the monetary market (maturity) context31
. We
acquire the 1st and 2
nd factor and use them as independent variables in a new
regression for dynamic correlation series post crisis32
. Analogous to Hull and White
(2013) we argue that the 1st principal component may be attributed to risk in overnight
interbank leading (small risk, non default component), while the 2nd
principal
component to the default risk from the counterparties engaging to the agreement
(default component)33
. Additionally for bank credit risk we employ the iTraxx senior
financial index (5year). For liquidity we employ the TED spread (3m Euribor-3m
overnight interest swap as proxy to 3month treasury bill).
We also expand model by adding dummies to capture the political contagion
of crisis contingent theories measuring the political contagion or differently the
political risk for the following legislative elections: dgr2012 (Greek (6) June 17,
2012), dsp2011 (Spanish elections November 20, 2011); dit2013 (Italian elections on
the February 24, 2013); dp2011 (Portuguese elections June 5, 2011); dir2011 (Irish
30
Factor analysis depicts the covariance between variables in terms of factors i.e. underlying random
quantities. We express the observed OIS series in terms of the following model: X F U ,
(0,1)F N (0, )U N . Where X is the vector of observed correlations among 6 maturities
(1m, 3m, 6m, 1y, 5y, 10y), μ is the vector of intercepts, A is a factor loadings matrix, F a common latent factor (or “level” factor), U is the error term and Ψ is the (diagonal) variance-covariance matrix of the error term. 31
OIS spread is the spread between Euribor minus OIS in all maturities available. The latter can be focused in monetary market 1, 3, 6, 12 months(<1y maturity which is the maximum maturity for Euribor since OIS continue up to 10years maturity since transactions are getting more common even to that length). As Morana (2013) argues: “the spread is also likely to reflect liquidity funding/hoarding risk, as well as the state of investors’ confidence. Overall, OIS spreads can be seen as indicators of banks assessment of the creditworthiness of other financial institutions and liquidity conditions, and more generally as a measure of stress conditions in the interbank market”. (p. 2-3). LIBOR and spread of LIBOR and OIS is often used to proxy risk free rates for derivatives valuation (see Hull and White (2013)). 32
Alternatively we could also employ factor model specification over sovereign CDS series instead of spreads (analogous to Friewald et al. (2014) who extract risk premia from the forward CDS curve) or just bond yield series. 33
Under a contemporaneous setting (affine term structure modeling i.e. how the term structure of OIS spread or CDS or just OIS evolve over time is beyond the present research framework analysis).
31
elections February 25, 2011) and dger2013 (German elections September 22, 2013)
in terms of an event study. Since elections create new political regimes we address the
dummy as 1 post the day of elections, 0 elsewhere (prior the election day).
For modeling setting since we use a battery of variables we have chosen as
more appropriate methodology the OLS (HAC estimator used).We employ the
benchmark model following the selection of the initial variables.
For the sovereign periphery-bank periphery CDS pair (insert table 13,
appendix 2 about here) the first model (1) delivered low R-squared (0.010) and
structural issues (DW statistic 0.098). We include a dynamic term (2) since by default
we expect a mean reverting behavior from the dependent variable (proved also by the
significance of the lag dependent term, as expected) hence we deliver a well
established model with R-squared (0.94) following robustness tests. The only
variables explaining the behavior of the dependent variable is the return on VSTOXX
(option implied volatility on equity) and the volatility risk premium (VRP, derived as
the difference of (log)VSTOXX minus the realised volatility of EUROSTOXX50). In
other words risk factors explain the dependent which is by nature a risk index. The
negative sign for the VRP can be translated under the following context: an increase
in the provision of volatility insurance reduces the correlation among (periphery)
sovereign-bank CDS.
We expand the model by including dummies for political risk (3). The used
frequency allows us to employ all of them. The model is robust (R-squared is
marginally higher). The same variables are important and once more the role of
Greece and Spain is shown as they are the only (periphery) countries where political
risk from a political regime change is significant.
Finally we follow general to specific methodology in dropping high not
significant variables pairwise having a basis the last model output. We deliver poor R-
squared and only one significant explanatory variable (bank risk index). We conclude
that the same variables continue to be significant, hence we leave the model as it is.
For the second case on core banks and periphery banks we follow the same
intuition (insert table 14, appendix 2 about here). Since the pair is in bank level we
expect specific variables to be more significant than the first one. As we see our
benchmark model (1) is not robust since R-squared is low.
We include in our model specification (2) a dynamic term since by default we
expect a mean reverting behavior from the dependent variable (proved also by the
32
significance of the lag dependent term, as expected) hence we deliver a well
established model with R-squared (0.97). The explanatory variables are adequate
significant (10% level); the second principal component on OIS spreads and the
itraxx sovereign risk proxy. The itraxx bank risk proxy is marginally significant
(0.101).
Finally we expand the model (3) by including dummies for political risk
(election regime shift). The used high frequency as the number of variables (10)
allows us to employ all of them (6, limit up to 10-1=9). We get significance for the
itraxx sovereign risk proxy, the second principal component of OIS spread, the itraxx
bank risk proxy (10% level). For the dummy variables we have a nearly suspected
result. Only the political risk from Germany as the core country and Italy, Ireland are
the ones who matter since in the last two the banking crisis adjustments was more
profound do be manipulated by political elections (shift in the political status). It is a
surprise not to see Spain also in the list but its governments maintained EU policies
signaling stability, hence no political risk is perceived from the political transition.
5. Summary and concluding remarks
In light of the previous sections and comparison among different
methodologies we find at least evidence for interdependence in sovereign, bank CDS
market (averaged intra EMU or domestic inter country), hence we are in line with
literature. Since definitions over interdependence, contagion and spillover effects are
mixed under different methodological approaches, we based our research on models
used by the majority of literature. Contagion is evident in 3 cases (sovereign
periphery-periphery CDS, core banks-periphery banks, sovereign core-US banks)
along country specific cases for France, Greece, Spain, Italy, Portugal.
Nevertheless according to Baur and Loffler (2013) Eurozone contagion
research offers mixed results. Due to the nature of CDS concerning derivative market
function further examination on CDS is needed. DCC graph outputs revealed that in
domestic level France besides periphery countries is also on the verge of potential
crisis. Incorporating structural modeling by using DCC series as the dependent
variable (in regional contagion cases) revealed only risk factors as explanatory
variables. We also delivered evidence for political risk following elections regimes
33
(Greece, Spain for the cross market contagion case and Germany, Ireland, Italy for the
bank market case).
Aggregated results in VAR setting (for all sampling periods) show that short
term linkages (in terms of Granger causality test) are more evident than long term
association (Johansen test for cointegration). Hence there is at least evidence for rigid
interdependence between averaged sovereign and banks’ sector CDS. The latter is
backed also by Granger causality bidirectional relations uncovered pre vs. post crisis.
Interdependence cases in parallel intuitively establish the feedback loop hypothesis
between sovereign and bank risk.
In our analysis we focused mainly on the relation among core and periphery
EMU and employed analysis by including or excluding Greece from the averaged
sovereign series. What was come rather as a surprise was that throughout models
there is a distinct period using periphery without Greece (as an outlier) from
September 2011-September 2012 (Greece was bailed out for the second time in
February 2012), where the series without Greece peak more than the series without
Greece. Having examined the weights based on 2012 GDP we come to the conclusion
that other more significant news (as the EMU’s “integrity-identity crisis” of
November-December 2011 and the potential brake of the union as the peak of an
“endogenous political crisis”) had impact over EMU (weights on Spain, Italy induced
larger impact as “too big to fail” countries). Hence the significance of the “ground
zero” country in EMU debt crisis as its continuous triggering on EMU risk seems to
have faded away post February 2012.
Generally we may argue that the banking crisis in EMU poses a larger threat
than sovereign one given the great risk response to turmoil on EMU’s integrity. The
vicious circle of twin crises evolves with debt burdened sovereign balance sheets and
bailed out financial institutions. The political risk, sovereign risk and banking risk are
creating self fulfilling expectations. The weights of the risks are concentrated in the
political-sovereign dipole vs. market-banking dipole. The clash of interest amid both
group of representative forces of those risks formulate the evolution of EMU.
Especially on the cross linkage between politics and markets (private sector) society’s
political institutions (governing the weights on the bargaining game among
governments and private sectors on the type of banking system) are the key analysis
factors hence overregulation versus less regulation issues are rather distractive
34
(Calomiris and Haber (2014)). Therefore the origin of the EMU crisis should be
researched in terms of political economy and not just of finance terms.
Our conclusions are useful for macroprudential regulators and bank officials
since there is information flow among CDS risk markets (sovereign vs. bank tier).
Also we derived useful policy intuition based on cross market impact; the question
that we set on the mitigation of the systematic risk between sovereign-bank sectors,
among core-periphery EMU or cross products with the inclusion of UK, US sectors
revealed namely the interlink amid EMU-UK risk.
Nevertheless the Minsky moments of the US and EMU crisis have led to
changes in structural relations, new institutions (TARP, ESM, European Banking
Union November 4, 2014) extended to global markets. Post Lehman era (post its
official bankruptcy on September 15, 2008 regarding private banking institutions) and
Greek revision of government data (post November 27, 2009) ending in the inability
of the country to borrow from international markets and the subsequent 1st bailout
agreement (May 2010, regarding states) where the triggers of a debt burdened
international architectural design. Both ended the investors’ euphoria started in 2002
with expansionary banking credit in private and sovereign sector. The greatest
negative impact was that thereafter investors were highly uncertain whether rigid
structures and pillars of the international capital flow as well regulatory mechanisms
were adequate to uphold their role when asset based crises erupt. Large economies as
US, UK, Germany seem to have equalized potential losses due to contagion or
interdependence.
35
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Data appendix
Table1. Selected Financial Intermediaries per country*
Core Eurozone
Germany Deutsche Bank AG, Commerzbank AG, LB
Badenwuerttemberg
France Credit Agricole SA, BNP Paribas, Societe Generale
Periphery Eurozone
Greece Alpha Bank AE, National Bank of Greece SA, EFG Eurobank.
Ireland The Governor and Co Bank of Ireland, Allied Irish Banks plc
sub (proxy)
Italy Banca MDP di Sienna, Unicredito Italiano SPA, Intesa
Sanpaolo SPA
Portugal Banco Espirito Santo SA, Banco Comr Portugues SA
Spain BBV Argentaria, Banco Santander, Banco Pop. Espanol
Control countries
UK HSBC, Royal Bank of Scotland, Lloyds Bank
USA Bank of America, Citigroup, Goldman Sachs
*Selection based on series availability by Datastream. Our selection depicts systemic banks
for each country (SIFI’s)
Table 2. Weights for averaged Countries’ CDS series*
Country level Series Weights
Core
Eurozone
CORECDS
Germany GECDS 0.58
France FRCDS 0.42
Periphery Eurozone PERCDS
Greece GRCDS 0.058
Ireland IRCDS 0.052(0.056)
Italy ITCDS 0.504(0.536)
Portugal PCDS 0.050(0.056)
Spain SPCDS 0.330(0.352)
Control
countries
UK UKCDS 1
USA USCDS 1
*Countries’ CDS are averaged based on their GDP (2012). The selection of the year 2012
depicts real economy performance for countries and not diluting effects on their overall
performance (crisis correction). EMU periphery for calculation purposes are grouped twice;
once including and the second excluding Greece as the ground zero of the euro debt crisis
(weights in parentheses).
Table 3. Weights for averaged Banks’ CDS series*
Bank level Weights Bank level Weights
Germany BCDSGE France BCDSFR
Commerzbank 0.213 BNP Paribas 0.381
44
Deutsche Bank 0.674 Societe Generale 0.250
LB
Badenwuettenmberg
0.113 Credit Agricole 0.369
Core eurozone BCDSCORE
Commerzbank 0.080
Deutsche Bank 0.252
LB
Badenwuettenmberg
0.042
BNP Paribas 0.239
Societe Generale 0.157
Credit Agricole 0.231
Greece BCDSGR Ireland BCDSIR
Alpha Bank 0.251 Allied Irish
Banks
0.448
EFG Eurobank 0.291 Bank of Ireland 0.552
National Bank of
Greece
0.459
Italy BCDSIT Portugal BCDSP
Banca MDP 0.120 Banco Espirito
Santo
0.485
Intesa Sanpaolo 0.371 Banco Portuguese 0.515
Unicredito 0.509
Spain BCDSSP
BBV Argentaria 0.308
Banco Santander 0.614
Banco Pop. Esp 0.077
Periphery
Eurozone
BCDSPER
Alpha Bank 0.013
EFG Eurobank 0.015
National Bank of
Greece
0.023
Allied Irish Banks 0.026
Bank of Ireland 0.033
Banca MDP 0.048
Intesa Sanpaolo 0.148
Unicredito 0.203
Banco Espirito Santo 0.018
Banco Portuguese 0.020
BBV Argentaria 0.140
Banco Santander 0.279
Banco Pop. Esp 0.035
* Banks’ CDS are averaged on their Total Assets (2012) for both country and regional level.
The selection of the year 2012 follows the insight on deducting diluting balance sheets effect
for banks (crisis correction).
Table 4. Summary statistics for Sovereign CDS (SCDS) & Bank CDS (BCDS) (full sample&
sub-samples)
45
GECDS FRCDS GRCDS IRCDS ITCDS PCDS SPCDS UKCDS USCDS CORE PER PER1
Mean 0.314 0.617 68.635 3.108 1.935 4.284 1.933 0.592 0.400 0.441 5.971 2.131
Max. 0.925 1.715 149.117 11.911 4.986 15.214 4.920 1.650 0.950 1.140 13.418 5.129
Min. 0.091 0.210 0.880 0.536 0.480 0.370 0.470 0.196 0.155 0.171 0.530 0.512
Std.Dev. 0.161 0.327 68.078 2.285 1.065 3.476 0.990 0.260 0.125 0.217 4.488 1.162
Skewness 0.988 1.147 0.254 0.916 1.022 1.009 0.683 1.129 1.278 1.009 0.154 0.885
Kurtosis 3.825 3.587 1.155 2.800 3.180 2.898 2.808 5.014 6.384 3.286 1.268 2.811
Jarque-Bera 274.303 335.288 218.591 202.973 251.7811 244.038 113.626 547.044 1074.721 248.413 184.741 189.223
Prob. 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Obs. 1433 1433 1433 1433 1433 1433 1433 1433 1433 1433 1433 1433
BCDSGE BCDSFR BCDSGR BCDSIR BCDSIT BCDSP BCDSSP BCDSUK BCDSUS BANKS CORE
BANKS PER
BANKS PER1
MMean 1.330 1.514 11.314 7.957 2.517 6.202 2.377 1.712 1.746 1.447 3.356 2.934
Max. 3.352 3.855 23.864 19.087 6.735 17.512 5.451 3.029 4.748 3.671 7.625 6.861
Min. 0.717 0.585 1.641 1.250 0.620 0.906 0.730 0.930 0.714 0.637 0.850 0.784
Std.Dev. 0.445 0.736 6.112 4.342 1.474 4.116 1.130 0.476 0.624 0.619 1.711 1.493
Skewness 1.240 1.062 0.269 0.306 0.668 0.639 0.452 0.786 1.824 1.132 0.332 0.369
Kurtosis 4.003 3.217 2.172 2.332 2.380 2.494 2.276 2.739 8.145 3.385 2.063 2.095
Jarque- Bera 427.574 272.534 58.203 49.021 129.769 112.813 80.224 151.763 2376.076 315.262 78.746 81.502
Prob. 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Obs. 1433 1433 1433 1433 1433 1433 1433 1433 1433 1433 1433 1433
Pre-crisis
GECDS FRCDS GRCDS IRCDS ITCDS PCDS SPCDS UKCDS USCDS CORE PER PER1
Mean 0.386 0.435 1.645 1.909 1.070 0.753 0.884 0.855 0.456 0.407 1.064 1.034
Max. 0.925 0.965 2.890 3.800 1.900 1.490 1.635 1.650 0.950 0.941 1.898 1.863
Min. 0.200 0.210 0.880 1.050 0.480 0.370 0.470 0.420 0.196 0.204 0.530 0.512
Std.Dev. 0.182 0.196 0.554 0.637 0.444 0.280 0.282 0.337 0.211 0.187 0.384 0.376
Skewness 1.227 0.932 0.570 1.083 0.477 0.752 0.766 0.566 0.751 1.106 0.546 0.544
Kurtosis 3.981 3.215 1.949 3.922 1.726 2.475 2.733 2.345 2.516 3.665 2.000 2.016
Jarque-
Bera 81.572 41.086 28.072 64.687 29.583 29.622 28.259 19.977 29.080 62.277 25.578 25.123
Prob. 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Obs. 280 280 280 280 280 280 280 280 280 280 280 280
BCDSGE BCDSFR BCDSGR BCDSIR BCDSIT BCDSP BCDSSP BCDSUK BCDSUS
BANKS
CORE
BANKS
PER
BANKS
PER1
Mean 1.115 0.865 3.594 3.170 1.094 1.519 1.122 1.480 2.480 0.959 1.370 1.252
Max. 1.567 1.384 6.645 6.560 2.445 2.692 2.004 2.638 4.748 1.453 2.706 2.498
Min. 0.739 0.626 1.641 1.250 0.620 0.906 0.764 0.977 1.215 0.730 0.850 0.800
Std.Dev. 0.202 0.158 1.437 1.404 0.387 0.363 0.284 0.415 0.879 0.165 0.423 0.381
Skewness 0.065 0.904 0.206 1.015 1.311 1.231 0.951 0.916 0.784 0.725 1.145 1.280
Kurtosis 2.259 3.225 1.694 3.305 4.427 4.078 3.078 2.876 2.759 2.762 3.667 3.984
Jarque-
Bera 6.594 38.742 21.852 49.250 104.074 84.3450 42.315 39.370 29.369 25.211 66.380 87.892
Prob. 0.036 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Obs. 280 280 280 280 280 280 280 280 280 280 280 280
Post-crisis
46
GECDS FRCDS GRCDS IRCDS ITCDS PCDS SPCDS UKCDS USCDS CORE PER PER1
Mean 0.296 0.661 84.903 3.400 2.145 5.141 2.187 0.528 0.386 0.450 7.163 2.398
Max. 0.792 1.715 149.117 11.911 4.986 15.214 4.920 0.949 0.650 1.140 13.418 5.129
Min. 0.091 0.254 1.521 0.536 0.720 0.611 0.667 0.196 0.155 0.171 0.798 0.758
Std.Dev. 0.150 0.337 66.373 2.441 1.066 3.352 0.931 0.189 0.088 0.223 4.210 1.131
Skewness 0.796 1.034 -0.159 0.623 0.893 0.904 0.595 0.078 -0.155 0.966 -0.247 0.766
Kurtosis 2.965 3.147 1.129 2.257 2.744 2.534 2.811 1.923 2.563 3.147 1.363 2.474
Jarque-Bera 122.113 206.571 173.019 101.169 156.450 167.673 69.912 56.870 13.792 180.582 140.399 126.255
Prob. 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.000 0.000 0.000
Obs. 1153 1153 1153 1153 1153 1153 1153 1153 1153 1153 1153 1153
BCDSGE BCDSFR BCDSGR BCDSIR BCDSIT BCDSP BCDSSP BCDSUK BCDSUS
BANKS
CORE
BANKS
PER
BANKS
PER1
Mean 1.383 1.672 13.189 9.119 2.862 7.340 2.681 1.769 1.567 1.565 3.838 3.342
Max. 3.352 3.855 23.864 19.087 6.735 17.512 5.451 3.029 2.420 3.671 7.625 6.861
Min. 0.717 0.585 2.562 1.919 0.649 1.095 0.730 0.930 0.714 0.637 0.920 0.784
Std.Dev. 0.472 0.735 5.286 4.004 1.433 3.795 1.046 0.473 0.367 0.631 1.551 1.372
Skewness 1.045 0.868 0.243 0.101 0.451 0.552 0.291 0.791 -0.146 0.908 0.165 0.182
Kurtosis 3.330 2.805 2.380 2.663 2.203 2.498 2.368 2.631 3.023 2.867 2.198 2.207
Jarque-
Bera 215.464 146.755 29.858 7.409 69.616 70.658 35.571 127.006 4.174 159.325 36.118 36.572
Prob. 0.000 0.000 0.000 0.024 0.000 0.000 0.000 0.000 0.123 0.000 0.000 0.000
Obs. 1153 1153 1153 1153 1153 1153 1153 1153 1153 1153 1153 1153
Figure 1. Sovereign CDS (levels)
0
20
40
60
80
100
120
140
160
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
GECDS_ FRCDS_ GRCDS_IRCDS_ ITCDS_ PCDS_
SPCDS_ UKCDS_ USCDS_
Figure 1.1. Sovereign CDS Eurozone, USA, UK.
0.0
0.4
0.8
1.2
1.6
2.0
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
GECDS_ FRCDS_
0
20
40
60
80
100
120
140
160
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
PCDS_ IRCDS_ ITCDS_
GRCDS_ SPCDS_
47
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
UKCDS_ USCDS_
Figure 2. Country-averaged Bank CDS (levels)
0
5
10
15
20
25
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
BCDSGE_ BCDSFR_ BCDSGR_BCDSIR_ BCDSIT_ BCDSP_
BCDSSP_ BCDSUK_ BCDSUS_
Figure 2.1 Country averaged bank CDS Eurozone, US,UK.
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
BCDSGE_ BCDSFR_
0
5
10
15
20
25
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
BCDSGR_ BCDSIR_ BCDSIT_
BCDSP_ BCDSSP_
0
1
2
3
4
5
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
BCDSUK_ BCDSUS_
48
Figure 3. Regional averaged sovereign/bank core vs. periphery CDS Eurozone
(levels)
0
2
4
6
8
10
12
14
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
CORE_ PER_
0
1
2
3
4
5
6
7
8
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
BANKSCORE_ BANKSPER_
0
2
4
6
8
10
12
14
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
PER1_(WITHOUT GR) PER_
Data appendix 1
Granger causality tests results (without control variables)
Table 1. Sovereign CDS
Pre crisis Post crisis
Causality
direction
F-test p-value F-test p-value
PER_>CORE_ 10.183** 0.037 8.153 0.5187
UKCDS_>CORE_ 7.662 0.104 19.402** 0.022
USCDS_>CORE_ 2.409 0.668 5.635 0.775
CORE_>PER_ 5.113 0.275 60.971*** 0.000
UKCDS_>PER_ 2.073 0.722 15.051* 0.089
USCDS_>PER_ 5.103 0.276 4.806 0.850
CORE_>UKCDS_ 4.738 0.315 62.763*** 0.000
PER_>UKCDS_ 12.305** 0.015 12.534 0.184
USCDS_>UKCDS_ 6.878 0.142 9.699 0.375
CORE_>USCDS_ 6.682 0.153 10.792 0.290
PER_>USCDS_ 3.833 0.429 17.364** 0.043
UKCDS_>USCDS_ 10.987** 0.026 8.690 0.466
*,**,*** significance at 10%, 5%,1% significance level
Table 2. Bank CDS
Pre crisis Post crisis
Causality direction F-test p-value F-test p-value
BANKSPER_>BANKSCORE_ 5.937 0.114 16.286*** 0.002
BCDSUK_>BANKSCORE_ 2.148 0.542 10.138** 0.038
49
BCDSUS_>BANKSCORE_ 12.481*** 0.005 25.526*** 0.000
BANKSCORE_>BANKSPER_ 13.862*** 0.003 2.241 0.691
BCDSUK_>BANKSPER_ 3.831 0.280 10.575** 0.031
BCDSUS_>BANKSPER_ 15.387*** 0.001 23.582*** 0.000
BANKSCORE_>BCDSUK_ 5.453 0.141 5.547 0.233
BANKSPER_>BCDSUK_ 11.315** 0.010 17.567*** 0.001
BCDSUS_>BCDSUK_ 13.497*** 0.003 47.041*** 0.000
BANKSCORE_>BCDSUS_ 1.109 0.774 3.496 0.478
BANKSPER_>BCDSUS_ 5.458 0.141 5.794 0.215
BCDSUK_>BCDSUS_ 1.665 0.644 2.268 0.686
*,**,*** significance at 10%, 5%,1% significance level
Table 3. Cross EMU
Pre crisis Post crisis
Causality direction F-test p-value F-test p-value
PER_>CORE_ 8.146** 0.043 0.376 0.828
BANKSCORE_>CORE_ 6.473* 0.090 1.601 0.448
BANKSPER_>CORE_ 9.808** 0.020 6.718** 0.034
CORE_>PER_ 5.596 0.133 17.280*** 0.000
BANKCORE_>PER_ 1.530 0.675 0.454 0.796
BANKSPER_>PER_ 2.411 0.491 12.255*** 0.002
CORE_>BANKCORE_ 4.892 0.179 7.654** 0.021
PER_>BANKSCORE_ 1.871 0.599 4.052 0.131
BANKSPER_>BANKSCORE_ 3.216 0.359 5.535* 0.062
CORE_>BANKSPER_ 4.527 0.209 7.801** 0.020
PER_>BANKSPER_ 1.303 0.728 0.991 0.609
BANKSCORE_>BANKSPER_ 15.713*** 0.001 4.398 0.110
*,**,*** significance at 10%, 5%,1% significance level
Table 4. All variables (cross country, cross market)
Pre-crisis Post-crisis
Causality direction F-test p-value F-test p-value
PER_>CORE_ 2.435 0.295 1.350 0.852
BANKSCORE_>CORE_ 3.335 0.188 10.468** 0.033
BANKSPER_>CORE_ 6.062** 0.048 21.179*** 0.000
UKCDS_>CORE_ 5.758* 0.056 8.469* 0.075
USCDS_>CORE_ 1.129 0.568 2.414 0.660
BCDSUK_>CORE_ 3.991 0.135 4.056 0.398
BCDSUS_>CORE_ 8.268** 0.016 9.749** 0.045
CORE_>PER_ 0.458 0.795 31.119*** 0.000
BANKCORE_>PER_ 0.705 0.702 4.430 0.350
BANKSPER_>PER_ 0.476 0.778 15.564*** 0.003
UKCDS_>PER_ 0.203 0.903 7.794* 0.099
USCDS_>PER_ 3.322 0.189 1.641 0.801
BCDSUK_>PER_ 1.166 0.558 1.811 0.770
50
BCDSUS_>PER_ 4.442 0.108 12.316** 0.015
CORE_>BANKCORE_ 5.906* 0.052 20.660*** 0.000
PER_>BANKSCORE_ 1.292 0.524 13.338*** 0.009
BANKSPER_>BANKSCORE_ 0.477 0.787 14.524*** 0.005
UKCDS_>BANKSCORE_ 3.366 0.185 7.729 0.102
USCDS_>BANKSCORE_ 2.732 0.251 1.507 0.825
BCDSUK_>BANKSCORE_ 0.159 0.923 8.218* 0.083
BCDSUS_>BANKSCORE_ 13.983 0.000 21.517*** 0.000
CORE_>BANKSPER_ 6.848** 0.032 14.052*** 0.007
PER_>BANKSPER_ 0.384 0.825 1.189 0.878
BANKSCORE_>BANKSPER_ 8.885 0.011 2.962 0.564
UKCDS_>BANKSPER_ 1.298 0.522 6.088 0.192
USCDS_>BANKSPER_ 1.341 0.511 1.956 0.743
BCDSUK_>BANKSPER_ 1.052 0.590 8.681* 0.069
BCDSUS_>BANKSPER_ 14.398*** 0.000 18.020*** 0.001
CORE_>UKCDS_ 1.360 0.505 35.896*** 0.000
PER_>UKCDS_ 11.037*** 0.004 7.044 0.133
BANKSCORE_>UKCDS_ 5.379* 0.067 2.871 0.579
BANKSPER_>UKCDS_ 1.667 0.434 10.131*** 0.038
USCDS_>UKCDS_ 4.860* 0.088 4.151 0.385
BCDSUK_>UKCDS_ 2.752 0.252 5.148 0.272
BCDSUS_>UKCDS_ 6.942** 0.031 10.594** 0.031
CORE_>USCDS_ 12.377*** 0.002 10.600** 0.031
PER_>USCDS_ 0.135 0.934 8.138* 0.086
BANKSCORE_>USCDS_ 6.348 0.041 6.720 0.151
BANKSPER_>USCDS_ 3.351 0.187 14.517*** 0.005
UKCDS_>USCDS_ 3.188 0.203 5.364 0.251
BCDSUK_>USCDS_ 1.257 0.533 5.125 0.274
BCDSUS_>USCDS_ 0.019 0.990 9.809** 0.043
CORE_>BCDSUK_ 3.073 0.215 13.645*** 0.008
PER_>BCDSUK_ 0.278 0.870 1.058 0.900
BANKSCORE_>BCDSUK_ 2.953 0.228 2.345 0.672
BANKSPER_>BCDSUK_ 2.157 0.340 19.887*** 0.000
UKCDS_>BCDSUK_ 2.319 0.313 10.218** 0.036
USCDS_>BCDSUK_ 1.153 0.561 1.255 0.868
BCDSUS_>BCDSUK_ 13.282*** 0.001 38.628*** 0.000
CORE_>BCDSUS_ 2.100 0.349 0.544 0.961
PER_>BCDSUS_ 6.491 0.038 11.635** 0.020
BANKSCORE_>BCDSUS_ 0.619 0.733 2.666 0.615
BANKSPER_>BCDSUS_ 4.571 0.101 5.451 0.244
UKCDS_>BCDSUS_ 3.704 0.156 7.837* 0.097
USCDS_>BCDSUS_ 5.095* 0.078 5.681 0.224
BCDSUK_>BCDSUS_ 2.287 0.318 1.725 0.786
51
Data Appendix 2
Figure 1. CDS Changes Return (ΔCDS%) Plots
-.3
-.2
-.1
.0
.1
.2
.3
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
RCORE_
-.4
-.3
-.2
-.1
.0
.1
.2
.3
.4
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
RPER_
-.0100
-.0075
-.0050
-.0025
.0000
.0025
.0050
.0075
.0100
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
RPER1
-.3
-.2
-.1
.0
.1
.2
.3
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
RBANKSCORE__
-.20
-.15
-.10
-.05
.00
.05
.10
.15
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
RBANKSPER_
-.4
-.3
-.2
-.1
.0
.1
.2
.3
.4
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
RGECDS_
-.3
-.2
-.1
.0
.1
.2
.3
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
RBCDSGE_
-.3
-.2
-.1
.0
.1
.2
.3
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
RFRCDS_
-.4
-.3
-.2
-.1
.0
.1
.2
.3
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
RBCDSFR_
52
-.6
-.4
-.2
.0
.2
.4
.6
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
RGRCDS_
-.3
-.2
-.1
.0
.1
.2
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
RBCDSGR_
-.3
-.2
-.1
.0
.1
.2
.3
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
RIRCDS_
-.3
-.2
-.1
.0
.1
.2
.3
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
RBCDSIR_
-.4
-.3
-.2
-.1
.0
.1
.2
.3
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
RITCDS_
-.4
-.3
-.2
-.1
.0
.1
.2
.3
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
RBCDSIT_
-.5
-.4
-.3
-.2
-.1
.0
.1
.2
.3
.4
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
RPCDS_
-.3
-.2
-.1
.0
.1
.2
.3
.4
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
RBCDSP_
-.4
-.3
-.2
-.1
.0
.1
.2
.3
.4
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
RSPCDS_
-.3
-.2
-.1
.0
.1
.2
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
RBCDSSP_
53
-.3
-.2
-.1
.0
.1
.2
.3
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
RUKCDS_
-.3
-.2
-.1
.0
.1
.2
.3
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
RBCDSUK_
-.3
-.2
-.1
.0
.1
.2
.3
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
RUSCDS_
-.4
-.3
-.2
-.1
.0
.1
.2
.3
.4
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
RBCDSUS_
All calculations for returns have been based on the change of CDS return (ΔCDS) after being
transformed in percentages points (%). Full sample diagrams comprise multiple volatility
regimes.
Bivariate Dynamic Conditional Correlations 3/11/2008-30/4/2014 and outputs (pre vs.
post crisis)
Panel I. Regional analysis (sovereign market)
.2
.3
.4
.5
.6
.7
.8
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
Figure 1. CORE vs PER
Table 1. CORE vs. PERIPHERY DCC OUTPUT
PRE-CRISIS POST-CRISIS
coefficients St. errors coefficients St. errors
ccore 2.990* 1.655 1.33E-0.5 0.957E-0.6
cper 1.546 1.076 1.74E-0.7 2.02E-0.7
αcore 0.231* 0.129 0.056* 0.012
αper 0.180* 0.083 0.093* 0.018
βcore 0.600* 0.166 0.093* 0.014
βper 0.731* 0.117 0.914* 0.025
dcccore 0.081* 0.045 0.027* 0.009
dccper 0.329 0.251 0.953* 0.017
LogLcore,per -1429.933 5261.720
54
.1
.2
.3
.4
.5
.6
.7
.8
.9
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
Figure 2. CORE vs. PER1
Table 2. CORE vs. PERIPHERY1 DCC OUTPUT
PRE-CRISIS POST-CRISIS
coefficients St. errors coefficients St. errors
ccore 1.09E-06 1.40E-06 4.82E-07 4.37E-07
cper1 7.05E-06 1.12E-05 5.69E-05 3.18E-05
αcore 0.112* 0.042 0.081* 0.018
αper1 0.126* 0.053 0.126* 0.026
βcore 0.891* 0.034 0.923* 0.015
βper1 0.884* 0.047 0.880* 0.020
dcccore -0.005 0.005 0.041* 0.041
dccper1 0.869* 0.200 0.923* 0.933
LogLcore,per1 1396.05 4574.818
-.1
.0
.1
.2
.3
.4
.5
.6
.7
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
Figure 3. CORE vs US
Table 3. CORE vs. US DCC OUTPUT
PRE-CRISIS POST-CRISIS
coefficients St. errors coefficients St. errors
ccore 1.09E-06 1.40E-06 4.82E-07 4.37E-07
cus 2.01E-06 4,17E-06 1.17E-05 4.24E-06
αcore 0.112* 0.042 0.081* 0.018
αus 0.048 0.028 0.247* 0.100
βcore 0.891* 0.034 0.923* 0.015
βus 0.946* 0.034 0.749* 0.060
dcccore 0.012* 3.73E-08 0.025* 0.011
dccus 1.015* 2.88E-08 0.941* 0.027
LogLcore,us 2793.464 6685.958
-.4
-.2
.0
.2
.4
.6
.8
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
Figure 4. CORE vs. UK
Table 4. CORE vs. UK DCC OUTPUT
PRE-CRISIS POST-CRISIS
coefficients St. errors coefficients St. errors
ccore 1.09E-06 1.40E-06 4.82E-07 4.37E-07
cuk 6.52E-06 1.22E-05 1.27E-05 5.41E-06
55
αcore 0.112* 0.042 0.081* 0.018
αuk 0.063 0.045 0.182* 0.041
βcore 0.891* 0.034 0.923* 0.015
βuk 0.930* 0.047 0.813* 0.040
dcccore -0.035 0.016 0.039* 0.010
dccuk 0.776* 0.124 0.932* 0.020
LogLcore,uk 1411.371 6180.140
-.2
.0
.2
.4
.6
.8
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
Figure 5. PER vs. US
Table 5. PER vs. US DCC OUTPUT
PRE-CRISIS POST-CRISIS
coefficients St. errors coefficients St. errors
cper 7.53E-06 1.30E-05 2.32E-05 3.30E-05
cus 2.01E-06 4.17E-06 1.17E-05 4.24E-06
αper 0.111* 0.050 0.125* 0.028
αus 0.048 0.028 0.247* 0.100
βper 0.894* 0.048 0.894* 0.024
βus 0.946* 0.034 0.749* 0.060
dccper 0.053 0.034 0.037* 0.017
dccus 0.853* 0.084 0.897* 0.056
LogLper,us 1293.276 4560.710
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
Figure 6. PER vs. UK
Table 6. PER vs. UK DCC OUTPUT
PRE-CRISIS POST-CRISIS
coefficients St. errors coefficients St. errors
cper 7.53E-06 1.30E-05 2.32E-05 3.30E-05
cuk 6.52E-06 1.22E-05 1.27E-05 5.41E-06
αper 0.111* 0.050 0.125* 0.028
αuk 0.063 0.045 0.182* 0.041
βper 0.894* 0.048 0.894* 0.024
βuk 0.930 0.047 0.813* 0.040
dccper 0.020 0.023 0.042* 0.011
dccuk 0.430 0.445 0.936* 0.018
LogLper,uk 1182.254 4047.071
56
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
Figure 7. US vs. UK
Table 7. US vs. UK DCC OUTPUT
PRE-CRISIS POST-CRISIS
coefficients St. errors coefficients St. errors
cus 2.01E-06 4.17E-06 1.17E-05 4.24E-06
cuk 6.52E-06 1.22E-05 1.27E-05 5.41E-06
αus 0.048 0.028 0.247* 0.100
αuk 0.063 0.045 0.182* 0.041
βus 0.946* 0.034 0.749* 0.060
βuk 0.930* 0.047 0.813* 0.040
dccus 0.014 0.027 0.150* 0.044
dccuk 0.856* 0.160 0.192 0.177
LogLus,uk 1331.098 6510.666
Regional analysis (bank market)
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
Figure 8. BANKSCORE vs. BANKSPER
Table 8. BANKSCORE vs. BANKSPER OUTPUT
PRE-CRISIS POST-CRISIS
coefficients St. errors coefficients St. errors
cbankscore 0.000 6.70E-05 3.08E-05 1.62E-05
cbanksper 9.51E-05 6.46E-05 8.24E-05 5.83E-05
αbankscore 0.435* 0.131 0.149* 0.033
αbanksper 0.413* 0.123 0.116* 0.028
βbankscore 0.553* 0.110 0.857* 0.027
βbanksper 0.623* 0.072 0.885* 0.030
dccbankscore 0.082* 0.065 0.015* 0.003
dccbanksper 0.054* 0.281 0.979* 0.005
LogLbankscore,banksper 1198.212 3760.190
Regional analysis (cross-market)
.0
.1
.2
.3
.4
.5
.6
.7
.8
.9
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
Figure 9. CORE vs. BANKSPER
57
Table 9. CORE vs. BANKSPER DCC OUTPUT
PRE-CRISIS POST-CRISIS
coefficients St. errors coefficients St. errors
ccore 1.09E-06 1.40E-06 4.82E-07 4.37E-07
cbanksper 9.51E-05 6.46E-05 8.24E-05 5.83E-05
αcore 0.112* 0.042 0.081* 0.018
αbanksper 0.413* 0.123 0.116* 0.028
βcore 0.891* 0.034 0.923* 0.015
βbanksper 0.623* 0.072 0.885* 0.030
dcccore 0.026 0.016 0.041* 0.011
dccbanksper 0.960* 0.032 0.925* 0.027
LogLcore,banksper 1325.180 4509.036
.2
.3
.4
.5
.6
.7
.8
.9
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
Figure 10. PER vs. BANKSPER
Table 10. PER vs. BANKSPER DCC OUTPUT
PRE-CRISIS POST-CRISIS
coefficients St. errors coefficients St. errors
cper 7.53E-06 1.30E-05 2.32E-05 3.30E-05
cbanksper 9.51E-05 6.46E-05 8.24E-05 5.83e-05
αper 0.111* 0.050 0.125* 0.028
αbanksper 0.413* 0.123 0.116* 0.028
βper 0.894* 0.048 0.894* 0.024
βbanksper 0.623* 0.072 0.885* 0.030
dccper 0.023* 0.008 0.048* 0.011
dccbanksper 0.980* 0.010 0.927* 0.017
LogLper,banksper 1080.061 2608.345
.0
.1
.2
.3
.4
.5
.6
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
Figure 11. CORE vs BCDSUS
Table 11. CORE vs. BCDSUS DCC OUTPUT
PRE-CRISIS POST-CRISIS
coefficients St. errors coefficients St. errors
ccore 1.09E-06 1.40E-06 4.82E-07 4.37E-07
cbcdsus 0.004 0.003 2.44E-05 1.85E-05
αcore 0.112* 0.042 0.081* 0.018
αbcdsus 0.418* 0.158 0.023 0.017
βcore 0.891* 0.034 0.923* 0.015
βbcdsus 0.511* 0.121 0.962* 0.027
dcccore -0.017* 0.003 0.038* 0.017
dccbcdsus 0.258 0.901 0.861* 0.050
58
LogLcore,bcdsus 940.4334 5296.622
Panel II. Domestic analysis (cross market)
.1
.2
.3
.4
.5
.6
.7
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
Figure 12. GECDS vs. BCDSGE
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
Figure 13. FRCDS vs. BCDSFR
-.3
-.2
-.1
.0
.1
.2
.3
.4
.5
.6
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
Figure 14. GRCDS vs. BCDSGR
.05
.10
.15
.20
.25
.30
.35
.40
.45
.50
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
Figure 15. IRCDS vs. BCDSIR
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
Figure 16. ITCDS vs. BCDSIT
59
.0
.1
.2
.3
.4
.5
.6
.7
.8
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
Figure 17. PCDS vs. BCDSP
.3
.4
.5
.6
.7
.8
.9
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
Figure 18. SPCDS vs. BCDSSP
.00
.05
.10
.15
.20
.25
.30
.35
.40
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
Figure 19. USCDS vs BCDSUS
-.1
.0
.1
.2
.3
.4
.5
.6
.7
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
Figure 20. UKCDS vs BCDSUK
Figure 21. Asymmetric vs. Symmetric core-periphery DCC series Core-Periphery
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
SYMMETRIC DCC ASYMMETRIC DCC
Figure 22. Asymmetric vs. Symmetric Core –Periphery1 DCC series
.1
.2
.3
.4
.5
.6
.7
.8
.9
IV I II III IV I II III IV I II III IV I II III IV I II III IV I II
2009 2010 2011 2012 2013 2014
SYMMETRIC DCC ASYMMETRIC DCC
60
Table 12.Descriptive Statistics for DCC series
RHO_
RCORERPER1 RHO_
RCORERPER2
RHO_ RPERRBANKSPER
1 RHO_
RPERRBANKSPER2 RHO_
RCORERBCDSUS1 RHO_
RCORERBCDSUS2
Mean 0.711136 0.535028 0.628926 0.698754 0.265521 0.361334
Max. 0.732122 0.768541 0.732630 0.887186 0.558267 0.700700
Min. 0.692145 0.134194 0.358918 0.266407 0.107879 0.187347
Std. Dev. 0.006151 0.114780 0.082591 0.106278 0.024660 0.065921
Skewness 0.303040 -0.461836 -0.967806 -1.120.824 5.593.043 1.027.550
Kurtosis 4.300.277 2.888.923 3.609.385 4.257.302 8.118.071 6.042.701
Jarque- Bera 2.392.487 4.158.052 4.787.111 3.173.535 72509.22 6.476.723
Prob. 0.000006 0.000000 0.000000 0.000000 0.000000 0.000000
Obs. 279 1153 279 1153 279 1153
RHO_
RPERRUSCDS1 RHO_
RPERRUSCDS2
RHO_ RUSCDSRUKCDS
1 RHO_
RPERRUKCDS2 RHO_ RBANKSCORE
RPANKSPER1 RHO_RBANKSCORE
RBANKSPER2
Mean 0.505731 0.348806 0.533848 0.407509 0.792765 0.858601
Max. 0.715826 0.659273 0.619721 0.740410 0.896644 0.923894
Min. 0.267037 0.013380 0.466942 -0.008803 0.686960 0.698725
Std. Dev. 0.088571 0.097721 0.027517 0.169194 0.027887 0.041323
Skewness -0.018982 -0.188269 0.644154 0.001589 -0.535328 -1.798097
Kurtosis 2.859.981 3.678.410 3.617.437 1.897.030 5.252744 7.140317
Jarque-Bera 0.244665 2.892.212 2.372.625 5.844.524 72.32100 1444.845
Prob. 0.884854 0.000001 0.000007 0.000000 0.000000 0.000000
Obs. 279 1153 279 1153 279 1153
RHO_ RCORE
RPERBANKS1
RHO_ RCORE
RPERBANKS2 RHO_ RCORE
RPER11 RHO_ RCORE
RPER22
Mean 0.512958 0.512437 0.704396 0.573363
Max. 0.689891 0.804613 0.728926 0.830081
Min. 0.287565 0.098956 0.680629 0.124242
Std. Dev. 0.098781 0.118665 0.007570 0.131246
Skewness -0.141407 -0.662869 0.227583 -0.710581
Kurtosis 2.092.381 3.599.721 4.239.142 2.886.570
Jarque-Bera 1.050.617 1.017.162 2.025.829 9.756.317
Prob. 0.005231 0.000000 0.000040 0.000000
Obs. 279 1153 279 1152
Suffix “1”: pre crisis, “2”:post crisis
61
Table 13. Regression output (sovereign periphery-bank periphery CDS)
VARIABLE (1) (2) (3)
C 0.698 (0.003)***
0.021 (0.005)***
0.034 (0.007)***
D(PERBSPROIS) 4.646 (3.530)
1.087 (0.932)
1.188 (0.925)
D(DSITRAXX5Y) 0.001 (0.001)
-0.000 (0.000)
-0.000 (0.000)
D(DSITRAXX SENFIN5Y
-0.000 (0.000)
-4.23E-05 (0.000)
-4.31E-05 (0.000)
D(OISSP1) 0.010 (0.054)
0.000 (0.010)
0.000 (0.010)
D(OISSP2) 0.026 (0.040)
-0.000 (0.008)
4.70E-06 (0.009)
D(SPRBLIQ) 49.002 (45.816)
-0.134 (9.966)
-0.278 (9.830)
RVSTOXX 5.441 (3.838)
6.198 (2.253)***
6.077 (2.207)***
D(VRP) 5.494 (3.836)
-6.204 (2.260)***
-6.085 (2.215)***
REUROSTOXX50 0.234 (0.457)
-0.116 (0.094)
-0.122 (0.092)
PHO-RPERBANKS PER2(-1)
0.969 (0.007)***
0.951 (0.010)***
DGER2013 -0.002 (0.003)
DGR2012 0.010 (0.004)**
DIR2011 -0.000 (0.002)
DIT2013 -0.002 (0.002)
DP2011 0.003 (0.003)
DSP2011 -0.010 (0.004)
R2 0.01 0.94 0.94
Obs. 1152 1152 1152
Table 14. Regression output (core banks-periphery banks CDS)
VARIABLE (1) (2) (3)
C 0.858 (0.003)***
0.007 (0.003)**
0.017 (0.006)***
D(PERBSPROIS) -0.422 (0.857)
0.339 (0.244)
0.332 (0.247)
D(DSITRAXX5Y) -0.000 (0.000)
-0.000 (0.000)
-0.000 (0.000)
D(DSITRAXX SENFIN5Y
-1.10E-05 (9.27E-0.5)
1.25E-05 (2.98E-05)
1.07E-05 (2.94E-05)
D(OISSP1) -0.004 (0.012)
0.000 (0.003)
0.000 (0.003)
D(OISSP2) 0.012 (0.012)
-0.006 (0.003)*
-0.006 (0.003)*
D(SPRBLIQ) -4.454 (11.076)
2.159 (3.148)
1.947 (3.068)
RVSTOXX -1.192 (1.546)
0.512 (0.794)
0.505 (0.792)
D(VRP) 1.121 (1.548)
-0.513 (0.800)
-0.505 (0.798)
REUROSTOXX50 0.000 (0.137)
-0.032 (0.028)
-0.033 (0.028)
PHO-RBANKSCORE RBANKSPER2 (-1)
0.991 (0.003)***
0.980 (0.007)***
DGER2013 -0.002 (0.001)*
DGR2012 -1.29E-05 (0.000)
DIR2011 -0.001 (0.000)*
DIT2013 0.000 (0.000)**
DP2011 0.001 (0.001)
DSP2011 0.000 (0.000)
R2 0.003 0.97 0.97
Obs. 1152 1152 1152
