Speculation in European Sovereign Debt Markets

Speculation in European Sovereign Debt
Markets
Remco C.J. Zwinkelsy
Bart Frijns
September 2015
Abstract
European sovereign debt markets have been under scrutiny ever
since the sovereign debt crisis of 2009. In this paper, we decompose
bond and CDS spreads into fundamental and non-fundamental parts
by means of a heterogeneous agent model. The model contains arbitrageurs who make use of the arbitrage opportunities between bond
and CDS markets, speculators who trade on price trends, and liquidity
traders. We …nd that bond markets are driven for 80% by liquidity
trading, 13% by credit news, and only 5.4% by speculation. The CDS
market is for 49% driven by credit news, 45% liquidity trading, and
5.5% speculation. The relative importance of the di¤erent types of
agents varies over time, though.
Department of Finance, Auckland University of Technology, New Zealand.
Department of Finance VU University Amsterdam and Tinbergen Institute, The
Netherlands. E: [email protected]; T: +31 20 59 85220.
We thank Andre Lucas, Maurizio Montone, participants of the 2015 WEHIA meeting in
Sophia Antipolis and the 2014 SNDE meeting in New York.
y
1
Keywords: Credit risk, sovereign bonds, CDS, heterogeneous
agent models.
JEL Classi…cations: C32, C5, G15.
2
1
Introduction
First, we must prevent speculative actions from causing so much
uncertainty on the market that prices no longer provide accurate
information [...].
Sarkozy, Juncker, Merkel, and Papandreou in a letter to Barroso, March
10, 2010.
The European sovereign debt crisis that erupted towards the end of 2009
has introduced unprecedented dynamics into European sovereign debt markets. Driven by fears that certain countries would not be able to repay their
debts, the yields of the peripheral countries increased to record levels. At
the same time, the yields of core countries reached all-time lows. As a consequence, there have been major changes in the correlation structures between
sovereigns, changing from virtually identical movements across countries to
opposite movements across core and peripheral countries. These changes illustrate the changes in perceived credibility of the ‘no-bailout clause’of the
European Union. The question arises, though, to what extent these dynamics are driven by the underlying fundamental credit risk or non-fundamental
factors, such as speculation.
The main objective of this paper is to decompose bond and CDS spreads
into fundamental and non-fundamental parts and to examine which part
drove the surge in spreads over the recent years. In achieving this objective,
we make the following contributions to the literature. First, to quantify the
extent of speculation in debt markets, we exploit the fact that bond and CDS
markets are theoretically driven by the same stochastic risk factor (Du¢ e,
3
1999), such that arbitrage forces should eliminate di¤erences between them.
We thus model both markets in a vector error correction model, which allows us to assess the strength of the arbitrage forces and whether there are
any limits to arbitrage. Second, we simultaneously model the sovereign bond
and CDS market using an empirical heterogeneous agent model. The advantage of this approach is that we do not have to make assumptions regarding
exogenous variables proxying for sovereign credit risk and can introduce different types of agents in both markets. We therefore make an important
contribution to the literature on heterogeneous agent models.
Empirically, we look at a set of 10 European sovereigns1 from 2008 until
2015. The empirical results reveal that bond and CDS markets were driven
by a combination of fundamental credit risk, liquidity-driven trading, speculation, and arbitrage. Interestingly, changes in CDS spreads are mainly
driven by fundamental credit risk news (49%) and liquidity demand (45%).
The bond market is less informative in the sense that it is driven for 13%
by credit news and for 80% by liquidity trading. Both markets are only for
approximately 5.5% driven by speculation. Arbitrage between the CDS and
bond markets, …nally, plays a neglegible role. Interestingly, the importance
of credit risk as a driver of bond and CDS markets is more important for peripheral countries than for core countries. We …nd the opposite for liquidity
trading.
In a frictionless market, a bond and a CDS are equivalent because both
are determined by the default probability of the underlying entity. However,
1
Austria, Belgium, France, Germany, Greece, Ireland, Italy, Netherlands, Portugal, and
Spain.
4
several costs and risks prevent arbitrage strategies to be fully implementable.2
These limits to arbitrage come in three forms, as suggested by Shleifer and
Vishny (1997), and Barberis and Thaler (2003). First, implementation costs
prevent complete arbitrage due to the costs incurred by arbitrageurs.3 Second, fundamental risks are a risk to the arbitrage process because the market
might move against the arbitrageur in the short run. Finally, noise trader
risk refers to the possibility that noise traders drive prices further from fundamentals in the short run, going against the arbitrageur’s position.
For our set of sovereigns, we …nd a signi…cant implementation cost of
the arbitrage which translates into a band around the long-run equilibrium
relation of approximately 50 basis points preventing complete convergence.
Given that the CDS and bond markets share the same fundamental, there
is no fundamental risk. Noise trader risk can take di¤erent forms. In our
model, noise trading is interpreted as liquidity trading and speculation (i.e.,
buying (selling) based on recent positive (negative) returns assuming these
trends will continue). We …nd that liquidity trading is the main driver of
variation in bond spreads (80%) whereas it is the second most important
driver for the CDS market (45%).
Our empirical model is inspired by the literature on heterogeneous agent
models (HAM), due to Brock and Hommes (1997, 1998).4 In these mod2
A famous example of limits to arbitrage is given by Froot and Dabora (1999), who
study the case of Royal Dutch Shell, which is listed on both the London and Amsterdam
stock exchange. Despite that the stocks are a claim to the same underlying cash‡ows, the
two market prices di¤er up to 35%.
3
Think of transaction costs, information costs, etc.
4
The HAM literature was initially fully theoretical, yielding insights from analytical
and simulation exercises. The modeling approach has by now, though, also proven itself
empirically; see Chen et al. (2012) for an overview. One of the …rst empirical results was
found by Boswijk et al. (2007) for the S&P500. After that, authors have found empirical
5
els, agents are boundedly rational in their formation of expectations. The
bounded rationality is captured by the fact that agents use heuristics in
forming expectations. Typically, agents are assumed either to be of the ‘fundamentalist’type expecting mean reversion towards a perceived fundamental
value, or of the ‘chartist’type expecting price trends to continue. Agents are
assumed to be able to switch between the two types conditional on past performance. Our model builds on this reasoning and extends it in several ways.
Instead of fundamentalists and chartists, we introduce arbitrageurs, speculators, and liquidity traders. Arbitrageurs expect the deviation between the
bond and CDS spreads to decrease in the next period. The introduction of arbitrageurs is driven by the fact that we simultaneously study bond and CDS
markets and these markets are driven by the same stochastic risk factor. The
empirical advantage of this approach over having traditional fundamentalists
in the model is that we do not have to make any assumptions regarding the
fundamental value. Chiarella et al. (2015), for example, develop and estimate a HAM for the CDS market building on a fundamental value based on
the reduced form model of Du¢ e (1999).
By simultaneously modeling the bond and CDS markets, we connect to
the heterogeneous agent models on multiple asset markets.5 Westerho¤ and
Dieci (2006) and Chiarella et al. (2005) both develop a HAM with two risky
assets. Westerho¤ and Dieci (2006) show that two fundamentally unrelated
support in international equity markets (De Jong et al., 2009), foreign exchange markets
(De Jong et al., 2010), commodity markets (Ter Ellen and Zwinkels, 2010), option markets
(Frijns et al., 2010), and real estate markets (Kouwenberg and Zwinkels, 2014). Chiarella
et al. (2015) propose and estimate a HAM for the sovereign CDS market. We extend
their work by simultaneously analyzing the CDS and bond markets and …nd evidence of
behavioral heterogeneity for all sovereigns in our study.
5
The typical HAM only considers one risky asset.
6
markets become related in the presence of common traders; Westerho¤ (2004)
and Chiarella et al. (2005) show that the presence of common investors can
introduce excess co-movement in markets. De Jong et al. (2009) develop and
estimate a HAM for multiple equity markets during the Asian crisis, in which
agents partially form expectations based on the returns in foreign markets.
Brock et al. (2009) simultaneously model a cash and derivative market in the
presense of trader heterogeneity and show that introducing more derivatives
might actually destabilize markets. To our best knowledge, we are the …rst to
simultaneously model the bond and CDS markets and estimate the resulting
empirical model.
Technically, our model reduces to a non-linear error correction model in
state-space representation and is an extension of De Jong et al. (2009) who
model a HAM with two asset markets using a nonlinear VAR. The nonlinearity is induced by the switching mechanism that allows agents to switch
strategies over time. This allows us to study the time-varying importance of
the di¤erent components. The error-correction model captures the arbitrage
relation between the cointegrated bond and CDS spreads.6 The state-space
representation, …nally, …lters the common credit risk component out of the
bond and CDS series when estimated simultaneously. This is related to the
permanent-transitory decomposition of Gonzalo and Granger (1995).
A host of papers have studied the determinants of sovereign bond and
CDS spreads; see Augustin (2014) for an overview. The main discussion
revolves around the question to what extent spreads are driven by global
6
For Greece, we do not …nd cointegration between the bond and CDS markets, forcing
us to exclude Greece from the sample.
7
risk factors due to the high comovment between sovereign spreads. Pan and
Singleton (2008) …nd that the …rst principal component of daily sovereign
CDS spreads explains up to 96% of spread variation; in our data this is 85%.
The global factors that are found to be important are the US equity, volatility,
and bond risk premia. Our state-space representation allows us to …lter out
the credit risk component from bond and CDS spreads. The …rst principal
component explains 66% of the variation in the …ltered credit news series.
This is substantially less than the …ndings of Pan and Singleton (2008) and
the …rst principal component of the raw CDS spread changes, implying that
the non-fundamental factors have a more global character than fundamental
factors do.
Our paper relates to the literature that studies the relation between bond
and CDS markets. Blanco, Brennan, and Marsh (2005) study the relation
between corporate bonds and CDS and …nd that the CDS market contributes
more to price discovery than the bond market. Palladini and Portes (2011)
study a set of six European countries and …nd the CDS market to lead the
bond market. Our results are consistent with this …nding.
The European sovereign debt crisis has also triggered renewed interest in
the concept of contagion. Results in the literature tend to be conditional
on the exact de…nition of the term contagion. Beirne and Fratzscher (2013)
de…ne fundamental contagion as an increased sensitivity of markets to fundamentals during crisis periods, and also …nd evidence for it. This is consistent
with our results both in the time-series and the cross-section. Speci…cally,
we …nd that for all countries the credit risk factor becomes more important
in the 2009-2010 period. In addition, peripheral countries tend to be more
8
strongly driven by credit risk than core countries. As in Bai et al. (2012),
we investigate the propagation of credit and liquidity shocks across countries. Based on a VAR analysis, we …nd that credit risk spreads positively
from Germany to all other countries illustrating Germany’s leading role in
Europe, and from Spain to …ve other countries illustrating the fear of bankruptcy of a large country like Spain. Portuguese credit risk shocks spread
negatively to core countries, suggesting a ‡ight-to-quality mechanism (see
Beber et al., 2009). We …nd much less evidence for the contagion of liquidity
risk, especially for the bond market. These results are consistent with Bai
(2012), who also …nd a more important fundamental credit risk channel than
liquidity channel.
The remainder of this paper is organized as follows. Section 2 presents our
empirical strategy. Section 3 describes the data and Section 4 the estimation
results. Section 7 concludes.
2
Empirical Model
In this section, we develop the model for the dynamic interaction between
the CDS and sovereign debt market, in the presence of di¤erent types of
traders: arbitrageurs, speculators, and liquidity traders.
Sovereign credit risk is traded in two markets: the sovereign bond market
and the sovereign credit default swap (CDS) market. Under full market e¢ ciency and refraining from liquidity risks and transaction costs, bond holders
who hedge the credit risk of the bond issuer using a CDS written on the same
reference entity should earn the risk free rate, see Du¢ e (1999), Lando (2004),
9
Hull and White (2000). Hence, we would expect that the bond spread (BSt ),
which is de…ned as the di¤erence between the bond yield and the risk-free
rate, is equal to the CDS spread (CDSt ). In this situation, both the CDS
spread and the bond spread are fully driven by the underlying sovereign
credit risk
t
and arbitrage immediately eliminates any dispersion between
the two markets.
However, when there are limits to arbitrage in the sovereign credit market,
the bond spread and CDS spread can diverge. Speci…cally, we assume that
the divergence from the arbitrage spread can occur in both markets K, where
K = fBS; CDSg. The CDS - bond basis is given by xK
t , such that
0
in which
1
B BSt C
@
A=
CDSt
t
t
is the level of credit risk.
0
B
+@
xBS
t
xCDS
t
1
C
A:
(1)
The equation presented above provides a common trend representation
of the model for bond spreads and the CDS market. Following the Granger
representation theorem, such a common trend representation can be written
as an error correction model. We continue by expressing the error correction
representation, but provide an economic motivation for the dynamic behavior
in this speci…cation based on the heterogeneous agent literature; see Hommes
(2006) for an overview.
We assume that the market for the bond spread and the CDS contains
two types of traders7 driving xK
t , which are active in both markets separately:
7
This is motivated by the empirical model of Barberis et al. (1998) and experimentally
shown by Bloom…eld and Hales (2002) that (educated) individuals tend to have regimeshifting beliefs, in which the regimes are given by trend or mean reversion.
10
Arbitrageurs and Speculators. In addition, we assume that there is a third
group of liquidity traders who trade randomly. Limits to arbitrage imply that
the two markets will be in equilibrium in the long-run, but that there can
be large and persistent deviations from the equilibrium relation in the short
are mean reverting processes. Arbitrageurs
and xCDS
run. Therefore, xBS
t
t
are assumed to trade on the expectation of mean reversion, i.e., that the
di¤erence between the bond spread and the CDS will converge to its longrun average. Hence their expectations of the future price movement in the
CDS and bond spread are given as
EtA CDSt+1 =
EtA BSt+1
where 0 >
K
=
CDS
BS
(CDSt
(CDSt
BSt + c)
(2)
BSt + c)
> 1 measures the expected speed of mean reversion and c
is a constant that represents implementation costs. In a perfect market,
= 1,
= 1, and c = 0. This implies that mean reversion is immediate
and complete. When there are limits to arbitrage, however, complete mean
reversion takes longer than one period and might not be complete due to
market frictions. Hence, arbitrageurs will not be active in the credit markets
when the di¤erence between the two spreads is not large enough to cover
costs, captured by c, and will not expect
K
= 1.
Speculative traders do not trade on the basis of the price di¤erence between BS and CDS, but trade on signals they extract from past price movements. This may cause the spread di¤erence to persist. Speci…cally, Specu-
11
lators trade on the lagged changes in CDS and BS, i.e.,
in which
EtS CDSt+1 =
CDS
1
EtS
BS
1
BSt+1
=
CDSt +
CDSt +
CDS
2
BS
2
BSt
(3)
BSt
measures the sensitivity to past changes in spreads.
We assume that traders are familiar with both arbitrage and speculative
trading strategies, and since these strategies are relatively simple to implement, can switch between strategies without any (signi…cant) costs. We
assume that switching occurs on the basis of the relative pro…tability of a
certain strategy, i.e. if, say, an arbitrage strategy is more pro…table (or making less losses) than any of the other strategies, then traders are inclined to
switch to that strategy; see Brock and Hommes (1997, 1998) and De Long et
al. (1990) for a similar mechanism.8 We measure pro…tability by considering
the forecast error a trader would make by following a certain strategy. The
forecast error,
i;K
t
for strategy i in market K is given by
K
i;K
t
=
L
X
Eti
l 1
Kt
l
Kt
l
;
(4)
l=0
with i 2 A; S for arbitrageurs and speculators, respectively. The optimal
lag-length LK is to be determined empirically.
Traders can change strategies based on the relative performance of each
trading rule and are expected to switch when one rule is more pro…table than
8
An alternative way of interpreting this mechanism is given by Shleifer and Vishny
(1997), who assume that arbitrageurs are intermediaries using other people’s capital. As
arbitrageurs generate negative returns, i.e., when spreads move against the parity relation,
these people will tend to withdraw capital causing the market capital devoted to arbitrage
to shrink.
12
the other. However, switching may not occur instantly as there may be some
stickiness to past trading rules. Hence, we de…ne a rule for switching between
possible strategies based on Brock and Hommes (1998), i.e.,
wtA;K =
wtS;K
where
K
K(
(
1 + exp
wtA;K )
= (1
=
S;K
t 1
S;K
t 1+
1
A;K
t 1 )
A;K
t 1 )
K(
(
1 + exp
A;K
t 1
A;K
t 1 +
(5)
1
S;K
t 1)
S;K
t 1)
;
is the so-called intensity of choice parameter and controls the in-
tensity by which market participants switch between the di¤erent strategies.
By construction, wti;K 2< 0; 1 > and wtA;K + wtS;K = 1. For instance, if
K
= 0 traders do not consider the pro…tability of any of the strategies and
thus wti;K =
1
2
K
8 t. On the other hand, when
! 1 traders become in…-
nitely sensitive to the relative performance of each strategy and completely
switch to the strategy with the highest performance measure.
The …nal model representing the changes in the CDS and bond spreads
can now be given as a combination of trading of the di¤erent trader types.
Speci…cally, we assume that the changes in the spreads are a¤ected by the
proportions of Arbitrage and Speculative traders, common shocks that represent the arrival of fundamental information to the market and idiosyncratic
shocks that are due to liquidity demand in both markets.
CDSt = wtA;CDS
wtS;CDS
BSt
CDS
CDS
1
= wtA;BS
wtS;BS
BS
BS
1
[CDSt
CDSt
[CDSt
CDSt
1
1
CDS
2
+
1
+
13
BSt
1
BSt
BS
2
1
BSt
+ c] +
1
+
t
+ "CDS
t
1 + c] +
BSt
1
+
t
+ "BS
t
(6)
in which
t
=
t
is the innovation to credit risk, and "CDS
and "BS
are
t
t
liquidity shocks for the CDS and bond market, respectively.
Equation (6) shows that the model de…ned above takes the form of a
vector error correction model (VECM) with time-varying parameters; see
Granger and Terasvirta (1993). This time-variation in the parameters is
controlled by the intensity of choice parameter
K
. If
K
= 0 , then there
is no switching between strategies and the model essentially reduces to a
standard VECM with three stochastic processes, one shared and two speci…c
to each market. The latter is related to the time-series issue of common
trends in cointegrated systems from the econometrics literature; see Gonzalo
and Granger (1995) and Stock and Watson (1988). To quantify the limits to
arbitrage and study the importance of sovereign credit risk news, we proceed
to estimate the model given by Equation (6) in the remainder of the paper.
3
Data and Methods
CDS data are collected from CMA through Bloomberg. Daily credit default swap spreads CDSt on ten9 Eurozone sovereigns are collected from
01/01/2004 until 01/13/2015. This equals a total of over ten years of daily
credit spreads adding up to 2,638 daily CDS spreads per sovereign entity.
Because there is no variation in the spreads prior to 2008, we restrict the
sample in our analysis to the period 01/01/2008 until 01/13/2015, providing
1,836 daily observations. The ten European sovereigns include Austria, Belgium, France, Germany, Greece, Ireland, Italy, The Netherlands, Portugal
9
Due to limited data availability we are restricted to ten European sovereigns.
14
and Spain. The 5-year USD CDS contracts are chosen since these are generally the most liquid. For each of these sovereign entities bond yields, BSt , are
collected from Thomson Reuters Datastream. These are zero-coupon bonds
with a constant maturity of 5 years. Both bond and credit spreads are bid
rates. Furthermore, we obtain interest rate swaps on EURIBOR with a 5year maturity as our measure of the risk-free interest rate rtf . For estimation
purposes, we transform all data to log-variables by taking ln(1 + CDSt ) and
ln(1 + BSt
rtf )
The model presented in Section 2 is essentially a VECM, hence for being
able to estimate it, the CDS and bond spread data need to be cointegrated.
The top part of Table 1 presents the cointegration test results obtained using
the Johanssen cointegration test.
< Insert Table 1 Here >
The cointegration test results indicate that the null-hypothesis of no cointegration is rejected for all countries, though marginally so for Ireland. The
null-hypothesis of at least one cointegrating relation is accepted for all countries. Hence, for all countries but Ireland the requirements for the estimation
of the VECM are satis…ed. The line labeled U R represents the result of a
Dickey-Fuller unit root test on the CDS - bond basis CDSt
BSt , hence
enforcing a cointegrating vector of ( 1; 1). This test is inspired by the theoretical restriction of
= 1. The results show that the null of a unit root
is rejected for all countries but Greece. Based on these …ndings, in the remainder of the paper we will continue with a set of nine countries, excluding
Greece, applying the restriction
= 1.
15
The lower part of Table 1 presents the descriptive statistics of changes
in CDS and bond spreads.10 All mean values are close to zero. The standard deviations and min-max spread, though, display substantial di¤erences
between countries. There is a clear divide between the core and peripheral European countries, with much higher volatilities, ranges, kurtosis, and
negative skewness for the peripheral countries.
<Insert Table 2 Here >
Table 2 presents the correlations between all bond and CDS spread changes.
The top left quadrant displays the correlations between the bond spreads of
the European countries. There is a substantial correlation between all countries, which is higher for countries that are located in the same region. The
outlier is Germany, with a somewhat lower correlation to all other countries
except for the Netherlands. The bottom right quadrant displays the correlations between the CDS spreads of the di¤erent countries. These are relatively
large for all countries. The di¤erence in the correlation structure between
bonds and CDS is a …rst indication that there is an important di¤erence
in the factors driving the spreads. The bottom left quadrant, …nally, illustrates the correlations between the bond and CDS markets. Of particular
interest are the values on the diagonal, representing the correlation between
one country’s bond and CDS spread. As expected, these values are typically
high, except for Germany with a slightly negative correlation of -19%.
As in Section 2, the model is e¤ectively a non-linear error correction
model. The fundamental news about the sovereign credit risk
10
t,
however, is
Note that these represent percentage changes in the spreads because we are working
with logarithmic values.
16
an unobserved variable. To distinguish between the liquidity process "t and
the fundamental credit risk news
representation in which
t,
we estimate the model in a state-space
CDSt and
BSt are the observed signals and
t
as an unobserved state variable. Here we assume that news on sovereign
credit risk is a random variable with
t
N (0;
2
), such that the only addi-
tional free parameter to be estimated is the variance term
2
. Because the
fundamental credit risk process is equal across the two markets, whereas the
liquidity process "K
t is speci…c to each market, we can identify the underlying
fundamental credit risk. Hence, the major advantage of simultaneously modelling the bond market and the CDS market is that it allows us to …lter out
the underlying fundamental credit risk process without having to make any
assumptions regarding (exogenous) factors that presumably drive sovereign
credit risk. The residual of the estimation procedure is then interpreted as
the demand for liquidity "K
t . With only one market under investigation, we
would not be able to distinguish between the fundamental credit risk and the
liquidity factor. Even with a state space model one market would not su¢ ce
because it remains unclear what the …ltered state variable would represent,
news or liquidity. Our VECM with a common news component is essentially
equal to a Permanent-Transitory decomposition; the common component
t
is a shared I(1) process whereas the xK
t terms are transitory I(0) processes.
Gonzalo and Granger (1995) propose a similar method to extract the common component from cointegrated series in an error correction model.
The model is estimated for both markets simultaneously by maximum
likelihood. To determine the optimal lag length LK for each market, we
estimate the model for all 100 combinations of LK , K 2 A; S between one
17
and ten, and use the Schwarz/Basian Information Criterion to select the
optimal model. Given the non-linearity of the switching function given by
Equation (4), the estimation procedure could be sensitive to starting values
of the coe¢ cients. Therefore, we …rst estimate the model without switching,
i.e., by setting
K
= 0, and use the estimated coe¢ cients of the static model
as starting values for estimation of the switching model. We set
A
=
S
=0
as starting value for the switching parameters. Experiments indicate that
the estimation results are insensitive to this choice.
4
Estimation Results
In this section, we present the estimation results of the model presented in
Section 2. We start by estimating the linear model, which sets all weights to
0.5, i.e. by setting
CDS
;
BS
= 0. Next, we estimate the nonlinear model by
…rst introducing switching in one market, then switching in the other, and
…nally allowing for switching in both markets. By comparing the likelihoods
of the di¤erent model con…gurations, we are able to determine to what extent
switching in either market adds to the explanatory power of the model.
4.1
Static Model
Table 3 presents the estimation results for the non-switching model with
K
= 0.
< Insert Table 3 Here >
18
Overall, the estimation results for the static model are consistent with
the theoretical framework consisting of arbitrageurs and speculators. For all
countries, we …nd signi…cant evidence for both arbitrage forces and speculative forces.
First, we consider the arbitrage dynamics in the bond and CDS markets. The estimates for c are negative and signi…cant for all countries but
Portugal, indicating that the CDS spread is higher than the bond spread in
equilibrium. On average, c is equal to
0:45 meaning that there is an approx-
imately 45 basis point implementation cost for arbitrageurs that prevent the
two spreads from complete convergence. The mean reversion parameter for
BS
the arbitrageurs in the bond market,
, is positive and signi…cant for all
countries. Hence, arbitrage forces are of importance in describing the bond
market dynamics. On average,
BS
is equal to 0:038 implying that arbi-
trageurs in the bond market expect the spread di¤erence between the bond
and CDS to decrease by almost 4% per day. From the perspective of the
CDS market, the evidence for arbitrage forces is substantially smaller. The
estimated values for
CDS
are only signi…cant for Ireland at the 10% level. In
terms of price leadership or price discovery, the parameter estimates imply
that the CDS market is leading the bond market as it is the bond market
that reacts to the cointegrating relationship. Speci…cally, the Gonzalo and
Granger (1995) component share, de…ned as the contribution of a speci…c
market to the common component for the CDS (bond) market is 89% (11%).
These results suggests that information on sovereign credit risk is …rst and
foremost digested in the CDS market before ‡owing to the bond market.
19
The estimated
coe¢ cients allow us to draw inferences on the existence
of speculative forces in the two credit markets. First, we observe that
1
is
positive for both the bond and the CDS market. Hence, lagged movements in
the CDS market positively a¤ect both the bond and the CDS markets. This
is an indication of speculative forces as positive autocorrelation is inconsistent with market e¢ ciency. On average, the bond market reacts stronger to
lagged changes in the CDS market than does the CDS market;
BS
1
>
CDS
.
1
Looking at the reaction of the markets to lagged changes in the bond market,
captured by
2,
we observe that only the bond market reacts to its own lags;
the CDS market is hardly a¤ected by lagged changes in the bond spread. For
all countries,
BS
2
is negative implying that an increase in the bond spread is
followed by a decrease. The absolute magnitude of
BS
2 ,
though, is smaller
than
BS
1 .
4.2
Allowing for Switching Between Strategies
Having established that arbitrage and speculative forces are relevant in explaining the spreads, we now address the question whether the relative importance of the two is constant over time. To examine this, we …rst allow for
switching only in the CDS market; switching only in the bond market; and
switching in both markets.
< Insert Table 4 Here >
Table 4 presents the estimated values of
CDS
and
BS
as well as the
result of the likelihood ratio test comparing the switching model to the static
model (LR
1) and comparing switching in both markets to switching in
20
one market only (LR
2).11 We present the results for the optimal lag L.
For the setup with switching in both markets, we have considered all 10
10
possible combinations of LA and LS .
First and foremost, the estimation results indicate that the model with
switching is optimal for all countries. The LR
1 statistics all reject the
null hypothesis that model (3) is equal to the static case. For Belgium and
Ireland, we only …nd signi…cant switching in the bond market whereas for the
Netherlands we only …nd signi…cant switching for the CDS market. Hence,
the impact of arbitrage and speculation is typically stable in neither the bond
market nor the CDS market.
Recall that a positive (negative)
implies that if either arbitrage or noise
is more pro…table in period t, traders will increase (decrease) their weight on
that strategy in period t + 1. For both the bond and the CDS market, we
observe that
is positive for six out of nine countries, implying that positive
feedback trading (i.e. positive past pro…tability of the strategy lead to an
increase in the weight put on that strategy) is the dominant strategy for
traders in both markets. For seven out of nine countries, switching is more
aggressive in the CDS market than in the bond market, i.e., j jCDS > j jBS :
Certain estimated ’s are clearly rather large, such as
CDS
BS
for Germany and
for Netherlands. This implies very aggressive switching between trading
rules when there are di¤erences past pro…tability of the trading strategies.12
11
The other coe¢ cient estimates have been omitted from the table for brevity. These,
however, are qualitatively similar to those for the static case as presented in Table 3.
12
This is the reason why the coe¢ cient itself is not signi…cant whereas the likelihood
ratio test indicates that allowing for switching is. The non-linear structure of the logit
function induces this paradoxical result. The signi…cance of the likelihood ratio test,
however, is conclusive; see Terasvirta (1997).
21
The optimal lag lengths, LA and LS , vary across countries and markets,
but are consistent whether we allow for switching in a single market or in
both simultaneously.
4.3
Forecasting
As an additional test of the switching model, we evaluate its forecasting
power. Speci…cally, we run rolling regressions with an expanding window
and form 10 dynamic out-of-sample forecasts for each country and market at
each step. The …rst in-sample period runs from January 1 2008 to December
12 2011 (1,000 daily observations), resulting in 804 out-of-sample forecasts
for each horizon.
< Insert Table 5 Here >
Table 5 presents the root-mean-square-error (RMSE) of the model over
the RMSE of a random walk forecast. A number less than one implies a
better forecasting performance for our model. For both markets, our model
outperforms the random walk forecast. For the CDS market, we observe that
the model outperforms the random walk forecast by on average 0:7%. There
is, however, quite some variation across countries and over the forecast horizon. Speci…cally, the model tends to perform better on the shorter forecast
horizons (3:7% outperformance on average) than the longer horizons (1:1%
underperformance). Furthermore, the model performs better for the peripheral countries Ireland, Italy, Portugal, and Spain (3% outperformance) than
the core countries (1:68% underperformance).
22
Our model show stronger forecasting power for the bond market than
for the CDS market. On average, the outperformance in the bond market
is 8:5%. Interestingly, whereas the performance decreased with the forecast
horizon for the CDS market, it increases with the forecast horizon for the
bond market. As for the CDS market, though, the performance of the model
is better for the peripheral countries (8:03% outperformance) than for the
core countries (6:9% outperformance).
Overall, the better forecasting performance of our model in the bond
market is in line with the …ndings observed in Table 3, which revealed that
the CDS market was dominant in terms of price discovery and did not adjust
to price discrepancies between the CDS and the bond market. This suggests
that the CDS market is driven much more by fundamental forces, which are
captured well by the random walk assumption used as a benchmark in this
forecasting exercise.
5
Which factors drive credit spreads?
Now that we have settled on the optimal model, we subsequently study how
and to what extent the di¤erent elements (fundamental news, arbitrage, speculative trading, and liquidity demand) a¤ect the credit spreads in the two
markets. To this end, we …rst study the …ltered news processes, followed
by the processes for arbitrage and speculative trading. Finally, we perform
a variance decomposition to see how important each of the elements is in
describing the credit spreads in the bond and CDS markets.
23
5.1
Fundamental News about Sovereign Credit Risk
The fundamental news component
t,
as described above, is obtained by
means of a Kalman Filter approach. Because we are simultaneously analyzing
both the bond and the CDS market, we can …lter out the fundamental value
without making any assumptions regarding the functional form or exogenous
variables. Figure 1 presents the …ltered news processes for all nine countries
and Table 6 presents the corresponding descriptive statistics.
< Insert Figure 1 Here >
< Insert Table 6 Here >
Figure 1 and Table 6 show that there is ample variation in the fundamental credit risk
t
for the European countries. The di¤erence in variation
between countries, however, is rather large. For Germany, the variation is
minimal whereas for Italy the estimated daily standard variation is 2%. The
di¤erence is again clear between peripheral countries with a standard deviation close to 2% and core countries with a standard deviation below 1%.
Figure 1 illustrates that there is also a substantial portion of comovement
among the sovereign credit risk measures. For all countries, the negative spike
on May 10 2010 is clearly recognizable, illustrating the positive reaction
to the announcement of the creation of the European Financial Stability
Facility (EFSF) by the EMU countries and the IMF and the installment of
the Securities Markets Programme by the ECB.
24
5.2
Arbitrage and Speculative Trading
The time variation in speculative trading is captured by the weights wti;K . As
illustrated in Section 4, the credit markets are better described by allowing
time variation in the proportion of arbitrageurs versus speculative traders.
Therefore, we present the properties of this time variation. Table 7 and
Figure 2 give the speculative trading weights in both markets, wtS;CDS and
wtS;BS (note that arbitrage weights are given by 1
wtS;K ).
< Insert Table 7 Here >
< Insert Figure 2 Here >
For all countries, we observe ample variation in the proportion of noise
traders active in the credit markets. The variability is directly related to
the absolute value of the estimated ’s as this regulates the sensitivity of
agents to di¤erences in performance. The Austrian, German, and Dutch
bond markets stand out as weights continuously jump from zero to one. For
…ve countries, the average proportion of speculative traders is higher in the
CDS market; this is especially so for core countries.
5.3
Relative Importance of Factors
To examine the contribution of each of the elements de…ned in the model to
the total variation in credit markets we perform a variance decomposition.
Speci…cally, we calculate the ratio of the variance of each of the elements relative to the variance of the bond and CDS spread changes
25
BSt and
CDSt ,
respectively.13 We de…ne news by
speculative trading by wtS;K
K
1
t,
arbitrage by wtA;K
CDSt
1
+
K
2
BSt
K
1
[CDSt
1
BSt
1
, and liquidity trad-
ing by "K
t . Table 8 presents the percentages of credit spread variation explained by each of the four elements over the full sample period.
<Insert Table 8 Here >
Table 8 shows that the news and liquidity elements are generally the most
in‡uential drivers of spread changes, followed by speculation and arbitrage,
respectively. For core countries, the liquidity element is the main driver
whereas for peripheral countries news on their credit risk is the main driver of
changes in credit spreads. For all countries, the CDS market is more a¤ected
by news than the bond market; this is consistent with the information share
estimates in the previous section. The bond market, on he other hand, is
more a¤ected by arbitrage, noise, and liquidity trading than the CDS market.
The in‡uence of speculative traders is, on average, limited to 5:5% for
the CDS market and 5:4% for the bond market. Only for Portugal we …nd
substantial evidence for speculation of up to 16:4% in the bond market.
<Insert Figure 3 Here >
Figure 3 presents the variance decomposition analysis broken down by
year. For all peripheral countries we observe that the impact of news is increasing over the years, especially in the CDS markets, until 2014 when it
tampers o¤ again. Hence, there appears to be an increased focus on market fundamentals during the European sovereign debt crisis, which decreases
13
This method does not take the covariances between elements into account. The sum
of variances of the elements, however, is always within 95% of the variance of the spread
changes. The covariances are therefore small.
26
+ c] ,
again as the crisis weakens. This growth mainly goes at the expense of liquidity trading; arbitrage and noise are quite stable over time. The exception
to the latter is Portugal, for which we observe a strong increase in speculative
trading in the bond market from 5 to 27% in 2010.
Whereas Figure 3 represents the decomposition of the variation in the
credit spreads, Figures 4 and 5 present the decomposition in the level of the
credit spread. Because the model is estimated in …rst di¤erences, we need to
make an assumption about the starting level of the bond and CDS spreads in
2008 in order to be able to visualize the level decomposition. We normalize
the levels of both the bond and CDS spread at 1/1/2008 at zero.
<Insert Figure 4 Here >
<Insert Figure 5 Here >
As shown before, the …gures con…rm that the spreads mainly consist of
fundamental credit risk and liquidity demand. The in‡uence of speculation,
as feared by policy makers, is only limited. The …gures do illustrate that the
e¤ect of arbitrage is larger than expected from the variance decomposition
results, especially for the bond market.
6
Contagion
Based on the decomposed fundamental credit risk and liquidity series, we
can further study the propagation of shocks through the European sovereign
debt markets. Speci…cally, we estimate a VAR for the …ltered fundamental
credit news series as well as for the estimated liquidity factors for both the
27
bond and CDS market. This allows us to separate fundamental contagion
(i.e., the spread of credit risk shocks) from non-fundamental contagion (i.e.,
the spread of liquidity shocks).14
< Insert Table 9 Here >
Table 9 presents the VAR estimation results for the fundamental contagion. First of all, we observe that the coe¢ cients on the diagonal are all
negative and signi…cant, implying that our credit news series have a meanreverting component. German credit risk shocks positively and signi…cantly
a¤ect the credit risk of all countries in the sample. Spanish credit risk shocks
are also positively related to the majority of countries, illustrating the fear of
a bankruptcy in a major European sovereign. Conversely, Portuguese credit
risk shocks are negatively related to the credit risk of the core countries.
This result illustrates that traders did not directly fear the consequences of
a bankruptcy of Portugal, and a ‡ight-to-quality instead.
< Insert Table 10 Here >
< Insert Table 11 Here >
Regarding the non-fundamental contagion, Table 11, based on liquidity
shocks, we …nd much less contagion across countries. Again, the German
and Spanish shocks appear to have some e¤ect, but much less so than for the
fundamental shocks. This is especially the case for the CDS market. In the
bond market, there are somewhat more relationships but it is hard to draw
consistent conclusions.
14
We restrict ourselves to these two elements because of the limited explanatory power
of arbitrage and speculation.
28
7
Conclusions
In this paper, we develop a model for arbitrage and speculative trading in the
European sovereign bond and CDS markets. We propose an empirical model
based on the heterogeneous agent literature, where traders can either follow
an arbitrage strategy (focusing on long-run mean-reversion) or a speculative
strategy (by extrapolating past price patterns). Empirically, we …nd evidence
of the presence of both types of traders in the markets, with a stronger
presence of arbitrage traders in the bond market than in the CDS market.
In addition, we observe that traders apply feedback rules and switch between
trading strategies based on their past performance. A variance decomposition
exercise illustrates that the e¤ect of speculation on credit spreads, as feared
by policymakers, was limited, and that it was mainly fundamental news that
drove both markets during the European Debt Crisis.
29
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34
Tables and Figures
Table 1: Descriptive Statistics
0 CE
1 CE
AU
0.000
0.440
BE
0.002
0.633
FR
0.001
0.503
GE
0.000
0.283
UR
0.001
0.000
0.020
0.046
Mean
Max
Min
StDev
Skew
Kurt
Obs
0.000
0.313
-0.278
0.043
0.684
11.39
1835
0.000
0.419
-0.266
0.040
0.787
16.09
1835
0.000
0.344
-0.333
0.045
0.365
10.35
1835
Mean
Max
Min
StDev
Skew
Kurt
Obs
0.000
0.158
-0.107
0.018
0.615
13.33
1835
0.000
0.100
-0.147
0.020
-0.137
9.15
1833
0.000
0.076
-0.100
0.016
-0.008
7.62
1835
GR
0.033
0.436
IR
0.109
0.834
IT
0.074
0.366
NL
0.001
0.290
PO
0.007
0.566
SP
0.036
0.495
0.791
0.030
0.005
Descriptives BSt
0.000
0.004
0.000
0.000
0.549
0.264
0.280
0.217
-0.527 -0.633 -0.450 -0.296
0.077
0.045
0.042
0.037
-0.099 -2.572 -0.695 -0.177
12.92
45.88
18.79
9.00
1835
1093
1835
1835
Descriptives CDSt
0.000
0.003
0.000
0.000
0.060
2.011
0.174
0.164
-0.067 -1.583 -0.248 -0.274
0.011
0.086
0.025
0.026
-0.038
4.565 -0.102 -0.129
8.22 298.48
15.16
13.42
1833
1396
1830
1835
0.037
0.050
0.003
0.000
0.426
-0.358
0.048
0.456
15.84
1835
0.001
0.264
-0.443
0.039
-0.479
19.26
1835
0.000
0.285
-0.412
0.039
-0.797
15.63
1835
0.000
0.110
-0.070
0.014
0.608
10.23
1830
0.000
0.190
-0.423
0.030
-0.976
28.02
1835
0.000
0.174
-0.277
0.026
-0.471
13.22
1833
This table shows the results of the cointegration test and the descriptive
statistics of the changes in bond and CDS spreads, respectively. CE
denotes cointegrating equations (p-values); UR denotes Unit Root (pvalues). We report cointegration and descriptive statistics for Austria
(AU); Belgium (BE); France (FR); Germany (GE); Greece (GR); Ireland
(IR); Italy (IT); the Netherlands (NL); Portugal (PO); and Spain (SP).
35
BSt
CDSt
36
AU
0.59
0.60
0.36
0.32
0.32
0.54
0.24
0.37
0.15
0.13
0.14
0.13
0.04
0.10
0.12
0.04
0.08
BE
FR
GE
IR
IT
NL
PO
SP
AU
BE
FR
GE
IR
IT
NL
PO
SP
0.27
0.31
0.28
0.26
0.22
0.29
0.26
0.20
0.28
0.60
0.22
0.40
0.49
0.51
0.32
0.52
BE
0.18
0.13
0.20
0.15
0.06
0.11
0.16
0.02
0.09
0.34
0.30
0.41
0.68
0.17
0.46
FR
0.22
0.23
0.21
0.21
0.38
0.31
0.17
0.34
0.31
0.48
0.30
0.52
0.54
0.37
0.42
0.38
0.35
0.39
0.54
0.33
0.38
0.50
0.34
0.45
0.75
IT
0.07
0.04
0.02
0.05
-0.03
0.00
0.10
-0.04
-0.01
0.18
0.41
NL
0.21
0.23
0.20
0.22
0.34
0.31
0.19
0.42
0.32
0.47
PO
0.34
0.38
0.33
0.31
0.38
0.49
0.30
0.36
0.49
SP
0.71
0.74
0.70
0.61
0.64
0.71
0.52
0.64
AU
Table 2: Correlations
0.75
0.68
0.64
0.72
0.67
0.59
0.70
BE
0.76
0.59
0.69
0.69
0.54
0.69
FR
0.57
0.62
0.69
0.51
0.61
0.69
0.56
0.71
0.59
CDSt
GE
IR
This table shows the correlations between bond and credit spread
changes. We report correlations for Austria (AU); Belgium (BE); France
(FR); Germany (GE); Ireland (IR); Italy (IT); the Netherlands (NL);
Portugal (PO); and Spain (SP).
-0.18
-0.23
-0.24
-0.19
-0.18
-0.21
-0.17
-0.16
-0.22
0.13
0.00
0.47
0.05
0.10
GE
BSt
IR
0.57
0.72
0.73
IT
0.46
NL
PO
37
LL
CDS
2
CDS
1
CDS
BS
2
BS
1
BS
c
7902.76
AU
-0.568***
(-14.632)
0.055***
(5.354)
0.464***
(5.693)
-0.233***
(-7.280)
-0.001
(-0.312)
0.418***
(14.30)
-0.006
(-0.299)
8090.26
FR
-0.621***
(-9.995)
0.038***
(3.895)
0.274**
(2.199)
-0.337***
(-9.987)
0.002
(0.553)
0.333***
(9.259)
0.005
(0.310)
7691.00
GE
-0.941***
(-4.767)
0.023***
(4.656)
-0.729***
(-2.895)
-0.353***
(-15.26)
0.001
(0.694)
0.355***
(11.074)
0.006
(1.143)
7525.76
IR
-0.210**
(-2.928)
0.030***
(4.465)
0.736***
(11.080)
-0.180***
(-6.662)
0.008*
(1.791)
0.341***
(9.691)
0.074***
(2.949)
7778.74
IT
-0.273***
(-6.190)
0.038***
(3.998)
0.386***
(6.329)
-0.043
(-1.019)
0.005
(0.740)
0.267***
(6.570)
0.110***
(3.186)
8154.80
NL
-0.633***
(-7.433)
0.031***
(4.040)
0.340***
(2.803)
-0.330***
(-12.67)
0.003
(1.454)
0.150***
(4.711)
0.003
(0.292)
This table shows the estimation results of the static model given by
Equation (6). *, **, *** represent signi…cance at the 10, 5, and 1%
level, respectively. We report results for Austria (AU); Belgium (BE);
France (FR); Germany (GE); Ireland (IR); Italy (IT); the Netherlands
(NL); Portugal (PO); and Spain (SP).
7949.26
BE
-0.468***
(-16.403)
0.067***
(5.880)
0.618***
(7.338)
-0.089**
(-2.362)
0.004
(0.696)
0.272***
(8.010)
0.042**
(2.634)
Table 3: Results Static Model
7341.68
PO
-0.076
(-1.062)
0.026***
(3.866)
0.819***
(23.280)
-0.044
(-1.447)
0.005
(0.835)
0.301***
(8.421)
0.065***
(2.700)
7610.96
SP
-0.257***
(-3.695)
0.030***
(4.157)
0.682***
(11.330)
-0.233***
(-6.958)
0.010
(1.633)
0.314***
(9.147)
0.007
(0.246)
38
L
1
2
LR
LR
L
1
2
BS
CDS
LR
LR
BS
10
14.15***
-2.60
-101181
(-0.003)
9
4.77***
-39.33***
-12.045***
(-2.352)
CDS
LR
LR
2
14.74***
-14.38***
7.399***
(4.396)
L
1
2
BS
CDS
-8.578***
(-541.0)
1.563**
(2.443)
10, 1
16.75***
-8.968***
(-502.59)
159796
(0.000)
2, 10
44.10
-8.042***
(-553.61)
649.99
(0.474)
2, 4
29.12***
3
20.95***
-40.21***
12.172***
(4.101)
6
1.34
-3.44*
3.836*
(1.878)
2
3.79*
-5.73**
4.502**
(2.018)
-5.936***
(-4.721)
7
44.13***
-17.03***
Portugal
-3.619*
(-1.657)
7
3.78*
-1.01
Ireland
1.292*
(1.652)
1
6.33**
-3.19*
Belgium
-7.027***
(-657.7)
-5.038***
(-4.812)
3, 10
61.16***
-7.451***
(-509.17)
-3.389
(-1.586)
6, 7
4.79**
-7.881***
(-446.67)
1.382
(1.615)
2, 1
9.52***
Table 4: Results Switching
1
16.70***
-11.56***
11.796***
(3.132)
10
5.55**
-4.73**
8.592***
(2.727)
5
6.53**
-20.43***
-15.601**
(-1.969)
This table shows the estimated switching parameters CDS and BS ,
as well as the results of the likelihood ratio tests of the switching model
versus the static model (LR 1) and the model with switching in one
market versus switching in both markets (LR 2). *, **, *** represent
signi…cance at the 10, 5, and 1% level, respectively.
1.506**
(2.438)
1
3.21*
-13.54***
Netherlands
143946
(0.002)
10
41.17***
-2.93*
Germany
645.582
(0.481)
4
14.19***
-14.93***
Austria
-6.697**
(-2.550)
7
12.55***
-15.71***
Spain
2.426
(1.648)
1
4.70**
-5.58**
Italy
7.178***
(3.075)
1
19.52***
-7.44***
France
-7.307***
(-435.8)
-7.854**
(-2.520)
1, 9
28.26***
-7.305***
(-473.61)
2.652*
(1.672)
10, 1
10.28***
-8.342***
(-444.85)
7.466***
(3.119)
5, 1
26.96***
Table 5: Forecasting Results
AU
BE
FR
GE
1
2
3
4
6
8
10
AVG
0.997
0.974
0.998
0.995
1.013
1.022
1.027
1.007
0.984
0.969
0.992
0.998
1.010
1.014
1.024
1.002
0.959
0.965
0.983
0.990
0.994
0.992
0.993
0.986
0.962
0.969
0.988
0.991
1.008
1.015
1.023
0.999
1
2
3
4
6
8
10
AVG
0.942
0.921
0.933
0.933
0.936
0.923
0.932
0.932
0.954
0.928
0.923
0.919
0.898
0.882
0.870
0.905
0.922
0.926
0.935
0.947
0.943
0.943
0.946
0.938
0.926
0.914
0.955
0.968
0.997
1.021
1.023
0.981
IR
CDS
0.911
0.917
0.923
0.922
0.929
0.932
0.934
0.926
TR
0.919
0.880
0.872
0.856
0.837
0.823
0.805
0.848
IT
NL
PO
SP
AVG
0.951
0.978
0.980
0.991
1.003
1.003
1.004
0.992
0.990
1.011
1.033
1.046
1.062
1.100
1.108
1.060
0.961
0.954
0.957
0.971
0.976
0.979
0.981
0.971
0.951
0.983
0.984
0.990
0.999
1.003
1.003
0.991
0.963
0.969
0.982
0.988
0.999
1.007
1.011
0.993
0.965
0.926
0.921
0.918
0.892
0.851
0.804
0.890
0.938
0.936
0.935
0.927
0.900
0.870
0.834
0.899
0.911
0.859
0.878
0.899
0.906
0.906
0.894
0.896
0.957
0.934
0.949
0.962
0.955
0.941
0.914
0.945
0.937
0.914
0.922
0.925
0.918
0.907
0.891
0.915
This table shows the forecasting results comparing the RMSE of the
model to that of a random walk forecast. A number < 1 represents outperformance of the model. AVG denotes average. We report forecasts
for Austria (AU); Belgium (BE); France (FR); Germany (GE); Ireland
(IR); Italy (IT); the Netherlands (NL); Portugal (PO); and Spain (SP).
Table 6: Credit Risk News
Mean
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
Observations
AU
0.000
0.057
-0.035
0.006
0.545
12.395
1814
BE
0.000
0.060
-0.089
0.012
-0.163
8.594
1814
FR
0.000
0.047
-0.059
0.009
0.053
7.446
1814
GE
0.000
0.000
0.000
0.000
-0.234
7.877
1814
IR
0.000
0.101
-0.179
0.015
-0.377
18.487
1814
IT
0.000
0.125
-0.220
0.020
-0.341
15.224
1814
NL
0.000
0.039
-0.023
0.005
0.657
10.473
1814
PO
0.000
0.117
-0.221
0.016
-0.820
28.332
1814
This table shows the descriptive statistics of the …ltered credit risk news
series t . We report descriptives for Austria (AU); Belgium (BE); France
(FR); Germany (GE); Ireland (IR); Italy (IT); the Netherlands (NL);
Portugal (PO); and Spain (SP).
39
SP
0.000
0.129
-0.210
0.019
-0.497
15.134
1814
40
Mean
Med
Max
Min
Stdv
Skew
Kurt
Obs
Mean
Med
Max
Min
Stde
Skew
Kurt
Obs
W S;BS
0.500
0.500
0.934
0.066
0.178
-0.042
3.126
1813
IT
W S;CDS
0.527
0.533
0.972
0.101
0.155
-0.002
2.783
1813
W S;BS
0.501
0.500
0.799
0.201
0.125
-0.067
3.003
1813
PO
W S;CDS W S;BS
0.539
0.427
0.552
0.428
1.000
0.880
0.001
0.045
0.259
0.148
-0.200
0.079
2.019
2.688
1813
1813
FR
W S;CDS W S;BS
0.490
0.506
0.485
0.511
1.000
0.999
0.002
0.001
0.258
0.335
0.103
-0.032
1.883
1.662
1813
1813
SP
W S;CDS
0.528
0.543
1.000
0.000
0.356
-0.107
1.580
1813
W S;BS
0.468
0.466
0.917
0.059
0.162
0.097
2.760
1813
GE
W S;CDS W S;BS
0.514
0.514
0.527
1.000
0.970
1.000
0.016
0.000
0.170
0.500
-0.321
-0.057
3.300
1.004
1813
1813
Table 7: Noise Trader Risk
NL
W S;CDS W S;BS
0.374
0.500
0.000
0.499
1.000
0.826
0.000
0.173
0.484
0.134
0.521
0.033
1.271
3.071
1813
1813
W S;CDS
0.529
0.536
0.988
0.010
0.179
-0.110
3.067
1813
BE
IR
W S;CDS
0.518
0.522
0.813
0.233
0.084
-0.219
3.683
1813
This table shows the descriptive statistics of the proportion of speculative traders in the bond and CDS markets, W S;BS and W S;CDS . We
report statistics for Austria (AU); Belgium (BE); France (FR); Germany (GE); Ireland (IR); Italy (IT); the Netherlands (NL); Portugal
(PO); and Spain (SP).
W S;BS
0.479
0.061
1.000
0.000
0.492
0.082
1.026
1813
W S;CDS
0.564
0.605
0.999
0.001
0.271
-0.375
2.091
1813
AU
W S;BS
0.482
0.491
0.855
0.101
0.099
-0.390
3.817
1813
Table 8: Variance Decomposition
AU
BE
FR
GE
IR
IT
NL
PO
SP
AVG
NEWS
BS
CDS
2.3% 18.7%
10.2% 67.6%
4.5% 65.3%
0.0%
0.0%
15.4% 65.8%
34.3% 81.9%
1.0% 21.4%
21.2% 44.7%
26.8% 78.2%
LIQUIDITY
BS
CDS
93.5% 72.5%
84.5% 27.7%
91.3% 29.3%
93.0% 97.0%
78.5% 26.8%
62.8% 12.8%
95.5% 75.5%
61.7% 48.5%
66.7% 16.7%
ARBITRAGE
BS
CDS
2.2%
0.0%
1.9%
0.1%
0.3%
0.1%
0.0%
0.1%
0.8%
0.2%
0.8%
0.0%
0.7%
0.0%
0.7%
0.0%
0.4%
0.0%
12.9%
80.8%
0.9%
49.3%
45.2%
0.1%
SPECULATION
BS
CDS
2.1%
8.7%
3.4%
4.7%
3.8%
5.3%
7.0%
2.9%
5.3%
7.1%
2.0%
5.4%
2.8%
3.1%
16.4%
6.8%
6.1%
5.1%
5.4%
5.5%
This table shows the resutls for the Variance Decomposition. *, **,
*** represent signi…cance at the 10, 5, and 1% level, respectively. We
report results for Austria (AU); Belgium (BE); France (FR); Germany
(GE); Ireland (IR); Italy (IT); the Netherlands (NL); Portugal (PO);
and Spain (SP).
41
42
SP(-1)
PO(-1)
NL(-1)
IT(-1)
IR(-1)
GE(-1)
FR(-1)
BE(-1)
AU(-1)
AU
-0.151***
(-3.977)
0.045**
(2.242)
-0.046*
(-1.666)
1.1E+13***
(3.370)
0.013
(0.876)
0.017
(1.092)
-0.032
(-0.700)
-0.044***
(-3.327)
0.035**
(2.059)
FR
0.024
(0.415)
0.074**
(2.420)
-0.206***
(-4.950)
1.2E+13**
(2.452)
-0.006
(-0.281)
0.035
(1.455)
0.007
(0.099)
-0.050**
(-2.465)
0.056**
(2.172)
GE
0.000
(0.181)
0.000*
(1.657)
0.000
(-0.159)
-0.104***
(-2.818)
0.000
(0.781)
0.000
(-0.237)
0.000
(0.902)
0.000
(-1.298)
0.000
(1.645)
IR
-0.164*
(-1.707)
0.065
(1.277)
0.042
(0.601)
6.4E+12
(0.783)
-0.075**
(-2.000)
0.005
(0.114)
-0.051
(-0.444)
0.011
(0.319)
0.048
(1.115)
IT
-0.163
(-1.329)
-0.041
(-0.620)
-0.090
(-0.018)
2.9E+13***
(2.772)
0.044
(0.919)
-0.093*
(-1.831)
-0.020
(-0.134)
-0.061
(-1.425)
0.167***
(3.059)
Table 9: Fundamental Contagion
NL
0.051*
(1.746)
0.051***
(3.261)
-0.006
(-0.307)
6.1E+12**
(2.418)
0.013
(1.138)
0.011
(0.893)
-0.228***
(-6.557)
-0.023**
(-2.283)
0.019
(1.446)
This table shows the estimation results of the VAR on the …ltered credit
risk news series. *, **, *** represent signi…cance at the 10, 5, and 1%
level, respectively. We report results for Austria (AU); Belgium (BE);
France (FR); Germany (GE); Ireland (IR); Italy (IT); the Netherlands
(NL); Portugal (PO); and Spain (SP).
BE
-0.024
(-0.316)
-0.217***
(-5.429)
0.011
(0.204)
2.1E+13***
(3.334)
0.017
(0.567)
0.026
(0.850)
0.073
(0.818)
-0.086***
(-3.279)
0.126***
(3.784)
PO
-0.288***
(-2.941)
-0.104**
(-2.003)
0.038
(0.543)
1.1E+13
(1.345)
0.121***
(3.140)
-0.037
(-0.905)
-0.012
(-0.105)
-0.170***
(-4.976)
0.194***
(4.450)
SP
-0.126
(-1.079)
0.053
(0.852)
-0.098
(-1.157)
1.9E+13*
(1.922)
0.019
(0.411)
0.055
(1.127)
-0.060
(-0.429)
-0.032
(-0.781)
-0.042
(-0.803)
43
SP(-1)
PO(-1)
NL(-1)
IT(-1)
IR(-1)
GE(-1)
FR(-1)
BE(-1)
AU(-1)
AU
-0.144***
(-3.787)
0.063
(1.082)
-0.128
(-1.582)
0.106**
(2.646)
0.060
(1.387)
0.098
(1.634)
-0.026
(-0.553)
-0.026
(-1.047)
0.135***
(2.357)
FR
0.025
(1.274)
0.027
(0.909)
-0.189***
(-4.511)
0.050***
(2.421)
-0.021
(-0.922)
0.021
(0.673)
-0.009
(-0.381)
0.012
(0.916)
0.076***
(2.569)
GE
0.037
(1.046)
0.058
(1.077)
0.022
(0.285)
-0.127***
(-3.387)
-0.001
(-0.028)
-0.014
(-0.244)
0.062
(1.384)
0.009
(0.383)
0.080
(1.495)
IR
0.025
(0.797)
0.078*
(1.661)
0.053
(0.808)
-0.039
(-1.195)
-0.103***
(-2.924)
0.022
(0.447)
-0.007
(-0.172)
0.011
(0.558)
0.054
(1.163)
IT
0.039
(1.576)
-0.055
(-1.472)
-0.025
(-0.467)
0.027
(1.031)
-0.030
(-1.055)
-0.051
(-1.306)
0.003
(0.084)
0.008
(0.470)
0.063*
(1.705)
Table 10: Liquidity Contagion CDS
NL
0.073***
(2.629)
0.116***
(2.727)
0.046
(0.780)
0.071***
(2.409)
-0.025
(-0.792)
0.015
(0.345)
-0.250***
(-7.099)
0.006
(0.332)
0.067
(1.596)
This table shows the estimation results of the VAR on the estimated
liquidity factor in the CDS market. *, **, *** represent signi…cance
at the 10, 5, and 1% level, respectively. We report results for Austria
(AU); Belgium (BE); France (FR); Germany (GE); Ireland (IR); Italy
(IT); the Netherlands (NL); Portugal (PO); and Spain (SP).
BE
0.032
(1.323)
-0.211***
(-5.678)
0.014
(0.261)
0.067**
(2.602)
-0.006
(-0.202)
0.029
(0.751)
0.017
(0.541)
-0.011
(-0.677)
0.138***
(3.759)
PO
-0.037
(-0.715)
-0.123
(-1.550)
0.124
(1.124)
0.061
(1.119)
0.015
(0.261)
0.075
(0.909)
-0.033
(-0.507)
-0.144***
(-4.283)
0.320***
(4.080)
SP
0.011
(0.389)
0.020
(0.476)
-0.043
(-0.742)
0.037
(1.265)
-0.060*
(-1.943)
0.013
(0.301)
-0.007
(-0.194)
0.053***
(2.962)
-0.054
(-1.308)
44
SP(-1)
PO(-1)
NL(-1)
IT(-1)
IR(-1)
GE(-1)
FR(-1)
BE(-1)
AU(-1)
AU
-0.092***
(-2.890)
-0.011
(-0.295)
0.051
(1.487)
-0.045***
(-3.084)
0.000
(-0.013)
0.066
(1.404)
0.041
(1.365)
-0.005
(-0.117)
0.030
(0.657)
FR
0.029
(0.859)
0.099***
(2.568)
-0.033
(-0.922)
-0.015
(-0.956)
-0.036
(-1.045)
-0.025
(-0.492)
-0.034
(-1.049)
-0.020
(-0.461)
0.006
(0.119)
GE
0.067
(1.108)
-0.107
(-1.556)
0.079
(1.229)
-0.001
(-0.039)
0.034
(0.556)
-0.254***
(-2.858)
-0.086
(-1.495)
0.064
(0.854)
0.267***
(3.113)
IR
-0.056**
(-2.009)
-0.022
(-0.688)
0.032
(1.065)
-0.001
(-0.051)
-0.038
(-1.365)
0.209***
(5.109)
-0.021
(-0.813)
-0.023
(-0.676)
0.001
(0.014)
IT
-0.003
(-0.139)
0.048**
(1.967)
-0.077***
(-3.379)
0.003
(0.302)
-0.002
(-0.070)
0.084***
(2.667)
-0.014
(-0.676)
-0.026
(-0.987)
-0.057*
(-1.872)
Table 11: Liquidity Contagion Bonds
NL
0.048
(1.269)
0.038
(0.896)
-0.014
(-0.350)
-0.024
(-1.398)
-0.040
(-1.040)
-0.041
(-0.747)
-0.008
(-0.227)
-0.011
(-0.238)
-0.005
(-0.088)
PO
-0.051***
(-2.342)
0.030
(1.227)
-0.008
(-0.348)
0.020**
(2.068)
0.037*
(1.670)
0.151***
(4.737)
-0.015
(-0.734)
-0.102***
(-3.769)
0.015
(0.486)
This table shows the estimation results of the VAR on the estimated
liquidity factor in the bond market. *, **, *** represent signi…cance
at the 10, 5, and 1% level, respectively. We report results for Austria
(AU); Belgium (BE); France (FR); Germany (GE); Ireland (IR); Italy
(IT); the Netherlands (NL); Portugal (PO); and Spain (SP).
BE
-0.019
(-0.685)
0.065**
(2.039)
-0.025
(-0.823)
-0.030**
(-2.330)
0.005
(0.160)
0.127***
(3.052)
-0.005
(-0.201)
-0.028
(-0.790)
-0.033
(-0.825)
SP
-0.004
(-0.164)
0.029
(1.087)
-0.049*
(-1.939)
0.011
(0.981)
-0.023
(-0.967)
0.078***
(2.231)
-0.040*
(-1.763)
0.002
(0.054)
0.001
(0.040)
45
-.04
-.02
.00
.02
.04
-6E-16
-4E-16
-2E-16
0E+ 00
2E-16
4E-16
2008
2008
2009
2009
2012
2013
2014
2011
2012
2010
2011
2013
2013
2014
2014
2012
2011
2012
-.3
-.2
-.1
2012
2013
2014
-.3
-.3
-.2
-.1
.0
2011
2014
-.2
-.1
.0
.1
.2
.0
2010
2013
2014
.1
NEWS_PORTUGAL
2010
2013
.1
2009
2009
2011
NEWS_IRELAND
2010
.2
2008
2008
2009
-.08
-.06
-.04
-.02
.00
.02
.04
.06
.2
-.2
2008
NEWS_BELGIUM
This Figure shows the …ltered credit risk news series.
2012
NEWS_NLANDS
2010
-.1
.0
.1
.2
-.12
-.04
2011
-.08
-.02
NEWS_GERMANY
-.04
.00
2010
.00
.02
2009
.04
.04
2008
.08
NEWS_AUSTRIA
.06
Figure 1: News
2008
2008
2008
2009
2009
2009
2011
2012
2011
2012
2010
2011
2012
NEWS_SPAIN
2010
NEWS_ITALY
2010
NEWS_FRANCE
2013
2013
2013
2014
2014
2014
46
2010
2011
2012
2013
2014
2008
2009
2010
2011
2012
2013
2014
2010
2011
2013
2014
WC_CDS_NLANDS
0.0
0.75
WC_CDS_PORTUGAL
2011
2013
2014
WC_TR_PORTUGAL
2012
0.0
0.2
0.4
0.4
0.2
0.6
0.6
2010
2014
0.8
2009
2013
WC_TR_IRELAND
2012
1.0
2008
2011
0.8
1.00
2010
WC_CDS_IRELAND
2009
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
2008
2008
2008
2009
2009
2009
2010
2011
2011
2011
WC_CDS_SPAIN
2010
WC_CDS_ITALY
2010
WC_CDS_FRANCE
This Figure shows the estimated proportion of speculative trader in the
CDS and bond markets, W N;T R and W N;CDS .
WC_TR_NLANDS
2008
0.0
0.2
0.4
1.0
0.00
2012
2014
0.00
2011
2013
0.2
0.4
0.6
0.8
0.25
2010
2012
WC_TR_GERMANY
1.0
0.50
2009
WC_CDS_GERMANY
2009
0.00
0.25
0.6
0.8
0.50
1.0
WC_TR_BELGIUM
0.75
WC_CDS_BELGIUM
1.00
0.25
2008
2008
WC_TR_AUSTRIA
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
WC_CDS_AUSTRIA
0.25
0.00
0.00
0.00
2009
0.50
0.25
0.25
2008
0.75
1.00
0.50
.2
0.50
1.00
0.75
0.00
.4
.6
.8
0.75
1.00
0.25
0.50
0.75
1.00
Figure 2: Noise Trader Risk
2013
2013
2013
WC_TR_SPAIN
2012
WC_TR_ITALY
2012
WC_TR_FRANCE
2012
2014
2014
2014
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
47
0.0
0.0
0.2
0.0
0.2
0.0
CDS
Ireland
CDS
0.2
0.0
0.2
0.0
0.2
0.0
France
CDS
Italy
CDS
Spain
CDS
2008 2009 2010 2011 2012 2013 2014 2008 2009 2010 2011 2012 2013 2014
Bonds
2008 2009 2010 2011 2012 2013 2014 2008 2009 2010 2011 2012 2013 2014
Bonds
2008 2009 2010 2011 2012 2013 2014 2008 2009 2010 2011 2012 2013 2014
Bonds
This Figure shows the variance decomposition results for each of the
years in the sample.
LIQUIDIT Y
NOISE
0.4
0.4
0.4
NEWS
ARB IT RA GE
0.6
0.6
0.6
2008 2009 2010 2011 2012 2013 2014 2008 2009 2010 2011 2012 2013 2014
0.8
0.8
CDS
0.8
Portugal
0.0
0.2
0.4
0.6
0.8
1.0
1.0
Bonds
2008 2009 2010 2011 2012 2013 2014 2008 2009 2010 2011 2012 2013 2014
Bonds
0.0
0.2
0.4
0.6
0.8
1.0
1.0
2008 2009 2010 2011 2012 2013 2014 2008 2009 2010 2011 2012 2013 2014
Belgium
2008 2009 2010 2011 2012 2013 2014 2008 2009 2010 2011 2012 2013 2014
Bonds
1.0
CDS
0.4
0.4
Netherlands
0.6
0.6
Bonds
0.8
0.8
2008 2009 2010 2011 2012 2013 2014 2008 2009 2010 2011 2012 2013 2014
1.0
1.0
CDS
0.2
0.2
Germany
0.4
0.4
Bonds
0.6
0.6
2008 2009 2010 2011 2012 2013 2014 2008 2009 2010 2011 2012 2013 2014
0.8
CDS
0.8
Austria
1.0
Bonds
1.0
Figure 3: Variance Decomposition
48
This Figure shows the decomposed credit spreads for the bond market.
Figure 4: Bond Spread Decomposition
49
This Figure shows the decomposed credit spreads for the CDS market.
Figure 5: CDS Spread Decomposition