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Working Paper Series Brasília n. 293 Oct. 2012 p. 1-79
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Contagion in CDS, Banking and Equity Markets
Rodrigo Cesar de Castro Miranda∗
Benjamin Miranda Tabak∗†
Mauricio Medeiros Junior‡
Abstract
This Working Paper should not be reported as representing the
views of the Banco Central do Brasil. The views expressed in
the paper are those of the authors and do not necessarily reflect
those of the Banco Central do Brasil.
We developed an endogenous testing strategy for finding contagion within stock mar-
kets indices, Credit Default Swaps spreads and banking sector indices. We present
evidence of strong contagion in specific cases and markets and show an analysis
of contagion to Brazil. Our results are important for the development of macro-
prudential policies.
JEL classification: G01; G15. Keywords: contagion; emerging markets; corre-
lation; coskewness; endogenous testing.
∗Departamento de Estudos e Pesquisas, Banco Central do Brasil.†Benjamin M. Tabak agradece apoio financeiro do CNPQ.‡Universidade de Brasılia.
3
1 Introduction
The study of financial contagion is becoming increasingly relevant as financial crises
such as the Global Financial Crisis1, and the European Sovereign Debt Crisis2 had
their effects spread throughout the world. The understanding of changes in the
volatility of worldwide financial markets, the way such changes spread and its im-
pact on the financial stability has become of great importance for governments and
regulatory agencies.
In this paper we aim to develop a framework for the analysis of contagion in the
recent crises which does not require a previously defined dating of a crisis period. Our
study includes an empirical analysis of the most affected countries due to financial
instability in the Global Financial Crisis and the European Sovereign Debt Crisis
and a more detailed presentation of the contagion to the Brazilian economy.
Contagion tests such as those developed by Forbes and Rigobon (2002) and Fry et al.
(2008) compare a previously defined “crisis period” with a “tranquil period” and
find contagion through statistically significant structural breaks. The proper choice
of those periods are generally regarded as “an essential component” (Hsiao et al.
(2011)). However in the literature those two periods are usually defined exogenously
from the model, with the boundaries gathered from some general consensus, which
identifies certain events with the start and end of a crisis. We believe that such
arbitrary dating may be hiding patterns in contagion effects that would be interesting
to study. Also it is our understanding that in many crises this arbitrary dating may
not be easy or even possible to make with any precision.
Because of this difficulty in defining the proper dating of each crisis, we propose
a contagion test which does not rely on exogenous definition of contagion dates,
but uses the same testing framework as the tests developed by Forbes and Rigobon
(2002) and Fry et al. (2008). For such ends an endogenous search for contagion is
proposed in which a window is moved along all possible dates and each window is
tested for contagion. The results of all tests are gathered and analysed.
We have apply this endogenous search to both the Global Financial Crisis and the
1The crisis which started with the collapse of the subprime market in the USA in 2007.2The crisis that started in late 2009 with the discovery of the fiscal problems of the Greek
government and is still under way.
4
ongoing European Sovereign Debt Crisis and find evidence of contagion consistent
with general consensus of the contagion periods. A special study is done on the
evidence of contagion to Brazil.
The remainder of this paper is structured as follows: section 2 reviews current
literature on contagion measurements, section 3 describes the datasets used in this
paper, section 4 describes the contagion measurement tests developed by Forbes
and Rigobon (2002) and Fry et al. (2008) used in this paper, section 5 outlines our
proposal for endogenous contagion tests, 6 presents the results and a commentary,
and section 7 concludes the paper.
2 Literature review
There is a growing body of literature on the topic of financial contagion. Dungey
et al. (2005) and Claessens and Forbes (2004) present reviews of the literature related
to empirical testing for contagion and the various methods used to analyse and
describe the contagion conditions in a certain market.
There are papers that develop alternative approaches to examine contagion channels,
for example, Manz (2010) and Castiglionesi (2007). Manz (2010) explore a global
game model of information-based financial contagion, where the failure of a single
firm can trigger the failure of another. Castiglionesi (2007) investigate the impact
of a central bank intervention in preventing financial contagion.
Tables 1, 2 and 3 present a summary of the contributions.
< Place Tables 1, 2 and 3 about here >
Overall we can infer from this literature that there are various competing models and
approaches to test for contagion. For the purposes of this paper, the main method
is the one proposed by Forbes and Rigobbon which looks for structural breaks in
correlations between markets, with proper adjustments for heteroscedascity. This
method was further developed by Fry et al. (2008) including an important addi-
tion as it proposes a framework for contagion tests in higher-order moments of the
5
distribution.
There seems to be a gap in the literature regarding the definition of the sample
to be tested. How long should be periods before and after contagion? These are
usually defined exogenously and arbitrarily for each crisis. Our main contribution is
the proposal of a search for contagion while endogenously setting the dating of the
contagion period.
3 Data
In this paper we perform endogenous tests for contagion through three distinct chan-
nels or dimensions: the equity market, the banking system and the sovereign CDS3
market. Testing for contagion in different channels allows for a better characteriza-
tion of how a given crisis might have affected a country, and also help in pointing
the direction for finer-grained studies.
The rationale for the choice of channels is as follows. Contagion in CDS spreads
indicates that during a crisis, the market expects it to trigger a rise in the country’s
probability of default, and therefore a decrease in its ability to finance itself and pay
its debts. Contagion in the banking sector reflects a loss of capital or of funding
in the affected country’s banking system. Contagion in the equity market indicates
an increased risk aversion in investors and less availability of funding for listed
companies in that specific country.
For the country equity market we used the MSCI standard country index. For CDS
spreads, Thomson Reuters Sovereign CDS. For the banking sector, Data Stream
Bank Sector index. We used the daily US Federal Funds Rate and Commodities
CRB Total Return index for controlling for fundamentals. All data was obtained
through Datastream.
The descriptive statistics of each series is calculated for three different periods: the
whole period from January 2006 to October 2011 (the full period), from January
2008 to October 2011 (the Global Financial Crisis period), and from December 2009
to October 2011 (the European Sovereign Debt Crisis). Tables 8, 9 and 10 show
the descriptive statistics for the banking sector for each of the above periods. The
3Credit Default Swaps.
6
effects of the Global Financial Crisis are visible in the volatility in the 2008 to 2011
period, which is generally higher than in the full period (the volatility is higher in 49
out of 54 countries), however if the periods of the European Sovereign Debt Crisis
is compared to the period of Global Financial Crisis, only four countries have their
volatility increased, namely Greece, Portugal (two of the countries central to the
European Sovereign Debt Crisis), Indonesia and Venezuela. Regarding skewness, 39
countries have their skewness increased in the subprime period relative to the full
period, and 30 countries have their skewness increased in the European crisis period,
including 17 European countries. According to Kraus and Litzenberger (1976) this
higher skewness signals an increase in risk aversion, and is important for one of the
tests used in this paper as described in section 4.
The descriptive tables for the MSCI equity indexes (tables 15, 16 17) are similar
to those of the banking sector, and the same analysis applies, but the CDS spreads
(tables 21 and 22) are different. The first difference is that the sample data begins at
December 2007 and therefore the in the CDS spreads case the period from January
2008 to October 2011 is regarded as the full period. Also, the volatility increases in
many more countries in the two periods of the European Sovereign Debt Crisis.
< place tables 8, 9, 10, 15, 16 17, 21 and 22 about here.>
4 Contagion testing
In our paper we adopt the same definition of contagion as that of Forbes and Rigobon
(2002):“a significant increase in cross-market linkages after a shock to one country
(or group of countries)”. This definition is formal enough for statistical testing
and is broad enough to signal various forms of contagion. Given this definition we
consider three possible measures of contagion. One based on correlation, and two
based on coskewness.
The logic of contagion tests based on correlation is that during a crisis contagion
from one market to another is signalled is through a significant increase in the
correlation of these markets. That is, if the prices of one market fall, the prices of
7
the the other also fall in a way that is stronger than predicted by their expected co-
movements. Regarding correlation tests for contagion, Forbes and Rigobon (2002)
observe that the higher volatility in times of crisis tends to increase the correlation
coefficients and therefore bias the contagion tests towards false positives, and thus a
better correlation-based test should take this into account. Fry et al. (2008) develop
the final form of the correlation-based contagion test used in this paper, which we
call the FR test.
The coskewness test are derived from an extension to the CAPM model due to Kraus
and Litzenberger (1976), which incorporates skewness, and a further development
by Harvey and Siddique (2000) which refined this model to include conditional
skewness. Both argues that risk aversion in a CAPM model which incorporates
skewness implies a preference for positive skewness. That is, a risk averse investor
will seek assets that increase the coskewness of a portfolio. On the other hand,
in order to add an asset to its portfolio that impacts its skewness negatively, the
risk averse investor will demand higher returns. Based on this observation, Fry
et al. (2008) argued that another signal of a crisis would be a shift towards positive
skewness as risk averse investors trade off smaller returns for positive skewness.
From this observation , Fry et al. (2008) developed two coskewness based tests, CS1
and CS2, which build on the FR test. Both tests aim to identify significant changes
in coskewness between the periods before a crisis and during a crisis.
The CS1 test for contagion verifies whether there is a significant decrease in the
source market returns and a related increase the volatility in the second market.
This implies that the crisis in the source market has been identified with positive
skewness (investors are seeking safer assets and accepting lower returns), and the
second market suffers contagion in the form of higher volatility.
The CS2 test for contagion verifies whether there is a significant increase in volatility
in the source market and a significant decrease of the average returns in the second
market, that is, the higher volatility in the source market affects investors in the
second market, which turn to safer assets (smaller returns) seeking positive skewness.
8
4.1 Contagion tests
In order to test for the occurrence of contagion from one market to another, we
separate each series i and j into a pre-crisis and a crisis period, and then compare
the joint behaviour of i and j. If the resulting statistic is greater than a critical
value, there is indication of contagion.
The first statistic (which in this paper is identified by FR) tests for an increase in the
correlation of the two series in the crisis period. The second and third statistics (in
this paper, CS1 and CS2 ) test for an increase in volatility in j and smaller returns
in i (CS1 ), and for smaller returns in j and increased volatility in i (CS2 ).
4.2 Correlation Contagion testing
The correlation-based testing for contagion used in this paper follows the proposal
by Forbes and Rigobon (2002) and further refined by Fry et al. (2008).
Let ρc and ρpre be the correlations between i and j in the crisis and pre-crisis periods.
The correlation of the crisis period is adjusted for the greater volatility of the crisis
period as per Forbes and Rigobon (2002), and the adjusted correlation νc is then
used for the calculation of the FR statistic:
νc =ρc√
1 + δ(1− ρ2c), (1)
δ =s2c,i − s2pre,is2pre,i
, (2)
where s2c,i and s2pre,i are the variances of i in the crisis and pre-crisis periods. Let Tc
be the number of observations in the crisis period and Tpre the number of observation
in the pre-crisis period.
FR(i→ j) =
(νc − ρpre√
V ar(νc − ρpre)
)2
, (3)
where
9
V ar(νc − ρpre) = V ar(νc) + V ar(ρpre)− 2Cov(νc, ρpre), (4)
V ar(νc) =1
2
(1 + δ)2
[1 + δ(1− ρ2c)]3
[1
Tc
((2− ρ2c)(1− ρ2c)2
)+
1
Tpre
(ρ2c(1− ρ2c)2
)], (5)
V ar(ρpre) =1
Tpre(1− ρ2pre)2, (6)
Cov(νc, ρpre) =1
2
1
Tpre
ρcρpre(1− ρ2c)(1− ρ2pre)(1 + δ)√[1 + δ(1− ρ2c)]3
. (7)
Under the null hypothesis of no contagion, the two-tailed adjusted correlation test
is asymptotically distributed χ21. Although the test is two-tailed, this paper only
concerns itself with increases in correlations, therefore the test indicates contagion
only if FR(i→ j) is greater than some critical value in χ21 and νc > ρpre.
4.3 Coskewness contagion testing
Fry et al. (2008) propose coskewness tests for contagion, which identifies contagion
from the value of i to the volatility of j and from the volatility of i to the value of j.
Let µT,k the mean of k in period T and σT,k the standard deviation of k in T , where
T can be either Tc ou Tpre and k can be either i or j.
The coskewness contagion test from i to the volatility of j, CS1(i → j; i1, j2), and
the coskewness contagion test from the volatility of i to j, CS2(i → j; i2, j1) are
given by:
CS1(i→ j; i1, j2) =
ψc(i1, j2)− ψpre(i1, j2)√4νc+2Tc
+4ρ2
pre+2
Tpre
2
, (8)
CS2(i→ j; i2, j1) =
ψc(i2, j1)− ψpre(i2, j1)√4νc+2Tc
+4ρ2
pre+2
Tpre
2
, (9)
where
ψc(im, jn) =
1
Tc
Tc∑t=1
(it − µc,iσc,i
)m(jt − µc,jσc,j
)n, (10)
ψpre(im, jn) =
1
Tpre
Tpre∑t=1
(it − µpre,iσpre,i
)m(jt − µpre,jσpre,j
)n. (11)
10
Under the null hypothesis of no contagion, the two-tailed adjusted correlation test
is asymptotically distributed χ21.
5 Endogenous test for contagion
In section 4.2 we presented contagion tests based on the structural break of some
property of a distribution. Those tests require the definition of two periods in the
series that are being tested, a crisis period and a pre-crisis period. However the
proper dating of the crisis and pre-crisis period can be difficult. Even when it is
possible to identify the start of a crisis more easily, the choice of its end may not be
so clear. In addition, a crisis may have recurring critical events, and multiple rounds
of contagion, or there might be many sources of contagion, and the contagion from
each source may be stronger at different dates.
Instead of an exogenously defined crisis and pre-crisis periods, in this paper we
define test windows over the entire period, test each window for contagion and then
consolidate the results, looking for the dates of the windows where the contagion
statistics are higher than a critical value. For additional robustness checks, we define
multiple window sizes. In this way we overcome the problem of crisis dating through
endogenous testing.
Given the series we are testing for contagion, we define a fixed length window and
move it across the sample. Each window consists of a pre-crisis period — which
for this paper was set to two years — and a crisis period — which for robustness
testing was defined to be of three lengths: 4, 6 and 8 months. Each window starts
one week after the previous window’s start. We did this for each pair of markets i
and j in each test dimension (country equity, sovereign CDS, bank equity indexes).
We test each window by estimating a Vector Auto Regressive (VAR) model of the
source and destination market in each period (crisis and pre-crisis), controlling for
the pre-defined exogenous variables (US Federal Funds rate and Commodities index).
Each VAR is calculated with a fixed lag of 5 observations in order to eliminate resid-
ual autocorrelation. We then test the residuals for contagion as described in section
4. The result is a set of statistics for that particular test instance Ti,j,D,W,L(where
i is the source market, j the destination market, D the dimension being tested, W
11
the period window, and L the crisis window length). Such test instance is said to
be an instance of contagion if the following conditions are met:
• The volatility of the crisis period residuals must be greater than that of the
pre-crisis period
• For the FR statistic, the correlation of the crisis period residuals must be
greater than that of the pre-crisis period (we are only testing one tail of the
distribution).
• The tests results (Forbes-Riggobon correlation, CoSkewness Testing in both
directions) must be greater than a pre-established critical value (in this paper,
5.9915, which is the critical value for the χ2 distribution at 95% confidence
and 2 degrees of freedom).
These instances of contagion which meet all conditions above are all points in time
at which there was a structural break in the relationship of pairs of markets (be
it correlation of coskewness). The next step is the endogenous choice of contagion
periods. We opted to include all contagion periods in our analysis of results, as
the aim of this paper is to validate a method for finding the periods of contagion
through endogenous testing.
6 Empirical results
The empirical tests generated large volumes of data. The total number of contagion
tests for analysis was 750, 013, including 3 windows sizes, and three contagion chan-
nels (bank, equity, CDS), each having its own sample size and represented countries.
We have chosen to present the results in graphs with an accompanying analysis.
The results will be presented as follows: first, general contagion results, involving
all countries, are presented for each crisis, and then a more detailed analysis of
the contagion to Brazil is presented. The empirical results have shown that even
in absence of precise dating, the fixed-length moving window tests were capable of
identifying the most significant periods of contagion, which is further corroborated
by the fact that in most cases tests with different contagion windows agree with
each other.
12
We present two types of graphs for visualization. Both follow a common logic: we
show the evolution of the relevant indexes (normalized towards each index initial
value) and draw shaded areas to indicate contagion according to a color scale. The
shaded areas indicate the strength of the contagion statistic in window that starts
at each date. The difference in the graph types is the meaning of the strength of
contagion in the graph. In the general contagion graphs (in which contagion to all
countries is summarized), the strength of contagion is the number of different coun-
tries for which the relevant statistic indicates contagion4 at that date. The second
type is the localized contagion to Brazil, in which case the strength of contagion is
the value of the statistic, bearing in mind that only values above the certain critical
value5 are represented.
Most of the events that were referred to in the following analyses can be found in
US Federal Reserve (2008), Deutsche Bundesbank (2009, 2010), European Central
Bank (2010) and US Federal Reserve St Louis (2011).
6.1 General contagion
6.1.1 The Global Financial Crisis
Our tests for contagion through the banking sector show evidence of contagion in
the banking sector indexes throughout 2008 and into early 2009. These results are
shown graphically in figures 1, 2 and 3. Most of this contagion is detected through
structural breaks in coskewness, but both coskewness tests are consistent with each
other regarding countries affected and the contagion periods (with the CS2 statistic
detecting more instances of contagion). The results identify occurrences of contagion
from the USA to fifty-one countries (out of fifty-three) at some time in this period.
The results are also consistent across all contagion window sizes tested (four, six
and eight months). The number of countries affected in a single week peaks at 30
(from the CS2 test, 25 from CS1 test) in late February, and early March, which is
consistent with the increasing uncertainty fueled by events such as the collapse of the
subprime mortgage market stemming up to 400 billion dollars in losses (February
10th) and the nationalization of the Northern Rock (February 17h). This uncer-
4Contagion, in this paper, is indicated as a structural break in a statistic.5See section 5.
13
tainty persisted in spite of actions by the Federal Reserve and other Central Banks
beginning in December 2007 trying to increase liquidity available to banks, culmi-
nating with a fund of 200 billion dollars made available to banks by the US Federal
Reserve (March 7th). After this period, the number of countries simultaneously
affected by contagion drops, and raises again to 26 countries (from the CS1 test, 19
from CS2 test) in late September and early October as great uncertainty returns
in the wake of the Lehman Brothers bank filling for bankruptcy and the increasing
instability in the European banking sector. These results are shown graphically in
figures 1, 2 and 3.
< Place figures figures 1, 2 and 3 about here. >
The results for contagion in the MSCI equity indexes are shown graphically in fig-
ures 4, 5 and 6. The tests for contagion through equity also show ample contagion
throughout 2008, where 58 countries out of 66 tested positive for contagion, however
the contagion stops at a earlier date in 2009 than in the banking sector indexes, and
at an even earlier date (December 2008) in the tests with larger windows (6 and 8
months), which indicates that the general equity market begins its recovery faster
than the banking sector. There is one other significant difference from the banking
sector contagion in that most of the early contagion (in early 2008) is in the tests
with 8-month contagion windows, and also increases in the 6-month and 4-month
contagion windows as their end dates reach the year’s fourth quarter. This indicates
that for the general equity markets, as opposed to banks, the bulk of the contagion is
concentrated at the end of each contagion period, related to the events of September
and October (the Lehman collapse, the nationalization and partial nationalization of
European funds, the bail out funds approved by the US and European governments).
< Place figures figures 4, 5 and 6 about here. >
14
6.1.2 The European Sovereign Debt Crisis
Regarding the European Sovereign Debt Crisis, we tested for contagion originating
from the following countries: Portugal, Greece, Ireland, Italy and Spain. These
countries are at the forefront of most stories on the ongoing crisis. Also in accordance
to most coverage, the contagion tests for the European Debt Crisis have indicated
two clearly distinct periods of contagion, the first in early 2010 and the second in
late 2011.
The banking sector tests suggest that contagion in the European Sovereign Debt
Crisis is as widespread as in the Global Financial Crisis. In the first contagion
period 46 out of 49 tested countries are affected by contagion, and 48 countries in
the second period. One difference from the subprime crisis is that the FR tests are
relevant, indicating contagion to 18 countries in both periods. The results of the
FR tests as well as the CS1 and CS2 are show in figures 7, 8, 9, 10, 11 and 12.
< Place figures figures 7, 8, 9, 10, 11 and 12 about here. >
In the first period contagion is stronger starting from January 2010, reaching up
to 38 countries (from the CS2 test, 26 from CS1 test). The FR tests find most
contagion around late march through late April, with up 18 countries affected at
the same time. The epicenters of this first period seem to be the uncertainty caused
by the large Greek budget deficit, and downgrades in ratings of the Greek (starting
in December, 2009), Portuguese and Spanish debts (April 2010). By May, 2010 the
European Union agrees on a 750 billion dollars bailout plan and no new instances of
contagion are found until the second period. This suggests that such tests may be
helpful in assessing whether specific policies that are being undertaken are effective.
Initially it seems to be the case, for beginning in May, 2010 both the MSCI and bank
sector indexes for most of the source countries show a limited recovery. However by
late 2010 and early 2011 they drop once again and the second period of contagion
begins.
The second period is different from the first in that the most contagion is most rele-
vant at the end of each window, as evidenced by how the contagion windows starting
dates are aligned about 2 months apart, first the 8-month contagion windows, then
15
the 6-month windows and finally the 4-months windows. All of the contagion peri-
ods shown end in October 31th, 2011, which is the last date of the sample period in
this study. This is consistent with the stronger drops in the MSCI and bank sector
indexes of the source countries (and the rise in CDS spreads for their debts), and
also with events such as the resignation of Portugal’s Prime Minister (March 2011),
Portugal’s request for financial support from the European Union (April 2011) and
its ratings downgrades, the further downgrade of Greece’s and Ireland’s debts, and
the increasing uncertainty regarding Italy (which had its debt rating downgraded
in September) and Spain, both having to increase the yields on government issued
bonds.
The equity indexes show a very similar picture, with similar contagion strength at
similar dates. This indicates that unlike the subprime crisis, in which the bank
sector is hit first and then the equity market, in the European crisis both markets
suffer contagion at the same time. The only significant difference is in FR tests.
In the bank sector most instances of contagion are in the first period, while in the
equity market they are stronger in the second period of the crisis.
The analysis of the CDS spreads also divides the crisis into two periods, with slightly
different timing. In the first period, the contagion peak is in early May 2010 as CDS
spreads start to rise. Throughout the two crisis periods, 35 countries suffered conta-
gion in their CDS spreads according to the FR tests. The results for the CDS tests
are show in figure 13.
< Place figures figure 13 about here. >
It seems to us that contagion tests can be an important tool for regulators and
policy makers to assess the effectiveness of their policies and of the communication
of their actions.
6.2 Contagion to Brazil
The tests find evidence of contagion to Brazil in both the Global Financial Crisis and
the European Sovereign Debt Crisis. Of particular note is that most contagion is
16
found through the coskewness test, although there is also correlation-based contagion
from Greece to Brazil in the CDS spreads in 2010.
6.2.1 Global financial crisis 2008
Brazil is one of the countries affected by the Global Financial Crisis. The results
of the contagion tests to Brazil show evidence of contagion both in the bank sector
and in the general equity indexes. The first signals of contagion are detected in the
bank sector indexes. The figure 14 shows the results of the contagion tests from the
USA to Brazil, which are detected through the CS2 test with windows of 4, 6 and 8
months. We do not find any contagion to Brazil applying FR and CS1 tests for the
bank sector indexes. The contagion identified starts in the period from February 1,
2008 to June 13, 2008. For the 4-month window contagion test, the peak statistic
occurs in the period of March 21 2008 to July 21 2008, when the CS2 statistic is
22.54. Both the 6-month and the 8-month window tests have their peak starting in
February 2, 2008, with the 6-month window ending in August, 2008 and the 8-month
window ending in October, 2008. Both windows overlap the peak 4-month window.
From the tests results as shown on figure 14 we can observe that the 4, 6 and 8-
month CS2 tests possess similar findings, with many test instances overlapping, and
some intervals when the contagion results do not match exactly between the three
tests. The 6 and 8-month windows have both the most overlaps and the highest
statistics, with the strongest values in windows which start in February and run at
least until August, 2008.
The fact that the contagion is detected only in the CS2 statistic is significant, as
it signals increasing risk aversion (and therefore lower average returns) because of
the higher volatility in the US banking sector. In January 2008 the U.S. Federal
Reserve rates had a decrease of three quarters of a percentage point, which was
the biggest cut in 25 years, bringing the value down to 3.5%, and the major bond
insurer Municipal Bond Insurance Association (MBIA) announced $ 2.3 billions in
losses. In February 10, 2008 the at G7 meeting it was announced an estimated $ 400
billions in losses due to the collapse of the subprime mortgage market. In March
7th 2008 the U.S. Federal Reserve injected $200 billions to increase the liquidity of
U.S. economy, which was another signal of an unstable financial environment. In
17
March 16th 2008, J.P. Morgan Chase buys the Bear Sterns in a deal backed up by
the U.S. government. As the instability grew, risk aversion motivated investors to
seek positive skewness, which can account for the contagion to the Brazilian banking
sector throughout February and March.
<Place Figure 14 About Here>
The contagion analysis of the MSCI equity indexes also show contagion to Brazil
from the CS1 and CS2 tests. The results are shown in the figures 15 and 16. The
first observation from these graphs is that while our tests detect contagion in the
bank sector starting February, 2008, most contagion detected in the equity market
starts a few months later, becoming stronger after September, 2008, with the levels
of contagion statistics rising sharply after September 19th and peaking in early
October, 2008.
<Place Figure 15 and 16 About Here>
By May 2008, the worldwide financial market already faces an unstable environment
and this state is sustained during the following months. In July 11th the American
Federal regulators seize IndyMac Bank due to the reduced liquidity that U.S. econ-
omy faces, while the barrel of oil reaches a record price of $147.5. In July 14th 2008,
the American Financial authorities decide to assist Fannie Mae and Freddie Mac.
These government sponsored agencies have a total debt of $5 trillion and are helped
by American government as they are considered crucial to the U.S. housing market.
September 2008 is a month of highly significant events that brought about further
instability in the worldwide financial markets. In September 15th, 2008, the Lehman
Brothers announces its filing for bankruptcy. This single event is identified as the
“trigger event” of the Global Financial Crisis in some of the contagion literature (as
in Fry et al. (2008), Dungey and Yalama (2009)). We believe that the growing insta-
bility leading to the collapse of the Lehmann Brothers, and the greater instability
that follows may explain the contagion to the Brazilian economy.
We note that for the Global Financial Crisis, our tests indicate early contagion
in the banking sector, which is followed a few months later by the contagion in the
general equity market, which is stronger after the collapse of the Lehmann Brothers.
18
<Place figures 15 and 16 about here>
6.2.2 European sovereign debt crisis 2010/2011
Our tests indicate contagion from the European Sovereign Debt Crisis to Brazil.
Most of the contagion to Brazil detected by the tests is from Portugal and Spain,
with additional contagion from Italy and Greece. We find coskewness contagion
(CS1 and CS2 ) in both bank sector and equity market indexes, and correlation
contagion (FR) in the CDS spreads. Regarding the occurrences of contagion, our
tests break the European Sovereign Debt Crisis into two major periods: early 2010
and mid to late 2011. The graphs with results for the contagion tests in the bank
market are shown in figures 17 and 18.
<Place figures 17 and 18 about here>
Most contagion to Brazil is concentrated in early 2010. Coskewness contagion from
Portugal and Spain is detected in the bank sector indexes from January through
late April (early May for the CS2 tests) in 2010. The CS1 tests detect contagion
mostly through the 6 and 8-month window tests, with some periods of contagion in
all three window lengths, while the CS2 tests detect contagion in all window lengths
consistently in the whole period. The peak contagion statistics are in January 2010
in all coskewness tests for both Portugal and Spain. There is some isolated contagion
from Greece in April 2010, but only in the 8-month window tests.
<Place figures 19 and 20 about here>
As shown in figures 19 and 20 the results form the equity market tests are very simi-
lar to those of the bank sector indexes. The contagion to the Brazilian equity market
is also strongest in early 2010, mostly originating in Portugal or Spain. There is
very limited contagion from Greece in the CS1 tests with 8-month windows. The
main difference is that for the equity market indexes the contagion statistics have
their highest values in February while in the bank sector indexes it was in January.
<Place figure 21 about here>
There is also some correlation contagion detected in the CDS spreads, concentrated
19
around April 2010, originating from Italy and Greece. The contagion from Greece is
strongest both in terms of the value of the FR statistic and also in that it is present
in the tests of all window lengths. The contagion from Italy is detected only in the
6 and 8-month tests. The results for the contagion tests in CDS spreads are shown
in figure 21.
The end of 2009 and the beginning of 2010 see an increasing instability in the
European economy due to the budget deficit problems faced by Greece. In December
2009, the Greek government announces e300 billion, about 113% of its GDP, almost
the double of the Eurozone limit of 60%, and in the same month Standard & Poors
downgrades the Greek sovereign debt from A- to BBB. This is a first indication of a
financial crisis in the making in Greek economy. In February 11 2010, the European
Union (E.U.) leaders held the first emergency summit on Greece. The bank sector
and equity indexes for Portugal and Spain are also affected as the fiscal challenges
faced by those countries comes to light, and in April the ratings agencies downgrade
the Portuguese and Spanish sovereign debts. This fiscal crisis in Europe can be the
explanation for the contagion caused by Spain and Portugal in Brazilian economy,
specially since large Spanish banks and firms operate in Brazil.
In April 8th the Greek ten-year bond yield reaches 7.4%, and the spread on German
bonds reaches a Euro-era high of 442 basis points. A spike in the CDS spreads
happens in April which probably explains the CDS contagion to Brazil. In April
12 the E.U. finance ministers agree to provide e30 billion in loans to Greece in
coordination with the International Monetary Fund (IMF) which is to make available
another e15 billion in funds in the next year, after which very little contagion is
detected until 2011.
The second period of contagion is in mid to late 2011 when contagion to Brazil
is detected in both the bank and equity indexes with the CS1 and CS2 tests. In
comparing the 4, 6 and 8-month window tests a pattern appears in that the window
start dates (the dates that indicate a structural break in the statistic) are about
two-months apart for each tests. That pattern implies that the actual contagion is
stronger at the end of the windows being analysed, in September and October 2011.
Also the tests indicate contagion from both Italy and Greece in the second period,
in addition to Portugal and Spain.
20
The period from late 2010 through 2011 is marked by high volatility and generally
unstable environment, specially towards the end of the sample period. In November
2010 Ireland requests further funds from the European Union. In March 2011 the
Portuguese Prime Minister resigns, and in April Portugal also applies for further
financial support from the European Union. CDS spreads for Greece soars to 45
times their December 2008 levels, and the spreads for Portugal, Spain, Ireland and
Italy also rise sharply in 2011. All this contributed to general financial instability
which influences the contagion to Brazil in 2011.
7 Concluding remarks
In this paper we have developed an approach for the timing of the contagion in a
financial crisis through endogenous testing. We have shown that in the case of the
Global Financial Crisis and the European Sovereign Debt Crisis the timing thus
obtained is consistent with the important events and the general consensus of the
crisis dating.
Our results show that contagion has been pervasive in the Global Financial Crisis
and also in the European Sovereign Debt Crisis. The approach developed in this
paper allows identifying contagion for a wide range of markets and countries and
could be helpful for the design of specific stabilization policies. It is our understand-
ing that these tests might be an additional tool for regulators and policy makers to
assess the effectiveness of their policies and the communication of their actions.
References
Niklas Ahlgren and Jan Antell. Stock market linkages and financial contagion: A
cobreaking analysis. Quarterly Review of Economics and Finance, 50(2):157–166,
2010.
Riadh Aloui, Mohamed Safouane, Ben Aıssa, and Duc Khuong. Global financial
crisis , extreme interdependences , and contagion effects: The role of economic
structure? Journal of Banking & Finance, 35(1):130–141, 2011.
21
Sergio Andenmatten and Felix Brill. Measuring co-movements of cds premia during
the greek debt crisis. Diskussionsschriften dp1104, Universitaet Bern, Departe-
ment Volkswirtschaft, 2011.
Lieven Baele and Koen Inghelbrecht. Time-varying integration, interdependence and
contagion. Journal of International Money and Finance, 29(5):791–818, 2010.
Dirk Baur and Niels Schulze. Coexceedances in financial markets a quantile regres-
sion analysis of contagion. Emerging Markets Review, 6(1):21–43, 2005.
Dirk G Baur. Financial contagion and the real economy. Journal of Banking &
Finance, page forthcoming, 2011.
Dirk G Baur and Brian M Lucey. Flights and contagion – an empirical analysis of
stock bond correlations. Journal of Financial Stability, 5(4):339–352, 2009.
Vincent Bodart and Bertrand Candelon. Evidence of interdependence and contagion
using a frequency domain framework. Emerging Markets Review, 10(2):140–150,
2009.
Giovanni Calice and Christos Ioannidis. An empirical analysis of
the impact of the credit default swap index market on large com-
plex financial institutions. Accessed in 03.01.2012, 2009. URL
http://papers.ssrn.com/sol3/papers.cfm?abstract id=1447403.
Bertrand Candelon, Alain Hecq, and Willem F C Verschoor. Measuring common
cyclical features during financial turmoil: Evidence of interdependence not con-
tagion. Journal of International Money and Finance, 24(8):1317–1334, 2005.
Fabio Castiglionesi. Financial contagion and the role of the central bank. Journal
of Banking & Finance, 31(1):81–101, 2007.
Thomas C Chiang, Bang Nam, and Huimin Li. Dynamic correlation analysis of
financial contagion: Evidence from asian markets. Journal of International Money
and Finance, 26(7):1206–1228, 2007.
A Cipollini and G Kapetanios. Forecasting financial crises and contagion in asia
using dynamic factor analysis. Journal of Empirical Finance, 16(2):188–200, 2009.
22
Stijn Claessens and Kristin Forbes. International financial contagion: The the-
ory, evidence and policy implications. Accessed in 03.01.2012, 2004. URL
http://web.mit.edu/kjforbes/www/Shorter%20Articles/.
Giancarlo Corsetti, Marcello Pericoli, and Massimo Sbracia. ”some contagion ,
some interdependence”: More pitfalls in tests of financial contagion. Journal of
International Money and Finance, 24(8):1177–1199, 2005.
Virginie Coudert and Mathieu Gex. Contagion inside the credit default swaps market
: The case of the gm and ford crisis in 2005. Journal of International Financial
Markets, Institutions & Money, 20(2):109–134, 2010.
Deutsche Bundesbank. Annual report. 2009.
Deutsche Bundesbank. Annual report. 2010.
Mardi Dungey and Abdullah Yalama. Detecting contagion with correlation: Volatil-
ity and timing matter. CAMA Working Papers 2009-23, Australian National
University, Centre for Applied Macroeconomic Analysis, 2009.
Mardi Dungey, Renee Fry, Brenda Gonzalez-Hermosillo, and Vance L. Martin.
Sampling properties of contagion tests. Accessed in 03.01.2012, 2005. URL
http://www.essex.ac.uk/ccfea/summerschool/.
Mardi Dungey, Brenda Gonz, and Vance Martin. Contagion in international bond
markets during the russian and the ltcm crises. Journal of Financial Stability, 2
(1):1–27, 2006.
Mardi Dungey, Renee Fry, Brenda Gonzalez-Hermosillo, Vance L Martin, and Chris-
min Tang. Are financial crises alike? CAMA Working Papers 2008-15, Australian
National University, Centre for Applied Macroeconomic Analysis, 2008.
European Central Bank. Annual report. 2010.
Kam Fong, Sirimon Treepongkaruna, Robert Brooks, and Stephen Gray. Asset mar-
ket linkages: Evidence from financial, commodity and real estate assets. Journal
of Banking & Finance, 35(6):1415–1426, 2011.
23
Kristin Forbes and Roberto Rigobon. Contagion in latin america: Definitions, mea-
surement, and policy implications. NBER Working Papers 7885, National Bureau
of Economic Research, Inc, 2000.
Kristin J Forbes and Roberto Rigobon. No contagion, only interdependence: Mea-
suring stock market comovements. The Journal of Finance, 57(5):2223–2261,
2002.
Renee Fry, Vance L Martin, and Chrismin Tang. A new class of tests of contagion
with applications to real estate markets. CAMA Working Papers 2008-01, Aus-
tralian National University, Centre for Applied Macroeconomic Analysis, 2008.
Renee Fry, Yu Ling Hsiao, and Chrismin Tang. A comparison of seven
crises: Coskewness contagion testing. Accessed in 03.01.2012, 2010. URL
http://www.buseco.monash.edu.au/eco/research/seminars/renee-fry.pdf.
Brenda Gonzalez-Hermosillos, Renee Fry, Mardi Dungey, and Vance L Martinin.
Empirical modelling of contagion: a review of methodologies. Quantitative Fi-
nance, 5(1):9–24, 2005.
Campbell R. Harvey and Akhtar Siddique. Conditional skewness in asset pricing
tests. The Journal of Finance, 55:1263–1295, 2000.
Sebastian Heise and Reimer K. Derivatives and credit contagion
in interconnected networks. Accessed in 03.01.2012, 2011. URL
http://www.mth.kcl.ac.uk/~kuehn/published/CDS.pdf.
Cody Yu-ling Hsiao, Renee Fry, Cody Yu-ling Hsiao, and Chrismin Tang. Actu-
ally this time is different. CAMA Working Papers 2011-12, Australian National
University, Centre for Applied Macroeconomic Analysis, 2011.
Keunho Hwang. Contagion effects in cds markets. Accessed in 03.01.2012, 2008. URL
http://business.kaist.ac.kr/re center/fulltext/2008/2008-086.pdf.
Saleheen Khan and Kwang Woo Ken Park. Contagion in the stock markets: The
asian financial crisis revisited. Journal of Asian Economics, 20(5):561–569, 2009.
Alan Kraus and Robert H. Litzenberger. Skewness preference and the valuation of
risk assets. The Journal of Finance, 31(4):1085–1100, 1976.
24
Francis A Longstaff, Freddie Mac, Lehman Brothers, and Indymac Bank. The
subprime credit crisis and contagion in financial markets. Journal of Financial
Economics, 97(3):436–450, 2010.
Alberto Manconi, Massimo Massa, and Ayako Yasuda. The role of institutional
investors in propagating the crisis of 2007-2008. Journal of Financial Economics,
page forthcoming, 2011.
Michael Manz. Information-based contagion and the implications for financial
fragility. European Economic Review, 54(7):900–910, 2010.
Sheri Markose, Simone Giansante, Mateusz Gatkowski, and Ali Rais Shaghaghi. Too
interconnected to fail: Financial contagion and systemic risk in network model of
cds and other credit enhancement obligations of us banks. Economics Discussion
Papers 683, University of Essex, Department of Economics, February 2010.
Enrique G Mendoza and Vincenzo Quadrini. Financial globalization, financial crises
and contagion. Journal of Monetary Economics, 57(1):24–39, 2010.
Paolo Emilio Mistrulli. Assessing financial contagion in the interbank market: Max-
imum entropy versus observed interbank lending patterns. Journal of Banking &
Finance, 35(5):1114–1127, 2011.
Omar Pe, Gallo Dey, Fernando Avila Embriz, and Fabrizio Lo. Systemic risk, finan-
cial contagion and financial fragility. Journal of Economic Dynamics & Control,
34(11):2358–2374, 2010.
Roberto Rigobon. Contagion: How to measure it? In Preventing Currency Crises in
Emerging Markets, NBER Chapters, pages 269–334. National Bureau of Economic
Research, Inc, 2002.
Roberto Rigobon. Identification through heteroskedasticity. The Review of Eco-
nomics and Statistics, 85(4):777–792, 2003.
Caroline Van Rijckeghem and Beatrice Weder. Sources of contagion: is it finance or
trade? Journal of International Economics, 54(2):293–308, 2001.
Juan Carlos Rodriguez. Measuring financial contagion: A copula approach. Journal
of Empirical Finance, 14(3):401–423, 2007.
25
Dobromil Serwa and Martin T Bohl. Financial contagion vulnerability and re-
sistance: A comparison of european stock markets. Economic Systems, 29(3):
344–362, 2005.
US Federal Reserve. Annual report. 2008.
US Federal Reserve St Louis. The financial crisis - a timeline of events and policy
actions. http://timeline.stlouisfed.org/, 2011.
26
A Contagion literature contribution tables
A summary of the contributions from recent literature on financial contagion.
Table 1: Summary of the contributionsAuthors Period Contagion Market Method Contagion
evidence
Baele and
Inghelbrecht
(2010)
1973-2007 14 European countries Equity mar-
kets
Dynamic factor model Mixed find-
ings
Longstaff et al.
(2010)
2006-2008 CDOs - other markets in
US
CDOs mar-
ket
Correlation test YES
Manconi et al.
(2011)
2004-2007 Securitized bond market -
corporate bond market in
US
Bond Mar-
ket
Correlation test YES
Pe et al. (2010) 2007-2009 Mexico Interbank
market
Systemic Risk Network
Model (SyRNet)
YES
Ahlgren and
Antell (2010)
1980-2006 Effects between Germany,
Japan, UK and US and be-
tween Hong Kong, Korea,
Mexico and US
Stock mar-
ket
Correlation break NO
Rijckeghem
and Weder
(2001)
1994, 1996
and 1997
Mexican, Thai, and Rus-
sian currency crises - 18 in-
dustrialized countries
Bank lend-
ing market
Correlation test YES
Corsetti et al.
(2005)
October
1997
Stock market returns in
Hong Kong - 10 emerging
economies as well as the
G7 countries
Stock mar-
ket
Correlation break YES
Rodriguez
(2007)
1993-1998 Asian crisis and Mexican
crisis
Stock mar-
ket
Copula YES
Candelon et al.
(2005)
1994 and
1997
Mexican crises in Ar-
gentina, Venezuela,
Colombia, Chile and Hong
Kong crisis in Indone-
sia, Korea, Malaysia,
Singapore, Taiwan and
Thailand
Stock mar-
ket
Common cyclical features NO
Aloui et al.
(2011)
2004-2009 US - Brazil, Russia, India,
China (BRIC)
Stock mar-
ket
Copula NO
Baur and
Lucey (2009)
1994-2006 Thailand (July 1997),
Hong Kong (October
1997) and Russia (August
1998) - eight developed
countries
Stock and
bond mar-
ket
Test for flight-to qual-
ity, flight-from-quality and
cross-asset contagion
YES
Mendoza
and Quadrini
(2010)
1982-2008 Effects of shocks to bank
equities on asset prices
Net credit
market
Model’s quantitative pre-
dictions
YES
27
Table 2: Summary of the contributionsAuthors Period Contagion Market Method Evidence of
Contagion
Dungey et al.
(2006)
1998 Russian bond default and
the LTCM recapitalization
announcement - 12 coun-
tries
Bond mar-
ket
Latent factor model YES
Baur and
Schulze (2005)
1997-2001 Asia - Latin America and
Europe
Stock mar-
ket
Quantile regression YES
Fong et al.
(2011)
1987-2008 Between financial assets
(US stocks and Treasury
bonds), commodities (oil
and gold) and real estate
assets (US CaseShiller in-
dex)
Stock mar-
ket
General Markov switching
model
Mixed find-
ings
Hwang (2008) 2006-2008 Banks of Asia, Europe,
Latin America, North
America, and Oceania
CDS market Approach proposed by
Rigobon (2003)
NO
Markose et al.
(2010)
2008 US banks CDS market Systemic Risk Ratio,
Complex Adaptive Sys-
tem (CAS), Agent-based
Computational Economics
(ACE) and SCAP Stress
Test
YES
Andenmatten
and Brill
(2011)
2008-2010 Greek debt crisis - 39
countries
CDS market Approach proposed by
Forbes and Rigobon
(2002)
YES
Calice and
Ioannidis
(2009)
2005-2008 European and American
banks
CDS and eq-
uity market
SVAR Mixed find-
ings
Coudert and
Gex (2010)
2004-2007 General Motors (GM) and
Ford crisis in 2005 - US
and European firms
CDS market Dynamic measures of
correlations(EWMA and
DCC-GARCH)
YES
Rigobon
(2002)
1994-1998 Asian crisis, Mexican cri-
sis, Russian crisis and
LTCM crisis
Bond and
stock mar-
kets
OLS, PCA and new pro-
cedure based in Rigobon
(2003)
Mixed find-
ings
Dungey et al.
(2008)
1998-2001
and 2007
Russia, LTCM, Brazil,
dot-com, Argentina and
U.S. subprime mortgage
and credit crisis
Bond and
stock mar-
kets
Related to the work of ? Mixed find-
ings
Dungey and
Yalama (2009)
2004-2009 US - European equity mar-
kets during the global fi-
nancial crisis
Stock mar-
ket
The FR test and Hong test Mixed find-
ings
Forbes and
Rigobon
(2002)
1987-1996 1997 Asian crisis, 1994
Mexican crisis and 1987
U.S. market crash
Stock mar-
ket
Heteroskedasticity biases
tests for contagion based
on correlation coefficients
Mixed find-
ings
Fry et al.
(2008)
1997-1998
and 2007
Hong Kong crisis and the
US subprime mortgage cri-
sis
Real estate
and equity
markets
Coskewness and Lagrange
multiplier tests
YES
Fry et al.
(2010)
1995-2010 1997 Asian crisis, the 1998
Russian crisis, the 1998
LTCM, the 2000 dot-com
crisis and the global finan-
cial crisis
Equity mar-
ket
Correlation and coskew-
ness
YES
28
Table 3: Summary of the contributionsAuthors Period Contagion Market Method Contagion
evidence
Gonzalez-
Hermosillos
et al. (2005)
1997-1998 1997 Asian crisis Equity mar-
ket
Model of interdependence,
Bivariate and Multi-
variate testing, AR and
heteroskedastic dynam-
ics, Forbes and Rigobon
(2002)contagion test, the
BKS test and the DFGM
test
Mixed find-
ings
Heise and K
(2011)
2008 26 major US banks CDS market Macroscopic System Dy-
namics
YES
Hsiao et al.
(2011)
1997-1998
and 2008-
2010
Asian crisis, Russian crisis,
LTCM crisis, Brazil crisis,
dot-com crisis, Argen-
tinean crisis, sub-prime
crisis, great recession,
global financial crisis
Interdependencies, corre-
lation and coskewness
Mixed find-
ings
Forbes and
Rigobon
(2000)
1982-2000 Mexican Debt Crisis,
Asian Flu, the Russian
Crisis, the Brazilian Cri-
sis, Dot-com crisis - Latin
America
Bond and
stock mar-
kets
GARCH model and Het-
eroskedasticity biases tests
NO
Bodart and
Candelon
(2009)
1994 and
1997
Mexican crisis - 4 Latin
America countries and
Asian crisis - 7 Asian
countries
Equity mar-
ket
Frequency domain ap-
proach
YES
Cipollini and
Kapetanios
(2009)
1997-1998 East Asian countries Stock mar-
ket
Dynamic Factor model YES
Khan and
Ken Park
(2009)
1994-1999 Asian crisis - Thailand,
Malaysia, Indonesia, Ko-
rea and Philippines
Stock mar-
ket
Kalman filter YES
Mistrulli
(2011)
1989-2008 Italy Interbank
market
Maximum entropy method YES
Chiang et al.
(2007)
1990-2003 Asian crisis - 9 Asian mar-
ket and US
Stock mar-
ket
Dynamic conditional-
correlation model
YES
Serwa and
Bohl (2005)
1997-2002 6 Crisis markets - Western
European markets, and
emerging markets in Cen-
tral and Eastern Europe
Stock mar-
ket
Heteroscedasticity-
adjusted correlation
YES
Baur (2011) 1979-2009 Financial sector - real
economy of ten sectors in
25 developed and emerging
countries
Stock mar-
ket
Test four alternative types
of contagion
YES
29
B Descriptive statistics tables
The descriptive statistics are taken from log returns of each series and where available
are shown for four periods:
2006–2007 The first pre-crisis period, from January, 2006 to December 2007.
2008–2009 The Global Financial Crisis Period
2010 The first year of the European Sovereign Debt Crisis
2011 The second year of the European Sovereign Debt Crisis
In addition, we also provide the statistics for the following samples:
2006–2011 The full sample
2008–2011 The period of both crisis
Dec, 2009 to October, 2011 The full European Sovereign Debt Crisis period
The exception are the CDS statistics, as in our sample only data from December
2007 and onward was available (and not for all countries), and therefore the CDS
descriptive statistics start in 2008.
B.1 Bank sector indexes statistics
Tables 4, 5, 6, 7, 8, 9, 10, show the descriptive statistics the bank sector indexes for
the seven periods.
30
Country # Obs Mean Std Median Skewness Kurtosis
ARGENTINA 520 0.000619 0.014454 0.000157 -0.590350 9.604432
AUSTRALIA 520 0.000692 0.012697 0.000716 -0.532294 6.021981
AUSTRIA 520 0.000944 0.016696 0.001183 -0.157361 5.190378
BELGIUM 520 0.000328 0.013104 0.001145 -0.364033 4.520833
BRAZIL 520 0.001684 0.021202 0.002124 -0.306839 4.724507
BULGARIA 520 0.002283 0.020701 0.001134 0.747248 9.545587
CANADA 520 0.000412 0.009973 0.001026 -0.254174 5.403890
CHILE 520 0.000371 0.011165 0.000000 -0.161698 5.793267
CHINA 520 0.001567 0.018878 0.000150 0.391767 5.971363
COLOMBIA 520 0.000386 0.021112 0.000000 0.048372 7.314018
CYPRUS 520 0.002465 0.017936 0.002500 -0.501709 5.948538
CZECHREPUBLIC 520 0.001035 0.016890 0.001565 0.109021 7.718293
DENMARK 520 0.000362 0.012028 0.001054 -0.302966 4.925700
EGYPT 520 0.000661 0.017161 0.000000 -1.823305 27.178386
FINLAND 520 0.000577 0.017403 0.000122 0.029204 4.612701
FRANCE 520 0.000375 0.014179 0.000869 -0.250216 4.397100
GERMANY 520 0.000614 0.012967 0.000610 -0.182097 4.422766
GREECE 520 0.000806 0.014329 0.001305 -0.169234 5.452423
HONGKONG 520 0.000327 0.008691 0.000000 -0.138555 5.820530
HUNGARY 520 0.000823 0.022710 0.000647 -0.171828 3.943163
INDIA 520 0.001714 0.020704 0.000960 -0.254030 4.703531
INDONESIA 361 0.000000 0.000000 0.000000 - -
IRELAND 520 0.000050 0.016773 0.000731 0.017026 5.388401
ISRAEL 520 0.000318 0.016469 0.000502 -0.399313 4.617180
ITALY 520 0.000557 0.011216 0.000839 -0.278641 4.121290
JAPAN 520 -0.000734 0.016168 0.000000 0.093503 3.872499
KOREA 520 0.000257 0.028400 0.000000 0.023133 5.377866
LUXEMBURG 520 0.000756 0.013405 0.000295 3.412165 43.786469
MALAYSIA 520 0.000974 0.010155 0.001157 -0.504143 5.896913
MALTA 520 0.000403 0.011806 0.000438 0.193134 8.698110
MEXICO 520 0.001216 0.014609 0.001690 -0.371231 5.256371
NETHERLANDS 520 0.000613 0.013984 0.001308 -0.464848 6.225914
NORWAY 520 0.000662 0.015996 0.001003 -0.323443 4.519159
PAKISTAN 520 0.001230 0.018852 0.001697 -0.552796 4.196439
PERU 520 0.001411 0.016898 0.000457 0.313987 18.755265
PHILIPPINES 520 0.001383 0.015109 0.001166 -0.441561 6.097302
POLAND 520 0.001385 0.017769 0.000957 0.027664 3.641643
PORTUGAL 520 0.000991 0.011117 0.000829 0.100541 5.873792
ROMANIA 520 0.001759 0.020131 0.000852 0.163712 5.587951
RUSSIA 520 0.001973 0.023664 0.000794 0.193051 7.485284
SINGAPORE 520 0.000759 0.013859 0.001845 -0.240927 5.956405
SLOVENIA 520 0.001347 0.013470 0.000584 1.689543 16.160637
SOUTHAFRICA 520 0.000217 0.023299 0.000475 -0.064671 3.959052
SPAIN 520 0.000784 0.011505 0.001016 -0.216854 4.258924
SRILANKA 520 0.000624 0.010685 0.000000 0.869581 10.850093
SWEDEN 520 0.000567 0.015798 0.000772 0.032543 4.389861
SWITZERLAND 520 0.000198 0.012517 0.000174 -0.262788 4.162156
TAIWAN 520 0.000564 0.015868 0.000000 -0.090499 4.924303
THAILAND 520 0.000676 0.020058 0.000000 -1.973022 30.290357
TURKEY 520 0.000790 0.026937 -0.000079 -0.272811 4.878868
UK 520 0.000008 0.012329 0.000530 -0.361721 5.109540
USA 520 -0.000344 0.011470 0.000000 -0.150048 8.593604
VENEZUELA 520 0.001529 0.029243 0.000000 2.829551 49.217398
Table 4: Banking sector descriptive stats Jan/2006 – Dec/2007
31
Country # Obs Mean Std Median Skewness Kurtosis
ARGENTINA 522 -0.000218 0.019219 0.000394 -0.166238 6.070029
AUSTRALIA 522 -0.000281 0.032362 -0.001075 -0.200889 5.369414
AUSTRIA 522 -0.001279 0.039603 -0.001543 -0.113223 5.059871
BELGIUM 522 -0.002664 0.047303 -0.000735 -0.417577 6.396159
BRAZIL 522 0.000067 0.032723 0.000300 -0.151083 7.932044
BULGARIA 522 -0.003971 0.040555 -0.002275 -0.721949 10.321024
CANADA 522 -0.000089 0.028503 0.000170 -0.180192 8.255457
CHILE 522 0.000275 0.019884 0.000000 -0.165119 10.457416
CHINA 522 0.000067 0.032922 0.000000 0.488135 7.400816
COLOMBIA 522 0.000270 0.025083 0.000000 -0.281690 5.723798
CYPRUS 522 -0.002231 0.034734 -0.001428 0.250953 5.088625
CZECHREPUBLIC 522 -0.000228 0.039097 0.000000 -0.275926 6.796642
DENMARK 522 -0.001152 0.031426 -0.002474 -0.251032 5.574151
EGYPT 522 -0.000498 0.015026 0.000124 -1.776116 17.627665
FINLAND 522 -0.000681 0.037086 -0.001082 -0.237518 6.657119
FRANCE 522 -0.001025 0.037400 -0.002314 0.258936 5.674337
GERMANY 522 -0.001529 0.038498 -0.000937 -0.044350 7.132181
GREECE 522 -0.001929 0.034206 -0.000827 0.099985 4.487083
HONGKONG 522 -0.000514 0.028428 -0.000255 -0.672463 14.073710
HUNGARY 522 -0.001078 0.049641 0.001101 0.061224 6.621774
INDIA 522 -0.000452 0.033511 0.000000 0.331826 6.188885
INDONESIA 380 0.000000 0.000000 0.000000 - -
IRELAND 522 -0.004671 0.083086 -0.003454 -1.240117 17.323549
ISRAEL 522 -0.000088 0.027220 -0.001369 0.367847 5.646763
ITALY 522 -0.001230 0.030095 -0.000042 -0.028779 5.904428
JAPAN 522 -0.001139 0.027175 0.000000 0.291976 5.391789
KOREA 522 -0.000900 0.067186 0.000000 -0.115952 9.339942
LUXEMBURG 522 -0.000914 0.020376 -0.000682 -0.236124 6.587301
MALAYSIA 522 0.000020 0.014838 0.000000 -0.359588 6.544997
MALTA 522 -0.000720 0.015286 -0.000337 -0.289260 5.983743
MEXICO 522 -0.000256 0.023154 0.000000 0.094989 7.081671
NETHERLANDS 522 -0.004875 0.068464 -0.001938 -13.076160 244.443736
NORWAY 522 -0.000622 0.050715 -0.001225 0.115408 6.873780
PAKISTAN 522 -0.002169 0.023758 -0.000836 0.054612 4.172016
PERU 522 0.000606 0.012429 0.000788 -0.805350 8.065648
PHILIPPINES 522 -0.000625 0.018576 0.000000 -0.481604 7.956635
POLAND 522 -0.000948 0.035219 0.000763 -0.139609 5.389508
PORTUGAL 522 -0.001848 0.023867 -0.001353 -0.016061 5.612927
ROMANIA 522 -0.001923 0.036717 0.000000 -0.580941 6.648371
RUSSIA 522 -0.000983 0.046500 -0.000038 0.200840 10.901804
SINGAPORE 522 0.000014 0.023456 0.000000 -0.013562 6.005075
SLOVENIA 522 -0.002198 0.021867 -0.000532 -0.462847 9.385384
SOUTHAFRICA 522 -0.000140 0.033148 -0.000714 -0.168576 4.891392
SPAIN 522 -0.000554 0.030489 -0.000399 0.129597 6.061723
SRILANKA 522 0.000411 0.015162 0.000000 1.624311 14.418828
SWEDEN 522 -0.000715 0.040995 -0.001448 0.468607 5.508695
SWITZERLAND 522 -0.000965 0.034512 -0.002369 0.435666 5.755983
TAIWAN 522 -0.000109 0.025917 0.000000 0.095901 4.108610
THAILAND 522 0.000012 0.022480 0.000000 -0.549933 7.567879
TURKEY 522 -0.000449 0.036183 -0.000823 -0.020338 6.090708
UK 522 -0.001666 0.040308 -0.002041 -0.060167 7.777896
USA 522 -0.001289 0.048103 -0.002385 0.191373 6.518418
VENEZUELA 522 -0.000261 0.008758 0.000000 0.252503 7.715279
Table 5: Banking sector descriptive stats Jan/2008 – Dec/2009
32
Country # Obs Mean Std Median Skewness Kurtosis
ARGENTINA 260 0.002414 0.017867 0.001202 0.169234 5.556356
AUSTRALIA 260 0.000201 0.019202 -0.000084 -0.218550 3.934581
AUSTRIA 260 0.000452 0.025794 0.000205 0.230524 6.632592
BELGIUM 260 -0.001289 0.028583 -0.000796 0.903465 10.809238
BRAZIL 260 0.000302 0.018864 0.000051 -0.173708 4.211232
BULGARIA 260 -0.000418 0.017617 -0.001356 0.261312 4.490354
CANADA 260 0.000441 0.013726 0.001581 -0.256896 4.016748
CHILE 260 0.002024 0.013083 0.002740 -0.041173 4.366897
CHINA 260 0.000049 0.014778 0.000084 0.021502 3.131485
COLOMBIA 260 0.002181 0.014557 0.000000 -0.329838 4.339596
CYPRUS 260 -0.001894 0.026939 -0.002422 0.185961 3.235068
CZECHREPUBLIC 260 0.000404 0.023770 -0.001256 0.226601 4.258737
DENMARK 260 0.000430 0.022157 0.000263 0.324360 6.855495
EGYPT 260 0.001113 0.010518 0.000342 -0.173304 4.300743
FINLAND 260 0.000407 0.024921 0.000448 2.003740 21.780264
FRANCE 260 -0.000898 0.028810 -0.001139 1.002511 11.253187
GERMANY 260 -0.000598 0.019890 -0.000154 0.062628 4.496898
GREECE 260 -0.003601 0.034919 -0.004368 0.582235 4.670117
HONGKONG 260 -0.000049 0.012156 0.000187 -0.515819 5.541941
HUNGARY 260 -0.000692 0.034556 0.000740 -0.172197 7.382550
INDIA 260 0.001262 0.015915 0.001779 -0.435324 3.875134
INDONESIA 260 0.000000 0.000000 0.000000 - -
IRELAND 260 -0.003849 0.056453 -0.007688 -0.221161 5.787060
ISRAEL 260 0.000393 0.014101 0.000095 -0.086436 3.306542
ITALY 260 -0.001645 0.026168 -0.002249 0.813684 10.358664
JAPAN 260 0.000332 0.013072 0.000000 0.069497 2.967295
KOREA 260 0.000701 0.040904 0.000000 0.034640 6.018607
LUXEMBURG 260 -0.000472 0.009419 -0.000528 0.013897 3.911840
MALAYSIA 260 0.001321 0.009555 0.001812 -0.233469 5.989394
MALTA 260 0.000043 0.015163 -0.000485 0.945892 18.583767
MEXICO 260 0.001378 0.014541 0.000841 0.351298 5.067308
NETHERLANDS 260 -0.001042 0.017234 -0.001947 -0.273342 7.819482
NEWZEALAND 171 0.000547 0.009766 0.000533 -0.315885 4.514111
NORWAY 260 0.000963 0.023912 -0.000762 0.258029 4.059644
PAKISTAN 260 0.000244 0.012576 0.000000 -0.375174 5.048039
PERU 260 0.000972 0.008239 0.000399 0.318768 5.355755
PHILIPPINES 260 0.001597 0.013568 0.000853 -0.157904 4.408128
POLAND 260 0.000381 0.021642 0.001070 0.081533 5.553242
PORTUGAL 260 -0.001810 0.024308 -0.001382 0.570875 7.673021
ROMANIA 260 -0.000652 0.024113 -0.000339 -0.117407 8.845307
RUSSIA 260 0.000944 0.022162 0.001076 -0.047801 4.863803
SINGAPORE 260 0.000261 0.011515 0.000400 0.163337 4.976735
SLOVENIA 260 -0.000635 0.011837 -0.000810 -0.049364 3.618023
SOUTHAFRICA 260 0.000890 0.019088 0.002344 0.026081 5.044331
SPAIN 260 -0.001761 0.029433 -0.000146 1.018609 12.246563
SRILANKA 260 0.003212 0.013180 0.000908 1.083285 6.254862
SWEDEN 260 0.000600 0.022704 0.001292 0.411054 6.898844
SWITZERLAND 260 -0.000011 0.017232 0.000685 -0.109586 4.900890
TAIWAN 260 0.001842 0.015556 0.001566 0.401084 4.805720
THAILAND 260 0.001545 0.015097 0.001145 0.301041 4.799045
TURKEY 260 0.000642 0.021905 0.001684 0.002890 5.788560
UK 260 0.000011 0.020324 -0.000708 0.550977 7.815149
USA 260 0.000415 0.018136 -0.000007 -0.129302 3.557702
VENEZUELA 260 -0.002715 0.043073 0.000000 -15.573435 248.346078
Table 6: Banking sector descriptive stats Jan/2010 – Dec/2010
33
Country # Obs Mean Std Median Skewness Kurtosis
ARGENTINA 232 -0.003004 0.017004 -0.002122 -0.510635 4.995972
AUSTRALIA 232 -0.000739 0.019847 -0.001339 -0.023724 4.127064
AUSTRIA 232 -0.004236 0.029528 -0.003259 -0.079110 4.723811
BELGIUM 232 -0.005627 0.038382 -0.003081 -0.042788 4.687930
BRAZIL 232 -0.001782 0.022607 -0.000176 -0.644381 5.314769
BULGARIA 232 -0.000799 0.023865 -0.000494 0.002609 4.756793
CANADA 232 -0.000732 0.015003 0.000339 -0.315002 4.140143
CHILE 232 -0.001232 0.017683 0.000000 -0.746383 7.086944
CHINA 232 -0.001474 0.020306 -0.000161 -0.185083 4.977996
COLOMBIA 232 -0.000946 0.011642 0.000000 -0.196672 3.143590
CYPRUS 232 -0.006432 0.035665 -0.006673 0.381228 5.518195
CZECHREPUBLIC 232 -0.001710 0.025660 0.000000 -0.648221 5.691419
DENMARK 232 -0.002485 0.027842 -0.003374 -0.008343 4.565555
EGYPT 232 -0.002706 0.013759 -0.000224 -1.843563 10.284430
FINLAND 232 -0.001569 0.026423 -0.001238 -0.031959 4.203201
FRANCE 232 -0.003367 0.038967 -0.002635 0.151611 6.506396
GERMANY 232 -0.002371 0.030902 0.000051 0.274376 6.429108
GREECE 232 -0.005725 0.049432 -0.005662 0.379080 5.864355
HONGKONG 232 -0.001566 0.016588 -0.000085 -0.758618 6.327160
HUNGARY 232 -0.002816 0.038020 -0.003180 -0.212706 5.819219
INDIA 232 -0.002285 0.018282 -0.000268 -0.085514 3.440229
INDONESIA 232 0.000000 0.144218 0.000000 0.000000 23.200000
IRELAND 232 -0.006115 0.045972 -0.009433 1.297932 10.270732
ISRAEL 232 -0.002426 0.023027 -0.000449 -0.534392 4.878025
ITALY 232 -0.002929 0.036485 -0.000204 -0.385368 3.728629
JAPAN 232 -0.000715 0.016915 0.000000 -0.567729 7.420883
KOREA 232 -0.001748 0.040308 0.000000 -0.156287 7.023475
LUXEMBURG 232 -0.003459 0.019097 -0.000238 -4.838602 42.668485
MALAYSIA 232 -0.000645 0.010905 0.000177 -0.300810 5.412049
MALTA 232 -0.000897 0.011235 0.000484 -0.838203 7.474445
MEXICO 232 -0.001420 0.018882 0.000516 -1.084153 7.619657
NETHERLANDS 232 -0.001551 0.018425 -0.002434 -0.393083 5.369323
NEWZEALAND 232 -0.000135 0.009824 0.000262 -0.115865 4.703125
NORWAY 232 -0.001852 0.028752 -0.000273 -0.288607 4.012006
PAKISTAN 232 -0.000895 0.012210 -0.000521 -0.026181 4.466422
PERU 232 -0.001293 0.015064 -0.000376 -0.961530 12.595904
PHILIPPINES 232 -0.000143 0.013407 0.000537 -0.370692 4.454036
POLAND 232 -0.001863 0.024362 -0.000218 -0.670403 5.545151
PORTUGAL 232 -0.004749 0.030448 -0.000880 -0.462161 4.646785
ROMANIA 232 -0.000946 0.020977 0.000000 -0.317095 4.533513
RUSSIA 232 -0.001624 0.025181 -0.001237 -0.704336 5.172727
SINGAPORE 232 -0.001002 0.015687 0.000474 -0.474628 4.395265
SLOVENIA 232 -0.004764 0.024619 -0.003051 -2.464696 19.635465
SOUTHAFRICA 232 -0.001400 0.020720 -0.000006 -0.057645 3.939442
SPAIN 232 -0.001540 0.028099 -0.000826 0.120031 3.770457
SRILANKA 232 -0.001753 0.009298 -0.001000 -0.285172 5.520857
SWEDEN 232 -0.001683 0.028560 0.000041 -0.131322 4.696123
SWITZERLAND 232 -0.001833 0.021796 -0.001224 -0.255338 5.541279
TAIWAN 232 -0.001536 0.019500 0.000000 -0.349441 4.092210
THAILAND 232 -0.000657 0.018943 0.000000 0.211807 4.456940
TURKEY 232 -0.002280 0.025095 -0.000131 -0.561058 4.116105
UK 232 -0.002144 0.023667 -0.000981 -0.174394 4.539747
USA 232 -0.002071 0.024528 -0.001832 -0.428191 6.418209
VENEZUELA 232 0.001376 0.010422 0.000000 5.262368 60.838490
Table 7: Banking sector descriptive stats Jan/2011 – Oct/2011
34
Country # Obs Mean Std Median Skewness Kurtosis
ARGENTINA 1520 0.000277 0.017177 0.000171 -0.229732 6.835930
AUSTRALIA 1520 0.000188 0.023047 0.000028 -0.277147 7.989611
AUSTRIA 1520 -0.000448 0.029470 0.000000 -0.158627 7.205670
BELGIUM 1520 -0.001444 0.033975 0.000000 -0.368549 10.122763
BRAZIL 1520 0.000506 0.025598 0.000738 -0.268991 8.732057
BULGARIA 1520 -0.000626 0.029087 -0.000101 -0.734311 15.003113
CANADA 1520 0.000164 0.019357 0.000845 -0.260754 13.785753
CHILE 1520 0.000484 0.015844 0.000000 -0.309311 11.383357
CHINA 1520 0.000438 0.024236 0.000044 0.454605 9.780172
COLOMBIA 1520 0.000503 0.020598 0.000000 -0.192374 7.304729
CYPRUS 1520 -0.000900 0.028666 0.000000 0.180206 6.003422
CZECHREPUBLIC 1520 0.000215 0.028377 0.000000 -0.308673 9.589707
DENMARK 1520 -0.000535 0.024076 0.000000 -0.206195 7.636399
EGYPT 1520 -0.000058 0.014992 0.000000 -1.765675 23.327777
FINLAND 1520 -0.000000 0.027870 0.000000 0.015810 10.265886
FRANCE 1520 -0.000645 0.029904 0.000000 0.376884 8.808647
GERMANY 1520 -0.000607 0.027627 0.000147 -0.042368 10.902596
GREECE 1520 -0.001837 0.031547 -0.000432 0.309306 7.796332
HONGKONG 1520 -0.000186 0.019054 0.000000 -0.868470 24.524904
HUNGARY 1520 -0.000469 0.037620 0.000394 -0.049813 8.751924
INDIA 1520 0.000457 0.024953 0.000193 0.160699 7.862234
INDONESIA 1219 0.000000 0.062806 0.000000 0.000000 121.900000
IRELAND 1520 -0.002938 0.057616 -0.001464 -1.287004 27.341084
ISRAEL 1520 -0.000056 0.021327 0.000090 0.079993 6.770575
ITALY 1520 -0.000797 0.025414 0.000056 -0.044954 7.768945
JAPAN 1520 -0.000683 0.020356 0.000000 0.175482 6.994878
KOREA 1520 -0.000291 0.048266 0.000000 -0.129694 13.217639
LUXEMBURG 1520 -0.000235 0.015410 0.000000 0.481849 16.089519
MALAYSIA 1520 0.000538 0.011974 0.000774 -0.440788 7.333518
MALTA 1520 -0.000210 0.013607 0.000065 0.042441 10.399546
MEXICO 1520 0.000447 0.018462 0.000894 -0.172936 8.242122
NETHERLANDS 1520 -0.001744 0.042101 -0.000298 -19.475685 591.431951
NEWZEALAND 387 0.000384 0.009739 0.000672 -0.209628 4.759168
NORWAY 1520 0.000060 0.034333 0.000000 0.068874 11.711000
PAKISTAN 1520 -0.000445 0.019177 0.000000 -0.222376 4.951777
PERU 1520 0.000686 0.014015 0.000396 -0.167932 18.053746
PHILIPPINES 1520 0.000548 0.015956 0.000706 -0.478870 7.474220
POLAND 1520 0.000075 0.026409 0.000665 -0.238014 7.148179
PORTUGAL 1520 -0.001173 0.021483 -0.000103 -0.057301 7.102457
ROMANIA 1520 -0.000217 0.027675 0.000419 -0.558774 8.820957
RUSSIA 1520 0.000334 0.033234 0.000273 0.112011 15.356631
SINGAPORE 1520 0.000244 0.017588 0.000817 -0.098380 7.783160
SLOVENIA 1520 -0.001061 0.017889 -0.000118 -0.461926 12.777591
SOUTHAFRICA 1520 0.000054 0.026172 0.000249 -0.143108 5.632362
SPAIN 1520 -0.000325 0.024980 0.000145 0.325985 9.483063
SRILANKA 1520 0.000706 0.012617 0.000000 1.405133 13.703261
SWEDEN 1520 -0.000039 0.029357 0.000056 0.420787 8.255542
SWITZERLAND 1520 -0.000409 0.024041 -0.000091 0.356036 9.170021
TAIWAN 1520 0.000377 0.020266 0.000000 0.008914 5.178420
THAILAND 1520 0.000449 0.020065 0.000000 -0.910101 15.196544
TURKEY 1520 -0.000019 0.029474 0.000150 -0.148022 6.524280
UK 1520 -0.000739 0.027517 -0.000070 -0.119351 13.099772
USA 1520 -0.000678 0.031281 -0.000321 0.157962 12.945845
VENEZUELA 1520 0.000157 0.025524 0.000000 -11.320139 377.635042
Table 8: Banking sector descriptive stats Jan/2006 – Oct/2011
35
Country # Obs Mean Std Median Skewness Kurtosis
ARGENTINA 999 0.000100 0.018449 0.000204 −0.123585 5.946978
AUSTRALIA 999 −0.000074 0.026915 −0.000522 −0.213112 6.378156
AUSTRIA 999 −0.001173 0.034283 −0.000832 −0.095523 5.827920
BELGIUM 999 −0.002368 0.040805 −0.000736 −0.256626 7.356004
BRAZIL 999 −0.000106 0.027613 0.000043 −0.226727 8.952072
BULGARIA 999 −0.002141 0.032529 −0.001350 −0.816347 13.578731
CANADA 999 0.000035 0.022771 0.000741 −0.218984 10.852019
CHILE 999 0.000543 0.017811 0.000000 −0.316818 10.391194
CHINA 999 −0.000150 0.026602 0.000000 0.483408 9.496352
COLOMBIA 999 0.000564 0.020345 0.000000 −0.331368 7.281956
CYPRUS 999 −0.002652 0.032779 −0.002561 0.338258 5.132196
CZECHREPUBLIC 999 −0.000212 0.032814 0.000000 −0.282936 7.867617
DENMARK 999 −0.001003 0.028397 −0.001651 −0.138754 5.915573
EGYPT 999 −0.000432 0.013731 0.000000 −1.694319 16.210771
FINLAND 999 −0.000301 0.032008 0.000000 0.033294 8.776015
FRANCE 999 −0.001177 0.035437 −0.001403 0.390945 6.765878
GERMANY 999 −0.001243 0.032757 −0.000244 0.016336 8.340887
GREECE 999 −0.003214 0.037449 −0.002550 0.380549 5.963933
HONGKONG 999 −0.000454 0.022651 0.000000 −0.752590 18.593731
HUNGARY 999 −0.001143 0.043412 0.000000 −0.002737 7.360835
INDIA 999 −0.000203 0.026898 0.000000 0.285913 8.040067
INDONESIA 857 0.000000 0.074918 0.000000 0.000000 85.700000
IRELAND 999 −0.004496 0.069995 −0.005375 −1.029934 19.008164
ISRAEL 999 −0.000257 0.023476 −0.000229 0.176396 6.450960
ITALY 999 −0.001503 0.030268 −0.000829 0.027220 5.838007
JAPAN 999 −0.000657 0.022243 0.000000 0.184494 6.908431
KOREA 999 −0.000576 0.055911 0.000000 −0.117218 10.987523
LUXEMBURG 999 −0.000751 0.016347 −0.000291 −0.332502 8.766579
MALAYSIA 999 0.000312 0.012823 0.000402 −0.396463 7.268782
MALTA 999 −0.000529 0.014458 −0.000097 0.021313 10.399840
MEXICO 999 0.000047 0.020183 0.000436 −0.100037 8.035865
NETHERLANDS 999 −0.002972 0.050909 −0.001965 −16.696454 419.555382
NEWZEALAND 387 0.000384 0.009739 0.000672 −0.209628 4.759168
NORWAY 999 −0.000253 0.040753 −0.000749 0.092368 8.929714
PAKISTAN 999 −0.001277 0.019267 −0.000517 −0.056588 5.433668
PERU 999 0.000309 0.012249 0.000396 −0.894981 11.575265
PHILIPPINES 999 0.000114 0.016379 0.000000 −0.482095 7.931801
POLAND 999 −0.000607 0.029933 0.000570 −0.207570 6.335064
PORTUGAL 999 −0.002301 0.025189 −0.001165 0.049395 5.596920
ROMANIA 999 −0.001246 0.030852 0.000000 −0.580146 8.073172
RUSSIA 999 −0.000518 0.037253 0.000000 0.136301 14.179717
SINGAPORE 999 −0.000024 0.019255 0.000341 −0.047331 7.419775
SLOVENIA 999 −0.002315 0.019701 −0.001095 −0.705897 11.027426
SOUTHAFRICA 999 −0.000031 0.027572 0.000000 −0.163387 5.911217
SPAIN 999 −0.000903 0.029663 −0.000187 0.352572 7.236268
SRILANKA 999 0.000750 0.013524 0.000000 1.510010 13.592059
SWEDEN 999 −0.000355 0.034375 0.000000 0.419085 6.599048
SWITZERLAND 999 −0.000725 0.028248 −0.000908 0.373010 7.258719
TAIWAN 999 0.000279 0.022230 0.000000 0.033808 4.783225
THAILAND 999 0.000331 0.020088 0.000000 −0.359746 7.426137
TURKEY 999 −0.000440 0.030731 0.000225 −0.093754 6.911419
UK 999 −0.001128 0.032757 −0.001006 −0.067045 9.875840
USA 999 −0.000852 0.037693 −0.000891 0.152230 9.313252
VENEZUELA 999 −0.000557 0.023353 0.000000 −25.439265 753.789405
Table 9: Banking sector descriptive stats Jan/2008 – Oct/2011
36
Country # Obs Mean Std Median Skewness Kurtosis
ARGENTINA 499 0.000560 0.017334 0.000204 -0.057935 5.717306
AUSTRALIA 499 0.000128 0.019075 -0.000169 -0.111158 4.156502
AUSTRIA 499 -0.001201 0.027076 -0.000680 -0.011726 5.858733
BELGIUM 499 -0.002217 0.031797 -0.000979 0.388937 7.203956
BRAZIL 499 -0.000333 0.020347 0.000000 -0.533844 5.435512
BULGARIA 499 -0.000454 0.020506 -0.000164 0.130430 5.271565
CANADA 499 0.000125 0.013891 0.000853 -0.214017 4.058771
CHILE 499 0.000857 0.014974 0.000000 -0.623822 7.737594
CHINA 499 -0.000465 0.017075 0.000000 -0.088337 5.056296
COLOMBIA 499 0.000847 0.013288 0.000000 -0.222420 4.189126
CYPRUS 499 -0.003150 0.030349 -0.003128 0.410094 5.002686
CZECHREPUBLIC 499 -0.000217 0.023860 0.000000 -0.204633 5.206237
DENMARK 499 -0.000747 0.024465 -0.000885 0.141478 5.708774
EGYPT 499 -0.000285 0.012192 0.000000 -1.355495 9.902941
FINLAND 499 0.000090 0.024919 0.000190 1.017155 13.643666
FRANCE 499 -0.001426 0.032665 -0.001083 0.593204 8.618215
GERMANY 499 -0.001032 0.024769 0.000000 0.313632 7.656481
GREECE 499 -0.004847 0.040618 -0.005013 0.568219 6.443282
HONGKONG 499 -0.000427 0.013699 0.000102 -0.494875 5.644240
HUNGARY 499 -0.001247 0.034987 0.000000 -0.213237 6.959210
INDIA 499 0.000004 0.016754 0.000153 -0.266853 3.720002
INDONESIA 499 0.000000 0.098222 0.000000 0.000000 49.900000
IRELAND 499 -0.004650 0.051861 -0.008012 0.327392 7.163293
ISRAEL 499 -0.000305 0.018353 0.000092 -0.562845 6.029427
ITALY 499 -0.001794 0.029934 -0.001259 0.085260 5.938956
JAPAN 499 -0.000365 0.015297 0.000000 -0.202679 6.647856
KOREA 499 -0.000402 0.039956 0.000000 -0.033912 6.556532
LUXEMBURG 499 -0.000663 0.010428 -0.000225 -0.675478 6.253649
MALAYSIA 499 0.000609 0.010028 0.001385 -0.337045 6.053091
MALTA 499 -0.000350 0.013275 0.000000 0.530044 18.299885
MEXICO 499 0.000382 0.016274 0.000692 -0.599147 7.946137
NETHERLANDS 499 -0.000960 0.016964 -0.002193 -0.065016 5.960400
NEWZEALAND 387 0.000384 0.009739 0.000672 -0.209628 4.759168
NORWAY 499 0.000029 0.025465 -0.000113 -0.053582 4.436030
PAKISTAN 499 -0.000258 0.012647 0.000000 -0.205055 4.596011
PERU 499 0.000026 0.011842 0.000250 -1.029248 16.337375
PHILIPPINES 499 0.000949 0.013383 0.000937 -0.271944 4.527753
POLAND 499 -0.000305 0.022599 0.000570 -0.363852 5.885418
PORTUGAL 499 -0.002853 0.026199 -0.000710 0.105719 5.578052
ROMANIA 499 -0.000661 0.022746 -0.000165 -0.214138 7.403778
RUSSIA 499 0.000166 0.023133 0.000000 -0.386177 5.322866
SINGAPORE 499 0.000002 0.013034 0.000577 -0.255953 5.006339
SLOVENIA 499 -0.002536 0.016856 -0.001453 -1.212101 13.477349
SOUTHAFRICA 499 0.000164 0.019533 0.000726 -0.030259 4.632312
SPAIN 499 -0.001357 0.028291 -0.000023 0.641368 9.030264
SRILANKA 499 0.001294 0.011365 0.000000 1.108516 7.176818
SWEDEN 499 -0.000044 0.024922 0.000878 0.096465 5.857601
SWITZERLAND 499 -0.000530 0.019109 0.000000 -0.174123 5.648804
TAIWAN 499 0.000709 0.017054 0.000000 -0.110874 4.813782
THAILAND 499 0.000673 0.016874 0.000000 0.198442 4.886133
TURKEY 499 -0.000091 0.023362 0.001487 -0.331747 5.005869
UK 499 -0.000658 0.021691 -0.000691 0.102080 6.101784
USA 499 -0.000457 0.020801 -0.000576 -0.363792 6.633971
VENEZUELA 499 -0.000849 0.031833 0.000000 -20.119339 437.217466
Table 10: Banking sector descriptive stats Dec/2009 – Oct/2011
37
B.2 MSCI Country indexes statistics
Tables 11, 12, 13, 14, 16 and 17 show the descriptive statistics the MSCI country
equity indexes for the seven periods.
38
Country # Obs Mean Std Median Skewness Kurtosis
ARGENTINA 520 0.000846 0.017782 0.000651 -0.208959 4.798341
AUSTRALIA 520 0.000890 0.013292 0.001510 -0.632447 6.454690
AUSTRIA 520 0.000579 0.012990 0.001386 -0.541992 5.884360
BAHRAIN 520 0.000364 0.010185 0.000000 2.996774 38.286081
BELGIUM 520 0.000435 0.011027 0.001530 -0.548814 4.520440
BRAZIL 520 0.001725 0.020911 0.002696 -0.490065 4.302018
CANADA 520 0.000757 0.010872 0.001741 -0.537235 4.283505
CHILE 520 0.000841 0.011782 0.001068 -0.645643 5.566684
CHINA 520 0.002051 0.017397 0.002238 -0.160746 5.498462
COLOMBIA 520 0.000479 0.022051 0.002006 -0.245157 14.764874
CROATIA 520 0.002005 0.013655 0.001288 0.262324 13.294707
CZECHREPUBLIC 520 0.001288 0.014361 0.001376 -0.179035 6.956332
DENMARK 520 0.000994 0.011626 0.001681 -0.633381 5.281207
EGYPT 520 0.001053 0.015736 0.000261 -0.918309 8.350375
ESTONIA 520 0.000143 0.013441 -0.000108 -0.057157 6.988337
FINLAND 520 0.001168 0.013784 0.000823 0.015325 4.456801
FRANCE 520 0.000712 0.011015 0.001257 -0.366784 4.152581
GERMANY 520 0.001076 0.010885 0.001871 -0.347284 3.959327
GREECE 520 0.001023 0.012410 0.001728 -0.277737 5.237771
HONGKONG 520 0.001061 0.011942 0.000849 -0.275570 5.075962
HUNGARY 520 0.000741 0.017986 0.002000 -0.204697 4.026996
INDIA 520 0.001802 0.017015 0.002281 -0.429665 5.471179
INDONESIA 520 0.001786 0.018422 0.002109 -0.580856 7.124782
IRELAND 520 0.000225 0.013736 0.000342 -0.400185 5.248912
ISRAEL 520 0.000416 0.010127 0.000745 -0.636544 5.942767
ITALY 520 0.000513 0.009838 0.001118 -0.369214 3.724629
JAPAN 520 -0.000012 0.012210 -0.000020 -0.251113 4.313685
JORDAN 520 -0.000447 0.012746 0.000000 -0.780242 9.936499
KAZAKHSTAN 520 0.001284 0.029982 0.000087 0.627127 11.170791
KOREA 520 0.000702 0.014911 0.000662 -0.583663 5.838034
KUWAIT 520 0.000446 0.013016 0.000000 -0.893779 16.938982
LEBANON 520 0.000091 0.018185 0.000000 -1.403022 21.293336
MALAYSIA 520 0.001218 0.010109 0.001369 -0.594602 6.987275
MAURITIUS 520 0.002228 0.012111 0.000000 1.098253 15.599280
MEXICO 520 0.000791 0.016337 0.001725 -0.153466 4.926219
MOROCCO 520 0.001644 0.012336 0.001691 -0.465081 4.942078
NETHERLANDS 520 0.000772 0.010312 0.001153 -0.325177 4.563700
NEWZEALAND 520 0.000259 0.011732 0.000744 -0.427878 6.379799
NIGERIA 520 0.001979 0.011771 0.000598 0.227659 4.897794
NORWAY 520 0.001151 0.017131 0.002114 -0.444493 4.714416
OMAN 520 0.000534 0.009108 0.000000 0.048296 9.201677
PAKISTAN 520 0.000477 0.016662 0.001104 -0.740466 4.851580
PERU 520 0.001996 0.019269 0.003542 -0.548223 4.588871
PHILIPPINES 520 0.001471 0.016989 0.001145 -0.259130 6.687476
POLAND 520 0.000951 0.017851 0.000863 -0.104573 3.686954
PORTUGAL 520 0.001068 0.008600 0.001197 0.076103 5.864717
QATAR 520 -0.000316 0.014567 0.000000 -0.359833 6.369394
ROMANIA 520 0.001267 0.018246 0.000483 0.068931 6.768563
RUSSIA 520 0.001223 0.019306 0.001922 -0.665536 7.979180
SINGAPORE 520 0.001085 0.012806 0.001442 -0.388667 5.565967
SLOVENIA 520 0.002199 0.010562 0.001789 0.235567 8.666904
SOUTHAFRICA 520 0.000570 0.019085 0.002300 -0.578010 4.733782
SPAIN 520 0.001066 0.010439 0.001212 -0.264432 4.145100
SRILANKA 520 0.000350 0.010025 0.000000 0.770890 11.165805
SWEDEN 520 0.000622 0.014542 0.001267 -0.387740 4.979926
SWITZERLAND 520 0.000515 0.009372 0.000832 -0.405290 4.404238
TAIWAN 520 0.000419 0.013314 0.000870 -0.675334 5.361030
THAILAND 520 0.000786 0.017714 0.000353 -1.802797 26.753301
TUNISIE 520 0.000735 0.008794 0.000742 -0.058747 4.469819
TURKEY 520 0.000834 0.025893 0.001030 -0.545774 5.748264
UKRAINE 413 0.000087 0.016268 0.000400 -2.340255 24.358527
UAE 520 -0.000802 0.017418 0.000000 -0.859768 8.320485
UK 520 0.000536 0.010545 0.001303 -0.445534 4.698241
USA 520 0.000315 0.008261 0.000649 -0.415447 5.316359
VIETNAM 282 0.001848 0.022208 -0.000179 0.098082 2.923233
Table 11: Equity descriptive stats Jan/2006 – Dec/2007
39
Country # Obs Mean Std Median Skewness Kurtosis
ARGENTINA 522 -0.000630 0.031565 0.000000 -0.676475 8.355339
AUSTRALIA 522 -0.000415 0.027970 0.000372 -0.742863 7.063586
AUSTRIA 522 -0.001619 0.032617 -0.000432 -0.006413 5.412181
BAHRAIN 522 -0.002374 0.019937 0.000000 -3.782410 41.767013
BELGIUM 522 -0.001321 0.025327 -0.000778 -0.649712 7.460249
BRAZIL 522 -0.000124 0.035753 0.002447 -0.259078 8.072565
CANADA 522 -0.000390 0.026578 0.001743 -0.604105 6.852810
CHILE 522 0.000248 0.021736 0.000936 -0.076695 12.906676
CHINA 522 -0.000518 0.029354 -0.000022 0.100753 6.354587
COLOMBIA 522 0.000468 0.022789 0.001178 -0.453372 8.396858
CROATIA 522 -0.001097 0.021593 -0.000646 -0.108687 6.256155
CZECHREPUBLIC 522 -0.000805 0.030122 -0.000108 -0.017307 11.062476
DENMARK 522 -0.000680 0.024229 -0.001064 -0.169802 7.168720
EGYPT 522 -0.000941 0.023525 0.000000 -1.058140 9.765555
ESTONIA 522 -0.001529 0.025817 -0.001139 0.127002 5.793096
FINLAND 522 -0.001458 0.027669 -0.001336 0.157542 5.269906
FRANCE 522 -0.000675 0.024921 -0.000132 0.156972 7.320849
GERMANY 522 -0.000855 0.024589 0.000000 0.187850 6.821451
GREECE 522 -0.001738 0.029157 -0.000025 0.001595 5.034867
HONGKONG 522 -0.000599 0.021786 0.000000 -0.023554 7.270199
HUNGARY 522 -0.000817 0.037264 -0.000474 0.049896 7.621452
INDIA 522 -0.000685 0.028838 0.000000 0.311754 7.708579
INDONESIA 522 -0.000125 0.027754 0.000212 -0.158252 7.660588
IRELAND 522 -0.002309 0.033513 -0.000007 -0.500004 6.636141
ISRAEL 522 0.000067 0.014562 0.000654 -0.718045 6.697428
ITALY 522 -0.001019 0.025516 0.000029 0.172732 6.759841
JAPAN 522 -0.000615 0.020831 0.000232 0.016509 6.299697
JORDAN 522 -0.001002 0.017016 0.000000 -0.701378 9.120107
KAZAKHSTAN 522 -0.000580 0.032302 0.000000 -0.165086 7.478323
KOREA 522 -0.000557 0.031321 0.000000 0.010118 15.447137
KUWAIT 522 -0.001460 0.021863 0.000000 -0.958032 8.297954
LEBANON 522 0.000167 0.018974 0.000000 0.885043 10.115525
MALAYSIA 522 -0.000342 0.014211 0.000000 -0.763098 10.957938
MAURITIUS 522 -0.000488 0.019532 0.000000 0.047269 8.376366
MEXICO 522 -0.000295 0.026892 0.000084 0.107765 7.227184
MOROCCO 522 -0.000431 0.014355 -0.000481 -0.291509 6.251182
NETHERLANDS 522 -0.000716 0.024136 -0.000525 0.002264 7.082815
NEWZEALAND 522 -0.000897 0.022297 0.000289 -0.361856 5.387964
NIGERIA 522 -0.002288 0.019386 -0.002767 -0.235836 5.113835
NORWAY 522 -0.000871 0.035716 0.000480 -0.257848 5.263887
OMAN 522 -0.000891 0.021531 0.000000 -1.207930 16.044832
PAKISTAN 522 -0.001517 0.022826 -0.000442 -0.401504 5.205023
PERU 522 -0.000048 0.030766 0.000011 -0.127687 6.219585
PHILIPPINES 522 -0.000576 0.020775 0.000000 -0.697042 8.727952
POLAND 522 -0.000975 0.031853 0.000580 -0.096070 5.232543
PORTUGAL 522 -0.000893 0.020871 -0.000460 -0.003334 9.135857
QATAR 522 -0.000638 0.024495 0.000000 -0.616162 9.094411
ROMANIA 522 -0.001909 0.033735 -0.000470 -1.599134 17.345486
RUSSIA 522 -0.001261 0.041255 0.000178 -0.257657 10.736110
SERBIA 392 -0.003471 0.036395 -0.004324 -0.236674 6.430652
SINGAPORE 522 -0.000325 0.021852 -0.000362 -0.098915 5.489506
SLOVENIA 522 -0.001639 0.022035 -0.001576 -0.236514 6.284205
SOUTHAFRICA 522 -0.000158 0.028265 0.000000 -0.226892 6.004521
SPAIN 522 -0.000480 0.025380 -0.000605 0.039316 7.038078
SRILANKA 522 0.000142 0.019406 -0.000510 2.079440 19.537550
SWEDEN 522 -0.000481 0.030605 -0.000823 0.268368 5.103936
SWITZERLAND 522 -0.000331 0.019235 -0.000311 0.233749 6.466864
TAIWAN 522 -0.000205 0.020816 0.000000 -0.051049 4.422822
THAILAND 522 -0.000324 0.022868 -0.000388 -0.626861 8.480781
TUNISIE 522 0.000321 0.013018 -0.000146 0.172785 10.622998
TURKEY 522 -0.000675 0.031943 -0.000525 -0.042855 6.200162
UKRAINE 522 -0.003007 0.031041 -0.000678 0.349628 12.857489
UAE 522 -0.001975 0.029105 0.000000 -0.456881 10.834926
UK 522 -0.000742 0.024521 0.000024 0.053110 7.412752
USA 522 -0.000518 0.021563 0.000399 -0.134534 7.575500
VIETNAM 522 -0.001202 0.024058 -0.000122 -0.048941 2.514720
Table 12: Equity descriptive stats Jan/2008 – Dec/2009
40
Country # Obs Mean Std Median Skewness Kurtosis
ARGENTINA 260 0.002042 0.018667 0.001270 0.439523 6.098574
AUSTRALIA 260 0.000366 0.016435 0.000930 -0.327588 3.679823
AUSTRIA 260 0.000273 0.020618 0.001475 0.220774 5.917466
BAHRAIN 260 -0.000931 0.008774 0.000000 -0.454812 5.191933
BELGIUM 260 -0.000087 0.016691 0.000195 0.635314 8.240452
BOSNIAHERZEGOVINA 87 0.000189 0.016984 -0.000912 -0.030638 3.948141
BRAZIL 260 0.000143 0.017175 -0.000063 -0.230065 4.761486
CANADA 260 0.000643 0.012613 0.001903 -0.411932 3.886686
CHILE 260 0.001344 0.010970 0.001152 0.134203 3.314583
CHINA 260 0.000088 0.012999 0.000072 -0.317653 3.541116
COLOMBIA 260 0.001315 0.013190 0.001803 -0.454813 5.302668
CROATIA 260 0.000195 0.012255 0.000732 0.163373 10.676216
CZECHREPUBLIC 260 -0.000296 0.015722 -0.000085 -0.216544 5.019189
DENMARK 260 0.001003 0.015852 0.001031 0.263651 4.372019
EGYPT 260 0.000348 0.013645 0.000000 -0.794998 6.511912
ESTONIA 260 0.001714 0.021777 0.000499 0.411022 4.247085
FINLAND 260 0.000262 0.017646 0.000411 -0.169336 5.722278
FRANCE 260 -0.000268 0.018217 0.000460 0.343936 6.828954
GERMANY 260 0.000225 0.015710 0.000570 -0.067903 4.278798
GREECE 260 -0.002396 0.028853 -0.002524 0.199769 3.774845
HONGKONG 260 0.000691 0.009663 0.000103 -0.192169 3.434767
HUNGARY 260 -0.000435 0.027437 0.000972 0.320575 7.953884
INDIA 260 0.000682 0.013636 0.001089 -0.313251 4.237998
INDONESIA 260 0.001044 0.016128 0.000617 0.285689 6.200387
IRELAND 260 -0.000844 0.023887 -0.000949 -0.041411 5.259281
ISRAEL 260 0.000083 0.010887 0.000279 -0.019852 4.086518
ITALY 260 -0.000746 0.019876 -0.000166 0.380963 8.062099
JAPAN 260 0.000482 0.011200 0.000492 -0.094412 3.077469
JORDAN 260 -0.000492 0.008601 0.000000 0.320165 5.632516
KAZAKHSTAN 260 -0.000714 0.018799 0.000000 -0.470232 10.957752
KOREA 260 0.000867 0.016243 0.001174 -0.457518 3.838379
KUWAIT 260 0.001185 0.012507 0.000693 -0.153828 6.412753
LEBANON 260 -0.000530 0.007240 -0.000323 0.378301 4.671420
MALAYSIA 260 0.001083 0.008929 0.001549 -0.329017 4.799828
MAURITIUS 260 0.000254 0.008833 0.000000 0.191823 5.168770
MEXICO 260 0.000889 0.013188 0.001878 -0.210102 5.704802
MOROCCO 260 0.000396 0.010892 -0.000510 -0.548873 9.245455
NETHERLANDS 260 -0.000024 0.016464 -0.000723 0.272814 6.382829
NEWZEALAND 260 0.000120 0.012705 0.000057 -0.357519 3.570471
NIGERIA 260 0.000800 0.013519 0.000394 0.086634 3.863810
NORWAY 260 0.000275 0.020294 0.000151 -0.050223 4.387783
OMAN 260 0.000492 0.005987 0.000000 -0.379531 8.451582
PAKISTAN 260 0.000685 0.010713 0.000000 -0.281880 4.854798
PERU 260 0.001540 0.017064 0.000943 0.146513 3.655308
PHILIPPINES 260 0.001018 0.013786 0.001430 -0.351417 3.807563
POLAND 260 0.000457 0.021432 0.000340 -0.049087 5.730015
PORTUGAL 260 -0.000608 0.018185 -0.000334 0.669638 9.605761
QATAR 260 0.000881 0.008067 0.000096 0.122132 8.297967
ROMANIA 260 -0.000073 0.023686 0.000891 -0.399656 10.254904
RUSSIA 260 0.000610 0.017649 0.001235 -0.179192 5.486611
SERBIA 260 0.000165 0.015128 0.000262 0.057542 5.859954
SINGAPORE 260 0.000651 0.011273 0.001880 -0.387939 4.261979
SLOVENIA 260 -0.000666 0.010754 -0.000553 0.282540 4.532226
SOUTHAFRICA 260 0.001030 0.016307 0.001572 0.186126 5.866033
SPAIN 260 -0.001126 0.023631 -0.000047 0.776173 11.243115
SRILANKA 260 0.002085 0.011819 0.000169 1.063687 6.955879
SWEDEN 260 0.001047 0.018843 0.001258 0.158168 5.115656
SWITZERLAND 260 0.000361 0.011812 0.000562 -0.201879 4.190408
TAIWAN 260 0.000647 0.012128 0.001109 -0.436023 3.932560
THAILAND 260 0.001580 0.014418 0.000411 0.168335 5.245883
TUNISIE 260 0.000323 0.009959 0.000002 0.419781 6.890828
TURKEY 260 0.000648 0.019414 0.002280 -0.101929 5.855123
UKRAINE 260 0.001534 0.019702 0.000523 -0.801596 10.417805
UAE 260 -0.000132 0.013914 0.000000 0.243288 8.241891
UK 260 0.000194 0.014028 0.000258 0.155790 5.696669
USA 260 0.000476 0.011190 0.000614 -0.225280 5.101728
VIETNAM 260 0.000294 0.013641 0.000260 -0.186979 3.767218
Table 13: Equity descriptive stats Jan/2010 – Dec/2010
41
Country # Obs Mean Std Median Skewness Kurtosis
ARGENTINA 230 -0.002481 0.019779 -0.001762 -0.737521 6.776926
AUSTRALIA 230 -0.000754 0.018522 0.000188 -0.157956 4.289497
AUSTRIA 230 -0.002513 0.023999 -0.001509 -0.184360 5.108082
BAHRAIN 230 -0.001463 0.009967 0.000000 -2.603198 16.041593
BELGIUM 230 -0.000836 0.018074 -0.000550 -0.332470 4.080350
BOSNIAHERZEGOVINA 230 -0.000439 0.013827 -0.000765 0.065609 3.626564
BRAZIL 230 -0.001282 0.018779 0.000626 -0.863956 5.718022
CANADA 230 -0.000837 0.015402 0.000141 -0.453022 4.382858
CHILE 230 -0.001145 0.018702 -0.000497 -0.648196 8.034540
CHINA 230 -0.001113 0.018209 -0.000050 -0.264291 4.754727
COLOMBIA 230 -0.000346 0.011841 0.000571 -0.201971 4.075044
CROATIA 230 -0.000489 0.011555 0.001010 -0.871638 7.112253
CZECHREPUBLIC 230 -0.000669 0.018075 0.000801 -0.689400 4.679694
DENMARK 230 -0.000930 0.017790 -0.000065 -0.048880 3.998866
EGYPT 230 -0.002533 0.018201 0.000000 -1.915656 11.990007
ESTONIA 230 -0.001158 0.020441 -0.000831 -0.345332 4.027575
FINLAND 230 -0.001809 0.023701 -0.000288 -0.167668 3.963422
FRANCE 230 -0.001236 0.022497 -0.001479 -0.230243 4.202352
GERMANY 230 -0.001074 0.022909 -0.000761 -0.236850 3.994851
GREECE 230 -0.004245 0.034273 -0.003964 0.790795 6.221471
HONGKONG 230 -0.001090 0.014653 -0.000009 -0.314201 5.258651
HUNGARY 230 -0.001756 0.028115 -0.002162 -0.173095 4.356134
INDIA 230 -0.001852 0.015674 -0.001685 -0.084288 4.143250
INDONESIA 230 -0.000019 0.019129 0.000530 -1.001071 7.425512
IRELAND 230 -0.000256 0.021628 -0.001479 -0.367248 3.484207
ISRAEL 230 -0.001619 0.015901 0.000232 -0.833540 5.901425
ITALY 230 -0.001357 0.025441 0.000055 -0.428554 3.691321
JAPAN 230 -0.000808 0.014973 0.000094 -0.740517 10.657505
JORDAN 230 -0.000940 0.008659 0.000000 -0.619473 7.287210
KAZAKHSTAN 230 -0.001379 0.018855 -0.000528 0.069215 4.290977
KOREA 230 -0.000690 0.021767 0.000040 -0.355614 4.459894
KUWAIT 230 -0.001100 0.011003 -0.000135 -1.208071 8.115693
LEBANON 230 -0.001256 0.009120 -0.000755 -0.422533 11.270067
MALAYSIA 230 -0.000481 0.010719 0.000008 -0.348983 4.067166
MAURITIUS 230 -0.000206 0.009845 0.000000 0.003647 5.390530
MEXICO 230 -0.000738 0.016356 0.001067 -0.928897 6.783684
MOROCCO 230 -0.000664 0.012261 -0.000290 -0.012487 4.216591
NETHERLANDS 230 -0.001038 0.019326 -0.000484 -0.322140 3.976769
NEWZEALAND 230 -0.000312 0.013666 0.001228 -0.348878 4.322888
NIGERIA 230 -0.000921 0.015081 -0.001439 0.390389 4.467638
NORWAY 230 -0.000750 0.022574 0.001135 -0.396028 3.922649
OMAN 230 -0.001159 0.011526 0.000000 -2.148801 31.241868
PAKISTAN 230 -0.000167 0.011993 0.000000 -0.199160 4.925187
PERU 230 -0.001270 0.023332 0.000000 -1.674083 13.478874
PHILIPPINES 230 -0.000202 0.013977 0.000000 -0.244808 4.923977
POLAND 230 -0.001551 0.023965 0.000224 -0.688663 5.741849
PORTUGAL 230 -0.001266 0.019503 0.000563 -0.469797 3.841634
QATAR 230 0.000018 0.009023 0.000000 -0.512926 7.656043
ROMANIA 230 -0.000719 0.021147 0.000296 -0.120780 5.765134
RUSSIA 230 -0.000918 0.021847 0.000439 -0.732673 5.176919
SERBIA 230 -0.000614 0.022673 -0.000126 -0.007889 7.964228
SINGAPORE 230 -0.001019 0.014881 0.000108 -0.327865 4.200460
SLOVENIA 230 -0.000778 0.013468 -0.000334 -0.610135 5.537706
SOUTHAFRICA 230 -0.001117 0.019489 0.001410 -0.204053 3.803078
SPAIN 230 -0.000937 0.023568 -0.000542 -0.072885 3.600136
SRILANKA 230 -0.001191 0.008789 -0.000895 0.540729 6.181678
SWEDEN 230 -0.001317 0.024782 0.000778 -0.259596 4.465005
SWITZERLAND 230 -0.000698 0.014841 0.000514 -0.448295 4.487408
TAIWAN 230 -0.001236 0.015192 0.000000 -0.412497 3.971755
THAILAND 230 -0.000480 0.018608 0.000000 0.027635 4.820016
TUNISIE 230 -0.000278 0.012611 0.000688 -0.133030 4.226516
TURKEY 230 -0.001881 0.021048 -0.001412 -0.546257 3.958538
UKRAINE 230 -0.002203 0.020744 0.000000 -0.238596 5.066952
UAE 230 -0.000934 0.013466 0.000000 -1.169151 8.224598
UK 230 -0.000480 0.016631 0.001193 -0.426315 3.959316
USA 230 -0.000278 0.014562 0.000553 -0.687257 6.245591
VIETNAM 230 -0.001985 0.016967 -0.001777 -0.097450 3.771423
Table 14: Equity descriptive stats Jan/2011 – Oct/2011
42
Country # Obs Mean Std Median Skewness Kurtosis
ARGENTINA 1520 0.000100 0.023749 0.000097 -0.648122 10.407716
AUSTRALIA 1520 0.000181 0.020564 0.001043 -0.789943 9.392955
AUSTRIA 1520 -0.000547 0.023996 0.000630 -0.118036 7.797749
BAHRAIN 1520 -0.001127 0.014145 0.000000 -3.543363 62.185804
BELGIUM 1520 -0.000387 0.018835 0.000452 -0.621236 9.998253
BOSNIAHERZEGOVINA 303 -0.000088 0.014865 -0.000852 0.031516 3.908923
BRAZIL 1520 0.000449 0.026235 0.001653 -0.390706 10.516170
CANADA 1520 0.000168 0.018507 0.001589 -0.765423 10.675140
CHILE 1520 0.000500 0.016745 0.000918 -0.309711 15.033928
CHINA 1520 0.000431 0.021775 0.000321 -0.026810 8.349454
COLOMBIA 1520 0.000531 0.019862 0.001524 -0.382613 12.892814
CROATIA 1520 0.000304 0.016438 0.000628 -0.155375 9.459280
CZECHREPUBLIC 1520 0.000092 0.021658 0.000725 -0.167203 15.189253
DENMARK 1520 0.000152 0.018330 0.000487 -0.231657 8.745586
EGYPT 1520 -0.000202 0.018803 0.000000 -1.233462 11.587458
ESTONIA 1520 -0.000319 0.020732 -0.000336 0.078479 6.603934
FINLAND 1520 -0.000231 0.021415 0.000001 -0.018133 6.554834
FRANCE 1520 -0.000110 0.019486 0.000418 0.053128 8.699583
GERMANY 1520 0.000038 0.019013 0.000860 -0.014370 8.115973
GREECE 1520 -0.001138 0.025499 0.000087 0.254448 6.748238
HONGKONG 1520 0.000182 0.016060 0.000137 -0.170013 9.569008
HUNGARY 1520 -0.000287 0.028685 0.000479 0.001429 9.329575
INDIA 1520 0.000340 0.021248 0.000384 0.114617 9.966842
INDONESIA 1520 0.000790 0.021828 0.001091 -0.353143 9.073073
IRELAND 1520 -0.000843 0.024744 0.000000 -0.569761 8.968553
ISRAEL 1520 0.000007 0.012732 0.000603 -0.718195 7.116459
ITALY 1520 -0.000416 0.020239 0.000372 0.023294 8.293588
JAPAN 1520 -0.000223 0.015927 0.000132 -0.159480 8.207871
JORDAN 1520 -0.000698 0.013343 0.000000 -0.759645 11.468494
KAZAKHSTAN 1520 -0.000038 0.027865 0.000000 0.173812 10.319037
KOREA 1520 0.000161 0.022830 0.000555 -0.167151 19.748072
KUWAIT 1520 -0.000290 0.016349 0.000000 -1.088686 12.261511
LEBANON 1520 -0.000162 0.016057 0.000000 -0.162523 18.919493
MALAYSIA 1520 0.000474 0.011576 0.000694 -0.740501 10.734990
MAURITIUS 1520 0.000606 0.014494 0.000000 0.189039 12.477336
MEXICO 1520 0.000254 0.020119 0.001223 -0.056124 9.226836
MOROCCO 1520 0.000423 0.012859 0.000220 -0.356079 6.070788
NETHERLANDS 1520 -0.000048 0.018209 0.000150 -0.079819 8.996920
NEWZEALAND 1520 -0.000166 0.016476 0.000453 -0.478219 7.318902
NIGERIA 1520 -0.000095 0.015644 0.000000 -0.203392 5.793870
NORWAY 1520 0.000099 0.026037 0.001338 -0.363949 7.291133
OMAN 1520 -0.000176 0.014630 0.000000 -1.581024 28.417407
PAKISTAN 1520 -0.000296 0.017783 0.000000 -0.589464 6.447348
PERU 1520 0.000771 0.024079 0.001040 -0.453057 8.329494
PHILIPPINES 1520 0.000477 0.017546 0.000526 -0.554297 8.513527
POLAND 1520 -0.000067 0.024833 0.000607 -0.223842 6.483831
PORTUGAL 1520 -0.000146 0.016896 0.000481 -0.045590 10.572525
QATAR 1520 -0.000164 0.017374 0.000000 -0.726043 13.601927
ROMANIA 1520 -0.000272 0.025801 0.000175 -1.358108 19.893588
RUSSIA 1520 0.000011 0.028827 0.001208 -0.450947 16.392521
SERBIA 869 -0.001573 0.028228 -0.001460 -0.341692 9.084150
SINGAPORE 1520 0.000292 0.016480 0.000774 -0.238630 7.035233
SLOVENIA 1520 -0.000003 0.015869 0.000214 -0.386351 9.315148
SOUTHAFRICA 1520 0.000206 0.022283 0.001241 -0.307999 6.805811
SPAIN 1520 -0.000036 0.020756 0.000152 0.137066 9.474181
SRILANKA 1520 0.000387 0.014075 0.000000 2.087479 25.795738
SWEDEN 1520 0.000139 0.023215 0.000604 0.111528 6.702306
SWITZERLAND 1520 0.000089 0.014513 0.000459 0.029140 8.038494
TAIWAN 1520 0.000055 0.016318 0.000272 -0.263969 5.447000
THAILAND 1520 0.000396 0.019293 0.000000 -0.853644 13.141273
TUNISIE 1520 0.000403 0.011176 0.000323 0.080494 9.053787
TURKEY 1520 -0.000041 0.026577 0.000753 -0.256426 6.700474
UKRAINE 1413 -0.001167 0.023828 0.000000 -0.123970 16.351153
UAE 1520 -0.001069 0.021306 0.000000 -0.651927 14.710058
UK 1520 -0.000057 0.017785 0.000937 -0.081592 10.171762
USA 1520 0.000009 0.015278 0.000585 -0.307144 11.462364
VIETNAM 1282 -0.000255 0.020786 0.000000 -0.041464 3.145887
Table 15: Equity descriptive stats Jan/2006 – Oct/2011
43
Country # Obs Mean Std Median Skewness Kurtosis
ARGENTINA 999 −0.000289 0.026336 0.000000 −0.665997 9.923078
AUSTRALIA 999 −0.000188 0.023481 0.000558 −0.716040 8.028118
AUSTRIA 999 −0.001134 0.028065 0.000000 −0.035061 6.202611
BAHRAIN 999 −0.001904 0.015773 0.000000 −4.223003 56.748574
BELGIUM 999 −0.000815 0.021822 −0.000217 −0.527424 8.256507
BOSNIAHERZEGOVINA 303 −0.000088 0.014865 −0.000852 0.031516 3.908923
BRAZIL 999 −0.000215 0.028617 0.000703 −0.326483 10.654594
CANADA 999 −0.000138 0.021437 0.001425 −0.681445 8.844188
CHILE 999 0.000322 0.018827 0.000829 −0.232269 13.874436
CHINA 999 −0.000411 0.023711 0.000000 0.050543 8.214997
COLOMBIA 999 0.000558 0.018644 0.001180 −0.491798 10.275349
CROATIA 999 −0.000582 0.017664 0.000171 −0.193388 8.239386
CZECHREPUBLIC 999 −0.000531 0.024608 0.000094 −0.104979 13.443666
DENMARK 999 −0.000286 0.020989 −0.000245 −0.135520 7.480652
EGYPT 999 −0.000856 0.020201 0.000000 −1.250361 11.525038
ESTONIA 999 −0.000559 0.023667 −0.000721 0.106451 5.547608
FINLAND 999 −0.000960 0.024447 −0.000477 0.040992 5.638391
FRANCE 999 −0.000538 0.022678 −0.000054 0.116426 7.108771
GERMANY 999 −0.000501 0.022085 0.000069 0.063174 6.671449
GREECE 999 −0.002263 0.030097 −0.001174 0.338758 5.257484
HONGKONG 999 −0.000275 0.017827 0.000000 −0.103768 9.055935
HUNGARY 999 −0.000822 0.032913 −0.000239 0.053049 8.011567
INDIA 999 −0.000422 0.023128 0.000000 0.269635 10.022243
INDONESIA 999 0.000273 0.023408 0.000497 −0.263543 9.021251
IRELAND 999 −0.001400 0.028859 −0.000717 −0.479460 7.202580
ISRAEL 999 −0.000215 0.013899 0.000437 −0.689162 6.727606
ITALY 999 −0.000900 0.023926 0.000000 0.085262 6.416161
JAPAN 999 −0.000334 0.017565 0.000331 −0.127371 7.922977
JORDAN 999 −0.000830 0.013655 0.000000 −0.745329 11.996533
KAZAKHSTAN 999 −0.000726 0.026700 0.000000 −0.180974 9.284597
KOREA 999 −0.000120 0.026028 0.000461 −0.092893 17.470916
KUWAIT 999 −0.000673 0.017841 0.000000 −1.064744 10.604452
LEBANON 999 −0.000293 0.014846 −0.000209 1.013963 14.640625
MALAYSIA 999 0.000087 0.012263 0.000108 −0.744882 11.236728
MAURITIUS 999 −0.000238 0.015537 0.000000 0.026819 11.250929
MEXICO 999 −0.000025 0.021842 0.001011 −0.016509 9.312995
MOROCCO 999 −0.000212 0.013090 −0.000373 −0.295488 6.561740
NETHERLANDS 999 −0.000475 0.021186 −0.000518 −0.011503 7.341113
NEWZEALAND 999 −0.000387 0.018478 0.000358 −0.437651 6.487457
NIGERIA 999 −0.001175 0.017235 −0.001222 −0.169532 5.329364
NORWAY 999 −0.000448 0.029636 0.000568 −0.294160 6.321088
OMAN 999 −0.000556 0.016797 0.000000 −1.546270 24.346119
PAKISTAN 999 −0.000666 0.018315 −0.000058 −0.519908 6.987909
PERU 999 0.000134 0.026233 0.000104 −0.386898 8.283976
PHILIPPINES 999 −0.000039 0.017824 0.000000 −0.680397 9.237596
POLAND 999 −0.000596 0.027786 0.000500 −0.195215 5.955043
PORTUGAL 999 −0.000778 0.019871 −0.000073 0.031669 8.301670
QATAR 999 −0.000085 0.018684 0.000000 −0.805097 14.273817
ROMANIA 999 −0.001073 0.028952 0.000000 −1.422148 18.381921
RUSSIA 999 −0.000620 0.032708 0.000699 −0.361104 14.534933
SERBIA 869 −0.001573 0.028228 −0.001460 −0.341692 9.084150
SINGAPORE 999 −0.000120 0.018099 0.000190 −0.168532 6.628864
SLOVENIA 999 −0.001148 0.017928 −0.000790 −0.308279 8.030689
SOUTHAFRICA 999 0.000016 0.023796 0.000848 −0.221498 6.952377
SPAIN 999 −0.000610 0.024456 −0.000318 0.195838 7.428956
SRILANKA 999 0.000406 0.015788 0.000000 2.146766 23.856492
SWEDEN 999 −0.000112 0.026649 0.000112 0.164863 5.655919
SWITZERLAND 999 −0.000134 0.016577 0.000002 0.095203 6.965467
TAIWAN 999 −0.000134 0.017692 0.000000 −0.151779 5.113252
THAILAND 999 0.000193 0.020081 0.000000 −0.502575 8.660526
TUNISIE 999 0.000230 0.012239 0.000103 0.125044 8.970781
TURKEY 999 −0.000496 0.026941 0.000660 −0.118989 7.133430
UKRAINE 999 −0.001686 0.026327 0.000000 0.134383 14.113276
UAE 999 −0.001209 0.023089 0.000000 −0.580033 14.834415
UK 999 −0.000366 0.020575 0.000331 −0.015809 8.478733
USA 999 −0.000150 0.017881 0.000543 −0.249353 9.168852
VIETNAM 999 −0.000849 0.020350 0.000000 −0.109278 3.177836
Table 16: Equity descriptive stats Jan/2008 – Oct/2011
44
Country # Obs Mean Std Median Skewness Kurtosis
ARGENTINA 499 0.000109 0.018700 0.000000 -0.226449 7.101001
AUSTRALIA 499 0.000067 0.017082 0.000633 -0.250976 4.227932
AUSTRIA 499 -0.000758 0.021893 0.000246 -0.059832 5.786825
BAHRAIN 499 -0.001349 0.009633 0.000000 -1.230487 12.623571
BELGIUM 499 -0.000322 0.016996 0.000121 0.117470 6.253678
BOSNIAHERZEGOVINA 303 -0.000088 0.014865 -0.000852 0.031516 3.908923
BRAZIL 499 -0.000341 0.017645 0.000334 -0.609343 5.598528
CANADA 499 0.000123 0.013665 0.001191 -0.479907 4.573549
CHILE 499 0.000502 0.014800 0.000885 -0.694475 10.115246
CHINA 499 -0.000304 0.015164 0.000018 -0.314372 5.199479
COLOMBIA 499 0.000654 0.012533 0.001156 -0.334408 4.873995
CROATIA 499 -0.000114 0.011824 0.000840 -0.276485 9.429732
CZECHREPUBLIC 499 -0.000341 0.016454 0.000647 -0.474896 4.981620
DENMARK 499 0.000033 0.016483 0.000000 0.102761 4.378811
EGYPT 499 -0.000659 0.016078 0.000000 -1.388301 11.472864
ESTONIA 499 0.000292 0.020917 0.000000 0.145705 4.207848
FINLAND 499 -0.000433 0.019984 0.000391 -0.217476 5.128752
FRANCE 499 -0.000389 0.019654 0.000000 0.032410 5.499638
GERMANY 499 -0.000150 0.018708 0.000372 -0.197007 4.749641
GREECE 499 -0.003078 0.031209 -0.002998 0.615851 5.320881
HONGKONG 499 0.000087 0.011949 0.000077 -0.345832 5.975461
HUNGARY 499 -0.000847 0.027145 0.000590 0.031796 6.480334
INDIA 499 -0.000107 0.014359 0.000000 -0.199387 4.354495
INDONESIA 499 0.000748 0.017294 0.001019 -0.545519 7.636729
IRELAND 499 -0.000345 0.022534 -0.000962 -0.154349 4.659823
ISRAEL 499 -0.000410 0.012974 0.000270 -0.670612 6.718332
ITALY 499 -0.000782 0.021711 0.000000 -0.060524 5.504702
JAPAN 499 -0.000041 0.013040 0.000331 -0.581022 9.508755
JORDAN 499 -0.000590 0.008710 0.000000 0.016184 6.696117
KAZAKHSTAN 499 -0.000844 0.018462 0.000000 -0.233077 8.189073
KOREA 499 0.000477 0.018314 0.001007 -0.461707 4.789761
KUWAIT 499 0.000222 0.011915 0.000180 -0.488428 6.961360
LEBANON 499 -0.000748 0.008151 -0.000349 -0.176549 9.715981
MALAYSIA 499 0.000516 0.009523 0.001063 -0.359015 4.672165
MAURITIUS 499 -0.000008 0.009592 0.000000 -0.018951 5.135656
MEXICO 499 0.000243 0.014257 0.001381 -0.733075 7.329816
MOROCCO 499 -0.000006 0.011554 -0.000262 -0.291966 6.284027
NETHERLANDS 499 -0.000204 0.017190 -0.000174 -0.015163 5.193283
NEWZEALAND 499 0.000196 0.013115 0.000272 -0.321685 4.104165
NIGERIA 499 0.000055 0.014354 0.000000 0.188487 4.182801
NORWAY 499 0.000034 0.020817 0.000568 -0.225382 4.289030
OMAN 499 -0.000146 0.009419 0.000000 -1.851864 35.561196
PAKISTAN 499 0.000307 0.011506 0.000000 -0.229241 4.794159
PERU 499 0.000142 0.020068 0.000000 -1.206176 12.648962
PHILIPPINES 499 0.000529 0.013702 0.000000 -0.308476 4.449188
POLAND 499 -0.000272 0.022311 0.000406 -0.423779 6.044031
PORTUGAL 499 -0.000688 0.018442 0.000349 0.087508 6.778892
QATAR 499 0.000610 0.009044 0.000000 0.257843 9.268141
ROMANIA 499 -0.000315 0.022482 0.000428 -0.288242 8.669177
RUSSIA 499 0.000087 0.019380 0.000913 -0.524510 5.681420
SERBIA 499 -0.000341 0.018896 -0.000199 -0.010731 8.811906
SINGAPORE 499 0.000158 0.012597 0.000919 -0.372473 4.657478
SLOVENIA 499 -0.000761 0.011862 -0.000573 -0.220509 5.567176
SOUTHAFRICA 499 0.000233 0.017503 0.001405 -0.065576 4.905238
SPAIN 499 -0.000819 0.023052 -0.000122 0.412457 8.084429
SRILANKA 499 0.000948 0.010688 0.000000 1.192211 7.577856
SWEDEN 499 0.000200 0.021232 0.001139 -0.128726 5.181575
SWITZERLAND 499 0.000067 0.012944 0.000620 -0.349664 4.684990
TAIWAN 499 0.000091 0.013321 0.000236 -0.506548 4.373768
THAILAND 499 0.000797 0.016288 0.000000 0.015299 5.430844
TUNISIE 499 0.000164 0.011160 0.000551 0.026786 5.452785
TURKEY 499 0.000001 0.020083 0.001809 -0.337833 4.998559
UKRAINE 499 -0.000402 0.019493 0.000000 -0.473172 8.123001
UAE 499 -0.000203 0.017075 0.000000 2.260078 33.894359
UK 499 0.000021 0.015021 0.000805 -0.218509 4.916218
USA 499 0.000255 0.012473 0.000601 -0.567985 6.856672
VIETNAM 499 -0.000524 0.015692 0.000000 -0.183749 3.803942
Table 17: Equity descriptive stats Dec/2009 – Oct/2011
45
B.3 Sovereign CDS spread statistics
Tables 18, 19, 20, 21 and 22 show the descriptive statistics the CDS spreads for the
five periods for which they are available.
Note that not all countries have CDS spreads for the whole periods.
46
Country # Obs Mean Std Median Skewness Kurtosis
ARGENTINA 522 0.001317 0.041315 0.000000 3.326189 33.896042
TUNISIE 501 0.001043 0.059633 0.000000 5.045761 78.922953
VENEZUELA 479 0.001349 0.033199 0.000000 3.573090 34.943896
CZECHREPUBLIC 479 0.001573 0.072876 0.000000 1.948356 30.478335
DOMINICANREPUBLIC 522 0.001150 0.044678 0.000000 -1.257649 28.137099
GERMANY 522 0.002795 0.051759 0.000000 2.188162 25.396919
BRAZIL 522 0.000393 0.065547 0.000000 6.979211 115.604875
FRANCE 522 0.002759 0.054641 0.000000 3.352453 45.891190
GREECE 522 0.004761 0.041696 0.000000 0.595373 9.715107
HONGKONG 479 -0.000113 0.048769 0.000000 -0.193687 31.763053
IRELAND 321 0.002330 0.046552 0.000000 1.050997 6.379686
JAMAICA 479 0.001516 0.033594 0.000000 2.859913 27.363920
JAPAN 479 0.002926 0.081011 0.000000 10.417450 183.041296
BAHRAIN 437 0.001532 0.045142 0.000000 2.507035 36.916401
BELGIUM 522 0.002513 0.048760 0.000000 1.580080 13.333677
DENMARK 304 -0.003071 0.046438 0.000000 -0.874661 8.744948
NORWAY 280 -0.002577 0.039141 0.000000 0.607373 12.361512
SPAIN 522 0.003124 0.049998 0.000000 0.235379 12.454675
SWEDEN 304 -0.000862 0.045578 0.000000 -0.170891 8.283786
THAILAND 479 -0.000246 0.054706 0.000000 0.344212 15.957368
NETHERLANDS 450 0.002568 0.057476 0.000000 3.756742 43.639038
LEBANON 479 -0.001665 0.032464 0.000000 2.240368 45.392508
MALAYSIA 322 -0.002222 0.061084 0.000000 0.370918 13.172433
URUGUAY 302 -0.002859 0.039090 0.000000 0.266266 26.748545
CHINA 519 0.001815 0.058057 0.000000 2.771780 32.498421
AUSTRIA 177 -0.003588 0.044734 0.000000 0.126881 5.941687
BULGARIA 479 0.000727 0.052517 0.000000 6.307349 89.170729
CHILE 479 0.000118 0.092202 0.000000 2.898802 53.837006
COLOMBIA 479 -0.000644 0.058506 0.000000 0.839006 39.909413
COSTARICA 322 -0.001490 0.061910 0.000000 0.097794 14.270618
CROATIA 479 0.001653 0.059392 0.000000 6.682041 101.147667
CYPRUS 522 0.003809 0.045264 0.000000 0.271737 21.394009
ELSALVADOR 479 0.000653 0.048662 0.000000 1.891289 35.411624
ESTONIA 321 -0.001423 0.057689 0.000000 1.431788 17.605919
GUATEMALA 511 0.000830 0.046322 0.000000 4.164132 59.747733
HUNGARY 522 0.002854 0.057496 0.000000 5.713631 69.556246
ICELAND 366 0.001005 0.040929 0.000000 4.342878 37.798311
INDONESIA 322 -0.002270 0.057767 0.000000 1.221901 13.226648
IRAQ 479 -0.000350 0.031565 0.000000 1.421093 89.192664
ITALY 522 0.002918 0.049089 0.000000 0.117266 15.947884
KAZAKHSTAN 479 -0.000304 0.065768 0.000000 7.677292 130.434274
LATVIA 321 0.001082 0.052263 0.000000 0.361041 11.738496
LITHUANIA 479 0.001474 0.055760 0.000000 4.821989 69.916550
MALTA 522 0.003809 0.044088 0.000000 1.160362 16.534217
PANAMA 479 -0.000501 0.060156 0.000000 3.009897 57.788644
PERU 479 -0.000372 0.070534 0.000000 6.911964 121.923701
POLAND 479 0.001478 0.077801 0.000000 4.684009 86.417289
PORTUGAL 304 -0.000138 0.044048 0.000000 0.090896 5.362624
SERBIA 479 0.000725 0.047709 0.000000 0.941635 73.265933
SINGAPORE 304 -0.000827 0.014414 0.000000 -17.349447 302.003300
SLOVENIA 522 0.004287 0.072956 0.000000 1.249112 17.957136
SOUTHAFRICA 479 -0.000737 0.060573 0.000000 0.235804 36.270684
PHILIPPINES 479 -0.000742 0.046834 0.000000 0.977518 16.158682
TURKEY 479 -0.000566 0.045698 0.000000 1.605441 18.057612
ROMANIA 479 0.000676 0.051019 0.000000 2.124739 27.659831
RUSSIA 479 0.000698 0.063477 0.000000 1.942384 19.978415
SLOVAK 321 0.000548 0.075066 0.000000 0.508429 15.161646
VIETNAM 479 0.000381 0.047007 0.000000 2.435305 32.618819
ISRAEL 479 0.001016 0.048876 0.000000 6.011108 77.769961
QATAR 522 0.002105 0.051395 0.000000 0.438958 9.394681
UKRAINE 304 -0.001889 0.048030 0.000000 0.830255 25.140715
UK 300 0.000907 0.042302 0.000000 0.236459 5.066597
MEXICO 521 0.001200 0.062654 0.000000 1.087168 25.140901
FINLAND 467 0.001483 0.051698 0.000000 -0.042995 13.338786
KOREA 522 0.001178 0.063672 0.000000 0.232466 22.855812
MOROCCO 522 0.001186 0.058002 0.000000 3.858036 94.502280
USA 518 0.003022 0.059794 0.000000 4.464252 51.748992
AUSTRALIA 299 -0.002274 0.053718 0.000000 -0.765010 13.175627
Table 18: CDS descriptive stats Jan/2008 – Dec/2009
47
Country # Obs Mean Std Median Skewness Kurtosis
ARGENTINA 260 -0.001606 0.030629 -0.003447 0.391084 6.064805
TUNISIE 260 0.000010 0.019200 0.000000 2.801287 42.547128
VENEZUELA 260 -0.000291 0.032205 -0.000007 0.116180 5.374867
CZECHREPUBLIC 260 -0.000487 0.049569 0.000000 -0.055964 15.401235
DOMINICANREPUBLIC 260 -0.002007 0.024755 0.000000 -5.723325 54.125167
GERMANY 260 0.001947 0.065806 0.000000 -0.865376 9.209708
BRAZIL 260 -0.000450 0.031791 0.000000 -0.448024 7.227619
FRANCE 260 0.003743 0.054133 0.000000 -0.343868 4.934111
GREECE 260 0.005050 0.061035 0.000161 -2.107475 25.168136
HONGKONG 260 0.000085 0.054247 0.000000 -0.881899 25.553871
IRELAND 260 0.005181 0.058833 0.000106 -0.395510 8.857472
JAMAICA 260 -0.001497 0.037038 0.000000 -4.122947 73.648682
JAPAN 260 -0.001126 0.109655 0.000000 0.050159 6.836691
BAHRAIN 260 -0.000768 0.019962 0.000000 -5.401579 56.211310
BELGIUM 260 0.004667 0.055086 0.000000 -0.125767 6.252421
DENMARK 260 0.001552 0.051765 0.000000 1.625235 17.336081
NORWAY 260 0.001160 0.036341 0.000000 0.629831 11.684207
SPAIN 260 0.003984 0.069025 0.000052 -0.674792 8.836711
SWEDEN 260 -0.002273 0.042306 0.000000 -0.172513 6.621763
THAILAND 260 0.000282 0.039808 0.000000 0.926386 7.670081
NETHERLANDS 260 0.002715 0.040996 0.000000 -0.369892 8.082945
LEBANON 260 0.000108 0.017098 0.000000 -1.825775 32.671165
MALAYSIA 260 -0.000666 0.040930 0.000000 0.305640 6.796002
NEWZEALAND 172 0.001167 0.026709 0.000000 0.527921 13.392769
URUGUAY 260 -0.002340 0.040069 0.000000 -1.567920 12.930648
CHINA 260 -0.000377 0.042460 0.000000 0.107727 6.837183
AUSTRIA 260 0.000708 0.056891 0.000000 0.141924 32.455007
BULGARIA 260 -0.000107 0.043606 0.000000 0.779566 13.511333
CHILE 260 0.001134 0.058916 0.000000 0.486441 8.666402
COLOMBIA 260 -0.000975 0.032480 -0.000112 -0.048060 5.212221
COSTARICA 260 0.000220 0.038433 0.000000 0.587235 8.592204
CROATIA 260 0.000087 0.039724 0.000000 -0.151921 12.905024
CYPRUS 260 0.002706 0.030544 0.000000 3.250898 30.197282
ELSALVADOR 260 -0.001093 0.030852 0.000000 -0.343065 11.030014
ESTONIA 260 -0.003111 0.038812 0.000000 -0.049607 8.917665
GUATEMALA 260 -0.002523 0.061525 0.000000 -1.161935 11.771523
HUNGARY 260 0.001411 0.043129 0.000000 1.209795 15.385694
ICELAND 260 -0.001155 0.022705 0.000000 1.967274 25.658956
INDONESIA 260 -0.001414 0.033911 0.000000 -0.478167 10.873060
IRAQ 260 -0.001056 0.012812 0.000000 -9.911114 146.147345
ITALY 260 0.002520 0.069110 0.000115 -1.029878 10.521829
KAZAKHSTAN 260 -0.001456 0.037930 0.000000 -1.206068 13.555036
LATVIA 260 -0.003200 0.027238 0.000000 -0.380992 7.571148
LITHUANIA 260 -0.001099 0.034701 0.000000 -0.246841 9.129300
MALTA 260 0.002706 0.030652 0.000000 3.329430 30.895247
PANAMA 260 -0.001300 0.039015 0.000000 -0.500837 7.159489
PERU 260 -0.000355 0.036400 0.000000 -0.376463 5.568319
POLAND 260 -0.000162 0.050728 0.000000 -0.641569 12.303168
PORTUGAL 260 0.006369 0.076824 0.000301 -1.766593 17.005019
SERBIA 260 -0.001244 0.020056 0.000000 -16.031340 258.003861
SINGAPORE 260 0.000967 0.013023 0.000000 6.808875 72.547670
SLOVENIA 260 -0.000492 0.048773 0.000000 0.778395 12.254199
SOUTHAFRICA 260 -0.001005 0.042465 0.000000 -1.371593 15.588019
PHILIPPINES 260 -0.001032 0.031229 0.000000 -0.446429 9.675038
TURKEY 260 -0.001612 0.035624 0.000000 -0.937759 12.306201
ROMANIA 260 0.000079 0.036583 0.000000 0.125824 14.186846
RUSSIA 260 -0.001410 0.042413 0.000000 -0.578888 10.263344
SLOVAK 260 -0.000302 0.047696 0.000000 -0.134091 14.632398
VIETNAM 260 0.000897 0.032557 0.000000 -0.125522 4.704493
ISRAEL 260 -0.000632 0.022100 0.000000 0.680123 15.917664
QATAR 260 -0.001041 0.028166 0.000000 -1.069629 12.583895
UKRAINE 260 -0.003436 0.027592 0.000000 -0.920600 7.585586
UK 260 -0.000424 0.040980 0.000000 -0.548249 7.004359
MEXICO 260 -0.000679 0.032447 -0.000923 -0.440348 6.586397
FINLAND 260 0.001030 0.042230 0.000000 -1.632680 21.889339
KOREA 260 0.000483 0.046473 0.000000 0.473142 5.146851
MOROCCO 260 0.000145 0.010424 0.000000 0.133039 54.748438
USA 260 0.000575 0.036896 0.000000 0.398557 13.565642
AUSTRALIA 260 0.001056 0.040700 0.000000 1.157218 13.281735
Table 19: CDS descriptive stats Jan/2010 – Dec/2010
48
Country # Obs Mean Std Median Skewness Kurtosis
ARGENTINA 230 0.002689 0.028354 0.000000 0.404520 6.973216
TUNISIE 230 0.002720 0.039212 0.000000 2.856749 26.587602
VENEZUELA 230 0.000193 0.020438 -0.000002 0.269804 3.922982
CZECHREPUBLIC 230 0.002980 0.038716 0.000095 1.037758 12.862311
DOMINICANREPUBLIC 230 0.002308 0.026292 0.000000 3.291856 37.471582
GERMANY 230 0.001174 0.061322 0.000106 0.291181 7.503443
BRAZIL 230 0.002352 0.034781 0.000602 0.238775 7.004584
FRANCE 230 0.002533 0.055447 0.000000 0.042527 6.248187
GREECE 230 0.007979 0.054748 0.005576 -1.014332 17.547101
HONGKONG 230 0.003061 0.045425 0.000000 0.561058 8.440580
IRELAND 230 0.000937 0.034183 0.000085 -0.340926 5.812668
JAMAICA 230 -0.000964 0.025682 0.000000 -11.425521 159.175751
JAPAN 230 0.000126 0.119551 0.000140 -0.049287 4.227193
BAHRAIN 230 0.003365 0.031889 0.000000 2.252805 22.870112
BELGIUM 230 0.001885 0.055870 0.000598 -0.177896 4.040418
DENMARK 230 0.005009 0.050056 0.000243 0.065845 5.410078
NORWAY 230 0.003019 0.043994 0.000141 1.002241 13.540197
SPAIN 230 0.001286 0.056940 0.001076 -0.046388 4.218084
SWEDEN 230 0.003494 0.069046 0.000208 -0.547344 6.084638
THAILAND 230 0.003079 0.031043 0.000173 -0.023394 8.031034
NETHERLANDS 230 0.002694 0.039128 0.000251 0.094666 5.170631
LEBANON 230 0.001574 0.014812 0.000000 2.271422 18.733982
MALAYSIA 230 0.003212 0.035950 0.000177 0.817384 7.212536
NEWZEALAND 230 0.002251 0.031508 0.000109 2.639827 21.814513
URUGUAY 230 0.001528 0.058755 -0.000091 0.980394 22.227306
CHINA 230 0.003545 0.036175 0.000092 0.449611 6.540537
AUSTRIA 230 0.002557 0.080501 0.000000 0.703592 9.434010
BULGARIA 230 0.002125 0.030350 0.000000 0.344644 5.577496
CHILE 230 0.002465 0.042006 -0.000083 0.826389 10.760147
COLOMBIA 230 0.002310 0.040147 -0.000210 0.167844 8.101925
COSTARICA 230 0.001745 0.049323 0.000000 -0.161325 6.338060
CROATIA 230 0.003217 0.025103 0.000000 1.178611 12.472479
CYPRUS 230 0.006603 0.056028 0.000000 3.129018 25.412635
ELSALVADOR 230 0.002496 0.050600 0.000000 1.644506 21.962810
ESTONIA 230 0.002337 0.028162 0.000100 2.188406 16.826504
GUATEMALA 230 0.001346 0.025502 -0.000092 0.412372 11.452634
HUNGARY 230 0.001775 0.026653 0.000121 0.086741 4.755220
ICELAND 230 -0.000138 0.024195 0.000000 1.856771 21.605464
INDONESIA 230 0.002320 0.037443 0.000000 0.693942 8.967184
IRAQ 230 0.000232 0.042305 0.000000 3.269663 51.080722
ITALY 230 0.003840 0.059711 0.000438 0.280847 3.940187
KAZAKHSTAN 230 0.002336 0.034433 0.000000 0.525819 6.902061
LATVIA 230 0.001207 0.023972 0.000000 0.540025 8.040973
LITHUANIA 230 0.001485 0.023245 0.000000 0.547808 8.274320
MALTA 230 0.000678 0.017027 0.000000 4.458037 63.519518
PANAMA 230 0.002622 0.037266 0.000000 0.093043 7.993675
PERU 230 0.002169 0.040678 0.000000 0.134272 5.430795
POLAND 230 0.003214 0.033125 0.000131 -0.083355 5.738823
PORTUGAL 230 0.003924 0.044056 0.000819 0.180890 7.088092
SERBIA 230 0.001797 0.026837 0.000000 3.509857 37.765677
SINGAPORE 230 0.002662 0.017583 0.000000 1.429554 13.906031
SLOVENIA 230 0.007716 0.040768 0.000063 0.866122 6.754075
SOUTHAFRICA 230 0.002293 0.036674 0.000000 0.035013 8.450698
PHILIPPINES 230 0.002122 0.035212 0.000000 0.231912 6.706976
TURKEY 230 0.003206 0.032215 0.000354 -0.510459 6.622967
ROMANIA 230 0.001386 0.028909 0.000000 0.164458 5.112883
RUSSIA 230 0.002854 0.038410 0.000076 0.127335 7.289450
SLOVAK 230 0.005671 0.057856 0.000000 0.558240 6.074919
VIETNAM 230 0.001309 0.024736 0.000079 0.263863 7.471589
ISRAEL 230 0.002550 0.027178 0.000051 0.595756 8.202837
QATAR 230 0.001232 0.025577 0.000000 0.537009 6.800246
UKRAINE 230 0.000000 0.000000 0.000000 - -
UK 230 0.000931 0.045442 0.000167 0.581077 7.121332
MEXICO 230 0.002216 0.030914 0.000000 0.458884 8.339171
FINLAND 230 0.003089 0.037689 0.000270 0.353511 5.705511
KOREA 230 0.002276 0.037964 0.000010 0.206046 6.362144
MOROCCO 230 0.002796 0.031002 0.000006 3.758198 34.843938
USA 230 0.000838 0.034121 0.000000 1.430980 12.665456
AUSTRALIA 230 0.002531 0.038059 0.000107 0.557361 11.150888
Table 20: CDS descriptive stats Jan/2011 – Oct/2011
49
Country # Obs Mean Std Median Skewness Kurtosis
ARGENTINA 999 0.000667 0.036064 0.000000 2.717684 32.003800
AUSTRALIA 776 −0.000256 0.045042 0.000000 −0.159262 14.821526
AUSTRIA 654 −0.000392 0.062295 0.000000 0.557590 18.200475
BAHRAIN 914 0.001228 0.036563 0.000000 2.408831 45.848711
BELGIUM 999 0.002667 0.051765 0.000000 0.558958 8.589816
BRAZIL 999 0.000315 0.052430 0.000000 7.098875 147.859907
BULGARIA 956 0.000548 0.045818 0.000000 4.955833 80.261917
CHILE 956 0.000521 0.074638 0.000000 2.819740 63.782916
CHINA 996 0.001415 0.049897 0.000000 2.321791 32.352903
COLOMBIA 956 −0.000404 0.048406 0.000000 0.730276 43.646167
COSTARICA 799 −0.000259 0.051835 0.000000 0.089656 13.950099
CROATIA 956 0.001387 0.048303 0.000000 6.230173 117.543281
CYPRUS 999 0.004323 0.044875 0.000000 1.837461 27.575629
CZECHREPUBLIC 956 0.000838 0.060035 0.000000 1.769134 35.476429
DENMARK 781 0.000393 0.048787 0.000000 0.335416 11.690373
DOMINICANREPUBLIC 999 0.000630 0.036789 0.000000 −1.319235 37.124956
ELSALVADOR 956 0.000806 0.044619 0.000000 1.813215 34.208741
ESTONIA 798 −0.001159 0.044761 0.000000 1.317726 21.723911
FINLAND 944 0.001494 0.045931 0.000000 −0.358015 15.392883
FRANCE 999 0.002752 0.053660 0.000000 1.753440 28.234679
GERMANY 999 0.002091 0.057537 0.000000 0.545793 14.845344
GREECE 999 0.005108 0.049369 0.000000 −1.276638 22.879190
GUATEMALA 988 0.000135 0.047536 0.000000 1.302119 36.955197
HONGKONG 956 0.000478 0.048534 0.000000 −0.349087 27.809270
HUNGARY 999 0.002143 0.048593 0.000000 5.180196 73.945048
ICELAND 843 −0.000081 0.032143 0.000000 4.345895 47.298824
INDONESIA 799 −0.001038 0.045339 0.000000 1.046513 16.092220
IRAQ 956 −0.000547 0.029443 0.000000 2.452830 106.599766
IRELAND 798 0.002842 0.048041 0.000000 0.143652 9.055459
ISRAEL 956 0.000674 0.038538 0.000000 6.229431 101.738760
ITALY 999 0.002837 0.056776 0.000000 −0.415650 11.491156
JAMAICA 956 0.000073 0.032940 0.000000 −1.359259 61.397918
JAPAN 956 0.001144 0.099858 0.000000 2.768754 44.617494
KAZAKHSTAN 956 −0.000193 0.052993 0.000000 7.245129 155.910000
KOREA 999 0.001037 0.054520 0.000000 0.280729 23.192218
LATVIA 798 −0.000590 0.038473 0.000000 0.404465 17.008493
LEBANON 956 −0.000547 0.025609 0.000000 2.156275 60.489925
LITHUANIA 956 0.000479 0.044700 0.000000 4.700026 86.101444
MALAYSIA 799 −0.000433 0.048711 0.000000 0.392071 14.714432
MALTA 999 0.002851 0.036422 0.000000 1.741780 23.304022
MEXICO 998 0.000661 0.050199 0.000000 1.110896 32.403447
MOROCCO 999 0.001177 0.044666 0.000000 4.697910 142.079802
NETHERLANDS 927 0.002400 0.049002 0.000000 2.875273 41.583313
NORWAY 757 0.000067 0.039634 0.000000 0.794734 13.360240
PANAMA 956 −0.000289 0.050173 0.000000 2.515393 60.889050
PERU 956 −0.000095 0.056586 0.000000 6.662594 147.601528
PHILIPPINES 956 −0.000407 0.040139 0.000000 0.760838 16.734720
POLAND 956 0.001202 0.062967 0.000000 4.327481 102.291832
PORTUGAL 781 0.003123 0.056683 0.000000 −1.379271 20.058011
QATAR 999 0.000981 0.041565 0.000000 0.417041 12.421170
ROMANIA 956 0.000421 0.042958 0.000000 1.811502 29.792628
RUSSIA 956 0.000388 0.053260 0.000000 1.582664 21.786007
SERBIA 956 0.000467 0.037712 0.000000 0.622306 101.915031
SINGAPORE 781 0.000784 0.015161 0.000000 −3.657404 119.023902
SLOVAK 798 0.001409 0.062687 0.000000 0.461875 15.360088
SLOVENIA 999 0.003487 0.061183 0.000000 1.266708 20.543994
SOUTHAFRICA 956 −0.000311 0.051131 0.000000 −0.035026 38.237330
SPAIN 999 0.002555 0.056566 0.000000 −0.281454 9.770292
SWEDEN 781 −0.000541 0.052130 0.000000 −0.416998 8.032207
THAILAND 956 0.000445 0.046157 0.000000 0.401946 17.177093
TUNISIE 978 0.001176 0.047738 0.000000 5.455129 101.359365
TURKEY 956 −0.000172 0.040311 0.000000 0.919692 17.536542
UK 777 0.000166 0.041685 0.000000 −0.114036 5.389267
UKRAINE 781 −0.001879 0.033922 0.000000 0.719311 40.301827
URUGUAY 779 −0.001681 0.040406 0.000000 −0.327733 16.359530
USA 995 0.001606 0.049377 0.000000 4.258795 59.796620
VENEZUELA 956 0.000586 0.030535 0.000000 2.367364 26.628425
VIETNAM 956 0.000615 0.039130 0.000000 2.101939 34.807681
Table 21: CDS descriptive stats Jan/2008 – Oct/2011
50
Country # Obs Mean Std Median Skewness Kurtosis
ARGENTINA 499 -0.000252 0.028946 -0.001094 0.357882 6.668351
AUSTRALIA 499 0.001372 0.038423 0.000000 1.032713 13.511329
AUSTRIA 499 0.000900 0.066533 0.000000 0.564674 17.901656
BAHRAIN 499 0.000861 0.027136 0.000000 0.649124 31.577847
BELGIUM 499 0.002988 0.054183 0.000000 -0.225982 5.252255
BRAZIL 499 0.000241 0.032105 0.000000 -0.162602 7.520071
BULGARIA 499 0.000331 0.037544 0.000000 0.714186 13.840448
CHILE 499 0.000800 0.052405 -0.000027 0.665981 9.967181
CHINA 499 0.000781 0.038777 0.000000 0.196995 7.194946
COLOMBIA 499 -0.000193 0.035104 -0.000161 0.002861 7.527988
COSTARICA 499 0.000557 0.043222 0.000000 0.114563 7.671166
CROATIA 499 0.001108 0.033485 0.000000 0.069085 15.247661
CYPRUS 499 0.005049 0.043974 0.000000 3.655463 34.817380
CZECHREPUBLIC 499 0.000251 0.043105 0.000000 0.060177 16.697722
DENMARK 499 0.002406 0.049130 0.000000 0.954798 13.268237
DOMINICANREPUBLIC 499 -0.000608 0.029419 0.000000 -3.881214 64.397151
ELSALVADOR 499 0.000962 0.039823 0.000000 1.602036 27.828534
ESTONIA 499 -0.001168 0.034097 0.000000 0.202171 11.675637
FINLAND 499 0.001517 0.039076 0.000000 -1.004510 17.537206
FRANCE 499 0.002986 0.052457 0.000000 -0.107679 5.907406
GERMANY 499 0.001583 0.062408 0.000000 -0.418747 8.943607
GREECE 499 0.006315 0.057372 0.000881 -1.759424 23.117006
GUATEMALA 499 -0.000578 0.047791 0.000000 -1.339605 17.450899
HONGKONG 499 0.000894 0.047353 0.000000 -0.511545 24.624644
HUNGARY 499 0.001431 0.036199 0.000000 1.087324 16.780460
ICELAND 499 -0.000743 0.023225 0.000000 1.980218 24.183483
INDONESIA 499 -0.000457 0.034025 0.000000 0.245032 11.167025
IRAQ 499 -0.000713 0.026572 0.000000 4.104353 138.037804
IRELAND 499 0.003089 0.048327 0.000000 -0.365474 10.690519
ISRAEL 499 0.000236 0.023959 0.000000 0.536288 12.276669
ITALY 499 0.003164 0.063560 0.000000 -0.631143 8.837527
JAMAICA 499 -0.000706 0.033102 0.000000 -5.019670 89.309283
JAPAN 499 -0.000676 0.113454 0.000000 0.006389 5.501244
KAZAKHSTAN 499 -0.000262 0.035209 0.000000 -0.596768 12.307207
KOREA 499 0.000483 0.042257 0.000000 0.296775 6.002949
LATVIA 499 -0.001743 0.024943 0.000000 -0.204784 8.313861
LEBANON 499 0.000560 0.015611 0.000000 -0.395315 31.591486
LITHUANIA 499 -0.000624 0.029421 0.000000 -0.150939 10.655136
MALAYSIA 499 0.000294 0.037901 0.000000 0.434674 7.510840
MALTA 499 0.002102 0.025728 0.000000 3.804894 39.362861
MEXICO 499 -0.000025 0.030984 -0.000074 -0.078394 7.798596
MOROCCO 499 0.000700 0.023786 0.000000 2.535595 59.824021
NETHERLANDS 499 0.002142 0.039162 0.000000 -0.220840 7.111587
NEWZEALAND 388 0.001322 0.029020 0.000000 2.076294 21.566779
NORWAY 499 0.001547 0.039199 0.000000 0.909234 14.286317
PANAMA 499 -0.000004 0.037001 0.000000 -0.352763 7.968557
PERU 499 0.000200 0.037198 0.000000 -0.165578 5.785470
PHILIPPINES 499 -0.000343 0.031677 0.000000 -0.031225 8.898170
POLAND 499 0.000989 0.042882 0.000000 -0.585206 13.524710
PORTUGAL 499 0.005479 0.062825 0.000125 -1.635177 20.365619
QATAR 499 -0.000454 0.028231 0.000000 -0.759411 11.323204
ROMANIA 499 0.000225 0.032606 0.000000 0.110994 12.977437
RUSSIA 499 -0.000108 0.039970 0.000000 -0.306752 9.639548
SERBIA 499 0.000322 0.023450 0.000000 -2.722603 103.502557
SINGAPORE 499 0.001730 0.015203 0.000000 3.295032 31.512667
SLOVAK 499 0.001978 0.051953 0.000000 0.361288 9.812454
SLOVENIA 499 0.002782 0.044405 0.000000 0.804704 11.451951
SOUTHAFRICA 499 -0.000147 0.038879 0.000000 -0.953885 14.534170
SPAIN 499 0.002519 0.062468 0.000012 -0.510670 7.933039
SWEDEN 499 -0.000279 0.054849 0.000000 -0.505832 7.885526
THAILAND 499 0.000730 0.035300 0.000000 0.555264 8.261535
TUNISIE 499 0.001096 0.030593 0.000000 3.086982 37.477083
TURKEY 499 0.000092 0.033646 0.000000 -0.785786 10.608785
UK 499 0.000024 0.041450 0.000000 -0.306502 5.486762
UKRAINE 499 -0.002024 0.020210 0.000000 -1.455906 14.118940
URUGUAY 499 -0.000850 0.040362 0.000000 -0.670351 11.551356
USA 499 0.000300 0.034402 0.000000 0.555518 12.818523
VENEZUELA 499 -0.000408 0.027642 -0.000282 0.217423 5.974959
VIETNAM 499 0.000691 0.028709 0.000000 -0.014453 5.935101
Table 22: CDS descriptive stats Dec/2009 – Oct/2011
51
C Contagion results
C.1 General contagion
C.1.1 Global Financial Crisis
52
03/0
606
/06
09/0
612
/06
03/0
706
/07
09/0
712
/07
03/0
806
/08
09/0
812
/08
03/0
906
/09
09/0
912
/09
03/1
006
/10
09/1
012
/10
03/1
106
/11
09/1
111
/11
4 months
6 months
8 months
Subprime crisis − BANK − FR
Date
Inde
x va
lue
(nor
mal
ized
)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6USACRB INDEX
5 10 15 20 25 30 35 40 45 50Contagion − countries affected
Figure 1: Global Financial Crisis contagion - Bank sector - FR
53
03/0
606
/06
09/0
612
/06
03/0
706
/07
09/0
712
/07
03/0
806
/08
09/0
812
/08
03/0
906
/09
09/0
912
/09
03/1
006
/10
09/1
012
/10
03/1
106
/11
09/1
111
/11
4 months
6 months
8 months
Subprime crisis − BANK − CS1
Date
Inde
x va
lue
(nor
mal
ized
)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6USACRB INDEX
5 10 15 20 25 30 35 40 45 50Contagion − countries affected
Figure 2: Global Financial Crisis contagion - Bank sector - CS1
54
03/0
606
/06
09/0
612
/06
03/0
706
/07
09/0
712
/07
03/0
806
/08
09/0
812
/08
03/0
906
/09
09/0
912
/09
03/1
006
/10
09/1
012
/10
03/1
106
/11
09/1
111
/11
4 months
6 months
8 months
Subprime crisis − BANK − CS2
Date
Inde
x va
lue
(nor
mal
ized
)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6USACRB INDEX
5 10 15 20 25 30 35 40 45 50Contagion − countries affected
Figure 3: Global Financial Crisis contagion - Bank sector - CS2
55
4 months
6 months
8 months
03/0
606
/06
09/0
612
/06
03/0
706
/07
09/0
712
/07
03/0
806
/08
09/0
812
/08
03/0
906
/09
09/0
912
/09
03/1
006
/10
09/1
012
/10
03/1
106
/11
09/1
111
/11
Subprime crisis − COUNTRY − FR
Date
Inde
x va
lue
(nor
mal
ized
)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2USACRB INDEX
10 20 30 40 50 60Contagion − countries affected
Figure 4: Global Financial Crisis contagion - Equity - FR
56
4 months
6 months
8 months
03/0
606
/06
09/0
612
/06
03/0
706
/07
09/0
712
/07
03/0
806
/08
09/0
812
/08
03/0
906
/09
09/0
912
/09
03/1
006
/10
09/1
012
/10
03/1
106
/11
09/1
111
/11
Subprime crisis − COUNTRY − CS1
Date
Inde
x va
lue
(nor
mal
ized
)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2USACRB INDEX
10 20 30 40 50 60Contagion − countries affected
Figure 5: Global Financial Crisis contagion - Equity - CS1
57
4 months
6 months
8 months
03/0
606
/06
09/0
612
/06
03/0
706
/07
09/0
712
/07
03/0
806
/08
09/0
812
/08
03/0
906
/09
09/0
912
/09
03/1
006
/10
09/1
012
/10
03/1
106
/11
09/1
111
/11
Subprime crisis − COUNTRY − CS2
Date
Inde
x va
lue
(nor
mal
ized
)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2USACRB INDEX
10 20 30 40 50 60Contagion − countries affected
Figure 6: Global Financial Crisis contagion - Equity - CS2
58
C.1.2 European Financial Crisis
59
03/0
8
06/0
8
09/0
8
12/0
8
03/0
9
06/0
9
09/0
9
12/0
9
03/1
0
06/1
0
09/1
0
12/1
0
03/1
1
06/1
1
09/1
111
/11
4 months
6 months
8 months
European sovereign debt crisis − BANK − FR
Date
Inde
x va
lue
(nor
mal
ized
)
0
0.2
0.4
0.6
0.8
1
1.2
1.4GREECEPORTUGALIRELANDITALYSPAINCRB INDEX
5 10 15 20 25 30 35 40 45 50Contagion − countries affected
Figure 7: European Sovereign Debt Crisis contagion - Bank sector - FR
60
03/0
8
06/0
8
09/0
8
12/0
8
03/0
9
06/0
9
09/0
9
12/0
9
03/1
0
06/1
0
09/1
0
12/1
0
03/1
1
06/1
1
09/1
111
/11
4 months
6 months
8 months
European sovereign debt crisis − BANK − CS1
Date
Inde
x va
lue
(nor
mal
ized
)
0
0.2
0.4
0.6
0.8
1
1.2
1.4GREECEPORTUGALIRELANDITALYSPAINCRB INDEX
5 10 15 20 25 30 35 40 45 50Contagion − countries affected
Figure 8: European Sovereign Debt Crisis contagion - Bank sector - CS1
61
03/0
8
06/0
8
09/0
8
12/0
8
03/0
9
06/0
9
09/0
9
12/0
9
03/1
0
06/1
0
09/1
0
12/1
0
03/1
1
06/1
1
09/1
111
/11
4 months
6 months
8 months
European sovereign debt crisis − BANK − CS2
Date
Inde
x va
lue
(nor
mal
ized
)
0
0.2
0.4
0.6
0.8
1
1.2
1.4GREECEPORTUGALIRELANDITALYSPAINCRB INDEX
5 10 15 20 25 30 35 40 45 50Contagion − countries affected
Figure 9: European Sovereign Debt Crisis contagion - Bank sector - CS2
62
4 months
6 months
8 months
03/0
8
06/0
8
09/0
8
12/0
8
03/0
9
06/0
9
09/0
9
12/0
9
03/1
0
06/1
0
09/1
0
12/1
0
03/1
1
06/1
1
09/1
111
/11
European sovereign debt crisis − COUNTRY − FR
Date
Inde
x va
lue
(nor
mal
ized
)
0
0.2
0.4
0.6
0.8
1
1.2
1.4GREECEPORTUGALIRELANDITALYSPAINCRB INDEX
10 20 30 40 50 60Contagion − countries affected
Figure 10: European Sovereign Debt Crisis contagion - Equity - FR
63
4 months
6 months
8 months
03/0
8
06/0
8
09/0
8
12/0
8
03/0
9
06/0
9
09/0
9
12/0
9
03/1
0
06/1
0
09/1
0
12/1
0
03/1
1
06/1
1
09/1
111
/11
European sovereign debt crisis − COUNTRY − CS1
Date
Inde
x va
lue
(nor
mal
ized
)
0
0.2
0.4
0.6
0.8
1
1.2
1.4GREECEPORTUGALIRELANDITALYSPAINCRB INDEX
10 20 30 40 50 60Contagion − countries affected
Figure 11: European Sovereign Debt Crisis contagion - Equity - CS1
64
4 months
6 months
8 months
03/0
8
06/0
8
09/0
8
12/0
8
03/0
9
06/0
9
09/0
9
12/0
9
03/1
0
06/1
0
09/1
0
12/1
0
03/1
1
06/1
1
09/1
111
/11
European sovereign debt crisis − COUNTRY − CS2
Date
Inde
x va
lue
(nor
mal
ized
)
0
0.2
0.4
0.6
0.8
1
1.2
1.4GREECEPORTUGALIRELANDITALYSPAINCRB INDEX
10 20 30 40 50 60Contagion − countries affected
Figure 12: European Sovereign Debt Crisis contagion - Equity - CS2
65
12/0
8
03/0
9
06/0
9
09/0
9
12/0
9
03/1
0
06/1
0
09/1
0
12/1
0
03/1
1
06/1
1
09/1
111
/11
4 months
6 months
8 months
European sovereign debt crisis − CDS − FR
Date
Inde
x va
lue
(nor
mal
ized
)
0
5
10
15
20
25
30
35
40
45
50GREECEPORTUGALIRELANDITALYSPAINCRB INDEX
10 20 30 40 50 60Contagion − countries affected
Figure 13: European Sovereign Debt Crisis contagion - CDS - FR
66
C.2 Contagion to Brazil
C.2.1 Global Financial Crisis
67
03/0
606
/06
09/0
612
/06
03/0
706
/07
09/0
712
/07
03/0
806
/08
09/0
812
/08
03/0
906
/09
09/0
912
/09
03/1
006
/10
09/1
012
/10
03/1
106
/11
09/1
111
/11
4 months
6 months
8 months
Subprime crisis − Contagion to BRAZIL − BANK − CS2
Date
Inde
x va
lue
(nor
mal
ized
)
0
0.5
1
1.5
2
2.5
3USABRAZILCRB INDEX
5 10 15 20 25 30 35Contagion to BRAZIL − CS2 statistic
Figure 14: Global Financial Crisis contagion to Brazil - Bank sector - CS2
68
03/0
606
/06
09/0
612
/06
03/0
706
/07
09/0
712
/07
03/0
806
/08
09/0
812
/08
03/0
906
/09
09/0
912
/09
03/1
006
/10
09/1
012
/10
03/1
106
/11
09/1
111
/11
4 months
6 months
8 months
Subprime crisis − Contagion to BRAZIL − COUNTRY − CS1
Date
Inde
x va
lue
(nor
mal
ized
)
0
0.5
1
1.5
2
2.5
3USABRAZILCRB INDEX
5 10 15 20 25 30 35Contagion to BRAZIL − CS1 statistic
Figure 15: Global Financial Crisis contagion to Brazil - Equity market - CS1
69
03/0
606
/06
09/0
612
/06
03/0
706
/07
09/0
712
/07
03/0
806
/08
09/0
812
/08
03/0
906
/09
09/0
912
/09
03/1
006
/10
09/1
012
/10
03/1
106
/11
09/1
111
/11
4 months
6 months
8 months
Subprime crisis − Contagion to BRAZIL − COUNTRY − CS2
Date
Inde
x va
lue
(nor
mal
ized
)
0
0.5
1
1.5
2
2.5
3USABRAZILCRB INDEX
5 10 15 20 25 30 35 40 45Contagion to BRAZIL − CS2 statistic
Figure 16: Global Financial Crisis contagion to Brazil - Equity market - CS2
70
C.2.2 European Financial Crisis
71
03/0
8
06/0
8
09/0
8
12/0
8
03/0
9
06/0
9
09/0
9
12/0
9
03/1
0
06/1
0
09/1
0
12/1
0
03/1
1
06/1
1
09/1
111
/11
4 months
6 months
8 months
European sovereign debt crisis − Contagion to BRAZIL − BANK − CS1
Date
Inde
x va
lue
(nor
mal
ized
)
0
0.2
0.4
0.6
0.8
1
1.2
1.4BRAZILGREECEPORTUGALIRELANDITALYSPAINCRB INDEX
2 4 6 8 10 12 14Contagion to BRAZIL − CS1 statistic
Figure 17: European Sovereign Debt Crisis contagion to Brazil - Bank sector - CS1
72
03/0
8
06/0
8
09/0
8
12/0
8
03/0
9
06/0
9
09/0
9
12/0
9
03/1
0
06/1
0
09/1
0
12/1
0
03/1
1
06/1
1
09/1
111
/11
4 months
6 months
8 months
European sovereign debt crisis − Contagion to BRAZIL − BANK − CS2
Date
Inde
x va
lue
(nor
mal
ized
)
0
0.2
0.4
0.6
0.8
1
1.2
1.4BRAZILGREECEPORTUGALIRELANDITALYSPAINCRB INDEX
5 10 15 20 25 30 35Contagion to BRAZIL − CS2 statistic
Figure 18: European Sovereign Debt Crisis contagion to Brazil - Bank sector - CS2
73
03/0
8
06/0
8
09/0
8
12/0
8
03/0
9
06/0
9
09/0
9
12/0
9
03/1
0
06/1
0
09/1
0
12/1
0
03/1
1
06/1
1
09/1
111
/11
4 months
6 months
8 months
European sovereign debt crisis − Contagion to BRAZIL − COUNTRY − CS1
Date
Inde
x va
lue
(nor
mal
ized
)
0
0.2
0.4
0.6
0.8
1
1.2
1.4BRAZILGREECEPORTUGALIRELANDITALYSPAINCRB INDEX
2 4 6 8 10 12 14 16 18Contagion to BRAZIL − CS1 statistic
Figure 19: European Sovereign Debt Crisis contagion to Brazil - Equity - CS1
74
03/0
8
06/0
8
09/0
8
12/0
8
03/0
9
06/0
9
09/0
9
12/0
9
03/1
0
06/1
0
09/1
0
12/1
0
03/1
1
06/1
1
09/1
111
/11
4 months
6 months
8 months
European sovereign debt crisis − Contagion to BRAZIL − COUNTRY − CS2
Date
Inde
x va
lue
(nor
mal
ized
)
0
0.2
0.4
0.6
0.8
1
1.2
1.4BRAZILGREECEPORTUGALIRELANDITALYSPAINCRB INDEX
5 10 15 20 25 30 35Contagion to BRAZIL − CS2 statistic
Figure 20: European Sovereign Debt Crisis contagion to Brazil - Equity - CS2
75
12/0
8
03/0
9
06/0
9
09/0
9
12/0
9
03/1
0
06/1
0
09/1
0
12/1
0
03/1
1
06/1
1
09/1
111
/11
4 months
6 months
8 months
European sovereign debt crisis − Contagion to BRAZIL − CDS − FR
Date
Inde
x va
lue
(nor
mal
ized
)
0
5
10
15
20
25
30
35
40
45
50BRAZILGREECEPORTUGALIRELANDITALYSPAINCRB INDEX
5 10 15 20 25 30Contagion to BRAZIL − FR statistic
Figure 21: European Sovereign Debt Crisis contagion to Brazil - CDS - FR
76
Banco Central do Brasil
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http://www.bcb.gov.br/?TRABDISCLISTA
Working Paper Series
The Working Paper Series of the Central Bank of Brazil are available for download at http://www.bcb.gov.br/?WORKINGPAPERS
253 Bank Efficiency and Default in Brazil: causality tests
Benjamin M. Tabak, Giovana L. Craveiro and Daniel O. Cajueiro
Oct/2011
254 Macroprudential Regulation and the Monetary Transmission Mechanism Pierre-Richard Agénor and Luiz A. Pereira da Silva
Nov/2011
255 An Empirical Analysis of the External Finance Premium of Public Non-Financial Corporations in Brazil Fernando N. de Oliveira and Alberto Ronchi Neto
Nov/2011
256 The Self-insurance Role of International Reserves and the 2008-2010 Crisis Antonio Francisco A. Silva Jr
Nov/2011
257 Cooperativas de Crédito: taxas de juros praticadas e fatores de viabilidade Clodoaldo Aparecido Annibal e Sérgio Mikio Koyama
Nov/2011
258 Bancos Oficiais e Crédito Direcionado – O que diferencia o mercado de crédito brasileiro? Eduardo Luis Lundberg
Nov/2011
259 The impact of monetary policy on the exchange rate: puzzling evidence from three emerging economies Emanuel Kohlscheen
Nov/2011
260 Credit Default and Business Cycles: an empirical investigation of Brazilian retail loans Arnildo da Silva Correa, Jaqueline Terra Moura Marins, Myrian Beatriz Eiras das Neves and Antonio Carlos Magalhães da Silva
Nov/2011
261 The relationship between banking market competition and risk-taking: do size and capitalization matter? Benjamin M. Tabak, Dimas M. Fazio and Daniel O. Cajueiro
Nov/2011
262 The Accuracy of Perturbation Methods to Solve Small Open Economy Models Angelo M. Fasolo
Nov/2011
263 The Adverse Selection Cost Component of the Spread of Brazilian Stocks Gustavo Silva Araújo, Claudio Henrique da Silveira Barbedo and José Valentim Machado Vicente
Dec/2011
77
264 Uma Breve Análise de Medidas Alternativas à Mediana na Pesquisa de Expectativas de Inflação do Banco Central do Brasil Fabia A. de Carvalho
Jan/2012
265 O Impacto da Comunicação do Banco Central do Brasil sobre o Mercado Financeiro Marcio Janot e Daniel El-Jaick de Souza Mota
Jan/2012
266 Are Core Inflation Directional Forecasts Informative? Tito Nícias Teixeira da Silva Filho
Jan/2012
267 Sudden Floods, Macroprudention Regulation and Stability in an Open Economy P.-R. Agénor, K. Alper and L. Pereira da Silva
Feb/2012
268 Optimal Capital Flow Taxes in Latin America João Barata Ribeiro Blanco Barroso
Mar/2012
269 Estimating Relative Risk Aversion, Risk-Neutral and Real-World Densities using Brazilian Real Currency Options José Renato Haas Ornelas, José Santiago Fajardo Barbachan and Aquiles Rocha de Farias
Mar/2012
270 Pricing-to-market by Brazilian Exporters: a panel cointegration approach João Barata Ribeiro Blanco Barroso
Mar/2012
271 Optimal Policy When the Inflation Target is not Optimal Sergio A. Lago Alves
Mar/2012
272 Determinantes da Estrutura de Capital das Empresas Brasileiras: uma abordagem em regressão quantílica Guilherme Resende Oliveira, Benjamin Miranda Tabak, José Guilherme de Lara Resende e Daniel Oliveira Cajueiro
Mar/2012
273 Order Flow and the Real: Indirect Evidence of the Effectiveness of Sterilized Interventions Emanuel Kohlscheen
Apr/2012
274 Monetary Policy, Asset Prices and Adaptive Learning Vicente da Gama Machado
Apr/2012
275 A geographically weighted approach in measuring efficiency in panel data: the case of US saving banks Benjamin M. Tabak, Rogério B. Miranda and Dimas M. Fazio
Apr/2012
276 A Sticky-Dispersed Information Phillips Curve: a model with partial and
delayed information Marta Areosa, Waldyr Areosa and Vinicius Carrasco
Apr/2012
277 Trend Inflation and the Unemployment Volatility Puzzle
Sergio A. Lago Alves May/2012
278 Liquidez do Sistema e Administração das Operações de Mercado Aberto
Antonio Francisco de A. da Silva Jr. Maio/2012
279 Going Deeper Into the Link Between the Labour Market and Inflation
Tito Nícias Teixeira da Silva Filho May/2012
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280 Educação Financeira para um Brasil Sustentável Evidências da necessidade de atuação do Banco Central do Brasil em educação financeira para o cumprimento de sua missão Fabio de Almeida Lopes Araújo e Marcos Aguerri Pimenta de Souza
Jun/2012
281 A Note on Particle Filters Applied to DSGE Models Angelo Marsiglia Fasolo
Jun/2012
282 The Signaling Effect of Exchange Rates: pass-through under dispersed information Waldyr Areosa and Marta Areosa
Jun/2012
283 The Impact of Market Power at Bank Level in Risk-taking: the Brazilian case Benjamin Miranda Tabak, Guilherme Maia Rodrigues Gomes and Maurício da Silva Medeiros Júnior
Jun/2012
284 On the Welfare Costs of Business-Cycle Fluctuations and Economic-Growth Variation in the 20th Century Osmani Teixeira de Carvalho Guillén, João Victor Issler and Afonso Arinos de Mello Franco-Neto
Jul/2012
285 Asset Prices and Monetary Policy – A Sticky-Dispersed Information Model Marta Areosa and Waldyr Areosa
Jul/2012
286 Information (in) Chains: information transmission through production chains Waldyr Areosa and Marta Areosa
Jul/2012
287 Some Financial Stability Indicators for Brazil Adriana Soares Sales, Waldyr D. Areosa and Marta B. M. Areosa
Jul/2012
288 Forecasting Bond Yields with Segmented Term Structure Models
Caio Almeida, Axel Simonsen and José Vicente Jul/2012
289 Financial Stability in Brazil
Luiz A. Pereira da Silva, Adriana Soares Sales and Wagner Piazza Gaglianone
Aug/2012
290 Sailing through the Global Financial Storm: Brazil's recent experience with monetary and macroprudential policies to lean against the financial cycle and deal with systemic risks Luiz Awazu Pereira da Silva and Ricardo Eyer Harris
Aug/2012
291 O Desempenho Recente da Política Monetária Brasileira sob a Ótica da Modelagem DSGE Bruno Freitas Boynard de Vasconcelos e José Angelo Divino
Set/2012
292 Coping with a Complex Global Environment: a Brazilian perspective on emerging market issues Adriana Soares Sales and João Barata Ribeiro Blanco Barroso
Oct/2012
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