Contagion in CDS, Banking and Equity MarketsContagion in CDS, Banking and Equity Markets Rodrigo C esar de Castro Miranda Benjamin Miranda Tabaky Mauricio Medeiros Juniorz Abstract

ISSN 1518-3548 CNPJ 00.038.166/0001-05

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.


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.


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.

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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


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


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


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


32


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


33


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


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


35


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


36


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


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


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


40


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


41


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


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


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


43


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


44


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


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


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


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


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


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


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


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


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


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


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


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



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



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



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



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



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


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


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


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



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


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



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

78

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

79

Documents

Contagion in CDS, Banking and Equity MarketsContagion in CDS, Banking and Equity Markets Rodrigo C esar de Castro Miranda Benjamin Miranda Tabaky Mauricio Medeiros Juniorz Abstract