Contagion in CDS, Banking and Equity Markets

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Working Paper Series Brasília n. 293 Oct. 2012 p. 1-79

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Rodrigo C´esarde 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 markets 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 macroprudential policies.

JEL classiﬁcation: G01; G15. Keywords: contagion; emerging markets; correlation; coskewness; endogenous testing.

∗Departamento de Estudos e Pesquisas, Banco Central do Brasil. †Benjamin M. Tabak agradece apoio ﬁnanceiro do CNPQ. ‡Universidade de Bras´ılia.

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 impact 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 addition 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 channels 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 comovements. 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:

ρc νc = , (1) p 2 1 + δ(1 − ρc ) 2 2 sc,i − spre,i δ = 2 , (2) spre,i

2 2 where sc,i and spre,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.

!2 νc − ρpre FR(i → j) = p , (3) V ar(νc − ρpre) where

9 V ar(νc − ρpre) = V ar(νc) + V ar(ρpre) − 2Cov(νc, ρpre), (4) 2 1 (1 + δ) 1 2 2 2 1 2 2 2 V ar(νc) = 2 3 (2 − ρc )(1 − ρc ) + ρc (1 − ρc ) , (5) 2 [1 + δ(1 − ρc )] Tc Tpre 1 2 2 V ar(ρpre) = (1 − ρpre) , (6) Tpre 2 2 1 1 ρcρpre(1 − ρc )(1 − ρpre)(1 + δ) Cov(νc, ρpre) = . (7) p 2 3 2 Tpre [1 + δ(1 − ρc )]

Under the null hypothesis of no contagion, the two-tailed adjusted correlation test 2 is asymptotically distributed χ1. Although the test is two-tailed, this paper only concerns itself with increases in correlations, therefore the test indicates contagion 2 only if FR(i → j) is greater than some critical value in χ1 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.

Letµ ˆT,k the mean of k in period T andσ ˆT,k the standard deviation of k in T , where

T can be either Tc ou Tpre and k can be either i or j.

1 2 The coskewness contagion test from i to the volatility of j, CS1(i → j; i , j ), and 2 1 the coskewness contagion test from the volatility of i to j, CS2(i → j; i , j ) are given by:

 2 1 2 1 2 1 2 ψc(i , j ) − ψpre(i , j ) CS1(i → j; i , j ) = , (8)  q 4ρ2 +2  4νc+2 + pre Tc Tpre  2 2 1 2 1 2 1 ψc(i , j ) − ψpre(i , j ) CS2(i → j; i , j ) = , (9)  q 4ρ2 +2  4νc+2 + pre Tc Tpre where

Tc m n 1 X it − µˆc,i jt − µˆc,j ψ (im, jn) = , (10) c T σˆ σˆ c t=1 c,i c,j Tpre m n 1 X it − µˆpre,i jt − µˆpre,j ψ (im, jn) = . (11) pre T σˆ σˆ pre t=1 pre,i pre,j

10 Under the null hypothesis of no contagion, the two-tailed adjusted correlation test 2 is asymptotically distributed χ1.

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 channels (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 countries 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 figures 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 relevant 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 periods 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 contagion 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.

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.

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. economy 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 instability 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.

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 similar 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.

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 understanding 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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26 A Contagion literature contribution tables

A summary of the contributions from recent literature on ﬁnancial contagion.

Table 1: Summary of the contributions Authors Period Contagion Market Method Contagion evidence Baele and 1973-2007 14 European countries Equity mar- Dynamic factor model Mixed ﬁnd- Inghelbrecht kets ings (2010)

Longstaﬀ et al. 2006-2008 CDOs - other markets in CDOs mar- Correlation test YES (2010) US ket

Manconi et al. 2004-2007 Securitized bond market - Bond Mar- Correlation test YES (2011) corporate bond market in ket US

Pe et al. (2010) 2007-2009 Mexico Interbank Systemic Risk Network YES market Model (SyRNet)

Ahlgren and 1980-2006 Eﬀects between Germany, Stock mar- Correlation break NO Antell (2010) Japan, UK and US and be- ket tween Hong Kong, Korea, Mexico and US

Rijckeghem 1994, 1996 Mexican, Thai, and Rus- Bank lend- Correlation test YES and Weder and 1997 sian currency crises - 18 in- ing market (2001) dustrialized countries

Corsetti et al. October Stock market returns in Stock mar- Correlation break YES (2005) 1997 Hong Kong - 10 emerging ket economies as well as the G7 countries

Rodriguez 1993-1998 Asian crisis and Mexican Stock mar- Copula YES (2007) crisis ket

Candelon et al. 1994 and Mexican crises in Ar- Stock mar- Common cyclical features NO (2005) 1997 gentina, Venezuela, ket Colombia, Chile and Hong Kong crisis in Indone- sia, Korea, Malaysia, Singapore, Taiwan and Thailand

Aloui et al. 2004-2009 US - Brazil, Russia, India, Stock mar- Copula NO (2011) China (BRIC) ket

Baur and 1994-2006 Thailand (July 1997), Stock and Test for ﬂight-to qual- YES Lucey (2009) Hong Kong (October bond mar- ity, ﬂight-from-quality and 1997) and Russia (August ket cross-asset contagion 1998) - eight developed countries

Mendoza 1982-2008 Eﬀects of shocks to bank Net credit Model’s quantitative pre- YES and Quadrini equities on asset prices market dictions (2010)

27 Table 2: Summary of the contributions Authors Period Contagion Market Method Evidence of Contagion Dungey et al. 1998 Russian bond default and Bond mar- Latent factor model YES (2006) the LTCM recapitalization ket announcement - 12 countries

Baur and 1997-2001 Asia - Latin America and Stock mar- Quantile regression YES Schulze (2005) Europe ket

Fong et al. 1987-2008 Between ﬁnancial assets Stock mar- General Markov switching Mixed ﬁnd- (2011) (US stocks and Treasury ket model ings bonds), commodities (oil and gold) and real estate assets (US CaseShiller index)

Hwang (2008) 2006-2008 Banks of Asia, Europe, CDS market Approach proposed by NO Latin America, North Rigobon (2003) America, and Oceania

Markose et al. 2008 US banks CDS market Systemic Risk Ratio, YES (2010) Complex Adaptive Sys- tem (CAS), Agent-based Computational Economics (ACE) and SCAP Stress Test

Andenmatten 2008-2010 Greek debt crisis - 39 CDS market Approach proposed by YES and Brill countries Forbes and Rigobon (2011) (2002)

Calice and 2005-2008 European and American CDS and eq- SVAR Mixed ﬁnd- Ioannidis banks uity market ings (2009)

Coudert and 2004-2007 General Motors (GM) and CDS market Dynamic measures of YES Gex (2010) Ford crisis in 2005 - US correlations(EWMA and and European ﬁrms DCC-GARCH)

Rigobon 1994-1998 Asian crisis, Mexican cri- Bond and OLS, PCA and new pro- Mixed ﬁnd- (2002) sis, Russian crisis and stock mar- cedure based in Rigobon ings LTCM crisis kets (2003)

Dungey et al. 1998-2001 Russia, LTCM, Brazil, Bond and Related to the work of ? Mixed ﬁnd- (2008) and 2007 dot-com, Argentina and stock mar- ings U.S. subprime mortgage kets and credit crisis

Dungey and 2004-2009 US - European equity mar- Stock mar- The FR test and Hong test Mixed ﬁnd- Yalama (2009) kets during the global ﬁ- ket ings nancial crisis

Forbes and 1987-1996 1997 Asian crisis, 1994 Stock mar- Heteroskedasticity biases Mixed ﬁnd- Rigobon Mexican crisis and 1987 ket tests for contagion based ings (2002) U.S. market crash on correlation coeﬃcients

Fry et al. 1997-1998 Hong Kong crisis and the Real estate Coskewness and Lagrange YES (2008) and 2007 US subprime mortgage cri- and equity multiplier tests sis markets

Fry et al. 1995-2010 1997 Asian crisis, the 1998 Equity mar- Correlation and coskew- YES (2010) Russian crisis, the 1998 ket ness LTCM, the 2000 dot-com crisis and the global ﬁnan- cial crisis

28 Table 3: Summary of the contributions Authors Period Contagion Market Method Contagion evidence Gonz´alez- 1997-1998 1997 Asian crisis Equity mar- Model of interdependence, Mixed ﬁnd- Hermosillos ket Bivariate and Multi- ings et al. (2005) variate testing, AR and heteroskedastic dynamics, Forbes and Rigobon (2002)contagion test, the BKS test and the DFGM test

Heise and K 2008 26 major US banks CDS market Macroscopic System Dy- YES (2011) namics

Hsiao et al. 1997-1998 Asian crisis, Russian crisis, Interdependencies, corre- Mixed ﬁnd- (2011) and 2008- LTCM crisis, Brazil crisis, lation and coskewness ings 2010 dot-com crisis, Argen- tinean crisis, sub-prime crisis, great recession, global ﬁnancial crisis

Forbes and 1982-2000 Mexican Debt Crisis, Bond and GARCH model and Het- NO Rigobon Asian Flu, the Russian stock mar- eroskedasticity biases tests (2000) Crisis, the Brazilian Cri- kets sis, Dot-com crisis - Latin America Bodart and 1994 and Mexican crisis - 4 Latin Equity mar- Frequency domain ap- YES Candelon 1997 America countries and ket proach (2009) Asian crisis - 7 Asian countries

Cipollini and 1997-1998 East Asian countries Stock mar- Dynamic Factor model YES Kapetanios ket (2009)

Khan and 1994-1999 Asian crisis - Thailand, Stock mar- Kalman ﬁlter YES Ken Park Malaysia, Indonesia, Ko- ket (2009) rea and Philippines

Mistrulli 1989-2008 Italy Interbank Maximum entropy method YES (2011) market

Chiang et al. 1990-2003 Asian crisis - 9 Asian mar- Stock mar- Dynamic conditional- YES (2007) ket and US ket correlation model Serwa and 1997-2002 6 Crisis markets - Western Stock mar- Heteroscedasticity- YES Bohl (2005) European markets, and ket adjusted correlation emerging markets in Cen- tral and Eastern Europe

Baur (2011) 1979-2009 Financial sector - real Stock mar- Test four alternative types YES economy of ten sectors in ket of contagion 25 developed and emerging countries

29 B Descriptive statistics tables

The descriptive statistics are taken from log returns of each series and where available are shown for four periods:

2006–2007 The ﬁrst pre-crisis period, from January, 2006 to December 2007.

2008–2009 The Global Financial Crisis Period

2010 The ﬁrst year of the European Sovereign Debt Crisis

2011 The second year of the European Sovereign Debt Crisis

In addition, we also provide the statistics for the following samples:

2006–2011 The full sample

2008–2011 The period of both crisis

Dec, 2009 to October, 2011 The full European Sovereign Debt Crisis period

The exception are the CDS statistics, as in our sample only data from December 2007 and onward was available (and not for all countries), and therefore the CDS descriptive statistics start in 2008.

B.1 Bank sector indexes statistics

Tables 4, 5, 6, 7, 8, 9, 10, show the descriptive statistics the bank sector indexes for the seven periods.

30 Country # Obs Mean Std Median Skewness Kurtosis ARGENTINA 520 0.000619 0.014454 0.000157 -0.590350 9.604432 AUSTRALIA 520 0.000692 0.012697 0.000716 -0.532294 6.021981 AUSTRIA 520 0.000944 0.016696 0.001183 -0.157361 5.190378 BELGIUM 520 0.000328 0.013104 0.001145 -0.364033 4.520833 BRAZIL 520 0.001684 0.021202 0.002124 -0.306839 4.724507 BULGARIA 520 0.002283 0.020701 0.001134 0.747248 9.545587 CANADA 520 0.000412 0.009973 0.001026 -0.254174 5.403890 CHILE 520 0.000371 0.011165 0.000000 -0.161698 5.793267 CHINA 520 0.001567 0.018878 0.000150 0.391767 5.971363 COLOMBIA 520 0.000386 0.021112 0.000000 0.048372 7.314018 CYPRUS 520 0.002465 0.017936 0.002500 -0.501709 5.948538 CZECHREPUBLIC 520 0.001035 0.016890 0.001565 0.109021 7.718293 DENMARK 520 0.000362 0.012028 0.001054 -0.302966 4.925700 EGYPT 520 0.000661 0.017161 0.000000 -1.823305 27.178386 FINLAND 520 0.000577 0.017403 0.000122 0.029204 4.612701 FRANCE 520 0.000375 0.014179 0.000869 -0.250216 4.397100 GERMANY 520 0.000614 0.012967 0.000610 -0.182097 4.422766 GREECE 520 0.000806 0.014329 0.001305 -0.169234 5.452423 HONGKONG 520 0.000327 0.008691 0.000000 -0.138555 5.820530 HUNGARY 520 0.000823 0.022710 0.000647 -0.171828 3.943163 INDIA 520 0.001714 0.020704 0.000960 -0.254030 4.703531 INDONESIA 361 0.000000 0.000000 0.000000 - - IRELAND 520 0.000050 0.016773 0.000731 0.017026 5.388401 ISRAEL 520 0.000318 0.016469 0.000502 -0.399313 4.617180 ITALY 520 0.000557 0.011216 0.000839 -0.278641 4.121290 JAPAN 520 -0.000734 0.016168 0.000000 0.093503 3.872499 KOREA 520 0.000257 0.028400 0.000000 0.023133 5.377866 LUXEMBURG 520 0.000756 0.013405 0.000295 3.412165 43.786469 MALAYSIA 520 0.000974 0.010155 0.001157 -0.504143 5.896913 MALTA 520 0.000403 0.011806 0.000438 0.193134 8.698110 MEXICO 520 0.001216 0.014609 0.001690 -0.371231 5.256371 NETHERLANDS 520 0.000613 0.013984 0.001308 -0.464848 6.225914 NORWAY 520 0.000662 0.015996 0.001003 -0.323443 4.519159 PAKISTAN 520 0.001230 0.018852 0.001697 -0.552796 4.196439 PERU 520 0.001411 0.016898 0.000457 0.313987 18.755265 PHILIPPINES 520 0.001383 0.015109 0.001166 -0.441561 6.097302 POLAND 520 0.001385 0.017769 0.000957 0.027664 3.641643 PORTUGAL 520 0.000991 0.011117 0.000829 0.100541 5.873792 ROMANIA 520 0.001759 0.020131 0.000852 0.163712 5.587951 RUSSIA 520 0.001973 0.023664 0.000794 0.193051 7.485284 SINGAPORE 520 0.000759 0.013859 0.001845 -0.240927 5.956405 SLOVENIA 520 0.001347 0.013470 0.000584 1.689543 16.160637 SOUTHAFRICA 520 0.000217 0.023299 0.000475 -0.064671 3.959052 SPAIN 520 0.000784 0.011505 0.001016 -0.216854 4.258924 SRILANKA 520 0.000624 0.010685 0.000000 0.869581 10.850093 SWEDEN 520 0.000567 0.015798 0.000772 0.032543 4.389861 SWITZERLAND 520 0.000198 0.012517 0.000174 -0.262788 4.162156 TAIWAN 520 0.000564 0.015868 0.000000 -0.090499 4.924303 THAILAND 520 0.000676 0.020058 0.000000 -1.973022 30.290357 TURKEY 520 0.000790 0.026937 -0.000079 -0.272811 4.878868 UK 520 0.000008 0.012329 0.000530 -0.361721 5.109540 USA 520 -0.000344 0.011470 0.000000 -0.150048 8.593604 VENEZUELA 520 0.001529 0.029243 0.000000 2.829551 49.217398

Table 4: Banking sector descriptive stats Jan/2006 – Dec/2007

31 Country # Obs Mean Std Median Skewness Kurtosis ARGENTINA 522 -0.000218 0.019219 0.000394 -0.166238 6.070029 AUSTRALIA 522 -0.000281 0.032362 -0.001075 -0.200889 5.369414 AUSTRIA 522 -0.001279 0.039603 -0.001543 -0.113223 5.059871 BELGIUM 522 -0.002664 0.047303 -0.000735 -0.417577 6.396159 BRAZIL 522 0.000067 0.032723 0.000300 -0.151083 7.932044 BULGARIA 522 -0.003971 0.040555 -0.002275 -0.721949 10.321024 CANADA 522 -0.000089 0.028503 0.000170 -0.180192 8.255457 CHILE 522 0.000275 0.019884 0.000000 -0.165119 10.457416 CHINA 522 0.000067 0.032922 0.000000 0.488135 7.400816 COLOMBIA 522 0.000270 0.025083 0.000000 -0.281690 5.723798 CYPRUS 522 -0.002231 0.034734 -0.001428 0.250953 5.088625 CZECHREPUBLIC 522 -0.000228 0.039097 0.000000 -0.275926 6.796642 DENMARK 522 -0.001152 0.031426 -0.002474 -0.251032 5.574151 EGYPT 522 -0.000498 0.015026 0.000124 -1.776116 17.627665 FINLAND 522 -0.000681 0.037086 -0.001082 -0.237518 6.657119 FRANCE 522 -0.001025 0.037400 -0.002314 0.258936 5.674337 GERMANY 522 -0.001529 0.038498 -0.000937 -0.044350 7.132181 GREECE 522 -0.001929 0.034206 -0.000827 0.099985 4.487083 HONGKONG 522 -0.000514 0.028428 -0.000255 -0.672463 14.073710 HUNGARY 522 -0.001078 0.049641 0.001101 0.061224 6.621774 INDIA 522 -0.000452 0.033511 0.000000 0.331826 6.188885 INDONESIA 380 0.000000 0.000000 0.000000 - - IRELAND 522 -0.004671 0.083086 -0.003454 -1.240117 17.323549 ISRAEL 522 -0.000088 0.027220 -0.001369 0.367847 5.646763 ITALY 522 -0.001230 0.030095 -0.000042 -0.028779 5.904428 JAPAN 522 -0.001139 0.027175 0.000000 0.291976 5.391789 KOREA 522 -0.000900 0.067186 0.000000 -0.115952 9.339942 LUXEMBURG 522 -0.000914 0.020376 -0.000682 -0.236124 6.587301 MALAYSIA 522 0.000020 0.014838 0.000000 -0.359588 6.544997 MALTA 522 -0.000720 0.015286 -0.000337 -0.289260 5.983743 MEXICO 522 -0.000256 0.023154 0.000000 0.094989 7.081671 NETHERLANDS 522 -0.004875 0.068464 -0.001938 -13.076160 244.443736 NORWAY 522 -0.000622 0.050715 -0.001225 0.115408 6.873780 PAKISTAN 522 -0.002169 0.023758 -0.000836 0.054612 4.172016 PERU 522 0.000606 0.012429 0.000788 -0.805350 8.065648 PHILIPPINES 522 -0.000625 0.018576 0.000000 -0.481604 7.956635 POLAND 522 -0.000948 0.035219 0.000763 -0.139609 5.389508 PORTUGAL 522 -0.001848 0.023867 -0.001353 -0.016061 5.612927 ROMANIA 522 -0.001923 0.036717 0.000000 -0.580941 6.648371 RUSSIA 522 -0.000983 0.046500 -0.000038 0.200840 10.901804 SINGAPORE 522 0.000014 0.023456 0.000000 -0.013562 6.005075 SLOVENIA 522 -0.002198 0.021867 -0.000532 -0.462847 9.385384 SOUTHAFRICA 522 -0.000140 0.033148 -0.000714 -0.168576 4.891392 SPAIN 522 -0.000554 0.030489 -0.000399 0.129597 6.061723 SRILANKA 522 0.000411 0.015162 0.000000 1.624311 14.418828 SWEDEN 522 -0.000715 0.040995 -0.001448 0.468607 5.508695 SWITZERLAND 522 -0.000965 0.034512 -0.002369 0.435666 5.755983 TAIWAN 522 -0.000109 0.025917 0.000000 0.095901 4.108610 THAILAND 522 0.000012 0.022480 0.000000 -0.549933 7.567879 TURKEY 522 -0.000449 0.036183 -0.000823 -0.020338 6.090708 UK 522 -0.001666 0.040308 -0.002041 -0.060167 7.777896 USA 522 -0.001289 0.048103 -0.002385 0.191373 6.518418 VENEZUELA 522 -0.000261 0.008758 0.000000 0.252503 7.715279

Table 5: Banking sector descriptive stats Jan/2008 – Dec/2009

32 Country # Obs Mean Std Median Skewness Kurtosis ARGENTINA 260 0.002414 0.017867 0.001202 0.169234 5.556356 AUSTRALIA 260 0.000201 0.019202 -0.000084 -0.218550 3.934581 AUSTRIA 260 0.000452 0.025794 0.000205 0.230524 6.632592 BELGIUM 260 -0.001289 0.028583 -0.000796 0.903465 10.809238 BRAZIL 260 0.000302 0.018864 0.000051 -0.173708 4.211232 BULGARIA 260 -0.000418 0.017617 -0.001356 0.261312 4.490354 CANADA 260 0.000441 0.013726 0.001581 -0.256896 4.016748 CHILE 260 0.002024 0.013083 0.002740 -0.041173 4.366897 CHINA 260 0.000049 0.014778 0.000084 0.021502 3.131485 COLOMBIA 260 0.002181 0.014557 0.000000 -0.329838 4.339596 CYPRUS 260 -0.001894 0.026939 -0.002422 0.185961 3.235068 CZECHREPUBLIC 260 0.000404 0.023770 -0.001256 0.226601 4.258737 DENMARK 260 0.000430 0.022157 0.000263 0.324360 6.855495 EGYPT 260 0.001113 0.010518 0.000342 -0.173304 4.300743 FINLAND 260 0.000407 0.024921 0.000448 2.003740 21.780264 FRANCE 260 -0.000898 0.028810 -0.001139 1.002511 11.253187 GERMANY 260 -0.000598 0.019890 -0.000154 0.062628 4.496898 GREECE 260 -0.003601 0.034919 -0.004368 0.582235 4.670117 HONGKONG 260 -0.000049 0.012156 0.000187 -0.515819 5.541941 HUNGARY 260 -0.000692 0.034556 0.000740 -0.172197 7.382550 INDIA 260 0.001262 0.015915 0.001779 -0.435324 3.875134 INDONESIA 260 0.000000 0.000000 0.000000 - - IRELAND 260 -0.003849 0.056453 -0.007688 -0.221161 5.787060 ISRAEL 260 0.000393 0.014101 0.000095 -0.086436 3.306542 ITALY 260 -0.001645 0.026168 -0.002249 0.813684 10.358664 JAPAN 260 0.000332 0.013072 0.000000 0.069497 2.967295 KOREA 260 0.000701 0.040904 0.000000 0.034640 6.018607 LUXEMBURG 260 -0.000472 0.009419 -0.000528 0.013897 3.911840 MALAYSIA 260 0.001321 0.009555 0.001812 -0.233469 5.989394 MALTA 260 0.000043 0.015163 -0.000485 0.945892 18.583767 MEXICO 260 0.001378 0.014541 0.000841 0.351298 5.067308 NETHERLANDS 260 -0.001042 0.017234 -0.001947 -0.273342 7.819482 NEWZEALAND 171 0.000547 0.009766 0.000533 -0.315885 4.514111 NORWAY 260 0.000963 0.023912 -0.000762 0.258029 4.059644 PAKISTAN 260 0.000244 0.012576 0.000000 -0.375174 5.048039 PERU 260 0.000972 0.008239 0.000399 0.318768 5.355755 PHILIPPINES 260 0.001597 0.013568 0.000853 -0.157904 4.408128 POLAND 260 0.000381 0.021642 0.001070 0.081533 5.553242 PORTUGAL 260 -0.001810 0.024308 -0.001382 0.570875 7.673021 ROMANIA 260 -0.000652 0.024113 -0.000339 -0.117407 8.845307 RUSSIA 260 0.000944 0.022162 0.001076 -0.047801 4.863803 SINGAPORE 260 0.000261 0.011515 0.000400 0.163337 4.976735 SLOVENIA 260 -0.000635 0.011837 -0.000810 -0.049364 3.618023 SOUTHAFRICA 260 0.000890 0.019088 0.002344 0.026081 5.044331 SPAIN 260 -0.001761 0.029433 -0.000146 1.018609 12.246563 SRILANKA 260 0.003212 0.013180 0.000908 1.083285 6.254862 SWEDEN 260 0.000600 0.022704 0.001292 0.411054 6.898844 SWITZERLAND 260 -0.000011 0.017232 0.000685 -0.109586 4.900890 TAIWAN 260 0.001842 0.015556 0.001566 0.401084 4.805720 THAILAND 260 0.001545 0.015097 0.001145 0.301041 4.799045 TURKEY 260 0.000642 0.021905 0.001684 0.002890 5.788560 UK 260 0.000011 0.020324 -0.000708 0.550977 7.815149 USA 260 0.000415 0.018136 -0.000007 -0.129302 3.557702 VENEZUELA 260 -0.002715 0.043073 0.000000 -15.573435 248.346078

Table 6: Banking sector descriptive stats Jan/2010 – Dec/2010

33 Country # Obs Mean Std Median Skewness Kurtosis ARGENTINA 232 -0.003004 0.017004 -0.002122 -0.510635 4.995972 AUSTRALIA 232 -0.000739 0.019847 -0.001339 -0.023724 4.127064 AUSTRIA 232 -0.004236 0.029528 -0.003259 -0.079110 4.723811 BELGIUM 232 -0.005627 0.038382 -0.003081 -0.042788 4.687930 BRAZIL 232 -0.001782 0.022607 -0.000176 -0.644381 5.314769 BULGARIA 232 -0.000799 0.023865 -0.000494 0.002609 4.756793 CANADA 232 -0.000732 0.015003 0.000339 -0.315002 4.140143 CHILE 232 -0.001232 0.017683 0.000000 -0.746383 7.086944 CHINA 232 -0.001474 0.020306 -0.000161 -0.185083 4.977996 COLOMBIA 232 -0.000946 0.011642 0.000000 -0.196672 3.143590 CYPRUS 232 -0.006432 0.035665 -0.006673 0.381228 5.518195 CZECHREPUBLIC 232 -0.001710 0.025660 0.000000 -0.648221 5.691419 DENMARK 232 -0.002485 0.027842 -0.003374 -0.008343 4.565555 EGYPT 232 -0.002706 0.013759 -0.000224 -1.843563 10.284430 FINLAND 232 -0.001569 0.026423 -0.001238 -0.031959 4.203201 FRANCE 232 -0.003367 0.038967 -0.002635 0.151611 6.506396 GERMANY 232 -0.002371 0.030902 0.000051 0.274376 6.429108 GREECE 232 -0.005725 0.049432 -0.005662 0.379080 5.864355 HONGKONG 232 -0.001566 0.016588 -0.000085 -0.758618 6.327160 HUNGARY 232 -0.002816 0.038020 -0.003180 -0.212706 5.819219 INDIA 232 -0.002285 0.018282 -0.000268 -0.085514 3.440229 INDONESIA 232 0.000000 0.144218 0.000000 0.000000 23.200000 IRELAND 232 -0.006115 0.045972 -0.009433 1.297932 10.270732 ISRAEL 232 -0.002426 0.023027 -0.000449 -0.534392 4.878025 ITALY 232 -0.002929 0.036485 -0.000204 -0.385368 3.728629 JAPAN 232 -0.000715 0.016915 0.000000 -0.567729 7.420883 KOREA 232 -0.001748 0.040308 0.000000 -0.156287 7.023475 LUXEMBURG 232 -0.003459 0.019097 -0.000238 -4.838602 42.668485 MALAYSIA 232 -0.000645 0.010905 0.000177 -0.300810 5.412049 MALTA 232 -0.000897 0.011235 0.000484 -0.838203 7.474445 MEXICO 232 -0.001420 0.018882 0.000516 -1.084153 7.619657 NETHERLANDS 232 -0.001551 0.018425 -0.002434 -0.393083 5.369323 NEWZEALAND 232 -0.000135 0.009824 0.000262 -0.115865 4.703125 NORWAY 232 -0.001852 0.028752 -0.000273 -0.288607 4.012006 PAKISTAN 232 -0.000895 0.012210 -0.000521 -0.026181 4.466422 PERU 232 -0.001293 0.015064 -0.000376 -0.961530 12.595904 PHILIPPINES 232 -0.000143 0.013407 0.000537 -0.370692 4.454036 POLAND 232 -0.001863 0.024362 -0.000218 -0.670403 5.545151 PORTUGAL 232 -0.004749 0.030448 -0.000880 -0.462161 4.646785 ROMANIA 232 -0.000946 0.020977 0.000000 -0.317095 4.533513 RUSSIA 232 -0.001624 0.025181 -0.001237 -0.704336 5.172727 SINGAPORE 232 -0.001002 0.015687 0.000474 -0.474628 4.395265 SLOVENIA 232 -0.004764 0.024619 -0.003051 -2.464696 19.635465 SOUTHAFRICA 232 -0.001400 0.020720 -0.000006 -0.057645 3.939442 SPAIN 232 -0.001540 0.028099 -0.000826 0.120031 3.770457 SRILANKA 232 -0.001753 0.009298 -0.001000 -0.285172 5.520857 SWEDEN 232 -0.001683 0.028560 0.000041 -0.131322 4.696123 SWITZERLAND 232 -0.001833 0.021796 -0.001224 -0.255338 5.541279 TAIWAN 232 -0.001536 0.019500 0.000000 -0.349441 4.092210 THAILAND 232 -0.000657 0.018943 0.000000 0.211807 4.456940 TURKEY 232 -0.002280 0.025095 -0.000131 -0.561058 4.116105 UK 232 -0.002144 0.023667 -0.000981 -0.174394 4.539747 USA 232 -0.002071 0.024528 -0.001832 -0.428191 6.418209 VENEZUELA 232 0.001376 0.010422 0.000000 5.262368 60.838490

Table 7: Banking sector descriptive stats Jan/2011 – Oct/2011

34 Country # Obs Mean Std Median Skewness Kurtosis ARGENTINA 1520 0.000277 0.017177 0.000171 -0.229732 6.835930 AUSTRALIA 1520 0.000188 0.023047 0.000028 -0.277147 7.989611 AUSTRIA 1520 -0.000448 0.029470 0.000000 -0.158627 7.205670 BELGIUM 1520 -0.001444 0.033975 0.000000 -0.368549 10.122763 BRAZIL 1520 0.000506 0.025598 0.000738 -0.268991 8.732057 BULGARIA 1520 -0.000626 0.029087 -0.000101 -0.734311 15.003113 CANADA 1520 0.000164 0.019357 0.000845 -0.260754 13.785753 CHILE 1520 0.000484 0.015844 0.000000 -0.309311 11.383357 CHINA 1520 0.000438 0.024236 0.000044 0.454605 9.780172 COLOMBIA 1520 0.000503 0.020598 0.000000 -0.192374 7.304729 CYPRUS 1520 -0.000900 0.028666 0.000000 0.180206 6.003422 CZECHREPUBLIC 1520 0.000215 0.028377 0.000000 -0.308673 9.589707 DENMARK 1520 -0.000535 0.024076 0.000000 -0.206195 7.636399 EGYPT 1520 -0.000058 0.014992 0.000000 -1.765675 23.327777 FINLAND 1520 -0.000000 0.027870 0.000000 0.015810 10.265886 FRANCE 1520 -0.000645 0.029904 0.000000 0.376884 8.808647 GERMANY 1520 -0.000607 0.027627 0.000147 -0.042368 10.902596 GREECE 1520 -0.001837 0.031547 -0.000432 0.309306 7.796332 HONGKONG 1520 -0.000186 0.019054 0.000000 -0.868470 24.524904 HUNGARY 1520 -0.000469 0.037620 0.000394 -0.049813 8.751924 INDIA 1520 0.000457 0.024953 0.000193 0.160699 7.862234 INDONESIA 1219 0.000000 0.062806 0.000000 0.000000 121.900000 IRELAND 1520 -0.002938 0.057616 -0.001464 -1.287004 27.341084 ISRAEL 1520 -0.000056 0.021327 0.000090 0.079993 6.770575 ITALY 1520 -0.000797 0.025414 0.000056 -0.044954 7.768945 JAPAN 1520 -0.000683 0.020356 0.000000 0.175482 6.994878 KOREA 1520 -0.000291 0.048266 0.000000 -0.129694 13.217639 LUXEMBURG 1520 -0.000235 0.015410 0.000000 0.481849 16.089519 MALAYSIA 1520 0.000538 0.011974 0.000774 -0.440788 7.333518 MALTA 1520 -0.000210 0.013607 0.000065 0.042441 10.399546 MEXICO 1520 0.000447 0.018462 0.000894 -0.172936 8.242122 NETHERLANDS 1520 -0.001744 0.042101 -0.000298 -19.475685 591.431951 NEWZEALAND 387 0.000384 0.009739 0.000672 -0.209628 4.759168 NORWAY 1520 0.000060 0.034333 0.000000 0.068874 11.711000 PAKISTAN 1520 -0.000445 0.019177 0.000000 -0.222376 4.951777 PERU 1520 0.000686 0.014015 0.000396 -0.167932 18.053746 PHILIPPINES 1520 0.000548 0.015956 0.000706 -0.478870 7.474220 POLAND 1520 0.000075 0.026409 0.000665 -0.238014 7.148179 PORTUGAL 1520 -0.001173 0.021483 -0.000103 -0.057301 7.102457 ROMANIA 1520 -0.000217 0.027675 0.000419 -0.558774 8.820957 RUSSIA 1520 0.000334 0.033234 0.000273 0.112011 15.356631 SINGAPORE 1520 0.000244 0.017588 0.000817 -0.098380 7.783160 SLOVENIA 1520 -0.001061 0.017889 -0.000118 -0.461926 12.777591 SOUTHAFRICA 1520 0.000054 0.026172 0.000249 -0.143108 5.632362 SPAIN 1520 -0.000325 0.024980 0.000145 0.325985 9.483063 SRILANKA 1520 0.000706 0.012617 0.000000 1.405133 13.703261 SWEDEN 1520 -0.000039 0.029357 0.000056 0.420787 8.255542 SWITZERLAND 1520 -0.000409 0.024041 -0.000091 0.356036 9.170021 TAIWAN 1520 0.000377 0.020266 0.000000 0.008914 5.178420 THAILAND 1520 0.000449 0.020065 0.000000 -0.910101 15.196544 TURKEY 1520 -0.000019 0.029474 0.000150 -0.148022 6.524280 UK 1520 -0.000739 0.027517 -0.000070 -0.119351 13.099772 USA 1520 -0.000678 0.031281 -0.000321 0.157962 12.945845 VENEZUELA 1520 0.000157 0.025524 0.000000 -11.320139 377.635042

Table 8: Banking sector descriptive stats Jan/2006 – Oct/2011

35 Country # Obs Mean Std Median Skewness Kurtosis ARGENTINA 999 0.000100 0.018449 0.000204 −0.123585 5.946978 AUSTRALIA 999 −0.000074 0.026915 −0.000522 −0.213112 6.378156 AUSTRIA 999 −0.001173 0.034283 −0.000832 −0.095523 5.827920 BELGIUM 999 −0.002368 0.040805 −0.000736 −0.256626 7.356004 BRAZIL 999 −0.000106 0.027613 0.000043 −0.226727 8.952072 BULGARIA 999 −0.002141 0.032529 −0.001350 −0.816347 13.578731 CANADA 999 0.000035 0.022771 0.000741 −0.218984 10.852019 CHILE 999 0.000543 0.017811 0.000000 −0.316818 10.391194 CHINA 999 −0.000150 0.026602 0.000000 0.483408 9.496352 COLOMBIA 999 0.000564 0.020345 0.000000 −0.331368 7.281956 CYPRUS 999 −0.002652 0.032779 −0.002561 0.338258 5.132196 CZECHREPUBLIC 999 −0.000212 0.032814 0.000000 −0.282936 7.867617 DENMARK 999 −0.001003 0.028397 −0.001651 −0.138754 5.915573 EGYPT 999 −0.000432 0.013731 0.000000 −1.694319 16.210771 FINLAND 999 −0.000301 0.032008 0.000000 0.033294 8.776015 FRANCE 999 −0.001177 0.035437 −0.001403 0.390945 6.765878 GERMANY 999 −0.001243 0.032757 −0.000244 0.016336 8.340887 GREECE 999 −0.003214 0.037449 −0.002550 0.380549 5.963933 HONGKONG 999 −0.000454 0.022651 0.000000 −0.752590 18.593731 HUNGARY 999 −0.001143 0.043412 0.000000 −0.002737 7.360835 INDIA 999 −0.000203 0.026898 0.000000 0.285913 8.040067 INDONESIA 857 0.000000 0.074918 0.000000 0.000000 85.700000 IRELAND 999 −0.004496 0.069995 −0.005375 −1.029934 19.008164 ISRAEL 999 −0.000257 0.023476 −0.000229 0.176396 6.450960 ITALY 999 −0.001503 0.030268 −0.000829 0.027220 5.838007 JAPAN 999 −0.000657 0.022243 0.000000 0.184494 6.908431 KOREA 999 −0.000576 0.055911 0.000000 −0.117218 10.987523 LUXEMBURG 999 −0.000751 0.016347 −0.000291 −0.332502 8.766579 MALAYSIA 999 0.000312 0.012823 0.000402 −0.396463 7.268782 MALTA 999 −0.000529 0.014458 −0.000097 0.021313 10.399840 MEXICO 999 0.000047 0.020183 0.000436 −0.100037 8.035865 NETHERLANDS 999 −0.002972 0.050909 −0.001965 −16.696454 419.555382 NEWZEALAND 387 0.000384 0.009739 0.000672 −0.209628 4.759168 NORWAY 999 −0.000253 0.040753 −0.000749 0.092368 8.929714 PAKISTAN 999 −0.001277 0.019267 −0.000517 −0.056588 5.433668 PERU 999 0.000309 0.012249 0.000396 −0.894981 11.575265 PHILIPPINES 999 0.000114 0.016379 0.000000 −0.482095 7.931801 POLAND 999 −0.000607 0.029933 0.000570 −0.207570 6.335064 PORTUGAL 999 −0.002301 0.025189 −0.001165 0.049395 5.596920 ROMANIA 999 −0.001246 0.030852 0.000000 −0.580146 8.073172 RUSSIA 999 −0.000518 0.037253 0.000000 0.136301 14.179717 SINGAPORE 999 −0.000024 0.019255 0.000341 −0.047331 7.419775 SLOVENIA 999 −0.002315 0.019701 −0.001095 −0.705897 11.027426 SOUTHAFRICA 999 −0.000031 0.027572 0.000000 −0.163387 5.911217 SPAIN 999 −0.000903 0.029663 −0.000187 0.352572 7.236268 SRILANKA 999 0.000750 0.013524 0.000000 1.510010 13.592059 SWEDEN 999 −0.000355 0.034375 0.000000 0.419085 6.599048 SWITZERLAND 999 −0.000725 0.028248 −0.000908 0.373010 7.258719 TAIWAN 999 0.000279 0.022230 0.000000 0.033808 4.783225 THAILAND 999 0.000331 0.020088 0.000000 −0.359746 7.426137 TURKEY 999 −0.000440 0.030731 0.000225 −0.093754 6.911419 UK 999 −0.001128 0.032757 −0.001006 −0.067045 9.875840 USA 999 −0.000852 0.037693 −0.000891 0.152230 9.313252 VENEZUELA 999 −0.000557 0.023353 0.000000 −25.439265 753.789405

Table 9: Banking sector descriptive stats Jan/2008 – Oct/2011

36 Country # Obs Mean Std Median Skewness Kurtosis ARGENTINA 499 0.000560 0.017334 0.000204 -0.057935 5.717306 AUSTRALIA 499 0.000128 0.019075 -0.000169 -0.111158 4.156502 AUSTRIA 499 -0.001201 0.027076 -0.000680 -0.011726 5.858733 BELGIUM 499 -0.002217 0.031797 -0.000979 0.388937 7.203956 BRAZIL 499 -0.000333 0.020347 0.000000 -0.533844 5.435512 BULGARIA 499 -0.000454 0.020506 -0.000164 0.130430 5.271565 CANADA 499 0.000125 0.013891 0.000853 -0.214017 4.058771 CHILE 499 0.000857 0.014974 0.000000 -0.623822 7.737594 CHINA 499 -0.000465 0.017075 0.000000 -0.088337 5.056296 COLOMBIA 499 0.000847 0.013288 0.000000 -0.222420 4.189126 CYPRUS 499 -0.003150 0.030349 -0.003128 0.410094 5.002686 CZECHREPUBLIC 499 -0.000217 0.023860 0.000000 -0.204633 5.206237 DENMARK 499 -0.000747 0.024465 -0.000885 0.141478 5.708774 EGYPT 499 -0.000285 0.012192 0.000000 -1.355495 9.902941 FINLAND 499 0.000090 0.024919 0.000190 1.017155 13.643666 FRANCE 499 -0.001426 0.032665 -0.001083 0.593204 8.618215 GERMANY 499 -0.001032 0.024769 0.000000 0.313632 7.656481 GREECE 499 -0.004847 0.040618 -0.005013 0.568219 6.443282 HONGKONG 499 -0.000427 0.013699 0.000102 -0.494875 5.644240 HUNGARY 499 -0.001247 0.034987 0.000000 -0.213237 6.959210 INDIA 499 0.000004 0.016754 0.000153 -0.266853 3.720002 INDONESIA 499 0.000000 0.098222 0.000000 0.000000 49.900000 IRELAND 499 -0.004650 0.051861 -0.008012 0.327392 7.163293 ISRAEL 499 -0.000305 0.018353 0.000092 -0.562845 6.029427 ITALY 499 -0.001794 0.029934 -0.001259 0.085260 5.938956 JAPAN 499 -0.000365 0.015297 0.000000 -0.202679 6.647856 KOREA 499 -0.000402 0.039956 0.000000 -0.033912 6.556532 LUXEMBURG 499 -0.000663 0.010428 -0.000225 -0.675478 6.253649 MALAYSIA 499 0.000609 0.010028 0.001385 -0.337045 6.053091 MALTA 499 -0.000350 0.013275 0.000000 0.530044 18.299885 MEXICO 499 0.000382 0.016274 0.000692 -0.599147 7.946137 NETHERLANDS 499 -0.000960 0.016964 -0.002193 -0.065016 5.960400 NEWZEALAND 387 0.000384 0.009739 0.000672 -0.209628 4.759168 NORWAY 499 0.000029 0.025465 -0.000113 -0.053582 4.436030 PAKISTAN 499 -0.000258 0.012647 0.000000 -0.205055 4.596011 PERU 499 0.000026 0.011842 0.000250 -1.029248 16.337375 PHILIPPINES 499 0.000949 0.013383 0.000937 -0.271944 4.527753 POLAND 499 -0.000305 0.022599 0.000570 -0.363852 5.885418 PORTUGAL 499 -0.002853 0.026199 -0.000710 0.105719 5.578052 ROMANIA 499 -0.000661 0.022746 -0.000165 -0.214138 7.403778 RUSSIA 499 0.000166 0.023133 0.000000 -0.386177 5.322866 SINGAPORE 499 0.000002 0.013034 0.000577 -0.255953 5.006339 SLOVENIA 499 -0.002536 0.016856 -0.001453 -1.212101 13.477349 SOUTHAFRICA 499 0.000164 0.019533 0.000726 -0.030259 4.632312 SPAIN 499 -0.001357 0.028291 -0.000023 0.641368 9.030264 SRILANKA 499 0.001294 0.011365 0.000000 1.108516 7.176818 SWEDEN 499 -0.000044 0.024922 0.000878 0.096465 5.857601 SWITZERLAND 499 -0.000530 0.019109 0.000000 -0.174123 5.648804 TAIWAN 499 0.000709 0.017054 0.000000 -0.110874 4.813782 THAILAND 499 0.000673 0.016874 0.000000 0.198442 4.886133 TURKEY 499 -0.000091 0.023362 0.001487 -0.331747 5.005869 UK 499 -0.000658 0.021691 -0.000691 0.102080 6.101784 USA 499 -0.000457 0.020801 -0.000576 -0.363792 6.633971 VENEZUELA 499 -0.000849 0.031833 0.000000 -20.119339 437.217466

Table 10: Banking sector descriptive stats Dec/2009 – Oct/2011

37 B.2 MSCI Country indexes statistics

Tables 11, 12, 13, 14, 16 and 17 show the descriptive statistics the MSCI country equity indexes for the seven periods.

38 Country # Obs Mean Std Median Skewness Kurtosis ARGENTINA 520 0.000846 0.017782 0.000651 -0.208959 4.798341 AUSTRALIA 520 0.000890 0.013292 0.001510 -0.632447 6.454690 AUSTRIA 520 0.000579 0.012990 0.001386 -0.541992 5.884360 BAHRAIN 520 0.000364 0.010185 0.000000 2.996774 38.286081 BELGIUM 520 0.000435 0.011027 0.001530 -0.548814 4.520440 BRAZIL 520 0.001725 0.020911 0.002696 -0.490065 4.302018 CANADA 520 0.000757 0.010872 0.001741 -0.537235 4.283505 CHILE 520 0.000841 0.011782 0.001068 -0.645643 5.566684 CHINA 520 0.002051 0.017397 0.002238 -0.160746 5.498462 COLOMBIA 520 0.000479 0.022051 0.002006 -0.245157 14.764874 CROATIA 520 0.002005 0.013655 0.001288 0.262324 13.294707 CZECHREPUBLIC 520 0.001288 0.014361 0.001376 -0.179035 6.956332 DENMARK 520 0.000994 0.011626 0.001681 -0.633381 5.281207 EGYPT 520 0.001053 0.015736 0.000261 -0.918309 8.350375 ESTONIA 520 0.000143 0.013441 -0.000108 -0.057157 6.988337 FINLAND 520 0.001168 0.013784 0.000823 0.015325 4.456801 FRANCE 520 0.000712 0.011015 0.001257 -0.366784 4.152581 GERMANY 520 0.001076 0.010885 0.001871 -0.347284 3.959327 GREECE 520 0.001023 0.012410 0.001728 -0.277737 5.237771 HONGKONG 520 0.001061 0.011942 0.000849 -0.275570 5.075962 HUNGARY 520 0.000741 0.017986 0.002000 -0.204697 4.026996 INDIA 520 0.001802 0.017015 0.002281 -0.429665 5.471179 INDONESIA 520 0.001786 0.018422 0.002109 -0.580856 7.124782 IRELAND 520 0.000225 0.013736 0.000342 -0.400185 5.248912 ISRAEL 520 0.000416 0.010127 0.000745 -0.636544 5.942767 ITALY 520 0.000513 0.009838 0.001118 -0.369214 3.724629 JAPAN 520 -0.000012 0.012210 -0.000020 -0.251113 4.313685 JORDAN 520 -0.000447 0.012746 0.000000 -0.780242 9.936499 KAZAKHSTAN 520 0.001284 0.029982 0.000087 0.627127 11.170791 KOREA 520 0.000702 0.014911 0.000662 -0.583663 5.838034 KUWAIT 520 0.000446 0.013016 0.000000 -0.893779 16.938982 LEBANON 520 0.000091 0.018185 0.000000 -1.403022 21.293336 MALAYSIA 520 0.001218 0.010109 0.001369 -0.594602 6.987275 MAURITIUS 520 0.002228 0.012111 0.000000 1.098253 15.599280 MEXICO 520 0.000791 0.016337 0.001725 -0.153466 4.926219 MOROCCO 520 0.001644 0.012336 0.001691 -0.465081 4.942078 NETHERLANDS 520 0.000772 0.010312 0.001153 -0.325177 4.563700 NEWZEALAND 520 0.000259 0.011732 0.000744 -0.427878 6.379799 NIGERIA 520 0.001979 0.011771 0.000598 0.227659 4.897794 NORWAY 520 0.001151 0.017131 0.002114 -0.444493 4.714416 OMAN 520 0.000534 0.009108 0.000000 0.048296 9.201677 PAKISTAN 520 0.000477 0.016662 0.001104 -0.740466 4.851580 PERU 520 0.001996 0.019269 0.003542 -0.548223 4.588871 PHILIPPINES 520 0.001471 0.016989 0.001145 -0.259130 6.687476 POLAND 520 0.000951 0.017851 0.000863 -0.104573 3.686954 PORTUGAL 520 0.001068 0.008600 0.001197 0.076103 5.864717 QATAR 520 -0.000316 0.014567 0.000000 -0.359833 6.369394 ROMANIA 520 0.001267 0.018246 0.000483 0.068931 6.768563 RUSSIA 520 0.001223 0.019306 0.001922 -0.665536 7.979180 SINGAPORE 520 0.001085 0.012806 0.001442 -0.388667 5.565967 SLOVENIA 520 0.002199 0.010562 0.001789 0.235567 8.666904 SOUTHAFRICA 520 0.000570 0.019085 0.002300 -0.578010 4.733782 SPAIN 520 0.001066 0.010439 0.001212 -0.264432 4.145100 SRILANKA 520 0.000350 0.010025 0.000000 0.770890 11.165805 SWEDEN 520 0.000622 0.014542 0.001267 -0.387740 4.979926 SWITZERLAND 520 0.000515 0.009372 0.000832 -0.405290 4.404238 TAIWAN 520 0.000419 0.013314 0.000870 -0.675334 5.361030 THAILAND 520 0.000786 0.017714 0.000353 -1.802797 26.753301 TUNISIE 520 0.000735 0.008794 0.000742 -0.058747 4.469819 TURKEY 520 0.000834 0.025893 0.001030 -0.545774 5.748264 UKRAINE 413 0.000087 0.016268 0.000400 -2.340255 24.358527 UAE 520 -0.000802 0.017418 0.000000 -0.859768 8.320485 UK 520 0.000536 0.010545 0.001303 -0.445534 4.698241 USA 520 0.000315 0.008261 0.000649 -0.415447 5.316359 VIETNAM 282 0.001848 0.022208 -0.000179 0.098082 2.923233

Table 11: Equity descriptive stats Jan/2006 – Dec/2007

39 Country # Obs Mean Std Median Skewness Kurtosis ARGENTINA 522 -0.000630 0.031565 0.000000 -0.676475 8.355339 AUSTRALIA 522 -0.000415 0.027970 0.000372 -0.742863 7.063586 AUSTRIA 522 -0.001619 0.032617 -0.000432 -0.006413 5.412181 BAHRAIN 522 -0.002374 0.019937 0.000000 -3.782410 41.767013 BELGIUM 522 -0.001321 0.025327 -0.000778 -0.649712 7.460249 BRAZIL 522 -0.000124 0.035753 0.002447 -0.259078 8.072565 CANADA 522 -0.000390 0.026578 0.001743 -0.604105 6.852810 CHILE 522 0.000248 0.021736 0.000936 -0.076695 12.906676 CHINA 522 -0.000518 0.029354 -0.000022 0.100753 6.354587 COLOMBIA 522 0.000468 0.022789 0.001178 -0.453372 8.396858 CROATIA 522 -0.001097 0.021593 -0.000646 -0.108687 6.256155 CZECHREPUBLIC 522 -0.000805 0.030122 -0.000108 -0.017307 11.062476 DENMARK 522 -0.000680 0.024229 -0.001064 -0.169802 7.168720 EGYPT 522 -0.000941 0.023525 0.000000 -1.058140 9.765555 ESTONIA 522 -0.001529 0.025817 -0.001139 0.127002 5.793096 FINLAND 522 -0.001458 0.027669 -0.001336 0.157542 5.269906 FRANCE 522 -0.000675 0.024921 -0.000132 0.156972 7.320849 GERMANY 522 -0.000855 0.024589 0.000000 0.187850 6.821451 GREECE 522 -0.001738 0.029157 -0.000025 0.001595 5.034867 HONGKONG 522 -0.000599 0.021786 0.000000 -0.023554 7.270199 HUNGARY 522 -0.000817 0.037264 -0.000474 0.049896 7.621452 INDIA 522 -0.000685 0.028838 0.000000 0.311754 7.708579 INDONESIA 522 -0.000125 0.027754 0.000212 -0.158252 7.660588 IRELAND 522 -0.002309 0.033513 -0.000007 -0.500004 6.636141 ISRAEL 522 0.000067 0.014562 0.000654 -0.718045 6.697428 ITALY 522 -0.001019 0.025516 0.000029 0.172732 6.759841 JAPAN 522 -0.000615 0.020831 0.000232 0.016509 6.299697 JORDAN 522 -0.001002 0.017016 0.000000 -0.701378 9.120107 KAZAKHSTAN 522 -0.000580 0.032302 0.000000 -0.165086 7.478323 KOREA 522 -0.000557 0.031321 0.000000 0.010118 15.447137 KUWAIT 522 -0.001460 0.021863 0.000000 -0.958032 8.297954 LEBANON 522 0.000167 0.018974 0.000000 0.885043 10.115525 MALAYSIA 522 -0.000342 0.014211 0.000000 -0.763098 10.957938 MAURITIUS 522 -0.000488 0.019532 0.000000 0.047269 8.376366 MEXICO 522 -0.000295 0.026892 0.000084 0.107765 7.227184 MOROCCO 522 -0.000431 0.014355 -0.000481 -0.291509 6.251182 NETHERLANDS 522 -0.000716 0.024136 -0.000525 0.002264 7.082815 NEWZEALAND 522 -0.000897 0.022297 0.000289 -0.361856 5.387964 NIGERIA 522 -0.002288 0.019386 -0.002767 -0.235836 5.113835 NORWAY 522 -0.000871 0.035716 0.000480 -0.257848 5.263887 OMAN 522 -0.000891 0.021531 0.000000 -1.207930 16.044832 PAKISTAN 522 -0.001517 0.022826 -0.000442 -0.401504 5.205023 PERU 522 -0.000048 0.030766 0.000011 -0.127687 6.219585 PHILIPPINES 522 -0.000576 0.020775 0.000000 -0.697042 8.727952 POLAND 522 -0.000975 0.031853 0.000580 -0.096070 5.232543 PORTUGAL 522 -0.000893 0.020871 -0.000460 -0.003334 9.135857 QATAR 522 -0.000638 0.024495 0.000000 -0.616162 9.094411 ROMANIA 522 -0.001909 0.033735 -0.000470 -1.599134 17.345486 RUSSIA 522 -0.001261 0.041255 0.000178 -0.257657 10.736110 SERBIA 392 -0.003471 0.036395 -0.004324 -0.236674 6.430652 SINGAPORE 522 -0.000325 0.021852 -0.000362 -0.098915 5.489506 SLOVENIA 522 -0.001639 0.022035 -0.001576 -0.236514 6.284205 SOUTHAFRICA 522 -0.000158 0.028265 0.000000 -0.226892 6.004521 SPAIN 522 -0.000480 0.025380 -0.000605 0.039316 7.038078 SRILANKA 522 0.000142 0.019406 -0.000510 2.079440 19.537550 SWEDEN 522 -0.000481 0.030605 -0.000823 0.268368 5.103936 SWITZERLAND 522 -0.000331 0.019235 -0.000311 0.233749 6.466864 TAIWAN 522 -0.000205 0.020816 0.000000 -0.051049 4.422822 THAILAND 522 -0.000324 0.022868 -0.000388 -0.626861 8.480781 TUNISIE 522 0.000321 0.013018 -0.000146 0.172785 10.622998 TURKEY 522 -0.000675 0.031943 -0.000525 -0.042855 6.200162 UKRAINE 522 -0.003007 0.031041 -0.000678 0.349628 12.857489 UAE 522 -0.001975 0.029105 0.000000 -0.456881 10.834926 UK 522 -0.000742 0.024521 0.000024 0.053110 7.412752 USA 522 -0.000518 0.021563 0.000399 -0.134534 7.575500 VIETNAM 522 -0.001202 0.024058 -0.000122 -0.048941 2.514720

Table 12: Equity descriptive stats Jan/2008 – Dec/2009

40 Country # Obs Mean Std Median Skewness Kurtosis ARGENTINA 260 0.002042 0.018667 0.001270 0.439523 6.098574 AUSTRALIA 260 0.000366 0.016435 0.000930 -0.327588 3.679823 AUSTRIA 260 0.000273 0.020618 0.001475 0.220774 5.917466 BAHRAIN 260 -0.000931 0.008774 0.000000 -0.454812 5.191933 BELGIUM 260 -0.000087 0.016691 0.000195 0.635314 8.240452 BOSNIAHERZEGOVINA 87 0.000189 0.016984 -0.000912 -0.030638 3.948141 BRAZIL 260 0.000143 0.017175 -0.000063 -0.230065 4.761486 CANADA 260 0.000643 0.012613 0.001903 -0.411932 3.886686 CHILE 260 0.001344 0.010970 0.001152 0.134203 3.314583 CHINA 260 0.000088 0.012999 0.000072 -0.317653 3.541116 COLOMBIA 260 0.001315 0.013190 0.001803 -0.454813 5.302668 CROATIA 260 0.000195 0.012255 0.000732 0.163373 10.676216 CZECHREPUBLIC 260 -0.000296 0.015722 -0.000085 -0.216544 5.019189 DENMARK 260 0.001003 0.015852 0.001031 0.263651 4.372019 EGYPT 260 0.000348 0.013645 0.000000 -0.794998 6.511912 ESTONIA 260 0.001714 0.021777 0.000499 0.411022 4.247085 FINLAND 260 0.000262 0.017646 0.000411 -0.169336 5.722278 FRANCE 260 -0.000268 0.018217 0.000460 0.343936 6.828954 GERMANY 260 0.000225 0.015710 0.000570 -0.067903 4.278798 GREECE 260 -0.002396 0.028853 -0.002524 0.199769 3.774845 HONGKONG 260 0.000691 0.009663 0.000103 -0.192169 3.434767 HUNGARY 260 -0.000435 0.027437 0.000972 0.320575 7.953884 INDIA 260 0.000682 0.013636 0.001089 -0.313251 4.237998 INDONESIA 260 0.001044 0.016128 0.000617 0.285689 6.200387 IRELAND 260 -0.000844 0.023887 -0.000949 -0.041411 5.259281 ISRAEL 260 0.000083 0.010887 0.000279 -0.019852 4.086518 ITALY 260 -0.000746 0.019876 -0.000166 0.380963 8.062099 JAPAN 260 0.000482 0.011200 0.000492 -0.094412 3.077469 JORDAN 260 -0.000492 0.008601 0.000000 0.320165 5.632516 KAZAKHSTAN 260 -0.000714 0.018799 0.000000 -0.470232 10.957752 KOREA 260 0.000867 0.016243 0.001174 -0.457518 3.838379 KUWAIT 260 0.001185 0.012507 0.000693 -0.153828 6.412753 LEBANON 260 -0.000530 0.007240 -0.000323 0.378301 4.671420 MALAYSIA 260 0.001083 0.008929 0.001549 -0.329017 4.799828 MAURITIUS 260 0.000254 0.008833 0.000000 0.191823 5.168770 MEXICO 260 0.000889 0.013188 0.001878 -0.210102 5.704802 MOROCCO 260 0.000396 0.010892 -0.000510 -0.548873 9.245455 NETHERLANDS 260 -0.000024 0.016464 -0.000723 0.272814 6.382829 NEWZEALAND 260 0.000120 0.012705 0.000057 -0.357519 3.570471 NIGERIA 260 0.000800 0.013519 0.000394 0.086634 3.863810 NORWAY 260 0.000275 0.020294 0.000151 -0.050223 4.387783 OMAN 260 0.000492 0.005987 0.000000 -0.379531 8.451582 PAKISTAN 260 0.000685 0.010713 0.000000 -0.281880 4.854798 PERU 260 0.001540 0.017064 0.000943 0.146513 3.655308 PHILIPPINES 260 0.001018 0.013786 0.001430 -0.351417 3.807563 POLAND 260 0.000457 0.021432 0.000340 -0.049087 5.730015 PORTUGAL 260 -0.000608 0.018185 -0.000334 0.669638 9.605761 QATAR 260 0.000881 0.008067 0.000096 0.122132 8.297967 ROMANIA 260 -0.000073 0.023686 0.000891 -0.399656 10.254904 RUSSIA 260 0.000610 0.017649 0.001235 -0.179192 5.486611 SERBIA 260 0.000165 0.015128 0.000262 0.057542 5.859954 SINGAPORE 260 0.000651 0.011273 0.001880 -0.387939 4.261979 SLOVENIA 260 -0.000666 0.010754 -0.000553 0.282540 4.532226 SOUTHAFRICA 260 0.001030 0.016307 0.001572 0.186126 5.866033 SPAIN 260 -0.001126 0.023631 -0.000047 0.776173 11.243115 SRILANKA 260 0.002085 0.011819 0.000169 1.063687 6.955879 SWEDEN 260 0.001047 0.018843 0.001258 0.158168 5.115656 SWITZERLAND 260 0.000361 0.011812 0.000562 -0.201879 4.190408 TAIWAN 260 0.000647 0.012128 0.001109 -0.436023 3.932560 THAILAND 260 0.001580 0.014418 0.000411 0.168335 5.245883 TUNISIE 260 0.000323 0.009959 0.000002 0.419781 6.890828 TURKEY 260 0.000648 0.019414 0.002280 -0.101929 5.855123 UKRAINE 260 0.001534 0.019702 0.000523 -0.801596 10.417805 UAE 260 -0.000132 0.013914 0.000000 0.243288 8.241891 UK 260 0.000194 0.014028 0.000258 0.155790 5.696669 USA 260 0.000476 0.011190 0.000614 -0.225280 5.101728 VIETNAM 260 0.000294 0.013641 0.000260 -0.186979 3.767218

Table 13: Equity descriptive stats Jan/2010 – Dec/2010

41 Country # Obs Mean Std Median Skewness Kurtosis ARGENTINA 230 -0.002481 0.019779 -0.001762 -0.737521 6.776926 AUSTRALIA 230 -0.000754 0.018522 0.000188 -0.157956 4.289497 AUSTRIA 230 -0.002513 0.023999 -0.001509 -0.184360 5.108082 BAHRAIN 230 -0.001463 0.009967 0.000000 -2.603198 16.041593 BELGIUM 230 -0.000836 0.018074 -0.000550 -0.332470 4.080350 BOSNIAHERZEGOVINA 230 -0.000439 0.013827 -0.000765 0.065609 3.626564 BRAZIL 230 -0.001282 0.018779 0.000626 -0.863956 5.718022 CANADA 230 -0.000837 0.015402 0.000141 -0.453022 4.382858 CHILE 230 -0.001145 0.018702 -0.000497 -0.648196 8.034540 CHINA 230 -0.001113 0.018209 -0.000050 -0.264291 4.754727 COLOMBIA 230 -0.000346 0.011841 0.000571 -0.201971 4.075044 CROATIA 230 -0.000489 0.011555 0.001010 -0.871638 7.112253 CZECHREPUBLIC 230 -0.000669 0.018075 0.000801 -0.689400 4.679694 DENMARK 230 -0.000930 0.017790 -0.000065 -0.048880 3.998866 EGYPT 230 -0.002533 0.018201 0.000000 -1.915656 11.990007 ESTONIA 230 -0.001158 0.020441 -0.000831 -0.345332 4.027575 FINLAND 230 -0.001809 0.023701 -0.000288 -0.167668 3.963422 FRANCE 230 -0.001236 0.022497 -0.001479 -0.230243 4.202352 GERMANY 230 -0.001074 0.022909 -0.000761 -0.236850 3.994851 GREECE 230 -0.004245 0.034273 -0.003964 0.790795 6.221471 HONGKONG 230 -0.001090 0.014653 -0.000009 -0.314201 5.258651 HUNGARY 230 -0.001756 0.028115 -0.002162 -0.173095 4.356134 INDIA 230 -0.001852 0.015674 -0.001685 -0.084288 4.143250 INDONESIA 230 -0.000019 0.019129 0.000530 -1.001071 7.425512 IRELAND 230 -0.000256 0.021628 -0.001479 -0.367248 3.484207 ISRAEL 230 -0.001619 0.015901 0.000232 -0.833540 5.901425 ITALY 230 -0.001357 0.025441 0.000055 -0.428554 3.691321 JAPAN 230 -0.000808 0.014973 0.000094 -0.740517 10.657505 JORDAN 230 -0.000940 0.008659 0.000000 -0.619473 7.287210 KAZAKHSTAN 230 -0.001379 0.018855 -0.000528 0.069215 4.290977 KOREA 230 -0.000690 0.021767 0.000040 -0.355614 4.459894 KUWAIT 230 -0.001100 0.011003 -0.000135 -1.208071 8.115693 LEBANON 230 -0.001256 0.009120 -0.000755 -0.422533 11.270067 MALAYSIA 230 -0.000481 0.010719 0.000008 -0.348983 4.067166 MAURITIUS 230 -0.000206 0.009845 0.000000 0.003647 5.390530 MEXICO 230 -0.000738 0.016356 0.001067 -0.928897 6.783684 MOROCCO 230 -0.000664 0.012261 -0.000290 -0.012487 4.216591 NETHERLANDS 230 -0.001038 0.019326 -0.000484 -0.322140 3.976769 NEWZEALAND 230 -0.000312 0.013666 0.001228 -0.348878 4.322888 NIGERIA 230 -0.000921 0.015081 -0.001439 0.390389 4.467638 NORWAY 230 -0.000750 0.022574 0.001135 -0.396028 3.922649 OMAN 230 -0.001159 0.011526 0.000000 -2.148801 31.241868 PAKISTAN 230 -0.000167 0.011993 0.000000 -0.199160 4.925187 PERU 230 -0.001270 0.023332 0.000000 -1.674083 13.478874 PHILIPPINES 230 -0.000202 0.013977 0.000000 -0.244808 4.923977 POLAND 230 -0.001551 0.023965 0.000224 -0.688663 5.741849 PORTUGAL 230 -0.001266 0.019503 0.000563 -0.469797 3.841634 QATAR 230 0.000018 0.009023 0.000000 -0.512926 7.656043 ROMANIA 230 -0.000719 0.021147 0.000296 -0.120780 5.765134 RUSSIA 230 -0.000918 0.021847 0.000439 -0.732673 5.176919 SERBIA 230 -0.000614 0.022673 -0.000126 -0.007889 7.964228 SINGAPORE 230 -0.001019 0.014881 0.000108 -0.327865 4.200460 SLOVENIA 230 -0.000778 0.013468 -0.000334 -0.610135 5.537706 SOUTHAFRICA 230 -0.001117 0.019489 0.001410 -0.204053 3.803078 SPAIN 230 -0.000937 0.023568 -0.000542 -0.072885 3.600136 SRILANKA 230 -0.001191 0.008789 -0.000895 0.540729 6.181678 SWEDEN 230 -0.001317 0.024782 0.000778 -0.259596 4.465005 SWITZERLAND 230 -0.000698 0.014841 0.000514 -0.448295 4.487408 TAIWAN 230 -0.001236 0.015192 0.000000 -0.412497 3.971755 THAILAND 230 -0.000480 0.018608 0.000000 0.027635 4.820016 TUNISIE 230 -0.000278 0.012611 0.000688 -0.133030 4.226516 TURKEY 230 -0.001881 0.021048 -0.001412 -0.546257 3.958538 UKRAINE 230 -0.002203 0.020744 0.000000 -0.238596 5.066952 UAE 230 -0.000934 0.013466 0.000000 -1.169151 8.224598 UK 230 -0.000480 0.016631 0.001193 -0.426315 3.959316 USA 230 -0.000278 0.014562 0.000553 -0.687257 6.245591 VIETNAM 230 -0.001985 0.016967 -0.001777 -0.097450 3.771423

Table 14: Equity descriptive stats Jan/2011 – Oct/2011

42 Country # Obs Mean Std Median Skewness Kurtosis ARGENTINA 1520 0.000100 0.023749 0.000097 -0.648122 10.407716 AUSTRALIA 1520 0.000181 0.020564 0.001043 -0.789943 9.392955 AUSTRIA 1520 -0.000547 0.023996 0.000630 -0.118036 7.797749 BAHRAIN 1520 -0.001127 0.014145 0.000000 -3.543363 62.185804 BELGIUM 1520 -0.000387 0.018835 0.000452 -0.621236 9.998253 BOSNIAHERZEGOVINA 303 -0.000088 0.014865 -0.000852 0.031516 3.908923 BRAZIL 1520 0.000449 0.026235 0.001653 -0.390706 10.516170 CANADA 1520 0.000168 0.018507 0.001589 -0.765423 10.675140 CHILE 1520 0.000500 0.016745 0.000918 -0.309711 15.033928 CHINA 1520 0.000431 0.021775 0.000321 -0.026810 8.349454 COLOMBIA 1520 0.000531 0.019862 0.001524 -0.382613 12.892814 CROATIA 1520 0.000304 0.016438 0.000628 -0.155375 9.459280 CZECHREPUBLIC 1520 0.000092 0.021658 0.000725 -0.167203 15.189253 DENMARK 1520 0.000152 0.018330 0.000487 -0.231657 8.745586 EGYPT 1520 -0.000202 0.018803 0.000000 -1.233462 11.587458 ESTONIA 1520 -0.000319 0.020732 -0.000336 0.078479 6.603934 FINLAND 1520 -0.000231 0.021415 0.000001 -0.018133 6.554834 FRANCE 1520 -0.000110 0.019486 0.000418 0.053128 8.699583 GERMANY 1520 0.000038 0.019013 0.000860 -0.014370 8.115973 GREECE 1520 -0.001138 0.025499 0.000087 0.254448 6.748238 HONGKONG 1520 0.000182 0.016060 0.000137 -0.170013 9.569008 HUNGARY 1520 -0.000287 0.028685 0.000479 0.001429 9.329575 INDIA 1520 0.000340 0.021248 0.000384 0.114617 9.966842 INDONESIA 1520 0.000790 0.021828 0.001091 -0.353143 9.073073 IRELAND 1520 -0.000843 0.024744 0.000000 -0.569761 8.968553 ISRAEL 1520 0.000007 0.012732 0.000603 -0.718195 7.116459 ITALY 1520 -0.000416 0.020239 0.000372 0.023294 8.293588 JAPAN 1520 -0.000223 0.015927 0.000132 -0.159480 8.207871 JORDAN 1520 -0.000698 0.013343 0.000000 -0.759645 11.468494 KAZAKHSTAN 1520 -0.000038 0.027865 0.000000 0.173812 10.319037 KOREA 1520 0.000161 0.022830 0.000555 -0.167151 19.748072 KUWAIT 1520 -0.000290 0.016349 0.000000 -1.088686 12.261511 LEBANON 1520 -0.000162 0.016057 0.000000 -0.162523 18.919493 MALAYSIA 1520 0.000474 0.011576 0.000694 -0.740501 10.734990 MAURITIUS 1520 0.000606 0.014494 0.000000 0.189039 12.477336 MEXICO 1520 0.000254 0.020119 0.001223 -0.056124 9.226836 MOROCCO 1520 0.000423 0.012859 0.000220 -0.356079 6.070788 NETHERLANDS 1520 -0.000048 0.018209 0.000150 -0.079819 8.996920 NEWZEALAND 1520 -0.000166 0.016476 0.000453 -0.478219 7.318902 NIGERIA 1520 -0.000095 0.015644 0.000000 -0.203392 5.793870 NORWAY 1520 0.000099 0.026037 0.001338 -0.363949 7.291133 OMAN 1520 -0.000176 0.014630 0.000000 -1.581024 28.417407 PAKISTAN 1520 -0.000296 0.017783 0.000000 -0.589464 6.447348 PERU 1520 0.000771 0.024079 0.001040 -0.453057 8.329494 PHILIPPINES 1520 0.000477 0.017546 0.000526 -0.554297 8.513527 POLAND 1520 -0.000067 0.024833 0.000607 -0.223842 6.483831 PORTUGAL 1520 -0.000146 0.016896 0.000481 -0.045590 10.572525 QATAR 1520 -0.000164 0.017374 0.000000 -0.726043 13.601927 ROMANIA 1520 -0.000272 0.025801 0.000175 -1.358108 19.893588 RUSSIA 1520 0.000011 0.028827 0.001208 -0.450947 16.392521 SERBIA 869 -0.001573 0.028228 -0.001460 -0.341692 9.084150 SINGAPORE 1520 0.000292 0.016480 0.000774 -0.238630 7.035233 SLOVENIA 1520 -0.000003 0.015869 0.000214 -0.386351 9.315148 SOUTHAFRICA 1520 0.000206 0.022283 0.001241 -0.307999 6.805811 SPAIN 1520 -0.000036 0.020756 0.000152 0.137066 9.474181 SRILANKA 1520 0.000387 0.014075 0.000000 2.087479 25.795738 SWEDEN 1520 0.000139 0.023215 0.000604 0.111528 6.702306 SWITZERLAND 1520 0.000089 0.014513 0.000459 0.029140 8.038494 TAIWAN 1520 0.000055 0.016318 0.000272 -0.263969 5.447000 THAILAND 1520 0.000396 0.019293 0.000000 -0.853644 13.141273 TUNISIE 1520 0.000403 0.011176 0.000323 0.080494 9.053787 TURKEY 1520 -0.000041 0.026577 0.000753 -0.256426 6.700474 UKRAINE 1413 -0.001167 0.023828 0.000000 -0.123970 16.351153 UAE 1520 -0.001069 0.021306 0.000000 -0.651927 14.710058 UK 1520 -0.000057 0.017785 0.000937 -0.081592 10.171762 USA 1520 0.000009 0.015278 0.000585 -0.307144 11.462364 VIETNAM 1282 -0.000255 0.020786 0.000000 -0.041464 3.145887

Table 15: Equity descriptive stats Jan/2006 – Oct/2011

43 Country # Obs Mean Std Median Skewness Kurtosis ARGENTINA 999 −0.000289 0.026336 0.000000 −0.665997 9.923078 AUSTRALIA 999 −0.000188 0.023481 0.000558 −0.716040 8.028118 AUSTRIA 999 −0.001134 0.028065 0.000000 −0.035061 6.202611 BAHRAIN 999 −0.001904 0.015773 0.000000 −4.223003 56.748574 BELGIUM 999 −0.000815 0.021822 −0.000217 −0.527424 8.256507 BOSNIAHERZEGOVINA 303 −0.000088 0.014865 −0.000852 0.031516 3.908923 BRAZIL 999 −0.000215 0.028617 0.000703 −0.326483 10.654594 CANADA 999 −0.000138 0.021437 0.001425 −0.681445 8.844188 CHILE 999 0.000322 0.018827 0.000829 −0.232269 13.874436 CHINA 999 −0.000411 0.023711 0.000000 0.050543 8.214997 COLOMBIA 999 0.000558 0.018644 0.001180 −0.491798 10.275349 CROATIA 999 −0.000582 0.017664 0.000171 −0.193388 8.239386 CZECHREPUBLIC 999 −0.000531 0.024608 0.000094 −0.104979 13.443666 DENMARK 999 −0.000286 0.020989 −0.000245 −0.135520 7.480652 EGYPT 999 −0.000856 0.020201 0.000000 −1.250361 11.525038 ESTONIA 999 −0.000559 0.023667 −0.000721 0.106451 5.547608 FINLAND 999 −0.000960 0.024447 −0.000477 0.040992 5.638391 FRANCE 999 −0.000538 0.022678 −0.000054 0.116426 7.108771 GERMANY 999 −0.000501 0.022085 0.000069 0.063174 6.671449 GREECE 999 −0.002263 0.030097 −0.001174 0.338758 5.257484 HONGKONG 999 −0.000275 0.017827 0.000000 −0.103768 9.055935 HUNGARY 999 −0.000822 0.032913 −0.000239 0.053049 8.011567 INDIA 999 −0.000422 0.023128 0.000000 0.269635 10.022243 INDONESIA 999 0.000273 0.023408 0.000497 −0.263543 9.021251 IRELAND 999 −0.001400 0.028859 −0.000717 −0.479460 7.202580 ISRAEL 999 −0.000215 0.013899 0.000437 −0.689162 6.727606 ITALY 999 −0.000900 0.023926 0.000000 0.085262 6.416161 JAPAN 999 −0.000334 0.017565 0.000331 −0.127371 7.922977 JORDAN 999 −0.000830 0.013655 0.000000 −0.745329 11.996533 KAZAKHSTAN 999 −0.000726 0.026700 0.000000 −0.180974 9.284597 KOREA 999 −0.000120 0.026028 0.000461 −0.092893 17.470916 KUWAIT 999 −0.000673 0.017841 0.000000 −1.064744 10.604452 LEBANON 999 −0.000293 0.014846 −0.000209 1.013963 14.640625 MALAYSIA 999 0.000087 0.012263 0.000108 −0.744882 11.236728 MAURITIUS 999 −0.000238 0.015537 0.000000 0.026819 11.250929 MEXICO 999 −0.000025 0.021842 0.001011 −0.016509 9.312995 MOROCCO 999 −0.000212 0.013090 −0.000373 −0.295488 6.561740 NETHERLANDS 999 −0.000475 0.021186 −0.000518 −0.011503 7.341113 NEWZEALAND 999 −0.000387 0.018478 0.000358 −0.437651 6.487457 NIGERIA 999 −0.001175 0.017235 −0.001222 −0.169532 5.329364 NORWAY 999 −0.000448 0.029636 0.000568 −0.294160 6.321088 OMAN 999 −0.000556 0.016797 0.000000 −1.546270 24.346119 PAKISTAN 999 −0.000666 0.018315 −0.000058 −0.519908 6.987909 PERU 999 0.000134 0.026233 0.000104 −0.386898 8.283976 PHILIPPINES 999 −0.000039 0.017824 0.000000 −0.680397 9.237596 POLAND 999 −0.000596 0.027786 0.000500 −0.195215 5.955043 PORTUGAL 999 −0.000778 0.019871 −0.000073 0.031669 8.301670 QATAR 999 −0.000085 0.018684 0.000000 −0.805097 14.273817 ROMANIA 999 −0.001073 0.028952 0.000000 −1.422148 18.381921 RUSSIA 999 −0.000620 0.032708 0.000699 −0.361104 14.534933 SERBIA 869 −0.001573 0.028228 −0.001460 −0.341692 9.084150 SINGAPORE 999 −0.000120 0.018099 0.000190 −0.168532 6.628864 SLOVENIA 999 −0.001148 0.017928 −0.000790 −0.308279 8.030689 SOUTHAFRICA 999 0.000016 0.023796 0.000848 −0.221498 6.952377 SPAIN 999 −0.000610 0.024456 −0.000318 0.195838 7.428956 SRILANKA 999 0.000406 0.015788 0.000000 2.146766 23.856492 SWEDEN 999 −0.000112 0.026649 0.000112 0.164863 5.655919 SWITZERLAND 999 −0.000134 0.016577 0.000002 0.095203 6.965467 TAIWAN 999 −0.000134 0.017692 0.000000 −0.151779 5.113252 THAILAND 999 0.000193 0.020081 0.000000 −0.502575 8.660526 TUNISIE 999 0.000230 0.012239 0.000103 0.125044 8.970781 TURKEY 999 −0.000496 0.026941 0.000660 −0.118989 7.133430 UKRAINE 999 −0.001686 0.026327 0.000000 0.134383 14.113276 UAE 999 −0.001209 0.023089 0.000000 −0.580033 14.834415 UK 999 −0.000366 0.020575 0.000331 −0.015809 8.478733 USA 999 −0.000150 0.017881 0.000543 −0.249353 9.168852 VIETNAM 999 −0.000849 0.020350 0.000000 −0.109278 3.177836

Table 16: Equity descriptive stats Jan/2008 – Oct/2011

44 Country # Obs Mean Std Median Skewness Kurtosis ARGENTINA 499 0.000109 0.018700 0.000000 -0.226449 7.101001 AUSTRALIA 499 0.000067 0.017082 0.000633 -0.250976 4.227932 AUSTRIA 499 -0.000758 0.021893 0.000246 -0.059832 5.786825 BAHRAIN 499 -0.001349 0.009633 0.000000 -1.230487 12.623571 BELGIUM 499 -0.000322 0.016996 0.000121 0.117470 6.253678 BOSNIAHERZEGOVINA 303 -0.000088 0.014865 -0.000852 0.031516 3.908923 BRAZIL 499 -0.000341 0.017645 0.000334 -0.609343 5.598528 CANADA 499 0.000123 0.013665 0.001191 -0.479907 4.573549 CHILE 499 0.000502 0.014800 0.000885 -0.694475 10.115246 CHINA 499 -0.000304 0.015164 0.000018 -0.314372 5.199479 COLOMBIA 499 0.000654 0.012533 0.001156 -0.334408 4.873995 CROATIA 499 -0.000114 0.011824 0.000840 -0.276485 9.429732 CZECHREPUBLIC 499 -0.000341 0.016454 0.000647 -0.474896 4.981620 DENMARK 499 0.000033 0.016483 0.000000 0.102761 4.378811 EGYPT 499 -0.000659 0.016078 0.000000 -1.388301 11.472864 ESTONIA 499 0.000292 0.020917 0.000000 0.145705 4.207848 FINLAND 499 -0.000433 0.019984 0.000391 -0.217476 5.128752 FRANCE 499 -0.000389 0.019654 0.000000 0.032410 5.499638 GERMANY 499 -0.000150 0.018708 0.000372 -0.197007 4.749641 GREECE 499 -0.003078 0.031209 -0.002998 0.615851 5.320881 HONGKONG 499 0.000087 0.011949 0.000077 -0.345832 5.975461 HUNGARY 499 -0.000847 0.027145 0.000590 0.031796 6.480334 INDIA 499 -0.000107 0.014359 0.000000 -0.199387 4.354495 INDONESIA 499 0.000748 0.017294 0.001019 -0.545519 7.636729 IRELAND 499 -0.000345 0.022534 -0.000962 -0.154349 4.659823 ISRAEL 499 -0.000410 0.012974 0.000270 -0.670612 6.718332 ITALY 499 -0.000782 0.021711 0.000000 -0.060524 5.504702 JAPAN 499 -0.000041 0.013040 0.000331 -0.581022 9.508755 JORDAN 499 -0.000590 0.008710 0.000000 0.016184 6.696117 KAZAKHSTAN 499 -0.000844 0.018462 0.000000 -0.233077 8.189073 KOREA 499 0.000477 0.018314 0.001007 -0.461707 4.789761 KUWAIT 499 0.000222 0.011915 0.000180 -0.488428 6.961360 LEBANON 499 -0.000748 0.008151 -0.000349 -0.176549 9.715981 MALAYSIA 499 0.000516 0.009523 0.001063 -0.359015 4.672165 MAURITIUS 499 -0.000008 0.009592 0.000000 -0.018951 5.135656 MEXICO 499 0.000243 0.014257 0.001381 -0.733075 7.329816 MOROCCO 499 -0.000006 0.011554 -0.000262 -0.291966 6.284027 NETHERLANDS 499 -0.000204 0.017190 -0.000174 -0.015163 5.193283 NEWZEALAND 499 0.000196 0.013115 0.000272 -0.321685 4.104165 NIGERIA 499 0.000055 0.014354 0.000000 0.188487 4.182801 NORWAY 499 0.000034 0.020817 0.000568 -0.225382 4.289030 OMAN 499 -0.000146 0.009419 0.000000 -1.851864 35.561196 PAKISTAN 499 0.000307 0.011506 0.000000 -0.229241 4.794159 PERU 499 0.000142 0.020068 0.000000 -1.206176 12.648962 PHILIPPINES 499 0.000529 0.013702 0.000000 -0.308476 4.449188 POLAND 499 -0.000272 0.022311 0.000406 -0.423779 6.044031 PORTUGAL 499 -0.000688 0.018442 0.000349 0.087508 6.778892 QATAR 499 0.000610 0.009044 0.000000 0.257843 9.268141 ROMANIA 499 -0.000315 0.022482 0.000428 -0.288242 8.669177 RUSSIA 499 0.000087 0.019380 0.000913 -0.524510 5.681420 SERBIA 499 -0.000341 0.018896 -0.000199 -0.010731 8.811906 SINGAPORE 499 0.000158 0.012597 0.000919 -0.372473 4.657478 SLOVENIA 499 -0.000761 0.011862 -0.000573 -0.220509 5.567176 SOUTHAFRICA 499 0.000233 0.017503 0.001405 -0.065576 4.905238 SPAIN 499 -0.000819 0.023052 -0.000122 0.412457 8.084429 SRILANKA 499 0.000948 0.010688 0.000000 1.192211 7.577856 SWEDEN 499 0.000200 0.021232 0.001139 -0.128726 5.181575 SWITZERLAND 499 0.000067 0.012944 0.000620 -0.349664 4.684990 TAIWAN 499 0.000091 0.013321 0.000236 -0.506548 4.373768 THAILAND 499 0.000797 0.016288 0.000000 0.015299 5.430844 TUNISIE 499 0.000164 0.011160 0.000551 0.026786 5.452785 TURKEY 499 0.000001 0.020083 0.001809 -0.337833 4.998559 UKRAINE 499 -0.000402 0.019493 0.000000 -0.473172 8.123001 UAE 499 -0.000203 0.017075 0.000000 2.260078 33.894359 UK 499 0.000021 0.015021 0.000805 -0.218509 4.916218 USA 499 0.000255 0.012473 0.000601 -0.567985 6.856672 VIETNAM 499 -0.000524 0.015692 0.000000 -0.183749 3.803942

Table 17: Equity descriptive stats Dec/2009 – Oct/2011

45 B.3 Sovereign CDS spread statistics

Tables 18, 19, 20, 21 and 22 show the descriptive statistics the CDS spreads for the ﬁve periods for which they are available.

Note that not all countries have CDS spreads for the whole periods.

46 Country # Obs Mean Std Median Skewness Kurtosis ARGENTINA 522 0.001317 0.041315 0.000000 3.326189 33.896042 TUNISIE 501 0.001043 0.059633 0.000000 5.045761 78.922953 VENEZUELA 479 0.001349 0.033199 0.000000 3.573090 34.943896 CZECHREPUBLIC 479 0.001573 0.072876 0.000000 1.948356 30.478335 DOMINICANREPUBLIC 522 0.001150 0.044678 0.000000 -1.257649 28.137099 GERMANY 522 0.002795 0.051759 0.000000 2.188162 25.396919 BRAZIL 522 0.000393 0.065547 0.000000 6.979211 115.604875 FRANCE 522 0.002759 0.054641 0.000000 3.352453 45.891190 GREECE 522 0.004761 0.041696 0.000000 0.595373 9.715107 HONGKONG 479 -0.000113 0.048769 0.000000 -0.193687 31.763053 IRELAND 321 0.002330 0.046552 0.000000 1.050997 6.379686 JAMAICA 479 0.001516 0.033594 0.000000 2.859913 27.363920 JAPAN 479 0.002926 0.081011 0.000000 10.417450 183.041296 BAHRAIN 437 0.001532 0.045142 0.000000 2.507035 36.916401 BELGIUM 522 0.002513 0.048760 0.000000 1.580080 13.333677 DENMARK 304 -0.003071 0.046438 0.000000 -0.874661 8.744948 NORWAY 280 -0.002577 0.039141 0.000000 0.607373 12.361512 SPAIN 522 0.003124 0.049998 0.000000 0.235379 12.454675 SWEDEN 304 -0.000862 0.045578 0.000000 -0.170891 8.283786 THAILAND 479 -0.000246 0.054706 0.000000 0.344212 15.957368 NETHERLANDS 450 0.002568 0.057476 0.000000 3.756742 43.639038 LEBANON 479 -0.001665 0.032464 0.000000 2.240368 45.392508 MALAYSIA 322 -0.002222 0.061084 0.000000 0.370918 13.172433 URUGUAY 302 -0.002859 0.039090 0.000000 0.266266 26.748545 CHINA 519 0.001815 0.058057 0.000000 2.771780 32.498421 AUSTRIA 177 -0.003588 0.044734 0.000000 0.126881 5.941687 BULGARIA 479 0.000727 0.052517 0.000000 6.307349 89.170729 CHILE 479 0.000118 0.092202 0.000000 2.898802 53.837006 COLOMBIA 479 -0.000644 0.058506 0.000000 0.839006 39.909413 COSTARICA 322 -0.001490 0.061910 0.000000 0.097794 14.270618 CROATIA 479 0.001653 0.059392 0.000000 6.682041 101.147667 CYPRUS 522 0.003809 0.045264 0.000000 0.271737 21.394009 ELSALVADOR 479 0.000653 0.048662 0.000000 1.891289 35.411624 ESTONIA 321 -0.001423 0.057689 0.000000 1.431788 17.605919 GUATEMALA 511 0.000830 0.046322 0.000000 4.164132 59.747733 HUNGARY 522 0.002854 0.057496 0.000000 5.713631 69.556246 ICELAND 366 0.001005 0.040929 0.000000 4.342878 37.798311 INDONESIA 322 -0.002270 0.057767 0.000000 1.221901 13.226648 IRAQ 479 -0.000350 0.031565 0.000000 1.421093 89.192664 ITALY 522 0.002918 0.049089 0.000000 0.117266 15.947884 KAZAKHSTAN 479 -0.000304 0.065768 0.000000 7.677292 130.434274 LATVIA 321 0.001082 0.052263 0.000000 0.361041 11.738496 LITHUANIA 479 0.001474 0.055760 0.000000 4.821989 69.916550 MALTA 522 0.003809 0.044088 0.000000 1.160362 16.534217 PANAMA 479 -0.000501 0.060156 0.000000 3.009897 57.788644 PERU 479 -0.000372 0.070534 0.000000 6.911964 121.923701 POLAND 479 0.001478 0.077801 0.000000 4.684009 86.417289 PORTUGAL 304 -0.000138 0.044048 0.000000 0.090896 5.362624 SERBIA 479 0.000725 0.047709 0.000000 0.941635 73.265933 SINGAPORE 304 -0.000827 0.014414 0.000000 -17.349447 302.003300 SLOVENIA 522 0.004287 0.072956 0.000000 1.249112 17.957136 SOUTHAFRICA 479 -0.000737 0.060573 0.000000 0.235804 36.270684 PHILIPPINES 479 -0.000742 0.046834 0.000000 0.977518 16.158682 TURKEY 479 -0.000566 0.045698 0.000000 1.605441 18.057612 ROMANIA 479 0.000676 0.051019 0.000000 2.124739 27.659831 RUSSIA 479 0.000698 0.063477 0.000000 1.942384 19.978415 SLOVAK 321 0.000548 0.075066 0.000000 0.508429 15.161646 VIETNAM 479 0.000381 0.047007 0.000000 2.435305 32.618819 ISRAEL 479 0.001016 0.048876 0.000000 6.011108 77.769961 QATAR 522 0.002105 0.051395 0.000000 0.438958 9.394681 UKRAINE 304 -0.001889 0.048030 0.000000 0.830255 25.140715 UK 300 0.000907 0.042302 0.000000 0.236459 5.066597 MEXICO 521 0.001200 0.062654 0.000000 1.087168 25.140901 FINLAND 467 0.001483 0.051698 0.000000 -0.042995 13.338786 KOREA 522 0.001178 0.063672 0.000000 0.232466 22.855812 MOROCCO 522 0.001186 0.058002 0.000000 3.858036 94.502280 USA 518 0.003022 0.059794 0.000000 4.464252 51.748992 AUSTRALIA 299 -0.002274 0.053718 0.000000 -0.765010 13.175627

Table 18: CDS descriptive stats Jan/2008 – Dec/2009

47 Country # Obs Mean Std Median Skewness Kurtosis ARGENTINA 260 -0.001606 0.030629 -0.003447 0.391084 6.064805 TUNISIE 260 0.000010 0.019200 0.000000 2.801287 42.547128 VENEZUELA 260 -0.000291 0.032205 -0.000007 0.116180 5.374867 CZECHREPUBLIC 260 -0.000487 0.049569 0.000000 -0.055964 15.401235 DOMINICANREPUBLIC 260 -0.002007 0.024755 0.000000 -5.723325 54.125167 GERMANY 260 0.001947 0.065806 0.000000 -0.865376 9.209708 BRAZIL 260 -0.000450 0.031791 0.000000 -0.448024 7.227619 FRANCE 260 0.003743 0.054133 0.000000 -0.343868 4.934111 GREECE 260 0.005050 0.061035 0.000161 -2.107475 25.168136 HONGKONG 260 0.000085 0.054247 0.000000 -0.881899 25.553871 IRELAND 260 0.005181 0.058833 0.000106 -0.395510 8.857472 JAMAICA 260 -0.001497 0.037038 0.000000 -4.122947 73.648682 JAPAN 260 -0.001126 0.109655 0.000000 0.050159 6.836691 BAHRAIN 260 -0.000768 0.019962 0.000000 -5.401579 56.211310 BELGIUM 260 0.004667 0.055086 0.000000 -0.125767 6.252421 DENMARK 260 0.001552 0.051765 0.000000 1.625235 17.336081 NORWAY 260 0.001160 0.036341 0.000000 0.629831 11.684207 SPAIN 260 0.003984 0.069025 0.000052 -0.674792 8.836711 SWEDEN 260 -0.002273 0.042306 0.000000 -0.172513 6.621763 THAILAND 260 0.000282 0.039808 0.000000 0.926386 7.670081 NETHERLANDS 260 0.002715 0.040996 0.000000 -0.369892 8.082945 LEBANON 260 0.000108 0.017098 0.000000 -1.825775 32.671165 MALAYSIA 260 -0.000666 0.040930 0.000000 0.305640 6.796002 NEWZEALAND 172 0.001167 0.026709 0.000000 0.527921 13.392769 URUGUAY 260 -0.002340 0.040069 0.000000 -1.567920 12.930648 CHINA 260 -0.000377 0.042460 0.000000 0.107727 6.837183 AUSTRIA 260 0.000708 0.056891 0.000000 0.141924 32.455007 BULGARIA 260 -0.000107 0.043606 0.000000 0.779566 13.511333 CHILE 260 0.001134 0.058916 0.000000 0.486441 8.666402 COLOMBIA 260 -0.000975 0.032480 -0.000112 -0.048060 5.212221 COSTARICA 260 0.000220 0.038433 0.000000 0.587235 8.592204 CROATIA 260 0.000087 0.039724 0.000000 -0.151921 12.905024 CYPRUS 260 0.002706 0.030544 0.000000 3.250898 30.197282 ELSALVADOR 260 -0.001093 0.030852 0.000000 -0.343065 11.030014 ESTONIA 260 -0.003111 0.038812 0.000000 -0.049607 8.917665 GUATEMALA 260 -0.002523 0.061525 0.000000 -1.161935 11.771523 HUNGARY 260 0.001411 0.043129 0.000000 1.209795 15.385694 ICELAND 260 -0.001155 0.022705 0.000000 1.967274 25.658956 INDONESIA 260 -0.001414 0.033911 0.000000 -0.478167 10.873060 IRAQ 260 -0.001056 0.012812 0.000000 -9.911114 146.147345 ITALY 260 0.002520 0.069110 0.000115 -1.029878 10.521829 KAZAKHSTAN 260 -0.001456 0.037930 0.000000 -1.206068 13.555036 LATVIA 260 -0.003200 0.027238 0.000000 -0.380992 7.571148 LITHUANIA 260 -0.001099 0.034701 0.000000 -0.246841 9.129300 MALTA 260 0.002706 0.030652 0.000000 3.329430 30.895247 PANAMA 260 -0.001300 0.039015 0.000000 -0.500837 7.159489 PERU 260 -0.000355 0.036400 0.000000 -0.376463 5.568319 POLAND 260 -0.000162 0.050728 0.000000 -0.641569 12.303168 PORTUGAL 260 0.006369 0.076824 0.000301 -1.766593 17.005019 SERBIA 260 -0.001244 0.020056 0.000000 -16.031340 258.003861 SINGAPORE 260 0.000967 0.013023 0.000000 6.808875 72.547670 SLOVENIA 260 -0.000492 0.048773 0.000000 0.778395 12.254199 SOUTHAFRICA 260 -0.001005 0.042465 0.000000 -1.371593 15.588019 PHILIPPINES 260 -0.001032 0.031229 0.000000 -0.446429 9.675038 TURKEY 260 -0.001612 0.035624 0.000000 -0.937759 12.306201 ROMANIA 260 0.000079 0.036583 0.000000 0.125824 14.186846 RUSSIA 260 -0.001410 0.042413 0.000000 -0.578888 10.263344 SLOVAK 260 -0.000302 0.047696 0.000000 -0.134091 14.632398 VIETNAM 260 0.000897 0.032557 0.000000 -0.125522 4.704493 ISRAEL 260 -0.000632 0.022100 0.000000 0.680123 15.917664 QATAR 260 -0.001041 0.028166 0.000000 -1.069629 12.583895 UKRAINE 260 -0.003436 0.027592 0.000000 -0.920600 7.585586 UK 260 -0.000424 0.040980 0.000000 -0.548249 7.004359 MEXICO 260 -0.000679 0.032447 -0.000923 -0.440348 6.586397 FINLAND 260 0.001030 0.042230 0.000000 -1.632680 21.889339 KOREA 260 0.000483 0.046473 0.000000 0.473142 5.146851 MOROCCO 260 0.000145 0.010424 0.000000 0.133039 54.748438 USA 260 0.000575 0.036896 0.000000 0.398557 13.565642 AUSTRALIA 260 0.001056 0.040700 0.000000 1.157218 13.281735

Table 19: CDS descriptive stats Jan/2010 – Dec/2010

48 Country # Obs Mean Std Median Skewness Kurtosis ARGENTINA 230 0.002689 0.028354 0.000000 0.404520 6.973216 TUNISIE 230 0.002720 0.039212 0.000000 2.856749 26.587602 VENEZUELA 230 0.000193 0.020438 -0.000002 0.269804 3.922982 CZECHREPUBLIC 230 0.002980 0.038716 0.000095 1.037758 12.862311 DOMINICANREPUBLIC 230 0.002308 0.026292 0.000000 3.291856 37.471582 GERMANY 230 0.001174 0.061322 0.000106 0.291181 7.503443 BRAZIL 230 0.002352 0.034781 0.000602 0.238775 7.004584 FRANCE 230 0.002533 0.055447 0.000000 0.042527 6.248187 GREECE 230 0.007979 0.054748 0.005576 -1.014332 17.547101 HONGKONG 230 0.003061 0.045425 0.000000 0.561058 8.440580 IRELAND 230 0.000937 0.034183 0.000085 -0.340926 5.812668 JAMAICA 230 -0.000964 0.025682 0.000000 -11.425521 159.175751 JAPAN 230 0.000126 0.119551 0.000140 -0.049287 4.227193 BAHRAIN 230 0.003365 0.031889 0.000000 2.252805 22.870112 BELGIUM 230 0.001885 0.055870 0.000598 -0.177896 4.040418 DENMARK 230 0.005009 0.050056 0.000243 0.065845 5.410078 NORWAY 230 0.003019 0.043994 0.000141 1.002241 13.540197 SPAIN 230 0.001286 0.056940 0.001076 -0.046388 4.218084 SWEDEN 230 0.003494 0.069046 0.000208 -0.547344 6.084638 THAILAND 230 0.003079 0.031043 0.000173 -0.023394 8.031034 NETHERLANDS 230 0.002694 0.039128 0.000251 0.094666 5.170631 LEBANON 230 0.001574 0.014812 0.000000 2.271422 18.733982 MALAYSIA 230 0.003212 0.035950 0.000177 0.817384 7.212536 NEWZEALAND 230 0.002251 0.031508 0.000109 2.639827 21.814513 URUGUAY 230 0.001528 0.058755 -0.000091 0.980394 22.227306 CHINA 230 0.003545 0.036175 0.000092 0.449611 6.540537 AUSTRIA 230 0.002557 0.080501 0.000000 0.703592 9.434010 BULGARIA 230 0.002125 0.030350 0.000000 0.344644 5.577496 CHILE 230 0.002465 0.042006 -0.000083 0.826389 10.760147 COLOMBIA 230 0.002310 0.040147 -0.000210 0.167844 8.101925 COSTARICA 230 0.001745 0.049323 0.000000 -0.161325 6.338060 CROATIA 230 0.003217 0.025103 0.000000 1.178611 12.472479 CYPRUS 230 0.006603 0.056028 0.000000 3.129018 25.412635 ELSALVADOR 230 0.002496 0.050600 0.000000 1.644506 21.962810 ESTONIA 230 0.002337 0.028162 0.000100 2.188406 16.826504 GUATEMALA 230 0.001346 0.025502 -0.000092 0.412372 11.452634 HUNGARY 230 0.001775 0.026653 0.000121 0.086741 4.755220 ICELAND 230 -0.000138 0.024195 0.000000 1.856771 21.605464 INDONESIA 230 0.002320 0.037443 0.000000 0.693942 8.967184 IRAQ 230 0.000232 0.042305 0.000000 3.269663 51.080722 ITALY 230 0.003840 0.059711 0.000438 0.280847 3.940187 KAZAKHSTAN 230 0.002336 0.034433 0.000000 0.525819 6.902061 LATVIA 230 0.001207 0.023972 0.000000 0.540025 8.040973 LITHUANIA 230 0.001485 0.023245 0.000000 0.547808 8.274320 MALTA 230 0.000678 0.017027 0.000000 4.458037 63.519518 PANAMA 230 0.002622 0.037266 0.000000 0.093043 7.993675 PERU 230 0.002169 0.040678 0.000000 0.134272 5.430795 POLAND 230 0.003214 0.033125 0.000131 -0.083355 5.738823 PORTUGAL 230 0.003924 0.044056 0.000819 0.180890 7.088092 SERBIA 230 0.001797 0.026837 0.000000 3.509857 37.765677 SINGAPORE 230 0.002662 0.017583 0.000000 1.429554 13.906031 SLOVENIA 230 0.007716 0.040768 0.000063 0.866122 6.754075 SOUTHAFRICA 230 0.002293 0.036674 0.000000 0.035013 8.450698 PHILIPPINES 230 0.002122 0.035212 0.000000 0.231912 6.706976 TURKEY 230 0.003206 0.032215 0.000354 -0.510459 6.622967 ROMANIA 230 0.001386 0.028909 0.000000 0.164458 5.112883 RUSSIA 230 0.002854 0.038410 0.000076 0.127335 7.289450 SLOVAK 230 0.005671 0.057856 0.000000 0.558240 6.074919 VIETNAM 230 0.001309 0.024736 0.000079 0.263863 7.471589 ISRAEL 230 0.002550 0.027178 0.000051 0.595756 8.202837 QATAR 230 0.001232 0.025577 0.000000 0.537009 6.800246 UKRAINE 230 0.000000 0.000000 0.000000 - - UK 230 0.000931 0.045442 0.000167 0.581077 7.121332 MEXICO 230 0.002216 0.030914 0.000000 0.458884 8.339171 FINLAND 230 0.003089 0.037689 0.000270 0.353511 5.705511 KOREA 230 0.002276 0.037964 0.000010 0.206046 6.362144 MOROCCO 230 0.002796 0.031002 0.000006 3.758198 34.843938 USA 230 0.000838 0.034121 0.000000 1.430980 12.665456 AUSTRALIA 230 0.002531 0.038059 0.000107 0.557361 11.150888

Table 20: CDS descriptive stats Jan/2011 – Oct/2011

49 Country # Obs Mean Std Median Skewness Kurtosis ARGENTINA 999 0.000667 0.036064 0.000000 2.717684 32.003800 AUSTRALIA 776 −0.000256 0.045042 0.000000 −0.159262 14.821526 AUSTRIA 654 −0.000392 0.062295 0.000000 0.557590 18.200475 BAHRAIN 914 0.001228 0.036563 0.000000 2.408831 45.848711 BELGIUM 999 0.002667 0.051765 0.000000 0.558958 8.589816 BRAZIL 999 0.000315 0.052430 0.000000 7.098875 147.859907 BULGARIA 956 0.000548 0.045818 0.000000 4.955833 80.261917 CHILE 956 0.000521 0.074638 0.000000 2.819740 63.782916 CHINA 996 0.001415 0.049897 0.000000 2.321791 32.352903 COLOMBIA 956 −0.000404 0.048406 0.000000 0.730276 43.646167 COSTARICA 799 −0.000259 0.051835 0.000000 0.089656 13.950099 CROATIA 956 0.001387 0.048303 0.000000 6.230173 117.543281 CYPRUS 999 0.004323 0.044875 0.000000 1.837461 27.575629 CZECHREPUBLIC 956 0.000838 0.060035 0.000000 1.769134 35.476429 DENMARK 781 0.000393 0.048787 0.000000 0.335416 11.690373 DOMINICANREPUBLIC 999 0.000630 0.036789 0.000000 −1.319235 37.124956 ELSALVADOR 956 0.000806 0.044619 0.000000 1.813215 34.208741 ESTONIA 798 −0.001159 0.044761 0.000000 1.317726 21.723911 FINLAND 944 0.001494 0.045931 0.000000 −0.358015 15.392883 FRANCE 999 0.002752 0.053660 0.000000 1.753440 28.234679 GERMANY 999 0.002091 0.057537 0.000000 0.545793 14.845344 GREECE 999 0.005108 0.049369 0.000000 −1.276638 22.879190 GUATEMALA 988 0.000135 0.047536 0.000000 1.302119 36.955197 HONGKONG 956 0.000478 0.048534 0.000000 −0.349087 27.809270 HUNGARY 999 0.002143 0.048593 0.000000 5.180196 73.945048 ICELAND 843 −0.000081 0.032143 0.000000 4.345895 47.298824 INDONESIA 799 −0.001038 0.045339 0.000000 1.046513 16.092220 IRAQ 956 −0.000547 0.029443 0.000000 2.452830 106.599766 IRELAND 798 0.002842 0.048041 0.000000 0.143652 9.055459 ISRAEL 956 0.000674 0.038538 0.000000 6.229431 101.738760 ITALY 999 0.002837 0.056776 0.000000 −0.415650 11.491156 JAMAICA 956 0.000073 0.032940 0.000000 −1.359259 61.397918 JAPAN 956 0.001144 0.099858 0.000000 2.768754 44.617494 KAZAKHSTAN 956 −0.000193 0.052993 0.000000 7.245129 155.910000 KOREA 999 0.001037 0.054520 0.000000 0.280729 23.192218 LATVIA 798 −0.000590 0.038473 0.000000 0.404465 17.008493 LEBANON 956 −0.000547 0.025609 0.000000 2.156275 60.489925 LITHUANIA 956 0.000479 0.044700 0.000000 4.700026 86.101444 MALAYSIA 799 −0.000433 0.048711 0.000000 0.392071 14.714432 MALTA 999 0.002851 0.036422 0.000000 1.741780 23.304022 MEXICO 998 0.000661 0.050199 0.000000 1.110896 32.403447 MOROCCO 999 0.001177 0.044666 0.000000 4.697910 142.079802 NETHERLANDS 927 0.002400 0.049002 0.000000 2.875273 41.583313 NORWAY 757 0.000067 0.039634 0.000000 0.794734 13.360240 PANAMA 956 −0.000289 0.050173 0.000000 2.515393 60.889050 PERU 956 −0.000095 0.056586 0.000000 6.662594 147.601528 PHILIPPINES 956 −0.000407 0.040139 0.000000 0.760838 16.734720 POLAND 956 0.001202 0.062967 0.000000 4.327481 102.291832 PORTUGAL 781 0.003123 0.056683 0.000000 −1.379271 20.058011 QATAR 999 0.000981 0.041565 0.000000 0.417041 12.421170 ROMANIA 956 0.000421 0.042958 0.000000 1.811502 29.792628 RUSSIA 956 0.000388 0.053260 0.000000 1.582664 21.786007 SERBIA 956 0.000467 0.037712 0.000000 0.622306 101.915031 SINGAPORE 781 0.000784 0.015161 0.000000 −3.657404 119.023902 SLOVAK 798 0.001409 0.062687 0.000000 0.461875 15.360088 SLOVENIA 999 0.003487 0.061183 0.000000 1.266708 20.543994 SOUTHAFRICA 956 −0.000311 0.051131 0.000000 −0.035026 38.237330 SPAIN 999 0.002555 0.056566 0.000000 −0.281454 9.770292 SWEDEN 781 −0.000541 0.052130 0.000000 −0.416998 8.032207 THAILAND 956 0.000445 0.046157 0.000000 0.401946 17.177093 TUNISIE 978 0.001176 0.047738 0.000000 5.455129 101.359365 TURKEY 956 −0.000172 0.040311 0.000000 0.919692 17.536542 UK 777 0.000166 0.041685 0.000000 −0.114036 5.389267 UKRAINE 781 −0.001879 0.033922 0.000000 0.719311 40.301827 URUGUAY 779 −0.001681 0.040406 0.000000 −0.327733 16.359530 USA 995 0.001606 0.049377 0.000000 4.258795 59.796620 VENEZUELA 956 0.000586 0.030535 0.000000 2.367364 26.628425 VIETNAM 956 0.000615 0.039130 0.000000 2.101939 34.807681

Table 21: CDS descriptive stats Jan/2008 – Oct/2011

50 Country # Obs Mean Std Median Skewness Kurtosis ARGENTINA 499 -0.000252 0.028946 -0.001094 0.357882 6.668351 AUSTRALIA 499 0.001372 0.038423 0.000000 1.032713 13.511329 AUSTRIA 499 0.000900 0.066533 0.000000 0.564674 17.901656 BAHRAIN 499 0.000861 0.027136 0.000000 0.649124 31.577847 BELGIUM 499 0.002988 0.054183 0.000000 -0.225982 5.252255 BRAZIL 499 0.000241 0.032105 0.000000 -0.162602 7.520071 BULGARIA 499 0.000331 0.037544 0.000000 0.714186 13.840448 CHILE 499 0.000800 0.052405 -0.000027 0.665981 9.967181 CHINA 499 0.000781 0.038777 0.000000 0.196995 7.194946 COLOMBIA 499 -0.000193 0.035104 -0.000161 0.002861 7.527988 COSTARICA 499 0.000557 0.043222 0.000000 0.114563 7.671166 CROATIA 499 0.001108 0.033485 0.000000 0.069085 15.247661 CYPRUS 499 0.005049 0.043974 0.000000 3.655463 34.817380 CZECHREPUBLIC 499 0.000251 0.043105 0.000000 0.060177 16.697722 DENMARK 499 0.002406 0.049130 0.000000 0.954798 13.268237 DOMINICANREPUBLIC 499 -0.000608 0.029419 0.000000 -3.881214 64.397151 ELSALVADOR 499 0.000962 0.039823 0.000000 1.602036 27.828534 ESTONIA 499 -0.001168 0.034097 0.000000 0.202171 11.675637 FINLAND 499 0.001517 0.039076 0.000000 -1.004510 17.537206 FRANCE 499 0.002986 0.052457 0.000000 -0.107679 5.907406 GERMANY 499 0.001583 0.062408 0.000000 -0.418747 8.943607 GREECE 499 0.006315 0.057372 0.000881 -1.759424 23.117006 GUATEMALA 499 -0.000578 0.047791 0.000000 -1.339605 17.450899 HONGKONG 499 0.000894 0.047353 0.000000 -0.511545 24.624644 HUNGARY 499 0.001431 0.036199 0.000000 1.087324 16.780460 ICELAND 499 -0.000743 0.023225 0.000000 1.980218 24.183483 INDONESIA 499 -0.000457 0.034025 0.000000 0.245032 11.167025 IRAQ 499 -0.000713 0.026572 0.000000 4.104353 138.037804 IRELAND 499 0.003089 0.048327 0.000000 -0.365474 10.690519 ISRAEL 499 0.000236 0.023959 0.000000 0.536288 12.276669 ITALY 499 0.003164 0.063560 0.000000 -0.631143 8.837527 JAMAICA 499 -0.000706 0.033102 0.000000 -5.019670 89.309283 JAPAN 499 -0.000676 0.113454 0.000000 0.006389 5.501244 KAZAKHSTAN 499 -0.000262 0.035209 0.000000 -0.596768 12.307207 KOREA 499 0.000483 0.042257 0.000000 0.296775 6.002949 LATVIA 499 -0.001743 0.024943 0.000000 -0.204784 8.313861 LEBANON 499 0.000560 0.015611 0.000000 -0.395315 31.591486 LITHUANIA 499 -0.000624 0.029421 0.000000 -0.150939 10.655136 MALAYSIA 499 0.000294 0.037901 0.000000 0.434674 7.510840 MALTA 499 0.002102 0.025728 0.000000 3.804894 39.362861 MEXICO 499 -0.000025 0.030984 -0.000074 -0.078394 7.798596 MOROCCO 499 0.000700 0.023786 0.000000 2.535595 59.824021 NETHERLANDS 499 0.002142 0.039162 0.000000 -0.220840 7.111587 NEWZEALAND 388 0.001322 0.029020 0.000000 2.076294 21.566779 NORWAY 499 0.001547 0.039199 0.000000 0.909234 14.286317 PANAMA 499 -0.000004 0.037001 0.000000 -0.352763 7.968557 PERU 499 0.000200 0.037198 0.000000 -0.165578 5.785470 PHILIPPINES 499 -0.000343 0.031677 0.000000 -0.031225 8.898170 POLAND 499 0.000989 0.042882 0.000000 -0.585206 13.524710 PORTUGAL 499 0.005479 0.062825 0.000125 -1.635177 20.365619 QATAR 499 -0.000454 0.028231 0.000000 -0.759411 11.323204 ROMANIA 499 0.000225 0.032606 0.000000 0.110994 12.977437 RUSSIA 499 -0.000108 0.039970 0.000000 -0.306752 9.639548 SERBIA 499 0.000322 0.023450 0.000000 -2.722603 103.502557 SINGAPORE 499 0.001730 0.015203 0.000000 3.295032 31.512667 SLOVAK 499 0.001978 0.051953 0.000000 0.361288 9.812454 SLOVENIA 499 0.002782 0.044405 0.000000 0.804704 11.451951 SOUTHAFRICA 499 -0.000147 0.038879 0.000000 -0.953885 14.534170 SPAIN 499 0.002519 0.062468 0.000012 -0.510670 7.933039 SWEDEN 499 -0.000279 0.054849 0.000000 -0.505832 7.885526 THAILAND 499 0.000730 0.035300 0.000000 0.555264 8.261535 TUNISIE 499 0.001096 0.030593 0.000000 3.086982 37.477083 TURKEY 499 0.000092 0.033646 0.000000 -0.785786 10.608785 UK 499 0.000024 0.041450 0.000000 -0.306502 5.486762 UKRAINE 499 -0.002024 0.020210 0.000000 -1.455906 14.118940 URUGUAY 499 -0.000850 0.040362 0.000000 -0.670351 11.551356 USA 499 0.000300 0.034402 0.000000 0.555518 12.818523 VENEZUELA 499 -0.000408 0.027642 -0.000282 0.217423 5.974959 VIETNAM 499 0.000691 0.028709 0.000000 -0.014453 5.935101

Table 22: CDS descriptive stats Dec/2009 – Oct/2011

51 C Contagion results

C.1 General contagion

C.1.1 Global Financial Crisis

52 Subprime crisis − BANK − FR 1.6 USA CRB INDEX 1.4 8 months

1.2

0.8 6 months

0.6

Index value (normalized) 0.4

4 months 0.2

03/06 06/06 09/06 12/06 03/07 06/07 09/07 12/07 03/08 06/08 09/08 12/08 03/09 06/09 09/09 12/09 03/10 06/10 09/10 12/10 03/11 06/11 09/1111/11 Date

5 10 15 20 25 30 35 40 45 50 Contagion − countries affected

Figure 1: Global Financial Crisis contagion - Bank sector - FR

53 Subprime crisis − BANK − CS1 1.6 USA CRB INDEX 1.4 8 months

1.2

0.8 6 months

0.6

Index value (normalized) 0.4

4 months 0.2

03/06 06/06 09/06 12/06 03/07 06/07 09/07 12/07 03/08 06/08 09/08 12/08 03/09 06/09 09/09 12/09 03/10 06/10 09/10 12/10 03/11 06/11 09/1111/11 Date

5 10 15 20 25 30 35 40 45 50 Contagion − countries affected

Figure 2: Global Financial Crisis contagion - Bank sector - CS1

54 Subprime crisis − BANK − CS2 1.6 USA CRB INDEX 1.4 8 months

1.2

0.8 6 months

0.6

Index value (normalized) 0.4

4 months 0.2

03/06 06/06 09/06 12/06 03/07 06/07 09/07 12/07 03/08 06/08 09/08 12/08 03/09 06/09 09/09 12/09 03/10 06/10 09/10 12/10 03/11 06/11 09/1111/11 Date

5 10 15 20 25 30 35 40 45 50 Contagion − countries affected

Figure 3: Global Financial Crisis contagion - Bank sector - CS2

55 Subprime crisis − COUNTRY − FR 2 USA 1.8 CRB INDEX

1.6 8 months 1.4

1.2

1 6 months 0.8

Index value (normalized) 0.6

0.4 4 months 0.2

03/06 06/06 09/06 12/06 03/07 06/07 09/07 12/07 03/08 06/08 09/08 12/08 03/09 06/09 09/09 12/09 03/10 06/10 09/10 12/10 03/11 06/11 09/1111/11 Date

10 20 30 40 50 60 Contagion − countries affected

Figure 4: Global Financial Crisis contagion - Equity - FR

56 Subprime crisis − COUNTRY − CS1 2 USA 1.8 CRB INDEX

1.6 8 months 1.4

1.2

1 6 months 0.8

Index value (normalized) 0.6

0.4 4 months 0.2

03/06 06/06 09/06 12/06 03/07 06/07 09/07 12/07 03/08 06/08 09/08 12/08 03/09 06/09 09/09 12/09 03/10 06/10 09/10 12/10 03/11 06/11 09/1111/11 Date

10 20 30 40 50 60 Contagion − countries affected

Figure 5: Global Financial Crisis contagion - Equity - CS1

57 Subprime crisis − COUNTRY − CS2 2 USA 1.8 CRB INDEX

1.6 8 months 1.4

1.2

1 6 months 0.8

Index value (normalized) 0.6

0.4 4 months 0.2

03/06 06/06 09/06 12/06 03/07 06/07 09/07 12/07 03/08 06/08 09/08 12/08 03/09 06/09 09/09 12/09 03/10 06/10 09/10 12/10 03/11 06/11 09/1111/11 Date

10 20 30 40 50 60 Contagion − countries affected

Figure 6: Global Financial Crisis contagion - Equity - CS2

58 C.1.2 European Financial Crisis

59 European sovereign debt crisis − BANK − FR 1.4 GREECE PORTUGAL 1.2 IRELAND 8 months ITALY SPAIN 1 CRB INDEX

0.8 6 months 0.6

Index value (normalized) 0.4

4 months 0.2

03/08 06/08 09/08 12/08 03/09 06/09 09/09 12/09 03/10 06/10 09/10 12/10 03/11 06/11 09/11 11/11 Date

5 10 15 20 25 30 35 40 45 50 Contagion − countries affected

Figure 7: European Sovereign Debt Crisis contagion - Bank sector - FR

60 European sovereign debt crisis − BANK − CS1 1.4 GREECE PORTUGAL 1.2 IRELAND 8 months ITALY SPAIN 1 CRB INDEX

0.8 6 months 0.6

Index value (normalized) 0.4

4 months 0.2

03/08 06/08 09/08 12/08 03/09 06/09 09/09 12/09 03/10 06/10 09/10 12/10 03/11 06/11 09/11 11/11 Date

5 10 15 20 25 30 35 40 45 50 Contagion − countries affected

Figure 8: European Sovereign Debt Crisis contagion - Bank sector - CS1

61 European sovereign debt crisis − BANK − CS2 1.4 GREECE PORTUGAL 1.2 IRELAND 8 months ITALY SPAIN 1 CRB INDEX

0.8 6 months 0.6

Index value (normalized) 0.4

4 months 0.2

03/08 06/08 09/08 12/08 03/09 06/09 09/09 12/09 03/10 06/10 09/10 12/10 03/11 06/11 09/11 11/11 Date

5 10 15 20 25 30 35 40 45 50 Contagion − countries affected

Figure 9: European Sovereign Debt Crisis contagion - Bank sector - CS2

62 European sovereign debt crisis − COUNTRY − FR 1.4 GREECE PORTUGAL 1.2 IRELAND 8 months ITALY SPAIN 1 CRB INDEX

0.8 6 months 0.6

Index value (normalized) 0.4

4 months 0.2

03/08 06/08 09/08 12/08 03/09 06/09 09/09 12/09 03/10 06/10 09/10 12/10 03/11 06/11 09/11 11/11 Date

10 20 30 40 50 60 Contagion − countries affected

Figure 10: European Sovereign Debt Crisis contagion - Equity - FR

63 European sovereign debt crisis − COUNTRY − CS1 1.4 GREECE PORTUGAL 1.2 IRELAND 8 months ITALY SPAIN 1 CRB INDEX

0.8 6 months 0.6

Index value (normalized) 0.4

4 months 0.2

03/08 06/08 09/08 12/08 03/09 06/09 09/09 12/09 03/10 06/10 09/10 12/10 03/11 06/11 09/11 11/11 Date

10 20 30 40 50 60 Contagion − countries affected

Figure 11: European Sovereign Debt Crisis contagion - Equity - CS1

64 European sovereign debt crisis − COUNTRY − CS2 1.4 GREECE PORTUGAL 1.2 IRELAND 8 months ITALY SPAIN 1 CRB INDEX

0.8 6 months 0.6

Index value (normalized) 0.4

4 months 0.2

03/08 06/08 09/08 12/08 03/09 06/09 09/09 12/09 03/10 06/10 09/10 12/10 03/11 06/11 09/11 11/11 Date

10 20 30 40 50 60 Contagion − countries affected

Figure 12: European Sovereign Debt Crisis contagion - Equity - CS2

65 European sovereign debt crisis − CDS − FR 50 GREECE 45 PORTUGAL IRELAND 8 months 40 ITALY SPAIN 35 CRB INDEX

25 6 months

Index value (normalized) 15

10 4 months 5

12/08 03/09 06/09 09/09 12/09 03/10 06/10 09/10 12/10 03/11 06/11 09/11 11/11 Date

10 20 30 40 50 60 Contagion − countries affected

Figure 13: European Sovereign Debt Crisis contagion - CDS - FR

66 C.2 Contagion to Brazil

C.2.1 Global Financial Crisis

67 Subprime crisis − Contagion to BRAZIL − BANK − CS2 3 USA BRAZIL CRB INDEX 2.5 8 months

1.5 6 months

Index value (normalized)

0.5 4 months

03/06 06/06 09/06 12/06 03/07 06/07 09/07 12/07 03/08 06/08 09/08 12/08 03/09 06/09 09/09 12/09 03/10 06/10 09/10 12/10 03/11 06/11 09/1111/11 Date

5 10 15 20 25 30 35 Contagion to BRAZIL − CS2 statistic

Figure 14: Global Financial Crisis contagion to Brazil - Bank sector - CS2

68 Subprime crisis − Contagion to BRAZIL − COUNTRY − CS1 3 USA BRAZIL CRB INDEX 2.5 8 months

1.5 6 months

Index value (normalized)

0.5 4 months

03/06 06/06 09/06 12/06 03/07 06/07 09/07 12/07 03/08 06/08 09/08 12/08 03/09 06/09 09/09 12/09 03/10 06/10 09/10 12/10 03/11 06/11 09/1111/11

Date

5 10 15 20 25 30 35 Contagion to BRAZIL − CS1 statistic

Figure 15: Global Financial Crisis contagion to Brazil - Equity market - CS1

69 Subprime crisis − Contagion to BRAZIL − COUNTRY − CS2 3 USA BRAZIL CRB INDEX 2.5 8 months

1.5 6 months

Index value (normalized)

0.5 4 months

03/06 06/06 09/06 12/06 03/07 06/07 09/07 12/07 03/08 06/08 09/08 12/08 03/09 06/09 09/09 12/09 03/10 06/10 09/10 12/10 03/11 06/11 09/1111/11

Date

5 10 15 20 25 30 35 40 45 Contagion to BRAZIL − CS2 statistic

Figure 16: Global Financial Crisis contagion to Brazil - Equity market - CS2

70 C.2.2 European Financial Crisis

71 European sovereign debt crisis − Contagion to BRAZIL − BANK − CS1 1.4 BRAZIL GREECE 1.2 PORTUGAL 8 months IRELAND ITALY 1 SPAIN CRB INDEX

0.8 6 months 0.6

Index value (normalized) 0.4

4 months 0.2

03/08 06/08 09/08 12/08 03/09 06/09 09/09 12/09 03/10 06/10 09/10 12/10 03/11 06/11 09/11 11/11 Date

2 4 6 8 10 12 14 Contagion to BRAZIL − CS1 statistic

Figure 17: European Sovereign Debt Crisis contagion to Brazil - Bank sector - CS1

72 European sovereign debt crisis − Contagion to BRAZIL − BANK − CS2 1.4 BRAZIL GREECE 1.2 PORTUGAL 8 months IRELAND ITALY 1 SPAIN CRB INDEX

0.8 6 months 0.6

Index value (normalized) 0.4

4 months 0.2

03/08 06/08 09/08 12/08 03/09 06/09 09/09 12/09 03/10 06/10 09/10 12/10 03/11 06/11 09/11 11/11 Date

5 10 15 20 25 30 35 Contagion to BRAZIL − CS2 statistic

Figure 18: European Sovereign Debt Crisis contagion to Brazil - Bank sector - CS2

73 European sovereign debt crisis − Contagion to BRAZIL − COUNTRY − CS1 1.4 BRAZIL GREECE 1.2 PORTUGAL 8 months IRELAND ITALY 1 SPAIN CRB INDEX

0.8 6 months 0.6

Index value (normalized) 0.4

4 months 0.2

03/08 06/08 09/08 12/08 03/09 06/09 09/09 12/09 03/10 06/10 09/10 12/10 03/11 06/11 09/11 11/11 Date

2 4 6 8 10 12 14 16 18 Contagion to BRAZIL − CS1 statistic

Figure 19: European Sovereign Debt Crisis contagion to Brazil - Equity - CS1

74 European sovereign debt crisis − Contagion to BRAZIL − COUNTRY − CS2 1.4 BRAZIL GREECE 1.2 PORTUGAL 8 months IRELAND ITALY 1 SPAIN CRB INDEX

0.8 6 months 0.6

Index value (normalized) 0.4

4 months 0.2

03/08 06/08 09/08 12/08 03/09 06/09 09/09 12/09 03/10 06/10 09/10 12/10 03/11 06/11 09/11 11/11 Date

5 10 15 20 25 30 35 Contagion to BRAZIL − CS2 statistic

Figure 20: European Sovereign Debt Crisis contagion to Brazil - Equity - CS2

75 European sovereign debt crisis − Contagion to BRAZIL − CDS − FR 50 BRAZIL 45 GREECE PORTUGAL 8 months 40 IRELAND ITALY 35 SPAIN CRB INDEX 30

25 6 months

Index value (normalized) 15

10 4 months 5

12/08 03/09 06/09 09/09 12/09 03/10 06/10 09/10 12/10 03/11 06/11 09/11 11/11 Date

5 10 15 20 25 30 Contagion to BRAZIL − FR statistic

Figure 21: European Sovereign Debt Crisis contagion to Brazil - CDS - FR

76 Banco Central do Brasil

Trabalhos para Discussão Os Trabalhos para Discussão do Banco Central do Brasil estão disponíveis para download no website 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 Oct/2011 Benjamin M. Tabak, Giovana L. Craveiro and Daniel O. Cajueiro

254 Macroprudential Regulation and the Monetary Transmission Nov/2011 Mechanism Pierre-Richard Agénor and Luiz A. Pereira da Silva

255 An Empirical Analysis of the External Finance Premium of Public Nov/2011 Non-Financial Corporations in Brazil Fernando N. de Oliveira and Alberto Ronchi Neto

256 The Self-insurance Role of International Reserves and Nov/2011 the 2008-2010 Crisis Antonio Francisco A. Silva Jr

257 Cooperativas de Crédito: taxas de juros praticadas e fatores Nov/2011 de viabilidade Clodoaldo Aparecido Annibal e Sérgio Mikio Koyama

258 Bancos Oficiais e Crédito Direcionado – O que diferencia o mercado de Nov/2011 crédito brasileiro? Eduardo Luis Lundberg

259 The impact of monetary policy on the exchange rate: puzzling evidence Nov/2011 from three emerging economies Emanuel Kohlscheen

260 Credit Default and Business Cycles: an empirical investigation of Nov/2011 Brazilian retail loans Arnildo da Silva Correa, Jaqueline Terra Moura Marins, Myrian Beatriz Eiras das Neves and Antonio Carlos Magalhães da Silva

261 The relationship between banking market competition and risk-taking: Nov/2011 do size and capitalization matter? Benjamin M. Tabak, Dimas M. Fazio and Daniel O. Cajueiro

262 The Accuracy of Perturbation Methods to Solve Small Open Nov/2011 Economy Models Angelo M. Fasolo

263 The Adverse Selection Cost Component of the Spread of Dec/2011 Brazilian Stocks Gustavo Silva Araújo, Claudio Henrique da Silveira Barbedo and José Valentim Machado Vicente

77 264 Uma Breve Análise de Medidas Alternativas à Mediana na Pesquisa de Jan/2012 Expectativas de Inflação do Banco Central do Brasil Fabia A. de Carvalho

265 O Impacto da Comunicação do Banco Central do Brasil sobre o Jan/2012 Mercado Financeiro Marcio Janot e Daniel El-Jaick de Souza Mota

266 Are Core Inflation Directional Forecasts Informative? Jan/2012 Tito Nícias Teixeira da Silva Filho

267 Sudden Floods, Macroprudention Regulation and Stability in an Feb/2012 Open Economy P.-R. Agénor, K. Alper and L. Pereira da Silva

268 Optimal Capital Flow Taxes in Latin America Mar/2012 João Barata Ribeiro Blanco Barroso

269 Estimating Relative Risk Aversion, Risk-Neutral and Real-World Mar/2012 Densities using Brazilian Real Currency Options José Renato Haas Ornelas, José Santiago Fajardo Barbachan and Aquiles Rocha de Farias

270 Pricing-to-market by Brazilian Exporters: a panel cointegration Mar/2012 approach João Barata Ribeiro Blanco Barroso

271 Optimal Policy When the Inflation Target is not Optimal Mar/2012 Sergio A. Lago Alves

272 Determinantes da Estrutura de Capital das Empresas Brasileiras: uma Mar/2012 abordagem em regressão quantílica Guilherme Resende Oliveira, Benjamin Miranda Tabak, José Guilherme de Lara Resende e Daniel Oliveira Cajueiro

273 Order Flow and the Real: Indirect Evidence of the Effectiveness of Apr/2012 Sterilized Interventions Emanuel Kohlscheen

274 Monetary Policy, Asset Prices and Adaptive Learning Apr/2012 Vicente da Gama Machado

275 A geographically weighted approach in measuring efficiency in panel Apr/2012 data: the case of US saving banks Benjamin M. Tabak, Rogério B. Miranda and Dimas M. Fazio

276 A Sticky-Dispersed Information Phillips Curve: a model with partial and Apr/2012 delayed information Marta Areosa, Waldyr Areosa and Vinicius Carrasco

277 Trend Inflation and the Unemployment Volatility Puzzle May/2012 Sergio A. Lago Alves

278 Liquidez do Sistema e Administração das Operações de Mercado Aberto Maio/2012 Antonio Francisco de A. da Silva Jr.

279 Going Deeper Into the Link Between the Labour Market and Inflation May/2012 Tito Nícias Teixeira da Silva Filho

78 280 Educação Financeira para um Brasil Sustentável Jun/2012 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

281 A Note on Particle Filters Applied to DSGE Models Jun/2012 Angelo Marsiglia Fasolo

282 The Signaling Effect of Exchange Rates: pass-through under dispersed Jun/2012 information Waldyr Areosa and Marta Areosa

283 The Impact of Market Power at Bank Level in Risk-taking: Jun/2012 the Brazilian case Benjamin Miranda Tabak, Guilherme Maia Rodrigues Gomes and Maurício da Silva Medeiros Júnior

284 On the Welfare Costs of Business-Cycle Fluctuations and Economic- Jul/2012 Growth Variation in the 20th Century Osmani Teixeira de Carvalho Guillén, João Victor Issler and Afonso Arinos de Mello Franco-Neto

285 Asset Prices and Monetary Policy – A Sticky-Dispersed Information Jul/2012 Model Marta Areosa and Waldyr Areosa

286 Information (in) Chains: information transmission through production Jul/2012 chains Waldyr Areosa and Marta Areosa

287 Some Financial Stability Indicators for Brazil Jul/2012 Adriana Soares Sales, Waldyr D. Areosa and Marta B. M. Areosa

288 Forecasting Bond Yields with Segmented Term Structure Models Jul/2012 Caio Almeida, Axel Simonsen and José Vicente

289 Financial Stability in Brazil Aug/2012 Luiz A. Pereira da Silva, Adriana Soares Sales and Wagner Piazza Gaglianone

290 Sailing through the Global Financial Storm: Brazil's recent experience Aug/2012 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

291 O Desempenho Recente da Política Monetária Brasileira sob a Ótica da Set/2012 Modelagem DSGE Bruno Freitas Boynard de Vasconcelos e José Angelo Divino

292 Coping with a Complex Global Environment: a Brazilian perspective on Oct/2012 emerging market issues Adriana Soares Sales and João Barata Ribeiro Blanco Barroso