Stock Market Co-Movement in Latin America
Vitor Leone and Otavio Ribeiro de Medeiros1
Abstract
This paper investigates co-movement in five Latin-American stock markets (Argentina,
Brazil, Chile, Colombia, Ecuador, Mexico, Peru, and Venezuela) using common factor
analysis. The common factors are obtained using principal component analysis (PCA) and
therefore account for the maximum portion of the variance present in the stock exchanges
investigated. We test for co-movement in different periods so as to ascertain any changes that
have taken place from one period to the next. In particular, we examine rolling windows with
5-year, 3-year, 2-year, and 1-year periods. We also specify and estimate a vector
autoregressive model and test for co-movement between the eight markets during the sample
period by means impulse response functions. The results of both methods show that comovement between the exchanges over the entire sample period does not converge. However,
we find evidence of an increasing co-movement from 2002 to 2008, which implies a growing
integration between these markets. However, the trend towards increasing integration
between the stock markets seems to have suffered a setback in 2008 due to the world
financial crisis. Since then, a possible resume to the trend of increasing integration is unclear.
The impulse response analysis shows that Argentina, Brazil, Chile, Colombia, Mexico and
Peru present moderate response to shocks in each other’s markets and very low responses to
shocks in Ecuador and Venezuela’s markets. Also, responses of Ecuador and Venezuela’s
market returns to shocks in the other markets are very low.
1. Introduction
There has been a lot of academic interest in international stock-market co-movements
in recent years, as it can be seen from the number of papers published on the subject,
involving both developed and emerging markets. One motivation for this interest is related to
the transmission of shocks from one market to other markets in times of turbulence or crisis,
which is sometimes referred to as contagion or interdependence. Therefore, a portion of these
studies are concerned in finding out whether and, if it is the case, how the transmission of
crisis from one country’s to other countries’ stock markets has occurred. Another source of
academic interest in the subject is related to portfolio diversification. As widely known from
portfolio theory, the benefits of portfolio diversification exist only when assets included in
the portfolio have negative or zero correlations among them, so that there will be no gains
from diversifying a portfolio with assets from another country’s stock market which is highly
1
Vitor Leone; Division of Economics, Nottingham Business School, Nottingham Trent University,
Burton Street, Nottingham, UK, NG1 4BU vitor.leone@ntu.ac.uk
Otavio Ribeiro de Medeiros; Universidade de Brasília - UnB; Faculdade de Economia,
Administração e Contabilidade - FACE; Departamento de Ciências Contábeis e Atuariais - CCA.
otavio@unb.br
1
correlated with the original assets. In other words, diversifying portfolios internationally is
advantageous to investors only if stock markets in different countries do not co-move.
Another reason to study stock market co-movements has been to find out lead-lag
relationships between two different stock markets, so that an investor could be able to make
abnormal returns by trading with shares from the lagging market based on the behaviour of
share prices in the leading market. In the beginning, studies on international stock market comovements where mainly focused on developed stock markets and later on, on comovements between developed and emerging stock markets. More recently, a number of
papers have focused on groups of emerging markets which belong to a same economic,
political or social region.
To study stock-market co-movements across a group of countries is only meaningful
if there are justifiable economic or political reasons which can explain why their markets are
indeed correlated. There are various reasons why one should expect that stock markets of LA
countries present co-movements. First, they are periphery or semi-periphery countries most
of them with long and enduring historical, cultural and socio-political ties among them, most
speak the same language (except Brazil), and are located in the same geographical region.
Second, because trade connections amongst some of these countries has led them to establish
trade agreements, such as Mercosur, ALADI (Latin American Integration Association)
involving Argentina, Bolivia, Brazil, Chile, Colombia, Cuba, Ecuador, México, Paraguay,
Peru, Uruguay and Venezuela, and the Andean Community. The Andean Community.
Another initiative towards integration is the recent (May 2011) creation of MILA (Latin
American Integrated Markets), by which Chile, Colombia and Peru have integrated their
stock markets, so that these countries have a reasonable degree of economic or financial
integration. Besides, BOVESPA is by far the largest stock market in Latin America and it is
also a reference to other South American countries. There are several studies showing that the
NYSE and BOVESPA are cointegrated and present co-movements with respect to each other.
Mercosur (Southern Common Market) was officially established in March 1991. It is
composed of the following countries in South America: Brazil, Paraguay, Uruguay and
Argentina. The main objective of Mercosur is to eliminate trade barriers between countries,
increasing trade between them. Another objective is to establish zero tariffs between
countries and in the future, a single currency. The Andean Community of Nations, formerly
Andean Pact, consists of Bolivia, Colombia, Ecuador and Peru, with Brazil, Argentina, Chile,
Paraguay and Uruguay as associated members. It was created in 1969 to integrate member
countries economically and culturally, seeks pluralism in the political and economic
progressive convergence towards the formation of a common Latin American market, besides
representing the interests of member countries in agreements with other economic blocks or
international organizations.
Latin American stock markets have grown significantly during the last decades,
especially those of Brazil, Mexico, Chile, and Colombia. Neverthless, the size of their
markets presents very distinct figures. Table 1 shows the sizes of the selected stock markets
by market capitalization in decreasing order as of December 2010.
This paper is aimed at investigating co-movement in five Latin-American stock
markets (Argentina, Brazil, Chile, Colombia, Ecuador, Mexico, Peru, and Venezuela) using
common factor analysis. The common factors are obtained using principal component
2
analysis (PCA) and account for the maximum portion of the variance present in the stock
exchanges investigated. We test for co-movement in different periods so as to ascertain any
changes that have taken place from one period to the next. In particular, we examine rolling
windows with 5-year, 3-year, 2-year, and 1-year periods. We also specify and estimate a
vector autoregressive model and test for co-movement between the eight markets during the
sample period by means impulse response functions.
The remaining parts of this paper are disposed as follows. Section 2 presents previous
empirical studies on international stock market co-movements, with especial emphasis in
emerging countries. Section 3 presents the methodology used to investigate co-movement
behaviour between the selected stock exchanges. Section 4 discusses our empirical results,
and Section 5 concludes.
Table 1: Selected Latin American Exchanges: Market Capitalization (USD 106)
Country
Stock Exchange
Market Capitalization
Brazil
Bolsa de Valores de Sao Paulo
1,545,565.7
Mexico
Bolsa Mexicana de Valores
454,345.2
Chile
Bolsa de Comercio de Santiago
341,798.9
Colombia
Bolsa de Valores de Colombia
208,501.7
Peru
Bolsa de Valores de Lima
103,347.5
Argentina
Bolsa de Comercio de Buenos Aires
63,909.8
Ecuador
Bolsa de Valores de Quito
4,562.1
Venezuela
Bolsa de Valores de Caracas
3,991.0
Source: World Federation of Exchanges http://www.world-exchanges.org/ and World Bank
Position in December 2010.
2. Previous Studies
Studies on co-movements among international stock markets are quite numerous. The
first studies on the topic were focused on investigating co-movements between developed
stock markets (Bessler and Yang, 2003; Fraser and Oyefeso, 2005; Eun and Shim, 1989;
Goetzmann, Li and Rouwenhorst, 2005). Some other studies concentrate on co-movements
between developed and emerging stock markets were they explore the interrelationships
between these two types of countries (Valadkhani, Chancharat and Harvie, 2008; Evans and
MacMillan, 2009). A third group of studies are concerned with co-movements between
emerging markets, generally belonging to a common continent or geographical region. Since
a generalized literature review on stock market co-movement would be quite extensive and
tedious, we discuss briefly below selected papers which have concentrated on co-movements
involving emerging markets in countries belonging to Latin American and its neighbouring
region, the Caribbean.
Christofi and Pericli (1999) investigate the short-run dynamics between five major
Latin American stock markets (Argentina, Brazil, Chile, Colombia and Mexico). They
estimate the joint distribution of stock returns by a vector autoregressive (VAR) model with
innovations following an exponential GARCH process from May 1992 to May 1997. Their
results show that these countries have significant first and second moment time dependencies
and that these markets reveal stronger volatility than mean spillovers. Further, they affirm
that these markets exhibit stronger volatility spillovers than other regions of the world.
3
Meric, Leal, Ratner and Meric (2001) examined the stability of correlations and the
benefits of international portfolio diversification through investment in Argentina, Brazil,
Chile and Mexico, the four largest Latin American markets, from the point of view of a U.S.
investor, before, during and after the 1987 market crash. They use principal components
analysis to study changes in co-movement patterns of the selected equity markets from the
precrash period to the postcrash period and during the postcrash period and Box’s M statistic
to study the intertemporal stability of the variance–covariance matrix of the equity market
index returns. They find that correlations were rising in time and that there are no significant
gains to a domestically well diversified U.S. investor from holding a well-diversified
portfolio of Latin American stocks in their most recent sample periods (1991-1993).
Accordingly, they argue that investment in Latin America should be made through a careful
selection of countries and securities instead of the purchasing of a broad index of Latin
American stocks.
Lopes and Migon (2002) aimed at measuring the transmission of shocks by crossmarket correlation coefficients following Forbes and Rigobon’s (2000) notion of “shiftcontagion”. By combining traditional factor model techniques with stochastic volatility
models they study the dependence among Latin American stock price indexes and the North
American index. Factor variances are modelled by a multivariate stochastic volatility
structure by allowing the factor loadings, in the factor model structure, to have a time-varying
structure and to capture changes in the series’ weights over time. They claim that their results
show that some sort of contagion is present in most of the series’ covariance during periods
of economic instability or crises.
Fujii (2005) studies the causal linkages among several emerging stock markets in Asia
and Latin America since 1990 using daily observations of stock indices and the GARCH
family of econometric models. He performs residual cross-correlation function tests to
investigate cross-market causality both in the first and second moments of the stock returns,
finding significant causal linkages both within each region and across the two regions. His
rolling test results suggest that the significance of the causality varies considerably over time
and that the causal linkages become stronger at the time of major financial crises.
Lorde, Francis and Greene (2009) scrutinise co-movement, common features and
efficiency in three Caribbean Community (CARICOM) stock markets. Stock markets of
Barbados, Jamaica and Trinidad and Tobago are investigated in the period from 1991 to
2006, aiming to find cointegration and common features among the three markets analysed.
They do not succeed in finding evidence of long-run or short-run co-movement, or common
features. They conclude that the three exchanges analysed are weakly efficient, that these
markets are segmented, and that there may be benefits from regional diversification of
security portfolios.
Harrison and Moore (2010) analyse co-movement in five Caribbean stock markets
(Barbados, Jamaica and Trinidad and Tobago, The Bahamas and Guyana) using common
factor analysis, obtained by principal component analysis. They divide their sample period in
10-year, 5-year and 3-year windows and test for co-movement in different periods to
determine changes that might have taken place from one period to the next. They also use
impulse response functions after specifying a vector autoregressive model to test for comovement between the five markets during the sample period. They affirm that both tests fail
4
to find any evidence of co-movement between the exchanges over the entire sample period,
but they find evidence of periodic co-movement, particularly between exchanges in
Barbados, Jamaica and Trinidad and Tobago.
De Barba and Ceretta (2011) explore the potential time-varying behaviour of long-run
stock market relationships among four Latin American emerging capital markets (Brazil,
Argentina, Chile and Mexico) and the US, considering the period of the recent financial crisis
of 2007/2008, and testing for cointegration with the Engle-Granger method before, during
and after the crisis period. Their results intend to show that Latin American equity markets
seem to respond differently to shocks in the US stock markets in the long-run. They sustain
that relationships between Argentina and Brazil and the United States have changed over
time, as their markets have become more integrated, but Chile’s and Mexico’s relationships
with the US did not change significantly during or after the crisis period. They sustain that
this is evidence that, for international diversification, each country should be analysed
individually, and that analysing Latin America as a group could lead to mistaken conclusions
about international diversification opportunities.
3. Methods
3.1. Sample and Data
Our sample includes eight Latin American stock markets: Argentina, Brazil, Chile,
Colombia, Ecuador, Mexico, Peru, and Venezuela. While Mexico is in Central America2, all
other countries lie in South America. Our data series are monthly closing price indices
referring to the eight stock exchanges which represent the stock markets of these countries.
These stock market indices are known as Merval (Argentina), Ibovespa (Brazil), IPSA
(Chile), IGBC (Colombia), IPC (Mexico), IGVBL (Peru), and IBC (Venezuela). Monthly
returns are represented in continuous compounding form and, accordingly, are calculated as
rit   ln pit , where ∆ is the first difference operator, ln is the natural log operator, pit is the
stock market index of the i-th country, in period t. The sample period extends from December
2001 to October 2011. All data series were obtained from Thomson Reuters Datastream®.
3.2. Principal Components
The first method utilized here is known as factor model. Factor models break up the
structure of a series group into factors which are common to all series and a portion which is
particular to each series. The most usual factor model is Principal Components Analysis
(PCA). PCA is aimed at explaining the variance-covariance structure of a set of variables
through a few linear combinations of these variables. Its general objectives are data reduction
and interpretation. In general, p components are necessary to represent the total system
variability, but there are many situations where much of the variability can be represented by
a reduced number k of components. If so, there is nearly as much information in the k
components as in the original p variables. The k principal components can then substitute the
2
Although some references situate Mexico in North America, the United Nations locates Mexico in Central
America. See: http://unstats.un.org/unsd/methods/m49/m49regin.htm#americas
5
original p variables so that the original data set is reduced to a data set of n measurements on
k principal components (Tsay, 2005).
Let y = yit be a vector of stock market indicators for country i = 1,...,8 for period t =
1,...,T.
r
yit   ij f jt   it
(1)
j 1
where λij are the factor loading coefficients associated with each of the r common factors and
εit it is a white noise IID error term. The common factors are obtained by principal
component analysis and represent the maximum portion of the variance for the chosen stock
exchanges. Becker and Hall (2009) demonstrate that a set of variables converge if the general
factor representation given by Equation (1) can be constrained to a single factor. Using the
calculated monthly returns, principal component analysis is employed to test for convergence
over the sample period considered. The measure of convergence utilized is the percentage R2
of the first factor, which is a measure of the total variation in returns explained by the first
factor. The closer this value is to 1, the greater the degree of convergence between the
returns. In addition, if the percentage R2 over period 1 is less than that in some consequent
period 2, then convergence has accelerated over the selected interval.
3.3. VAR model and Impulse Response Functions
In a second method used to investigate co-movements within the LA region, we
specify and estimate a vector autoregressive (VAR) model and test for co-movement between
the eight LA markets during the sample period through impulse response functions. Since the
individual coefficients in estimated VAR models are often difficult to interpret, the
practitioners of this technique often estimate the so-called impulse response function (IRF).
The IRF traces out the response of the dependent variable in the VAR system to shocks in the
error. If there are several lags in each equation, it is not always easy to interpret each
coefficient, especially if the signs of the coefficients alternate. For this reason one examines
the impulse response function (IRF) in VAR modelling to find out how the dependent
variable responds to a shock administered to one or more equations in the system. This
provides a means of evaluating the extent to which shocks on one exchange are transmitted to
other exchanges, and the length of time these shocks last. Therefore, prior to conducting the
impulse response analysis, a VAR model involving the returns of the eight exchanges
investigated must first be obtained.
p
Yt  A0   Ak X t k   t
(2)
k 1
where Xt-k is a n x 1 column vector of stock exchange returns at period t-k, A0 is an n x 1
column vector of intercept terms, Ak is an n x n column matrix of coefficients, p is the
number of lags and εt is an n x 1 column vector of error terms that may be correlated.
Once the VAR model has been specified, impulse response analysis is employed to
evaluate the co-movement between the eight stock exchanges. Impulse response analysis
traces the effect of a shock in one of the VAR equations on current and future values of the
6
endogenous variables included in the VAR. Generalised impulses are used since these are not
significantly influenced by the VAR ordering (Pesaran and Shin, 1998).
4. Empirical Results
In this Section we describe and analyse the results obtained. First, we present and
discuss our sample’s descriptive statistics and then we present the results of our factor
analysis, and finally the results of the impulse response analysis.
4.1. Descriptive Statistics
Table 1 provides summary statistics for the monthly returns between 2002 and 2011.
The highest mean returns were in Venezuela (2.8%) and Peru (2.5%), while the lowest mean
returns were in Ecuador (0.8%) and Chile (1.0%). The highest volatilities, measured by
standard deviation, were observed in Argentina (0.105) and Peru (0.103), which can be
regarded thus as the riskier markets within the group. The lowest volatilities are found in
Chile (0.046) and Mexico (0.056). With respect to the distribution of returns, all of the
selected markets present non-normal returns, according to the Jarque-Bera test, with the
exception of Chile. The majority of these markets have a distribution of returns with negative
skewness, which means that the proportion of months with negative returns tends to be higher
than those with positive returns. The exceptions are Chile, Ecuador and Venezuela, which
have positive skewness. Besides, all of these markets have leptokurtic returns, which means
that their return distribution present heavy tails.
Table 1: Descriptive Statistics of Monthly Returns of Latin American Stock Exchanges (2002-2011)
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
Jarque-Bera
Probability
Observations
ARG
BRA
CHI
COL
ECU
MEX
PER
VEN
0.022
0.022
0.397
-0.458
0.105
-0.229
7.790
107.070
0.000
111
0.012
0.017
0.165
-0.285
0.075
-0.699
4.335
17.275
0.000
111
0.010
0.007
0.149
-0.101
0.047
0.063
3.290
0.461
0.794
111
0.023
0.026
0.178
-0.247
0.074
-0.583
4.694
19.570
0.000
111
0.008
0.003
0.437
-0.191
0.065
2.651
19.742
1426.347
0.000
111
0.015
0.029
0.124
-0.197
0.056
-0.782
3.875
14.859
0.001
111
0.025
0.028
0.309
-0.505
0.103
-1.052
8.823
177.290
0.000
111
0.028
0.018
0.361
-0.287
0.082
0.452
6.463
59.238
0.000
111
4.2. Principal Component Analysis
In this section we report our results from applying the common factor approach to the
sample of Latin American stock market index returns over the period 2002 to 2011. For the
whole sample period, the first principal component is able to explain approximately 44.73%
of the variance in the data. This suggests that most of the variance in stock market return
indices in Latin America cannot be attributed to one factor over the full sample period. The
clear implication is that there is incomplete convergence over the period. As a result, the
study attempts to assess whether convergence has been period-specific or increasing over
time.
7
To identify whether stock market co-movement is episodic, Table 2 provides the R2
for the first principal component over 5-year, 3-year, 2-year and 1-year rolling windows for
the Latin American stock exchanges over the sample period.
In the case of the 5-year rolling windows there is evidence of increasing convergence
throughout the period, with the first principal component explaining 38% of the variance in
2002-2006 and growing gradually in the following periods to reach 53% in 2007-2011.
Similar results are also obtained when 3-year windows are employed, with the first principal
component explaining 37% of the variance in 2002-2004 and growing gradually in the
following periods to reach 51% in 2009-2011.
Table 2: R2 of the First Principal Component of the eight LA Stock Exchanges
full period
2002-2011
5-year windows
2002-2006
0.4473
0.3787
3-year windows
2002-2004
0.3675
2-year windows
2002-2003
0.3834
2003-2007 2004-2008 2005-2009 2006-2010 2007-2011
0.3918
0.5063
0.5128
0.5256
0.5307
2003-2005 2004-2006 2005-2007 2006-2008 2007-2009
0.3728
0.4238
0.4234
0.564
0.5601
2003-2004 2004-2005 2005-2006 2006-2007 2007-2008
0.3812
0.3895
0.459
0.465
0.5552
2008-2010 2009-2011
0.5685
0.5141
2008-2009 2009-2010
0.5944
0.515
2010-2011
0.4664
Analogous growth in R2 can be seen when 2-year and 1-year rolling windows are
employed. These results indicate a gradual convergence between the Latin America stock
exchanges from 2002 to 2011. It should be mentioned though that by observing the behaviour
of the R2 in the 3-year, 2-year and 1-year rolling windows, one can see that this goodness of
fit measure reaches its maximum value in 2008-2010 for the 3-year rolling window, in 20082009 for the 2-year rolling window and 2008 for the 1-year rolling window, respectively.
This suggests that the convergence between the stock exchanges has suffered an interruption
in its growth trend due to the world financial crisis which started in September 2008. After
this event, it is apparent that R2 falls to levels lower than those shown before the crisis. The
R2 results observed after that make it unclear whether the trend towards increasing integration
has been resumed or whether it has just stabilized. Figure 1 shows the evolution of R2 for the
first principal component from 2002 to 2011 when 5-year, 3-year, 2-year and 1-year rolling
windows are considered.
The PCA results discussed above shows the magnitude to which variations in the
returns of the selected LA countries share common structures. An additional important
issue is to find to what extent a certain exchange's returns vary around the common factor
independently of the other market's returns. If convergence were complete, the factor
loadings would be equal to 1 for all exchanges in the sample.
Figure 1: R2 of First Component for 5-year, 3-year, 2-year and 1-year rolling windows
8
0.7
0.6
0.5
5-year windows
0.4
3-year windows
0.3
2-year windows
0.2
1-year windows
0.1
0
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
To detect the intensity of independence, Figure 2 charts the factor loadings on the first
principal component for each country over three 3-year fixed intervals considered. The
results show that none of the weights are close to 1. This means that a large portion of
fluctuations in monthly returns cannot be explained by the first principal component in any of
the exchanges considered. It becomes also clear that while factor loadings on the first
principal component for Argentina, Brazil, Chile, Colombia, Mexico and Peru are positive
and have moderate sizes, i.e. from 0.3 to 0.6, those for Ecuador and Venezuela are very low
or even negative, indicating that the degree of integration of these two markets with respect to
the others is quite low.
Figure 2: Factor Loadings of the First Principal Component (3-year windows)
ARG
0.6
VEN
0.4
BRA
0.2
0
-0.2
PER
CHI
-0.4
MEX
COL
ECU
2002-2004
9
ARG
0.5
VEN
0.4
BRA
0.3
0.2
0.1
PER
CHI
0
MEX
COL
ECU
2005-2007
ARG
VEN
PER
0.5
0.4
0.3
0.2
0.1
0
-0.1
BRA
CHI
MEX
COL
ECU
2008-2010
4.2 VAR model and Impulse response analysis
We now describe the results obtained from estimating the VAR model and analyse the
co-movements between the eight LA markets during the sample period through IRF, which
reveals the response of the dependent variable in the VAR system to shocks in the error term.
It is a method to assess intensity in which shocks on one market are spilled over to other
markets, and the duration of these shocks.
The sequential likelihood ratio (LR) statistic (Lütkepohl, 1991) with the small sample
modification proposed by Sim’s (1980) indicates that the optimal lag length is three for the
sample period utilized. The autocorrelation LM test for the VAR model estimated with eight
lags test cannot reject the null of no autocorrelation between the residuals at 5%. The JarqueBera normality test shows that the null of multivariate normality of the VAR residuals is
rejected at 5%. The test results show that this rejection is a consequence of residual kurtosis
rather than skewness. The ordering of stock market index returns entering the VAR model
10
was done based on the exchanges’ market capitalization, with the highest market
capitalization coming first and the smaller market capitalization last. This was made under
the assumption that largest stock exchanges might tend to lead smaller exchanges.
Accordingly, the ordering became: Brazil, Mexico, Chile, Colombia, Peru, Argentina,
Ecuador and Venezuela.
Figure 3 shows the impulse responses of the eight LA stock exchanges. The
horizontal axis gives the number of lags, while the vertical axis gives a measure of the
responses of each market expressed in standard deviations.
Similar to the findings from the principal component analysis, the responses of returns
on the exchanges in Ecuador and Venezuela relative to innovations in the other markets were
relatively small. In the case of Venezuela, the mean absolute response in the first period was
just -0.0060 and 0.0037 in the second period. Similarly in the case of Ecuador, the mean
response was about -0.0034 and 0.0008 in the first and second periods respectively. The
marginal responses to shocks on the other exchanges also dissipate relatively rapidly. In both
countries the impact almost disappears on all markets after the fourth lag.
All other markets presented much greater responses to shocks on the other markets.
Peru and Argentina were the countries with the highest mean responses in the first period,
with 0.0395 and 0.0357, respectively. They are followed by Brazil (0.0293), Colombia
(0.0228), Mexico (0.0218), and Chile (0.0143).
Considering the first period, Mexico, Colombia and Peru show stronger individual
responses to shocks in Argentina, while Brazil and Argentina show greater individual
responses to shocks in Mexico, and Chile to shocks in Brazil. The highest response observed
is that of Argentina with respect to shocks in Mexico (0.0654) and that of Peru with respect to
Argentina (0.06278).
11
Figure 3: Impulse Responses of each of the eight stock markets to shocks in the other markets
Response of RBRA to Generalized One
S.D. Innovations
Response of RMEX to Generalized One
S.D. Innovations
.08
Response of RCHI to Generalized One
S.D. Innovations
.06
.05
.05
.04
.06
.04
.04
.03
.02
.02
.03
.02
.01
.01
.00
.00
.00
-.02
-.01
1
2
RBRA
RCOL
RECU
3
4
RMEX
RPER
RVEN
5
-.01
1
RCHI
RARG
2
RBRA
RCOL
RECU
Response of RCOL to Generalized One
S.D. Innovations
3
4
RMEX
RPER
RVEN
5
1
RCHI
RARG
Response of RPER to Generalized One
S.D. Innovations
.08
3
4
RMEX
RPER
RVEN
5
RCHI
RARG
Response of RARG to Generalized One
S.D. Innovations
.12
.10
.10
.08
.06
.08
.04
2
RBRA
RCOL
RECU
.06
.06
.04
.04
.02
.02
.02
.00
.00
.00
-.02
-.02
1
2
RBRA
RCOL
RECU
3
4
RMEX
RPER
RVEN
5
-.02
1
RCHI
RARG
2
RBRA
RCOL
RECU
Response of RECU to Generalized One
S.D. Innovations
3
4
RMEX
RPER
RVEN
5
RCHI
RARG
1
2
RBRA
RCOL
RECU
3
RMEX
RPER
RVEN
4
5
RCHI
RARG
Response of RVEN to Generalized One
S.D. Innovations
.08
.10
.08
.06
.06
.04
.04
.02
.02
.00
.00
-.02
-.02
1
2
RBRA
RCOL
RECU
3
RMEX
RPER
RVEN
4
5
RCHI
RARG
1
2
RBRA
RCOL
RECU
3
RMEX
RPER
RVEN
4
5
RCHI
RARG
5. Conclusion
This paper studies stock market co-movement between stock exchanges in Latin
American countries. Two methods were used: principal component analysis and impulse
response analysis. The principal components method provides an evaluation of the degree to
which the variance of the stock markets can be represented by a single factor, while the
impulse response technique verifies the degree to which shocks on one market are transmitted
to the other markets in the region.
The results from the principal component analysis indicate that although there is no
evidence of convergence over the entire sample period, there is some evidence of a gradual
12
trend toward convergence throughout the sample period, from 2002 to 2008, as it can be seen
by observing the trajectory of R2 of the first component in 5-year, 3-year, 2-year and 1-year
rolling windows. In 2008, it seems there is a break point in this convergence due to the world
financial crisis and from then on, the new trend is still uncertain. It can also be seen that while
the markets of Argentina, Brazil, Chile, Colombia, Mexico and Peru are moderately
integrated with each other, Ecuador and Venezuela seem to be very weakly correlated with
the other markets in the group.
The results from the impulse response approach were quite similar. Within the subgroup of Argentina, Brazil, Chile, Colombia, Mexico and Peru, innovations were transmitted
rapidly across the exchanges. However, the exchanges in Ecuador and Venezuela had little
impact on the other exchanges and in turn were not significantly influenced by shocks in
those other exchanges.
The present study suggests that co-movement among stock markets in Latin America
did not converge over the period 2002-2011, but presented a trend toward a greater
convergence and hence toward greater integration, although interrupted by the outbreak of
the financial crisis in 2008. Whether Latin American stock markets will resume the trend
towards higher convergence and integration shown before the financial crisis is to be verified
in the following years.
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