Download Stock Market Integration and the Determinants of Co

Global Stock Market Integration and
the Determinants of Co-movements:
Evidence from developed and emerging countries
Author(s): Asma Mobarek1
Abstract
This study examines the extent of cross-country returns co-movement between the stock
markets of five developed benchmark countries [US, UK, Japan, Germany and France] and five
emerging benchmark countries [Brazil, Russia, India, China and South Africa] countries, vis-à-vis
a total country sample composed by 20 countries. The Geweke (1982) Measure of Feedback
methodology along with a set of pooled cross-country time-series regressions is used to identify
and explain the changes in stock market integration. The general findings for the Geweke
contemporaneous feedback measures provide supportive evidence of increased stock market
integration. Our pooled cross-country time-series regression analysis has shown that countries’
economic integration, as measured by the explanatory variables explain almost 32 percent of
the variation in the contemporaneous Geweke feedback measure on a global scale. This
explanatory power becomes stronger for the group of developed markets (49.74 percent) and
for countries that are part of the European economical and political union (69.82 percent). The
results also reported that several variables as significantly associated with the evolution of stock
markets integration over time. These statistically significant variables include, on a global level,
import dependence, stock markets’ size differential and their relative size, difference in annual
GDP growth rate as well as the time trend.
1
Associate Professor, Stockholm University Business School. The author acknowledges NASDAQ
OMX Foundation for financially supporting the project. The author also thankful to Angelo
Fiorante and also Federica Vitali, for their efforts as a research assistant in the project.
1. Introduction
The movement towards a synchronized stock market landscape has gained momentum,
especially during the past two decades, where tighter economical and financial linkages among
developed economies have grown stronger. However, the rises of many important emerging
markets, which have been a major driver of global growth the past decades, have opened up
additional channels for cross-border relations. Other causes behind the rapid increase in world
trade, capital movements, and foreign investments between world economies are due to
market liberalization/deregulation, technological advances and removals of statutory controls.
Many of these factors have contributed to more interlinked economies, which in turn, are said
to have given rise to a higher degree of stock market synchronization, especially in volatile time
periods, e.g. eruption of a financial crisis, war, or political instability. The aftermath of historical
financial crises, including the latest one in 2007, have opened up a tremendous interest for
determining the underlying factors that might explain how stock markets are correlated with
one and other for better understanding the causes of the sudden and simultaneous
deterioration of wealth that occurs during crises periods. To investigate the propensity of one
country to be affected by global shocks have enormous value for preventing future crises. The
extent of financial and economical integration between a country-pair may indeed be reflected
by the degree of stock markets co-movement that they exhibit. In fact, the dynamic structures
of international economies have clearly intensified the complexity behind stock market
performances. As our countries become more economically interlinked, explaining the
formation of price co-movement between stock markets on an international level is significant
for better understanding this higher interdependency and integration. However, the
contemporary research in stock market integration has not sufficiently focused on determining
the driving forces behind co-movement although this information would be most effective for
policy-makers and investors that are keen to know how economic linkages may influence the
countries financial stability, diversification possibilities and what types of common and specific
shocks stock markets are most vulnerable against. This study assesses how stock market
integration and the co-movement between country-pairs, distinguishing between developed
and emerging markets, has been affected in terms of timing and intensity during 1995 – 2010. A
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greater degree of co-movements in stock prices is seen as a reflection of greater stock market
integration. It also takes a closer look at the outcome of significant financial meltdowns
occurring within this period, e.g., the Asian crises, the dotcom-bubble, the financial crisis of
2007 and other country-specific crises. It investigates plausible economical and financial
underlying factors that are said to characterize and influences the extent of integration
between pairs of countries. A two-step analysis is employed to assess which underlying factors
can explain stock market integration and the degree of co-movement. First, the Geweke (1982)
feedback measure, outlined in Section 3.1, is estimated between country pairs. By considering a
dynamic interrelationship between two countries’ daily stock market returns, the year-on-year
feedback measures demonstrate how co-movement evolves over time, reflecting changes in
stock market integration by increases or decreases in the measures (see Bracker et al., 1999;
Johnson & Soenen, 2002, 2003). Second, the estimated feedback measures are employed in a
pooled cross-country time-series regression, outlined in section 3.2, including significant
economical, financial and country-specific factors hypothesized to influence the degree of stock
market integration. The data sample covers 20 countries - ten developed and ten emerging –
from 1995 to 2010.
The major findings of the paper are as follows: The general findings for the Geweke
contemporaneous feedback measures provide supportive evidence of increased stock market
integration. A reasonably clear time trend is identified, where the extent of contemporaneous
co-movement across markets has intensified over time, especially for emerging countries,
which consequently suggests that greater market efficiency is being fostered at the
international level. On the other hand, the results of the Geweke unidirectional feedback
measures indicate a tendency that some markets are more likely to lead other markets than
vice versa. However, there is a less distinctive time trend in the movements of the annual twoways unidirectional feedback measures, suggesting that leader-follower relationships are likely
to change over time periods. These alterations might be due to possible changes in a country’s
economy and market conditions, but also the stability of global markets. Nevertheless, the
highly sophisticated market of the US and the emerging markets of Brazil and Russia appear to
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affect other rather than be affected. However, the distinction with previous studies is that not
only is the contemporaneous measures larger on average, but higher significance levels are
reported for the unidirectional measures of feedback, suggesting that cross-market adjustment
persist over time more often than occasionally for this study’s time period 1995-2010. The
study also reports that countries’ economic integration, can explain almost 32 percent of the
variation in the contemporaneous Geweke feedback measure on a global scale over the 15-year
period, 1995-2009. This explanatory power becomes stronger for the group of developed
markets (49.74 percent) and for countries that are part of the European economic and political
union (69.82 percent). The results also point out several variables as significantly associated
with the evolution of stock markets integration over time. These statistically significant
variables include, on a global level, import dependence, stock markets’ size differential and
their relative size, difference in annual GDP growth rate as well as the time trend.
The rest of the paper is organized as follows: section 2 reviews the literature on stock market
integration, section 3 outlines the already mentioned research method more in detail, section 4
presents the empirical results, and section 5 provides the conclusions.
2. Literature review:
The current state of literature offers numerous studies that examine the presence of stock
market integration, with the notion that markets have been exhibiting tighter co-movements
with one and other, and that they are more integrated than never due to closer financial and
economical linkages. However, it is clear that less has been said concerning the determinants of
stock market co-movement and economic integration, which makes it still an intriguing
research topic where there seems to be many missing pieces of the puzzle.
A good number of studies on the correlation between stock markets at an international level
have been concerned with measuring the extent and direction of the co-movement by using
multivariate GARCH, vector auto-regression (VAR), Unit root test, and various co-integration
tests. Several of these studies report that during periods of financial crisis the stock market comovement is greater than before the crisis occurred. Liu et al. (1998) employs a vector
autoregressive analysis to examine the dynamic structure of international transmission in stock
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returns for six countries – the U.S, Japan, Hong Kong, Singapore, Taiwan and Thailand – for the
period 1985-1990 capturing the October 1987 stock market crash. They conclude that the
degree of interdependence among the Asian-Pacific markets increased substantially after the
1987 stock market crash and where the U.S market possesses an influential role affecting these
markets. In addition, the risk reduction benefits of international portfolio diversification have
been reduced due to the higher interdependence that has been observed in these markets.
Similarly, Arshanapalli et al. (1995), conclude that the co-integration structure that links these
markets increased substantially after the 1987 collapse. However, Longin & Solnik (1995)
examines the correlation for seven major European countries over the period 1960-90
indicating that not only is the international covariance and correlation matrices unstable over
time, but that correlation rises in periods when the conditional volatility of markets is large.
Karolyi & Stulz (1996) explores the co-movements between the Japanese and U.S stock markets
from 1988-1992, showing that correlation and covariance are high when markets move a lot,
hence demonstrating the shortcomings of international diversification in times of high volatility
which is when it is most needed. In light of the benefits of international portfolio diversification
(see e.g. Solnik, 1995), there is a range of studies that deals with emerging stock markets, which
are said to have lower exposure to world factors, thus having lower levels of integration and
therefore may offer greater opportunities for risk diversification across countries. Moreover,
Ampomah (2008) presents evidence that African stock markets are still segmented from global
markets offering strong diversification benefits.
Another type of studies has provided evidence on which markets dictates over other markets.
An early study by Eun & Shim (1989) highlights the influence and power that the U.S stock
market has on the stock markets of eight other developed countries. Findings indicate that a
substantial amount of interdependence exists, where the U.S stock market represents the most
influential world economy having by far a dominant position when it comes to producing
valuable information that affects world stock markets. Empirically they found that innovations
in the US stock market were rapidly transmitted to the rest of the world, whereas innovations
in other markets did not have much effect on the US market.
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A very few studies evidence on the determinants of stock market co-movement has been
presented by Pretorius (2002), which examined ten emerging stock markets for the period
1995–2000 by employing a cross-section and a time-series model. The major findings showed
that only bilateral trade and the industrial production growth differential were significant for
explaining the correlation between two countries on a cross-sectional basis. Similar results
were achieved by the time-series regression. The model explained 40% of the variation in the
correlation coefficient, thus 60% could be due to contagion or other explanatory variable that
was not included in the analysis. Forbes & Rigobon (2002) pointed out that traditional tests for
contagion based on cross-market correlation coefficient are problematic due to the bias
introduced by changing volatility in market returns, i.e. heteroskedasticity. During a crisis
period when stock market volatility increases, the estimates of cross-market correlation will be
biased upward. The paper reevaluates several crisis periods with a method that corrects for this
heteroskedasticity, finding that there was no contagion during these periods of turmoil. They
conclude that the higher levels of market co-movement during the observed periods are mostly
due to interdependence, which depend on the linkages that economies have with each other.
Wälti (2005) follows Forbes and Rigabon (2002) correction model for determining the
macroeconomic variables underlying co-movements between stock market return for fifteen
industrialized countries for the period 1973–1997. Results show that trade and financial
integration contributes positively to stock market synchronization, while a fixed exchange rate
regime increases co-movements. Other factors such as the similarity of economic structure
across countries, informational asymmetries and a common language also contribute to stock
market synchronization. Serra (2000) found that emerging markets’ returns are mainly driven
by country specific factors and less by industry specific factors. Cross-market correlation is not
affected by the industrial composition of the indices, making cross-market diversification a
better option than cross-industry diversification. However, significant loss of diversification
benefits may occur if the industrial mix is totally ignored. Morgado & Tavares (2007) examines
the impact of bilateral indicators of economic integration on the correlation of stock return of
40 developed and emerging markets for the period 1970–1990. Results showed that bilateral
trade intensity affects the correlation positively, whereas the asymmetry of output growth, the
dissimilarity of export structure and the real exchange rate volatility have negative effects on
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stock return correlation. Lin & Cheng (2008) apply a non-linear Multinominal Logit Model
(MNLM) in which co-movement is categorized in three outcomes: (i) negative co-movements,
(ii) positive co-movements and (iii) no co-movements. The empirical analyze of the economic
determinants that affect the stock market co-movement relationship between Taiwan and its
four major trading partners (Mainland China, US, Japan and Hong Kong) are stock market return
volatility, the rate of change in exchange rate and interest rate differentials.
Other types of studies, such as Bracker & Koch (1999) suggest that countries that experience
greater economic integration should also experience greater co-movement in their respective
capital markets. Their study addresses questions whether, how and why, the correlation
structure changes over time. By testing the stability of the correlation matrix over different
periods and modeling potential economic determinants of the correlation structure for ten
national stock indices during 1972–1993, they provide evidence of the dramatic evolution in the
correlation matrix over both short- and long-time horizon. Results indicate that the degree of
international integration (measured as the magnitude of the correlation structure) is positively
associated with (1) world market volatility and (2) trend; while it is negatively related to (3)
exchange rate volatility, (4) term structure differential across markets, (5) real interest rate
differentials, and (6) the return on a world market index. However, it is concluded that further
analyzes on potential economic determinants of the correlation structure is needed to fully
understand what makes market move in tandem. Other similar studies like, Bracker et al.,
(1999); Johnson & Soenen, (2002, 2003) investigate how and why different pairs of
international stock markets display differing degree of co-movement over time. The main
empirical results from these studies show that; (Bracker et al., 1999) Several macroeconomic
factors are significantly associated with the extent of stock market integration over time, e.g.
trade, geographic distance, stock market size differential, time trend, and real interest
differential; (Johnson & Soenen, 2002) Asian stock markets become more integrated with the
Japanese stock market over time, especially since 1994, where increased export share from
Asian economies to Japan and greater foreign direct investment from Japan to other Asian
economies contributes to greater co-movement; (Johnson & Soenen, 2003) Indicating that a
high share of trade with the US has a strong positive effect on stock market co-movements for
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equity markets of the Americas, whereas increased bilateral exchange rate volatility and a
higher ratio of stock market capitalization relative to the US contribute to lower co-movement.
In short, the main emphasis of previous mentioned studies has been determining how
integrated markets are by examining the extent of the co-movements that stock markets
exhibit. By flipping the coin, we find a smaller amount of studies that attempt to determine why
stock markets are integrated. The main objective of this research paper is to fill the gap is to
unfold the determinants and the driving forces behind stock market relationships including
both developed and emerging markets, which may indeed be of greater value for investors that
struggles with portfolio-diversification choices and for policy-makers and regulatory bodies that
are keen to know what types of determinants and treaties with other countries that might
affect the national stock market, especially during turmoil periods.
3. Methodology and Data Sample
3.1 Measuring Stock Market Integration: Geweke (1982) Measures of Feedback
Geweke (1982) provides a cardinal methodology for measuring the degree of co-movement (or
interrelationship) between pairs of stock markets, which indicates how integration between
country pairs evolves over time (Bracker et al., 1999; Geweke, 1982, 1984). An increase (or
decrease) in the year-to-year feedback measure reflects an increase (or decrease) in the extent
of stock market integration. The measure of feedback technique has been chosen since it has
certain advantages over other means (e.g. VAR2 or Granger Causality3) that might be used for
testing the relationship between two stock markets. It identifies not only the presence of
significant information flows between two markets, but also the extent of this feedback.
Moreover, it reveals how integration, as well as how the leader/follower relationship changes
over time.
2
“The VAR approach is deficient in its failure to incorporate potential long-term relations and, therefore, may
suffer from specification bias”. (Mukherjee & Naka, 1995)
3
The Granger Causality (1969) test for casual relation can only reveal if the hypothesis under consideration holds
or not.
8
The objective of implementing Geweke’s feedback measures on stock market co-movement is
to capture the degree to which daily stock returns (i) move together in the two countries on the
same day and (ii) the degree to which daily stock returns in the two countries lead and lag each
other. The Geweke contemporaneous and unidirectional feedback measures are calculated
annually between pairs of countries using daily stock market returns. In the first stage of the
analysis, the model specification considers a dynamic interrelationship between the daily stock
market return of country i and j, (rit and rjt), to hypothetically depend upon: (i) past returns in
the other market, (ii) its own past returns, and (iii) the idiosyncratic noise. The restricted
regression equations (1) and (2) are specified as follows:
M2
M1
k 1
k 1
rit   0   ak rjtk  bk ritk   it ,
M2
M1
k 1
k 1
rjt  0   c k ritk  dk rjtk   jt ,

v ar( it )  2i
[1]
v ar( jt )  2j
[2]
wi t h t he va r i-acnc
ova
e r i a nc e m a t r i x of re
i nd
dua
 j )lt :s (
i s
t a

C

Y 

D
  i  t2i  i 
 o   v   2   Y
 j   ti  j j
e
t
e
j



rY  c m i ,o
j)
t
v ti
The residuals εit and εjt are assumed to be white noise, i.e., normally distributed

where z = i or j and
( n
N(0,σεz2),
Cov(εzt, εz,t-1)=0. Despite the fact that the residuals εit and εjt are
assumed to be serially uncorrelated, they may exhibit contemporaneous correlation4 with each
other. The regression equations [1] and [2] can be solved by applying the Seemingly Unrelated
Regression (SUR), a technique that account for the contemporaneous correlation among the
residuals (Judge et al., 1988). The initiative behind Eq. [1] and [2] measures the nature and
extent of the interrelationship between daily stock returns in the two countries, e.g. coefficient
4
Estimates derived with OLS techniques may be inefficient when error terms may exhibit contemporaneous correlation. See Zellner (1962).
9
a
n
ak display how the second market (j) leads the first market (i) across days, while coefficient ck
display how the first market (i) leads the second market (j) across days (Bracket et al., 1999).
Following Bracket et al. (1999) the lag length of M1 and M2 are chosen to be 10 and 5 business
days, respectively. In the second stage of the analysis, it is assumed that there is no
interrelationship among the price series of the two different stock markets, (i.e. coefficients ak
and ck will be equal to zero for k = 1,2,…,M2) hence, the unrestricted regression equations [3]
and *4+ incorporates only the country’s own lagged returns to explain its current daily return,
and they can be estimated with ordinary least squares (OLS).
M1
rit = a + å bk´ rit-k + mit ,
´
0
var(mit ) = s m2i
[3]
var(m jt ) = s m2 j
[4]
k=1
M1
rjt = b + å dk´ rjt-k + m jt ,
´
0
k=1
The residuals
variance,
µit and µjt are independently and identically distributed with zero means and
where z = i or j, and Cov(i,t ,  j,t )  0 i.e., the residuals does not exhibit
contemporaneous correlation, thus Ordinary Least Squares (OLS) technique is appropriate for
solving Eq. [3] and [4]. At this stage, three null hypotheses may be identified from the

considerations related with the above analysis. They are formulated as follows:
H1: There is no contemporaneous relation between rit and rjt on the same day.
H2: There is no unidirectional relationship from rjt to rit across days (i.e. ak = 0, for any k)
H3: There is no unidirectional relationship from rit to rjt across days (i.e. ck = 0, for any k)
According to Geweke (1982) Measure of Feedback, the interrelationship among the stock
markets of two different countries can be measured by the following Log-likelihood Ratio
statistics:
10
GMFi* j = (n)ln éë(s m2i ´ s m2 j ) / Y ùû
~ c12 under H1;
GMFj®i = (n)ln (s m2i / s e2i )
~ c M2 2 under H2;
GMFi® j = (n)ln (s m j / s
2
2
ej
a
a
)
a
~ c M2 2 under H3;
The yearly Geweke measures demonstrate how the co-movement of daily returns between a
pair of countries evolves over time, e.g. where an increase (or decrease) in GMF from year t1 to
t1+n (n=1,2…T) reflects an increase (or decrease) in the extent of stock market integration for
that pair of countries. The likelihood-ratio test statistics forms the Geweke feedback measure,
and it is calculated for each country pair and for each year from the residual variances and covariances from the restricted [Eq. 1 and 2] and unrestricted [Eq. 3 and 4] country pair
regressions.
3.2 Modeling for Determinants: Pooled Cross-Country Time-series Regression
The second step of this analysis is specifically aimed at investigating the statistical significance
of various macroeconomic and financial factors, indicators of economic integration between
two countries, in explaining the evolution of the degree of co-movement between their stock
markets over time. At this purpose, a pooled cross-country (more specifically, cross-country
pair) time-series regression has been estimated with the contemporaneous Geweke measure of
feedback CGMFij,t for countries i and j at time t acting as dependent variable, across pairs of the
20 countries included in the study. The pooled regression model representing the potential
determinants of equity markets interdependence takes the following form:
A
B
C
a 1
b 1
c 1
CGMFij,t   0   a Tradeta  b Macrotb  c Developmenttc  Trendt   t
[5]
The explanatory variables included in the regression model [5] outlined above are described in

Table 1.
11
Table 1. Potential determinants of stock market integration
(A.) Measures of the nature and extent of bilateral trade relationships
Xi = (Xij /Xi ) t
Exports from country i to country j , relative to i 's total export
Xj = (Xji /Xj ) t
Exports from country j to country i , relative to j 's total export
Mi = (Mij /Mi )t
Imports of country i from country j , relative to i 's total import
Mj = (Mji /Mj )t
Imports of country j from country i , relative to j 's total import
(B.) Macroeconomic factors
I = (πi -πj )t
Inflation differantial between markets i and j
RI = (ri -rj )t
Real interest rate differential between markets i and j
Gr = (gi -gj) t
GDP annual growth rate differential between country i and j
(C.) Measure of financial development
S = (size i -size j )t
Percent of world equity market share of country i minus that in j
MV = (MV j /MV i )t Ratio of stock market capitalization of country j to that of country i , expressed in US dollars
T
Variable for the time trend t (i.e. t = 1,2,…,T years)
In terms of bilateral trade relationships, four different variables have been considered in order
to encompass the point of view of both countries in each pair, hence revealing the two
different sides of the same coin. Indeed, although theoretically total exports from country i to j
should equal total import of country j from i, Xij=Mji, the measures used in this study are relative
measures, as also specified by Bracker et al. (1999). Export from country i to j is compared to
country i’s total export and, vice versa, import of country j from i is compared to j’s total
import, so that the theoretically same amount of bilateral trade Xij=Mji becomes relatively more
important for one of the two trading parties. The importance of including four measures of
bilateral trade relationships relies in the fact that each of the four could have a different impact
on the co-movement of two given stock markets. While export from the point of view of both
countries seems to be always positively related to the sensitiveness of one country’s stock
market to its partner’s stock market activities, the same is not valid for import. As fully
explained by Bracker et al. (1999), stock market performance is considered an indicator of the
future economic outline of a country, so that the possibilities of increasing export to that
country should always be positively linked to its stock market movements. On the other hand,
an increasing import dependence of country i on j (and vice versa) may entail positive stock
market co-movements whereas a decreasing dependence may generate a negative effect.
Indeed, when the economy of the importing country performs well, this country is likely to
12
import more from its partner thus boosting the latter’s economic performance as well. Hence,
larger import dependence between two countries should be positively associated with greater
co-movements between their stock markets. A reduction in the import dependence may boost
the ability of exporting firms in the less dependent importing country to compete on the global
market with the exporting firms of its partner country, thus driving their stock markets apart.
Hence, the degree of relative import dependence may have either a positive or a negative
effect on stock markets integration. The macroeconomic factors included, inflation rate
differential, real interest rate differential and GDP annual growth rate differential, are expected
to be negatively related to the co-movements in a stock markets’ pair. Indeed, the larger these
differences become, the larger the divergence between the economies of the two countries
and hence the less their stock markets will be influenced by each other. The third group of
variables includes indicators of the stage of stock markets’ development, such as stock markets
size differential and relative size. More specifically, the stock market capitalization of a country
may be a measure of the ease or difficulty, in terms of liquidity and costs, of trading on that
stock market. While a large difference in market size for a pair of countries may determine less
co-movements between their respective stock markets, the relative size of the two markets in
the pair have opposite effects. Last, a time trend is included in the regression to encompass the
possibility that stock market inter-dependence has increased over time, due to the advanced
communications technology, the eased flow of information, trade and capital across borders
and the increasing cross-listing of stocks and mergers between stock markets of different
countries.
As a preliminary test of poolability, we found the applicability of pooled regression model using
the following, (Kunst, 2009):
H01: yit= α + βXit + vit………………………………………………………………………………………………………………….(6)
H11: yit= α + βXit + µi + vit…………………………………………………………………………………………………………(7)
Or H01: µi=0 i=1995, 1996,……….2009
The test Statistic is
13
F=
……………………………………………………..(8)
Follows F distribution with (T-1), (N-1)(T-K) df
SSR = Residuals sum squares under the null hypothesis
SSU= Residuals sum squares under the alternative hypothesis
T= Number of years, N= Number of observations, K= Number of parameters=10
F=
Our regression analysis extends the works of Braker et al. (1999) and of Johnson and Soenen
(2002; 2003) in one fundamental way, which is the large span of countries included in our
analysis. Whereas the number of explanatory variables considered is on average the same, no
previous study has incorporated as many as twenty countries. In addition, the regression model
presented above has been estimated for groups of markets in the same geographical area as
well as by differentiating markets according to the most distinctive characteristic, which is their
level of development.
3.3 Data Sample and Summary Statistics
The 20 countries included in the data sample are specified in Table 2. Out of these 20 countries,
10 are used in the analysis of stock market integration. These are referred as the base country
group, which consists of five developed [US, UK, Japan, Germany and France] and five emerging
[Brazil, Russia, India, China and South Africa] countries. The sample covers a 16-year period
from 1995-2010. Daily stock returns are calculated as the log change in the daily index closing
price as follows:
rz 
P
(
100
t ln
z,t /P
z,t
1)
where z = market i or j, and Pz,t represent the closing price of the markets on day t. The daily
MSCI stock index time-series expressed in US dollars have been extracted from the Thomson

Financial DataStream. The pooled cross-country time-series regression uses yearly data, also
14
extracted from Thomson Financial DataStream, consisting of cross-sections of country-pair
observations covering a 15-year period from 1995-2009 (2010 has been excluded due to nonavailability of dataset).
Table 2. Description of data sample and price indices
Table 3, provides descriptive statistics for the daily stock index returns for the 20 sample
countries, where the test of normality is rejected for all return series. Table 4, presents the
correlation coefficient of returns. The highest correlation coefficients are mostly found
between developed markets, especially between European countries.
15
Table 3: Summary statistics on daily index returns (%)
16
Table 4: Correlation coefficient on daily index
returns
17
4. Empirical Results
4.1 Stock market integration analysis: Geweke (1982) Measures of Feedback
The result statistics of the annual Geweke measures of feedback (GMF), contemporaneous and
two-way unidirectional feedback, estimated with regression [1] – [4] during the sample period
1995-2010 between the base countries – United States, United Kingdom, Japan, Germany,
France, Brazil, Russia, India, China and South Africa – vis-à-vis the total country sample (see
section 3.3) are reported in Appendix [A] and summarized in Table [5]. The corresponding
results for the base countries are illustrated in Figures [1] – [3], which summarizes the average
annual GMF across time as well as presenting a marketwise differentiation – total country
sample average, developed country average and emerging country average.
The contemporaneous feedback measures results, summarized in Table [5]: Geweke 1, report
high percentages [94% - 99%] of significance present across the country sample. The highest
average country-pairs contemporaneous feedback measures is found in France, Germany and
the United Kingdom, with each exceeding 100, whereas India’s average is the lowest one at just
over 40, and the remaining eight countries fall in the range of 49-82. The year-on-year
contemporaneous measure of feedback [See Appendix A for selected countries] clearly indicate
that stock market integration have intensified, where larger measures denote greater
contemporaneous relationship between stock return patterns from country pairs. The trend
towards a global stock market landscape that takes into account information flows from other
markets has clearly gain momentum the past decade. Figure [1] reflects this evolution of stock
market integration during the 16-year [1995 – 2010] sample period. It illustrates how stock
markets have been witnessing stronger co-movement with time. The estimated measures
indicate significant inter-market relationship across the base country group and the total
country sample. Although the overall results are highly significant across countries, the
developed markets seem to be more extraordinary affected by each other. As seen from Figure
[1], the average contemporaneous feedback measures for developed markets consistently
exceed the average contemporaneous feedback measures for emerging markets, in particular
for UK, Germany and France.
18
Table 5. Summary of Geweke Measure of Feedback (GMF)
According to Johnson & Soenen (2009), this greater extent of stock market integration may be
attributed to the presence of more favorable economic and political climate towards business
in developed markets. Nonetheless, from 2005 and onwards, emerging markets, in particular
Brazil, Russia, China and South Africa, have enjoyed a tightening of co-movement. This greater
extent of co-movement appears to be rightful considering the increased importance of these
countries’ economies. One might argue that emerging markets have become more
sophisticated and efficient with time. Furthermore, the notion that financial crises periods
change the co-moving behaviors of stock markets seems to be also present in the results. A
further investigation of today’s global financial crisis (2007-) and previous economic
meltdowns, e.g. Asian crisis (1997-98) and the dot-com bubble (2000-02), are to a certain
degree reflected by the feedback measures, which are apparent as upward peaks during the
climax period of the crisis, followed by a stabilizing or a slight plunge in the measures. However,
crisis periods seem to foster a new and higher equilibrium level of co-movement, evidence that
are in line with previous studies, e.g. Liu et al. (1998); Longin and Solnik (1995); and
Arshanapalli et al. (1995).
19
Figure 1. Geweke 1 – Contemporaneous feedback measures
Note: Figures summarizes the average annual feedback measures across time and presents a marketwise differentiation – total country sample
average, developed country average and emerging country average. The tables in the appendix provide the result statistics for each country
pair.
20
The average unidirectional feedback measures, summarized in Table [5]: Geweke 2, from each
base country to all others are considerably lower than the contemporaneous feedback levels,
with all of them being within the range [5.2–19.7]. However, a fairly high percentage of the
year-by-year unidirectional feedback measures from each country to the others are significant
at the 5% level, with the minimum being 6% (for Japan) and the maximum being 65% (for Brazil)
and the rest are within 15% - 50%. Surprisingly, it appears that there is more often than
occasionally a delay with which these stock markets fully incorporate information from other
markets. The United States, Brazil and Russia appears to have higher influence compared to
other markets, since the 50%, 65% and 40% significance of the annual unidirectional feedback
measures, respectively, is fairly higher. However, the other base markets, besides Japan and
China, show also a relatively high percentage of significance. Figure [2] illustrates the
unidirectional feedback measures from the ten base countries to the total country sample. As
mentioned above, interesting features are particularly present in US, Brazil and Russia.
Information flows from these markets in particular are demonstrated to be significant across
days. Additionally, the unidirectional feedback measures variation across time appears to
increase substantially across periods of financial meltdowns. The uncertainty arising from
crises, shown by an alteration in volatility, is clearly reflected by the unidirectional feedback
measures, which illustrate how markets continue to exhibit co-movement across days. For
Russia, this is clearly illustrated by the peak in the feedback measure, which represents the
“Ruble crisis” that hit the country in 1998, triggered by the Asian crisis that erupted one year
before. Furthermore, the booming economy of Brazil reveals further how unidirectional
feedback has intensified over the sample period, especially during the years prior to the
financial crisis of 2007. The nature of crises and market uncertainty appears to extend periods
of co-movement between country pairs, which are more pronounced during financial crises
and/or booming years. The results of financial crises or booming economies seem to add
complexity in how efficient markets are able to incorporate or transmit information flows.
However, identifying a clear time trend, as for the contemporaneous feedback measures, the
unidirectional appears to fluctuate more around crisis periods, but the increase does not persist
with time.
21
Figure 2. Geweke 2 – Unidirectional Measure of Feedback (Base country  Others)
Note: Figures summarizes the average annual feedback measures across time and presents a marketwise differentiation – total country
sample average, developed country average and emerging country average. The tables in the appendix provide the result statistics for each
country pair.
22
The average unidirectional feedback measures in the opposite direction, summarized in Table
[5]: Geweke 3, from all other markets to each base markets, reveals additional country-specific
differences. Besides reporting relatively lower estimates than the contemporaneous feedback
levels, the number of significances is slightly lower than the previous unidirectional feedback
measure. The range for the mean values is between [23.03-3.68], where Japan, in this case,
have the highest percentage of significant estimates, 56%, followed by China [38%]. In contrast,
the estimates for US, Brazil and Russia, only 19%, 4% and 11%, respectively, are significant.
Hence, these patterns imply that some markets may have greater tendency of leading other
markets, whereas the opposite is true for others. This seems to be the cases for the latter
mention countries, whereas for Japan and China there is a higher tendency that they are being
lead by others. The leader-follower relationship is subtler and less pronounced for UK,
Germany, France, India and South Africa. Although a good number of significant estimates are
reported for the two-way unidirectional feedback measures, the differences for these countries
are less striking. In Figure [3], it is clear that Japan, as of 2007, has been affected to a higher
degree by the delayed influence of the stock markets of other countries. Moreover, South
Africa was also affected in 1998 by what seems to have been spillover effect from the Asian
crisis that started in 1997, which is illustrated by the spike in the average unidirectional
feedback measures from emerging countries.
23
Figure 3. Geweke 3 – Unidirectional Measure of Feedback (Others  Base Country)
Note: Figures summarizes the average annual feedback measures across time and presents a marketwise differentiation – total country
sample average, developed country average and emerging country average. The tables in the appendix provide the result statistics for each
country pair.
24
Moreover, the preliminary analysis of F test statistic of pool ability is significant at 1% level as
the tabulated F value with (14, 945) df is 2.10. So the null hypothesis is rejected, which supports
the use of pooled regression model with the dataset.
4.2 Pooled Regression Analysis
The empirical results from the pooled regression analysis over the 15-year period 1995-2009
are presented in Table 5.a for all 190-country pairs, for 45 pairs of developed countries and 45
pairs of emerging markets. The separation between developed and emerging countries finds its
rationale in the fact that the different level of development may entail economic, financial,
political and regulatory conditions that are distinctive and typical for each of the two groups
but that are not directly measurable and hence could not be included in the regression as
explanatory variables. The pooled cross-pair time-series regression has also been estimated for
group of countries located in the same geographical zone. The rationale lies in the fact that
stock markets which have over-lapping trading hours, are more likely to systematically co-move
with each other on the same day than with markets in distant regions. Three regional areas
have been defined5: Europe including 21 pairs, Asia with 28 pairs and the Americas consisting of
10 pairs. Table 5.b presents pooled regression results for these three regions.
Two models have been estimated:
-
Model 1 with all explanatory variables.
-
Model 2 with all explanatory variables, except real interest rate differential, which proved
to be insignificant in the univariate regression results.
The results in Table 5.a and 5.b show that goodness-of-fit statistics, the adjusted R2 and the
F-statistic, indicate that the explanatory variables included in model 1 explain a significant
portion of stock markets co-movement on the same day. It should be noted that, for model
2, where the insignificant variable Real interest rate differential is removed, these measures
5
Europe: France, Germany, Italy, Sweden, UK, Russia, South Africa.
Asia: Australia, China, Japan, Hong Kong, Malaysia, India, Indonesia, Korea.
Americas: Argentina, Brazil, Chile, Canada and USA.
25
of goodness of fit always increase; implying that model 2 better fits the underlying data.
Considering the all country-pairs regression, model 2 is able to explain almost 32 percent of
the variation in the contemporaneous Geweke feedback measure. When comparing this
measure between developed and emerging markets, it appears clear that the economic
integration among developed markets, as represented by the explanatory variables as well
as by intangible characteristics proper of this group, explains almost 50 percent of their
stock markets’ integration on the same day. The same is not valid for emerging markets, for
which only 30 percent of their same-day financial markets co-movement is explained by
economic integration. As suggested by Johnson & Soenen (2009), this greater extent of
developed countries’ stock market integration, apart from economic integration as proved
by regression results, may also be attributed to the presence of a more favorable economic
and political climate towards business in these countries compared to the emerging ones.
Table 5.a - Results of the pooled regressions on contemporaneous Geweke measures
Expected sign
ALL COUNTRIES
DEVELOPED
+
Model 1
-0.120
3.681
Model 2
-15.884 ***
-48.065
Model 1
1.397
-182.458 ***
+
30.563
106.214
728.302 *** 1546.485 ***
Intercept
Xi
Xj
Mi
?
139.015 ***
157.836 ***
Mj
Size
log(MV)
Infllation
GDP growth
Real interest
T
# of obs
?
?
+
226.081
-19.560
3.736
0.076
1.042
-0.120
6.600
2450
384.402
-45.226
4.966
0.024
1.730
Adjusted R2
F-statistic
***
***
***
***
***
***
***
***
8.812 ***
2774
29.09%
101.456 ***
31.69%
143.950 ***
56.058
366.240
127.165
10.913
-0.805
-1.552
-1.059
9.080
538
Model 2
-38.485 ***
-433.392 ***
203.865
**
***
**
209.265
108.828 **
6.663
-1.915
-0.548
EMERGING
Model 1
-12.131 **
135.516
Model 2
-9.012
168.748
158.823
173.553
26.949
***
13.913 ***
666
218.517
582.777 ***
5.542 **
-0.077
0.444
0.106
5.919 ***
632
35.90%
31.072 ***
49.74%
74.110 ***
29.62%
27.554 ***
3.207
206.651
563.765 ***
4.435
-0.029
0.233
29.88%
31.639 ***
5.724 ***
648
** Significance at the 0.05 level. *** Significance at the 0.01 level.
When considering the three regional blocks, the adjusted R2 for model 2 is highest in Europe
with 70 percent of contemporaneous co-movement explained by economic integration. This
result is not surprising if one considers that European countries, excluding Russia and South
Africa, are part of an economic, political and monetary union. In the Asiatic region, economic
factors can explain almost 51 percent of the evolution of the Geweke feedback measure over
26
time whereas in the Americas only 32 percent of the variation in the contemporaneous
relationships over the 15-year sample is explained by economical integration.
Table 5.b - Results of the pooled regressions on contemporaneous Geweke measures
Expected sign
EUROPE
Intercept
Xi
+
Model 1
Model 2
-28.251
-112.982 ***
3211.827 *** 3704.471 ***
Xj
+
-193.390
Mi
?
Mj
Size
log(MV)
Inflation
GDP growth
Real interest
T
# of obs
?
?
+
Adjusted R2
F-statistic
-105.938
-1968.281 *** -1728.129 ***
384.582
882.419 **
18.830
-0.078
5.740 **
0.400
16.257 ***
214
289.103
604.941
-2.980
0.289
10.909 ***
52.53%
24.575 ***
ASIA
Model 1
-12.414 **
507.601 ***
AMERICAS
Model 2
-12.977 **
518.355 ***
Model 1
40.778 **
26.099
Model 2
48.495 ***
59.441
98.431
107.607
274.763
249.975
-86.261
-105.775
-23.015
-33.097
67.810
8.522 **
-0.483
1.186 **
25.013 ***
297
-40.099
65.630
8.961 ***
-0.773
1.192 **
-0.639
8.117 ***
413
8.106 ***
413
-143.147
11.670
8.115
0.577
-1.321
-0.100
7.046 ***
142
69.82%
77.103 ***
50.81%
43.550 ***
50.75%
48.178 ***
30.40%
7.157 ***
-33.315
-189.185
68.012
11.843 **
0.116
-1.731
6.749 ***
150
31.88%
8.749 ***
** Significance at the 0.05 level. *** Significance at the 0.01 level.
The variables included in the pooled regressions are always jointly significant at the 1 percent
level and, on average, four of the ten explanatory variables enter into model 2 at 5 percent
level of significance. More specifically, one or several measures of bilateral trade relationships
are able to significantly influence stock market integration over time in the developed markets’
group, in the European and Asiatic regions as well as in the all countries’ sample. This finding
does not conflict with the empirical results provided by Braker et al. (1999) nor with those
presented by Johnson and Soenen (2002; 2003). In these studies, indicators of bilateral trade
are found to be significantly associated with the evolution of stock market integration over
time. The interesting result that inflation rate differential and real interest rate differential are
never statistically significant in influencing the variation of the contemporaneous Geweke
measures is also in accordance with these studies, excluding Johnson and Soenen (2002) that
found greater differential inflation and differential real interest to be significant in reducing the
co-movement among Asian markets. When the measures of financial development are
considered, results are less consistent with those from other studies. Size differential is found
27
to be statistically significant in the all countries’ sample as well as in the developed and
emerging markets’ groups, whereas it is not in the regional blocks. Bracker et al. (1999) show
that, in a group of developed markets, size differential is statistically significant in explaining the
degree of contemporaneous co-movement. However, in the study conducted by Bracker et al.
(1999), a greater size differential negatively affects stock markets’ interdependence, as
expected, whereas in our study this effect is positive. The relative size indicator (natural
logarithm of the variable MV, as described in table 1) is positively associated with greater
contemporaneous co-movement in the all countries’ sample, in the Pacific region and in the
Americas. Conversely, this indicator is negatively related to stock markets’ integration in the
American block according to Johnson and Soenen (2003) and it is not significant among Asian
countries according to Johnson and Soenen (2002). Finally, all regions and all groups of
countries always exhibit a significant trend towards increasing same-day co-movement over
time throughout the 15-year sample, as also empirically demonstrated by other studies.
5. Conclusions
This study examines the degree of cross-country returns co-movement between the stock
markets of five developed [US, UK, Japan, Germany and France] and five emerging [Brazil,
Russia, India, China and South Africa] countries, vis-à-vis a total country sample composed by
20 countries. The Geweke (1982) Measure of Feedback methodology along with a set of pooled
cross-country time-series regressions is used to identify and explain the changes in stock
market integration. The general findings for the Geweke contemporaneous feedback measures
provide supportive evidence of increased stock market integration. A reasonably clear time
trend is identified, where the extent of contemporaneous co-movement across markets has
intensified over time, especially for emerging countries, which consequently suggests that
greater market efficiency is being fostered at the international level. On the other hand, the
results of the Geweke unidirectional feedback measures indicate a tendency that some markets
are more likely to lead other markets than vice versa. However, there is a less distinctive time
trend in the movements of the annual two-ways unidirectional feedback measures, suggesting
that leader-follower relationships are likely to change over time periods. These alterations
28
might be due to possible changes in a country’s economy and market conditions, but also the
stability of global markets. Nevertheless, the highly sophisticated market of the US and the
emerging markets of Brazil and Russia appear to affect other rather than be affected. Similar
findings have been reported for the US by Eun and Shim (1991), Bracker et al. (1999) and
Johnson and Soenen (2003). However, the distinction with previous studies is that not only is
the contemporaneous measures larger on average, but higher significance levels are reported
for the unidirectional measures of feedback, suggesting that cross-market adjustment persist
over time more often than occasionally for this study’s time period 1995-2010. Our pooled
cross-pair time-series regression analysis has shown that countries’ economic integration, as
measured by the explanatory variables included in model 2, can explain almost 32 percent of
the variation in the contemporaneous Geweke feedback measure on a global scale over the 15year period, 1995-2009. This explanatory power becomes stronger for the group of developed
markets (49.74 percent) and for countries that are part of the European economic and political
union (69.82 percent). The results also point out several variables as significantly associated
with the evolution of stock markets integration over time. These statistically significant
variables include, on a global level, import dependence, stock markets’ size differential and
their relative size, difference in annual GDP growth rate as well as the time trend.
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Appendix
A. Geweke Measure of Feedback
A1. United States
32
33
34
A2. United Kingdom
35
36
37
A3. Japan
38
39
40
A4. Germany
41
42
43
A5. France
44
45
46
A6. Brazil
47
48
49
A7. Russia
50
51
52
A8. India
53
54
55
A9. China
56
57
58
A10. South Africa
59
60
61