Article
An Empirical Study of Co-movement
in Selected Stock Exchanges
Ritesh Patel1
Asia-Pacific Journal of Management
Research and Innovation
12(1) 23–30
© 2016 Asia-Pacific
Institute of Management
SAGE Publications
sagepub.in/home.nav
DOI: 10.1177/2319510X16647293
http://apjmri.sagepub.com
Abstract
This study explores the co-movement amongst the selected stock exchanges, that is, ‘BSE’, ‘Hangseng’, ‘MXX’, ‘RTS’, ‘BVSP’, ‘FTSE100’, ‘Nikkei’ and ‘NASDAQ’. This study is done using the daily index of all stock markets from 1 January 1998 to 30 June 2015. This
study has been done using various tests like ‘The ADF test’, ‘The PP test’, ‘The Granger causality test’ and ‘The Johansen co-integration
test’. ADF & PP test show that the series are non-stationary. The result of the Granger causality test indicates that the BSE is Grangercaused by BVSP, FTSE 100, MXX, NASDAQ and the RTS market. The Mexican (MXX) and Russian (RTS) stock exchanges have an
impact on Brazilian (BVSP) stock exchange. The Return of the FTSE 100 is affected by BVSP, MXX and NASDAQ, whereas Hangseng
is affected by BSE, BVSP, FTSE 100, MXX and NASDAQ. None of the stock exchanges in the study have an impact on the MXX and
Nikkei. NASDAQ is affected by BVSP, FTSE 100 and Hangseng markets. The stock exchange RTS (Russia) has the dependency on the
FTSE-100 (UK) and Hangseng Stock Exchange. RTS depend on FTSE 100 and Hangseng. The co-integration test is carried out which
indicates the long-term relationship amongst all the selected markets. Principal component analysis makes the fragmentation of market
in the two regions, the South Asian and the Latin American stock market (BSE, BVSP, NASDAQ, MXX, Hangseng and FTSE 100) and
the Northeast Asian stock market (RTS & Nikkei).
Keywords
Co-movement, developed stock market, co-integration, Granger causality, emerging markets
Introduction
The stock market has remained an important concern
for economists, researchers, investors, governments and
policymakers. The stock market is considered as the
barometer for the financial growth of a country which
reflects the changes in the economy and direction in which
it is moving. The Indian stock market has witnessed many
global ups and downs in past few years. The capital market
is not only affected by ‘domestic factors’ but also directed
by ‘international factors’. With a stage of development,
every market is affected by other international markets in
either a positive or a negative manner. The literature suggests that researchers are trying to comprehensively establish the relationship among various markets. Researchers
have made an attempt to study the co-movement among
selected markets (Arshanapalli & Doukas, 1983; Cheung
& Mak, 1992; Hilliard, 1979). Currently, co-movement
of stock exchanges is among the most popular topics in
financial research.
1 Assistant
To fullfil the present gap, this article examines the comovement among selected stock markets. In this study,
the researcher had examined the co-movement of all the
selected stock markets covering the period from January
1998 to June 2015. This period was selected considering
various global ups and downs, global recession, growth
of the world market and emergence of the new market in
the world. This study aims at exploring the short- and longterm relationship between the eight stock exchanges. From
this present study, FIIs (Foreign Institutional Investor),
individual investors, banks, institutional investors, public
investors and HNI (High net worth individual) will be benefited and affected. All these stakeholders can take their
decision for investment in the overseas market by looking
at the short-term and long-term integration of BSE with
other selected markets. Moreover, if FIIs, individual investors, banks, institutional investors, public investors, HNI
want to reduce the risk of their investment portfolio, they
can invest in the stock market of such a country which
has negative or very less correlation with BSE. It can help
them to reduce the unsystematic risk.
Professor, S.V. Institute of Management, Kadi, Gujarat, India.
Corresponding author:
Ritesh Patel, L.I.G. Second, Block Number -3, Room Number -25, G.I.D.C Colony, Behind Police Chowky, Odhav, Ahmadabad 382415, India.
E-mail: [email protected]
24
Asia-Pacific Journal of Management Research and Innovation 12(1)
Review of Literature
Research Methodology
Various studies, directly or indirectly related to the objective of this study, have been reviewed. There are various
studies which evaluate various aspects of the co-movement,
that is, ‘prices’, ‘return’, ‘risk’, ‘volatility’ etc. Past studies
show that the Indian capital market is affected by many
international markets. Masih and Masih (2001), in a study
of the co-movement among the stock markets of nine developed countries using multivariate analysis, revealed the
existence of the short-term and long-term co-movement
among the UK, USA and Japanese markets. Forbes and
Rigobon (2002) examined the co-movement between stock
markets of Hong Kong, Brazil, Canada, Germany and
South Africa during various international financial crises
and found co-movement among all these markets. Beine
and Candelon (2011) carried out a study to check the impact
of trade liberalisation of co-movement using panel data
framework which revealed a positive impact of trade
and financial liberalisation on the co-movement of the stock
markets. A study on co-movement between the stock exchanges of Turkey, Russia and Hungary undertaken by
Aktar (2009) using unit root test, Granger causality test and
vector error correction model found that there is price
linkage and co-integration among these three stock indices.
A few researchers have carried out a study of co-movement
among markets of particular countries. Modi, Patel and
Patel (2010) has found that among eight markets of
the world, DOWJONES is most influential market and the
stock return of each market moves with movement in
another market. In a study, Jakpar, Vejayon, Johari and
Myint (2013) examined the co-movement of stock market
volatility between China and ASEAN-5 countries and found
that China’s market is affected by Indonesian, Thailand and
the Singapore markets and vice versa, whereas as there is
no relationship between China’s market and the Malaysian
and Philippines markets.
Again, a few researchers have undertaken studies where
they found no co-movement among stock markets. For
instance, Bekaert, Hodrick and Zhang (2009) have studied
the international stock return co-movement by using
various risk-based factor models by comparing various
markets of different countries, where the researchers have
revealed that in both cases there is no strong evidence on
co-movement. Scheicher (2001) found that there is less comovement and interrelationship among Hungary, Poland
and the Czech Republic markets. Researchers have also
revealed that these markets are not related strongly with
local markets. Alvi (2014) studied the co-movement among
Pakistan’s stock market and other nation’s stock market by
applying co-integration approach, where the researcher
reveals that KSE-100 is not co-moved with any stock
market and there is no co-integration among these markets.
This article is an attempt to capture the tendency in
co-movement of other markets with BSE.
This article has used correlation, unit root test, Granger
causality test and the Johansen multivariate co-integration
test to determine co-movement among selected markets.
The ADF and PP test are used to check whether the data
are stationary or not. To perform various statistical tests,
Eviews 7 and SPSS 20 are used. Here, data are taken from
Yahoo Finance, for the period from 1 January 1998 to 30
June 2015. The study covers eight stock exchanges, namely,
‘BSE Sensex (India)’, ‘Hangseng (Hong Kong)’, ‘MXX
(Mexico)’, ‘RTS (Russia)’, ‘BVSP (Brazil)’, ‘FTSE-100
(U.K.)’, ‘Nikkei (Japan)’ and ‘NASDAQ (US market)’. All
the selected eight stock markets are national indices. These
eight countries are selected on the basis of the stage of
their economic development. Out of eight countries, four
countries, namely, ‘India (BSE Sensex)’, ‘Mexico (MXX)’,
‘Russia (RTS)’ and ‘Brazil (BVSP)’, are developing economies. Moreover, these four countries are the Emerging
Markets Index countries. The other four countries, namely,
USA (NASDAQ), Hong Kong (Hangseng), the United
Kingdom (FTSE-100) and Japan (Nikkei), are developed
economies. Moreover, India has good trade relations with
these four developed economies. Thus, the Indian market
is associated with the market of these countries. These
markets have been selected in order to have analyses of comovement of the Indian market with developing and the
developed economies. The daily index returns of the stock
markets are computed using the following formula:
rt = log (p 1 - P0) / P0(1)
Where rt is the return from the exchange for day t, p1
and P0 are the closing value of exchange at day t and t − 1,
respectively.
Objectives of the Present Study
The objective of this study is ‘to analyse the long-run and
short-run relationship among all selected eight markets’.
Unit Root Test
To perform Granger causality and co-integration analysis it
is important to check whether all the time series variables
are non-stationary with unit root I(1) and stationary with
unit root I(0) at the difference before using them in cointegration analysis. The augmented Dicky–Fuller test
(Dickey, Bell & Miller, 1986) and Phillips–Perron Test
(Phillips & Perron, 1988) was used to examine whether the
series are stationary or not.
The ADF test is used to check whether the series is
stationary or not.
n
D y t = a + byt - 1 + | cD y t - i + u t(2)
i =1
Patel25
The appropriate lag order of k in (1) was chosen on the
basis of the criteria of Schwarz information criteria.
The PP test is used to check whether the series is
stationary or not.
Dyt = py t - 1 + u t(3)
Granger Causality Test
Granger (1986) reveals the condition that if the historical
values of X predict the Y in an improved manner, then it can
be concluded that X causes Y. The variable fails to Grangercause yt if
Pr (y t + m | X t) = Pr (y t + m W t)(4)
where Pr (y t + m | X t) signifies conditional probability of yt,
Xt is the set of all information available at time t, whereas
the other part Pr (y t + m W t) indicates the conditional probability of yt obtained by excluding all information on Xt
from yt; this set of information is represented as Wt. Causal
relations between stationery series can be calculated using
the below given formula
k
k
j =1
j =1
k
k
j =1
j =1
X t = a 0 + | c j x t - j + | b j y t - j + u xt(5)
y t = a 0 + | c j x t - j + | b j y t - j + u yt(6)
where, k is a suitably chosen positive integer, cj and bj,
j = 0, 1,...,k are parameters and a is a constant; and ut is
Figure 1. Co-movement of Selected Markets
Source: Author’s calculations.
disturbance terms with zero means and finite variances.
H0 states that Y does not cause X. The lag is determined as
per the Akaike Information Criteria (AIC).
Test of Co-integration
Johansen and Juselius (1990) has developed the technique
defining a vector of n potentially endogenous variables Zt.
Z t = AiZ a - 1 + ... + A K Z t - K + UD t + n et(7)
where Ai is coefficients’ matrix, n is a constant, Dt are seasonal dummies orthogonal to the constant term n, and ft is
assumed to be an independent and identically distributed
Gaussian process.
Result and Discussion
This Figure 1 shows the momentum of co-movement
among selected markets. From the graph it is observed that
during year 1998, BVSP, Hangseng, MXX and NASDAQ
reported a high growth rate of 143.88 per cent, 77.08
per cent, 80.01 per cent and 86.29 per cent, respectively.
During the year 2007, due to global recession, BSE, BVSP,
FTSE-100, Hangseng, MXX and NASDAQ markets have
fallen down with 51.92 per cent, −41.22 per cent, −32.18
per cent, −47.99 per cent, −24.51 per cent and −42.02
per cent, respectively. In year 2008, BSE and BVSP had
‘grown’ with rate of 78.50 per cent and 82.66 per cent,
respectively. All these markets have made recovery during
year 2008–2009 and grown, but from year 2010 markets
started to fall, which then recovered in the 2011–2014
period. It shows the turbulent movement with ups and
downs in last 15 years.
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Asia-Pacific Journal of Management Research and Innovation 12(1)
correlated with BSE. FTSE 100 is highly correlated with
NASDAQ (0.79) and vice versa. Nikkei is not much
correlated with any market and RTS is highly correlated
with BVSP Market (0.79).
Descriptive Statistics
The descriptive statistics of the eight selected stock markets
are reported in the Table 1.
Skew is a measure of symmetry. A normal distribution
has skewness equal to 0. Kurtosis measures peakeness.
Kurtosis of a normal distribution is 3 or more than 3. Here,
kurtosis is less than 3.00. The values of kurtosis are shown
in the above Table 1. Thus, the descriptive statistics indicate that all the markets are not normally distributed and
are characterised as leptokurtic and skewed.
Analysis of Top Rise and Fall
Below given figure 2 shows Analysis of top rise and fall of
all markets. The BVSP has the highest daily rise of 14.72
per cent with the fall of 9.15 per cent, followed by RTS
with the highest one-day rise of 12.14 per cent and the fall
of 12.76 per cent. In the same line, Hangseng has risen and
fallen with 10.61 per cent and 9.58 per cent, respectively.
Lowest difference between fall and rise is in FTSE-100
(0.20 per cent), BSE (0.30 per cent), Hangseng (1.03 per
cent), NASDAQ (1.35 per cent) and MXX (1.38 per cent).
Maximum spread between fall and rise is seen in BVSP
(5.57 per cent). Nikkei and RTS are two markets where the
average of the 10 highest falls is more than the average
of the 10 highest rises.
Correlation
Table 2 shows the correlation matrix. It is seen from the
table that BSE is highly correlated with MXX (0.97),
Hangseng (0.91) and BVSP (0.88) and partially positively
correlated with NASDAQ (0.65), RTS (0.55) and FTSE
100 (0.51). BVSP is highly correlated with MXX (0.92),
whereas Hangseng (0.91) and MXX (0.97) are highly
Table 1. Descriptive Statistics
Particulars
BSE
BVSP
FTSE 100
Hangseng
MXX
NASDAQ
Nikkei
RTS
Mean
11,732
37,265
5,604
17,243
21,897
2,578
12,546
1,089
Median
10,858.5
38,405
5,766.6
16,998
20,438
2,339
11,543
1,178
Maximum
29,682
73,517
7,104
31,638
46,357
5,210
20,868
2,487.9
Minimum
2,600
4,761
3,287
6,660
2,856
1,114
7,054
131
Std. Dev.
7,540.1
21,074
843
5,307
14,372
3,264
603.4
906.83
Skewness
0.421
0.007
−0.493
0.060
0.176
1.05
0.53
0.06
Kurtosis
2.01
.43
2.32
1.88
1.46
2.42
.20
1.86
Jarque-Bera
Probability
268.1
0.00
391.8
227.99
200.81
394.45
746.18
279.90
207.27
0.00
0.00
0.00
0.00
0.00
0.00
0.00
BVSP
FTSE 100
Hangseng
MXX
NASDAQ
Nikkei
RTS
0.88
0.51
0.91
0.97
0.65
0.13
0.55
0.33
0.90
0.92
0.37
−0.15
0.79
0.62
0.46
0.79
0.31
0.09
0.90
0.66
−0.01
0.54
0.58
0.05
0.63
0.28
0.06
Source: Author’s calculations.
Table 2. Correlation Matrix
Stock Market
BSE
BSE
BVSP
0.88
FTSE 100
0.51
0.33
Hangseng
0.91
0.90
0.62
MXX
0.97
0.92
0.46
NASDAQ
0.65
0.37
0.79
0.66
0.58
Nikkei
0.13
−0.15
0.31
−0.01
0.05
0.28
RTS
0.55
0.79
0.09
0.54
0.63
0.05
Source: Author’s calculations.
0.90
0.12
0.11
Patel27
Figure 2. Major One-Day Increase and Decrease of All Exchanges
Source: Author’s calculations.
Unit Root Test
This study has tested the stationarity of all these series in
Eviews 7 econometric software. The ADF and PP test
are used to examine the presence of stochastic and deterministic trends in the given data. The result of augmented
Dickey–Fuller test and Phillips–Perron test are shown in
the Table 3.
All the stock exchanges have been tested for the stationarity using the ADF and PP Test. The H0 for the unit root
test could not be rejected at 10 per cent significance level.
However, at the first difference, we reject the H0 at 1 per
cent, 5 per cent and 10 per cent level and it can be concluded that all the series are stationary and integrated in the
same order, that is, I(1). Table 4 shows result of the Granger
causality test performed on 2,829 cbservations.
Table 3. Unit Root Test
ADF Test
Level
Stock Market
With
Intercept
PP Test
First Difference
With
Intercept
and Trend
With
Intercept
With
Intercept
and Trend
Level
First Difference
With
Intercept
With
Intercept
and Trend
With
Intercept
With
Intercept
and Trend
BSE
−1.2
−1.19
−19.54
−19.56
–1.24
−1.23
–127.36
−127.38
BVSP
−1.98
−1.99
−23.77
−23.78
−1.99
−1.97
−130.14
−130.15
FTSE 100
−2.21
−2.24
−116.96
−116.95
−2.25
−2.30
−117.89
117.90
Hangseng
−1.88
−1.94
−119.12
−119.11
−1.90
−1.93
−119.12
−119.11
MXX
−1.94
−1.96
−36.09
−36.10
−1.97
−1.98
−115.59
−115.62
NASDAQ
−1.04
−1.06
−140.21
−140.23
−1.07
−1.09
−139.11
−139.10
Nikkei
−2.31
−2.40
−25.54
−25.56
−2.37
−2.42
−163.67
−163.68
RTS
−2.02
−2.05
−142.03
−142.02
−2.09
−2.13
−142.66
−142.65
Source:Author’s calculations.
Notes:
The critical values of ADF and PP test are −3.438, −2.864 and −2.568 with only intercept and −3.970, −3.415 and −3.129 with intercept and
trend at 1 per cent, 5 per cent and 10 per cent significance levels, respectively.
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Asia-Pacific Journal of Management Research and Innovation 12(1)
Table 4. Granger Causality Test
Cause
Caused By
BSE
p-value
BVSP
p-value
FTSE 100
BSE
BVSP
FTSE 100
Hangseng
MXX
NASDAQ
Nikkei
RTS
–
0.20
0.47
25.43
0.65
1.40
0.64
1.22
–
0.81
0.62
0.00
0.51
0.24
0.52
0.29
56.16
–
42.88
229.21
0.43
5.56
0.08
1.13
–
0.00
0.00
0.65
0.03
0.92
0.32
0.000
0.25
–
p-value
0.000
0.77
–
Hangseng
0.75
0.41
p-value
0.47
79.96
MXX
p-value
NASDAQ
11.83
0.000
36.16
142.0
1.36
38.20
0.42
2.77
0.00
0.25
0.00
0.65
0.01
0.12
–
1.13
4.29
0.95
3.65
0.66
0.87
–
0.32
0.014
0.38
0.02
3.43
42.45
196.36
–
1.26
1.08
1.05
0.03
0.00
0.00
–
0.28
0.33
0.34
0.10
14.30
102.42
0.04
–
0.87
1.66
p-value
0.000
0.90
0.00
0.00
0.95
–
0.41
0.19
Nikkei
1.82
0.13
1.56
1.10
0.13
1.44
–
1.04
p-value
0.16
0.87
0.21
0.33
0.87
0.23
–
0.35
RTS
3.67
2.64
0.84
0.95
3.08
0.61
0.23
–
p-value
0.02
0.02
0.43
0.39
0.46
0.53
0.79
–
Source: Author’s calculations.
Granger Causality Test
Table 4 shows results from pairwise result for all markets.
From the F-statistic and the ‘p-Value’, it is observed that
return of BSE depends on all the market, except the
Hangseng and Nikkei. Return of BVSP depends on MXX
and the RTS. Return of the FTSE 100 is affected by BVSP,
MXX and NASDAQ, whereas Hangseng is affected
by BSE, BVSP, FTSE 100, MXX and NASDAQ. MXX
and Nikkei are not affected by any market. NASDAQ is
affected by BVSP, FTSE 100 and Hangseng markets. RTS
depend on FTSE 100 and Hangseng. It reveals that the
current prices advance the forecast ability of stock prices.
Cointegration Test
Johansen and Juselius (1990) in their research had
developed the multivariate approach to trace maximise
eigenvalue test to analyse the number of co-integrations.
Table 5 gives the co-integration results.
Table 5. Johansen Test
Hypothesized No. of CE
Eigenvalue
None (r = 0)*
0.021334
At most 1 (r # 1)
0.017876
68.41737
At most 2 (r # 2)
0.009715
36.74299
95.75366
1.0000
At most 3 (r # 3)
0.005324
19.60077
69.81889
1.0000
At most 4 (r # 4)
0.005004
10.22716
47.85613
1.0000
At most 5 (r # 5)
0.000717
1.418818
29.79707
1.0000
At most 6 (r # 6)
0.000777
0.159010
15.49471
1.0000
At most 7 (r # 7)
0.000785
0.022610
Source:Author’s calculations.
Notes: Trace test indicates no cointegration at the 0.05 level.
*denotes rejection of the hypothesis at the 0.05 level.
**MacKinnon-Haug-Michelis (1999) p-values.
Trace Statistic (m)
106.2855
Critical Value 0.05
Prob.**
159.5297
0.9843
125.6154
0.9995
3.841466
0.8804
Patel29
Table 6. Highest Eigenvalue Results
Hypothesised No. of CE
Eigenvalue
Max-Eigen Statistic
Critical Value 0.05
Prob.**
None (r = 0)*
0.021334
37.86814
52.36261
0.6287
At most 1 (r # 1)
0.017876
31.67438
46.23142
0.6784
At most 2 (r # 2)
0.009715
17.14222
40.07757
0.9963
At most 3 (r # 3)
0.005324
9.373617
33.87687
1.0000
At most 4 (r # 4)
0.005004
8.808339
27.58434
0.9958
At most 5 (r # 5)
0.000717
1.259809
21.13162
1.0000
At most 6 (r # 6)
7.77E−05
0.136399
14.26460
1.0000
At most 7 (r # 7)
1.29E−05
0.022610
3.841466
0.8804
Source: Author’s calculations.
Here, H0 was ‘There is no co-integration, r = 0’,
whereas it was accepted at 5 per cent level as the trace
value (λ = 106.28) is lower than the critical value
(λ = 159.52). But researcher at r = 1 where the trace statistics value (X = 68.41), the H0 fails to reject as the trace
value (λ = 68.41) is less than the critical value (λ = 125.60).
In the same line, for two or more co-integrations, in all
cases, null hypothesis fails to reject. Hence, the researcher
has concluded that there is a long-run relationship amongst
the selected stock exchanges. Furthermore, the maximum
eigenvalue test is shown in Table 6.
Here, the researcher had tested seven H0, that is, the
null of nil, of one, of two, of three, of four, of five, of six
and of seven co-integration vectors. It is revealed from
study that the maximum eigenvalue test supports the
co-integration among the selected markets.
Figure 3. Component’s Plotting
Source: Author’s calculations.
Principal Component Analysis
Here, the value of KMO test value of the sample is 0.763
which is more than 0.05 and further reveals that the variables measure common factor. The result shows factorable
inter-correlation matrix.
From the Figure 3, it is can be observed that, in ‘BSE’,
‘BVSP’, ‘NASDAQ’, ‘MXX’, ‘Hangseng’ and ‘FTSE 100’
returns lie on component 1, whereas ‘RTS’ and ‘Nikkei’
returns lie on component 2. Component 1 can be defined as
‘the South Asian’ and ‘the Latin American stock exchanges’
and the component 2 can be defined as ‘the Northeast Asian
stock exchanges’.
30
Asia-Pacific Journal of Management Research and Innovation 12(1)
Conclusion
The present study is performed ‘to find out a relationship
between the selected international markets’. This study is
carried out by using data of ‘BSE Sensex’, ‘Hangseng’,
‘MXX’, ‘RTS’, ‘BVSP’, ‘FTSE-100’, ‘Nikkei’ and the
‘NASDAQ exchange’ from the period of 1 January 1998 to
30 June 2015. The result of correlation analysis revealed
that BSE is highly correlated with MXX (0.97), Hangseng
(0.91) and BVSP (0.88) and partially positively correlated
with NASDAQ (0.65), RTS (0.55) and FTSE 100 (0.51).
BSE is highly correlated with BVSP, Hangseng and MXX
as they are the Emerging Markets Index. Moreover, countries like India, Brazil and Mexico are the fastest growing
economies which result in good economic relations of India
with these nations. NASDAQ, FTSE-100 and RTS are the
markets belonging to high-income economies. BSE is moderately correlated with these markets as India is a lower
middle-income country. Analysis on the average of top rises
and falls shows that BVSP is the market with the highest
daily rise of 14.72 per cent, whereas RTS has witnessed the
highest daily fall of 12.76 per cent. The result of Granger
causality test indicates that BSE depends on all the markets,
except Hangseng and Nikkei. Return of BVSP depends on
MXX and RTS. Return of FTSE 100 is affected by BVSP,
MXX and NASDAQ, whereas Hangseng is affected by
BSE, BVSP, FTSE 100, MXX and NASDAQ. MXX and
Nikkei are not affected by any of the selected markets.
NASDAQ is affected by BVSP, FTSE 100 and Hangseng
markets. RTS depends on FTSE 100 and Hangseng. Hence,
from the study it is revealed that there is a short-term relationship amongst the selected variables. Outcome of the
Johansen co-integration test indicates that there is a longrun relationship amongst the selected stock markets. The
results are in line with previous studies conducted by
Akhtar (2009), Alexakis (2009), Modi, Patel and Patel
(2010), and Patel and Patel (2012a, 2012b). It can be concluded that the following are the reasons for relation
between the markets: (i) the economic integration amongst
all the selected markets in terms of foreign trade, (ii) the
cross-border investments by the selected markets in other
countries, (iii) the economic stage of each selected country
and (iv) the growth of BSE, BVSP, RTS and MXX as the
Emerging Markets Index. The result of the principal component analysis indicates that markets are fragmented
in two regions, namely, ‘the south Asian’ and ‘the Latin
American stock market’ (BSE, BVSP, NASDAQ, MXX,
Hangseng and FTSE 100) and the North Asian Stock market
(RTS and Nikkei).
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