Zahirullah, PhD Scholar, University of Peshawar
Email: zaheer1854@gmail.com
Adeel Rahim, Lecturer Sarhad University, PhD Scholar, University of
Peshawar
Email: adeel.ba@suit.edu.pk
Abstract
This study is
conducted to check the co-movement between the equity markets of BRICS
countries that is Brazil, Russia, India and China with Pakistan. These
countries are selected because they are world emerging markets. Co-movement is
checked through co-integration analysis. Ten years monthly stock indices are
taken for analysis that is from July 2006 to June 2016. No cointegration is
found between Pakistani equity market and the BRICS countries. So, this favours
the international portfolio investors. Furthermore, Brazilian market is found to
be the most attractive market for the portfolio investors in order to maximize
their wealth by minimizing their risk levels. The future research can be
extended by using high frequency data in order to check the comovement and
cointegration.
Key Words: Comovement, cointegration, BRICS, equity
markets, ADF, financial risk, diversification
No country is self-sufficient; every country depends
on one another in way or another. The
changes made in modes of trade and business has raised many options and
opportunities to the investors to maximize their wealth. There are many factors
involved in the advancement of trade, business and investment. Some of the
factors responsible for the advancement of the business include technology,
trade reforms, political and economic interests and the interdependency on one
another. The various trade agreements, trade liberalization and economic
integration between various countries have opened doors for the investors to
invest across borders in order to take advantages from the investment
opportunities. All this allow investors to diversify their investments in order
to increase their profits with the minimum level of risk taken. Co-integration
is a statistical term which is a property of time series variables.
BRICS Countries and Pakistan
The concept of BRICS was first coined by Goldman Sachs
(2001) in his research paper. He said that BRICS countries that are Brazil,
Russia, India and China will play a key role in the world’s economy in the
future. In another paper in 2003 by Goldman Sachs, tried to predict the future
of world economy over the next 50 years. In that paper it has been predicted
that BRICS countries would be key players in world’s economy. It is forecasted
that BRICS together will have greater economy than the Group of Six(USA, UK,
France, Germany, Japan and Italy) of the world in not more than the next 40
years. It has also been forecasted that over the next 50 years of time span
only US and Japan can defend their position to be in the Group of Six. This
situation will be only due to the emergence of new markets. According to the
paper BRICS countries have many problems especially the infrastructure and
their political situation.
Today we clearly see that the predictions made by
Goldman Sachs (2001) are on its way to be proved. BRICS are gaining its
importance economically as well as the politically. Few achievements they made
over the last few years or so include the joining of World Trade Organization
(WTO) by China in 2001, a step towards modernizing its industries. Brazil has
gained tremendous momentum in lifting its economy over the last few years.
Brazil joined strategic partnership with its two other members that is India
and China.
Purpose of
the Study
Purpose of the study is give guidelines to the policy makers, academia and
investors to invest in markets which suit them well. The study will give all
the stated parties an edge over the others while considering these countries
for policy, research and an investment purpose. The objective of the study is
to examine and verify the co-movement between Pakistan stock market and markets
of Brazil, Russia, India and China. They are collectively termed as BRICS
countries and are the emerging markets of the world. To check the co-movement
through co-integration analysis is also an objective of the study. The study
will contribute in the field of investment and trade, policy making and further
research. The study will give guidance to the portfolio investors to minimize
their risk and maximize their wealth. This paper
consists of mainly four chapters. The first chapter is the “Introduction”.
Second chapter is about the work done in the related field in past that is the
“Literature Review”. Third chapter defines the “Methodology and Data Analysis”
and final chapter is the ultimate “Conclusion and Suggestions”.
Literature Review
Number of studies have been undertaken to analyze the comovments
amongst different stock markets. Tahir and Sabir (2013) inquired the
relationship between developed markets and South Asian Markets and their impact
on Karachi Stock Exchange (KSE) Pakistan. They analyzed the data from July 1999
to June 2011 for four major South Asian Markets and four developed markets.
They used correlation, unit root, and Granger Causality and co integration and
found no relation amongst the developed markets and South Asian markets under
correlation test whereas they stated that India and US markets are correlated.
They further found a strong relationship between Karachi Stock Exchange and
Chinese Stock Exchange under Granger Casuality test. They suggested the
managers that KSE shall be included in developed market portfolios as KSE has
no cointegration with the developed markets. So, they suggest the investors to
invest in South Asian Markets as it will be beneficial in minimizing the risk
and maximizing their wealth.
Chang, Nieh
and Wei (2006) conducted a study to check a long term equilibrium linkage
between Taiwan’s equity market and the four developed European markets of
France, Germany, United Kingdom and Netherlands.
Chang et al (2006)analyzed the data and
concluded that there is no cointegration after testing the four pairs of
statistical tests that are PO, HI, JJ and KPSS. With this result they said that Taiwani investors can take long term
financial benefits by diversifying their portfolio and investing in the selected
European markets. They said that these findings will help investors in the long
run.
Camelia and
Ilie (2012) conducted a study to check the impact of stock market variability
on the economic activities of Romania and the reasons responsible for the crash
of Romanian stock market in the context of correlation and co movement of stock
markets. They checked the correlation and the co movement of Romanian Stock
market with North American and European markets. Finally they said that
Romanian investors imported the financial crisis by investing heavily in North
American and European markets as a strong correlation and co movement was found
amongst them.
Cheung et al., (2006) stated that structure of
information is different in the crisis than in the non-crisis situations. This
has been observed after the Asian financial crisis in 1997. They have checked
the relationship amongst four East Asian markets and US market. After the
analysis they said that it is the US market which leads the four Asian markets
all the time that is before, during and after the financial crisis. They also
stated that during the crisis time Japanese currency is responsible for
affecting these four markets.
Chow, Huang
and Niu (2012) applied regression and time varying correlation to check the
integration amongst East Asian markets namely Korea, China, Hong Kong, Taiwan
and Singapore and co movement with US market. Chow et al., (2012) found that
integration has been increased amongst Korea, Hong Kong Taiwan and Singapore
whereas integration of China has been increased since the opening of Shanghai
stock market in 1990 and the US recent crisis has affected the economies of
East Asian Countries. They have an interesting finding that despite the
investment and trade relations Japan and US have not so close integration
rather China’s linkages has been increased with US.
Friedman and
Shachmuove (2008) conducted a study in which they used VAR model by taking
eight European countries daily returns and they find that UK, France, Germany
and Netherland have strong linkages whereas the smaller markets are
independent. They have further investigated that UK has 91% of innovations of
its own and it is leading market, so it is not influenced by others whereas
others are affected by the larger economies like Germany and France.
Worthington
and Higgs (2007) have examined the relationship and integration amongst the
Asian equity markets which include three developed markets that is Hong Kong,
Singapore and Japan and eight emerging economies namely Korea, Malaysia,
Philippines, Taiwan, China, India and Thailand. They tested unit root test,
cointegration and Granger causality test on the data and stated that there is
long term relationship amongst them. They added that they also have causal
relationship. They concluded that there is strong integration amongst the
stated Asian markets just like European Union has it.
Busse, Goyal
and Wahal (2009) examined the investment opportunities available for the US
investors globally and came to the conclusion that due to many differences
between the developed market and the emerging markets there is high level of
variability in returns as well as the associated risk of the investment. With
these findings they stated that it is harder for the investors to perform
superior as compared to others.
Lucey and
Muckley (2000) conducted a study and examined the short term as well as long
run interrelationship between the Asian and European markets extended to the
United States of America. They concluded that in short term Asian markets do
not have that level of cointegration with European and US market as that
between US and European markets. On other hand they concluded that Asian market
have long run interdependencies with US markets and they further founded that
this long run interdependencies are absent between US and European markets. So
present their final remarks as that in long run correction are not informative
from the diversification point of view. So they suggested US investors to take
co integration into account rather than correlation if he has inclination
towards diversification.
Ali et al.,
(2011) said that the key factors in arousing the interest of investors to
invest in other stocks to subject of co-movement between the stocks of
different countries is the increasing level of globalization. They conducted a
study in which they had tried to investigate the co movement between Pakistani
equity market and seven other countries which include U.S.A, U.K, India,
Malaysia, Taiwan, Singapore, Indonesia and Japan. They investigated this
through co integration test and monthly stock prices have been used for a
period of ten years that is from July1998 to June 2008.They concluded that
Pakistani equity market has no co-integration with the markets of U.S.A, U.K,
Singapore , Taiwan and Malaysia. Here they have suggested the investor to
invest freely and it will help them to minimize their risk and maximize their
wealth. While Co-integration do exists between Pakistan equity market and
China, Indonesia, India and Japanese equity market and the stock prices move
together. So here the investor has very low chance of risk minimization while
investing in these countries Stocks along with the investment in Pakistani
equity market. During the data analysis they had tested the variables for the unit
root to give order to the integration by employing Augmented Dickey Fuller
statistics (ADF). After establishing the order of integration, co integration
test was applied to predict (check) the long run relationship. Finally Granger
Causality has been applied to check the short run relationship among the stock
indices of different markets. The study shows that co integration vary with the
selection of frequency of stock that is suing , daily, weekly, monthly
,quarterly stock indices/prices.
Trivedia and
Birav (2013) said that financial crisis over the globe (1997, 2008 etc.) has
inclined the interest of the research to work in this particular field that is
theoretical and applied finance. Stock market is one of the most attractive
areas for conducting an applied research. Many policy makers, financial
investors and academicians have provided many results by analyzing this issue.
They investigated the co movements between the emerging and developed stock
equity from the global financial crisis perspective. They took daily indices
closing prices for the period of ten years that is from January 2003 to January
2013. They run series of integration test and found that co-movements,
interdependency and inter-linkages do exists between developed and under developed
equity markets .They found that the highest return payer country is Hungary and
emerging stock market country with relatively high risk ratio is Romania. While
among the developed equity markets France and Japan were found with highest
return payer and having risk ratio. They used advanced level econometric models
and technologies.
Aktan and
Bulut (2008) conducted a research to examine the impact of corporate
entrepreneurship on the financial performance of the firms in Turkey. The data
taken for analysis include 2032 respondents of active 312 firms. They run
multiple regression and confirmatory factor analysis to test the hypothesis.
They found that there is a positive correlation between the tested hypotheses
that is the innovativeness, risk orientation (level of risk taking) relative
aggressiveness and proactiveness of entrepreneurs firms with the financial
performance.
Ruxanda and
Stoenescu (2009) tested bivariant and multi variant co integration and their
applications in different stock markets which include stock exchange indexes
from Romania, France and U.S.A. They have used stock prices daily. Dickey
fuller and unit root test were used and they found that non stationary series were three and every
series is of 1st order integration They also tested Engle Granger procedure and
found that BAT(Romania stock series) and CAC40 (France stock exchange) are
integrated. So finally they estimated the error correction model. So they found
that around 2 % of the gap between the two series is adjusted every day.
Finally they run Johansen procedure and concluded that co integration system is
formed between the three series. Eventually they came to the conclusion that co
integrated variables can be used to generate error correction model.
Zhou (2013)
conducted a study of co movement relation between U.S.A, U.K, Japan and the
Asian emerging markets and he found that there exists a common long term
equilibrium relationship of some emerging markets with some developed markets.
He also said the interdependency increase between the Asian emerging markets
and developed markets after 1997 Asian financial crisis. Levy and Sarnat (1970)
stated that one of the reasons for going for diversification internationally is
that stock prices move together within a close economy where the investing
cannot minimize their risk and maximize their wealth.
Grubel and
Fadner (1971) said that the stock prices of both global and international move
together and have no or weak relationship which results in higher gains and
lower loss due to changes only in exchange rates. They further stated that co
movement of stock prices across countries are not related due to different
government setups, policies, Management, and economic dependencies. Sodie, et al. (1999) found that the risk can be
minimized if investors invest in unrelated stock and there are higher
possibilities and securities across countries have low correlation. So
investors can easily diversify their portfolio by investing in a cross
countries securities.
Worthington
(2003) checked the stock prices linkage of Asian markets for the period of
January 1998 and February 2000. Six emerging markets of Taiwan, Indonesia,
Thailand, Philippines, Korea and Malaysia and three developed markets of Japan,
Singapore and Hong Kong. By using multi co integration analysis results. He has
shown that there is lower causal relationship among the Asian markets so he
suggested that investors can go for diversification in the Asian markets. Kawan
et al., (1995) studied the markets of Australia, U.K, Japan, Hong Kong,
Singapore, U.S, South Korea, Taiwan and Germany and came to the conclusion that
these markets have lead-lag relationships. Ghosh, et al. (1999) found that there exist a long run equilibrium
relationship between some of the Asian markets and some of the emerging
markets. Singh (2010) came to the conclusion that Indian and Chinese markets
have co integration with developed markets of U.S.A, U.K, Japan, and Hong Kong
after the mortgage crisis of 2008 under the granger causality analysis.
Zhou (2008)
found out that the Chinese markets has the highest return also with the higher
risk level while India has the highest standard deviation. They showed that
Indian and Indonesian markets have higher co integration with U.K. China has Co
integration with U.S.A. Singapore has co integration with Japan and Philippines
have co integration with Japan and U.K. So they have lower diversification
opportunities among themselves. They found that after the Asian financial
crisis in 1997 the positive correlations have increased among these countries
so here investors have little chance to minimize their risk of portfolio.
Romana (2013)
quantified the impact of global financial crisis on the emerging capital
markets. They found out that the emerging capital markets have asymmetric
volatility effects taking in account the fact that negative information has
more intense and adverse effects than the positive information. BRICS economies
that are Brazil, Russia, India and China have emerging capital markets and
empirical analysis is based on this. They have used the methodology including
unit root test, granger causality test, BDS test, and Augmented Dickey Fuller
stationary test and Johansen co integration method. Romana (2003) stated that
internationalization of capital emerging market have very deep implication with
the 2007 global financial crisis that started from U.S. Wong, Perum, Terrell,
and Lim (2004) found that there exists comovement between some Asian emerging
markets and the developed where as some emerging markets differ from other
developed markets where they have long term equilibrium relationship. They
found that the co integration has been increased since 1997 Asian financial
crisis, so, the advantage of diversification is adversely affected.
Methodology
Most of the
studies have shown that most of the markets have co movements amongst them. Due
to globalization, formation of trade blocks and trade and investment contracts
between markets, have increased the opportunities for the investors all over
the globe. Researchers have shown that many markets have integration amongst
them. Using correlation, unit root,
and Granger Causality and co integration Tahir and Sabir (2013) found no
relation amongst the developed markets and South Asian markets under
correlation test whereas they found that India and US markets are correlated.
They further found a strong relationship between Karachi Stock Exchange and
Chinese Stock Exchange under Granger Casuality test. They suggested the
managers that KSE shall be included in developed market portfolios as KSE has
no cointegration with the developed markets. So, they suggest the investors to
invest in South Asian Markets as it will be beneficial in minimizing the risk
and maximizing their wealth.
The more the
co integration or co movement, the less it is beneficial for the investors to
diversify its portfolio. Here the motive of the investor is diversifying its
portfolio and maximizing their wealth. If the co integration is low then it
will be beneficial for the investors to invest in those markets. All this is
backed by the portfolio investment theory which says that investors can
maximize their wealth and minimize their risk through diversification that is
by making a portfolio of investments rather than stick to just one type of
securities or market. This means not putting all the eggs in a single basket.
Research Design
The research
is quantitative as degree of co movement and co integration has to be checked
amongst the selected markets. In quantitative research we have to deal with numerical
data, explaining a phenomenon. Secondly
various statistical tests are applied on the data to get the ultimate results.
Here in this study the co movement has to be checked between Pakistani equity
market and the four emerging markets of the world namely Brazil, Russia, India
and China (BRICS) through co integration analysis. The co movement differs
amongst the stock indices from time to time. This inconsistent behaviour is due
to the selection of time period, the frequency of observation taken that is
daily, weekly or monthly and the selection of stock markets to be tested.
Data
Collection and Sample
The data type
for the research is secondary one and is collected from the secondary sources. Websites
of the stock markets under study and yahoo finance site are used for collecting
the required data. Monthly stock indices are taken of each market from their
websites and yahoo finance. The data consists of 120 monthly observations for
the period from July, 2006 to June, 2015 for each. The reasons behind taking
monthly indices not daily, quarterly or annual stock indices is that quarterly
or annual data may result in false results when not compromising on the
available degrees of freedom required in selecting appropriate lag structures
(Patra, & Poshakwale, 2006). The following are stock market of the selected
countries for the study.
Table 1 Selected Countries and their Stock Markets
Country |
Index |
Pakistan |
Karachi Stock Exchange (KSE-100 Index) |
Brazil |
Brazil Stock Exchange (BOVESPA) |
Russia |
Russia stock market (MICEX) |
India |
Bombay Stock Exchange (BSE Sensex) |
China |
Hong Kong Stock Exchange (Hang Seng Index) |
South Africa |
Johannesburg Stock Exchange (JSE) |
Specification
of Variables
The variables to be tested are the degree of co
movement and correlation of stock markets indices between Pakistani equity
market and BRICS countries that is Brazil, Russia, India and China. Variables
are formed in such a way that co-movement and co-integration is checked in the
following sequence;
·
Pakistan and Brazil
·
Pakistan and Russia
·
Pakistan and India
·
Pakistan and China
·
Pakistan and South Africa.
The data that is to be tested is taken from the stock
markets websites and yahoo finance. The tests to be tested on the required data
include unit root test, Engle Granger test and cointegration analysis. Data is
normalized in two cases. Firstly logarithmic transformation is taken to check
the stationarity of the data. Secondly natural logarithm is taken to check
non-stationarity of the data for the co-integration purpose.
I)
Log (P0/P1)
II)
Log (P1)
Unit Root
Test (ADF)
To check the long run relationship between Pakistan
stock market (KSE) and equity markets of the BRICS countries. It is important
to verify whether the data series is stationary or non-stationary. For this entire,
augmented Dickey Fuller unit root test has been in use. It has the following model in mathematical
form.
∆Yt=β0+β1Yt-1+β2∆Yt-1+ᶓt
β1 is examined
to check the stationarity of the data. Where
β1=σ-1
Co-integration Test
Engle
Granger test is employed to check the co-integration between time series data
of Pakistani equity market and BRICS countries markets. The data sets are said
to be co-integrated if certain common deviation or drift is found amongst them.
Linear combination of Wt and Ut must be stationary if they are
co-integrated.
Mathematically;
Wt = βUt - Vt
So
at first step all the data is transformed in logarithmic form then unit root
test (ADF) is employed to check the data whether it is stationary or
non-stationary. Then data is passed through co-integration test for which Engle
Granger test is employed.
Data
Analysis and Interpretation
After devising the methodology, the time series data
is passed through many statistical tests. At the first step descriptive
analysis is performed, after that, unit root test (ADF) is employed. At the
third step after verifying the stationarity via unit root test the
co-integration test is conducted by running the test of Engle Granger.
Descriptive
Analysis
Descriptive statistics is performed after the logged
transformation of the data for the selected markets.
Table 2 Descriptive Statistics of the Selected
Countries
Country |
Mean |
Standard Deviation |
Maximum |
Minimum |
Skewness |
Kurtosis |
Pakistan |
0.0193 |
0.0732 |
0.2022 |
-0.2204 |
-0.6359 |
1.2893 |
Brazil |
0.0265 |
0.0621 |
0.1445 |
-0.1215 |
-0.3267 |
-0.2846 |
Russia |
0.0238 |
0.0763 |
0.1472 |
0.1821 |
0.6282 |
0.0300 |
India |
0.0214 |
0.0744 |
0.1461 |
-0.1983 |
-1.0332 |
1.0784 |
China |
0.0132 |
0.0556 |
0.1441 |
-0.1703 |
-0.6441 |
1.5934 |
Mean is the average value. Standard deviation is the
level of risk. Skewness is the data distribution of the time series around the
mean whereas kurtosis shows the peakness or flatness of the time series.
The table above shows the values of descriptive
statistics in terms of mean, standard deviation, stocks maximum and minimum
returns, the skewness and kurtosis of the time series. Mean is the average
value of the time series. Brazil has the highest mean value and China has the
lowest mean value. Russia has the highest level of standard deviation followed
by India and Pakistan respectively. Skewness shows the normal distribution of
the data. The skewness value for Pakistan, Brazil, India and China is negative
which means that extreme values lies on the left and most of the values are
clustered on the right side of the mean whereas Russia also has the same data
distribution but with positive value of 0.6282 which is also less than zero.
Kurtosis is a statistical term which shows the height or flatness of the data
distribution. All the time series of the selected markets have values less than
3 which mean that normal distribution has wider peakness and is more flat than
normal. Also, the extreme values have less probability and most of the data is
concentrated around the mean. This type of distribution is termed as
platykurtic.
Unit Root
Test (ADF)
The Augmented Dickey Fuller Test is employed to check
the stationarity of the time series.
Table 3 Augmented Dickey Fuller Test
Level |
||
Indices |
With Constant |
With Constant and Trend |
Pakistan |
0.3186 |
0.9905 |
Brazil |
0.9503 |
0.02795 |
Russia |
0.7755 |
0.9306 |
India |
0.972 |
0.4356 |
China |
0.7964 |
0.8262 |
*The table presents P-value @ 5% significance level
which shows stationarity of the series.
The above table is the result of the Unit Root Test
(ADF) which shows that time series of all the selected markets are
non-stationary with P-value greater than 0.05(5%) with constant at level. The
time series is stationary for Brazil having P-value less than 0.05(5%) with
constant and trend. Here H0 is rejected while rest of the series is found to be
non-stationary. Means H0 is accepted and H1 is rejected.
Engle
Granger Test
After running ADF test with the help of which we found
whether the data is stationary or non-stationary, Engle Granger test is
employed to check the cointegration amongst the markets.
Table 4 Engle Granger Test
Country |
P-Value |
Pakistan and Brazil |
0.9263 |
Pakistan and Russia |
0.2706 |
Pakistan and India |
0.8207 |
Pakistan and China |
0.8527 |
Pakistan and South Africa |
0.9281 |
*Co-integration
test showing P-value @ 5% significance level.
Analyzing the results of the Engle Granger output
stated in the Table above, it can be inferred that step one tests the
stationarity of KSE which shows that KSE time series is non-stationary. Step
two shows that the time series of Brazil is also non-stationary. As a result of
the cointegrating regression, it can be inferred that the regression residual
is also non-stationary which shows that the two series are not cointegrated
having P-value greater than 0.05 that is 5%. Similarly co-integration is
examined for KSE and Russia. Results were the same for both series as cited
above that time series of both the markets are non-stationary and the residual
P-value is also greater than 5%(0.05) which means that no cointegration is
found amongst them. Similar findings are found for the other two combinations
that is no combination of time series found to be co-integrated.
Conclusion
After conducting the study regarding the co-movement
between Pakistan stock market and markets of the selected countries namely
Brazil, Russia, India and China through co-integration analysis by using their
monthly indices, it has been concluded that Brazil has the highest returns
where as Russia has the highest level of standard deviation. So, it is inferred
from the analysis that Brazil is the most attractive market from the investment
point of view for portfolio managers or investors where investors can minimize
their level of risk and maximize their wealth. After analyzing the
co-integration test of Engle Granger it has been concluded that Pakistan has no
co-integration with any of the selected markets means that Pakistan is not
co-integrated with Brazil, Russia, India or China. It is recommended for the
Pakistani portfolio investors to invest freely in the selected markets without
any hesitation as no co-integration is reported amongst them.
The research
can be extended by examining the co-integration for the higher frequency (daily
or weekly). The other area to extend
this research is to examine the relation of Pakistani equity market with other
markets. This study is supplement for the academia for further investigation of
relationship amongst these markets by using different models and variables.
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