long run relationship between south asian equity markets and equity markets of developed world

International Journal of Management and Strategy http://www.facultyjournal.com/ (IJMS) 2012, Vol. No.3, Issue 5, July-Dec.2012 ISSN: 2231-0703

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LONG RUN RELATIONSHIP BETWEEN SOUTH ASIAN EQUITY MARKETS AND

EQUITY MARKETS OF DEVELOPED WORLD

Muhammad Mansoor, University of Sargodha, Mianwali- Pakistan

Arshad Hassan, Muhammad Ali Jinnah University, Islamabad- Pakistan

RanaHaroonHussain, University of Sargodha, Bhakkr- Pakistan

ABSTRACT

This study examines the relationship between the Major Asian Equity markets and the Developed equity

markets. This study used weekly stock prices indices of KSE100, BSE500, AORD500,ASPI250, CAC40,

FTSE100, IBEX35, Nikkie 225, S&P500 and S&P/Tsx Composite Index for the period 1st week of

January-2000 to last week of March/2012.This study applied Descriptive statistics, Augmented Dickey

Fuller Test, Phillips perron test,Johansen and jelseluis Co-integration test, Granger causality test, Vector

error correction model and Variance decomposition test find out the relationship among the Asian and

developed equity markets. This study concludes that the South Asian equity markets have no

relationshipwith developed equity markets in long run while show significant relationships in short run.

This study assistance the investors in decision making to achieve diversification by making investment in

South Asian equity market and developed equity markets.

Key words: Portfolio, Diversification, Stock market, Globalization, Co-integration, unit root test, South

Asian Countries, Devloped countries.

INTRODUCTION:

Today world becoming global village. Countries are reducing the barriers to promote

globalization to achieve maximum profit and maximize the wealth of the shareholders. WTO is

one of the strong institutions who promote globalization by promoting globalization of

production, globalization of markets and globalization of financial markets. So we can say the

objective of globalization to maximize profitability and minimize unsystematic risk. During the

last decades the interrelation among the stock markets is increases significantly specially in the

last two decades the co movements among equity markets are increased significantly. No doubt

the objective of globalization of market is to diversify risk but strong relationship and co


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movements among the equity markets make limited the diversification. International

Diversification suggests investing in those equity markets, which are low correlated. Number of

studies conducted on the correlation and integration of equity markets which helps the investors

in making investment decision about the portfolio diversification.

The purpose of this study is to analyze or explore the relationship among the major South Asian

stock markets i.e. Pakistan, India and Sri Lanka and developed countries like Australia, France,

UK, Spain, Japan, USA and Canada. This study focuses the liberalization and deregulation in

major South Asian equity markets and their impact on developed equity markets. This study also

focuses on whether the major South Asian markets attract the investors or not and whether

investors can take benefits from diversification by investing in emerging equity markets. In this

study we target the three major South Asian markets and equity markets of seven developed

countries. The indices are used in KSE100(Pakistan), SENSEX30(India), ASPI 250(Sri Lanka),

AORD500(Australia), CAC40(France), FTSE100(UK), IBEX35(Spain), Nikkei225(Japan), S&P

500(USA) and S&P/TSX Composite Index (Canada).

The trend of foreign direct investment was influenced due to process of deregulation and

liberalization in the world. In United Kingdom the liberalization of equity markets occur in 1975,

in Japan deregulation and liberalization of equity markets occur in 1978-1979, liberalization of

equity markets occur in Pakistan in 1991. Due to the liberalization the investor has an

opportunity to invest in local and foreign equity markets. Investors have an opportunity to

diversify their portfolios by investing in negatively correlated equity markets. If the investor

select those equity markets which are integrated or have positive relationship than it will increase

the portfolio risk. Finally the basic purpose of diversification is to minimize risk so investor will

make investment in negatively correlated securities. Furthermore the founder of diversification

concept is Markowitz (1952,59), who explained that the investor should invest in different

countries if they want to minimize their risk. Lot of studies conducted on major stock markets

shows that the south Asian markets are emerging markets and they are not co-integrated with the

developed stock markets. Hassan et al (2008) and Lamba(2005).

There are three objectives of this study. Firstly to identify long run relationship exists between

equity markets of south Asian and developed countries. Secondly to explain the lead and lag


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relationship between equity markets south Asia and developed world. Finally to study the short

term dynamics the relationship between south Asian markets and developed world.

Second Chapter covers review of the Literature regarding to theoretical and empirical work on

Major south Asian and developed countries stock markets. Third chapter explain data used

methodology adopted for this study. Fourth chapter presents the results and discuss the results of

the study. Last chapter conclude the study and suggest implication on the base of chapter 4.

LITERATURE REVIEW:

Hasan et al.(2008) investigated long run relationship among Pakistan, US, UK, Germany,

Canada, Italy Australia, Japan and France for the period 2000 to 2006 by taking weekly values

of stock market returns. This study used Johansen and Juselius multivariate Cointegration

analysis. Results shows that KSE is not co-integrated with the US, UK, Germany, Canada, Italy

and Australian market while KSE is co-integrated with the France and Japan. It further used

impulse response and variance decomposition analysis which result shows KSE is generally

independent but US and UK Markets have small impact on KSE. This study concluded that the

investors of developed countries need to make diversify their portfolio by investing in Karachi

Stock market. Hatemi-J et al(2007) examine the relationship among stock exchange of Australia,

US, UK, Hong Kong, Singapore, Taiwan and Korea by applying for 1 January 1993 to 10

September 2001. The bootstrap Granger-causality tests and their results shows that there is low

correlation occur between Australia and these markets. This study concluded that for the

Australian investors have opportunities to invest in these markets and diversify their portfolio.

Azmi et al(2004) conduct the study on stock market interdependence between

ASEAN(Association of Southeast Asian Nations, i.e. Malaysia, Singapore, Thailand, Indonesia

and the Philippines) market for the period Jan 1990–June 2004 by employing Cointegration test,

VECM analysis and vector decomposition analysis for pre control and past control period in long

run and short run. This study concluded that Malaysian stock return causes short run

disequilibrium for other ASEAN market in post control period.Park(2010) studied between the


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Asia Equity markets including Thailand, Malaysia, Indonesia, Singapore, the Philippines, Korea,

Japan, China, Hong Kong, Taiwan, and India and the US Equity market for the time period

2005-2008. This study noted that there is strong co movement of the Asian developed market

including Japan, Singapore, Hong Kong on other Asian equity markets. The study results show

that Japan and Philippines markets are more sensitive with the change in US market and the

linkage between the US market with Thailand, Malaysia and China is very weak.

Lamba (2005) conducted a study on the relationship between the South Asian equity markets and

developed country Equity market. This extensive study discussed stock markets of Pakistan,

India, Sri Lanka, UK, US and Japan for the period july1997-Feb2003.It used multivariate

Cointegration framework and vector error correction model which results shows that Indian

Equity market is highly influenced by US,UK and Japan equity market and this influenced

increases in for time period Jan 2000 to February 2003.It reported that Pakistani and Sri Lankan

markets not affected by developed country market as compare to India and there is no significant

relationship exist between Indian equity market and both Asian markets. Subhani et al(2011)

investigate the linkage between the South Asian stock markets including Pakistan, India,

Bangladesh, Nepal stock exchanges by employing Johansen Co-Integration analysis for the

period May-1995 to May-2011.This study used Augmented dickey fuller test to check the

stationary of data. It concluded that Pakistan stock market is co-integrated with Bangladesh stock

market and not co-integrated with Indian and Nepal stock exchanges. Therefore by investing in

Pakistan Stock market there chance for Nepal and Indian investor to achieve diversification.

Rivero et al.(2010) studied the relationship among the developed countries equity markets

including US,JAPAN and US by using boosting-based classification technique for the period

june,1986-june2004.It reported that there is causality relation between US market to both

Japanese and UK market and S&P has more incremental information which can help to predict

the increase and decrease trends of returns in other three stock markets. Zhang(2011) investigates

the Linkage between the Asian countries including Japan, Singapore, Chinese mainland, Hong

Kong and US counties stock market for the period 1991-2007 by employing Augmented Dickey

fuller(ADF) unit root test and Phillips Peron tests and VAR equation. This study concluded that

US equity markets has strong effect on Asian markets except the Chinese mainland stock market


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and linkage among the Asian markets (excluding china) increased after the Asian financial

crises.

Al Hedi(2004) conduct study on relationship of Stock market integration and expected gain

from international portfolio diversification by using GARCH approach on developed and

developing market. It reported that there is integration among the stock markets and investor can

gain more profits or gain from international diversification as compare to investing in domestic

market portfolio.Arouri(2008) investigate the hypothesis of market integration by applying

Nonlinear Error Correction Models (NECM),the Exponential Switching Transition ECM

(ESTECM) and the nonlinear ECM-Rational Polynomial (NECM-RP),KPSS and R/S test for the

Philippines and Mexico equity markets for the period December 1988-december 2008. Results

shows that the Mexico is highly integrated with world as compare to Philippines.

Ahlgren et al.(2010) testing the cointegration between the Finland, France, Germany, Sweden,

UK and USA for the period january1980 to February 1997 by employing Johansen maximum

likelihood(ML) method and likelihood ratio(LR) tests. The Study result shows that international

stock prices are not co integrated. This study use the both monthly, quarterly stock prices and

concluded that Johansen‟s LR tests for Cointegration are sensitive to the lag length specification

in the VAR model. This study found generally more evidence for Cointegration in higher order

VAR models. Naryan et al.(2006) explore the relationship among the Australian stock market

and G7 countries including Canada, Italy, Japan, UK, France, Germany, USA by applying the

both johansen and Gregory and Hansen tests for the period 1960-2003. This study concluded that

Australian equity market has pair wise linkage with Canada, Italy, Japan and the UK but not

linkage with French, German and Americans equity markets.

This study attractiveness is, it covers the long period 1st week of January 2000-last week of

march2012. Other studies Hasan et al(2008) and Lamba(2005) conducted on South Asian equity

markets and developed Equity markets used less spam of time. This study used most recent stock

returns prices of equity markets which represents the current picture of equity markets to

investors. Husssain et al.(2012) investigate the relationship by taking one South Asian equity

market(KSE) with other equity markets.Hasan et al. investigate the relationship by taking one


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South Asian equity market(KSE) with developed equity markets. Hasan and hamid(2011)

investigate the relationship by taking two South Asian equity markets with developed equity

markets.

METHODOLOGY:

In this study we take weekly stock prices indexes of KSE100, BSE200, AORD500,ASPI250,

CAC40, FTSE100, IBEX35, NIKKEI225, S&P500 and S&P/TSX Composite Index for the

period 1st week of January-2000 to last week of March/2012.To calculate the continuous

compounded rate of return we use the following Equation:

Rt = ln(Pt / Pt- 1 )

Rt = Return for Given Period„t‟, Pt = Price at closing time, Pt-1 = Price at the opening time , ln = Natural

Log

In this study we analyze relationship among the stock exchange market by applying the

following tests includingDescriptive statistics shows the trend of returns in the equity markets in

the sense of which equity market shows high return, low return and volatility in returns.

Generally Correlation Matrix technique used to explains the degree of relationship among the

different variable or series. So here we use this technique to explore the relationship among the

time series data about stock market returns. VAR technique is use to determine the proper lag

length because for the application of Johnson and Julius Approach the stationary of data is

essential.For the application of co integration Johnson and Julius Approach is essential that data

should be stationary and integrated at same level. To get achieve the 1st objective means

stationary of data we use different unit root test. These include augmented dickey fuller test

(ADF) and Philip Peron test.Co integration is a technique which tells you about the co movement

among two series in long run. Granger theorem explain that if the co integration test applied

among two variables and the results shows that these two variables are co integrated than the

cause and effect relationship exist among these variable may be in one direction and in both

directions.Impulse response function explains the changes in standard deviation. Variance

decomposition can be defined as decomposition of variance due to changes in same series or


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other series.Vector error correction model is used to find short run relationship among the

variables.

RESULTS

Table 4.1Descriptive Statistics

KSE

(pakistan)

SENSEX

(india)

ASPI 250

(srilanka)

AORD

(Australia)

CAC 40

(france)

FTSE

(UK)

IBEX

(spain)

NIKKEI

(japan)

S&P

(usa)

TSX

(canada)

Mean 0.003824 0.001817 0.003529 0.000541 -0.00018 0.000181 -0.00052 -0.00096 -3.64E-05 0.000605

Median 0.007621 0.005063 0.001009 0.002987 2.64E-05 0.00028 0.002683 0.001712 0.001213 0.003926

Maximum 0.12795 0.131709 0.179536 0.081012 0.092208 0.050323 0.118234 0.114496 0.113559 0.128171

Minimum -0.20098 -0.17381 -0.11327 -0.1771 -0.05635 -0.04779 -0.23827 -0.27884 -0.20084 -0.17542

Std. Dev. 0.036241 0.035845 0.029459 0.022134 0.015713 0.0118 0.032828 0.031721 0.027481 0.02698

Skewness -0.99041 -0.54483 0.62088 -1.21002 0.042962 -0.15702 -0.87247 -1.27312 -0.77636 -0.88222

Kurtosis 6.92368 5.357002 7.377948 10.62872 6.163043 4.579899 8.616395 12.46654 9.149885 8.979096

Jarque-Bera 512.7562 178.9658 549.6353 1700.099 265.7411 68.86762 918.0423 2550.621 1067.825 1031.484

Probability 0 0 0 0 0 0 0 0 0 0

Table 4.1 shows then descriptive of stock markets. The table shows the Mean, Median,

Maximum, Minimum, Standard deviation, Skewness and Kurtosis. The result shows that Karachi

stock exchange and Colombo stock exchange show high return. Sensex stock exchange, AORD,

FTSE and TSX shows positive returns in that time period.CAC40, IBEX and NIKKIE shows

negative returns in this period. Standard deviation of KSE is high as compare to other equity

markets which shows the Karachi stock exchange is more volatile as compare to the other equity

markets. KSE shows high return because it more volatile than other equity markets.


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Unit Root Analysis

Table 4.3

ADF (Level) ADF (1st Difference) Phillips-Perron (Level) Phillips-Perron (1stDifferenc)

KSE -1.49926

-21.4546

-1.5137

-21.5937

SENSEX

-0.36022

-15.5051

-0.45998

-24.4422

ASPI -0.45395

-21.6208

-0.48324

-22.2542

AORD

-1.45412

-25.9649

-1.47416

-25.9552

CAC 40 -2.11501

-24.3204

-1.99994

-24.5384

FTSE

-2.78021

-24.2293

-2.74201

-24.3811

IBEX

-1.50121

-28.0541

-1.62629

-27.9598

NIKKEI -2.10171 -26.2219

-2.11963

-26.2021

S&P -2.20543 -26.5101

-2.13294

-26.5379

TSX -1.24247 -28.0615 -1.38141

-27.9087

Critical Values

1% -3.440419 -3.440435 -3.440387 -3.4404

5% -2.865874 -2.865881 -2.86586 -2.86587

10% -2.569136 -2.56914 -2.569128 -2.56913

Augmented dickey fuller test and Phillips perron test reveal that the time series is not stationary


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at the level but stationary at 1st difference level.

Multivariate Co integration Analysis Trace Statistics

Table 4.4

Hypothesis Eigen Value Trace Statistics Critical Value

5%

Remarks

KSE r = 0* 0.145648 312.5418 239.2354 Trace test shows

2cointegrating

equations at the

0.05

level

SENSEX r ≤ 1* 0.088644 212.4277 197.3709 COLOMBO r ≤ 2 0.070893 153.3932 159.5297 AORD r ≤ 3 0.063824 106.6269 125.6154 CAC 40 r ≤ 4 0.033426 64.68157 95.75366

FTSE r ≤ 5 0.027612 43.05951 69.81889 IBEX r ≤ 6 0.017539 25.25133 47.85613 NIKKEI r ≤ 7 0.010526 13.99760 29.79707 S&P r ≤ 8 0.008307 7.267852 15.49471 TSX r ≤ 9 0.003081 1.962558 3.841466

At the 5% significant level there are 2 co integrating equations. The multivariate analysis shows

that the stock markets are integrated.

Bivariate Co-Integration (KSE)

Table 4.5 Hypothesis Eigen Value Trace Statistics Critical Value Remarks

KSE- AORD r = 0

r ≤ 1

0.008241 7.519592 15.49471

No cointegration

No cointegration

No cointegration

No cointegration

No cointegration

No cointegration

No cointegration

0.003542 2.256833 3.841466

KSE-CAC 40 r = 0

r ≤ 1

0.009964 8.359232 15.49471

0.003125 1.990412 3.841466

KSE-FTSE r = 0

r ≤ 1

0.021182 15.69886 15.49471

0.003269 2.082461 3.841466

KSE-IBEX r = 0

r ≤ 1

0.006629 6.830023 15.49471

0.00408 2.599869 3.841466

KSE-NIKKEI r = 0

r ≤ 1

0.009215 8.402998 15.49471

0.003947 2.515343 3.841466

KSE-S&P r = 0

r ≤ 1

0.011658 10.08032 15.49471

0.004115 2.622512 3.841466

KSE-TSX r = 0

r ≤ 1

0.012372 10.47459 15.49471

0.004013 2.557203 3.841466


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The results shows that Pakistani stock exchange is not cointegrated with Australian, France, UK,

Japan, Spain, USA and Canada equity markets.

Bivariate Co Integration (BSE)

Table 4.6

Hypothesis Eigen Value Trace Statistics Critical Value Remarks

SENSEX-AORD

r = 0

r ≤ 1 0.006318 4.191636 15.49471

No cointegration

No cointegration

No cointegration

No cointegration

No cointegration

No cointegration

No cointegration

0.000252 0.160464 3.841466

SENSEX-CAC 40

r = 0

r ≤ 1 0.009221 6.07274 15.49471

0.000285 0.181058 3.841466

SENSEX-FTSE

r = 0

r ≤ 1 0.01847 12.02036 15.49471

0.000257 0.163699 3.841466

SENSEX- IBEX

r = 0

r ≤ 1 0.005235 3.342285 15.49471

6.88E-06 0.004377 3.841466

SENSEX-

NKKKEI

r = 0

r ≤ 1 0.00767 5.254793 15.49471

0.000562 0.357686 3.841466

SENSEX- S&P

r = 0

r ≤ 1 0.010797 7.718729 15.49471

0.00128 0.814701 3.841466

SENSEX- TSX

r = 0

r ≤ 1 0.013753 9.03368 15.49471

0.000355 0.225819 3.841466

The Table 4.6 shows that Indian stock exchange is not co integrated with Australian, France,

UK, Japan, Spain, USA and Canada equity markets. so investment in BSE SENSEX is a best

opportunity for the investors of develop countries to diversify their portfolios and minimize their

risk.


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Bivariate Co-Integration(CSE)

Table 4.7

Hypothesis Eigen Value Trace Statistics Critical Value Remarks

COLOMBO-AORD r = 0

r ≤ 1 0.005857 3.844433 15.49471

No cointegration

No cointegration

Co integrated

No cointegration

No cointegration

No cointegration

No cointegration

0.00017 0.108132 3.841466

COLOMBO-CAC

40

r = 0

r ≤ 1 0.021299 14.14886 15.49471

0.000717 0.456482 3.841466

COLOMBO-FTSE r = 0

r ≤ 1 0.032161 21.05866 15.49471

0.000421 0.267898 3.841466

COLOMBO-IBEX r = 0

r ≤ 1 0.006291 4.267632 15.49471

0.000399 0.253857 3.841466

COLOMBO-

NIKKEI

r = 0

r ≤ 1 0.010979 8.350885 15.49471

0.002088 1.329564 3.841466

COLOMBO-S&P r = 0

r ≤ 1 0.016857 12.49097 15.49471

0.002636 1.678603 3.841466

COLOMBO-TSX r = 0

r ≤ 1 0.01302 8.856059 15.49471

0.000819 0.52121 3.841466

Table 4.7 describes the Bivariate co integration relationship between Colombo stock exchange

and developed stock exchange. The results shows that Colombo stock exchange is not co-

integrated with Australian, France, Japan, Spain, USA and Canada equity markets but co-

integrated with UK stock market. Investors of all developed countries included in this study

except UK investor has potential to make investment in Sri Lankan stock exchange and take the

advantage of diversification.


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Null Hypothesis: F-Statistics Probability

∆ AORD does not Granger Cause ∆ KSE 0.25783 0.6118

∆ KSE does not Granger Cause ∆ AORD 3.12499 0.0776

∆ CAC 40 does not Granger Cause ∆ KSE 1.89861 0.1687

∆ KSE does not Granger Cause ∆ CAC 40 0.02131 0.884

∆ FTSE does not Granger Cause ∆ KSE 2.0381 0.1539

∆ KSE does not Granger Cause ∆ FTSE 4.66571 0.0311

∆ IBEX does not Granger Cause ∆ KSE 0.34208 0.5588

∆ KSE does not Granger Cause ∆ IBEX 2.44861 0.1181

∆ NIKKEI does not Granger Cause ∆ KSE 0.19203 0.6614

∆ KSE does not Granger Cause ∆ NIKKEI 0.84354 0.3587

∆ S&P does not Granger Cause ∆ KSE 0.22564 0.6349

∆ KSE does not Granger Cause ∆ S&P 2.3697 0.1242

∆ TSX does not Granger Cause ∆ KSE 0.4421 0.5064

∆ KSE does not Granger Cause ∆ TSX 7.1785 0.0076

∆ AORD does not Granger Cause ∆ SENSEX 0.07906 0.7787

∆ SENSEX does not Granger Cause ∆ AORD 0.81492 0.367

∆ CAC 40 does not Granger Cause ∆ SENSEX 0.52886 0.4674

∆ SENSEX does not Granger Cause ∆ CAC 40 0.45024 0.5025

∆ FTSE does not Granger Cause ∆ SENSEX 0.25534 0.6135

∆ SENSEX does not Granger Cause ∆ FTSE 2.97084 0.0853

∆ IBEX does not Granger Cause ∆ SENSEX 1.78757 0.1817

∆ SENSEX does not Granger Cause ∆ IBEX 1.07832 0.2995

∆ NIKKEI does not Granger Cause ∆ SENSEX 2.51637 0.1132

∆ SENSEX does not Granger Cause ∆ NIKKEI 0.22026 0.639

∆ S&P does not Granger Cause ∆ SENSEX 3.78046 0.0523

∆ SENSEX does not Granger Cause ∆ S&P 1.61552 0.2042

∆ TSX does not Granger Cause ∆ SENSEX 2.75018 0.0977

∆ SENSEX does not Granger Cause ∆ TSX 10.2953 0.0014

∆ AORD does not Granger Cause ∆ COLOMBO 0.41443 0.52

∆ COLOMBO does not Granger Cause ∆ AORD 1.48789 0.223

∆ CAC 40 does not Granger Cause ∆ COLOMBO 10.7344 0.0011

∆ COLOMBO does not Granger Cause ∆ CAC 40 0.1514 0.6973

∆ FTSE does not Granger Cause ∆ COLOMBO 10.9008 0.001

∆ COLOMBO does not Granger ∆ Cause FTSE 3.93801 0.0476

∆ IBEX does not Granger Cause ∆ COLOMBO 1.5246 0.2174

∆ COLOMBO does not Granger Cause ∆ IBEX 0.81988 0.3656

∆ NIKKEI does not Granger Cause ∆ COLOMBO 4.52995 0.0337

∆ COLOMBO does not Granger Cause ∆ NIKKEI 0.31224 0.5765

∆ S&P does not Granger Cause ∆ COLOMBO 6.66636 0.01

∆ COLOMBO does not Granger Cause ∆ S&P 2.78428 0.0957

∆ TSX does not Granger Cause ∆ COLOMBO 3.08738 0.0794

∆ COLOMBO does not Granger Cause ∆ TSX 5.89071 0.0155


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Granger Causality Test

Table 4.8

Results of granger causality test indicates that KSE does not granger causes the stock returns in

other equity markets except Sensex, Ftse, Tsx. Only unidirectional causality is found from KSE

to Sensex,Ftse, Tsx. Similarly results showed that Colombo stock returns cause granger in

sensex and sensex stock returns cause granger in TSX stock returns. Results showed that Stock

Returns in Nikkei granger cause stock returns in Colombo and Stock returns in Colombo Equity

Market granger cause stock returns in TSX. The results revealed that FTSE granger cause to

Colombo stock exchange and Colombo cause granger to FTSE. However unidirectional causality

exists from CAC40 to Colombo stock returns.

Table 4.9 Variance Decomposition Analysis of KSE

Period S.E.

KSE

(pakistan)

SENSEX

(india)

COLOMB

O

(srilanka)

AORD

(Australia

)

CAC 40

(france)

FTSE

(UK)

IBEX

(spain)

NIKKEI

(japan)

S&P

(usa)

TSX

(canada

)

1 0.035884 100 0 0 0 0 0 0 0 0 0

2 0.036524 98.90687 0.241973 0.284236 0.341862 0.018681 0.083699 0.037755 0.067382 0.000319 0.01722

3 0.036556 98.79011 0.285881 0.318461 0.352305 0.021663 0.086651 0.03769 0.068174 0.016794 0.02227

4 0.036557 98.78312 0.289282 0.321402 0.352607 0.021781 0.086663 0.037692 0.068187 0.016866 0.0224

5 0.036557 98.7828 0.289412 0.321528 0.352613 0.021782 0.086664 0.037692 0.068203 0.01688 0.02242

6 0.036557 98.78278 0.289423 0.321535 0.352614 0.021782 0.086664 0.037692 0.068204 0.016881 0.02242

7 0.036557 98.78278 0.289423 0.321536 0.352614 0.021782 0.086664 0.037692 0.068204 0.016881 0.02242

8 0.036557 98.78278 0.289423 0.321536 0.352614 0.021782 0.086664 0.037692 0.068204 0.016881 0.02242

9 0.036557 98.78278 0.289423 0.321536 0.352614 0.021782 0.086664 0.037692 0.068204 0.016881 0.02242

10 0.036557 98.78278 0.289423 0.321536 0.352614 0.021782 0.086664 0.037692 0.068204 0.016881 0.02242


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Table 4.9 indicates that the change in Karachi stock exchange is occurs due to innovations or

fluctuation in itself. We interpret as the other developed and developing stock markets not cause

change in KSE if any change or fluctuation or innovation occur in these stock exchange.


International Journal of Management and Strategy ISSN: 2231-0703 15

Table 4.10 Variance Decomposition Analysis of BSE

The table 4.10 indicates that the change in Bombay stock exchange (sensex) explained by due to

its own innovations or fluctuation. In the other way we interpret as the other developed and

developing stock markets not cause change in Bombay stock exchange (Sensex) if any change or

fluctuation or innovation occur in these stock exchange.

Per

iod S.E.

KSE

(Pakistan)

SENSEX

(India)

COLMBO

(Sri

Lanka)

AORD

(Australia)

CAC 40

(France)

FTSE

(UK)

IBEX

(Spain)

NIKKEI

(Japan)

S&P

(USA)

TSX

(Canada

)

1 0.035964 1.211264 98.78874 0 0 0 0 0 0 0 0

2 0.036153 1.353305 97.84841 0.195813 0.206444 0.012456 9.14E-06 0.007202 0.054484 0.283882 0.03799

3 0.036156 1.353086 97.83522 0.204383 0.206445 0.012542 1.14E-05 0.007578 0.05742 0.285302 0.03801

4 0.036156 1.353103 97.83454 0.204694 0.206492 0.012542 1.19E-05 0.007578 0.057423 0.28541 0.03821

5 0.036157 1.353102 97.8345 0.204714 0.206494 0.012542 1.33E-05 0.007578 0.057424 0.285411 0.03822

6 0.036157 1.353102 97.8345 0.204715 0.206494 0.012542 1.33E-05 0.007578 0.057424 0.285411 0.03822

7 0.036157 1.353102 97.8345 0.204715 0.206494 0.012542 1.33E-05 0.007578 0.057424 0.285411 0.03822

8 0.036157 1.353102 97.8345 0.204715 0.206494 0.012542 1.33E-05 0.007578 0.057424 0.285411 0.03822

9 0.036157 1.353102 97.8345 0.204715 0.206494 0.012542 1.33E-05 0.007578 0.057424 0.285411 0.03822

10 0.036157 1.353102 97.8345 0.204715 0.206494 0.012542 1.33E-05 0.007578 0.057424 0.285411 0.03822



Table 4.11Variance Decomposition Analysis of CSE

The above table 4.11 indicates that the change in Colombo stock exchange explained by due to

its own innovations or fluctuation. In the other way we interpret as the other developed and

developing stock markets not cause change in Colombo stock exchange if any change or

fluctuation or innovation occur in these stock exchange.

Peri

od S.E.

KSE

(pakistan)

SENSEX

(india)

COLOMBO

(srilanka)

AORD

(Australia)

CAC 40

(france)

FTSE

(UK)

IBEX

(spain)

NIKKEI

(japan)

S&P

(usa)

TSX

(canada

)

1 0.028804 0.005394 0.362819 99.63179 0 0 0 0 0 0 0

2 0.02964 0.030621 2.583574 96.01739 0.014807 0.151848 0.039082 0.00626 0.592714 0.034914 0.52879

3 0.029687 0.040382 2.730224 95.80453 0.035659 0.152015 0.040776 0.006243 0.600124 0.061184 0.52886

4 0.029688 0.04038 2.732398 95.80165 0.035814 0.152094 0.040819 0.006268 0.600563 0.061189 0.52882

5 0.029688 0.040387 2.732565 95.80143 0.035825 0.152094 0.040819 0.006277 0.600561 0.061197 0.52884

6 0.029688 0.040388 2.732572 95.80142 0.035826 0.152094 0.040819 0.006277 0.600561 0.061198 0.52884

7 0.029688 0.040388 2.732572 95.80142 0.035826 0.152094 0.040819 0.006277 0.600561 0.061198 0.52884

8 0.029688 0.040388 2.732572 95.80142 0.035826 0.152094 0.040819 0.006277 0.600561 0.061198 0.52884

9 0.029688 0.040388 2.732572 95.80142 0.035826 0.152094 0.040819 0.006277 0.600561 0.061198 0.52884

10 0.029688 0.040388 2.732572 95.80142 0.035826 0.152094 0.040819 0.006277 0.600561 0.061198 0.52884



Table 4.12Error Correction Model

Dependent variable is dPAK

637 observations used for estimation from 2 to 638

Regressor Coefficient Standard Error T-Ratio[Prob]

dSENSEX .0028050 .0036463 .76925[.442]

dCOLOMBO.020186 .0073241 2.7562[.006]

dAUS .0021854 .0045320 .48221[.630]

dFRNCE.22426 .048795 4.5960[.000]

dUK -.022703 .020701 -1.0967[.273]

dSPAIN .051469 .018462 2.7878[.005]

dJPN -.077878 .034026 -2.2888[.022]

dAMRCA .12608 .027025 4.6652[.000]

dCANADA.52344 .039028 13.4117[.000]

ecm(-1) -.12410 .017035 -7.2854[.000]

List of additional temporary variables created: dPAK = PAK-PAK(-1) dSENSEX = SENSEX-SENSEX(-1) dCOLOMBO = COLOMBO-COLOMBO(-1) dAUS = AUS-AUS(-1) dFRNCE = FRNCE-FRNCE(-1) dUK = UK-UK(-1) dSPAIN = SPAIN-SPAIN(-1) dJPN = JPN-JPN(-1) dAMRCA = AMRCA-AMRCA(-1) dCANADA = CANADA-CANADA(-1)

ecm = PAK -.022602*SENSEX -.16266*COLOMBO -.017609*AUS + .075488*FRNCE + .18294*UK -.41473*SPAIN -.30712*JPN + .053244*AMRCA -.51731*CANADA

R-Squared .53392 R-Bar-Squared .52419

S.E. of Regression .018611 F-stat. F( 9, 627) 79.2963[.000]

Mean of Dependent Variable .6049E-3 S.D. of Dependent Variable .026980

Residual Sum of Squares .21578 Equation Log-likelihood 1641.0

Akaike Info. Criterion 1627.0 Schwarz Bayesian Criterion 1595.8

DW-statistic 2.3662

The results of error correction model indicates that coefficient of ECM(-1) value is -.12410.

which means that the Adjustment process is low and 12.41 percent of the previous years

disequilibrium in share prices from its equilibrium path will be corrected this year. The result



shows that American, French, Spanish and Canadian equity markets show no relationship with

Karachi stock market in long run but these equity markets are statically significant in short term.


Dependent variable is dSENSEX



dCOLOMBO .12047 .041197 2.9242[.004]

dAUS .025862 .0085624 3.0204[.003]

dFRNCE .039406 .023806 1.6553[.098]

dUK.0036160 .039378 .091829[.927]

dSPAIN -.0016270 .035451 -.045894[.963]

dJPN -.0010339 .026625 -.038832[.969]

dAMRCA.032952 .016544 1.9917[.047]

dCANADA -.0017437 .026311 -.066273[.947]

dPAK -.073771 .033259 -2.2181[.027]

ecm(-1) -.024472 .0068550 -3.5699[.000]

List of additional temporary variables created: dSENSEX = SENSEX-SENSEX(-1)

dCOLOMBO = COLOMBO-COLOMBO(-1) dAUS = AUS-AUS(-1)

dFRNCE = FRNCE-FRNCE(-1) dUK = UK-UK(-1)

dSPAIN = SPAIN-SPAIN(-1) dJPN = JPN-JPN(-1)

dAMRCA = AMRCA-AMRCA(-1) dCANADA = CANADA-CANADA(-1) dPAK = PAK-PAK(-1)

ecm = SENSEX -.32625*COLOMBO -1.0568*AUS -1.6103*FRNCE -.14776*UK +

.066486*SPAIN + .042247*JPN -1.3465*AMRCA + .071254*CANADA + 3.0145*PAK



Mean of Dependent Variable .0038244 S.D. of Dependent Variable .036241



DW-statistic 1.7264



The results of error correction model indicate that coefficient of ECM (-1) value is -.02447. This

means that the Adjustment process is low and 02.44 percent of the previous year‟s


shows that Australian and American equity markets show no relationship with Bombay stock

market in long run but these equity markets are statically significant in short term.


Dependent variable is dCOLOMBO



dSENSEX.0052678 .0056378 .93438[.350]

dAUS .0060010 .0070269 .85400[.393]

dFRNCE .50747 .074495 6.8121[.000]

dUK -.018855 .032149 -.58647[.558]

dSPAIN -.0024261 .028733 -.084436[.933]

dJPN .25361 .047453 5.3443[.000]

dAMRCA -.017365 .013581 -1.2787[.201]

dCANADA -.019410 .021332 -.90991[.363]

dPAK .22845 .055122 4.1445[.000]

ecm(-1) -.035474 .011456 -3.0966[.002]

List of additional temporary variables created: dCOLOMBO = COLOMBO-COLOMBO(-1) dSENSEX = SENSEX-SENSEX(-1) dAUS = AUS-AUS(-1) dFRNCE = FRNCE-FRNCE(-1) dUK = UK-UK(-1) dSPAIN = SPAIN-SPAIN(-1) dJPN = JPN-JPN(-1) dAMRCA = AMRCA-AMRCA(-1) dCANADA = CANADA-CANADA(-1) dPAK = PAK-PAK(-1)

ecm = COLOMBO -.14850*SENSEX -.16916*AUS -.68700*FRNCE + .53150*UK + .068390*SPAIN -.52220*JPN + .48952*AMRCA + .54717*CANADA -1.0223*PAK



Mean of Dependent Variable .0018166 S.D. of Dependent Variable .035845



DW-statistic 2.0703



The results of error correction model indicate that coefficient of ECM (-1) value is -.03547. This

means that the Adjustment process is low and 03.47 percent of the previous year‟s


shows that Japanese equity markets show no relationship with Colombo stock market in long run

but these equity markets are statically significant in short term. The UK equity market

integratedin long run and also in short run UK equity market statically significant with Colombo

stock market in short term.

CONCLUSION

In this study we take major south Asian stock markets and developed stock markets. No doubt

there is diversity in the social, economic and political environment in these both type of south

Asian and developed country, in spite of that this study investigate whether these countries stock

markets have closely integrated or not.This study reveals that Karachi stock exchange is more

volatile than other stock exchanges because it shows high return at high risk. As compare to

KSE, Colombo stock exchange shows low return comparatively at low at low risk, TSX and

FTSE shows low return at low risk. So high level of returns in the emerging equity markets

(including KSE,SENSEX,CSE) attract full for the investors who wants to make investments in

the equity markets. This study aimed to help the investors in decision making about the

investments. This study help full for investors that they can get the advantage diversification and

reduce the unsystematic risk by investing in the international equity markets. So ADF and PP

tests are used for the stationary of data and found that data is integrated at same order. Bivariate

co integration which indicates that KSE and BSE also not show co movements with developed

stock markets in the long run. The resutls of the impulse response function shows standard

deviation change in a market because of one standard deviation change in other market. Resutls

the response of KSE to the changes in the developed equity markets. Results showed that

change in NYSE brings change in KSE. However, results of Impulse Response Function shows

that KSE,BSE and CSE returns are not influnced by the shocks in the other marekts. Finally it is

conclude that the investors can minimize their country systematic risk by making investment in

the developed equity markets and emerging equity markets. This study support to the Hassan et

al.(2009) and Hussain et al.(2012) concluded that major south Asian markets including India and

Pakistan equity markets are independent.



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APPENDIX

Annexure I

Impulse response

-.02

.00

.02

.04

2 4 6 8 10

Response of SER01 to SER01

-.02

.00

.02

.04

2 4 6 8 10


-.02

.00

.02

.04

2 4 6 8 10


-.02

.00

.02

.04

2 4 6 8 10


-.02

.00

.02

.04

2 4 6 8 10


-.02

.00

.02

.04

2 4 6 8 10


-.02

.00

.02

.04

2 4 6 8 10


-.02

.00

.02

.04

2 4 6 8 10


-.02

.00

.02

.04

2 4 6 8 10


-.02

.00

.02

.04

2 4 6 8 10


-.02

.00

.02

.04

2 4 6 8 10


-.02

.00

.02

.04

2 4 6 8 10


-.02

.00

.02

.04

2 4 6 8 10


-.02

.00

.02

.04

2 4 6 8 10


-.02

.00

.02

.04

2 4 6 8 10


-.02

.00

.02

.04

2 4 6 8 10


-.02

.00

.02

.04

2 4 6 8 10


-.02

.00

.02

.04

2 4 6 8 10


-.02

.00

.02

.04

2 4 6 8 10


-.02

.00

.02

.04

2 4 6 8 10


-.02

.00

.02

.04

2 4 6 8 10


-.02

.00

.02

.04

2 4 6 8 10


-.02

.00

.02

.04

2 4 6 8 10


-.02

.00

.02

.04

2 4 6 8 10


-.02

.00

.02

.04

2 4 6 8 10


-.02

.00

.02

.04

2 4 6 8 10


-.02

.00

.02

.04

2 4 6 8 10


-.02

.00

.02

.04

2 4 6 8 10


-.02

.00

.02

.04

2 4 6 8 10


-.02

.00

.02

.04

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.005

.000

.005

.010

.015

2 4 6 8 10


-.005

.000

.005

.010

.015

2 4 6 8 10


-.005

.000

.005

.010

.015

2 4 6 8 10


-.005

.000

.005

.010

.015

2 4 6 8 10


-.005

.000

.005

.010

.015

2 4 6 8 10


-.005

.000

.005

.010

.015

2 4 6 8 10


-.005

.000

.005

.010

.015

2 4 6 8 10


-.005

.000

.005

.010

.015

2 4 6 8 10


-.005

.000

.005

.010

.015

2 4 6 8 10


-.005

.000

.005

.010

.015

2 4 6 8 10


-.01

.00

.01

.02

.03

2 4 6 8 10


-.01

.00

.01

.02

.03

2 4 6 8 10


-.01

.00

.01

.02

.03

2 4 6 8 10


-.01

.00

.01

.02

.03

2 4 6 8 10


-.01

.00

.01

.02

.03

2 4 6 8 10


-.01

.00

.01

.02

.03

2 4 6 8 10


-.01

.00

.01

.02

.03

2 4 6 8 10


-.01

.00

.01

.02

.03

2 4 6 8 10


-.01

.00

.01

.02

.03

2 4 6 8 10


-.01

.00

.01

.02

.03

2 4 6 8 10


-.01

.00

.01

.02

.03

2 4 6 8 10


-.01

.00

.01

.02

.03

2 4 6 8 10


-.01

.00

.01

.02

.03

2 4 6 8 10


-.01

.00

.01

.02

.03

2 4 6 8 10


-.01

.00

.01

.02

.03

2 4 6 8 10


-.01

.00

.01

.02

.03

2 4 6 8 10


-.01

.00

.01

.02

.03

2 4 6 8 10


-.01

.00

.01

.02

.03

2 4 6 8 10


-.01

.00

.01

.02

.03

2 4 6 8 10


-.01

.00

.01

.02

.03

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


-.01

.00

.01

.02

2 4 6 8 10


Response to Cholesky One S.D. Innovations ± 2 S.E.

(Here Ser01 represents Kse, Ser02 represents Bse,Ser03 represents Cse,Ser04 represents the AORD, Ser05 represents the

CAC40,Ser06 represents FTSE,Ser07 represents IBEX,Ser08 represents NIKKEI,Ser09 represents the S&P.Ser10 represents

TSX.)

long run relationship between south asian equity markets and equity markets of developed world

Documents

developed equity markets

globalization of markets

integration of equity

major asian equity markets

major south asian markets

south asian countries

developed countries

objective of globalization