Efficiency of Capital Markets: A Study with Special Reference To G7 Nations Stock Markets

 

Dr. M. Babu¹, C. Hariharan²

¹Assistant Professor, Bharathidasan School of Management, Bharathidasan University, Tiriuchirappalli.

²Ph. D Research Scholar, Bharathidasan School of Management, Bharathidasan University, Tiriuchirappalli.

 

ABSTRACT:

This study aims to explore the linkages among G7 nations’ stock market indices, namely, CAC 40, FTSE 100, FTSE MIB, GDAXI, NIKKEI 225, NYSE COMPOSITE and S&P TSX COMPOSITE during the study period from April 2004 to March 2014 by using Co integration Test, Vector Error Correction Model (VECM) and Granger Causality Test. The results of Johansen Co integration Test found that daily returns of G7 nations stock market indices were Co Integrated, The co efficient value of Vector Error Correction Model implied that the FTSE MIB, GADAXI and S&P Tesx Composite did not witness short term relationship with CAC 40 INDEX, FTSE 100, NIKKEI 225 and NYSE COMPOSITE INDEX, rest of the indices recorded short run relationship. The results of Granger Causality Test exhibited bidirectional relationship between following indices - S&P TSX COMPOSITE and CAC 40 INDEX, S&P TSX COMPOSITE and FTSE MIB, S&P TSX COMPOSITE and NYSE COMPOSITE INDEX, CAC 40 and FTSE MIB. Unidirectional causal relationship was found between FTSE 100 and GDAXI, FTSE MIB and NIKKEI 225, NIKKEI 225 and S&P TSX COMPOSITE. Finally the study concluded that G7 nations’ stock market indices recorded both Short and Long run linkages during the study period, hence international investors G7 nations' investors could go for both long run and short run diversification for reducing their investment risk in future, if investors diversify their investment in to listed companies of G7 nations' stock market indices.

 

 

 

1. INTRODUCTION:

International financial markets have developed rapidly throughout the last four decades, for the reasons of internationalization, securitization, and liberalization. It is universally accepted that the net effect of these developments has been to expand the set of states of nature against which market operators can insure and/or upon which they can speculate.

 

Financial integration is great when there stay alive a complete set of international financial markets that allow economic and financial market participants to insure against the full set of anticipated states of nature.

 

Co movement is clearly defined as a pattern of positive correlation. However, positive correlation is unclear term and can show several types of associations. Exactly, co movement represents a fact of an asset price moving with other asset price. Moving with is the movement that is shared by all assets or movement that all assets have in common. Increasing globalization among economies of the world has increased attention of academics and investors to the subject of co movement among the stock markets around the world. In today’s express moving finance globe, there are rich factors integrating financial markets to each other. The existence of healthy trading and economic relations, the growth in liberalization activities of governments, the development of international finance and trade, rapid developments in trading systems and telecommunication, and the establishment of common trading union such as European Union, NAFTA, SAARC, BRICS, G7 and ASEAN are some factors causal to financial integration. Similarly, reported positive impact of financial and trade liberalization reforms on the level of cross country equity market linkages. Likewise, the formulation of strong policy coordination and economic ties between associated countries can ultimately connect their stock indices over time. It is evident from the literature that, not only the developed countries but also the financial markets of developing countries become interrelated. However, the intensity of interdependence among equity markets varies in developed and developing economies. Hence the study investigates the co movement among G7 stock market indices. 

 

2. Literature Review:

Rajkumar, G. S. (2015) investigated the relationship between Indian stock market index daily returns and the three stock markets indices daily returns of the ASEAN countries - Indonesia, Malaysia, and Singapore during the study period 2004 - 2014. Using the Granger-causality and co-integration test, the study found that there existed significant short-term unidirectional influenced from the Indian stock market to the three ASEAN countries stock markets while no long-term relation (no co-integration) were found between the Indian equity market with that of three ASEAN countries viz. Indonesia, Malaysia and Singapore equity markets. Guidi and Ugur (2014) investigated integration of South-Eastern European stock markets, namely, Bulgaria, Croatia, Romania, Slovenia and Turkey, within developed countries counterparts namely, Germany, UK and USA. Using static co-integration analysis, this study found that the South-Eastern European markets were co-integrated with German and UK markets but no such evidence was noticed with the USA markets during the period 2000–2013. The dynamic co-integration analysis revealed co integration among the South-Eastern European markets and their developed counterparts. Dasgupta (2014) examined short- and long-run integration and linkages of Indian stock markets with BRIC stock markets by using Johansen Co-integration test and Granger Causality test. The study found that Indian stock markets recorded co-movement with Brazilian, Russian and Chinese stock markets during the study period from January 2003 to December 2012. Lehkonen (2014) explored the co-movements between BRICs and developed markets, namely, Canada, Hong Kong, Australia, UK, Germany and Japan, by using Dynamic Conditional Correlation Analysis. The outcome of the study revealed that BRIC stock markets were linked with UK, Germany and Japan. Małgorzata Doman and Ryszard Doman (2013) examined the impact of globalization and crises on the power of linkages between stock markets. An analysis was performed for join up of national stock markets, represent by the daily returns on stock indices, including the SandP 500, DAX, FTSE, CAC 40, RTS, WIG 20, NIKKEI, STI, BSE, and BOVESPA.  A systematic and significant increase in the mean level of the linkages between the national stock markets was observed. K. Malarvizhi and M.Jaya (2012) analyzed the dynamic relationship between the exchange rate and Indian stock market index, NIFTY. ADF Test, Johansen Maximum Likelihood Test, Pairwise Granger Causality Test were used to analyze the dynamic relationship. The Johansen maximum likelihood test results clearly showed that there was no co movement between NIFTY and exchange rate. Pairwise Granger causality test found that there was bidirectional causal relationship between exchange rate and Nifty. Jagroop Kaur (2012) examined the relationship between US stock exchange and Islamic countries stock exchanges, namely, Indonesia, Israel, Malaysia and Pakistan. The research found relationship between US and Islamic Countries’ stock markets and Islamic stock markets were affected by the US stock markets. Searat Ali and Babar Zaheer Butt (2011) investigated the co movement of Pakistan’s Equity Market with the markets of India, China, Indonesia, Singapore, Taiwan, Malaysia, Japan, USA and UK by using Co Integration Test for the monthly stock prices, during the period from July 1998 to June 2008. The results revealed that there was no co movement between Pakistan’s equity market and the markets of UK, USA, Taiwan, Malaysia and Singapore. Burcu Kiran (2011) studied the relationship between Oil prices and stock market index of G7 nations by using Robinson Tests. The study found that there was fractional co integration relationship between oil prices and DAX 30, Dow Jones, FTSE 100 and SP-TSX indices. Kui Fan, et al.(2009) examined the relationship between the Chinese and selected international main stock markets, namely, U.S., U.K., Japan and Hong Kong. A significant trend of long-term co-movement between the Chinese and other international stock markets was noticed since 1999 and in the short term, Chinese market was directly or indirectly adapted by the other stock markets. Walid M.A. Ahmed (2008) investigated the long and short-run dynamic linkages between the stock exchange of Egypt and its counterparts in Group of Seven (G7) countries. The study used Johansens co integration and Variance Decomposition Analyses. No pair wise long-run co integration relationships were noticed between Egyptian and G7 countries across the pre- and post-attack periods. Alessandra Bonfiglioli (2005) analyzed co movements between Germany and US stock markets by using Co integration analysis. The empirical study found no long-term interdependence between US and German stock markets. A. Tahai and Robert W (2004) examined the financial co integration of G7 industrialized equity markets and found that G7 industrialized nation’s equity markets were co integrated during the study period March 1978 to December 1997.  M. Kabihassan (2001) investigated the dynamic linkages between U.S, Japan, U.K and German Stock market indices, by using VECM and Co integration. The study found short run and long run relationships between U.S, Japan, U.K and German Stock market indices during the study period from April 1984 to May 1991. 

 

3.METHODOLOGY AND DATA SOURCE:

3.1Objectives of the Study:

1.    To examine whether the G7 countries' stock markets index daily returns are normally distributed during the study period.

2.    To evaluate whether the G7 countries' stock markets index daily returns are stationary during the study period.

3.    To examine the volatility of G7 countries' stock index daily returns during the study period.

4.    To find the long run and short run Co Movement among G7 countries' stock markets Index daily returns during the study period.

5.    To know the causal relationship between G7 nations' stock markets Index daily returns during the study period.

 

3.2 Null Hypotheses of the Study:

NH₀₁: The G7 countries' stock market index daily price returns are not normally distributed during the study period.

NH₀₂: The daily index returns of G7 stock markets are not stationary during the study period.

NH₀₃: There is no significant volatility in the daily index returns of G7 countries' stock markets during the study period.

NH₀₄: There is no long run relationship between the daily index returns of G7 countries' stock markets during the study period.

NH₀₅: There is no short run relationship between daily index returns of G7 countries' stock markets during the study period.

NH₀₆: There is no causal relationship between daily index returns of G7 countries' stock markets during the study period.

              

3.3   Sample selection:

The G7 nations namely, Canada, France, Germany, Italy, Japan, UK, US stock markets were considered for the analysis of this study. From these countries, top stock markets were selected on the basis of domestic market capitalization data taken from World Federation of Exchanges. Benchmark indices were selected from listed stock indices in the respective stock exchanges websites. The names of major indices are given in Table – 1.

 

G7 (Developed Countries)	STOCK EXCHANGE	INDEX
CANADA	Toronto Stock Exchange	S&P/TSX Composite index
FRANCE	Euronext paries	CAC 40
GERMANY	Frankfurt Stock Exchange	GDAXI
ITALY	Borsa Italiana	FTSE MIB
JAPAN	Tokyo Stock Exchange	Nikkei 225
UK	London Stock Exchange	FTSE 100 Index
US	New York Stock Exchange	NYSE Composite index

Source: Data collected from the World Federation of Exchanges, investingonline.com and official websites of stock markets. 

 

3.4   Data Collection:

The daily prices of G7 nations’ stock market indices were collected for analyzing the stock market linkages. The daily closing prices of selected sample indices were collected from Yahoo Finance, FTSE Website and other data were collected from various books, journals and websites.

 

3.5   Period of the Study:

The present study covered a period of ten years from April 2004 to March 2014, for analyzing the inter linkages among G7 countries stock markets.

 

3.6 Tools used for the Study:

For testing the null hypothesis, Statistic/Econometric tools like, Descriptive Statistics, Kolmogorov–Smirnov Test and Shapiro-Wilk Test, Augmented Dickey Fuller Test, Phillips – Perron Test, GARCH (1, 1) Model, Johansen Co Integration Test, Vector Error Correction Model and Granger Causality Test were used.

 

4. EMPIRICAL RESULTS AND DISCUSSION:

Table - 2 shows the descriptive statistics which include mean, median, maximum, minimum, standard deviation, skewness, kurtosis and Jarque-Bera for Group Seven nations' (G7) stock markets indices, namely, CAC 40, FTSE 100 INDEX, FTSE MIB, GDAXI, NIKKEI 225, NYSE COMPOSITE INDEX, S&P TSX COMPOSITE INDEX during the study period April 2004 to March 2014. NYSE COMPOSITE INDEX recorded the highest mean return of 0.000275, with the standard deviation of 0.013563. The second largest mean return was recorded by S&P TSX COMPOSITE INDEX (0.000267), with the lowest standard deviation of 0.011761, followed by the FTSE 100 INDEX mean return at 0.000226 with the standard deviation of 0.011919. NIKKEI 225 recorded the highest standard deviation of 0.015722, with mean returns of 0.000221, followed by FTSE MIB and CAC 40, with high standard deviation of 0.015449 and 0.01426 respectively, with the mean return of 0.0000281 and 0.000183. Negative mean return was recorded in GDAXI index at 0.000252, with the standard deviation of 0.013814 during the study period. CAC 40 index, FTSE 100 INDEX, FTSE MIB, GDAXI returns were positively skewed but NIKKEI 225, NYSE COMPOSITE INDEX, S&P TSX COMPOSITE INDEX returns were negatively skewed during the study period. The sample indices returns of G7 stock markets were leptokurtic as the kurtosis value was greater than the three. Jarque-Bera Test confirmed that the sample indices of G7 stock markets, namely, CAC 40, FTSE 100 INDEX, FTSE MIB, GDAXI, NIKKEI 225, NYSE COMPOSITE INDEX, S&P TSX COMPOSITE INDEX returns were normally distributed during the study period from April 2004 to March 2014.

 

 

Table - 2 Results of Descriptive Statistics for sample indices returns of G7 countries’ stock markets during the study from period April 2004 to March 2014.

	CAC 40	FTSE 100 INDEX	FTSE MIB	GDAX INDEX	NIKKEI 225	NYSE COMPOSITE INDEX	S &P TSX COMPOSITE INDEX
Mean	0.00018	0.00022	0.00002	-0.0002	0.00022	0.000275	0.000267
Median	0.00039	0.00012	0.00068	-0.0009	0.00044	0.000778	0.000656
Maximum	0.11176	0.09838	0.11487	0.07716	0.14150	0.122162	0.098233
Minimum	-0.0903	-0.0884	-0.0823	-0.1023	-0.1140	-0.09726	-0.093241
Std. Dev.	0.01426	0.01191	0.01544	0.01381	0.01572	0.013563	0.011761
Skewness	0.24543	0.04389	0.11628	0.16474	-0.3394	-0.165886	-0.501189
Kurtosis	10.6078	12.2457	9.02727	9.64453	11.1440	13.6696	13.55288
Jarque-Bera	6330.18	9290.07	3882.27	4717.20	6834.45	11945.81	11985.92

 Source: Data collected from yahoo finance and FTSE computed from E Views 7

 

The results of normality test for sample indices daily returns of G7 countries' stock markets by using Kolmogorov-Smirnov and Shapiro-Wilk Tests, during the study period from April 2004 to March 2014, are shown in the Table – 3. The significant value of both the tests Kolmogorov-Smirnov and Shapiro-Wilk was 0.000 for sample indices of G7 nations namely, CAC 40, FTSE 100 INDEX, FTSE MIB, GDAXI, NIKKEI 225, NYSE COMPOSITE INDEX and S&P TSX COMPOSITE INDEX and this significant value was less than the critical value of 0.05. These results proved that sample indices of G7 stock markets daily returns were normally distributed. Hence the Null Hypothesis NH₀₁ “The G7 nations’ stock market index daily returns are not normally distributed during the study period” is rejected

.

 

Table - 3. Summary of Normality Test for daily returns of G7 nations stock market indices during the study period from April 2004 to March 2014.

Source: Data collected from yahoo finance and FTSE, computed from SPSS 17

df – degrees of freedom

 

The results of stationary test, by using Augmented Dickey Fuller Test and Phillips-Perron Test, for sample indices of G7 nations’ stock markets daily returns during the study period from April 2004 to March 2014, are displayed in the Table – 4. Augmented Dickey Fuller Test statistic values of CAC 40 (-33.22623), FTSE 100 INDEX (-26.06543), FTSE MIB (-50.92903), GDAXI (-50.30337), NIKKEI 225 (-51.83306), NYSE COMPOSITE INDEX  (-39.58426) and S&P TSX COMPOSITE INDEX (-23.50265) were less than the test critical value at 1%, 5% and 10% level. The Phillips-Perron Test statistic values of CAC 40 (-54.49792), FTSE 100 INDEX (-54.0033), FTSE MIB (-51.02479), GDAXI (-50.37302), NIKKEI 225 (-51.93483), NYSE COMPOSITE INDEX (-55.49443) and S&P TSX COMPOSITE INDEX     (-52.87044) were also less than the test critical value at 1%, 5% and 10% level. The Augmented Dickey Fuller Test and Phillips-Perron Tests proved that the G7 nations’ stock market sample indices daily returns attained stationarity during the study period. Hence reject the Null Hypothesis namely NH₀₂ “The daily index returns of G7 Stock markets are not stationary during the study period”.

 

Table - 4 Results of Augmented Dickey-Fuller Test and Phillips-Perron Test for G7 countries’ Stock markets sample indices daily returns during the study period from April 2004 to March 2014.

INDEX	Augmented Dickey-Fuller test statistic (T-statistics)	Phillips-Perron test statistic (T-statistics)	Test critical values
			1% level	5% level	10% level
CAC 40	-33.2262	-54.49792	-3.432662	-2.862447	-2.567298
FTSE 100	-26.0654	-54.0033	-3.432668	-2.86245	-2.567299
FTSE MIB	-50.929	-51.02479	-3.432711	-2.862469	-2.56731
GDAXI	-50.3034	-50.37302	-3.432714	-2.86247	-2.56731
NIKKEI 225	-51.8331	-51.93483	-3.432819	-2.862517	-2.567335
NYSE COMPOSITE	-39.5843	-55.49443	-3.432757	-2.862489	-2.567321
S&P TSX COMPOSITE	-23.5027	-52.87044	-3.432717	-2.862472	-2.567311

Source: Data collected from yahoo finance and FTSE computed from E Views 7

 

The results of GARCH (1, 1) model for G7 nations’ stock market indices daily returns are shown in Table – 5. The sums of co efficient values of ARCH (1) and GARCH (1) for all sample indices of G7 nations were close to one and the probability values indicate that there was significant difference at 1%, 5% and 10% level. The results clearly indicate that G7 Indices daily returns were volatile, the FTSE MIB and FTSE 100 employed high volatility when comparing to other indices during the study period from April 2004 to March 2014. Hence the Null Hypothesis NH₀₃, “There is no significant volatility in the daily index returns of G7 countries’ stock markets during the study period”, is rejected.

 

Table – 5 Results of GARCH (1, 1) Model for G7 nations’ Stock markets indices daily returns during the study period from April 2004 to March 2014.

Source: Data collected from yahoo finance and FTSE computed from E Views 7

β₁ – Co efficient of ARCH (1)

β₂ - Co efficient of GARCH (1)

 

Table – 6 exhibits the results of Trace Statistics and Max-Eigen Statistic values of Johansen co integration test, for G7 nations' stock markets indices, during the study period from April 2004 to March 2014. The values of Trace Statistics and Max-Eigen Statistic for all the indices of G7 nations were greater than the test critical values of 0.05. These results denote long run relationship between G7 stock indices, namely, CAC 40, FTSE 100 INDEX, FTSE MIB, GDAXI, NIKKEI 225, NYSE COMPOSITE INDEX and S&P TSX COMPOSITE INDEX. Hence the results confirmed co integration in G7 nations' stock market indices. Therefore, rejecting the null hypothesis, namely, NH₀₄, “There is no long run relationship between daily index returns of G7 countries’ stock markets during the study period”.

 

Table - 6 Results of Johansen Co-Integration test for G7 Countries’ Stock Markets Indices during the study period from April 2004 to March 2014.

	Hypothesized No. of CE(s)	Trace Statistic	0.05 Critical Value	Max-Eigen Statistic	0.05 Critical Value
CAC 40 and FTSE 100	None	1123.297	15.49471	584.5504	14.2646
	At most 1	538.7469	3.841466	538.7469	3.841466
CAC 40 and FTSE MIB	None	1387.356	15.49471	956.3836	14.2646
	At most 1	430.9722	3.841466	430.9722	3.841466
CAC 40 and GDAXI	None	1091.405	15.49471	573.1004	14.2646
	At most 1	518.3049	3.841466	518.3049	3.841466
FTSE 100 and FTSE MIB	None	1078.038	15.49471	558.9745	14.2646
	At most 1	519.0635	3.841466	519.0635	3.841466
FTSE 100 and GDAXI	None	1061.369	15.49471	560.91	14.2646
	At most 1	500.4593	3.841466	500.4593	3.841466
FTSE 100 and NIKKEI 225	None	1017.238	15.49471	514.6248	14.2646
	At most 1	502.6137	3.841466	502.6137	3.841466
FTSE MIB and GDAXI	None	1061.927	15.49471	559.7196	14.2646
	At most 1	502.2075	3.841466	502.2075	3.841466
FTSE MIB and NIKKEI 225	None	1025.397	15.49471	523.909	14.2646
	At most 1	501.4877	3.841466	501.4877	3.841466
FTSE MIB and NYSE COMPOSITE	None	1083.592	15.49471	582.7629	14.2646
	At most 1	500.8288	3.841466	500.8288	3.841466
GDAXI and NIKKEI 225	None	1005.004	15.49471	520.9862	14.2646
	At most 1	484.0177	3.841466	484.0177	3.841466
GDAXI and NYSE COMPOSITE	None	1060.219	15.49471	550.1037	14.2646
	At most 1	510.1156	3.841466	510.1156	3.841466
GDAXI and SandP TSX COMPOSITE	None	1057.974	15.49471	539.3263	14.2646
	At most 1	518.6478	3.841466	518.6478	3.841466
NIKKEI 225 and CAC 40	None	1056.351	15.49471	553.3124	14.2646
	At most 1	503.0389	3.841466	503.0389	3.841466
NIKKEI 225 and NYSE COMPOSITE	None	1044.675	15.49471	544.6617	14.2646
	At most 1	500.0132	3.841466	500.0132	3.841466
NIKKEI 225 and S&P TSX COMPOSITE	None	1019.031	15.49471	533.2216	14.2646
	At most 1	485.8091	3.841466	485.8091	3.841466
NYSE COMPOSITE and CAC 40	None	1113.022	15.49471	600.3515	14.2646
	At most 1	512.6706	3.841466	512.6706	3.841466
NYSE COMPOSITE INDEX and FTSE 100	None	1073.354	15.49471	555.6948	14.2646
	At most 1	517.6591	3.841466	517.6591	3.841466
NYSE COMPOSITE and S&P TSX COMPOSITE	None	1085.801	15.49471	598.6001	14.2646
	At most 1	487.201	3.841466	487.201	3.841466
S&P TSX COMPOSITE and CAC 40	None	1312.852	15.49471	890.4824	14.2646
	At most 1	422.3692	3.841466	422.3692	3.841466
S&P TSX COMPOSITE and FTSE 100	None	1074.988	15.49471	556.0543	14.2646
	At most 1	518.9335	3.841466	518.9335	3.841466
S&P TSX COMPOSITE and FTSE MIB	None	1173.366	15.49471	653.5621	14.2646
	At most 1	519.8037	3.841466	519.8037	3.841466

Source: Data collected from yahoo finance and FTSE computed from E Views 7

 

Table – 7 reveals the results of Vector Error Correction Model, for G7 nations stock market indices, Namely, CAC 40, FTSE 100, FTSE MIB, GDAXI, NIKKEI 225, NYSE COMPOSITE and S&P TSX COMPOSITE, during the study period from April 2004 to March 2014. The coefficient values of VECM for CAC 40 INDEX with other G7 were stock indices,  namely, FTSE 100 (0.160485), FTSE MIB (-0.22178), GDAXI (-0.096915), NIKKEI 225 (0.001398), NYSE COMPOSITE (0.021465) and S&P TSX COMPOSITE (-0.751476). The results revealed short term relationship between CAC 40 and FTSE 100, NIKKEI 225, NYSE COMPOSITE as the value was greater than the critical value of 0.05 whereas other three indices did not show any relationship with CAC 40. The results of VECM Coefficient for FTSE 100 INDEX with other G7 nations' stock market indices were CAC 40 INDEX, FTSE MIB, GDAXI, NIKKEI 225, NYSE COMPOSITE INDEX and S&P TSX COMPOSITE INDEX at 6.231117, -1.381936, -0.603886, 0.00871, 0.133748 and -4.682534 respectively. These coefficient values revealed that FTSE 100 recorded short term relationship with CAC 40 INDEX, NIKKEI 225, NYSE COMPOSITE INDEX and S&P TSX COMPOSITE INDEX, as the coefficient values were greater than the critical value of 0.05 and the rest of the indices did not exhibit short term relationship with FTSE100 INDEX. The coefficient values of VECM were FTSE MIB INDEX with CAC 40 INDEX (-4.508977), FTSE 100 INDEX (-0.723623), GDAXI (0.436986), NIKKEI 225(-0.006303), NYSE COMPOSITE INDEX (-0.096783) and S&P TSX COMPOSITE (3.388387). These results denote FTSE MIB INDEX did not establish short term relationship with other G7 stock markets except GDAXI and S&P TSX COMPOSITE. The GDAXI and S&P TSX COMPOSITE coefficient value was greater the critical value of 0.05. The values of co efficient for GDAXI and other G7 indices were CAC 40 (-10.31836), FTSE 100 (-1.65594), FTSE MIB (2.288404), NIKKEI 225 (-0.014423), NYSE COMPOSITE (-0.221479) and S&P COMPOSITE (7.753997). These results indicate that GDAXI enjoyed short term relationship only with FTSE MIB and S&P COMPOSITE, as the value was greater than the critical values of 0.05. The coefficient values of NIKKEI 225, CAC 40, FTSE 100, FTSE MIB, GDAXI, NYSE COMPOSITE and S&P TSX COMPOSITE were 715.4192, 114.814, -158.6655, -69.3346, 15.35615 and -537.6203 respectively. The coefficient values of Vector Error Correction Model confirmed that NIKKEI 225 enjoyed short term relationship with CAC 40, FTSE 100 and NYSE COMPOSITE as the values were less than the critical value of 0.05. VECM coefficient model for NYSE COMPOSITE INDEX with other G7 stock indices, were CAC 40 INDEX (46.58845), FTSE 100 INDEX (7.476742), FTSE MIB (-10.33238), GDAXI (-4.515103), NIKKEI 225 (0.06512) and S&P TSX COMPOSITE INDEX (-35.0101). These results revealed short term relationship between NYSE COMPOSITE and CAC 40 INDEX, FTSE 100 INDEX and NIKKEI 225, as the value reported were greater than the critical value of 0.05. But other three indices did not exhibit any relationship with NYSE COMPOSITE. S&P TSX COMPOSITE INDEX with CAC 40 INDEX, FTSE 100 INDEX, FTSE MIB, GDAXI, NIKKEI 225 and NYSE COMPOSITE INDEX co efficient values were -1.330715, -0.21356, 0.295126, 0.128966, -0.00186 and -0.028563 respectively. These values confirmed that S&P TSX COMPOSITE INDEX witnessed short term relationship with FTSE MIB and GDAXI, as the values were less than the critical value of 0.05 and the remaining indices did not show short term relationship with S&P TSX COMPOSITE. Table – 8 clearly highlights that CAC 40, FTSE 100, Nikkei 225 and NYSE Composite were employed short run relationship with each other, further, FTSE MIB, GDAXI and S&P TSX Composite were has the short run relationship during the study period. These results confirmed the rejection of Null Hypothesis NH₀₅ “There is no short run relationship between daily index returns of G7 countries’ stock markets during the study period”.

 

Table – 7 Results of Vector Error Correction model for daily returns of G7 countries’ stock markets indices during the study period from April 2004 to March 2014.

Variable	Co efficient		Standard Error		T - Statistics
CAC 40 and
FTSE 100	0.160485		-0.03276		4.89828
FTSE MIB	-0.22178		-0.02788		-7.9544
GDAXI	-0.096915		-0.02732		-3.547
NIKKEI 225	0.001398		-0.02484		0.05627
NYSE COMPOSITE	0.021465		-0.03017		0.7115
S&P TSX COMPOSITE	-0.751476		-0.03763		-19.97
FTSE 100 and
CAC 40	6.231117		-0.17377		35.858
FTSE MIB	-1.381936		-0.17454		-7.9175
GDAXI	-0.603886		-0.17022		-3.5477
NIKKEI 225	0.00871		-0.15469		0.0563
NYSE COMPOSITE	0.133748		-0.18801		0.71138
S&P TSX COMPOSITE	-4.682534		-0.23497		-19.928
FTSE MIB  and
CAC 40		-4.508977		-0.12518		-36.019
FTSE 100		-0.723623		-0.14776		-4.8974
GDAXI		0.436986		-0.12317		3.54774
NIKKEI 225		-0.006303		-0.11198		-0.0563
NYSE COMPOSITE		-0.096783		-0.13603		-0.7115
S&P TSX COMPOSITE		3.388387		-0.15293		22.156
GDAXI and
CAC 40		-10.31836		-0.28796		-35.832
FTSE 100		-1.65594		-0.33824		-4.8958
FTSE MIB		2.288404		-0.28913		7.91489
NIKKEI 225		-0.014423		-0.2563		-0.0563
NYSE COMPOSITE INDEX		-0.221479		-0.31105		-0.712
S&P TSX COMPOSITE		7.753997		-0.38844		19.9616
NIKKEI 225 and
CAC 40		715.4192		-19.9703		35.8241
FTSE 100		114.814		-23.4484		4.89646
FTSE MIB		-158.6655		-20.0502		-7.9134
GDAXI		-69.3346		-19.5511		-3.5463
NYSE COMPOSITE		15.35615		-21.5685		0.71197
S&P TSX COMPOSITE		-537.6203		-26.9788		-19.928
NYSE COMPOSITE and
CAC 40		46.58845		-1.30025		35.8305
FTSE 100		7.476742		-1.52782		4.89373
FTSE MIB		-10.33238		-1.30584		-7.9124
GDAXI		-4.515103		-1.27207		-3.5494
NIKKEI 225		0.06512		-1.1563		0.05632
S&P TSX COMPOSITE		-35.0101		-1.75418		-19.958
S&P TSX COMPOSITE and
CAC 40		-1.330715		-0.03706		-35.907
FTSE 100		-0.21356		-0.04363		-4.8949
FTSE MIB		0.295126		-0.03354		8.79791
GDAXI		0.128966		-0.0363		3.55297
NIKKEI 225		-0.00186		-0.03305		-0.0563
NYSE COMPOSITE		-0.028563		-0.04008		-0.7126

Source: Data collected from yahoo finance and FTSE computed from E Views 7

 

Table – 8  Summary of Vector Error Correction model for daily returns of G7 countries’ stock markets indices during the study period from April 2004 to March 2014.

G7 Indices	CAC 40	FTSE 100	FTSE MIB	GDAXI	NIKKEI 225	NYSE COMPOSITE	S&P TSX COMPOSITE
CAC 40	-	YES	NO	NO	YES	YES	NO
FTSE 100	YES	-	NO	NO	YES	YES	NO
FTSE MIB	NO	NO	-	YES	NO	NO	YES
GDAXI	NO	NO	YES	-	NO	NO	YES
NIKKEI 225	YES	YES	NO	NO	-	YES	NO
NYSE COMPOSITE	YES	YES	NO	NO	YES	-	NO
S&P TSX COMPOSITE	NO	NO	YES	YES	NO	NO	-

 

Table – 9 presents the results of Granger Causality analysis for G7 countries’ stock market indices, during the study period from April 2004 to March 2014. The results of probability values of Granger Causality Test for FTSE MIB and CAC 40, CAC 40 and FTSE MIB, S&P TSX COMPOSITE and CAC 40, CAC 40 and S&P COMPOSITE, FTSE 100 index and GDAXI, FTSE MIB and NYSE COMPOSITE INDEX, S&P COMPOSITE and FTSE MIB, FTSE MIB and S&P COMPOSITE, NIKKEI 225 and S&P COMPOSITE, S&P COMPOSITE and NYSE COMPOSITE, NYSE COMPOSITE and S&P COMPOSITE were less than the significant value of 0.05. Further, the F statistic values for these indices were greater than three. These results indicate bidirectional causation between FTSE MIB and CAC 40, S&P TSX COMPOSITE and CAC 40, S&P COMPOSITE and FTSE MIB, S&P COMPOSITE and NYSE COMPOSITE. In addition, unidirectional causation was noticed between FTSE 100 index and GDAXI, FTSE MIB and NYSE COMPOSITE, NIKKEI 225 and S&P COMPOSITE during the study period. Hence reject the Null Hypothesis NH_06, “There is no Causal relationship between the G7 countries’ stock markets index returns during the study period”.

 

Table - 9 Results of Granger Causality Test for daily returns of G7 countries’ stock markets sample indices during the study period from April 2004 to March 2014.

Null Hypotheses	F-Statistic	Prob.	Accept / Reject the Null Hypothesis
FTSE 100 INDEX does not Granger Cause CAC 40	0.03817	0.9626	Accepted
CAC 40 does not Granger Cause FTSE 100 INDEX	1.92541	0.146	Accepted
FTSE MIB does not Granger Cause CAC 40	8.93767	0.0001	Rejected.
CAC 40 does not Granger Cause FTSE MIB	40.6951	0.00004	Rejected.
GDAXI does not Granger Cause CAC 40	1.95844	0.1413	Accepted
CAC 40 does not Granger Cause GDAXI	0.84285	0.4306	Accepted
NIKKEI 225 does not Granger Cause CAC 40	1.55542	0.2113	Accepted
CAC 40 does not Granger Cause NIKKEI 225	1.13255	0.3224	Accepted
NYSE COMPOSITE does not Granger Cause CAC 40	0.55522	0.574	Accepted
CAC 40 does not Granger Cause NYSE COMPOSITE	1.06832	0.3437	Accepted
S&P TSX COMPOSITE  does not Granger Cause CAC 40	15.1319	0.00003	Rejected.
CAC 40 does not Granger Cause S&P TSX COMPOSITE	38.1723	0.000005	Rejected.
FTSE MIB does not Granger Cause FTSE 100	1.88592	0.1519	Accepted
FTSE 100 INDEX does not Granger Cause FTSE MIB	0.1486	0.8619	Accepted
GDAXI does not Granger Cause FTSE 100	2.38089	0.0927	Accepted
FTSE 100  does not Granger Cause GDAXI	5.74823	0.0032	Rejected.
NIKKEI 225 does not Granger Cause FTSE 100	0.57298	0.5639	Accepted
FTSE 100   does not Granger Cause NIKKEI 225	2.68303	0.0686	Accepted
NYSE COMPOSITE  does not Granger Cause FTSE 100	2.17651	0.1137	Accepted
FTSE 100   does not Granger Cause NYSE COMPOSITE INDEX	0.79278	0.4527	Accepted
S&P TSX COMPOSITE INDEX does not Granger Cause FTSE 100 INDEX	0.59167	0.5535	Accepted
FTSE 100 INDEX does not Granger Cause S & P TSX COMPOSITE	2.18648	0.1125	Accepted
GDAXI does not Granger Cause FTSE MIB	1.46193	0.232	Accepted
FTSE MIB does not Granger Cause GDAXI	0.27453	0.7599	Accepted
NIKKEI 225 does not Granger Cause FTSE MIB	1.55013	0.2124	Accepted
FTSE MIB does not Granger Cause NIKKEI 225	0.16081	0.8515	Accepted
NYSE COMPOSITE does not Granger Cause FTSE MIB	1.75881	0.1725	Accepted
FTSE MIB does not Granger Cause NYSE COMPOSITE	4.80154	0.0083	Rejected.
S&P TSX COMPOSITE does not Granger Cause FTSE MIB	21.5489	0.000005	Rejected.
FTSE MIB does not Granger Cause S&P TSX COMPOSITE	70.1642	0.00002	Rejected.
NIKKEI 225 does not Granger Cause GDAXI	0.81425	0.4431	Accepted
GDAXI does not Granger Cause NIKKEI 225	1.56645	0.209	Accepted
NYSE COMPOSITE does not Granger Cause GDAXI	1.1332	0.3222	Accepted
GDAXI does not Granger Cause NYSE COMPOSITE	0.74495	0.4749	Accepted
S&P TSX COMPOSITE   does not Granger Cause GDAXI	0.701	0.4962	Accepted
GDAXI does not Granger Cause S&P TSX COMPOSITE	1.31526	0.2686	Accepted
NYSE COMPOSITE INDEX does not Granger Cause NIKKEI 225	1.23302	0.2916	Accepted
NIKKEI 225 does not Granger Cause NYSE COMPOSITE	2.02019	0.1329	Accepted
S & PTSX COMPOSITE   does not Granger Cause NIKKEI 225	0.7721	0.4622	Accepted
NIKKEI 225 does not Granger Cause S &P TSX COMPOSITE	3.261	0.0385	Rejected.
S & P TSX COMPOSITE   does not Granger Cause NYSE COMPOSITE	3.8967	0.0204	Rejected.
NYSE COMPOSITE   does not Granger Cause S&P TSX COMPOSITE	3.88777	0.0206	Rejected.

Source: Data collected from yahoo finance & FTSE computed using E Views 7

 

5. SUMMARY AND CONCLUSION:

The study empirically examined the Co Movement among G7 nations’ stock markets indices during the study period April 2004 to March 2014. For empirical analysis, econometrics tools namely Kolmogorov–Smirnov Test and Shapiro-Wilk Test, Augmented Dickey Fuller Test, Phillips – Perron Test, GARCH (1, 1) Model, Johansen Co Integration Test, Vector Error Correction Model and Granger Causality Test were used to analyze the stock market integration. The results indicated that, Kolmogorov - Smirnov and Shapiro-Wilk (K – S) Tests revealed the sample indices of G7 nations' stock markets daily returns were normally distributed. Augmented Dickey Fuller Test and Phillips-Perron Test indicated that all sample indices of G7 nations' stock markets daily returns attained stationarity at level difference. GARCH (1, 1) model revealed that the G7 nations' stock market indices were highly volatile. The Johansen co integration test exhibited long run Co-movement between sample indices of G7 stock indices, namely, CAC 40, FTSE 100, FTSE MIB, GDAXI, NIKKEI 225, NYSE COMPOSITE and S&P TSX COMPOSITE INDEX. The Vector Error Correction Model exhibit CAC 40, NIKKEI 225 and NYSE COMPOSITE, did not exhibit short term relationship with FTSE MIB INDEX, GADAXI and S&P TESX COMPOSITE INDEX. S&P TSX COMPOSITE recorded bidirectional causation with CAC 40 INDEX, FTSE MIB and NYSE COMPOSITE INDEX as the F statistic values of Granger Causality Test were greater than three and probability values were less than 0.05. CAC 40 index experienced bidirectional causal relationship with FTSE MIB. Unidirectional causal relationship was found between the following indices- FTSE 100 and GDAXI, FTSE MIB and NIKKEI 225, NIKKEI 225 and S&P TSX COMPOSITE among the G7 nations' stock market indices. Finally the study suggested that the international investors will have the opportunity for getting long run benefits from G7 nations' stock market indices as these indices recorded long run relationship. Hence investors can invest in listed companies of these indices for long run benefits. 

 

6. REFERENCES:

1.     Alessandra Bonfiglioli (2005), “Explaining co-movements between stock markets: The case of US and Germany”, Journal of International Money and Finance, No. 24, pp. 1299 – 1316.

2.     Burcu Kiran (2011), “Fractional Co integration Relationship between Oil Prices and Stock Markets: An Empirical Analysis From G7 Countries”, PRAGUE ECONOMIC PAPERS, No. 2, pp. 179 – 189.

3.     Colm Kearney and Brian M. Lucey (2004), “International equity market integration: Theory, evidence and implications”, International Review of Financial Analysis, Vol. 13, pp - 571– 583.

4.     Dasgupta, R. (2014). “Integration and dynamic linkages of the Indian stock market with BRIC – An Empirical Study” Asian Economic and Financial Review, Vol. 4, No. 6, pp. 715–731.

5.     Guidi, F. and Ugur, M. (2014). An analysis of South-Eastern European stock markets: evidence on co-integration and portfolio diversification benefits. Journal of International Financial Markets, Institutions and Money, Vol. 30, pp. 119–136.

6.     Heikki Lehkonen and Kari Heimonen (2014), “Timescale-dependent stock market comovement: BRICs vs. developed markets”, Journal of Empirical Finance, Vol. 28, pp - 90–103.

7.     Jagroop Kaur (2012), “Are the Stocks Exchanges interlinked: a Case of Islamic Countries and US”, BIZ and Bytes- A Quarterly Journal of Management and Technology, Vol.1, No.4, pp. 128 – 137.

8.     Kabihassan M (2001), “Short run and long run dynamic linkages among International stock Markets”, International Review of Economics and Finance,  Vol.5, No. 4, pp. 387-405.

9.     Kui Fan, Zudi Lu and Shouyang Wang (2009), “Dynamic Linkages between the China and International Stock Markets”, Asia-Pacific Finan Markets Vol.16, pp.211–230.

10.   Lehkonen, H. (2014). “Timescale-dependent stock market co movement: BRICs vs. Developed Markets” Journal of Empirical Finance, Vol. 28, pp. 90–103.

11.   Małgorzata Doman and Ryszard Doman (2013), “Dynamic linkages between stock markets: the Effects of Crises and Globalization”, Port Econ J, Vol. 12, pp. 87–112.

12.   Rajkumar, G. S. (2015). “Linkages between India and three Asean stock markets: A co-integration approach” Journal of Commerce and Accounting Research, 4(1).

13.   Searat Ali and Babar Zaheer Butt (2011), “Co movement between Emerging and Developed Stock Markets: An Investigation through Co integration Analysis”, World Applied Sciences Journal, Vol. 12, No.  4, pp. 395-403.

14.   Tahai A and Robert W (2004), “An Examination of Financial Integration for Group of Seven (G7) Industrialized Countries using an I(2) Co integration Model”, Applied Financial Economics,  No. 14, pp. 327 – 335.

15.   Walid M. A. Ahmed (2008), “Co integration and Dynamic linkages of International Stock Markets: An Emerging Market Perspective”, Electronic copy available at http://mpra.ub.uni-muenchen.de/26986/

 

 

 

 

Accepted on 11.06.2017                © A&V Publications all right reserved