^{Nexus amidst Asian Continent Stock Exchange Indices}

 

S. Rajamohan1, G. Arivalagan2

1Professor, Alagappa Institute of Management, Alagappa University, Karaikudi-630003

2Research Scholar, Alagappa Institute of Management, Alagappa University, Karaikudi-630003

 

ABSTRACT:

^{This paper investigates the co movements between the Asian countries stock indices.  The study aims to explore the long run relationship between the stock market indices. The researcher has used daily closing price of the index for the period of April 2003 to March 2016. The sample area is Asian continent countries such as china, India, Japan, Korea, Indonesia, Malaysia, Pakistan, Russia and Singapore. All the data are used in the raw to found the ADF at first order difference. The co integration is used to find the relationship among the stock indices. Granger causality is used to analyse the causal effect on the stock market indices.}

 

 

INTRODUCTION:

Globalization and trade liberalization opened the opportunities for foreign and domestic investors. International liberalizations removed the blocks for foreign inflows to domestic country. The removal of barrier has made the goods and services internationalized for foreign. With increased market integration, the current world financial markets have become more closely correlated and interdependent over time. Also the common stock markets have been opened for foreign investors for investments. This provided an opportunity for many investors to maximize their wealth by looking for different investments opportunities around the globe.

 

The investors and policy makers can easily understand the information about the global financial markets for the purpose of taking financial decision relate to future investment and risk management.

 

The existence of low returns from different national stock markets has been used frequently to justify the international diversification of the portfolios. Another reason for investors to consider global investments is return enhancement. Securities issued by countries with higher growth rates are expected to earn higher rates of returns. So the individual and institutional investors began to diversify their risk by looking around the world. The investors were unable to invest in global well developed equity markets of the world before liberalization. Now a day’s all around the globe the investors are able to gain advantage by investing in developing and well developed equity markets. Liberalization opened opportunities for investors to invest in foreign markets but these also caused the foreign markets to lead and lag each other.

 

The purpose of study is to discover the dynamics association ship between Asian countries equity stock markets. This study aims to provide visions about the relationships of selective Asian countries stock markets. For this purpose, we have selected nine equity markets from Asia. These equity markets include: China, India, Indonesia, Japan, Korea, Malaysia, Pakistan, Russia and Singapore. This study will explore the integration of developed, developing and emerging markets.

 

REVIEW OF LITERATURE:

Wong W.K., Penm J and Terell R.D (2004) attempted to found the relationship of Asian emerging countries stock market with the major established economies of the world. For this purpose, they have employed weekly stock prices of the Asian countries from Jan, 1981 to Dec, 2002. They used co integration for this purpose to explore the integration in diverse mix of the Asian countries. The researcher found the evidence of relationships between the emerging equity markets of Asia. However, they said that the emerging markets exhibit some short run integration among them. They said that their study will be helpful for investors in terms of decision making and foreign investments because they provided the association ship of emerging with the developed equity markets.

 

Ceylan and Dogan (2004) examined the market co movement of OIC countries. In this study the researcher has included Pakistan, Lebanon, Morocco, Jordon, Oman, Kuwait and Egypt equity market. The researchers found the evidence of association of Lebanon with Kuwait and their results also reveal that the market of Turkey and Egypt.

 

Islam et al (2005) studied the exploring relationship of Malaysia, Singapore and India equity markets. In order to explore the dynamics of these equity markets they used the multivariate approach of co-integration. The Granger Causality test was used to analyse the causality of the equity markets. This study is based on the equity closing price, the daily data taken from July, 1997 to Feb, 2005. Their results pointed to unidirectional flow from Singapore equity market to Malaysian equity market. While the other market has been found bidirectional flow.

 

Lamba A.S (2005) analysis the short and long run relationship between South Asian and developed equity markets such as Japan, UK and US. He used the Granger causality for examining the causal flow of the market. He found that there is only one response from the Indian equity markets to the developed markets while the other markets of Pakistan and Srilanka shows no such trends.

 

Glezakos et al (2007) studied the exploring integration of different equity markets of the world in comparison with Athens stock market. In this study they included the developed markets, USA, Japan, France and Germany, England, Spain, Italy, Holland and Belgium with the Athens market for the period 2000-2006. They confirmed integration of Athens market with different markets. They found the evidence of multidirectional spill over in different markets. Ismail et al. (2009) made an attempt to explore the Asian equity markets with well-established market of US. They have used four markets; Hong Kong, South Korea, Malaysia and India. Their study is undertaken by using monthly indices from 1996 to 2008. They found the evidence of relationship of US market with Asian by using the analyses of VAR model.

 

Sharma P (2011) studied integration of Asia emerging equity markets with US equity market. The researcher used the co-integration for exploring the association ship between these equity markets. His analyses confirmed that the emerging markets are influenced by the US market. So the emerging market investors cannot earn benefit by investing in US market.

 

Ali, Zaheer Butt and ur Rehman (2011) studied the integration of Pakistan market with a diverse mix of economies. They used the developed market of US, Japan, UK and China and other markets India, Indonesia, Singapore, Malaysia and Indonesia for this purpose. The monthly data taken from 1998 to 2008 have been analysed by using the co integration analyses. They found that the equity market of Pakistan is not integrated with the equity market of Singapore, UK, USA, Malaysia and Taiwan.

 

Hussain et al. (2012) empirically examined the association of Pakistan equity market with the East Asian Stock Markets. The researcher had taken stock indices monthly data from 2000 to 2010. They used co-integration and error correction technique. Their analyses confirmed no relationship between the equity markets of East Asia. However, they found unidirectional flow from Japan to equity market of Pakistan and to equity market of China.

 

Aslam et al. (2012) carried out a study for finding integration among developed equity markets and Karachi Stock Exchange for period 1999 to 2012. They have applied co-integration analyses for this purpose. They found that Pakistan equity market is weakly integrated with the developed stock markets. They also found that KSE have influence on France stock market, London stock market and US stock market.

 

Khan and Aslam (2014) carried out a study for finding integration among developed equity markets and Karachi Stock Exchange for the period 1999 to 2012. They have applied co integration analyses for this purpose. They found that Pakistan equity market is weakly integrated with the developed stock markets. They also found that KSE has an influence on France stock market, London stock market and US stock market.

 

OBJECTIVES OF THE STUDY:

1.    To explore the long run association ship among the selected Asian countries.

2.    To examine the causal relationship between the stock indices.

 

METHODOLOGY:

This study covers the various Asian countries stock market indices presented in Table 1. The study comprises the daily closing price of the indices from April 1, 2003 to 31^st March 2016 which obtained 2425 observations. The daily data is used to find the nexus among the Asian countries stock market and the researcher has used descriptive statistics, VAR model, Johansen co-integration and granger causality.

 

Stock Market Indices:

Stock markets have its own indices. Table 1 shows Asia major stock exchanges and their indices.

 

Table 1: Stock Market Indices.

Source: Authors Estimation

 

Behaviour of the stock returns of the selected Asia stock Market:

Descriptive statistics is carried out to examine the behaviour of the stock returns of the selected stock market. It is analyse the mean, range, standard deviation, skewness and kurtosis.

 

Table 2: Descriptive Statistics

Indices Name	Mean	Maximum	Minimum	Std.dev	Skewness	Kurtosis	Jarque-bera	Probability
JCI	0.00103	0.136241	-0.14726	0.01688	-0.68477	15.3082	15503.1	0
KOPSI	0.00054	0.138635	-0.11172	0.01572	-0.38409	11.3159	7050.14	0
KLCI	0.00041	0.058101	-0.07178	0.00885	-0.43599	11.5292	7430.43	0
NIKKEI 225	0.00030	0.104443	-0.13664	0.01711	-0.69816	10.0030	5154.52	0
KSE 100	0.00103	0.097738	-0.09407	0.01552	-0.21330	8.31639	2875.41	0
RTS	0.00036	0.202039	-0.40961	0.02716	-1.17748	31.2407	81178.7	0
Sensex	0.00086	0.1599	-0.13793	0.01774	-0.36243	12.0435	8320.32	0
SSEC	0.00028	0.090345	-0.12763	0.01973	-0.30671	6.99213	1649.013	0
STI	0.000328	0.214742	-0.11978	0.013583	1.19515	35.11051	104803	0

Source: Authors Estimation

 

 

 

Table 2 describes the analyse of nine stock market indices.  Among the selected stock market indices, all the stock market has the positive mean value. It shows all markets give positive return to the investors. The STI, RTS, and Sensex represent the maximum daily return of respectively 21.47 per cent, 20.20 percent and 15.99 percent. RTS (40.96 percent) represent the minimum daily return. The maximum daily standard deviation of RTS 2.71 percent. All the markets data are found negatively skewed except STI which is found positively skewed. All the selected stock market indices data are highly significant in kurtosis, from that the frequency Distributions of underlying variables are fat-tail degree of distribution.

 

Assessment of stationarity of data - Unit Root Test:

The researcher has used different equity market data for the study. The researcher to test the stationary of the data. Here they used two tests for analysing the stationary such as Augmented Dickey Fuller and Phillip Perron test. These two tests have been applied to know about the stationary of different equity markets. The results are presented in Table 3.

 

Table 3: Assessment of stationarity ADF and PP Unit Root Test

Variables	ADF T Statistic		P VALUE		PP T-Statistic			P Value
	@Level	@First Difference	@Level	@First Difference	@Level	@First Difference		@Level	@First Difference
JCI	-0.912	-31.2257*	0.7847	0*	-0.873	-49.0208*		0.7969	0.0001*
KOPSI	-2.147	-51.1421*	0.2263	0.0001*	-2.135	-51.1449*		0.2306	0.0001*
KLCI	-1.468	-31.7321*	0.5498	0*	-1.460	-46.4299*		0.5536	0.0001*
NIKKEI 225	-1.645	-51.124*	0.459	0.0001*	-1.586	-51.1542*		0.4892	0.0001*
KSE 100	0.4496	-43.5982*	0.985	0*	0.4884	-43.8455*		0.9864	0*
RTS	-1.956	-46.7189*	0.3064	0.0001*	-2.079	-46.8397*		0.2533	0.0001*
Sensex	-1.241	-48.0521*	0.6585	0.0001*	-1.237	-48.0381*		0.6602	0.0001*
SSEC	-1.641	-48.8822*	0.4613	0.0001*	-1.660	-48.8842*		0.4515	0.0001*
STI	-2.510	-50.92*	0.113	0.0001*	-2.538	-50.9599*		0.1064	0.0001*
Critical values
1%							-3.432
5%							-2.862
10%							-2.567

Source: Authors Estimation *1^st difference

 

Table 3 explain the stationarity of the data at level and first difference. All the data are non- stationary at level with intercept in both tests. The researcher taking the first difference to make data the non-stationary to stationary. However, after first order difference with intercept the data are found to be stationary at 1,5 and 10 per cent significance level in ADF and PP test.

 

Table 4 VAR Statistics

Lag	Log L	LR	FPE	AIC	SC	HQ
0	-172185	NA	3.20E+49	142.3691	142.393	142.3778
1	-115867	112124.1	2.09E+29	95.88863	96.15197*	95.9844
2	-115643	445.6565	1.88e+29*	95.78547*	96.28821	95.96829*
3	-115545	191.7334	1.89E+29	95.78786	96.53	96.05774
4	-115476	137.1453	1.93E+29	95.81286	96.79441	96.16981
5	-115415	118.7358	2.00E+29	95.8454	97.06634	96.2894
6	-115356	114.5527	2.07E+29	95.8795	97.33984	96.41056
7	-115266	174.783	2.08E+29	95.88774	97.58748	96.50586
8	-115194	139.5613*	2.13E+29	95.91072	97.84987	96.6159

Source: Authors Estimation

* indicates lag order selected by the criterion

 

Table 4 presents the result of VAR lag order selection criteria. The VAR statistics confirm the Schwarz and Akaike information criterion significance at lag two which confirms that it is the suitable lag value to test the co integration between the Asian countries stock market indices.

 

LR: sequential modified LR test statistic (each test at 5% level)

FPE: Final prediction error

AIC: Akaike information criterion

 

SC: Schwarz information criterion

HQ: Hannan-Quinn information criterion

 

Long Run Equilibrium Relationship between The Asian Stock Indices:

The number of co-integration relationship among the underlying variables is examined by the Johansen and Juselius test. The Johansen and Juselius co-integration test was used to exploring the long run association ship between the selected equity markets indices. This analyse have the two test i.e. Trace test and Maximum Eigen value test, it was presented in Table 5.

 

 

Table 5 Long run relationship between the Asian Stock Indices-Johansen’s Co-Integration Test

Hypothesized No. of CE(s)	Trace Test				Maximum Eigenvalue test
	Eigenvalue	Statistic	Critical Value	Prob.**	Statistic	Critical Value	Prob.**
None *	0.225257	4330.941	197.3709	0.0001	617.8967	58.43354	0.0001
At most 1 *	0.208641	3713.044	159.5297	0.0001	566.5214	52.36261	0.0001
At most 2 *	0.19867	3146.523	125.6154	0.0001	536.2087	46.23142	0.0001
At most 3 *	0.186447	2610.314	95.75366	0.0001	499.5598	40.07757	0.0001
At most 4 *	0.182879	2110.754	69.81889	0.0001	488.9637	33.87687	0.0001
At most 5 *	0.17123	1621.791	47.85613	0.0001	454.6937	27.58434	0.0001
At most 6 *	0.158934	1167.097	29.79707	0.0001	419.0383	21.13162	0.0001
At most 7 *	0.154214	748.0587	15.49471	0.0001	405.4916	14.2646	0.0001
At most 8 *	0.131943	342.5671	3.841466	0	342.5671	3.841466	0

Source: Authors Estimation

Table 5 shows the result of long-run equilibrium relationship between the Sensex and JCI, KOPSI, KLCI, NIKKEI 225, KSE 100, RTS, SSEC and STI. The Trace test and Maximum Eigenvalue test confirms that the Asian continent stock exchange indices have the long-run relationship with the Sensex index. It was statistically proved by the Trace test and Maximum Eigenvalue P-values.

Vector Error Correction:

Assuming one co-integrating vector, the short run and long run interaction of the underlying variables the VECM has been estimated based on the Johansen co-integration methodology. The results are presented in Table 6.

 

Table 6 Results Vector Error Correction Model

SENSEX (-1)	JCI  (-1)	KOPSI  (-1)	KLCI  (-1)	NIKKEI  (-1)	KSR 100  (-1)	RTS (-1)	SSEC (-1)	STI (-1)	C
1	1.650182	-1.041391	17.30393	0.966751	-1.055529	1.002018	0.83749	-13.70491	-8272.262
	(-1.10528)	(-2.36961)	(-3.9946)	(-0.22761)	(-0.13442)	(-1.14412)	(-0.41648)	(-1.95917)
	[ 1.49299]	[-0.43948]	[ 4.33183]	[ 4.24743]	[-7.85218]	[ 0.87579]	[ 2.01088]	[-6.99527]
Error Correction:	D(SENSEX)	D(JCI)	D(KOPSI)	D(MALA)	D(NKY)	D(PAK)	D(RUS)	D(SSEC)	D(STI)
CointEq1	-0.006079	0.000657	-0.00013	-6.19E-05	-0.00325	0.013043	-0.00027	0.001026	0.000484
	-0.00305	-0.00052	-0.00027	-0.00013	-0.00248	-0.0024	-0.00037	-0.00074	-0.00041
	[-1.99088]	[ 1.25765]	[-0.50548]	[-0.47156]	[-1.30995]	[ 5.42745]	[-0.71900]	[ 1.39270]	[ 1.18462]
F-statistic	3.363962	4.912746	8.765162	7.873461	9.19058	5.770492	1.932706	1.865761	5.876522

Source: Authors Estimation

Standard errors in ( ) and t-statistics in [ ]

 

Table 6 describe the result of Vector Error Correction Model. In Table 7, the t- statistics are given in [ ] brackets and the co-efficient of error term are given in () brackets. The coefficients of JCI returns, KLCI returns, NIKKEI 225 returns, RTS returns and SSEC returns are positive and statistically insignificant, while the coefficient of KOPSI returns, KSE 100 returns and STI returns are negative and statistically significant. The results revels that the relationship between JCI, KLCI, NIKKEI 225, RTS and Sensex is negative while the relationship between the KOPSI, KSE 100, STI and Sensex is positive. The sign of the error correction coefficient in determination of Sensex is negative (-

 

0.00607) and the corresponding t-value (-1.99) and the F-statistic is (3.363). this indicates that return on Sensex is significantly to re-establish the equilibrium relationship.

 

Causal Effects of Asian Stock Market Indices:

The Granger causality test is used to know about the unidirectional and bidirectional causal flow between the equity stock market indices. Granger causality test is followed for determining and forecasting the one market to other market. It tells about the casual flow between the variables. Table 7 presents the analyses of Granger causality test between the selected variables of the study.

Table 7 Causal effects of Asian stock market indices - Granger Causality

Null Hypothesis:	F-Statistic	Prob.
JCI does not Granger Cause SENSEX	0.5235	0.5925
SENSEX does not Granger Cause JCI	17.0288	5.00E-08
KOPSI does not Granger Cause SENSEX	0.89323	0.4095
SENSEX does not Granger Cause KOPSI	18.5897	1.00E-08
KLCI does not Granger Cause SENSEX	0.53512	0.5857
SENSEX does not Granger Cause KLCI	19.8903	3.00E-09
NIKKEI225 does not Granger Cause SENSEX	2.63817	0.0717
SENSEX does not Granger Cause NIKKEI225	27.3081	2.00E-12
KSE100 does not Granger Cause SENSEX	0.69677	0.4983
SENSEX does not Granger Cause KSE100	8.74229	0.0002
RTS does not Granger Cause SENSEX	7.01782	0.0009
SENSEX does not Granger Cause RTS	1.91935	0.1469
SSEC does not Granger Cause SENSEX	4.27168	0.0141
SENSEX does not Granger Cause SSEC	4.4727	0.0115
STI does not Granger Cause SENSEX	0.57524	0.5626
SENSEX does not Granger Cause STI	4.2653	0.0142

Source: Authors Estimation

 

Table 7 describes the causal relationship between the equity stock market indices. Sensex has causes JCI, KOPSI, KLCI, Nikkei, KSR 100 and not causes with STI.  The RTI has influenced the Sensex. So that the unidirectional causality Relationship with Sensex is following JCI, KOPSI, KLCI, Nikkei, KSR 100 and RTI to Sensex. There is one bidirectional causality found between SSEC and Sensex.

 

CONCLUSION:

This study observed the co integration of Indian stock market index Sensex with the Asian continent stock market of china, Indonesia, Korea, Malaysia, Japan, Pakistan Russia and Singapore. The daily market index prices are taken for the period 2003-2016. From the descriptive statistic it is found that STI and Sensex has highest returns among others and RTS has the highest standard deviation among others. From the Granger causality test, Sensex has the unidirectional relationship among all stock markets.

It means Indian stock market movements have affected Asian countries stock market returns such as Korea, Indonesia, Malaysia, Japan, Pakistan and Singapore. India and china markets have the bidirectional causality i.e. any economic change was happening in one country it affects the both countries markets either positively or negatively. So this is good condition for the global investors (FDI & FII) and Domestic investors for investing their investments in India because the market returns.

 

REFERENCES:

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10.  Sharma, P. (2011). “ Asian Emerging Economies and United States of America: Do They Offer a Diversification Benefit?,” 1(4), 85–92.

11.  Wong, W., Penm, J., Terell, R. D., and Ching, K. Y. (2004). The Relationship Between Stock Markets of Major Developed Countries And Asian Emerging Markets. Journal of Applied Mathematics and Decision Sciences, 8(4), 201–218. https://doi.org/ 10.1155/S1173912604000136.

 

 

Accepted on 22.09.2017                © A&V Publications all right reserved