it is a analysis done by using eviews software

1. A STUDY ON INTERLINKAGES BETWEEN
DIFFERENT STOCK MARKET
PROJECT REPORT
Submitted By
RAVICHANDRAN T
[Reg. No. 16397087]
Under the Guidance of
Dr. R. KASILINGAM
ASSOCIATE PROFESSOR

2. PROFILE OF THE COMPANY
•Name of the Company : Sharekhan
•Year of Establishment : 1922
•Headquarter : Mumbai – Maharashtra, INDIA
•Services : Depositor Services, Online Services and
Technical Research
•Number of Employees : Over 3500
•Website : www.sharekhan.com
•Slogan : Your Guide to The Financial Jungle
VISION
To be the best retail brokering Brand in the retail business of
stock market.
MISSION
To educate and empower the individual investor to make better
investment decision through quality advice and superior service.

3. Most of the world economies depend one on another.
Stock markets are always taken as a prominent indicator of the performance
of the economy of a country and the industry.
With the globalization of the capital markets, stock markets worldwide are
integrated.
Technological innovations have improved market integration
INTRODUCTION

4. To help investors reduce the portfolio risk
Objective of the study:
To find out integration of stock market
To find out dependency level of stock market
To understand the level of interdependence among the major
stock markets
To help investors in planning for international portfolio diversification.
Tools Used
ECONOMETRIC VIEWS (E-Views) is a Technical tool used for data analysis. Methods used in
E-Views are Ordinary Least Squares, Autoregressive Conditional Heteroskedasticity, Johansen
Cointegration Test and Vector Error Correction Estimates.
Study of global stock market is helpful for the prediction of Indian stock market.
To find out which stock markets are less correlated with other markets.

5. LIMITATIONS OF THE STUDY
It helps the investor in making the investment decision but not every investment is entirely
depending on the analysis done.
The tools used for analysis is subject to inherent limitations.
The study is done by using tools like E-Views.
The various sources from which the data are obtained for this Study are as follows:
 Financial Databases from various portals have been used in collecting the Index
Quotes of the various global equity markets.
Source of Data
The nature of data collected for this study is secondary.
Secondary Data refers to the information gathered from the Sources
that already exists

6. Data type used in the study
STOCK EXCHANGE INDEX
USA DOW JONES
JAPAN TOPIX
LONDON FTSE100
HANG KONG HANG SANG
CHINA SSE
GERMAN DAX
AUSTRALIA ASX
INDIA BSE
INDIA NSE

7. 2.8
3.0
3.2
3.4
3.6
3.8
4.0
4.2
05 06 07 08 09 10 11 12 13 14 15 16
L_ASTRALIAN
8.4
8.8
9.2
9.6
10.0
10.4
05 06 07 08 09 10 11 12 13 14 15 16
L_BSE
6.8
7.2
7.6
8.0
8.4
8.8
05 06 07 08 09 10 11 12 13 14 15 16
L_CHINA
8.2
8.4
8.6
8.8
9.0
9.2
9.4
05 06 07 08 09 10 11 12 13 14 15 16
L_GERMAN
9.4
9.6
9.8
10.0
10.2
10.4
05 06 07 08 09 10 11 12 13 14 15 16
L_HANG_KONG
6.4
6.6
6.8
7.0
7.2
7.4
7.6
05 06 07 08 09 10 11 12 13 14 15 16
L_JAPAN
8.2
8.4
8.6
8.8
9.0
L_LONDAN
7.2
7.6
8.0
8.4
8.8
9.2
L_NSE
8.8
9.0
9.2
9.4
9.6
9.8
10.0
L_USA

8. augemented dicky
fuller(ADF)test intercet and trend
result
data series at level at Ist order difference
t-statistic prob. t-statistic prob
usa -0.969476 0.7630 -4.292901 0.0007 I(1)
japan -1.32802 0.6155 -10.49635 0.0000 I(1)
londan -2.276926 0.1809 -11.93371 0.0000 I(1)
hang kong -2.719303 0.0732 -10.71887 0.0000 I(1)
china -1.514437 0.0690 -4.187663 0.0010 I(1)
german -1.481056 0.5404 -9.168105 0.0000 I(1)
astralia -2.422808 0.1373 -10.93667 0.0000 I(1)
india(bse) -2.150974 0.2253 -10.91161 0.0000 I(1)
india(nse) -1.967105 0.3011 -11.4147 0.0000 I(1)

9. -.3
-.2
-.1
.0
.1
.2
05 06 07 08 09 10 11 12 13 14 15 16
D_ASTRALIAN
-.3
-.2
-.1
.0
.1
.2
.3
05 06 07 08 09 10 11 12 13 14 15 16
D_BSE
-.3
-.2
-.1
.0
.1
.2
.3
05 06 07 08 09 10 11 12 13 14 15 16
D_CHINA
-.3
-.2
-.1
.0
.1
.2
05 06 07 08 09 10 11 12 13 14 15 16
D_GERMAN
-.3
-.2
-.1
.0
.1
.2
05 06 07 08 09 10 11 12 13 14 15 16
D_HANG_KONG
-.3
-.2
-.1
.0
.1
.2
05 06 07 08 09 10 11 12 13 14 15 16
D_JAPAN
-.15
-.10
-.05
.00
.05
.10
D_LONDAN
-.4
-.2
.0
.2
.4
D_NSE
-.20
-.15
-.10
-.05
.00
.05
.10
D_USA

10. Null hypotheses: F-Statistic Prob.
BSE does not Granger cause Australia 0.09293 0.9113
Germany does not Granger cause Australia 0.34614 0.7080
Hang-Kong does not Ganger cause Australia 0.16480 0.8482
BSE does not Granger cause China 0.27506 0.7599
Japan does not Granger BSE 1.43153 0.2427
London does no Granger cause BSE 0.03217 0.9683
Germany does not Granger cause China 0.02572 0.9746
China does not Granger cause Japan 1.41314 0.2471
London Does not Granger cause China 0.01765 0.9825
NSE does not Granger cause China 0.40402 0.6684
Japan does not Granger cause Germany 1.09740 0.3368
NSE does not Granger cause USA 0.05714 0.9445
London does not Granger cause USA 0.21440 0.8073
Pair wise Granger Causality Tests

11. Null hypothesis: F-Statistic Prob.
China does not granger cause Australia 3.21392 0.0432
China does not granger cause BSE 3.42425 0.0354
Hang Kong does not granger cause BSE 3.72047 0.0267
China does not granger cause NSE 4.00944 0.0203
Hang Kong does not granger cause NSE 4.37125 0.0144
Japan does not granger cause USA 3.58237 0.0306
USA does not granger cause London 3.06245 0.0499

12. INDEPENDE
NT
VARIABLE
DEPENDEN
T
VARIABLE
COEFFICIE
NT
STD.ERRO
R
T-
STATISTIC
PROB.
CHINA AUSTRALIA 0.224203 0.057312 3.911947 0.0001
CHINA BSE 0.298443 0.058241 5.124287 0.0000
HANG KONG BSE 0.786569 0.061783 12.73113 0.0000
CHINA NSE 0.305638 0.059439 5.142042 0.0000
HANG KONG NSE 0.792702 0.062989 12.58483 0.0000
USA LONDON 0.789805 0.050541 15.62714 0.0000
JAPAN USA 0.000158 0.053374 0.002966 0.9976
METHOD: LEAST SQUARES

13. Independent
Variable
Dependent
variable
Coefficient Std.Error t-Statistic Prob.
China Australia -0.007570 0.006530 -1.159366 0.2483
China BSE -0.003950 0.006478 -0.609795 0.5430
Hang Kong BSE -0.001519 0.004455 -0.341102 0.7335
China NSE -0.007796 0.006937 -1.123877 0.2630
Hang Kong NSE -0.004268 0.004595 -0.928746 0.3546
USA London -0.002618 0.001984 -1.319810 0.1890
Residual diagonestic check Heteroskedasticity test:
Breusch-pagan-Godfrey

14. Independent
Variable
Dependent
variable
Eigenvalue
Trace
Statistic
0.05
Critical value
Prob**
China Australia 0.088873 12.93716 3.84166 0.0003
China BSE 0.128375 19.09797 3.84166 0.000
Hang Kong BSE 0.090980 13.25896 3.841466 0.003
China NSE 0.129159 19.22305 3.841466 0.0000
Hang Kong NSE 0.105905 15.56013 3.84166 0.0001
USA London 0.091131 03.28205 3.84166 0.0003
Johansen cointegration test
Unrestricted Cointegration Rank test (trace)

15. Independent
Variable
Dependent
variable
Coefficient Std.Error T-statistic Inference
China Australia -1.575999 0.18406 -8.56244 Long run equilibrium
China BSE -1.589310 0.20269 -7.84091
Long run equilibrium
Hang Kong BSE -1.005470 0.07970 -12.6158
Long run equilibrium
China NSE -1.844133 0.21451 -8.59704
Long run equilibrium
Hang Kong NSE -1.14915 0.08448 -13.5171
Long run equilibrium
USA London -1.161820 0.01662 -15.1630
Long run equilibrium
VECTOR ERROR CORRECTION MODEL

16. FINDINGS
From OLS, China influencing Australia. Residual checking defines,
There is no Heteroskedasticity and also having long run equilibrium
relationship
From OLS, China influencing BSE. Residual checking defines, There is
no Heteroskedasticity and also having long run equilibrium relationship
From OLS, Hang Kong influencing BSE. Residual checking defines,
There is no Heteroskedasticity and also having long run equilibrium
relationship
From OLS, China influencing NSE. Residual checking defines, There is
no Heteroskedasticity and also having long run equilibrium relationship
From OLS, USA influencing London. Residual checking defines, There
is no Heteroskedasticity and also having long run equilibrium relationship
From OLS, Hang Kong influencing NSE. Residual checking defines,
There is no Heteroskedasticity and also having long run equilibrium
relationship

17. SUGGESTION
Since some of the stock market has an influencing on another stock
market investors will be able to decide whether to invest, hold or sale based
on the estimated situation.
With the help of Vector Error Correction Estimates investors can have a
clear idea about the long run influencing factor, Based on these factors the
investors will be able to decide what to do

18. Stock market is highly risk oriented. If we don’t have thorough knowledge of the
technical analysis, then we might lose the money. A return of the investment in the Stock
market depends on the volatility of the market. Stock market is highly volatile. Volatility
gives itself an ‘opportunity’ as well as ‘risk’ whichever way one may look at it, we can’t
wish it away.
“Don’t focus on making money; focus on protecting what you have.” If
investors fellow this strategy for a period of time then they can earn plenty of
money in the Stock market.
CONCLUSION