Inter Linkage between Macroeconomic Variables and Stock Indices Using Granger Causality and Co Integration Approach

International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 6 Issue 6, September-October 2022 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470
@ IJTSRD | Unique Paper ID – IJTSRD51868 | Volume – 6 | Issue – 6 | September-October 2022 Page 694
Inter-Linkage between Macroeconomic Variables and Stock
Indices: Using Granger Causality & Co-Integration Approach
Dr. Amit Manglani1
, Mr. Suraj Patel2
1
Assistant Professor, 2
Research Scholar,
1,2
Department of Commerce, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, Chhattisgarh, India
ABSTRACT
As exchange rate and GDP are the important factors which influence
the behavior of stock market. In this study we have examined the Co-
integration between macroeconomic variables and Indian stock
market and causality between exchange rate and GDP with stock
return. We have applied 42years data on yearly basis for GDP,
exchange rate and stock return and applied ADF test for checking
Stationarity, Correlogram for serial correlation, Johansen Co-
integration for association and Granger causality test for examine
multiple causal relation by controlling the effects of other variables,
then Impulse Response Function used for checking the
responsiveness of a time series to unexpected shocks in other time
series. The study found that exchange rate significantly granger
causes the stock return Indian stock market and long run co-
integration found to be significant in amongst the selected variables.
KEYWORDS: Stock Return, Stationarity, Granger Causality, and
Variance Decomposition Analysis
How to cite this paper: Dr. Amit
Manglani | Mr. Suraj Patel "Inter-
Linkage between Macroeconomic
Variables and Stock Indices: Using
Granger Causality & Co-Integration
Approach" Published in International
Journal of Trend in
Scientific Research
and Development
(ijtsrd), ISSN:
2456-6470,
Volume-6 | Issue-6,
October 2022,
pp.694-701, URL:
www.ijtsrd.com/papers/ijtsrd51868.pdf
Copyright © 2022 by author(s) and
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INTRODUCTION
As stock market is working since over a decades in
the country and it has participated prominent role in
the growth of the economy directly or indirectly.
From last few years, national as international
investors and researchers have taken more emphasis
on emerging financial market, especially in Asian
countries markets. Stock market provides attractive
investment opportunityto the investors. Since last one
decade Indian stock markets are providing more
attractive opportunities to the investors and this
market is growing sharply during the period. So it has
become very much profitable and attractive market
for the investors. As we know, after globalisation
Indian economy has been open for rest of the world.
By this, international investors can invest in Indian
stock market without extra restriction as it was
earlier. By applying granger causality, variance
decomposition and impulse response function, the
author depicted stock market leads to economic
activates except exchange rate. Exchange rate leads to
stock price (Ahmed, 2008). A study confirmed that
exchange rate and stock prices are bi-directionally
related to each other and positively related to each
other (Srivastava A. &., 2011). Evidence depicted
that growth rate and stock market are equally related
to each (Agrawalla, 2007). The macroeconomic
factors like IIPs, WPIs, affected more to the stock
market than global factors in the long run
(Srivastava, 2010).
As previous literatures also worked on the causality
and co-integration among the macroeconomic
variables and Indian stock market but previous
literature consists data for maximum of 12 to 15 years
only. Here we have used data for 42 years from 1980
to 2021 to examine the causality and co-integration
amongst the variables as well responsiveness of time
series to shocks. The first part of the article includes
introduction and literature review. Second part consist
objective, hypothesis and methodology. Third part of
the article depicts models of causality and co-
integration test, and then analysis and interpretation
IJTSRD51868

International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470
have been shown related to unit root test, serial
correlation, co-integration, VAR granger causality /
Block Exogeneity Wald test. And finally we have
presented discussion, managerial implication and
conclusion of the study.
Review of literature:
Nakaet studied long run relationship between BSE
stock price and select macroeconomic variables by
using monthly data from 1960 to 1995. The study
applied VECM to avoid potential misspecification
biases. The study observed that five variables are co-
integrated and exist long run equilibrium relation
among these variables (Nakaet, 2001). (Irabian)
analyse the dynamic interaction between stock price
and select macro-economic variables. Under the stock
monthly data from 1977 to 1996 have been used to
examine the co-integration and granger causality
between and among the variables. This study depicted
that stock price is influenced by changes in official
reserves and exchange rate in short run but there is no
co-integration in long run (Irabian, 1999).
Bhattacharya and Mukharjee (2006) empirically
analysed the relationship between macroeconomic
variables and Indian stock market. Under the study
VAR model and non-granger causality technique
applied, using data from April 1992 to Mar.2001.
Result submitted that there is no causality between
exchange rate, money supply, Index of Industrial
Production, Gross National Product and Forex reserve
and stock return. But inflation rate bi-directionally
causes with stock return (Bhattacharya and
Mukharjee, 2006). Another study examined the
relationship between stock return and macroeconomic
variables in India by using co-integration and error
correction model for the period from April 1992 to
Dec. 1997 on monthly basis. The study admitted that
long run co-integration does not exist between the
stock price and macro-economic variables also
inferred weak form of causality running from IIPs to
stock index (karnik, 2000). A study on the causal
relationship between S&P 500 and exchange rate for
short period of time has been conducted and by the
study, authors depicted that there is bidirectional
causality exists between both of these selected
variables. On the other hand, co-integration analysis
was failing to produce significant long run co-
integration between S&P 500 and USD exchange rate
(Sohrabian, 1992). The author studied the
relationship between BSE stock index and three select
macroeconomic variables i.e. IIPs, WPIs, Exchange
Rate. By using monthly data he has applied unit root,
correlation and granger causality to know relationship
and granger cause between the variables. Study
affirmed there is unit root in stock index, IIPs WPIs
and Sensex. On the other hand, unidirectional
causality of WPIs exists with Sensex as well as strong
correlation in between (Singh, 2010). Recent study
analysed the relationship between Indian stock market
index (BSE Sensex) and select variables namely IIPs,
WPIs, Money Supply, and exchange rate. Under the
study monthly data have been used from 1999 to
2017 and for the analysis causality, co-integration and
VECM has been applied to know the causality and
relationship. The results significantly depict that BSE
Sensex causes changes in the exchange rate and
money supply, FII, gold prices and IIP (Misra, 2018).
Naik and Padhi analysed the long run relationship
between stock index and macroeconomic variables
for the period from April 1994 to Jun 2011 by using
Johansen co-integration and VECM techniques. The
result showed co-integration among the variables
while stock price influenced by exchange rate and
interest rate insignificantly. IIPs and Money supply
are positively related to stock prices (Pramod
Kumar NAIK, Puja PADHI, 2012). A study
examine the causal relationship between macro-
economic variables and Indian stock index by using
different test i.e. unit root test, Ljung –Box Q test and
granger causality. The study affirmed there is
autocorrelation existing between stock index and
macro-economic variables. The study depicted
bilateral relationship between stock market and
macroeconomic variables (Tripathy, 2011).
Here we have deeply studied the previous literatures
concerned with our study and found that some
literatures are confined existence of long run
relationship between stock price and select
macroeconomic variables. On the other, some studies
are saying about non-existence of co integration
among the exogenous and endogenous value in long
run. With this, we have arrived at a point that there is
conflict between the previous result and need to
conduct further research using long period data for
confirming the relationship between the variables. So
we have used very long data for 41 years of select
macroeconomic variables for the purpose of study.
Objective of the study: The main objective of
the study –
1. To know the Stationarity in time series variables
2. To find the Causality among GDP Exchange rate
and stock return.
3. To examine the Co-integration among the
macroeconomic variables.
4. To examine the responsiveness of time series
variables to their own lagged shocks and shocks
of lagged movement of remaining variables.
Research Hypothesis:
H01: There is Stationarity problem in time series.

H02: GDP and Exchange rate do not significantly
cause to BSE return.
H03: There is no significant Co-integration among the
Variables.
Research Methodology:
1. Nature of the study: The study is of analytical
and Descriptive nature
2. Variables selection: The yearly data of Exchange
Rate, Annual GDP and BSE Sensex return have
been used for the period of 42 years.
3. Statistical and econometric tools: we have used
open access software for the purpose of finding
the relation among the variables and applied ADF
Unit root test, Correlogram, Johansen Co-
integration and Granger Causality and Variance
Decomposition Analysis.
4. Presentation of Data: Tabulation, Line & Bar
Graph have been used.
5. Data Description: Here, we have used average
yearly continuously compounded log return of
BSE Sensex as a benchmark of Indian Stock
market. Under the study, relative change in GDP
and exchange rate data used to confirm
Stationarity in data set without making any
transformation in data set for proper application
of econometric tools in the study.
Analysis and interpretation of the study:
Table 1- Descriptive statistics-
Variable Mean Std. Dev. Skewness Kurtosis Jarque-Bera P Value
GDP -0.000661 0.036990 0.058300 6.296378 18.58615 0.000092
Exchange Rate 5.597132 8.306994 1.338220 6.332432 31.96975 0.000000
BSE Return 0.205896 0.316407 0.289344 2.918577 19.17887 0.000068
Source: Authors own analysis
The above table presents the higher volatility in the time series of the variables and series is leptokurtic because
kurtosis value is more than and approximately 3 i.e. the distribution show high probability of return inclining to
zero with a high positive and negative return. The statistics of Jarque-Bera show significant that is greater than
zero (because of leptokurtic data). So Jarque-Bera depicts all the series are normally distributed.
Table – 2 Correlogram: Auto Correlation and Partial Auto correlation
Lag AC PAC Q-Stat. Prob.
1 -0.175 -0.175 1.3734 0.241
2 -0.122 -0.157 2.0568 0.358
3 -0.131 0.084 2.8701 0.412
4 0.010 0.034 2.8747 0.579
5 -0.152 -0.123 4.0282 0.545
6 0.064 0.007 4.2355 0.645
7 -0.011 -0.037 4.2421 0.752
8 0.000 0.030 4.2421 0.835
9 -0.246 -0.269 7.6309 0.572
10 0.005 -0.113 7.6322 0.665
11 0.033 -0.046 7.6956 0.740
12 0.002 0.033 7.6956 0.808
13 0.026 0.051 7.7384 0.860
Source: Authors own calculation
The result of Correlogram is showing that almost every lag has significant value which could be seen by Q-
statistics that’s indicate significant serial correlation and this is rejection of weak form of market efficiency. It
affirms existence of autocorrelation of macroeconomic variables and Indian stock market which implies
operation of market follows the information rationally.
Figure 1- Graphical Presentation of Data:
To analyse the trend of the time series data we have plot the graph of data set during the study period. With the
graph, we can say that there is high volatility in the series which indicates for checking Stationarity in data set so
that we could further apply the econometric tools for authentic research.

-.15
-.10
-.05
.00
.05
.10
.15
1980 1985 1990 1995 2000 2005 2010 2015 2020
ANNUAL_CHANGE_GDP_
-20
-10
0
10
20
30
40
1980 1985 1990 1995 2000 2005 2010 2015 2020
Annual Change in Exch Rate
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1980 1985 1990 1995 2000 2005 2010 2015 2020
BSE Stock_return
-20
-10
0
10
20
30
40
1980 1985 1990 1995 2000 2005 2010 2015 2020
BSE Stock_return
Annual Change in Exch Rate
ANNUAL_CHANGE_GDP_
Augmented Dickey–Fuller unit root test: (at level / without Difference)
H0: Variables have unit root Problem or data is not stationary.
Previous literatures confirmed that most of the financial time series is to be Non-stationaryor random walk when
the series are in their original form. So we have examined unit root problem in the time series before applying
any type of econometric technique in the study. The presence of Non-stationary or random walk in the series
may lead to fallacious inferences in the study. Thus, by applying augmented dickey-fuller (ADF) test, we have
scrutinize the presence of unit root in the data set/ series. ADF test is to be expressed as:
Table-03 Augmented Dickey–Fuller (ADF) test
Variable ADF test Prob. Null hypothesis Result
GDP -7.739291 0.0000 Rejected H0 Variable is stationary
Exchange Rate -4.737280 0.0004 Rejected H0 Variable is stationary
BSE Return -7.524013 0.0000 Rejected H0 Variable is stationary
Source: Authors.
Note: A null hypothesis is to be rejected when test statistics > Critical Value
Asymptotic Critical Values*
At Level 1% 5% 10%
Critical Value(C.V) -3.605593 -2.936942 -2.606857
Result of the ADF test showing that the three variables of time series i.e. log Sensex return, relative GDP and
Exchange rate are free from unit root problem or series are stationary because the prob. Value is less than 0.05
which confirms rejection of null hypothesis and now we can further use the data set for further study.
Table-04: Johansen Co-integration Test: at Lag Length = 0
H0 = There is no co-integration among the variables
In the process of examine the Co-integration among the variables the trace statistics test considered by following
equation (2):-

Result:
Variable H0
Eigen
value
Critical
Value
Trace
statistic
Critical
Value
Prob.**
BSE Stock Return to
GDP & Exchange
Rate
r = 0 47.62384* 21.13162 93.33649* 29.79707 0.0000
r = 1 31.03955* 14.26460 45.71264* 15.49471 0.0000
r = 2 14.67310* 3.841466 14.67310* 3.841466 0.0001
Source: The author’s
*denotes rejection of the Hypothesis at the 0.05 level
**Mackinnon-Haug-Michelis (1999) p-value
The result of Johansen Co-integration test based on the principle which has been discussed in the literature
concerned with Johansen Co-integration (Johansen 1995). In accordance with the principle when the value of
test statistics is less than the critical value at significance level of 5 %. The null hypothesis of no co-integration
among the variable rejected and we can confirm that there is long run association exist among GDP, Exchange
Rate and Sensex return as well as Eigen value & trace rank test also supports the co-integration among the
variables.
Table – 05 Granger Causality Test- At Lag – 2, Observation– 41
As it is all known, this test is one of most important test in the field of time series research,. We can complete
one research only with the help of this test significantly. So by this, we can understand how much important this
test.
Result:
Null Hypothesis F-Statistic Prob.
Annual Change in Exchange Rate does not Granger Cause Annual Change in GDP
Annual Change in GDP does not Granger Cause Annual Change in Exchange Rate
1.12214
0.77320
0.3373
0.4695
BSE Stock Return does not Granger Cause Annual Change GDP
Annual Change in GDP does not Granger Cause BSE Stock Return
0.23628
0.83051
0.7908
0.4440
BSE Stock Return does not Granger Cause Annual Change in Exchange Rate
Annual Change in Exchange Rate does not Granger Cause BSE Stock Return
0.33158
4.01708
0.7200
0.0269
The table - 05 presented pair wise causality between the variables with log 2 which is appropriate lag order
based on Akaike Information Criteria (AIC) for the sample period. Here annual change in GDP, Exchange rate
and stick returns are macroeconomic variables that have been selected for the study. Results are depicting that
the null hypothesis of annual change in exchange rate and annual change in GDP does not granger cause to each
other has been accepted because F-statistics and P value is greater than 0.05 significance level which confirm
there is no causal relation between both of these variables. Further, Hypothesis of BSE stock return and GDP
does not granger cause to one another is also accept because of high prob. Value (0.7908 & 0.4440) than 0.05
critical value that is indicating absence of causal relation between BSE stock return and annual change in GDP.
And if we will see the causality between exchange rate and BSE return, we can say there is significant causal
relation exist between annual change in exchange rate and stock return because p value (0.0269) is less than 0.05
which is significantly rejecting the null hypothesis of annual change in exch. Rate does not granger cause to BSE
return. But on the other hand, BSE Sensex return does not granger cause to exchange rate, as we can see in the
table where p value (0.7200) is greater than 5% significance level.
Finally we can say that change in exch. Rate leads to BSE Sensex return significantly in long run but annual
change in GDP does not do so. Thus we admit that exchange rate is a leading factor for stock return. So at the
time of taking decision regarding investment in stocks people should critically examine the changes take placed
in exchange rate in long time.

Table – 06Variance Decomposition:
Variance Decomposition of ANNUAL_CHANGE_IN_EXCH_RATE:
Period S.E.
ANNUAL_CHANGE
_IN_EXCH_RATE
ANNUAL_CHANGE
_GDP_
BSE_STOCK_RETURN
1 8.454357 100.0000 0.000000 0.000000
2 8.914901 97.58479 0.796539 1.618672
3 9.076320 95.16961 3.260537 1.569850
4 9.096783 94.80722 3.628401 1.564380
5 9.098799 94.79722 3.638828 1.563950
6 9.098951 94.79550 3.640518 1.563981
7 9.098993 94.79473 3.641211 1.564060
8 9.098996 94.79469 3.641254 1.564060
9 9.098997 94.79468 3.641254 1.564061
10 9.098997 94.79468 3.641256 1.564061
Variance Decomposition of ANNUAL_CHANGE_GDP_:
Period S.E.
ANNUAL_CHANGE
_IN_EXCH_RATE
ANNUAL_CHANGE
_GDP_
BSE_STOCK_RETURN
1 0.033759 1.168292 98.83171 0.000000
2 0.036309 11.42695 85.66895 2.904109
3 0.036406 11.54330 85.21488 3.241827
4 0.036426 11.56184 85.17796 3.260203
5 0.036428 11.56069 85.17426 3.265047
6 0.036429 11.56277 85.17211 3.265121
7 0.036429 11.56315 85.17155 3.265306
8 0.036429 11.56321 85.17148 3.265310
9 0.036429 11.56322 85.17147 3.265314
10 0.036429 11.56322 85.17147 3.265315
Variance Decomposition of BSE_STOCK_RETURN:
Period S.E.
ANNUAL_CHANGE
_IN_EXCH_RATE
ANNUAL_CHANGE
_GDP_
BSE_STOCK_RETURN
1 0.297267 2.858329 3.508028 93.63364
2 0.339792 21.30653 4.688019 74.00545
3 0.343038 21.09047 5.672621 73.23691
4 0.344415 21.52293 5.645658 72.83141
5 0.344458 21.51926 5.664976 72.81577
6 0.344490 21.52384 5.664878 72.81128
7 0.344491 21.52420 5.664852 72.81095
8 0.344492 21.52416 5.664840 72.81100
9 0.344492 21.52419 5.664838 72.81097
10 0.344492 21.52419 5.664840 72.81097
Cholesky Ordering: ANNUAL_CHANGE_IN_EXCH_RATE ANNUAL_CHANGE_GDP_
BSE_STOCK_RETURN

Figure: 02 Graph of Variance Decomposition
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9 10
ANNUAL_CHANGE_IN_EXCH_RATE
ANNUAL_CHANGE_GDP_
BSE_STOCK_RETURN
Variance Decomposition of A NNUA L_CHA NGE_IN_EXCH_RA TE
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9 10
ANNUAL_CHANGE_GDP_
BSE_STOCK_RETURN
Variance Decomposition of ANNUA L_CHANGE_GDP_
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9 10
ANNUAL_CHANGE_GDP_
BSE_STOCK_RETURN
V ariance Decomposition of BSE_STOCK_RETURN
Variance Decomposition using Cholesky (d.f. adjusted) Factors
The variance decomposition function provides something different method of analyzing the dynamics of vector
auto regression system. Variance decomposition depicts the proportion of change in endogenous variables, due
to shocks of their ‘own’ lag versus lagged shocks of other remaining variables in the system.
By analyzing the above figure 1, we can understand that exchange rate is affected by its own lagged shock rather
than other endogenous variables lagged shock and figure 2 shows annual change in GDP is slightly affected by
shock of remaining endogenous variables. But Sensex return is highly affected by the change in exchange rate
and slightly affected by shock of lag value of GDP. It means exchange rate change have high impact on Sensex
return than change in GDP. This indicated that any change in exchange rate in future would influence the
decision of investors and BSE return is so sensitive with respect to exchange rate.
Conclusion:
The time series data set comprises of 42 observations
of relative alter in GDP, Exchange rate and BSE
return. Correlogram affirmed the nearness of serial
auto correlation of macroeconomic factors with
Indian stock market. The study includes Johansen co-
integration test to examine long run association
among the variables and study affirmed that all
selected variables are interrelated to each other. On
the other hand, granger causality test exhibits
significant causal relationship and it was confirmed
that any change in exchange rate affect the stock
prices in long run. The analysis of Variance
Decomposition depicts that unexpected own lagged
shock as well as lagged shock of other variables
affects the price of dependent variables. Here
exchange rate has high composition to BSE return
than GDP. So investors should try to take in
consideration at the time of taking investment
decision. It also implicates that the rational investors
can obtain abnormal return in Indian stock market by
using historical data of macroeconomic variables and
they would be able to predict the fluctuations in stock
prices.
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