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http://www.dem.umk.pl/dem 

D Y N A M I C  E C O N O M E T R I C  M O D E L S  
DOI: http://dx.doi.org/10.12775/DEM.2018.002 Vol. 18 (2018) 35−47 

Submitted January 23, 2018  ISSN (online) 2450-7067 

Accepted September 5, 2018 ISSN (print) 1234-3862 

Kamal P. Upadhyaya, Raja Nag and Franklin G. Mixon Jr.

 

 
 

Stock Market Prices and the Macroeconomics of 

Emerging Economies: the Case of India 

 

A b s t r a c t. This paper investigates the relationship between stock prices and selected mac-

roeconomic variables in India. The empirical results suggest that, in the long run, output 

growth and the exchange rate are positively related to stock prices, while money supply ex-

hibits a negative relationship to stock market prices. In the short run most of the variation in 

the stock market is captured by its own innovation, although the exchange rate, the price level 

and the interest rate seem to have some effect on short-run stock prices. 

K e y w o r d s: stock prices, Indian economy, emerging economies, Asian economics 

J E L Classification: E00; E44 

Introduction 

 Despite having the seventh largest economy in the world, India is also 

currently home to one of the world’s fastest growing economies. This phe-

nomenon is relatively recent. However, the Indian economy was more or less 

stagnant until the early 1980s. After enacting some major economic reforms 

                                                 
 Correspondence to: Kamal P. Upadhyaya, University of New Haven, Department of 

Economics, 300 Boston Post Road, West Haven, CT 06516, United States, e-mail: 

kupadhyaya@newhaven.edu; Raja Nag, New York Institute of Technology, Department of 

Accounting and Finance, Old Westbury Campus, Old Westbury, NY 11568, United States,  

e-mail: dnag@nyit.edu; Franklin G. Mixon, Jr., Columbus State University, Center for Eco-

nomic Education, 4225 University Avenue, Columbus, GA 31907, United States, e-mail: 

mixon_franklin@columbusstate.edu. 
 The authors thank two anonymous referees for many helpful comments on a prior ver-

sion. Any remaining errors are our own. 



Kamal P. Upadhyaya, Raja Nag and Franklin G. Mixon, Jr. 

DYNAMIC ECONOMETRIC MODELS 18 (2018) 35–47 

36 

during the 1980s, the growth rate of India’s economy accelerated, at an aver-

age 6.27 percent annual growth from 1980 to 2014. In 2014, India’s econom-

ic growth rate stood at 7.24 percent, making that economy the fourteenth 

fastest growing economy in the world. In 2015, this growth rate increased to 

7.36 percent, establishing India as the world’s ninth fastest growing econo-

my. In response to this level of GDP growth, stock prices in India have also 

been growing steadily. In 2003, total Indian stock market capitalization was 

$279 billion dollars (slightly more than 40% of GDP), increasing to $1.516 

trillion (over 70% of GDP) in 2015. As a result, the growing Indian stock 

market has become an attractive investment opportunity not only for domes-

tic investors but also for foreign investors.  

 There are several factors that contribute to, and influence, a company’s 

stock price. It is in part determined by a firm’s own company fundamentals, 

such as earning per share, dividend per share, book value and other internal 

factors that may affect the growth of the firm. In addition to its own internal 

factors, various external factors, such as domestic as well as foreign macroe-

conomic variables, also affect the firm’s stock price
1
. According to Shleifer 

and Vishny (1997), successful corporate governance systems have signifi-

cant legal protections for investors. Unfortunately, developing countries 

often lack such legal protection, which has led to concentrated ownership of 

equity and large investors. Thus, the findings from studies such as those 

cited above with regard to the relationship between macroeconomic varia-

bles and stock market prices cannot necessarily be generalized for an emerg-

ing economy such as India. As such, the objective of this study is to explore, 

identify and analyze the effects of various macroeconomic variables on equi-

ty prices in the Indian stock market, and to determine if these effects differ 

from those of developed countries such as the U.S., U.K. and Japan. 

1. Literature Review 

 There is abundant theoretical literature that links the stock market with 

different macroeconomic variables (e.g., see Fama, 1981 and 1990; Chen, 

Roll and Ross, 1986). Empirical studies on the validity on this issue, howev-

er, began only few decades ago. One of the early studies on this topic is by 

Mukherjee and Naka (1995), which studied the relationship between macro-

economic variables such as the exchange rate, money supply, inflation, in-

                                                 
1 Given the strong link between the financial market and the macroeconomy, Andreou, 

Ghysels and Kourtellos (2013) use daily financial data to predict quarterly real economic 

activity. 



Stock Market Prices and the Macroeconomics of Emerging Economies 

DYNAMIC ECONOMETRIC MODELS 18 (2018) 35–47 

37 

dustrial production, long term government bond rates and call money rates 

and the Japanese stock market using a vector error correction model 

(VECM). Their findings suggest that there exists a long-run relationship 

between these macroeconomic variables and stock prices in Japan
2
. 

 Maysami and Koh (2000) developed a vector error correction model 

(VECM) to study the relationship between different macroeconomic varia-

bles and the stock market in Singapore. Their findings suggest that only 

price levels, money supply, short-term and long-term interest rates and ex-

change rates are cointegrated with Singapore’s stock market. They also 

found that industrial production and trade, which essentially are the 

measures of real economic activity in Singapore, are not cointegrated with 

its stock market. Wongbangpo and Sharma (2002) also employ a VECM 

model to study the effects of different macroeconomic variables such as 

gross national product, the consumer price index, money supply, interest rate 

and the exchange rate on the stock prices in five ASEAN countries (Indone-

sia, Malaysia, Philippines, Singapore and Thailand). From the estimated 

model they observed both a short-term and long-term relationship between 

the various macroeconomic variables and stock prices. They also suggest 

that there exists a bi-directional causality between these macroeconomic 

variables and stock prices in every sample country in their study. 

 Abugri (2008) investigates the effect of macroeconomic volatility on 

stock returns in four Latin American countries (Argentina, Brazil, Chile and 

Mexico) using a vector autoregressive (VAR) model. The macroeconomic 

variables considered in the study include exchange rates, interest rates, in-

dustrial production and money supply. In addition to these macroeconomic 

variables they also included the MSCI world index and the U.S.three-month 

treasury yield. Based on their empirical findings they argue that the effect of 

macroeconomic variables on stock returns varies from country to country. 

                                                 
2 Many researchers have studied the effects of macroeconomic variables on stock markets 

in different countries (e.g., see Fama, 1981; Chen, 1986; Geske and Roll, 1983; Huang and 

Kracaw, 1984; Kwon, Shin and Bacon, 1997; Humpe and Macmillan, 2008). Most of these 

studies are based on data from developed countries, such as the United States, United King-

dom, and Japan, where the financial sector is well developed. Corporate governance is at 

a mature state in these developed countries (Shleifer and Vishny, 1997). Although, according 

to Fama’s efficient market hypothesis (EMH) continuum, the stock market in the U.S. is 

semi-strong efficient, it is still the most efficient capital market in the world. This is due 

largely to its well-evolved and mature corporate governance structure. 



Kamal P. Upadhyaya, Raja Nag and Franklin G. Mixon, Jr. 

DYNAMIC ECONOMETRIC MODELS 18 (2018) 35–47 

38 

Thus, it is not possible to determine, a priori, the effect of a change of any 

individual macroeconomic variable on market returns
3
. 

 Humpe and Macmillan (2009) examine, using Johansen’s (1991) multi-

variate cointegration test, the importance of various macroeconomic varia-

bles in explaining long-term movements in the stock markets in both the 

U.S. and Japan. In doing so they include industrial production, the CPI, 

money supply, the long-term interest rate and stock prices in the statistical 

model.Their findings suggest that in the U.S., stock prices are positively 

related to industrial production and negatively related to both the CPI and 

long-term interest rates. The relationship between the stock returns and the 

U.S. money supply is found to be positive but insignificant. In case of Japan, 

stock prices are positively affected by industrial production but negatively 

affected by the money supply, while industrial production is negatively in-

fluenced by both the CPI and long-run interest rates. These authors conclude 

that the slump in the Japanese economy during the 1990s, and the subse-

quent liquidity trap experience there, may be responsible for these con-

trasting results.  

 Lastly, Al-Tamami and Rahman (2011) study the factors affecting stock 

prices in the United Arab Emirates (UAE). In doing so they develop a linear 

regression model in which stock prices are a function of earnings per share, 

dividend per share, oil prices, GDP, the CPI, interest rates and the money 

supply. Their analysis of time series data from 17 companies, divided into 

two groups – banks and non-banks – for the period 1995–2005 suggests that 

both GDP and the money supply positively affects both stock prices for 

banks and non-banks, whereas the CPI has a negative effect on both groups 

of stock prices. The interest rate fails to impact stock prices in either sub-

group. 

2. Methodology and Data 

 Based on the economics literature discussed above, the following model 

is developed, 

SP = f (IIP, MS, WPI, R, EXCH), (1) 

where the dependent variable, SP, represents India’s stock price index. On 

the right-hand side are national output, IIP, as measured by the index of in-

dustrial production, the M1 money supply, MS, the price level, WPI, as 

                                                 
3 An additional finding of interest in this study is that the global variables (i.e., the MSCI 

world index and the U.S. Treasury yield) are more consistent in explaining stock returns 

across the countries than are the domestic macroeconomic variables. 



Stock Market Prices and the Macroeconomics of Emerging Economies 

DYNAMIC ECONOMETRIC MODELS 18 (2018) 35–47 

39 

measured by the wholesale price index, the nominal interest rate, R, as 

measured by the 10-year Treasury rate, and the exchange rate, EXCH
4
. An 

increase in economic activity not only increases corporate profitability, it 

may also increase expected future cash flows. This in turn will have a posi-

tive effect on stock prices. Accordingly, a decrease in economic activity may 

lower corporate profitability and expected future cash flows, which in turn 

negatively affects stock prices. Therefore, we expect a negative relationship 

between stock prices (SP) and economic output (IIP). 
 The relationship between the money supply and stock prices is not as 

straightforward. On one hand, an increase in the money supply, ceteris pari-

bus, creates an excess supply of money balances and an excess demand for 

equity, resulting to an increase in equity prices (Dhakal, Kandil and Sharma, 

1993; Wongbangpo and Sharma, 2002). On the other hand, an increase in the 

money supply may lead to inflation, which can lower corporate profits by 

increasing costs. Therefore, it is possible that an increase in the money sup-

ply will negatively affect stock prices. As such, the relationship between MS 

and SP is an empirical question. The expected relationship between SP and 

WPI follows a similar path. In general, the price level is expected to have 

a negative effect on stock prices, given that inflation raises the costs of pro-

duction, lowers profit and reduces future cash flows of a firm (Fama and 

Schwert, 1977; Fama, 1981; Chen et al., 1986; DeFina, 1991). Wongbangpo 

and Sharma (2002) argue, however, that inflation can have a positive effect 

on stock prices given because equities serve as a hedge against inflation 

given that they represent claims on real assets. 

 The relationship between the interest rate and the stock prices is ex-

pected to be negative. There are primarily two reasons for this expectation. 

First, an increase in the interest rate makes interest earning assets more at-

tractive, leading investors to reallocate their portfolios by substituting equity 

for other assets. Second, an increase in the interest rate can reduce corporate 

profitability because of the concomitant increase in financing costs. In either 

case, stock prices (SP) are negatively related to the interest rate (R). Next, an 

increase in the exchange rate (i.e., currency depreciation) makes domestic 

goods (imported goods) relatively cheaper (more expensive) and, thus, more 

(less) competitive in the world (domestic) market. Under this circumstance, 

the volume of exports should increase and at the same time import-

competing domestic goods production also should increase. Under such 

                                                 
4 Ideally GDP would have been more appropriate than the index of industrial production 

(IIP) as a measure of overall economic activity in India. However, a lack of monthly data for 

GDP resulted in our use of industrial production data as a measure of economic activity. 



Kamal P. Upadhyaya, Raja Nag and Franklin G. Mixon, Jr. 

DYNAMIC ECONOMETRIC MODELS 18 (2018) 35–47 

40 

a scenario, corporate profits, as well as future cash flows, should rise. The 

opposite is true in case of a decrease in the exchange rate (i.e., currency ap-

preciation). Therefore, we expect a positive relationship between the ex-

change rate (EXCH) and stock prices (SP). 

 Monthly data from January 2006 to March 2016 are used to test the hy-

potheses developed above. For SP (stock prices), the Bombay Stock Ex-

change Index (SENSEX) is used
5
. SENSEX is a free-float market-weighted 

stock market index of established companies which are listed in the Bombay 

Stock Exchange (BSE). It is the S&P BSE Sensitive Index, which is widely 

considered as the benchmark index of domestic stock markets of India. The 

SENSEX has a free-float market capitalization of over $450 billion (US). 

Data on IPP data are obtained from the Ministry of Statistics and Program 

Implementation of the Government of India., while data for the money sup-

ply (MS), the price index (WPI), the interest rate (R) and the exchange rate 

(EXCH) are obtained from various issues of International Financial Statis-

tics, which is published by the International Monetary Fund. All of the data 

series, with the exception of the interest rate, are transformed into natural 

logs before the empirical estimation. 

3. Estimation and Empirical Findings 

 Macroeconomic time series data are typically not stationary. The use of 

non-stationary data series produces spurious results. Therefore, it is im-

portant to test the stationarity of the each data series before estimating the 

model. To ensure the stationarity of the data series employed in this study, 

both an augmented Dickey-Fuller test (Said and Dickey, 1984) and Phillips-

Perron test (Phillips and Perron, 1988; Perron, 1988) are conducted. Results 

from these tests are reported in Table 1. As indicated there, both the aug-

mented Dickey-Fuller test and the Phillips-Perron test statistics do not reject 

the null hypothesis of “the existence of a unit root” in level form, while they 

do reject this null in the case of first differences for each of the series. These 

results suggest that all of the data series are integrated of order one, or are 

I(1). 

 After establishing the stationarity of the data series, Johansen’s 

cointegration test (Johansen, 1988 and 1991; Johansen and Juselius, 1990) is 

conducted in order to test for long-run relationships among the variables. 

The AIC criterion is used to identify the optimal lag length. The Johansen’s 

                                                 
5 The data for this variable is collected from CNBC money control: 

(http://www.moneycontrol.com/stocks/hist_index_result.php?indian_indices=4). 



Stock Market Prices and the Macroeconomics of Emerging Economies 

DYNAMIC ECONOMETRIC MODELS 18 (2018) 35–47 

41 

cointegration test results are reported in Table 2, and indicate that one may 

reject the null hypothesis of “no cointegration”. The long-run relationship 

between stock price indices (SP) and the macroeconomic variables are de-

rived after normalizing the coefficient of SP to one, which occurs as fol-

lows
6
: 

SP  =  30.61IIP
***

 −10.07MS
***

 − 9.40WPI + 0.53R + 12.77EXCH
*** 

         (2) 

        (4.51)           (−3.13)         (−1.54)         (1.32)      (3.43)    

Table 1. Unit root test results 

 Augmented Dickey-Fuller Phillips-Perron 

Variable Level FD Level FD 

logSP −2.66 −10.07*** −2.92 −10.06*** 
logIIP −2.54 −3.86** −2.47 −21.55*** 
logMS −2.05 −6.75*** −2.99 −13.89*** 
logWPI −2.47 −6.44*** −1.76 −6.39*** 

R –2.99 –6.31*** –3.32**  –11.92*** 
logEXCH −2.59 −7.92*** −2.20 −7.84*** 

Notes: FD = first-difference. *** (**) denotes significance at the 1% ( 5%) critical level. 

 As indicated in (2) above, the coefficient of IIP, which is a proxy for 

overall economic activity, is both positive and statistically significant. This 

finding is consistent with the notion that an increase (decrease) in economic 

activity increases (decreases) corporate profits and expected future cash 

flows, which in turn increases (decreases) stock prices. 

Table 2. Johansen’s cointegration test results 

H0 Eigen Value Trace Statistic 5% Critical Value 

r = 0 0.314 123.50*** 95.75 
r ≤ 1 0.224 80.22*** 69.82 
r ≤ 2 0.205 50.97** 47.85 
r ≤ 3 0.129 24.53 29.79 
r ≤ 4 0.058 8.64 15.49 
r ≤ 5 0.015 1.73 3.84 

Note: *** (**) signifies rejection of hypothesis at 1% (5%) critical level. 

 As discussed in the previous section of this study, the relationship be-

tween the money supply and stock prices is not straightforward, given that 

on the one hand an increase in money supply may increase stock prices by 

creating an excess supply of money balances, while on the other hand an 

increase in the money supply may lower stock prices by increasing the price 

level. In case of India, it appears as though an increase in the money supply 

                                                 
6 The figures in the parentheses are t-values for the corresponding coefficients, where *** 

indicates significance at the 1% critical level. 



Kamal P. Upadhyaya, Raja Nag and Franklin G. Mixon, Jr. 

DYNAMIC ECONOMETRIC MODELS 18 (2018) 35–47 

42 

has a negative effect on stock prices in the long run, presumably because of 

its effect on the price level. This finding is consistent with Wongbangpo and 

Sharma’s (2002) findings for Indonesia and the Philippines.  

 The coefficient on the price level (WPI) is negative, which suggests that 

an increase in the price level raises the production costs, lowers corporate 

profits and reduces expected future cash flows. The coefficient, however, is 

not statistically significant at the conventional level. Likewise, the coeffi-

cient attached to the interest rate (R), which carries an unexpected sign, is 

not statistically significant. Wongbangpo and Sharma (2002) similarly found 

a positive long-run effect of long-term interest rates on stock prices for both 

Indonesia and the Philippines. Lastly, the coefficient of EXCH, which shows 

the long-run effect of the exchange rate (EXCH) on stock prices, is positive 

and statistically significant. This finding is consistent with that of Mukherjee 

and Naka (1995) in the case of Japan and Indonesia, as well as that for the 

Philippines and Malaysia in the study by Wongbangpo and Sharma (2002). 

 After establishing the long run relationships between the variables in (1) 

above, we proceed to identify the short run relationship of the various mac-

roeconomic variables in (1) stock prices (SP) in India. Although recent stud-

ies provide examples of the efficacy of various types of Granger causality 

(e.g., Piłatowska, M., Włodarczyk, A., Zawada, 2014; Syczewska, 2014; 

Geise, A. and Piłatowska, 2016), the relationships examined in this study are 

best understood through the Granger causality test derived from the VECM 

estimation
7
. The VECM Granger causality test results are reported in Table 

3. As reported there, when all the macroeconomic variables are taken togeth-

er the Chi-square value for SP is statistically significant. This indicates that 

national output (IIP), the money supply (MS), the price level (WPI), the in-

terest rate (R) and the exchange rate (EXCH) jointly Granger-cause stock 

prices (SP). Accordingly, we can observe that, with exception to price level 

(WPI), each of the variables in the model is jointly Granger-caused by the 

other variables. 

 

                                                 
7 Vector Error Correction (VECM) includes lags of the dependent variables, in addition to 

its own lags. Econometircs software used (E-views-7) to estimate this equation also generates 

the cointegration equation which is reported in (2). Our methodology here is consistent with 

other similar studies (Maysami and Koh, 2000; Wongbangpo and Sharma, 2002; Abugri, 

2008). Next, to ensure that the VECM estimation does not suffer from serial correlation, and 

that it is stable and robust, we conducted a Portmantau test (Castle and Hendry, 2010) for 

autocorrelation (for up to 10 lags). Encouragingly, the test result does not reject the null hy-

pothesis of no autocorrelation. 



Stock Market Prices and the Macroeconomics of Emerging Economies 

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43 

Table 3. VEC Granger causality/Block exogeneity Wald test results 

 Excluded Variables 

Dependent Variable ΔSP ΔIIP ΔMS ΔWPI ΔR ΔEXCH All 

ΔSP n/a 4.95 10.57 5.29 7.25 25.52*** 58.58*** 
ΔIIP 0.57 n/a 11.45 18.27** 7.66 9.95 61.67*** 
ΔMS 3.55 17.71*** n/a 9.48 6.61 5.64 59.65*** 
ΔWPI 2.61 4.43 7.54 n/a 4.62 3.54 31.28 

ΔR 2.92 7.14 11.74 11.34 n/a 3.21 54.32** 
ΔEXCH 25.30*** 7.97 6.61 6.78 2.60 n/a 56.53** 

Note: The numbers above are chi-square statistics, where *** (**) signifies the 1% (5%) critical level. 

 The effect of innovation in any variable on stock prices (SP) can be sen-

sitive to the ordering of the variables. Most of the literature places stock 

prices first, which are followed by the output level (i.e., GDP), CPI, money 

supply and interest rate. Given that the exchange rate is “pegged” in most 

developing countries it is considered as exogenous and typically placed last 

(see Naka and Tufte, 1997). The same ordering is followed in this study, 

with exception to the money supply. The ordering in this case is SP, IIP, MS, 

WPI, R and EXCH. Table 4 reports the variance decomposition, also referred 

to forecast error variance (FEV) decomposition. In addition to the forecast 

error variance (FEV) decomposition of stock prices due to a shock in the 

macroeconomic variables, Table 4 also includes the variance decomposition 

of other variables due to a shock in other macroeconomic variables, includ-

ing stock prices. 

Table 4. Variance decomposition 

 Forecast Error Variance of Stock Prices Explained by Innovations 

Steps Ahead SP IIP MS WPI R EXCH 

3 89.16 1.40 1.06 1.18 0.35 6.85 
6 91.35 1.33 0.75 0.63 1.17 4.77 
9 87.33 1.26 0.50 2.11 2.98 5.82 
12 85.02 1.15 1.20 3.56 3.28 5.78 
       

 Forecast Error Variance of Stock Prices Explained by Innovations in SP 

3 n/a 1.72 1.05 0.03 6.25 24.36 
6 n/a 11.62 1.49 0.24 9.39 35.79 
9 n/a 21.40 2.70 1.09 15.81 40.12 
12 n/a 22.90 3.41 2.16 21.67 40.72 

 From the FEV it appears that most of the variance in stock prices (SP) in 

the short horizon is mainly attributable to its own shock (e.g., 89 percent in 

third period, 91 percent in sixth period, and 85 percent in twelfth period). 

Interestingly, innovations in macroeconomic variables such as the output 

level (IIP) and money supply (MS) do not seem to attribute much in the vari-



Kamal P. Upadhyaya, Raja Nag and Franklin G. Mixon, Jr. 

DYNAMIC ECONOMETRIC MODELS 18 (2018) 35–47 

44 

ance of stock prices (SP). For example, IIP attributes less than two percent 

and MS attributes around one percent over the time period. The effects of the 

price level (WPI) and the interest rate (R) on the variance of SP are little 

more than three percent by the ninth and twelfth time period. The only mac-

roeconomic variable that has a larger effect on the variance of SP is the ex-

change rate (EXCH). Innovation in this variable attributes five percent or 

more from period three to period twelve. The effect of innovations on stock 

prices (SP) and other macroeconomic variables on the variance output level 

(IIP) is also of interest. It appears that a one standard deviation shock in SP 

contributes to 22.90, 21.67 and 40.72 percent, respectively, in IIP (output), 

the interest rate (R) and the exchange rate (EXCH), respectively, by within a 

year (i.e., period 12). The magnitude of the effect of innovation in SP is 

much smaller in terms of the money supply (MS) and the price level (WPI) 

compared with other variables. 

 The impulse response functions, which are available from the authors 

upon request, show the response of stock prices (SP) to a one standard devia-

tion shock to all the macroeconomic variables, and vice versa. In terms of 

the effect of a shock on SP on itself, the effect initially keeps on rising, it 

peaks by period 6 and thereafter starts declining, which is compatible with 

the variance decomposition result. In the case of IIP, an innovation on its 

effect on SP peaks at period two and becomes negative by period 12. Like-

wise, money supply and price level innovations also have some positive 

effect in the beginning, but eventually a negative effect prevails. Interest rate 

and exchange rate shocks have negative effects from the beginning to period 

12. Overall, the impulse response functions corroborate the variance decom-

position analyses. 

Conclusions 

 In the midst of a growing economy, the stock market in India is also 

expanding and maturing, thus providing an opportunity for both domestic 

and foreign investors. India’s stock market, like others, is affected by a num-

ber of macroeconomic variables, and while there are several empirical stud-

ies investigating the relationship between stock prices and the 

macroeconomy, particularly for developed economies, such a study of In-

dia’s emerging economy is currently missing from the economics literature. 

As such, this study is to investigate the relationship between stock market 

prices in India and selected macroeconomic variables, such as overall eco-

nomic output, the money supply, the price level, the interest rate and the 

exchange rate. Using monthly data from January 2006 to June 2016, both 



Stock Market Prices and the Macroeconomics of Emerging Economies 

DYNAMIC ECONOMETRIC MODELS 18 (2018) 35–47 

45 

short-run and long-run relationships are estimated using mix of approaches, 

including a vector error correction model (VECM), Johansen’s cointegration 

test, Granger causality tests and variance decomposition. 
 The empirical findings suggest that, in the long run, stock market prices 

in India are positively related to output growth, while money supply growth 

seems to be negatively related to stock prices. The Granger causality test 

indicates that, as a group, all of the macroeconomic variable in the model 

Granger-cause stock prices in India. Likewise, the combination of stock 

prices and the other variables (as a group) Granger-causes each of the mac-

roeconomic variables in model, with the exception of the price level. Lastly, 

variance decomposition and impulse response functions suggest that the 

stock market dynamically interacts with key macroeconomic variables. More 

specifically, although most of the variation in the stock market is captured by 

its own innovation, the exchange rate, the price level and the interest rate 

seem to have some effect on stock price variation as well. 

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