The present paper is aimed at analyzing the nature of the causal relationship between stock monetary values and macroeconomic sums in India, if any. By using the techniques of unit-root trials, cointegration and the Granger prove the causal relationships between the NSE Index ‘Nifty ‘ and the macroeconomic variables, viz. , Real effectual economic rate ( REER ) , Foreign Exchange Reserve ( FER ) , and Balance of Trade ( BoT ) , Foreign Direct Investment ( FDI ) , Index of industrial production ( IIP ) , Wholesale monetary value index ( WPI ) utilizing monthly informations for the period 1st April 2006 to 31st March 2010 have been studied.

The major findings of the survey are ( one ) there is no cointegration between Nifty and all other variables except Wholesale monetary value index ( WPI ) as per Johansen Cointegration trial. Therefore causal relationship between such macro economic variables holding no cointegration with nifty is non established. ( two ) Nifty does non Granger Cause WPI and WPI besides does non Granger Cause Nifty.

## Cardinal Wordss: Granger Causality, Macroeconomic Variables, Cointegration, Stock Monetary values

## JEL Classification: G1, E4

## Introduction

The motion of stock indices is extremely disposed to the alterations in basicss of the economic system and to the alterations in future chances outlooks. These outlooks are influenced by the micro and macro basicss which may be formed either logically or adaptively on economic basicss, every bit good as by many subjective factors which are unpredictable and besides not quantifiable. It is believed that domestic economic basicss play seminal function in the public presentation of stock market. However, in the epoch of globalization and integrating of universe economic systems, domestic economic variables are besides capable to alter due to the policies adopted and expected to be adopted by other states or some planetary events. The common external factors act uponing the stock return are stock monetary values in planetary economic system, the involvement rate, foreign investing and the exchange rate. For illustration, capital influxs and escapes are non determined by domestic involvement rate entirely but besides by the alterations in the involvement rate by major economic systems in the universe. Recently, it is experienced that contagious disease from the US bomber premier crisis has played important motion in the capital markets across the universe as foreign hedge financess unwind their places in assorted markets. Other firing illustration in India is the grasp of Indian currency due to increased influx of foreign exchange. It has resulted in a diminution in the stock monetary values of major export oriented companies particularly in Information engineering and fabric sectors. The modern fiscal theory dressed ores upon systematic factors as beginnings of hazard and contemplates that the long tally return on an single plus must retroflex the alterations in such systematic factors. This implies that securities market has an of import relationship with existent and fiscal sectors of the economic system. This relationship is by and large viewed in two ways. The first relationship considers the stock market as a prima index of the economic activity in the state, whereas the 2nd relationship focuses on the possible impact the stock market might hold on aggregative demand, preponderantly through aggregative ingestion and investing. The first instance states that stock market leads economic activity, whereas the 2nd instance suggests that it follows economic activity. Knowledge of the sensitiveness of stock market to macro economic behavior of cardinal variables and vice-versa is of import in many countries of investings and finance. This research may be helpful to grok this relationship.

Since the decennary of 1990 in India, a figure of steps have been adopted for economic liberalisation of the state. Coupled with this assorted other stairss have besides been taken to beef up the stock market such as gap of the stock markets to international investors, addition in the regulative power of SEBI, reforms in the capital markets, trading in derived functions, etc. These steps have resulted in notable betterments in the size and deepness of stock markets in India and they are get downing to play their due function. Soon, the motion in stock market in our state is viewed and analysed carefully by a big figure of planetary participants. An apprehension of the macro kineticss of Indian stock market can be valuable for bargainers, investors and besides for the policy shapers of the state. Consequences of the survey may assist in naming whether the motion of stock market is the consequence of some other variables or it is one of the causes of motion in other macro variable in the economic system. The survey besides expects to research whether the motion of stock market is associated with the economic system. In this context, the intent of this paper is to research such causal dealingss for India for the period of 2006 to 2010.

The complete paper is organised in the four subdivisions. Section I provides reappraisal of selected literature on the causal relationship between stock monetary values and macro economic variables. Section II discusses the informations and explains the methodological analysis for proving the stationarity, the being of cointegration, and the way of causality if any. Section III reports the consequences and their reading. Finally, Section IV trades with the reasoning comments.

## I. Review of Literature

Many empirical surveies have been conducted to analyze the causal relationship between stock market and macro economic variables. In retrospect of the literature, a figure of hypotheses support the being of a causal relation between stock monetary values and economic variables. Ma and Kao [ 1990 ] unearthed that a currency grasp has a negative consequence on the domestic stock market for an export-dominant state and a favorable consequence on the domestic stock market for an import-dominant state, which appears to be consistent with the goods market theory. Bahmani and Sohrabian [ 1992 ] set up a bi-directional causality between stock monetary values ( Standard & A ; Poor ‘s 500 index ) and the effectual exchange rate of the dollar in the short period of clip. However, co-integration analysis did non uncover any long tally relationship between the two variables. Abdalla and Murinde [ 1996 ] in their research analyze the relationship between exchange rates and stock monetary values in the emerging fiscal markets of India, Korea, Pakistan and the Philippines. As per their survey farmer causality trials consequences show uni-directional causality from exchange rates to stock monetary values in all the sample states, except Philippines. Ajayi and Mougoue [ 1996 ] , show important interactions between foreign exchange and stock markets by utilizing day-to-day informations for eight states, while Abdalla and Murinde [ 1996 ] papers that a state ‘s monthly exchange rates tends to take its stock monetary values but non the other manner around. Pan, et Al. [ 1999 ] examined the causal relationship between stock monetary values and exchange rates with the aid of day-to-day market informations and found that the exchange rates Granger-cause stock monetary values with less important causal dealingss from stock monetary values to interchange rate. They besides find that the causal relationship has been stronger in the wake of the Asiatic crisis.

Malliaris and Urrutia [ 1991 ] observed that the public presentation of the stock market might be used as a prima index for existent economic activities in the United States. For the United Kingdom, Thornton [ 1993 ] besides found that stock returns be given to take existent income. In related work and Chang and Pinegar [ 1989 ] besides concluded that there is a close relationship between stock market and the domestic economic activity.

Chen, Roll, and Ross [ 1986 ] , Bodie [ 1976 ] , Fama [ 1981 ] , Geske and Roll [ 1983 ] , Pearce and Roley [ 1983 ] , Pearce [ 1985 ] , James et. Al. [ 1985 ] , and Stulz [ 1986 ] and many documents have tried to demo empirical associations between macroeconomic variables and security returns. Bodie [ 1976 ] , Fama [ 1981 ] , Geske and Roll [ 1983 ] , Pearce and Roley [ 1983 ] and Pearce [ 1985 ] papers that rising prices and money growing has an reverse impact on equity market. Many experts nevertheless believe that positive effects will outweigh the negative effects and stock monetary values will finally lift due to growing of money supply [ Mukherjee and Naka, 1995 ] .

Mukherjee and Naka [ 1995 ] reveal in their survey that cointegration relation existed and positive relationship was found between the Nipponese industrial production and stock return. However, Cutler, Poterba, and Summers [ 1989 ] ( CPS ) find that Industrial Production growing is significantly positively correlated with existent stock returns over the period from 1926 to 1986, except the 1946- 85 sub-period.

In context of developing states Mustafa, K et Al. [ 2007 ] have done a survey to look into the empirical relationship between the stock market and existent economic system in Pakistan economic system by taking up assorted variables like per capita GDP, end product growing to stand for the Real economic system and stock market liquidness, size of stock market stand foring the Stock Market. Cointegration and Error Correction Model Technique has been adopted to set up the empirical relation, if any between the two from the clip period 1980- 2004. Husain, F. [ 2006 ] examined the causal relationship between stock monetary value and existent sector variables of Pakistan economic system, utilizing one-year informations from 1959-60 to 2004-05. It studied the causal relationship between them utilizing assorted econometric techniques like ECM, Engle-Granger carbon monoxide incorporating arrested developments and Augmented Dickey Fuller ( ADF ) Unit Root trials. The survey indicates the presence of a long tally relationship between the stock monetary values and existent sector variables.

More late, Humpe, A. , et Al. [ 2009 ] have tried to associate the macro economic variables with long term stock market motions in US and Japan within the model of a criterion discounted value theoretical account by utilizing monthly informations over 40 old ages. A cointegration analysis has been applied to pattern the long term relationship between the industrial production, money supply, the consumer monetary value index, long term involvement rates and stock monetary values in US and Japan. The writers have found a important relation between the macro economic variables and stock market in the long tally.

In Indian context, Abhay Pethe and Ajit Karnik [ 2000 ] has investigated the inter – relationships between stock monetary values and of import macroeconomic variables, viz. , exchange rate of rupee vis-a-vis the dollar, premier loaning rate, narrow money supply, and index of industrial production. The analysis and treatment are situated in the context of macroeconomic alterations, particularly in the fiscal sector, that have been taking topographic point in India since the early 1990s. Chakradhara Panda, et Al. [ 2001 ] explored the causal dealingss and vivacious interactions among pecuniary policy, existent activity, expected rising prices and stock market returns in the station liberalisation period by utilizing a vector-autoregression ( VAR ) attack. The major findings of their survey are ( one ) expected rising prices and existent activity do impact stock returns, ( two ) pecuniary policy loses its explanatory power for stock returns when expected rising prices and existent activity are present in the system, ( three ) the relationships of pecuniary policy, expected rising prices and existent activity with stock returns deficiency consistence, ( four ) there is no causal linkage between expected rising prices and existent activity. Bhattacharya and Mukherjee [ 2002 ] studied the nature of the causal relationship between stock monetary values and macro sums for the period of 1992-93 to 2000- 2001. The consequences of their survey show that there is no causal relationship between stock monetary value and macro economic variables like national income, money supply, and involvement rate but there exists a two manner causing between stock monetary value and rate of rising prices. Their consequence further points that index of industrial production lead the stock monetary value. Kanakaraj, A. et Al. [ 2008 ] have examined the tendency of stock monetary values and assorted macro economic variables between the clip periods 1997-2007. They have tried to research upon and reply that if the recent stock market roar can be explained in the footings of macro economic basicss and have concluded by urging a strong relationship between the two.

As per the reappraisal of the literature there is no unanimity with respect to the causal relationship between cardinal macro between cardinal macro economic variables and stock monetary values. This relationship is different in different stock markets and clip skylines in the literature. This paper makes an effort to add to the bing literature by supplying robust consequence which is based on more than one technique, about causal links for a period of 4 old ages monthly informations.

## II. Empirical Methodology and Data

For pulling utile illations clip series analysis must be based on stationary informations series. Generally a information series is said to be stationary if its mean and discrepancy are changeless ( non-changing ) over a given period of clip and the value of covariance between two clip periods depends merely on the distance or slowdown amid the two clip periods and non on the existent clip at which the covariance is computed. The correlativity between a series and its lagged values are assumed to depend merely on the length of the slowdown and non when the series started. This belongings is known as stationarity and any series obeying this is called a stationary clip series.

To prove the stationarity of a series three unit root trials have been applied. Stationarity of the clip series has been tested by utilizing Augmented Dickey Fuller ( ADF ) and Phillips Perron ( PP ) trials. [ Dickey and Fuller ( 1979, 1981 ) , Gujarati ( 2003 ) , Phillips and Perron ( 1988 ) , Enders ( 1995 ) ] . For proving void hypothesis of stationarity, KPSS trial has besides been applied for hardiness [ Kwiatkowski, Phillips, Schmidt. and Shin ( 1992 ) ] .

## Augmented Dickey Fuller ( ADF ) Trial

Augmented Dickey-Fuller ( ADF ) trial has been carried out which is the modified version of Dickey Fuller ( DF ) trial. ADF makes a parametric rectification in the original DF trial for higher-order correlativity by presuming that the series follows an AR ( P ) procedure. The ADF attack controls for higher-order correlativity by adding lagged difference footings of the dependant variable to the right-hand side of the arrested development. The Augmented Dickey-Fuller trial specification used here is as given below:

## P

## a?†yt = I±0 + I»yt-1+ I?I?ia?†yt-i +ut ( I )

## i=1

## Phillips-Perron ( PP ) Trial

Phillips and Perron ( 1988 ) follow a nonparametric method for commanding higher-order consecutive correlativity in a series. The trial arrested development for the Phillips-Perron ( PP ) trial is the AR ( 1 ) procedure. The ADF trial damagess for higher order consecutive correlativity by adding lagged differenced footings on the right-hand side and the PP trial makes a rectification to the t-statistic of the coefficient from the AR ( 1 ) arrested development to set the consecutive correlativity in Greenwich Mean Time. The rectification is nonparametric in nature. The of import asset of Phillips-Perron trial is that it is free from parametric mistakes. Phillips-Perron trial allows the perturbations to be decrepit dependant and heterogeneously distributed. In position of this, PP values have besides been checked for stationarity.

## KPSS Test

A major unfavorable judgment of the ADF unit root proving process is that it can non distinguish between unit root and near unit root processes particularly when utilizing short samples of informations. This prompted the usage of the KPSS trial, where the nothing is of stationarity against the option of a unit root. This guarantees that the option will be accepted ( void rejected ) merely when there is strong grounds for ( against ) it [ Kwiatkowski, Phillips, Schmidt. and Shin ( 1992 ) ] .

## Co-integration Trial

Using non-stationary series, cointegration analysis has been used to analyze whether there is any long tally equilibrium relationship. For case, when non-stationary series are used in arrested development analysis, one as a dependant variable and another as an independent variable, statistical illation become slippery [ Granger and Newbold, 1974 ] . If two variables are cointegrated, they would on norm, non float apart over a period of clip this construct provides insight into the long-term relationship between the two variables and proving for the cointegration between two variables.

In the present instance, Johansen ‘s Maximum Likelihood process for Cointegration has been applied.

## Granger Causality Test

The dynamic linkage is examined utilizing the construct of Granger ‘s causality trial ( 1969, 1988 ) . Granger causality trial is applied on a stationary series. This trial analyses the fact that between two given factors which one is the doing one and which factor is acquiring affected by another. The trial is based on following two regression equations:

Ns Ns

Yt = I? I±i Xt-i+ I? I?j Yt-j+ u1t __________________________________ ( II )

i=1 j=1

Ns Ns

Crosstalk = I? I»i Xt-i+ I? I?j Yt-j+ u2t __________________________________ ( III )

i=1 j=1

In the two equations given above it has been assumed that perturbations u1t and u2t are non correlated with each other. Equation ( II ) postulates that current Y is related to its ain yesteryear values as that of X and following equation ( III ) postulates a similar behavior of X. There are following four possibilities of cause and consequence:

Unidirectional causality from X to Y is indicated if the estimated coefficients on the lagged Ten in equation ( II ) are statistically different from Zero as a group ( i.e. I?I±i a‰ 0 ) and the set of estimated coefficients on the lagged Y in equation ( II ) is non statistically different from nothing ( i.e. I?I?j a‰ 0 ) .

Unidirectional causality from Y to X is indicated if the estimated coefficients on the lagged Ten in equation ( III ) are statistically different from Zero as a group ( i.e. I?I±i a‰ 0 ) and the set of estimated coefficients on the lagged Y in equation ( III ) is statistically different from nothing ( i.e. I?I?j a‰ 0 ) .

Feedback, or bilateral causality is suggested when the sets of X and Y coefficients are statistically important different from nothing in both the arrested development equations.

Independence is suggested when the sets of X and Y coefficients are non statistically important in both the instances.

## Lag-Length Standards

Determination of the slowdown length of an autoregressive procedure is one of the most hard undertakings in using econometrics techniques. To get the better of this trouble assorted slowdown length choice standards ( Akaike Information Criterion, Schwarz Information Criterion, Hannan-Quinn Criterion, Final Prediction Error, Corrected version of AIC ) have been proposed in the literature.

Asghar and Irum have compared Akaike Information Criterion, Schwarz Information Criterion, Hannan-Quinn Criterion, Final Prediction Error, Corrected version of AIC for slowdown length choice for three different instances that is under normal mistakes, under non-normal mistakes and under structural interruption by utilizing Monte Carlo simulation. The survey shows that the public presentation of all these standards improves with an addition in the sample size. For sample size of 30, although AIC and FPE have the highest chance of right appraisal but all other standards besides perform really good. For sample size equal to 60, chance of right appraisal for HQC is highest but AIC and SIC besides has chance of right appraisal near to that of HQC. For big sample size ( 120 or greater ) public presentation of SIC is the best. This shows that AIC and FPE are efficient but non asymptotically consistent where as SIC, AIC and HQC are asymptotically consistent standards. Liew and Khim [ 2004 ] have carried out this survey for both normal and non-normal mistakes. They found that HQC is the best for big samples. In the present survey slowdown length is determined on the footing of Hannan-Quinn Information Criteria.

## III. Empirical Analysis

The descriptive statistics for all four variables are calculated and presented in table 1. These variables are Real Effective Economic Rate, Balance of Trade, Foreign Exchange Reserve and NSE Nifty. The skewness coefficient, in surplus of integrity is taken to be reasonably utmost [ Chou 1969 ] . High or low kurtosis value indicates utmost leptokurtic or utmost platy-kurtic [ Parkinson 1987 ] . By and large values for nothing lopsidedness and kurtosis at 3 represents that the ascertained distribution is usually distributed. It is seen that the frequence distribution of the above mentioned variables are non normal. Jarque-Bera statistics besides indicates that the frequence distribution of the implicit in series does non suit normal distribution. Further, the coefficient of discrepancy indicates that the Foreign Direct Investment, Balance of Trade, Foreign Exchange Rate and Nifty are comparatively more volatile in comparing to Index of Industrial Production, Wholesale Price Index and Real Effective Exchange Rate.

## Table 1: Descriptive Statisticss

## Bot

## FDI

## FER

## IIP

## NIFTY_CL

## REER

## WPI

## A Mean

-33750.19

A 8658.104

A 1046881.

A 273.6208

A 4205.306

A 97.51708

A 224.6375

## A Median

-29714.00

A 7836.500

A 1166866.

A 269.2500

A 4305.400

A 97.56500

A 226.5500

## A Maximum

-15376.00

A 22529.00

A 1301645.

A 347.3000

A 6144.350

A 106.0900

A 250.5000

## A Minimum

-69925.00

A 2405.000

A 690730.0

A 225.2000

A 2674.600

A 87.48000

A 199.0000

## A Std. Dev.

A 14217.27

A 4646.714

A 214050.1

A 26.96276

A 872.2687

A 5.679449

A 15.35988

## A Lopsidedness

-0.759591

A 0.869245

-0.452362

A 0.620278

A 0.098875

-0.074652

A 0.116621

## Co-eff. of Discrepancy

-42.125

53.66896

20.44646

9.854061

20.7421

5.824056

6.83763

## A Kurtosis

A 2.751454

A 3.416896

A 1.530345

A 3.330791

A 2.359419

A 1.856711

A 1.689008

## A Jarque-Bera

A 4.739378

A 6.392298

A 5.956823

A 3.296808

A 0.898900

A 2.658803

A 3.546204

## A Probability

A 0.093510

A 0.040919

A 0.050874

A 0.192357

A 0.637979

A 0.264636

A 0.169805

## A Sum

-1620009.

A 415589.0

A 50250309

A 13133.80

A 201854.7

A 4680.820

A 10782.60

## A Sum Sq. Dev.

A 9.50E+09

A 1.01E+09

A 2.15E+12

A 34168.56

A 35760076

A 1516.039

A 11088.51

## A Observations

A 48

A 48

A 48

A 48

A 48

A 48

A 48

The first and simplest type of trial one can use to look into for stationarity is to really plot the clip series and look for grounds of tendency in mean, discrepancy, autocorrelation and seasonality. If any such forms are present so these are marks of non-stationarity. The seven clip series displayed in figure-1 exhibit different such forms. Foreign Exchange Reserve, Index of Industrial Production and Wholesale Price Index seem to exhibit a tendency in the mean since they have a clear upward incline. In fact, sustained upward or downward sloping forms ( additive or non-linear ) are marks of a non-constant mean. The clip series on Balance of Trade, Nifty and Real Effective Economic Rate in the figure contain an obvious tendency in both average and discrepancy. This is a mark of non-stationarity.

## Figure 1: Dataset Graph

Apart from ocular review, formal trial for stationarity is indispensable to choose for appropriate methodological construction. As a first measure, we tested all the variables ( Balance of Trade, Foreign Exchange Reserve, Foreign Direct Investment, Nifty, Real Effective Economic Rate, Index of Industrial Production and Wholesale monetary value index ) for stationarity by using ADF, PP unit root trial and KPSS stationarity trial. The consequence of ADF, PP and KPSS statistics are given in table-2. On the footing of ADF statistics and PP trial, all the series are found to be non-stationary at degrees except Foreign Direct Investment which is important at one per centum. Further, ADF statistics and PP trial rejects void hypotheses of unit root in instance of first differences for all the variables. In the terminal, KPSS trial is besides applied which has a void hypothesis that series is stationarity. In this instance, all variables are non stationary in degrees ( except nifty ) and stationary in first differences. Assuming all the variables are non-stationary at degrees and stationary at first differences on the footing of ADF, PP, KPSS trials and ocular reviews, Johansen ‘s attack of cointegration and Granger causality trial have been applied.

## Table 2: Unit of measurement Root Test

## Variables

## Null Hypothesis: Variable is non-stationary

## Null Hypothesis: Variable is non-stationary

## Null Hypothesis: Variable is stationary

## Augmented Dicky Fuller Test Statistic

## Phillips-Perron Test Statistic

## Kwiatkowski-Phillips-

## Schmidt-Shin trial statistic

## Degree

## First Difference

## Degree

## First Difference

## Degree

## First Difference

## t- statistic p-value

## t- statistic p-value

## t- statistic p-value

## t- statistic p-value

## LM-Stat.

## LM-Stat.

## Bot

## -2.389654 A

## 0.1500

## -7.779047*

## 0.0000*

## -2.389654 A

## 0.1500

## -7.724768*

## 0.0000*

## 0.515649**

## 0.043815

## FER

## -1.795759

## 0.3781

## A -5.191802*

## 0.0001*

## -1.651677 A

## 0.4488

## -5.360630*

## 0.0000*

## 0.759324*

## 0.341829

## NIFTY_CL

## -1.638304 A

## 0.4554

## -6.201501*

## 0.0000*

## -1.727747 A

## 0.4110

## -6.205292*

## 0.0000*

## 0.145549

## 0.086579

## REER

## -0.958878 A

## 0.7602

## -5.515513*

## 0.0000*

## -1.236302

## 0.6510

## -5.591141*

## 0.0000*

## 0.422529***

## 0.184626

## IIP

## 0.234639

## 0.9719

## -8.117466*

## 0.0000*

## -1.213731

## 0.6609

## -13.32941*

## 0.0000*

## 0.823505*

## 0.133640

## WPI

## -0.812230

## 0.8061

## -3.547469**

## 0.0109**

## -0.756054

## 0.8220

## -3.643894*

## 0.0085*

## 0.860559*

## 0.046077

## FDI

## -3.962301

## 0.0035*

## -10.05718*

## 0.0000*

## -3.955949

## 0.0035*

## -10.26543*

## 0.0000*

## 0.378648***

## 0.065507

## Asymptotic critical values* :

## 1 % Degree

## -3.48

## -3.48

## 0.74

## 5 % Degree

## -2.88

## -2.88

## 0.46

## 10 % Degree

## -2.57

## -2.57

## 0.35

## Figure 2: Dataset Graph

To research whether there is any long-term relationship between Indian stock markets and macro economic variables such as exports, exchange rate, index of industrial production, foreign direct investing, involvement rate and money supply, Johansen ‘s cointegration trial has been applied. The figure of slowdowns in cointegration analysis is chosen on the footing of Hannan-Quinn Information Criterion. Before discoursing the consequences, it is of import to discourse what it implies when two variables are cointegrated and when they are non. When two variables are cointegrated, it implies that the two clip series can non roll off in opposite waies for really long without coming back to a average distance finally. But it does non intend that on a day-to-day footing the two series have to travel in synchronism at all. When two series are non cointegrated it implies that the two clip series can roll off in opposite waies for really long without coming back to a average distance finally.

As is concluded by unit root trials that all the variables considered except the Foreign Direct Investment ( FDI ) are I ( 1 ) , while the FDI is I ( 0 ) . So for the testing of cointegration among the variables, the FDI is dropped from the farther analysis.

Consequences indicate that Nifty and Wholesale Price Index may be cointegrated in the long tally as the consequences vary depending on the varying premise about tendency and intercept. However, all other variables and Nifty are non cointegrated in the long tally under all premises. In instance of Balance of Trade – Nifty, Foreign Exchange Reserve – Nifty, Real Effective Exchange Rate – Nifty and Index of Industrial production – Nifty, there is no grounds of co-integration. ( See table-3 ) .

## Table 3: Johansen Co-Integration Test: Nifty and Other Macro Variables ( Number of Cointegrating Relations by Model )

Datas Tendency:

None

None

Linear

Linear

Quadratic

Test Type

No Intercept

Intercept

Intercept

Intercept

Intercept

No Trend

No Trend

No Trend

Tendency

Tendency

## NIFTY_CL – BOT ( 1 )

Trace

Max Eig

0

0

0

0

0

0

0

0

0

0

## NIFTY_CL – FER ( 1 )

Trace

Max Eig

0

0

0

0

0

0

0

0

0

0

## NIFTY_CL – REER ( 1 )

Trace

Max Eig

0

0

0

0

0

0

0

0

0

0

## NIFTY_CL – IIP ( 2 )

Trace

Max Eig

0

0

0

0

0

0

0

0

0

0

## NIFTY_CL – WPI ( 2 )

Trace

Max Eig

0

0

0

0

0

0

1

1

2

2*Critical values based on MacKinnon-Haug-Michelis ( 1999 )

**Appropriate slowdown is given in parentheses on the footing of Hannan-Quinn Information Criteria

## Table 4: Pairwise Granger Causality Trials

Slowdowns: 2

A Null Hypothesis:

Ob river

F-Statistic

Prob.A

A D ( WPI ) does non Granger Cause D ( NIFTY_CL )

A 45

A 0.38604

0.6822

A D ( NIFTY_CL ) does non Granger Cause D ( WPI )

A 0.99976

0.3770

Since there is no grounds of cointegration in the macro economic variables and Nifty series the trial of Granger Causality is non applied between Nifty and such variables except Wholesale Price Index which is cointegrated with Nifty under the theoretical account of Linear Trend & A ; Intercept and Quadratic Trend & A ; Intercept. The trial consequences in table 4 suggest that we fail to reject the void hypothesis of Granger non-causality from WPI to NIFTY_CL every bit good as the void hypothesis of Granger non-causality from NIFTY_CL to WPI. The consequences suggest that the NSE Index Nifty neither leads Wholesale Price Index nor Wholesale Price Index lead the Nifty. This implies that the stock market can non be used as a prima index for future growing in sweeping monetary value index in India.

## IV. Reasoning Remarks

The intent of the present survey is to research the relationships between stock monetary values and the cardinal macro variables stand foring existent and fiscal sector of the Indian economic system. These variables are the index of industrial production, foreign exchange militias, foreign direct investing, balance of trade, existent effectual exchange rate, sweeping monetary value index and NSE Nifty. The present analysis is based on monthly informations from April, 2006 to March, 2010.

Although at that place seems to be a important relationship between macro economic variables and stock market but consequences of our survey show that stock market roar is non much supported by the existent economic basicss. Even there is no mark of causality between the variables which are integrated of same order which farther concretizes the issue that stock markets in India are in their childhood stage as their impact on existent economic variables is less as that in developed states and moreover consequence of existent economic variables is about nil on stock market index in instance of causality. To work out this job monthly informations was used from April 2006 to March 2010 and the basic and believed to be “ index ” variables were used and studied and analysed by first using the basic statistical and analytical tools such as unit root trial, cointegration and eventually Granger causality.

The consequences shows that series of variables used are non stationary at degrees but at first difference. Further, there is no grounds of cointegration among the economic indexs chosen and Indian stock market except with rising prices ( Wholesale Price Index ) . Granger Causality trial was applied between the two variables found integrated of same degree I ( 1 ) i.e. Nifty and WPI. The analysis pointed that there are no mark of causality between the two variables and neither Nifty Granger causes WPI nor WPI causes Nifty. Therefore connoting that existent sector is non doing the vibraphone in stock market and even the volatility in it is due to some other external factors and non these existent economic factors. Adding to it, is one more ground that merely 2 to 3 % of the Indian population invests in stock market which makes it non so good representative of the Indian fiscal wellness.