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International Journal of Management (IJM) Volume 11, Issue 6, June 2020, pp. 989-1001, Article ID: IJM_11_06_087 Available online at http://iaeme.com/Home/issue/IJM?Volume=11&Issue=6 ISSN Print: 0976-6502 and ISSN Online: 0976-6510 DOI: 10.34218/IJM.11.6.2020.087

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A COMPARATIVE STUDY ON THE INDIAN STOCK MARKET’S EFFICIENCY DURING PRE

AND POST DERIVATIVE TRADING Abhijit Dutta*

Professor, Department of Commerce, Sikkim University, Gangtok, India

Madhabendra Si nhaAssistant Professor, Department of Management, Raiganj University,

West Bengal, India *Corresponding Author E mail: [email protected]

ABSTRACT In Indian stock market is yet to be proved as efficient. This paper tries to

understand the efficiency in term of a major event and its effect on the price formation in the stock exchanges. The models used are largely to understand the stability, stationary and Conditional Hetroscadasticity. Using SENSEX and NIFTY the study

finds that, the efficiency in Indian Stock market has increased post introduction of derivative trading.

Key words: Conditional Hetroscadasticity, Derivative Trading, Sensex, NIFTY, Market Efficiency, India

Cite this Article: Abhijit Dutta and Madhabendra Sinha, A Comparative Study on the Indian Stock Market’s Efficiency during Pre and Post Derivative Trading.

International Journal of Management, 11 (6), 2020, pp. 989-1001. http://iaeme.com/Home/issue/IJM?Volume=11&Issue=6

1. INTRODUCTION The Indian stock market has deepened over a period of time due to introduction of the

derivatives. It would be interesting to see the stylized return of the market due to introduction of derivatives trading to understand the integration and arbitrage opportunity in the market. It

could be expected that both the spot market and the futures market would be contemporaneously integrated. That apart these markets should not be auto co-related. If there would be no co-integration, the markets could wander without bound and as a result, there would be arbitrage opportunities. If the arbitrageurs could not act quickly, there would be lead lag relationships between the returns series of the market indices.

In this context it is to be understood that the deep market is a function of liquidity in the market. Thus, the study of stylization of return could very well be a function of increasing

A Comparative Study on the Indian Stock Market’s Efficiency during Pre and Post Derivative Trading


liquidity in the market. In an earlier by Patnaik and Gahan (2018) shows that there is an increasing integration between the available market price information and this is leading to an opportunity to integrate to the Asian Markets.

Bandivadekar and Ghosh (2003) studies shows that the market volatility in the derivative market is largely due to the information biases which are prevalent in Indian stock market.

This is a basic challenge to the formation of price in the market since this is a case of increased inefficiency in the market. Hence, it is important to find the degree of market

efficiency in the derivatives segment. This paper will try to address this section in a limited way by comparison of the pre and post derivative introduction in the Indian capital market.

Literature and studies show in the area shows that there are ample evidences of change in the return volatility due to introduction of new instruments in the market. The studies on the efficiency of the market dates back to the fect seventy’s. Black (1976) showed that leverage efwhere by a fall in the value of the firm’s stock causes the firm’s debt to equity ratio to rise

which leads to share holders to perceive that the future cash flows are relatively risky. Amihud and Mendelson (1991) found that there is a direct relationship between volatility and efficiency from trading evidences of Japanese stock market. Koutomous (1998) investigated the possibility of asymmetry in adjusted price of stock prices to show how efficiency can be reached through information sharing. Henery (1998) found asymmetric effect in Hong Sang Index. Xu (1998) examined the Shangahi Stock exchange for daily return and used stylized returns to understand the effect of efficiency of the stock market. He used co-integration for the index through several periods to find the stationery of the data and concluded that if the market is integrated over several period then the efficiency increases in the market and the asymmetry decreases. Frank, David and Chan (2000) studied the bond market in USA for a

period of 1980 to 2000 to understand the market efficiency of and used co-integration to understand the degree to which the data are stationery and then to understand the distribution of the data using GARCH models. Figleweski and Wang (2000) answered the asymmetric response between stocks and volatility can be actually to leverage and how much needs to be explained by other causes. Li et al (2003), studied the relationship between expected stock returns and volatility in the twelve largest international stock markets during January 1980 to

2001 using E-GARCH models and commented that the market lower volatility when the market is integrated. Sardar et al (2005) studied the efficiency in emerging financial market and concluded that the lower the volatility and the higher is the market efficiency. Rajani and

Reddy measured the stock market volatility in emerging economy and came to a similar conclusion. Cunado et al (2008) studied the Stock Market Volatility in Bull and Bear

conditions and concluded that the market is more volatile in Bull market, the market are lesser integrated and the efficiency also drops. Chang (2009) found out the volatility reaction to serial correlation and concluded that positive relations are found between futures volatility and absolute values of serial correlations in US Stock returns.

Roy and Karmakar (1995) measured the stock market volatility for the period 1935 to 1992 in Indian context and came to the conclusion that the Indian market has become more volatile and less integrated the world market, which has affected the stock market dimensions. Bandiavdekar and Ghosh (2003) studied derivative and volatility on Indian stock market and find that surrogate indices reduces the futures effect on volatility of the market. Dutta (2008) studied the volatility of the National Stock Market using stylized returns and concluded that

the volatility persist for very short period in the market making the market less efficient. Prashant and Kiran (2008) showed that the market volatility of the effect the efficiency and higher integration between the various market indices increases efficiency in the market.

The paper try to use in this context stylized return in Indian derivative market segment to understand the efficiency in term of a major event and its effect on the price formation in the

Abhijit Dutta and Madhabendra Sinha


stock exchanges. The models used are largely to understand the stability, stationary and Conditional Hetroscadasticity. Using SENSEX and NIFTY the study finds that, the efficiency in Indian Stock market has increased post introduction of derivative trading.

2. OBJECTIVE OF THE PAP ERThe objective of the paper is to study the market efficiencies through using co-integration between the two indices namely the BSE30SENSEX and NSE50NIFTY

3. HYPOTHESES The following Null hypothesis are being taken for the study

Ho1 = There is no co-integration between SENSEX and NIFTY in pre derivative trading period

Ho2 = There is no co-integration between SENSEX and NIFTY in post derivative trading period

4. DATA AND PERIOD OF STUDY The data used for the paper comprises of daily closing level data of stock market indices name earlier in the objective of the paper. The period under study has been 1996 to 2015. To total number of observation studied has been BSE SENSEX 6380, BSE- SENSEX Futures 2530, –NSE-NIFTY 3143 and NSE-NIFT Futures- 2537.

5. TECHNIQUES USED FOR THE STUDY The co-integration study has been conducted in two forms i.e at Level I (0), that is closing level data and the other at First distance (1) that is, return data obtaining from the closing level data. The following models are being employed for the data analysis under this study.

5.1. Descriptive Statistics and Normality Test a. Mean Return (R)

(1)

Where; Rt = Daily return in percentage n= Total number of observations n-1=Number of daily return series

b. Standard Deviation σ =

…… (2)

c. Skeweness =

(3)

d. Kurtosis = =

(4)

e. Jarque-Bera Test of Normality

(5) …….

f. Test of Stationary



Augmented Dickey Fuller test

(6) Phillip-Pearson (PP) Test It is used in time series analysis to test the null hypothesis that a time series is integrated of

order 1. It builds on the Dickey Fuller test of the null hypothesis in , where –is the first difference operator. Co-integration Regression Durbin-Watson Statistics

………… (7) g. Test of Co-Integration

5.2. The Granger Causality Test Regression analysis deals with the dependence of one variable on other variables. It does not necessarily imply causation. In other words, the existence of a relationship between variables does not prove causality or the direction of influence. If the regression involves time series data, the situation may be somewhat different as time does not run backward. Therefore if event A happens before B then A is supposed to influence B. In other words, events in the past can cause events to happen today. Futures events cannot. This is the moot idea behind the Granger Causality Test.

Granger defined the causality relationship based on two principles. The cause happens prior to its effect. The cause has unique information about the future values of its effect.

Given these two assumptions about causality, Granger proposed to test the following hypothesis for identification of a causal effect of X on Y:

………… (8) where P refers to probability, is an arbitrary non-empty set, and I (t) and I x(t) A –

respectively denote the information available as of time in the entire universe, and that in the t modified universe in which is excluded. If the above hypothesis is accepted, we say that X X Granger causes Y.

Granger Causality Test is of three types, No Causality is indicated if the estimated coeffient care not statistically signifiacant in

both the regression Unidirection causality is indicated if the estimated coefficent on the laggerd Value are

statistically different from zero as a group. Bidirectional Causality is indicated if the estimated coeffient are statistcally significant

and different from zero in both the equations. h. Model specification The model used in this equation is:

Y = Where, Y is the dependent variable taking the value of one of the Indices

X = The independent variable which takes the value of one of the indices. α, B and U are coefficient for constant, proportion and error term (or the stochastic factor) The single index model has been checked for causality and the co-integration of the indices series.



6. ANALYSIS OF RESULTS 6.1. Co integration and Causality Test between NSE NIFT and BSE SENSEX for Pre derivative Period In this BSE 30 SENSEX has been used as the depended variable and NSE 50 NIFTY has been used as the independent variable.

The primary requisite for the study is stationery of the data. If the levels of data are stationary, we would go for co-integration test. If this is not satisfied we would carry out the first difference variable and then test the unit root at next order of integration. 6.1.1. Unit root test in Data Level Table 1 below presents the unit root results or stationary test summery for the two variables. Both the variables are tested together to find out whether the unit root exist. It is observed that

both the variables have unit root which means that they are not stationery. The ADF test statistics is 5.90934 and the PP test 5.80166, the p-value for the ADF test is 0.2060 that of PP

test is 0.2145 both being grater that 0.05, thus the null hypothesis-there is unit root is not rejected.

Table 1 ADF and PP test for Unit root of BSE30 SENSEX and NSE50 NIFTY

Method Statistics Probability** Cross Section Observations Null: Unit root ( assumes common unit root process)

Levin, Lin & Chu t* -0.94766 0.1717 2 5368 Null: Unit root( assumes individual unit root process

Im, Pesaran and Shin W stat ADF Fisher Chi –Square PP-Fisher Chi Square

-0.99781

5.90934

5.80166

0.1592

0.2060

0.2145

2

2

2

5368

5368

5370

** Probability for Fisher tests are computed using asymptotic: Chi square distribution. All other tests assume asymptotic normality. Source: Computed. 6.1.2. Unit root Test for First Difference of Data Level After first differencing the unit root test is conducted again and the stationery status of the data is checked. From the table 2, it is observed that both the variables have no unit root. It means that the first difference level data are stationary. The p-value of the ADF test is 0.000 and the PP test is 0.000 and both being lower than 0.05, the null hypothesis of the presence of

unit root is rejected and therefore, the alternative hypothesis that there is no unit root is accepted. As there is no unit root, the present data is stationery.

Table 2 Group unit root test: summery of the first difference data of BSE30SENSEX and NSE50NIFTY

Method Statistics Probability** Cross Section Observations Null: Unit root ( assumes common unit root process)

Levin, Lin & Chu t* -81.2895 0.000 2 5368 Null: Unit root( assumes individual unit root process

Im, Pesaran and Shin W stat ADF Fisher Chi –Square PP-Fisher Chi Square

-68.6337

51.9146

52.5672

0.000

0.0000

0.0000

2

2

2

5368

5368

5370

** Probability for Fisher test is computed using asymptotic: Chi square distribution. All other tests assume asymptotic normality. Source: Computed.



6.1.3. Analysis of Descriptive statistics of the Level Data The table 3 below presents the descriptive statistics from the level. The average daily closing level price and standard deviation for the two stock indices are different for the period under study. The skewness for daily data is found to be negative, but is less than 1 for both the indices during the pre-derivative period indicating that the level data distribution is almost symmetric. Kurtosis less than three for both the indices during the period suggests that the underlying data is Platykurtic i.e, squat with short tails about the mean. This indicates that the data are not normally distributed. The J-B statistics to test the null hypothesis of normality in

the data rejects the normality distribution at 1 percent level of significance. The results conforms the well known fact that daily level data of the indices under consideration is not normally distributed.

Table 3 Descriptive statistics of BSE30SENSEX and NSE50NIFTY from the level data

Statistics BSE30SENSEX NSE50NIFTY Mean 964.6045 3276.070 Median 989.1650 3385.535 Maximum 1756.000 5933.560 Minimum 279.0200 862.8800 Std.Deviation 297.3562 1010.803 Skewness -0.226440 -0.381585 Kurtosis 2.927956 2.983434 Jargue-Bera 19.53955 54.14283 Probability 0.000057 0.000000 Sum 2151068 7305637 Sum Sq. Dev 1.97E+8 2.28E+09

Source: Computed 6.1.4. Long Run Model with Dependent Variable as BSE SENSEX The two variables are tested for their long run relationship or interdependence by means of

OLS Regression of SENSEX on NIFTY. The liner association can be tested between the variables by testing the significance of the Beta coefficient. As the table 4 indicate, that the coefficient is 3.381962 and p-value is 0.000, it indicates that there is relationship between the two variables.

Table 4 Regression Results of SENSEX on NIFTY in Level Data (Method: Least Square)

Variable Co-efficient Std. Error t-statistic Prob. C 13.814 94 7.332811 1.883990 0.0597 NSE50NIFTY 3.381962 0.007265 465.5344 0.0000 R-Squared 0.989820 Mean Dependent

Var. 3276.070

Adj. R –Squared

101.9880 SD Dependent Var 1010.803

S.E of Regression

23174644 Akaike info criterion

12.08848

Sum Squared residual

-13476.66 Schwarz Criterion 12.09360

Log Likelihood

-13476.66 Hannan-Quinn Criterion

12.09035

F-Statistics 216722.30 DRDW Statistics 0.049861 Prob (F-Statistics)

0.0000000 0.000000

Source: Computed In the above table it is noted that the co- tegration regression and D-W test indicate that in

the R- Square value being 0.98982 is greater that D-W statistics of 0.04961. Thus, the model developed is of spurious in nature. The stationary of the error term is tested in such a case. If



the residual error term is stationery we can conclude that there is long run relationship between SENSEX and NIFTY. After confirming the linear relationship we estimated the error term Ut. The analysis of the error term is given in table 5 below.

Table 5 Descriptive Statistics of the error term (Ut )

Mean -05.00E- 12 Std. Dev. 101.9651 Jarque-Bera 41.66393 Median 8.834094 Skewness -0.334135 Probability 0.000000 Maximum 260.6991 Kurtosis 2.957387 Sum -1.13E- 08Minimum -291.5383 Observations 2230 Sum. Sq. Dev 2317644 Source: Computed

The descriptive statistics of the residual obtained from the OLS regression of SENSEX on NIFTY indicate that the average and standard deviation for the error term are negative for the

period under discussion. The skewness statistics is found to be less than one for both the indices indicating that the error term is almost symmetrical. Kurtosis being less than three

suggests that the underlying data is platykuratic, hence data is not normally distributed. Application of the J-B statistics calculated to test the null hypothesis of normally in the data rejects the normality assumption at 1 % level of significance. The results confirm the well know fact that error term is not normally distributed but is skewed. The result confirms that the error term is not normally distributed and is skewed.

It is observed that the residual error term has no unit root and is stationary. The calculated value of the ADF test is -1.95197 which is less then Engle-Granger Test critical value of -3.433094 at 1 % level of significance. Thus, null hypothesis of the presence of the Unit root is rejected and the alternative hypothesis that there is no unit root is accepted. The values are given in the table below:

Table 6 Unit Root Test trough ADF Test is U t Null Hypothesis: Ut had a unit root Exogenous Constant, lag length 3 (Automatic Based on SIC, Max lag=32) –

t-statistics Prob.*

Augmented Dickey-Fuller Test Statistics Test Critical Values 1% level -3.433094 0.3085 5 % level -2.862638 10 % level -2.567400 *MacKinnon (1996) one sided p-values. Source: Computed

The table 7 below shows the results of the error correction Model of SENSEX on NIFTY. It is observed from the table that the value of R squared is less than the value of D-W

statistics. The Co-efficient of error term of 0.0063 is statistically significant as the p value is 0.00.

Table 7 Results of Error Correction Model Dependent Variable : SENSEX

Variable Co-efficient

Std. Error t-statistics Prob.

C 0.068064 0.136096 0.500116 0.6170 NIFTY 0.267645 0.002010 133.1715 0.0000 Ut 0.006300 0.001337 4.711332 0.0000 R-Squared 0.888383 Mean Dependent Variable 0.533055 Adj. R- Squared 0.888383 S.E of Regression 6.423269 S.D Dependent Variable 19.22611 Sum Squared Residual 91841.16 Akaike Info Criterion 6.559076 Log Likelihood -7303.091 Schwarz Criterion 6.566760 F-statistic 8867.587 Hannan- Quinn Criterion 6.561882 Prob. (F-Statistics) 0.000000 D-W Statistics 2.570828 Source: Computed



6.1.5. -integration and causality test between NSE 50 NIFTY and BSE 30 SENSEX in Cothe Post Derivative Period The primary requisite of the study is stationary of the data. If the data are stationery at level, we would go for co-integration test; otherwise, we would carry out the first difference of the

variables and test the unit root at the next order of integration. Unit root test is being conducted for both the variables in their level form.

The table 8 below presents the unit root test results or stationery with level data for the two variables like SENSEX and NIFTY. Both the variables are tested together to find out whether the unit root exists or not. The table shows that both the variables have unit root or that they are non-stationary. The ADF test statistics is 0.61951 with p-value of 0.9609 and the

PP test is 0.58335 with of 0.9649. Such p-value are greater than 0.05. Thus, the null hypothesis of the presence of the unit root is not rejected.

Table 8 Group Unit Root Test through ADF and PP Statistics in SENSEX and NIFTY during post Derivative Period

Method Statist ic Prob.** Cross Section Obs Levin, Lin & Chu t* 0.56481 0.7139 2 6270 Im, Pesaran and Shin W- Stat

1.43908 0.9249 2 6270

ADF Fisher Chi Square 0.61951 0.9609 2 6270 PP-Fisher Chi-Square 0.58335 0.9649 2 6270 ** Probability for Fisher tests are computed using an asymptotic Chi-square distribution. All other tests assume asymptotic normality. Source: Computed 6.1.6. Unit root or stationery test at First Difference of Level Data After first difference the unit root test is conducted again and the stationery status of the data is checked. From the table 9 below, it is observed that both the variables have unit root or that they are stationary. The p-value for the ADF test is 0.00000 and that of PP test is 0.00000 both being lower that 0.05, thus null hypothesis- there is unit root is rejected and alternative hypothesis that there is “no unit root” is not rejected. As there is no unit the present data is stationary.

Table 9 Group unit root test through ADF and PP statistics of SENSEX and NIFTY with their First Difference data

Method Statistics Prob.** Cross Section Observations Levin, Lin & Chu. t*

-98.3937 0.0000 2 6270

Im, Pesaran and Shin W- Stat

-83.9377 0.0000 2 6270

ADF-Fisher Chi- Square

36.8414 0.0000 2 6270

PP-Fisher Chi Square

36.8414 0.0000 2 6270

** Probabilities for Fisher test computed using an asymptotic Chi-square distribution. All other tests assume asymptotic normality. Source: Computed

6.1.7. Descriptive Statistics of SENSEX and NIFFTY The table 10 below presents the descriptive statistics obtained from the level data. It is

observed that the average positive level occur for the post-derivatives from 12.06/2000 to 31/12/2012 with 3137 observations. Average daily closing level and standard deviations for the two stock market Indices are different. The skwness data is less than one indicating that the data is symmetric. Kurtosos less than three for both the indices shows that underlying data is Platykurtic i.e., squat with short tails. J-B statistics to test the null hypothesis at 1 % level is



rejects the normality assumption. The data of the indices are not normally distributed and are skewed and are fat tailed.

Table 10 Descriptive statistics of SENSEX and NIFTY from the level data

Statistics BSE30SENSEX NSE50NIFTY Mean 10700.36 3232.393 Median 10352. 94 3077.500 Maximum 21004.120 6312.450 Minimum 2600.120 854.2000 Std. Dev. 5983.415 1758.918 Skewness 0.0708887 0.097990 Kurtosis 1.411903 1.442751 Jarque-Bera 332.2806 321.9910 Probability 0.000000 0.000000 Sum 33567028 10140018 Sum Squared 1.12E+1 9.70E+09 Observations 3137 3137

Source: Computed

6.1.8. Long Run Model with SENSEX as Dependent Variable The SENSEX and NIFTY are tested for their long run relationship or interdependence during

their post derivative period. The linear association can be tested between the variables by testing the significance of the beta co-efficient. As the table 11 below indicates that the co-efficient is 3.40 and p-value is 0.000, it indicates that there is relationship between the two indices.

Table 11 Long Rum Relationship between SENSEX and NIFTY through OLS Regression

Variables Co-efficient Std. Error t-statistics Prob. C -289.7782 7.187249 -40.31837 0.0000 NSE50NIFTY 3.4000000 0.001953 1740.782 0.0000 R-Squared 0.998967 Mean Dependent Var. 10700.36 Adj. R Squared 0.998966 S.D Dependent Var. 5983.415 S.E of Regression 192.3837 Akaike Info Criterion 13.35750 Sum Squared Residual

1.16E+08 Schwarz Criterion 13.36136

Log Likelihood -20949.24 Hann-Quinn Criterion 13.35888 F-Statistics 3030322 D-W Statistics 0.020500 Prob. (F-Statistics) 0.000000 Prob. (F- Statistcis) 0.000000 Note: Number of Included observations is 5367 Source: Computed

In the above table the R- Squared value is greater that the D-W statistics. Hence, the model developed is spurious in nature. In such a case we need to check if the error term is stationery or not. Subsequently the descriptive statistics of the residual is observed. 6.1.9. Descriptive statistics of Residuals The descriptive statistics of the residual given in the table 12 below indicate that the average and standard deviation for the error term are negative. The skewness is positive and greater than zero indicating error term is not symmetric. Kurtosis being more than three suggests that

the underlying data is Platykurtic and is not normal. Application of the J-B statistics calculated to test the null hypothesis of normality in the error series rejects the normality at

1% level of significance. The results confirm the well known fact that error term is not normally distributed but is skewed.



Table 12 Descriptive Statistics of the Error term

Mean 2.93E- 12 Std. Dev 192.3530 Jarque-Bera 1030.570 Median -33.04187 Skewness 1.151012 Probability 0.000000 Maximum 710.5680 Kurtosis 4.607852 Sum 8.32E- 09Minimum -394.8622 Observations 3137 Sum Sq. Dev 1.16E+08 Source: Computed

6.1.10. Test of Stationarity of the Residuals From the table 13 below we observe that the error term is stationery. The value of the ADF test is -3.802 with the p-value of 0.0029. The Engle-Granger Test critical value of -3.432246 is at 1 % level of significance. Thus we null hypothesis is rejected and the alternative

hypothesis is accepted and we conclude that the error term has no unit root and is thus stationery.

Table 13 Unit Root Test of the Error term

t-statistics Prob.* Augmented Dickey

Fuller test -3.801617 0.0000

Engle Granger Test 1% level

-3.432246

6.1.11. Error Correlation Model on SENSEX and NIFTY The table 14 below shows the results of the ECM model of SENSEX and Nifty and U t-1 . It is observed that the R-squared value of 0.98 is less than the value of the D-W test of 2.006972. Therefore, the model developed is not spurious in nature. The co-efficient of the error term is

significant as its p-value is 0.00%. The coefficient if Ut-1 is 0.002685 which means error correlation term is 0.2685 % daily.

Table 14 Results of Error term correlation model for SENSEX on NIFTY and U t-1.

Variables Co-efficient Std. Error t-statistics Prob. C 0.011679 0.143357 0.081471 0.9351 D(NIFTY) 0.299387 0.000759 394.5801 0.0000 U(- 1) 0.002685 0.000746 3.600476 0.0003 R-Squared 0.998967 Mean Dependent Var. 1.423693 Adj. R Squared 0.980295 S.D Dependent Var. 57.17247 S.E of Regression 8.025470 Akaike Info Criterion 7.004074

Sum Squared Residual

201790.8 Schwarz Criterion 7.006151

Log Likelihood -10979.39 Hann-Quinn Criterion 7.006151 F-Statistics 77983.54 D-W Statistics 2.006151 Prob. (F-Statistics) 0.000000 Prob. (F- Statistics) 0.000000 Source: Computed

6.1.12. Engle Granger Test for Co-Integration The table 15 below shows that there is no co-integration between BSE30SENSEX and

NSE50NIFTY as the Tau statistics is -3.802337, with p-value 0.0136 and as the Tau statistics is -3.79787t0 (p-value 0.0136) respectively. Null hypothesis that the series are not co-

integration is thus not rejected and the alternative hypothesis that the series are co-integration are not rejected. Thus, during the post derivative period, there is no long-run relationship –between SENSEX and NIFTY. It may be interpreted that the stock market in India becomes efficient in post-derivatives. Hence the scope of arbitrage is limited. This may be attributed to the introduction of derivatives.



Table 15 Engle-Granger Test of Co-integration Between SENSEX and NIFTY

Dependent Tau-statistics

Prob.* z-statistics Prob.*

BSE30SENSEX -3.802337 0.0136 -30.44497 0.0061 NSE50NIFTY -3.797870 0.0138 -30.45372 0.0061 MacKinnon (1996) p-values Intermediate Results

BSE30SENX NSE50NIFTY

Rho-1 -0.009708 -0.009711 Rho. S.E 0.002553 0.00257 Residual Variance 755.5518 65.47909 Long Run Residual Variance 755.5518 65.47909 Number of Lags 0 0 Number of Observations 3136 3136 Number of Stochastic trends 2 2

6.1.13. Granger Causality Test Table 16 below shows that the F Statistics is 2.43620 with the p-value 0.00004 rejects the null

hypothesis that there is no causality between the two series (NSE50NIFTY and BSE30SENSEX). However, the two series BSE30SENSEX and NSE50NIFTY F value

2.52718 with prob. 0.000002 has no causality. Thus we can say that there is Bi-directional causality between the two series.

Table 16 Pair wise Granger causality Test

Null Hypothesis Observations F-Statistics Prob. NSE50NIFTY does not Cause BSE30SENSEX 3101 2.43620 0.0000006 BSE30SENSEX does not Causes NSENIFTY 2.52718 0.0000002 Source: Computed

7. INTERPRETATION OF THE RESULTS The two variables were tested for two periods that is pre derivative trading and post derivative

trading period. It is assumed that, if the market is integrated, there are lesser chances of arbitrage in the market making it more inefficient. The data at first level of the pre derivative trading is stationery. The data level is almost symmetrical and squat with short tails. This all shows that the data are not normally distributed which should be the characteristics of the

BSE30SENSX and NSE50NIFTY has no relationship. The indices are not co-integrated. Since the model tends to be spurious in nature, the error term had to be checked. The error term shows that it is symmetrical and not normally distributed and is stationery.

In the post derivative trading period the level data is not symmetrical as there is unit root. At the first difference level there is no unit root and the data is stationery. The two indices are co-integrated. The data set is symmetrical, squat with fat tails and not normally distributed which indicate all the character of the market. Long run model shows that there is relationship between the two indices. The residual error is not symmetrical and not normal. The error term

has no unit root and is stationery. Error correlation model of BSE30SENSEX and NSE50NIFTY is not spurious in nature. There is no co-integration between the two indices during this period. The causal relationship is bi directional. There is no long run relationship in the post derivative trading period. This shows that chance of arbitrage and low and limited in this market and the market has become more efficient during the period.

8. CONCLUSION Data for a period of pre derivative trading and the post derivative trading for BSE30SENSEX and NSE50NIFTY association has been checked. The basic assumption if that during this two

period if the data are found to be stationery then the markets would be integrated. It was



found that the pre derivative trading the market indices are not stationery and that is spurious causation between the series. However, during the post derivative period the markets are

integrated at both level and first lag period. The error term is correlation model considering each indices as independent are non spurious hence the causation seem be real. This give to

rise to believe that post derivative trading the market have become more efficient and arbitrage opportunities have been less in this situation.

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