p-ISSN : 2231–167X, Impact Factor : 2.3982, Volume 07, No. 03, July, 2017, pp. 285-294
AN EMPIRICAL STUDY ON IMPACT OF SPOT PRICES ON THE FUTURES PRICES OF BANK NIFTY NSE INDEX
Dr. P.Sri Ram∗
ABSTRACT
Derivatives products have gained a significant place in the Indian context due to its core implications. This Paper is an endeavour to empirically study the causal interaction and the impact of Spot prices on the Futures prices of Bank Nifty NSE Index. The study holds the Futures Markets in three dimensions i.e. Near Month, Next Month and Far Month Futures Contracts. With the application of required Econometrics technique such as Co-integration Approach and Granger Causality Test, the results has shown that, there exist a Co-integrating long run association between all the three Contracts and Short run interaction between Next and Far Month Contracts. Further with the employment of OLS Model it has found that in all three Contracts Spot prices have an impact on the Futures Markets.
KEYWORDS: Index Spot Market, Index Futures Market, Co-integration Approach, OLS Estimate Analysis.
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Introduction
Derivatives Markets is the financial market for various types of Derivatives. A Derivative is a financial instrument which derived its value from the other form of Securities or a basket of Securities. It offers various types of risk protection and allows investors to adopt innovative investment strategies.
Derivatives are becoming popular and deserved a significant role in today’s financial and trade markets.
In the Indian context the benefit of Derivatives trading has urged to re-open the previously banned Derivatives trading in the post reforms periods. The Index Futures Trading in NSE and BSE is the first step which is initiated in the year 2000. Derivatives trading are the latest origin in India and it is considered to be the mirror look for Countries economy. Since it has gained an extreme position in Financial world, the study has been conducted by selecting a leading NSE Index i.e. Nifty Bank, to see and investigate the interaction between the Spot and Futures trading of this Index. The result of the study will support to understand a lead-relationship between the Spot and Futures Markets and this will be the significant contribution to the various users specially Derivatives Traders.This Paper with an inclusion of Introduction has been shared into Five Sections. Section-II deals with existing Literature Review on the similar study, Section-III has shown the Methodological background, Section-IV has pictured out the Analytical result and Section-V has highlighted the Findings and Conclusions of the Study.
Theoretical Background of Futures
In the Futures contracts recognized exchange act as an intermediary and contract executor between the buyer and the seller. Some of the salient features of futures contracts in relation of trading through clearing house of exchange are; no default risk, timely delivery and payment in transaction etc.
clearing house is a trade settlement agency which settle the daily trade of the investors. The clearing house by arranging an activity of mark to market protects the investors from default risk.
∗ Assistant Professor, Faculty of Commerce and Management, Goa University, Taleigoa Plateau, Panaji, Goa.
In the futures contract as per exchange norms to have a trading in contracts both the parties required to maintain an adequate sum of margin. The fluctuations in the prices of contracts on a daily basis due to the demand and supply forces are usually settled with margin amount and thereby this indicates one party gains and opposite party suffers losses.
Contract Specifications- NSE Index (Bank Nifty) Futures Bank Nifty Futures contract Specifications Parameters Specifications and Remarks
Ticker symbol Bank Nifty Contract Size 40 Units
Notional value Contract size multiplied by the Index level (for example, if the current Index value is 1000 then the notional value would be 1000 X 25 = Rs. 250000
Tick size 0.05
Trading hours As in equity derivative segment
Expiry date BANK NIFTY Futures contracts expire on the last Thursday of the expiry month. If the last Thursday is a trading holiday, the contracts expire on the previous trading day.
Contract month BANK NIFTY Futures contracts have a maximum of 3 month trading cycle- the near month, the next month, and the far month. A new contract is introduced on the trading day following the expiry of the near month contract.
Daily settlement price Last half hours weighted average price.
Final settlement price Final settlement price for a future contract shall be the closing price of the underlying Index in the normal market of the capital market segment of NSE on the last trading day of such futures contract.
Final Settlement procedure
Final settlement will be cash settled in INR based on final settlement price.
Final settlement day All open positions on expiry date shall be settled on the next working day of the expiry day (T+1)
Position limits The trading members/MF/FII position limit BANK NIFTY futures contracts shall be higher of Rs.500 crores or 15% of the totals open interest in the market. These limits would be applicable on open positions in all Futures contracts on underlying Index.
Source: NSE website Review of Literature
Dr. Nirmala Chandra Kar and Sarita Satapathy had put an emphasises to examine the causal relationship between spot and Futures prices of NSE CNX Nifty and some selected stocks of Nifty like TATA Motors, INFOSYS, ICICI, ACC and ONCG and the data used for the study is indicates 5 years daily closing data. By using a Johensen-Juselium, Co-integration Test, VECM, Impulse Response, Variance Decomposition and Granger Causality test they investigated the existence of long-run relationships between spot and futures prices of Nifty and all the five stocks. In the study it is also found that there is a unidirectional causal relationship running from spot prices to futures prices of Nifty, TATA Motors and ACC. Further the study also identified a Bidirectional causal relationship running from spot to futures prices and future to spot prices in the case of ICICI Bank, INFOSYS, and ONCG.
Ms. Shalini Bhatia by applying the Co-integration approach attempted to examine the long-run relationship between Nifty futures and spot Index. Further she has also used Error Correction Model to verify the existence of short-run relationship by using high frequency data. The study concluded by releasing a result that the price discovery happens in both the Futures and spot market. Further the result also indicates that the S&P CNX Nifty Future Index is more efficient than the S&P CNX Nifty Index and leads the spot Index by 10 to 25 minutes. The data set used for the study is 15576 pairs of observations collected for a period of 1 year i.e. from 1st April 2005 to 31st March 2006.
Manmohan Mall, R.K. Bal and P.K Mishra (February 2012) made an attempt to find the lead-lag relationship between spot and Index Futures market in India. The intention of examining the lead-lag relationship between market Index and its Future is to see how fast each market reacts to market wide information and how well their co-movements are indicated and in turn to see the possibility of Arbitrage.
The paper by using time series models like Co-integration Approach, VECM, left the result of existence of long-run equilibrium relationship between the spot market price Index and its futures. Finally it is also concluded that Index futures market Index leads to spot market in the long run only but not in the short run. The data for the purpose of study collected from June 12, 2000 to May 2011, based on daily observations.
Dr. Y.Nagaraju and Sumon Reddy’s studied the impact of Futures prices on Spot market and emphasis has been also made to find the causal effect of Futures prices on Spot market. For the purpose of this, author used 5 major Indices of NSE i.e. Nifty, Nifty Mid Cap 50, Bank Nifty, CNX IT and CNX Infra. Further data employed in the study consists of daily closing prices and Futures prices of 5 Indices for the period from 1st January 2012 to 30th September 2014. The study employed the ADF to find stationary of data and Granger causality Test to find whether one Time series is useful in forecasting another. The result had shown one-way causality from Spot market to Futures market but it is not seen significantly among 5 Indices. Further the study does not found two-way causality relationship between Spot and Futures market among the 5 Indices. And finally it is also argued that markets are efficient and there is no causal relationship between the Spot and Futures market.
The impact of introduction of Index Futures on Spot market volatility on S&P CNX Nifty and BSE Sensex by using ARCH/GARCH technique study conducted by SnehalBandivadekar and SaurabhGhosh. To find the volatility result, empirical analysis has been carried out and it had shown that because of introduction of Index Futures, Spot market volatility has decline and this is generally due to increased impact of news and reduced effect of uncertainty originating from the old news. Further to investigate whether the introduction of Futures has succeeds to reduce the Spot market volatility surrogates Indices like BSE 200 and Nifty Junior has been considered. The result shown that Futures effect plays a significant role in the reduction of volatility in the case of S&P CNX Nifty as well as BSE Sensex.
Methodology Objectives
• To Study the causal relationship between the Spot prices and Futures prices of Nifty Bank Index.
• To examine the Spot prices and its impact on the Futures prices of Nifty Bank Index.
Hypothesis
The following are the formulated Null Hypothesis:
• H1 : There is a presence of unit root in the series.
• H2 : There is no Causal relationship between the Spot and Future prices.
• H3 : Spot price does not Granger Cause to Futures prices.
• H4 : Futures price does not Granger Cause to Spot prices.
• H5 : Spot prices do not have impact on the Futures prices.
Data
The study of this paper are based on secondary daily Observations collected from a leading Stock Exchange of India Website i.e. NSE. The data are collected in accordance with Near, Next and Far Month Futures Contract traded in Bank Nifty NSE Index and Spot data for 10 years ranging from the year 2007 to 2016. The data for the study are taken as closing prices of both Spot and Futures of Bank Nifty Index. Further daily returns are collected for all the variables. To calculate the log normal returns we make the use of the following formulas;
= ln ( / ) = ln( / ) Where,
Rs– Spot daily returns, RF – Futures daily returns, St –Closing prices of Spot Nifty Bank Index, Ft – Closing prices of Futures Nifty Bank Index, St-1–Previous day’s closing Price of Spot Index, Ft-1– Previous day’s closing price of Futures Index, t- Corresponding time period.
To avoid the problem of heteroscedasticity and other outliers, the whole collected contract wise data are transformed into logarithm form.
Unit Root Test
In order to find the stationary of the variables, the first econometrics test has used is unit root test.
Although there are many techniques like graphical analysis, correlogram, Phillips-Perron (PP) test etc. to check for stationary properties but in the present study only the most and widely accepted technique under unit root test is ADF has applied.
ADF Test
This test helps to find unit root in the time series and it was propounded by Fuller in 1976 and Dickey and Fuller in 1979. This test is applicable to find stationary in time series data.
Johansen Co-integration Approach
This test was coined by Johansen and Juselius. This test is significant to determine the existence of long run relationship between the variables of the study.
VECM
The VECM is a general framework which helps to examine the dynamic short-run interrelationship for variables that are stationary in their differences (i.e. I (1)). The VECM model also takes into account co-integrating relationships among the variables.
Impulse Response Function
Impulse response model helps to know the response of one given variable to an impulse in other variable. It helps to investigate the impulse relationship between two variables. In study impulse response function will show how fluctuations in the value of variables will create a shock to the other variables of the study.
Forecast Error Variance Decomposition (FEVD)
Variance Decomposition model reports the quantum of information each variable has contributed towards the other variables in the auto relation. It has used in the study to determine the forecast the variance produced in one variable due to the abnormal shocks to the other variables.
Granger Causality Test
The granger causality test is a statistical hypothesis test that is used for prediction and forecasting analysis. It was first founded in 1969. It indicates that, if a variable ‘Y’ is set to granger cause to ‘X’, if ‘X’
can be predicted with greater accuracy by using past values of ‘Y’ variables. It expresses the ability of one variable to predict the other given variable.
OLS (Ordinary Least Square Method)
This method has used in study to find the impact of one variable on the other given variable. It gives the knowledge of how the change in prices of one variable produces changes in prices of other variable. It means that, if particular variable change by ‘X’ percentage, then how much percentage of changes occurs in other dependent variable.
Empirical Analysis and Findings Descriptive Statistics
Table 1.1: Descriptive Statistics of Spot and Futures Contract (Near Month, Next Month and Far Month Contract)
NIFTY BANK NEAR MONTH NEXT MONTH FAR MONTH
Spot Futures Spot Futures Spot Futures
Mean 11295.04 11316.32 11239.38 11280.25 11187.04 11248.12
Median 10558.35 10583.35 10523.35 10569.15 10484.00 10551.55
Std.Dev.( ) 4277.179 4295.287 4263.555 4298.451 4261.146 4306.926
Maximum 20555.25 20588.45 20555.25 20763.80 20555.25 20882.00
Minimum 3339.700 3313.900 3339.700 3304.200 3339.700 3296.00
Skewness 0.407482 0.407005 0.421527 0.423673 0.428498 0.436048
Kurtosis 2.221779 2.221559 2.246960 2.248477 2.251516 2.255904
Jarque-Bera 131.0535 130.9281 263.3884 264.4234 397.9498 403.9615 Observations 2477 2477 4947 4947 7377 7377 The above table 1.1 reveals the descriptive statistics of all the three types of contracts i.e. Near month, Next month and Far month Contract of Bank Nifty. The mean and median values of Futures of all three types of contracts have reported the higher values than their Spot mean and median values. The
standard deviation values in the table reveals the volatility in the Spot and Futures prices for Near, Next, and Far month contract. The volatility in the Future prices is higher than the Spot prices for all three types of contracts, which has been shown by higher standard deviation values of Futures. The existence of degree of symmetry in the data for all the three contracts has been explained by skeweness values. In the above table, it has been observed that the values of Skewness lie within a range of +/- 1 which indicates that the data are moderately symmetric in nature. The kurtosis depicts the behaviours of the data. The value of kurtosis of all types of contract lies below three which means that the behaviour of the data is Mesokurtic. The Jarque-Bera test values in the table are significantly high for all three contracts i.e. p-value= 0.000 which is the indication of asymmetric and residuals are not normally distributed.
Unit Root Test (Augmented Dicky Fuller Test)
The below table 1.2 has been clearly disclosed the Stationarity result of the Near, Next, and Far month contract. The ADF test has shown that in all the three contracts the p-values are less than the 0.05 level. It means that we reject the H0: LNFC and LNSC have unit root and we accept the H1: LNFC and LNSC have no unit root. Thus it means that the time series data of Spot and Futures are stationary at their first differences. Similarly, the evidence of stationarity has been also given by t-statistic values i.e. it is more than 0.01, 0.05 and 0.10 t-critical values and under this situation we accept the H1 hypothesis of having no unit root in the series.
Table 1.2: Unit Root Test of Spot and Futures Contract (Near Month, Next Month and Far Month Contract)
Contents t-Statistics Critical Level Prob. Decision
1% 5% 10%
Near Month Contract
LNFC -45.34346 -3.432798 -2.862507 -2.567330 0.0001 Reject LNSC -44.32370 -3.432798 -2.862507 -2.567330 0.0001 Reject Next
Month Contract
LNFC -25.96513 -3.431495 -2.861931 -2.567021 0.0000 Reject LNSC -25.97494 -3.431495 -2.861931 -2.567021 0.0000 Reject Far
Month Contract
LNFC -25.97883 -3.431063 -2.861740 -2.566918 0.0000 Reject LNSC -25.85354 -3.431063 -2.861740 -2.566918 0.0000 Reject
Co-integration Test (Johansen Co-integration Test)
The Johansen’s Co-integration test helps to find the existence of long-run Co-integration interaction between the Spot and Futures prices of Bank Nifty. The table 1.3 has indicated the result of the test. The body of the table has shown the result of presence of long run equilibrium relationship between the Spot and Futures prices of all three Contracts by indicating the rejection of H0 at 0.05 levels.
It has also shown the existence of 1 Co-integrating equations for Near and Next Month Contracts and 2 Co-integrating equation for Far Month Contract, which means that Spot and Futures prices of the study are Co-integrating or have long run associations at 1 and 2 Co-integrating equations. The same result of having long run relationship between spot and Futures prices has also supported by the Maximum Eigen value test. Thus the above result has stated that both Spot and Futures markets are having long run impact and holding this strong association between them.
Table 1.3: Co-integration Test of Spot and Futures Contract (Near Month, Next Month and Far Month Contract) Particulars Hypothesized
No. of CE(s)
Trace Statistics
0.05 Critical
Value
Prob.** Max-eigen Statistics
0.05 Critical
Value Prob.**
Near Month Contract
None* 126.9966 15.49471 0.0001 126.1157 14.26460 0.0001 At most 1* 0.880841 3.841466 0.3480 0.880841 3.841466 0.3480 Next Month
Contract
None * 297.7416 15.49471 0.0001 295.2401 14.26460 0.0001 At most 1 * 2.501456 3.841466 0.1137 2.501456 3.841466 0.1137 Far Month
Contract
None * 467.2023 15.49471 0.0001 461.9936 14.26460 0.0001 At most * 5.208666 3.841466 0.0225 5.208666 3.841466 0.0225
Vector Error Correction Model (VECM)
The Johansen Co-integration Test has reported the existence of long-run equilibrium among the Independent variable (Futures) and Dependent variable (Spot). To detect and further to verify the same relation in the short-run and also to identify the progress of error correction in the short-run to arrive at equilibrium, the VECM model has been used. The VECM model will depicts the existence of short-run relationship between the Spot and Futures prices of all three contracts.
Table 1.4 i. Vector Error Correction Model for Near Month Contract
NIFTY BANK D (LNF) D (LNS)
Error Correction Coefficient Prob. Coefficient Prob.
Coint Eq1 -1.606887 0.0005 0.089161 0.8430
D(LNF (-1)) 0.396837 0.2586 0.100108 0.7701
D(LNF (-2)) 0.255908 0.2108 0.152705 0.4439
D(LNS (-1)) -0.985176* 0.0054 -0.671412 0.0518
D(LNS (-2)) -0.587166* 0.0046 -0.475426* 0.0186
C 6.06E-06 0.9897 7.39E-06 0.9871
Note: *indicates rejection of Null hypothesis at 5% level
Table 1.4 ii. Vector Error Correction Model for Near Month Contract
NIFTY BANK D(LNF) D(LNS)
Error Correction Coefficient Prob. Coefficient Prob.
CointEq1 -1.321730 0.0000 0.314534 0.1261
D(LNF(-1)) -0.031100 0.8486 -0.271287 0.0871
D(LNF(-2)) 0.037770 0.6927 -0.062581 0.5011
D(LNS(-1)) -0.589392* 0.0004 -0.326692* 0.0434
D(LNS(-2)) -0.366625* 0.0002 -0.253686* 0.0079
C 4.11E-06 0.9920 4.11E-06 0.9918
Note: *indicates rejection of Null hypothesis at 5% level
Table 1.4.ii. Vector Error Correction Model for Near Month Contract
NIFTY BANK D(LNF) D(LNS)
Error Correction Coefficient Prob. Coefficient Prob.
CointEq1 -1.340359 0.0000 0.071953 0.1880
D(LNF(-1)) 0.176038* 0.0000 -0.076029 0.0706
D(LNF(-2)) 0.043502 0.1230 -0.051313 0.0646
D(LNS(-1)) -0.762315* 0.0000 -0.530631* 0.0000
D(LNS(-2)) -0.351094* 0.0000 -0.263986* 0.0000
C 1.09E-05 0.9759 8.12E-07 0.9982
Note: *indicates rejection of Null hypothesis at 5% level
The result of the Error Correction Term has been disclosed in the body of table 1.4.i, 1.4.ii, and 1.4.iii by having an independent variable a Spot market and dependent variable a Futures market. All the three tables have shown that the Co-integration equation is in negative sign indicating a statistical significant reflection in the Near, Next and Far month contract. It means that there is a flow of long-run relationship from the Spot market to the Futures market. In the VECM model error correction variables are describe as D(LNF(-1)), D(LNF(-2)), D(LNS(-1)), and D(LNS(-2)) which represents the one day and two day lags of Futures and Spot.
In the all above three table i.e. 1.4.i, 1.4.ii and 1.4.iii LNS (-1) and LNS (-2) of both Spot and Futures are significant at 0.05 level excepts the LNS (-1) of Spot in the Near month contract. It indicates the presence of Short run relationship between the Spot and Futures prices and it can be also say that in the short-run Spot prices do have an influence on the Futures prices. The insignificant LNS (-1) in the case of Near month contract has stated that one day lag of Spot do not have influence on current day Spot prices. In the Near and Next month contract in terms of LNF(-1) and LNF(-2) we accept Null hypothesis at 0.05, which indicates that one day and two days lags of Futures do not have influence on Spot and Futures prices. In the Far month contract only one day leg of Futures have influence on current Futures prices and it is insignificant for current Spot prices. Similarly two day lag of Futures in the Far month contract have no influence to the Spot and Futures prices.
Impulse Response Model
To reveals the interaction between the Spot and Futures prices of NIFTY BANK from VECM model, the use of impulse function has made. It facilitates how the prices of Spot and Futures are responding due to one standard deviation shock in Futures or Spot prices.
The above Figures 1.1, 1.2 and 1.3 has displayed the impulses of Near, Next and Far month contracts and this are shown in the form of LNS to LNS, LNS to LNF, LNF to LNS and LNF to LNF. All three figures has shown that, one standard deviation shock in LNS and LNF lead to a high fall in the Spot prices for a 2-periods, then it moves up gradually towards the 6-periods. The one standard deviation shock in LNF leads to a drastic change in LNF prices which fall below the horizontal axis has disclosed in graphs and then it moves up from 2-periods steeper to horizontal line. The shock of LNS leads to a steeper movement of LNF prices on a horizontal line for all periods. And lastly one standard deviation shock in LNF had made to LNF prices to fall below the horizontal line for 2-periods and then after it gradually moved up towards 8-periods.
Forecast Error Variance Decomposition
To examine and investigate the dynamic interaction between the Spot and Futures prices, Variance Decomposition Function has been estimated through VECM mechanism.
The result of Variance Decomposition for Near, Next and Far month contract has been portrayed in table 1.5.i, 1.5.ii and 1.5.iii. The result from all the three tables has shown that, the Variance Decomposition of BANK NIFTY Futures at maximum extends has explained by its own and the contribution of LNS is very less. However in the case of Bank Nifty Spot, the variance of LNS has explained at major contribution by LNF and a minor percentage by its own.
Variance Decomposition in LNS in the case of Spot in 1.5.iii, the share of LNF is around 78.98%
in 1st period and it starts increasing and decreasing for the remaining periods and contribution of LNS to its own variance is around 21.01% at initial year and then after it revolved around ups and down for the remaining periods. In overall, the contribution of NIFTY Bank LNS to the variation of its own and the Futures is quite insignificant.
Table 1.5.iii FEVD of NIFTY BANK Spot and Futures of Far Month Contract
Granger Causality Test
Table 1.6: Granger Causality Test of Spot and Futures Contract (Near Month, Next Month and Far Month Contract)
Null Hypothesis: No. of Observations F-Statistic Prob.**
Near Month Contract
LNS does not Granger Cause LNF 2474 2.04932 0.1290
LNF does not Granger Cause LNS 0.47936 0.6192
Next Month Contract
LNS does not Granger Cause LNF 4944 19.1157 5.E-09**
LNF does not Granger Cause LNS 0.63915 0.5278
Far Month
Contract LNS does not Granger Cause LNF 7374 56.2000 6.E-25**
LNF does not Granger Cause LNS 1.61263 0.1994
Note: *indicates the rejection of Null hypothesis at 5% level
The table 1.6 represented the result of causality relationship for Near, Next and Far Month Contract. From the result of Near, and Next Month Contract the Null hypothesis: LNS does not granger cause to LNF has been rejected and hypothesis: LNF does not granger cause to LNS has been accepted at 0.05 level. It means that there is running a one-way or unidirectional causality between the variables.
In the case of Near Month Contract both the Null hypothesis are accepted at 0.05 level. It means that both the LNS and LNF prices in Near Month Contract are independent in nature. There do not exist short run causality in them.
Ordinary Least Square Model (OLS)
Table 1.7: Ordinary Least Square Test of Spot and Futures Contract (Near Month, Next Month and Far Month Contract)
Variables Coefficient Prob. R-Squared D.W. Stat
LNS Near Month Contract 1.017114 0.0000** 0.985450 2.668978
LNS Next Month Contract 1.014856 0.0000** 0.965975 2.696812
LNS Far Month Contract 0.927942 0.0000** 0.780059 2.254382
It has been reported in the above table 1.7 for the Near Month Contract that the Null hypothesis:
There is no impact of Spot prices on the Futures prices has rejected at 0.05 level i.e. p-value is less than 0.05 level and accepted the alternative hypothesis: There is impact of Spot prices on the Futures prices i.e. p-value is less than 0.05 level. It means that Spot prices of near month contract do have an impact on the Futures prices. The coefficient of LNS in the table shows that for every 1% change in Spot prices, it produces changes around 1.017% in Futures prices. The reliability measure R-Squared indicated that around 98.54% of variation in Futures prices has been explained by Spot prices alone. Further the Autocorrelation problem in residuals has been solved by Durbin Watson Stat by indicating a result at above 2.
Findings and Conclusions
The study has stepped to examine the causal relationship between the Spot and Futures market of Bank Nifty NSE Index. It has also tries to determine the impact of Spot market on the Futures market.
The summary statistics has shown higher mean and median values of Futures for all three contracts. The study has found the moderately Skewed and Mesokurtic behaviour in the data. The Johansen Co- integration approach result has shown 1 Co-integration equation for Near and Next month contract and 2 Co-integration equations for Far month contract indicating a long run association among the Spot and Futures contracts. In terms of VECM we have found a significant result of having a short-run relationship between the Spot and Futures prices. In Impulse response graph, it has found that one Standard deviation shock in Spot and Futures lead a higher fall in Spot prices. In Variance Decomposition result, it has obtained that Futures are more active to explain the variation in Spot and even its own. Granger Causality result has reported a unidirectional relationship of Spot and Futures for Next and Far month contract and Independent nature in the case of Near month contract. This means that Spot Market play an active role to explain the movement in Futures. In the case of OLS result it has found that for all three month contracts Spot have an impact on Futures and it has also shown a better reliability in terms of explanation of variations in Futures.
Conclusion
Many Studies have been attempted to determine and examine the relationship between Spot and Futures Markets across a developed, developing and other emerging nations. However, derivatives trading in India are of recent origin and contribution of studies in this area is at limited scope. Having a focus towards this, the study has made an attempt to empirically examine and determine the relationship between the Spot and the Futures market of Indian Bank Nifty NSE Index. The study has also focused to find the impact of Spot market on the Futures market of the Index. The result of first objective has concluded that in all three Futures Contracts there exist a long run association supported by Co- integration Test and Short run Unidirectional relationship which are only in the case of Next and Far month for Near Month contract both the Spot and Futures prices are independent in nature in short run.
The result of impact has shown that for all Near, Next and Far Month Contract, the Spot market do have an impact on the Futures market. Finally the findings of this study got a support from Sarita Satapathy and Dr. NirmalaKar (2015) study on Causal relationship between Spot and Futures in Indian context, which has shown a result of long run relationship between the Spot and Futures prices. The impact of Spot market on the Futures market is also an evidence of this study which has supported by much research work. This result will be a significant contribution to the users of the Derivatives.
References
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