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Analysis of lead-lag estimates between spot and futures market for selected companies in Indian scenario.

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Introduction

The issue of price discovery on futures and spot markets and the lead-lag relationship are topics of interest to traders, financial economists and analysts. Although futures and spot markets react to the same information, the major question is which market reacts first. In an efficient capital market where all available information is fully and instantaneously utilized to determine the market price of securities, prices in the futures and spot market should move simultaneously without any delay. However, due to market frictions such as transaction cost, capital market microstructure effects etc., significant lead-lag relationship between these two markets has been observed. Inter-market linkages are of considerable interest to regulators, practitioners and academics. With no market frictions, the prices of securities and their derivatives must simultaneously reflect new information. If this was not the case then costless arbitrage profits would be possible. However, there are numerous market frictions cited in the literature which can cause prices in one market to lead or lag the other market.

The Indian capital market has witnessed a major transformation and structural change from the past one decade as a result of ongoing financial sector reforms initiated by the Government of India. One of the major objectives of these reforms was to bring the Indian capital market up to a certain international standard Due to such reforming process, one of the significant step taken in the secondary market is the introduction o derivative products in two major Indian stock exchanges viz. National Stock Exchange (NSE) and Bombay Stock Exchange (BSE), with a view to provide tools for risk management to investors and to improve the informational efficiency of the cash market. Though the onset of derivative trading has significantly altered the movement o stock prices in Indian spot market, it is yet to be proved whether the derivative products have served the purpose as claimed by the Indian regulators. Equity derivatives in India was started as a part of capital market reforms to hedge price risk resulted from greater financial integration between nations in the 1990s. These reforms were an integral part of financial sector reforms recommended by the Narasimham Committee Report on Financial System in September 1992. The reforms were aimed at enhancing, competition, transparency, and efficiency in the Indian financial market. The Indian capital market saw the launching of index futures on June 9, 2000 on BSE and on June 12, 2000 on the NSE. A year later options on index were also introduced for trading on these exchanges. Later, stock options on individual stocks were launched in July 2001. Stock futures entered the derivative segment on these exchanges from November 2001 onwards.

Significance of the Study

In India, very few work has been done in this area. The lead-lag analysis by Thenmozhi (2002) showed that the returns on futures lead the spot market returns. The study lent credence to the belief that the futures market tends to lead spot market and the index futures market serves as a primary market of price discovery. The study also showed that the cash index does not lead the futures returns. Though the futures lead the spot market returns by one day, the exact time by which the futures lead the spot market returns was not identified as the study was conducted using daily returns due to lack of data in terms of minute-by-minute or hourly returns. Mukherjee and Mishra (2006) used intra day data from April to September 2004 to investigate the lead-lag relationship between Nifty spot index and Nifty futures. They found that there was a strong bidirectional relationship among returns in the futures and the spot markets. The spot market was found to play a comparatively stronger leading role in disseminating information available to the market and therefore said to be more efficient. The results relating to the informational effect on the lead-lag relationship exhibit that though the leading role of the futures market would not strengthen even for major market-wide information releases, the role of the futures market in the matter of price discovery tends to weaken and sometimes disappear after the release of major firm-specific announcements. Two studies on the lead-lag relationship in the Indian market have come up with diametrically opposing views. According to Thenmozhi, futures markets lead the spot market whereas, according to Mukherjee and Mishra the spot market had a major role to play in price discovery and lead over the futures market. The general conclusion of previous research is that the returns in the futures market seem to lead cash market returns and there is some evidence of the predictive ability from cash to futures returns. It has been argued that the persistence in the lead-lag relationship between futures and spot prices can be traced to one or more market imperfections, such as transaction costs, liquidity differences between the two markets, non-synchronous trading effects, and the automation of one or the other market, short selling restrictions, different taxation regimes, dividend uncertainties and non-stochastic interest rates. The study used OLS estimation for calculating lead-lag coefficients. This model is estimated using the returns that are calculated as the subsequent log differences of closing prices of the spot and futures prices.

Objectives of the Study

The primary objective of the study is to examine the nature of lead-lag relationship between returns in the spot and the futures markets among the selected companies in the Indian economy. The Lead-Lag estimates between spot and futures prices are analyzed using Multiple Regression (Simultaneous equation modelling). Lead-lag estimates up to 5 lags/leads are examined for statistical significance. The study investigates whether the Spot market leads/lags the futures and vice-versa. The null hypotheses to be tested for significance is 'Futures price series lead the spot price series'.

Literature Review

Most of the empirical studies examining the temporal relationship between futures and cash returns supported the lead effect more than the lag effect.

Kawaller, Koch and Koch (1987) estimated the lead-lag relation between S&P 500 index futures and S&P 500 index. They probed the lead-lag effects using simultaneous equation model estimated by three stage least squares regression. Based on the minute-to-minute changes in both the index and the futures prices, a model was constructed to describe the dynamic intra-day price relationship between the index and futures prices. Prior studies had suggested leading role of the futures market which varied from five to forty minutes, while the spot market rarely led the futures market beyond one minute. While explaining the causes behind such relation, their study attributed the stronger leading role of the futures market to the infrequent trading of component stocks. Though, at the same time, Stoll and Whaley (1990) and Chan (1992) proved the existence of such relation even in case of highly traded stocks or after adjusting for infrequent trading of component stocks. Finnerty and Park (1987) also discovered a significant lead-lag relationship between futures and spot prices.

Herbst, McCormak, and West (1987) also observed that the S&P 500 and Value Line futures led the spot index between 0 to 16 minutes. Stoll and Whaley (1990) used ARIMA model and ordinary least squares to estimate the lead-lag between S&P 500 index futures, Major Market Index futures and the underlying spot market. The results indicated that S&P 500 and Major Market Index futures led the cash market by 10 minutes and they attributed this to faster dissemination of information into futures market. The findings were consistent with the evidence gathered by Koch and Koch (1987).

Schwarz and Laatsch (1991) examined the price leadership of index futures over the spot market and tested the dynamic efficiency of index futures as a price discovery vehicle. However, they used Garbade & Silber model to quantify the price discovery function of the futures market. The study was done on the Major Market Index for the sample period 1985 to 1988. The results showed that the spot and futures market were integrated such that average mispricing leading to arbitrage was eliminated within one to seven days.

Chan (1992) estimated the lead-lag relation between Major Market Index and Major Market Index futures under conditions of good and bad news, different trading intensities and under varying market wide movements. ARMA models were used and it was observed that the futures market led the spot market, and this was primarily due to faster information processing by the futures market. However, under bad news it was the cash index that led over the futures market while, there was no effect on the lead-lag relation during different trading intensities.

These findings were in line with the earlier studies of Koch and Koch (1987) and Stoll and Whaley (1990). Tang, Mak and Choi (1992) studied the causal relationship between stock index futures and cash index prices in Hong Kong, which revealed that futures prices caused cash index prices to change in the pre-crash period but not vice versa. In the post-crash period, they found that bi-directional causality existed between the two variables. Wahab and Malek (1993) studied daily data from January 1988 to May 1992 using error correction methodology. Their results revealed bi-directional causality between spot and futures returns. Evidence from other markets also postulated a significant lead-lag relationship. The research study of Chan (1992) suggested that informed traders should trade in the futures market around the release of macroeconomic announcements, while the leading role of futures market weakened through the discovery of stock specific information.

Tse (1995) examined the behaviour of prices in the Nikkei index and the corresponding SIMEX traded futures contract and found that lagged changes of the futures price affected the short-term adjustments of the futures price. Abhyankar (1995) investigated the lead-lag relationship between hourly returns in the FT-SE 100 stock index futures and the underlying cash index using hourly data for the period 1986-1990. They tested the lead-lag relation for periods of differential transactional costs, spot volume, spot volatility and good and bad news (measured by the size of returns). The results revealed that when transaction costs for the underlying asset fell, the futures lead of the spot index reduced, implying that transaction cost differential was the major driver for the lead-lag relationship. Teppo, Jukka and Vesa (1995) studied the two-way causality between the Finnish stock index futures and the stock index for a period of one year from 1989-1990. Granger Causality tests were applied on the daily returns due to non-availability of intra-day data. The results indicated that the futures market provided predictive information for both frequent and infrequently traded stocks while the reverse causality was found to be weak. Abhyankar (1998) revisited the relationship using 5-minute returns by regressing spot returns on lagged spot and futures returns, and futures returns on lagged spot and futures returns using E-GARCH model. He found that the futures returns led the spot returns by 15-20 minutes.

Apart from this, while examining the volatility spillover, Abhyankar (1995) and Tse (1999) had documented that unlike a lead-lag relation, there was a bi-directional or contemporaneous relationship among the spot and the derivative markets, with bad news having a greater impact on volatility. Min and Najand (1999) investigated possible lead-lag relationship in returns and volatilities between cash and futures markets in Korea. Utilizing intraday data from the newly established futures market in Korea, they found that the futures market lead the cash market by as long as 30 minutes. This result was consistent with previous studies for the U.S. and other countries futures markets. With regard to volatility interaction between spot and futures markets, they found that, unlike the above results for returns, a bidirectional causality was more prevalent between cash and futures markets, and this relationship was entirely sample dependent. They also found that the trading volume had significant explanatory power for volatility changes in both spot and futures markets.

Brooks, Rew and Ritson (2001) estimated the lead-lag relation between the FTSE 100 stock index futures and the FTSE 100 index. Based on the results obtained, they developed a trading strategy based on the predictive abilities of the futures market. The study was conducted using Co-integration and Error Correction model, ARMA model and vector auto-regressive model. The results indicated that futures led the spot market and this was attributable to faster flow of information into futures market mainly due to lower transaction costs. Frino and West (2002) examined the lead-lag relationship in returns on stock index futures and the underlying stock index for the Australian market between 1992 and 1997. On average across the sample period, futures returns was found to lead index returns by twenty to twenty-five minutes and there was some evidence of feedback from the equities market to the futures market. Analysis conducted on a year-by-year basis suggested that the extent to which the futures market leads the equities market had decreased over time and the relationship between the two markets had generally strengthened. This was consistent with an increase in the level of integration between the markets.

Reddy and Sebastin (2008) studied the temporal relationship between the equities market and the derivatives market segments of the stock market using various methods and by identifying lead-lag relationship between the value of a representative index of the equities market and the price of a corresponding index futures contract in the derivatives market. The study observed that price innovations appeared first in the derivatives market and were then transmitted to the equities market. The dynamics of such information transport between stock market and derivatives market were studied using the information theoretic concept of entropy, which captures non-linear dynamic relationship also.

Data and Sources

The research findings and literature on the lead-lag relation between the index futures and the spot index indicated that futures market was the main source of market wide information with the futures leading the spot market. There was very little evidence of spot index leading the futures market. Most of the studies attempting to examine the lead-lag relationship between the futures and the spot market used the simultaneous equation modeling solved by ordinary least squares method.

This study has used daily prices (closing, opening, high and low) in both spot market and futures market for the 40 sample individual stocks drawn from six leading sectors namely, Automobiles, Banking, Cement, Gas, Oil & Refineries, Information Technology and Pharmaceutical.

In terms of market capitalization, the leading stocks under each of the six selected sectors were shortlisted. On the basis of complete data availability of spot prices during the study period and a minimum 24 months of continuous daily futures data, we finalized the selected 40 companies.

The spot prices and the one-month futures prices of the selected stocks are taken for the study. The futures time series analyzed here uses data on the near month contract as they are most heavily traded. The study used data on daily opening, low, high and closing prices of the selected indices and individual stocks traded in the spot market. The futures data include the near-month prices of daily opening, low, high and closing.

The study was entirely based on time series data from secondary sources and collected from official website of National Stock Exchange (www.nse-india.com). The period of study is from 1st January 1997 to 31st May 2009.

Research Methodology

Methodology directs the researcher to conduct the research in a systematic manner and deals with sampling plan and various tools to carry out the analysis on the data collected.

In the first place, the daily returns based on spot and futures prices were computed. The price series consisted of open price, low, high, and closing prices for both spot and futures market. The returns for the futures contract and the spot index are defined as R[F.sub.t] = {Ln ([F.sub.t]/[F.sub.t-1])} and R[S.sub.t] = {Ln ([S.sub.t]/[S.sub.t- 1])}, respectively where [F.sub.t] and [S.sub.t] are the futures prices and spot prices on day t, respectively.

Lead-Lag Framework Using Multiple Regression Model

Lead-lag estimate is practically useful for traders and investors trading in the leading market because they can make arbitrage profits by trading in the leading market. It also reveals the possibility for long-run equilibrium between two markets which gives the chance for equilibrium price for investors and traders after adjusting the short-run price fluctuations. This study aims to examine the possible lead/lag relation between the spot and futures market.

Following Stoll and Whaley (1990), the following Multiple Regression (Simultaneous I equation modelling, two stage least square) is estimated to-examine the nature of lead-lag relationship between returns in the spot and the futures markets as presented below.

[R.sub.S,t] = [alpha] + [sub.k=-n] [[summation].sup.k=+n][[beta].sub.k][R.sub.F,t-k] + [[epsilon].sub.t] (1)

[R.sub.F,t] = [alpha] + [sub.k=-n] [[summation].sup.k=+n][[beta].sub.k][R.sub.S,t-k] + [[epsilon].sub.t] (2)

where [R.sub.S,t] and [R.sub.F,t] are the daily spot and futures market returns at time t, respectively; [[beta].sub.k] is a constant representing the regression co-efficient, n denotes the number of leads/lags and [[epsilon].sub.t] is the error term.

Any of the above two equations (Eq.1 and 2) may be taken for analyzing lead-lag relationship, as these are complementary simultaneous equations. Ourstudy takes into consideration only equation 1 and 2 for computing Lead-lag estimates up to 5 lags/leads (i.e., k = -5 to +5, or n = 5) by using significance level is tested at 1 percent, 5 percent and 10 percent. Positive value of the coefficients ([beta]) at k = 1,2,3,4 and 5 would indicate that returns in the futures market tend to lead those in the spot market, and positive values for the coefficients at leads k = -1, -2, -3,-4 and -5 would indicate that the spot market tends to lead the futures market. Hence, the coefficients with negative subscripts are the lead coefficients and the positive subscripts are the lag coefficients. If [[beta].sub.1] is statistically found to be of positive value, then we infer that returns in futures market lead that of spot by one time period (k = 1). Similarly if [[beta].sub.2], [[beta].sub.3], [[beta].sub.4], and [[beta].sub.5] have positive values then it indicates that futures market leads the spot market for 2,3,4 and 5 days, respectively. If the lead coefficients are significant then the spot market leads the futures and, if the lag coefficients are significant, the Spot market lags the futures. Higher values of adjusted [R.sup.2] (coefficient of determination) indicates higher extent of the 'goodness of fit' of the regression model. If the contemporaneous coefficient [[beta].sub.0] is the largest among all coefficients, it suggests that the two markets react simultaneously to new information.

All statistical analysis and estimations in this study are done using SPSS 11.0, EViews 3.0 and Microsoft Excel.

Analysis and Findings

Analysis of Lead-Lag Relationship Between Spot and Futures Market for Selected Companies in Automobile Sector

The values of the lead-lag estimates between spot and futures prices for the six selected Automobile companies are presented in Table I. The analysis performed up to 5 lead/lags attempts to measure the extent of relationship between spot and futures price series.

It is observed that futures market leads the spot market up to one day for three companies namely, M&M, Tata Motors and TVS Motors, as evidenced from the significant values of [[beta].sub.0] and [[beta].sub.-1]. But in case of Bajaj Auto, futures markets lead the spot up to 2 days with [[beta].sub.-2] coefficient of 0.523, statistically significant at 1 percent level. Futures market lead the spot up to 3 days in case of Maruti Udyog with [[beta].sub.-3]. coefficient of 0.006 (significant at 1 percent level), and up to 4 days leading by futures market over spot market for Hero Honda with [[beta].sub.-4] coefficient of 0.040 found significant at 5 percent level. The contemporaneous coefficient ([[beta].sub.0]) was highest for Maruti Udyog (0.913) and minimum for M & M (0.761). The coefficient of 1 day lag (spot lagging behind futures, [[beta].sub.-1]), being significant was witnessed to be maximum for Tata Motors (0.370) and lowest for Maruti Udyog (-0.332). Similarly, the coefficient of 2 days lag (spot lagging behind futures, [[beta].sub.-2]), being significant was witnessed to be maximum for Hero Honda (0.005) and minimum for Bajaj Auto (-0.523). It is to be noted that the all the lag coefficients up to 3 days are significant at 1 level level. Only Hero Honda showed significant co-efficient lag (up to 4 days) of -0.040 with significance at 5 percent level. Further, only three Automobile companies witnessed significant 1-day lead coefficients (i.e., spot market leading futures market). They are M & M, Tata Motors and TVS Motors with [[beta].sub.+1] value of -0.134, -0.062 and 0.282, respectively. The regression intercept (p) had the highest value of 0.006 for Tata Motors and lowest value of -0.004 for TVS Motors. The regression model (OLS) has maximum coefficient of determination (r2) value for Maruti Udyog (0.887) and minimum value of 0.519 for Hero Honda.

These results from Table I indicate that the lag coefficients are relatively stronger than lead coefficients, which means that the futures prices lead spot market for the six selected Automobile companies.

Analysis of Lead-Lag Relationship Between Spot and Futures Market for Selected Companies in Banking Sector

Table II presents the values of the lead-lag estimates between spot and futures prices for the nine selected banks. The analysis is performed up to 5 days lead and 5 days lag, and this measures the extent of relationship between spot and futures price series. For all the nine Banks, it is observed that futures market leads the spot market up to one day as evidenced from the significant values of [[beta].sub.-1], and similarly the spot market leads the futures market up to one day as evidenced from the significant values of [[beta].sub.+1]. The contemporaneous coefficients ([[beta].sub.0]) is found significant at 1 percent level for all the Banks and the highest value is witnessed for Canara Bank (0.928) and minimum for IDBI (-0.545). The coefficients of 1 day lag (spot lagging behind futures, [[beta].sub.-1]), being significant was evidenced to be maximum for Union Bank of India (0.611) followed by IDBI (0.355) and Canara Bank (0.246). Lowest value of 1 day lag coefficient was seen for ICICI Bank (-0.461) followed by Oriental Bank (-0.048) and Bank of Baroda (-0.035). Similarly, the coefficients of 1 day lead (spot leading futures, [[beta].sub.+1]), being significant was observed to be highest for HDFC Bank (0.256) followed by SBI (0.212) and Oriental Bank (0.134). Lowest value of 1 day lead coefficient was seen for PNB (-0.415) followed by Union Bank of India (-0.076) and Canara Bank (-0.011). It is to be noted that none of the lead or lag coefficients over 1 day are significant either at 1 percent or 5 percent level. The regression intercept ([alpha]) had the maximum value of 0.049 for IDBI and lowest value of -0.015 for ICICI Bank. The regression model showed highest value of coefficient of determination (r2) for Bank of Baroda (0.921) and minimum value of 0.618 for SBI.

These results from Table II indicate that the both lag and lead coefficients are significant up to one day, signifying that both spot and futures market lead/lag each other for the selected banks under study.

Analysis of Lead-Lag Relationship Between Spot and Futures Market for Selected Companies in Cement Sector

The values of the lead-lag estimates between spot and futures prices for the four selected Cement companies are presented in Table III. The analysis performed up to 5 lead/lags intends to measure the extent of relationship between spot and futures price series.

It is observed from Table III that futures market leads the spot market up to one day as evidenced from the significant values of [[beta].sub.0] and [[beta].sub.-1], in the case of Gujrat Ambuja Cements with 1 day lag coefficient of 0.055. But in case of India Cements, futures market led the spot up to 2 days with [[beta].sub.-2] coefficient of -0.042 found statistically significant at 5 percent level. Futures market lead the spot market up to 3 days in case of ACC with [[beta].sub.-3] coefficient of 0.002 (significant at 5 percent level) and for Grasim Cements with 3-day lag coefficient of -0.21 5, found significant at 5 percent level. The contemporaneous coefficient ([[beta].sub.0]) was highest for Grasim Cements (0.872) and the lowest for Gujrat Ambuja Cements (0.623). The coefficient of 1 day lag (spot lagging behind futures, [[beta].sub.-1]), being significant at 1 percent level was witnessed to be maximum for ACC (0.352) and minimum for India Cements (-0.642). Similarly, the coefficient of 2 day lag (spot lagging behind futures, [[beta].sub.-2]), being significant at was observed to be the highest for ACC (0.216) and the lowest for Grasim Cements (-0.054). It is to be noted that only two Cement companies showed significant lag coefficient up to 3 days. These are ACC and Grasim Cements with 3-day lag coefficient of 0.002 and -0.21 5, respectively with each of them being significant at 5 percent level. Further, none of the lead coefficients (i.e., spot market leading futures market) are found to be statistically significant. The regression model has maximum coefficient of determination (r2) value for ACC (0.895) and minimum value of 0.655for India Cements. The regression intercept ([beta]) had the highest value of 0.061 for Grasim Cements and the lowest value of 0.001 for Gujrat Ambuja Cements.

These results from Table III indicate that the lag coefficients are more significant, which means that the futures market lead the spot market for the selected Cement companies.

Analysis of Lead-Lag Relationship Between Spot and Futures Market for Selected Companies in Gas, Oil and Refineries Sector

Table IV presents the values of the lead-lag estimates between spot and futures prices for the selected eight companies in the Gas, Oil & Refineries sector. The lead-lag regression analysis is performed up to 5 lead/lags with the objective of measuring the extent of relationship between spot and futures price series. For all the selected companies, it is observed that futures market leads the spot market up to one day or two days as evidenced from the significant values of [[beta].sub.-1] and [[beta].sub.-2]. The coefficients of 1 day lag (spot lagging behind futures, [[beta].sub.-1]), being significant (at 1 percent level) was witnessed to be maximum for Bongaigaon Refineries (0.335) followed by ONGC (0.219) and IOC (0.156). Lowest value of 1 day lag coefficient was seen for GAIL (-0.655) followed by Reliance Industries (-0.418) and BPCL (-0.261). Only two companies showed significant (at 1 percent level) lag coefficients up to 2-days. They are IPCL and Reliance Industries with 2-day lag coefficient values of -0.008 and -0.189, respectively. None of the selected companies witnessed significant lag coefficients beyond three days. The contemporaneous coefficients ([[beta].sub.0]) was significant at 1 percent level for all these companies and the highest value was witnessed for GAIL (0.935) and minimum for IOC (0.593). It is to be noted that none of the lead or lag coefficients (excluding BPCL for 1 -day lead) are significant either at 1 percent or at 5 percent level.

Only in the case of BPCL, spot market leads the futures market up to one day as evidenced from the significant values of -0.035 for [[beta].sub.+1].The regression intercept ([alpha]) had the maximum value of 0.357 for Bongaigaon Refineries and minimum value of -0.1 52 for IOC. The regression model evidenced highest coefficient of determination ([r.sup.2]) value for ONGC (0.953) and lowest value of 0.579 for HPCL

These results from Table IV indicate that the lag and lead coefficients are significant up to one or two days, signifying that futures market lead spot market (i.e., spot lags behind futures) for the selected companies in the Gas, Oil & Refineries sector. The only exception being BPCL in which case both spot and futures market lead/ lag each other up to 1 day.

Analysis of Lead-Lag Relationship Between Spot and Futures Market for Selected Companies in Information Technology (IT) sector

Table V presents the values of the lead-lag estimates (performed up to 5 days lead and 5 days lags) between spot and futures prices for the selected seven IT companies.

For all these companies, it is observed from Table V that futures market leads the spot market up to one day as evidenced from the significant values of [[beta].sub.-1], and similarly the spot market leads the futures market up to one day as evidenced from the significant values of [[beta].sub.+1]. The contemporaneous coefficient ([[beta].sub.0]) was found significant at 1 percent level for all the IT companies and the highest [[beta].sub.0] value was witnessed for Satyam Computers (0.915) and the lowest for Infosys Tech (0.579). The coefficients of 1 day lag (spot lagging behind futures, [[beta].sub.-1]), being significant at 1 percent level, was witnessed to be maximum for Polaris (0.689) followed by Patni Computers (0.415) and Infosys Tech (0.277). Minimum value of 1 day lag coefficient was seen for I-Flex (-0.396) followed by Satyam Computers (-0.054) and WIPRO (0.094).Similarly, the coefficients of 1 day lead (spot leading futures, [[beta].sub.+1]), being significant was observed to be the highest for TCS (0.415) followed by I-Flex (0.123) and Infosys Tech (0.091). Lowest value of 1 day lead coefficient was seen for Polaris (-0.059) followed by Satyam Computers (-0.011) and Patni Computers (0.027). It is to be noted that none of the lead or lag coefficients over 1 day are significant either at 1 percent or 5 percent level. The regression intercept ([beta]) had the maximum value of 0.154 for Polaris and minimum value of -0.027 for TCS. The regression model analysis showed maximum coefficient of determination (r2) value for WIPRO (0.881) and minimum value of 0.6 37for I-Flex.

In similarity with the selected banks (as shown in Table II), it is found from Table V that all the seven IT companies witnessed significant lag and lead coefficients up to one day, signifying that both spot and futures market lead/ lag each other, the lag coefficients being relatively stronger (higher values) than lead coefficients.

Analysis of Lead-Lag Relationship Between Spot and Futures Market for Selected Companies in Pharmaceutical Sector

The values of the lead-lag estimates between spot and futures prices for the six selected Pharmaceutical companies are presented in Table VI. The analysis performed up to 5 lead/lags intends to measure the extent of relationship between spot and futures price series in the Pharmaceutical companies. It is observed that for these selected Pharmaceutical companies (except Ranbaxy) futures market leads the spot market up to two days as evidenced from the significant values of [P.sub.0], [[beta].sub.-1] and [[beta].sub.-2]. In case of Ranbaxy, futures market led the spot up to 3 days with [[beta].sub.-3] coefficient of -0.007 found statistically significant at 1 percent level. The contemporaneous coefficient ([[beta].sub.0]) was the highest for CIPLA (0.822) and the lowest for Dr. Reddy's (0.694). The coefficient of 1 day lag (spot lagging behind futures, [[beta].sub.-1]) , being significant was witnessed to be highest for DABUR (0.428) and lowest for Wockhardt (-0.079). Similarly, the coefficient of 2 day lag (spot lagging behind futures, [[beta].sub.-2]), being significant was observed to be the highest for GLAXO Pharma (0.299) and the lowest for Ranbaxy (-0.067). Further, none of the lead coefficients (i.e., spot market leading the futures market) are found to be statistically significant. The regression model has maximum coefficient of determination (r2) value for DABUR (0.929) and minimum value of 0.594 for Ranbaxy. The regression intercept ([beta]) had the highest value of 0.075 for CIPLA and the lowest value of -0.002 for Wockhardt.

These results from Table VI indicate that the lag coefficients are more significant, which means that the futures market lead the spot market for the six selected Pharmaceutical companies.

Conclusion

Multiple Regression (using Simultaneous equation modeling) is estimated to examine the nature of lead-lag relationship between returns in the spot and the futures markets. The lead-lag analysis shows that for each of the six Automobile companies, lag coefficients are relatively stronger than lead coefficients implying that the futures prices lead spot prices. In case of each of the nine selected banks, both lag and lead coefficients are significant up to one day signifying that both spot and futures market lead/lag each other. The futures market leads the spot market for each of the four selected cement companies. For the each of selected companies in the Gas, Oil & Refineries sector (excluding BPCL) futures market leads spot market (i.e., spot market lags behind futures market). All the seven selected IT Companies witnessed significant lag and lead coefficients up to one day, signifying that both spot and futures market lead/lag each other. For all the Pharmaceutical companies (except Ranbaxy) futures market leads the spot market up to two days.

The results of this study are especially important to stock exchange officials and regulators in designing trading mechanisms and contract specifications for derivative contracts, thereby enhancing their value as risk management tools. An area of further research could be a study on intra-day return dynamics between the cash and futures market.

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Sathya Swaroop Debasish

Reader, Department of Business

Administration, Utkal University, Bhubaneshwar.
Table I

Lead-Lag Estimates Between Spot and Futures Market for Selected
Companies in Automobile Sector

Companies      [alpha]   [beta]-5   [beta]-4      [beta]-3

Bajaj Auto     0.001     0.522      0.164         0.115
Hero Honda     0.000     -0.023     -0.040 (**)   -0.112 (*)
Maruti Udyog   0.003     -0.385     0.053         0.006 (*)
M & M          0.001     0.416      0.132         -0.231
Tata Motors    0.006     0.225      0.362         0.335
TVS Motors     -0.004    0.352      0.625         0.015

Companies      [beta]-2     [beta]-1     [beta]0     [beta] + 1

Bajaj Auto     -0.523 (*)   -0.025 (*)   0.826 (*)   -0.229
Hero Honda     0.005 (*)    0.256 (*)    0.611 (*)   0.002
Maruti Udyog   -0.411 (*)   -0.332 (*)   0.913 (*)   0.009
M & M          0.052        0.225 (*)    0.761 (*)   -0.134 (*)
Tata Motors    0.655        -0.370 (*)   0.846 (*)   -0.062 (*)
TVS Motors     -0.354       0.116 (*)    0.776 (*)   0.282 (*)

Companies      [beta] + 2   [beta] + 3   [beta] + 4   [beta] + 5

Bajaj Auto     0.326        0.015        0.031        -0.025
Hero Honda     -0.152       0.058        -0.128       0.052
Maruti Udyog   0.315        0.268        -0.328       0.259
M & M          0.212        -0.341       0.036        0.313
Tata Motors    -0.064       0.626        -0.413       -0.059
TVS Motors     -0.138       0.034        0.352        0.176

Companies      Adj. R2

Bajaj Auto     0.652
Hero Honda     0.519
Maruti Udyog   0.887
M & M          0.636
Tata Motors    0.599
TVS Motors     0.773

Note: (*) denote significance at 1% level, (**) denote significance
at 5% level, [alpha] is the regression intercept; [beta]'s are the
values of the coefficients for various lags/leads ; [[beta].sub.0]
represent the contemporaneous coefficients between spot and futures
market; [[beta].sub.-1], [[beta].sub.-2], [[beta].sub.-3],
[[beta].sub.-4] and [[beta].sub.-5] represent the lag coefficients
(spot market lagging behind futures market or Futures market
leading spot) over 1, 2, 3, 4 and 5 days lags respectively;
[[beta].sub.1], [[beta].sub.2], [[beta].sub.3], [[beta].sub.4] and
[[beta].sub.5] represent the lead coefficients (spot market leading
the futures market or Futures lagging behind spot) over 1, 2, 3, 4
and 5 days lags respectively; [R.sup.2] is the coefficient of
determination of the regression model

Table II

Lead-Lag Estimates Between Spot and Futures Market for Selected
Companies in Banking Sector

Companies             [alpha]   [[beta].sub.-5]   [[beta].sub.-4]

Bank of Baroda        -0.008    0.223             -0.365
Canara Bank           0.002     -0.046            0.046
HDFC Bank             0.012     0.521             0.125
ICICI Bank            -0.015    0.084             -0.006
IDBI                  0.049     -0.281            0.842
Oriental Bank         0.011     0.152             -0.064
PNB                   0.000     0.004             0.136
SBI                   -0.006    -0.065            -0.035
Union Bank of India   0.026     0.021             0.252

Companies             [[beta].sub.-3]   [[beta].sub.-2]

Bank of Baroda        -0.031            0.411
Canara Bank           0.512             0.521
HDFC Bank             0.612             0.631
ICICI Bank            -0.431            -0.005
IDBI                  0.081             -0.031
Oriental Bank         -0.215            0.136
PNB                   0.325             -0.216
SBI                   -0.415            -0.005
Union Bank of India   0.385             0.323

Companies             [[beta].sub.-1]   [[beta].sub.0]

Bank of Baroda        -0.035 *          0.751 *
Canara Bank           0.246 *           0.928 *
HDFC Bank             0.125 *           0.774 *
ICICI Bank            -0.461 *          0.695 *
IDBI                  0.355 *          -0.545 *
Oriental Bank         -0.048 *          0.794 *
PNB                   0.008 **          0.884 *
SBI                   0.081 *           0.816 *
Union Bank of India   0.611 *           0.796 *

Companies             [[beta].sub.+1]   [[beta].sub.+2]

Bank of Baroda        0.096 *          -0.003
Canara Bank           -0.011 *          0.054
HDFC Bank             0.256 *           0.358
ICICI Bank            -0.001 *         -0.124
IDBI                  0.002 *           0.006
Oriental Bank         0.134 *           0.186
PNB                   -0.415 *         -0.133
SBI                   0.212 *           0.042
Union Bank of India   -0.076 *         -0.069

Companies             [[beta].sub.+3]   [[beta].sub.+4]

Bank of Baroda        0.223             -0.035
Canara Bank           0.051             0.055
HDFC Bank             -0.068            0.128
ICICI Bank            0.035             0.325
IDBI                  -0.048            0.004
Oriental Bank         0.006             -0.139
PNB                   0.223             0.009
SBI                   0.246             0.011
Union Bank of India   -0.034            0.128

Companies             [[beta].sub.+5]   Adj.R2

Bank of Baroda        0.052             0.921
Canara Bank           0.013             0.856
HDFC Bank             0.245             .0774
ICICI Bank            -0.005            0.854
IDBI                  0.326             0.903
Oriental Bank         -0.128            0.886
PNB                   0.015             0.693
SBI                   -0.032            0.618
Union Bank of India   0.284             0.776

Note: * denote significance at 1% level, ** denote significance
at 5% level, [alpha] is the regression intercept; [beta]'s are the
values of the coefficients for various lags/leads; [[beta].sub.0]
represent the contemporaneous coefficients between spot and futures
market; [[beta].sub.-]1, [[beta].sub.-2], [[beta].sub.-3],
[[beta].sub.-4] and [[beta].sub.-5] represent the lag coefficients
(spot market lagging behind futures market or Futures market leading
spot) over 1, 2, 3, 4 and 5 days lags respectively; [[beta].sub.1],
[[beta].sub.2], [[beta].sub.3],[[beta].sub.4] and [[beta].sub.5]
represent the lead coefficients (spot market leading the futures
market or Futures lagging behind spot) over 1, 2, 3, 4 and 5 days
lags respectively; [R.sup.2] is the coefficient of determination of
the regression model.

Table III

Lead-Lag Estimates Between Spot and Futures Market for Selected
Companies in Cement Sector

Companies               [alpha]   [[beta].sub.-5]   [[beta].sub.-4]

ACC                     0.012     -0.325            0.251
Grasim Cements          0.061     0.056             -0.062
Gujrat Ambuja Cements   0.001     0.085             0.085
India Cements           0.005     0.012             0.312

Companies               [[beta].sub.-3]   [[beta].sub.-2]

ACC                     0.002 **           0.216 *
Grasim Cements          -0.215 **         -0.054 *
Gujrat Ambuja Cements   0.368             -0.565
India Cements           0.175             -0.042 **

Companies               [[beta].sub.-1]   [[beta].sub.0]

ACC                     0.352 *           0.775 *
Grasim Cements          -0.068 *          0.872 *
Gujrat Ambuja Cements   0.055 *           0.623 *
India Cements           -0.642 *          0.725 *

Companies               [[beta].sub.+1]   [[beta].sub.+2]

ACC                     -0.365            0.065
Grasim Cements          0.218             0.085
Gujrat Ambuja Cements   0.062             0.013
India Cements           0.823             -0.615

Companies               [[beta].sub.+3]   [[beta].sub.+4]

ACC                     0.0312            -0.516
Grasim Cements          0.645             0.051
Gujrat Ambuja Cements   0.082             -0.362
India Cements           -0.068            0.059

Companies               [[beta].sub.+5]   Adj.[R.sup.2]

ACC                     0.328             0.895
Grasim Cements          0.01168           0.776
Gujrat Ambuja Cements   0.965             0.823
India Cements           -0.045            0.655

Note: * denote significance at 1% level, ** denote significance
at 5% level, [alpha] is the regression intercept; [beta]'s are the
values of the coefficients for various lags/leads; [[beta].sub.0]
represent the contemporaneous coefficients between spot and futures
market; [[beta].sub.-1], [[beta].sub.-2], [[beta].sub.-3],
[[beta].sub.-4] and [[beta].sub.-5] represent the lag coefficients
(spot market lagging behind futures market or Futures market leading
spot) over 1, 2, 3, 4 and 5 days lags respectively; [[beta].sub.1],
[[beta].sub.2], [[beta].sub.3], [[beta].sub.4] and [[beta].sub.5]
represent the lead coefficients (spot market leading the futures
market or Futures lagging behind spot) over 1, 2, 3, 4 and 5 days
lags respectively; [R.sup.2] is the coefficient of determination of
the regression model.

Table IV

Lead-Lag Estimates Between Spot and Futures Market for Selected
Companies in Gas, Oil & Refineries sector

Companies               [alpha]   [[beta].sub.-5]   [[beta].sub.-4]

BPCL                    0.059     -0.658            0.028
Bongaigaon Refineries   0.357     0.325             0.384
GAIL                    -0.079    0.045             -0.651
HPCL                    0.014     -0.068            0.078
IOC                     -0.152    0.019             -0.094
IPCL                    0.124     -0.135            0.154
ONGC                    0.177     0.325             -0.025
Reliance Industries     0.045     0.065             0.361

Companies               [[beta].sub.-3]   [[beta].sub.-2]

BPCL                    -0.051            -0.017
Bongaigaon Refineries   0.841             0.009
GAIL                    0.061             0.068
HPCL                    0.039             0.811
IOC                     0.015             0.256
IPCL                    0.165             -0.008 *
ONGC                    0.238             0.032
Reliance Industries     -0.259            -0.189 *

Companies               [[beta].sub.-1]   [[beta].sub.0]

BPCL                    -0.261 *          0.652 *
Bongaigaon Refineries   0.335 *           0.912 *
GAIL                    -0.655 *          0.935 *
HPCL                    0.046 *           0.884 *
IOC                     0.156 *           0.593 *
IPCL                    -0.215 *          0.682 *
ONGC                    0.219 *           0.773 *
Reliance Industries     -0.418 *          0.856 *

Companies               [[beta].sub.+1]   [[beta].sub.+2]

BPCL                    -0.035 *          0.052
Bongaigaon Refineries   0.155             0.062
GAIL                    0.289             -0.142
HPCL                    0.665             0.0283
IOC                     -0.382            0.069
IPCL                    0.051             -0.058
ONGC                    0.211             0.125
Reliance Industries     -0.032            0.154

Companies               [[beta].sub.+3]   [[beta].sub.+4]

BPCL                    0.025             -.036
Bongaigaon Refineries   0.036             0.582
GAIL                    0.182             -0.185
HPCL                    -0.028            0.225
IOC                     0.036             0.346
IPCL                    0.826             -0.074
ONGC                    -0.627            0.081
Reliance Industries     0.186             0.096

Companies               [[beta].sub.+5]   Adj.[R.sup.2]

BPCL                    0.145             0.746
Bongaigaon Refineries   -0.023            0.743
GAIL                    0.561             0.882
HPCL                    0.384             0.579
IOC                     0.039             0.842
IPCL                    0.041             0.698
ONGC                    -0.846            0.953
Reliance Industries     0.115             0.712

Note: * denote significance at 1% level, ** denote significance
at 5% level, [alpha] is the regression intercept; [beta]'s are the
values of the coefficients for various lags/leads; [[beta].sub.0]
represent the contemporaneous coefficients between spot and futures
market; [[beta].sub.-1], [[beta].sub.-2], [[beta].sub.-3],
[[beta].sub.-4] and [[beta].sub.-5] represent the lag coefficients
(spot market lagging behind futures market or Futures market
leading spot) over 1, 2, 3, 4 and 5 days lags respectively;
[[beta].sub.1], [[beta].sub.2], [[beta].sub.3], [[beta].sub.4] and
[[beta].sub.5] represent the lead coefficients (spot market leading
the futures market or Futures lagging behind spot) over 1, 2, 3, 4
and 5 days lags respectively; [R.sup.2] is the coefficient of
determination of the regression model.

Table V

Lead-Lag Estimates Between Spot and Futures Market for Selected
Companies in information Technology (IT) sector

Companies          [alpha]   [[beta].sub.-5]   [[beta].sub.-4]

I-Flex             -0.005    -0.012            0.362
Infosys Tech       0.016     0.312             0.041
Patni Computers    0.021     0.614             -0.069
Polaris            0.154     0.018             0.0148
Satyam Computers   0.008     0.213             -0.682
TCS                -0.027    0.168             0.195
WIPRO              0.012     0.032             0.467

Companies          [[beta].sub.-3]   [[beta].sub.-2]   [[beta].sub.-1]

I-Flex             0.152             -0.635            -0.396 *
Infosys Tech       -0.163            0.015             0.277 *
Patni Computers    0.097             -0.046            0.41 5 *
Polaris            0.034             0.039             0.689 *
Satyam Computers   -0.007            -0.047            -0.054 *
TCS                0.615             -0.165            0.187 *
WIPRO              0.018             -0.012            0.094 *

Companies          [[beta].sub.0]   [[beta].sub.+1]   [[beta].sub.+2]

I-Flex             0.842 *          0.123 *           0.031
Infosys Tech       0.579 *          0.091 *           0.014
Patni Computers    0.692 *          0.027 *          -0.416
Polaris            0.883 *         -0.059 *           0.286
Satyam Computers   0.915 *         -0.011 *           0.194
TCS                0.687 *          0.415 *          -0.064
WIPRO              0.746 *          0.048 *           0.067

Companies          [[beta].sub.+3]   [[beta].sub.+4]   [[beta].sub.+5]

I-Flex             0.155             0.125             -0.032
Infosys Tech       -0.362            0.064             0.046
Patni Computers    0.014             -0.084            0.286
Polaris            0.398             0.013             -0.046
Satyam Computers   -0.029            0.328             0.366
TCS                0.619             0.016             0.049
WIPRO              -0.023            -0.147            0.112

Companies          Adj.[R.sup.2]

I-Flex             0.637
Infosys Tech       0.774
Patni Computers    0.698
Polaris            0.721
Satyam Computers   0.755
TCS                0.694
WIPRO              0.881

Note: * denote significance at 1% level, ** denote significance
at 5% level, [alpha] is the regression intercept; [beta]'s are
the values of the coefficients for various lags/leads ; [[beta].sub.0]
represent the contemporaneous coefficients between spot and futures
market; [[beta].sub.-1], [[beta].sub.-2], [[beta].sub.-3],
[[beta].sub.-4] and [[beta].sub.-5] represent the lag coefficients
(spot market lagging behind futures market or Futures market leading
spot) over 1, 2, 3, 4 and 5 days lags respectively; [[beta].sub.1],
[[beta].sub.2], [[beta].sub., [[beta].sub.4] and [[beta].sub.5]
represent the lead coefficients (spot market leading the futures
market or Futures lagging behind spot) over 1, 2, 3, 4 and 5 days lags
respectively; [R.sup.2] is the coefficient of determination of the
regression model.

Table VI
Lead-lag estimates between spot and futures market for selected
companies in pharmaceutical sector

Companies      [alpha]   [[beta].sub.-5]   [[beta].sub.-4]

CIPLA          0.075     0.032             0.362
Dr. Reddy's    0.023     -0.465            0.168
DABUR          0.019     0.289             0.023
GLAXO Pharma   0.004     0.038             0.069
Ranbaxy        0.028     -0.016            0.012
Wockhardt      -0.002    0.228             -0.385

Companies      [[beta].sub.-3]   [[beta].sub.-2]   [[beta].sub.-1]

CIPLA          -0.026            0.265 *           0.178 *
Dr. Reddy's    0.284             0.125 *           0.082 *
DABUR          0.032             0.085 *           0.428 *
GLAXO Pharma   0.016             0.299 *           0.259 *
Ranbaxy        -0.007 *         -0.067 *           0.155 *
Wockhardt      0.426             0.211 *          -0.079 *

Companies      [[beta].sub.0]   [[beta].sub.+1]   [[beta].sub.+2]

CIPLA          0.822 *          -0.046              0.262
Dr. Reddy's    0.694 *           0.524             -0.159
DABUR          0.723 *           0.112              0.013
GLAXO Pharma   0.816 *           0.039              0.062
Ranbaxy        0.718 *          -0.241              0.018
Wockhardt      0.774 *          0.032              -0.026

Companies      [[beta].sub.+3]   [[beta].sub.+4]   [[beta].sub.+5]

CIPLA          -0.466            0.053             -0.516
Dr. Reddy's    0.138             -0.419            0.095
DABUR          0.065             0.011             -0.036
GLAXO Pharma   0.096             0.336             0.132
Ranbaxy        -0.142            0.084             0.085
Wockhardt      0.036             -0.017            -0.629

Companies      Adj.[R.sup.2]

CIPLA          0.669
Dr. Reddy's    0.856
DABUR          0.929
GLAXO Pharma   0.635
Ranbaxy        0.594
Wockhardt      0.746

Note: * denote significance at 1% level, ** denote significance
at 5% level, [alpha] is the regression intercept; [beta]'s are the
values of the coefficients for various lags/leads; [[beta].sub.0]
represent the contemporaneous coefficients between spot and futures
market; [[beta].sub.-1], [[beta].sub.-2], [[beta].sub.-3],
[[beta].sub.-4] and [[beta].sub.-5] represent the lag coefficients
(spot market lagging behind futures market or Futures market leading
spot) over 1, 2, 3, 4 and 5 days lags respectively; [[beta].sub.1],
[[beta].sub.2], [[beta].sub.3], [[beta].sub.4] and [[beta].sub.5]
represent the lead coefficients (spot market leading the futures
market or Futures lagging behind spot) over 1, 2, 3, 4 and 5 days
lags respectively; [R.sup.2]  is the coefficient of determination
of the regression model.
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Date:Apr 1, 2012
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