An Empirical Analysis of Commodity Future Market in India

An Empirical Analysis of Commodity Future Market in India 11 Assistant Professor, Department of Business & Commerce, Manipal University, Jaipur. Abstract The present study attempts to investigate long and short run causality between spot price and future prices of selected agricultural commodities of NCDEX in India.The data looks at the spot and future daily closing prices of chana,soyabean,soyarefined,guargum,potato and Pepper. We found strong evidence of cointegration between the daily spot and one-month futures commodity prices of chana,soyabean,soya oil and pepper. We have not found any correlation between guargum and potato future and spot price. After identifying single cointegration vector between spot and future prices of the selected agricultural commodity, the Vector Error Correction Model (VECM) was employed to examine the causal nexus between future and spot market of the selected agricultural commodity.. This is consistent with market efficiency. Finally, VECM model and Wald test are used to measure long run as well as short run causality among these four commodities.the evidence shows that future leads to spot in case of soyabean and soya oil. Whereas in case of chana and pepper we found bi-directional relationship. Keywords: Agricultural commodity, Lead lag Relationship, Informational efficiency, Cointegration, Vector Error Correction Model(VECM) Introduction India is a agriculture-dominated economy. Farmers face yield risk and price risk as well. Commodity futures and derivatives play a crucial role in fixing a price, especially in agriculture. The present study is an investigation into the futures markets in agricultural commodities in India. Price discovery is defined as the process by which buyers and sellers arrive at a specific transaction cost. With respect to commodity futures, price discovery is the use of futures prices to determine expectations of cash (spot) prices in the future. Trying to establish price discovery on Indian commodity exchanges is a difficult task especially for agricultural commodities as there is no single spot price for a commodity corresponding to a specified quality that is used as a reference price by all users all over India. The Ministry has received a report on the price movements of eight agricultural commodities.. These include pepper, potato, cardamom, mentha oil, soya oil, soya bean and chana. We have selected six commodities out of the above mentioned eight commodities to study the causal effect of future or spot prices. These commodities are chana,soyabean,soya oil,guargum,potato and pepper. Kailash Chandra Pradhan, K. Sham Bhat study investigated price discovery, information and forecasting in Nifty futures markets. Johansen s (1988) Vector Error Correction Model (VECM) is employed to

investigate the causal relationship between spot and futures prices. This study compares the forecasting ability of futures prices on spot prices with three major forecasting techniques namely ARIMA, VAR and VECM model. The Johansen s VECM results found that the spot market leads the futures market and spot prices tend to discover new information more rapidly than futures prices. The findings from VECM perform well on a post-sample basis against the univariate ARIMA model and a VAR model. The results show clearly the importance of taking into account the long-run relationship between the futures and the spot prices in forecasting future spot prices. Pratap Chandra Pati and Purna Chandra Padhan examined the price discovery process and lead-lag relationship between NSE S&P CNX Nifty stock index futures and its underlying spot index. It investigates the long-term and short-term dynamics of prices between spot and futures market, using Johansen-Juselius cointegration test, Vector Error Correction Model (VECM), impulse response functions, and variance decomposition. The results support the existence of a long-run relationship between spot and futures prices. Further, VECM indicates short-run unidirectional causality from futures to spot market. In addition, the study finds unidirectional Granger causality from futures market to spot market through Toda-Yamamoto-Dolado-Lütkepohl (TYDL) causality test. K. Srinivasan, Malabika Deo employed Johansen s Cointegration test and Vector Error Correction Model (VECM) for analyzing the long run and speed of equilibrium between the between Mini gold spot and futures market by taking daily closing values for both the indices. The findings of the study reveal that, in the long run, both the markets are cointegrated and causal relationship exists between these two markets. The results shows that unidirectional causality is running from spot to futures market in long-run dynamics and spot market serves as a primary market for price discovery. Adamopoulos Antonios investigated the causal relationship between stock market development and credit market development for Spain using a vector error correction model (VECM). The purpose of this paper is to examine the long run relationship between these variables applying the Johansen cointegration analysis. Granger causality tests discovered a unidirectional causality between credit market development and stock market development with direction from credit market to stock market development and there is a unidirectional causal relationship between stock market developments..the direction is from productivity to stock market development for Spain. Therefore, it can be inferred that credit market development and productivity have a positive effect on stock market development. T Mallikarjunappa and E M Afsal found no significant leading or lagging effects in either spot or futures markets with respect to top twelve individual stocks. There exists a contemporaneous and bi-directional lead-lag relationship between the spot and the futures markets. As against the widely accepted hypothesis of futures market, with its cost and hedging advantages, leading the spot market, Indian futures market fails to supply early information to spot market. 12

Maran Marimuthu, Ng Kean Kok attempted to re-examine the dynamic relationship between the Malaysian, and the Tiger markets (Hong Kong, South Korea, Singapore and Taiwan). The Johansen multivariate cointegration test,vecm using a five-variable and Granger causality test are used to find correlation and lead lag. The results indicate that there is a long run relationship among the five markets, and that the Hong Kong and Taiwan markets appear to be the most influential markets in this region. P. Srinivasan, K. Sham Bhat applied Johansen s Cointegration technique followed by the Vector Error Correction Model (VECM) to examine the lead-lag relationship between NSE spot and futures market for selected twenty-one commercial banking stocks of India. The analysis reveals mixed findings. However, most of the selected commercial bank stocks in India reveal future leads to spot `and equal number of selected banking stocks reveals bi-directional and spot lead to future prices. Janchung Wang studied empirical evidence related to futures pricing for the SGX FTSE Xinhua China A50 and HKE share index futures markets. He investigated whether the cost of- carry model can describe the relationship between index futures prices and underlying stock indexes is examined. The results says that incorporating stock market volatility into pricing models appears beneficial for estimating prices on these two index futures. Raymond Li evaluates the relationship among the NYMEX futures prices for crude oil, unleaded gasoline, heating oil and the US trade-weighted exchange rate to determine the relationship between the US exchange rate and energy prices. In addition, the causal relationships among the energy futures prices are examined. Cointegration is detected among the variables, but contrary to the existing empirical literature, it is found that the US exchange rate can be excluded from the cointegrating space. The Granger causality tests and impulse response functions also indicate that the US exchange rate is not related to the energy prices. Tarık Doğru,Ümit Bulut examine the relation between closing prices and trading volume of US Dollar (USD) futures contracts in the Turkish Derivatives Exchange (TURKDEX). The results indicate that while there is not a relation between prices and volume in the short run, there is a relation that is from volume to prices in the long run. Accordingly, it may be said that the futures market in Turkey is not efficient by the efficient market hypothesis. Kaoru Kawamoto, Shigeyuki Hamori In this study, market efficiency and unbiasedness among such futures are defined and the concept of consistently efficient (or consistently efficient and unbiased) market within n-month maturity is introduced market efficiency and unbiasedness among WTI futures with different maturities are tested using cointegration analysis, and short-term market efficiency, using an error correction model and GARCH-M-ECM. The results show that WTI futures are consistently efficient within 8-month maturity and consistently efficient and unbiased within 2-month maturity. 13

Christos Floros examines the price discovery between futures and spot markets in South Africa over the period 2002 to 2006. We employ four empirical methods: (i) a cointegration test, (ii) a Vector Error Correction model, (iii) a Granger causality test, and (iv)an Error Correction model with TGARCH errors. Empirical results show that FTSE/JSE Top 40 stock index futures and spot markets are cointegrated. Furthermore, Granger causality, VECM and ECM-TGARCH(1,1) results suggest a bidirectional causality (feedback) between futures and spot prices. Methodology and Data Johansen s Cointegration and Vector Error Correction Model (VECM) were employed to examine the lead-lag relationship between spot and futures price of the selected agricultural commodity. Augmented Dickey-Fuller (1979) was employed to verify the stationary of the data series. Further, the necessary lag length of the data series was selected on the basis of Schwarz Information Criterion (SC). Johansen s Cointegration test is employed to examine long-run relationship among the variables after they are integrated in an identical order. After identifying single cointegration vector between spot and future prices of the selected agricultural commodity, the Vector Error Correction Model (VECM) was employed to examine the long-run relationship between the two and it is presented below: Where, SPOT t and FUT t are spot and future market prices of individual stocks at time t. u1t and u2t are white noise disturbance terms and ECT t-1 is the lagged error correction term. The data for study is the daily closing prices of spot and futures of Chana,Soyabean,soyarefined,Guargum,Potato and Pepper. The data set has been comprised from November 2006 to April 2012. The near month futures contract has been used for the study as they are heavily traded as compared to next month and far month future contracts. All the required data information for the study has been retrieved from the National Commodity Exchange of India(NCDEX) website. Empirical Results and Discussions As a preliminary investigation, Augmented Dickey Fuller tests was employed to test the stationarity of spot and future price series of selected agricultural commodities and its results are presented in Table-I. 14

Augmented Dickey-Fuller test statistic t-statistic Prob.* With Intercept -1.3987 0.5816 Future With Intercept and Trend -2.6027 0.2799 Without Intercept and Trend 0.5395 0.8314 Chana First Difference -11.2199 0.0000 With Intercept -2.1468 0.2269 With Intercept and Trend -2.9643 0.1460 Without Intercept and Trend 0.2680 0.7624 First Difference -9.3965 0.0000 With Intercept -1.0971 0.7190 Future With Intercept and Trend -1.7316 0.7369 Without Intercept and Trend 1.6608 0.9769 Soyabean First Difference -37.4564 0.0000 With Intercept -1.3021 0.6306 With Intercept and Trend -1.8628 0.6732 Without Intercept and Trend 1.6220 0.9749 First Difference -35.8564 0.0000 With Intercept -0.8136 0.8145 Future With Intercept and Trend -1.6700 0.7641 Without Intercept and Trend 1.2045 0.9419 Soya Oil First Difference -37.0632 0.0000 With Intercept -0.7706 0.8265 With Intercept and Trend -1.5715 0.8039 Without Intercept and Trend 1.2920 0.9507 First Difference -32.8630 0.0000 With Intercept 6.140315 1.0000 Future With Intercept and Trend 3.716311 1.0000 Without Intercept and Trend 3.681838 1.0000 Guargum First Difference -35.50233 0.0000 With Intercept 7.031055 1.0000 With Intercept and Trend 4.481443 1.0000 Without Intercept and Trend 3.445325 0.9999 First Difference -15.22392 0.0000 Potato Future With Intercept -1.532933 0.5164 With Intercept and Trend -1.344322 0.8757 15

Without Intercept and Trend 0.273803 0.7651 First Difference -22.2053 0.0000 With Intercept -1.512864 0.5266 With Intercept and Trend -1.323741 0.8810 Without Intercept and Trend 0.299936 0.7723 First Difference -24.48486 0.0000 With Intercept -0.4246 0.9026 Future With Intercept and Trend -1.7136 0.7451 Without Intercept and Trend 1.6353 0.9756 Pepper First Difference -37.7766 0.0000 With Intercept 0.3583 0.9811 With Intercept and Trend -1.1038 0.9268 Without Intercept and Trend 2.2410 0.9944 First Difference -33.4398 0.0000 Notes: * indicates significance at one per cent level. Optimal lag length is determined by the Schwarz Information Criterion (SC). The above Table I result reveals that both the data series of future and spot price of selected agricultural commodities are stationary after first difference. Johansen s Cointegration test is performed to examine the long-run relationship between spot and future markets of selected agricultural commodities and its results are presented in Table-II. Table-II: Johansen s Cointegration Test Results Commodity Hypothesized No. of CE(s) Eigen Value Trace Statistic Critical Value Prob.** Chana None 0.059178 9.629463 15.49471 0.0203 At most 1 0.006789 0.96729 3.841466 0.3254 Soyabean None * 0.030664 51.52274 15.49471 0.0000 At most 1 0.001179 1.879826 3.841466 0.1704 Soya Oil None * 0.040237 60.04337 15.49471 0.0000 At most 1 0.000312 0.452581 3.841466 0.5011 Guargum None * 0.064171 144.7169 15.49471 0.0001 At most 1 * 0.02456 39.46387 3.841466 0.0000 Potato None 0.019789 12.45223 14.2646 0.0948 At most 1 * 0.00698 4.363562 3.841466 0.0367 Pepper None * 0.065144 109.197 15.49471 0.0001 16

At most 1 1.22E-06 0.00198 3.841466 0.9611 Note : * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values The Table-II result reveals that presence of one cointegrating vector between Future and spot prices of Chana, Soyabean, Soya Oil and Pepper. We have not found any correlation between guargum and potato. After identifying single cointegration vector between spot and future prices of the selected agricultural commodity, the Vector Error Correction Model (VECM) was employed to examine the causal nexus between future and spot market of the selected agricultural commodity and its results are presented in Table-III. Besides, the vector error correction model is sensitive to the selection of optimal lag length and the necessary lag length of future and spot price series for the selected agricultural commodities are determined by the Schwarz Information Criterion (SC). Table III: Long Run Causality Analysis Long Run Causality Analysis Coefficient Std. Error t-statistic Prob. Inference Chana(Future to ) C(1) -0.133502 0.064759-2.06151 0.0411 Chana( to Future) C(1) -0.058546 0.069143-0.84674 0.3986 Soyabean(Future to ) C(1) -0.078582 0.012523-6.27519 0.0000 Soyabean( to Future) C(1) 0.559385 0.185775 3.011088 0.0026 Soya Oil (Future to ) C(1) -0.079219 0.021788-3.63585 0.0003 Soya Oil ( to Future) C(1) -0.032009 0.027948-1.14533 0.2523 Pepper(Future to ) C(1) -0.069002 0.01345-5.13036 0.0000 Pepper( to Future) C(1) -0.084877 0.028702-2.95723 0.0031 F S F S By and large, the above table results of vector error correction model reveal mixed findings. There is one-way causal linkage from future market to spot market prices for Soyabean and Soya Oil. This indicates that information gets reflected first in the future prices and then it transmitted to spot market prices of the Soyabean and Soya Oil. We found bidirectional causal relationship for Chana and Pepper in the long term. In the short run we found unidirectional causality Soyabean and Soya oil and pepper from future to spot. It is bidirectional in case of Chana. This reveals that in case of Soyabean and Soya oil and pepper future price leads but in case of chana both the spot and future markets price of plays a leading role through price discovery process and is said to be efficient and reacts more quickly to each other. 17

Table IV: Short Run Causality Analysis Chana(Future to ) Chana( to Future) Soyabean(Future to ) Soyabean( to Future) Soya Oil (Future to ) Soya Oil ( to Future) Pepper(Future to ) Pepper( to Future) Wald Test Result Test Statistic Value df Probability Inference F-statistic 26.18017 (2, 138) 0.0000 Chi-square 52.36033 2 0.0000 F-statistic 3.690448 (2, 138) 0.0275 F S Chi-square 7.380895 2 0.0250 F-statistic 35.66634 (3, 1587) 0.0000 Chi-square 106.999 3 0.0000 F-statistic 2.2093 (2, 1590) 0.1101 Chi-square 4.418601 2 0.1098 F-statistic 17.77834 (3, 1444) 0.0000 Chi-square 53.33502 3 0.0000 F-statistic 0.97413 (3, 1444) 0.4041 Chi-square 2.922391 3 0.4037 F-statistic 112.6022 (6, 1605) 0.0000 Chi-square 675.6135 6 0.0000 F-statistic 0.58551 (6, 1605) 0.7422 Chi-square 3.51306 6 0.7422 4. Conclusion Johansen s Cointegration technique followed by the Vector Error Correction Model (VECM) was employed to examine the lead-lag relationship between spot and futures market for selected Agricultural commodity. The empirical analysis was conducted for the daily data series from November 2006 to April 2012. Using Augmented Dickey-Fuller test which are robust to a wide variety of serial correlation and time-dependent heteroskedasticity, we found enough evidence for the presence of a unit root for all 6 commodities spot and future prices. We have also used the theory of cointegration to test to examine the longrun relationship between spot and future markets of selected agricultural commodities. We found strong evidence of cointegration between the daily spot and one-month futures commodity prices of chana,soyabean,soya oil and pepper. This is consistent with market efficiency. Finally, VECM model and Wald test are used to measure long run as well as short run causality among these four commodities. We found bidirectional causal relationship for Chana and Pepper in the long term. There is unidirectional relationship between soyabean and soya oil in long term. In the short run we found unidirectional causality Soyabean and Soya oil and pepper from future to spot. It is bidirectional in case of Chana. This reveals that in case of Soyabean and Soya oil and pepper future price leads but in case of chana both the spot and future markets price of plays a leading role through price discovery process and is said to be efficient and reacts more quickly to each other. 18

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