Indian J Econ Dev Volume 11 No. 1 (2015): 369-377
DOI No. 10.5958/2322-0430.2015.00043.8 Research Article
FUTURES MARKET IN MITIGATING PRICE RISK: AN EXPLORATIVE ANALYSIS OF CASTOR MARKET Rachana Kumari Bansal*, Y. C. Zala* and D.J. Parmar# ABSTRACT Instability of commodity prices has always been a major concern of the producers as well as the consumers in an agriculture dominated country like India. Farmers in a bid to avert the price risk often tend to go for distress sale and thereby reducing the potential returns. In order to cope up with this problem, futures trading has emerged as a viable option and serves as a risk-shifting function. This study has analyzed the movement of futures and spot prices or co-integration in castor and the data were collected from July, 2004 to December, 2013. The ADF (Augmented Dickey Fuller) test has been used to check the stationarity of the time series data and it followed the stationarity pattern at the first difference. The cointegration test has been used to find out whether there existed a long-run relationship between spot and futures prices and it behaves in an expected manner. The result of Granger test detected unidirectional Granger Causality from futures to spot markets. This phenomenon of price convergence and causality indicated its better hedging efficiency for farmers and exporters to mitigate the price risk. Keywords: ADF test, co-integration, futures trading, Granger Causality. JEL CLassification: C10, C46, M21, Q10, Q13
INTRODUCTION India being an agrarian economy, instability in commodity prices has always remained a major concern for producers as well as the consumers. Various other challenges have cropped into Indian agriculture during the post-WTO regime, for instance dragging technological process, depletion of water resources, stagnant productivity and more importantly, lagging market reforms. Fragmented rural markets are another challenge *
Research Scholar and Professor-cum-Head, Department of Agricultural Economics, B.A. College of Agriculture and #Associate Professor, Department of Agricultural Statistics, Anand Agricultural University, Anand-388110 (Gujarat) Email:
[email protected]
in efficient marketing/trading of agricultural commodities in India. Given the exposure of farmers to such risks and challenges, it makes their investment in farming an unprofitable proposition. There are various ways to cope with this problem. Market based risk management tools for commodities have assumed special significance in the liberalization era (Sahadevan, 2002). Apart from increasing the stability of the market, various factors in the farm sector can better manage their activities in an environment of unstable prices through futures markets. These markets serve as a risk-shifting function and can be used to lock-in prices instead of relying on uncertain price developments (Raipuria, 2002). The price risk refers to the probability of
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adverse movement in prices of commodities, services or assets. Many of them being seasonal tend to attract lower prices during the harvest season. The forward and futures contract are considered to be an efficient risk minimizing tools which insulate buyers and sellers from unexpected changes in future price movements. These contracts enable them to lock-in the prices of the products well in advance. Moreover, Futures prices give necessary indications to producers and consumers about the likely future ready price and demand and supply conditions of the commodity traded. The cash market or ready delivery market, on the other hand, is a time tested market system, which is used in all forms of business to transfer title of goods (Singh et al., 2005). The commodity futures market in India has witnessed unprecedented growth since 2003. Despite this phenomenal growth, the economic rationale and functioning of futures market is not clearly understood by many. This has led not only many misconceptions about the functioning of the futures market but has also kept many potential beneficiaries away from this market. The volume of trade and number of commodities traded in the futures market has increased over the years. The hedging and price discovery functions of futures markets promote more efficient production, storage, marketing and agro-processing operations, financing, and overall agricultural marketing performance. Futures trade provides a convenient mechanism through which a farmer, who is uncertain about the price of his produce, can cover his risk by selling a futures contract before the harvest day. The futures trading is based on an obligation between a buyer and a seller to fulfill the pre-determined standardized contract entered on the day of agreement for delivery in the future (Sendhil et al., 2013). The futures trading infuse efficiency in the functioning of a commodity market through availability and dissemination of information, which helps to stabilize and to decrease spot
price volatility (Tomek, 1980). Through hedging farmers can mitigate the price risk that they may face in the spot market with volatile prices. However, even in the well functioning markets, the movement of spot and futures prices would not be perfectly parallel, so that it can only reduce risks through executing opposite selling and buying in two markets rather than altogether removing them. Keeping all this in view, the present study tries to explore how futures prices moved with spot prices using co-integration technique and also investigates the efficiency of Futures market. METHODOLOGY Crop Selection India exports nearly 80 percent of castor seed production and there is significant fluctuation in the production of castor seed. The market participants like the farmers, traders, oil millers, exporters and industries which produce value added derivatives face an eternal price risk due to fluctuating production and world prices set by other trading countries. Hence, there is a need for futures contract to hedge their price risk. So, castor was purposively selected for the study. Data The data used for the study was entirely based on secondary source. The daily spot and futures prices of castor seed were obtained from the website of National Commodity Derivative Exchange of India Limited (NCDEX), www.ncdex.com, Mumbai, from July, 2004 to December, 2013 (for a period of 10 years-2285 observations). The sample period used in analysis was based on availability of data and by smoothing of data after adjusting holidays and non-trading days. Analytical Framework Integration Johansen’s (1988) multivariate approach was used to examine co-integration of futures market with spot market prices. Before testing for co-integration, the time series of prices was checked for its stationarity to avoid spurious or non-sense regression. The stationarity properties and unit roots in the time series were
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substantiated by Augmented Dickey-Fuller (ADF) test (Dickey and Fuller, 1979). This test was conducted on the level and first differences of price series. The time series variables that are integrated, may be of the same order, while the unit root test finds out which variables are integrated of order one, or I(1). The following ADF regression equation was tested for stationarity: DYt= b1 + b2t + dYt-1 + S aiDYt-i+ et Where, Yt is a vector to be tested for cointegration, t is the time or trend variable, DYt = (Y-Yt-i) and et is a pure white noise error-term. The null hypothesis that, d = 0; signifying unit root, states that the time series is nonstationary, while the alternative hypothesis, d < 0, signifies that the time series is stationary, thereby rejecting the null hypothesis. The Johansen’s Co-integration test based on the error-correction representation is as follows: Dzt= fk Dzt-k+ pzt-1 + m + et Where, zt is n 1 vector of I(1) processes (price of n market), The rank of p equals the number of co-integrating vectors, which is tested by maximum eigen value or trace value and likelihood ratio test statistics, m is a constant term has been used to capture the left out variables. The number of lags used in the model was decided on the basis of Akaike (1974). In this study the rank of p was determined by Johansen’s trace test. n
λ trace T ln(1 λi) for r= 0, 1, 2..., n-1 i r 1
Where, li are the Eigen values representing the strength of the correlation between the first difference and the error-correction. Then, the following hypotheses were tested: H0: rank of p = r (null hypothesis), and H1: rank of p > r (alternate hypothesis) Where, ‘r’ is the number of co-integration equations. The above test was carried out on the assumption of linear deterministic trend in original data and only intercept in the cointegrating equation. The co-integrating equation has only the intercept (no trend) because of differencing the price series while
checking for its stationarity, whereas the original price series follows a trend since the mean and variance are non constant over a period of time (non-stationary). The Vector Error Correction Model (VECM) was used to estimate the acceleration speed of short-run deviation to long run equilibrium. The error correction model isDSt = q0 + q1 DSt-1 + q2 DFt-1 + q3 et-1 + mt where, D denotes first difference operator mt is the random error term et-1 = (St - a- bFt-1) that is the one period lagged value of the error from the co integrating regression. Of particular interest is the coefficient of the error correction term, q3 that indicates the speed at which the series returns to equilibrium. For value of q3 that is negative (positive) and less than (equal to) zero, the series converges to (or diverges from) the long run equilibrium. Here St and Ft are spot and futures prices respectively. Granger Causality Test Correlation does not necessarily imply causation in any meaningful sense of that word. The Granger (1969) approach to the question of whether x causes y is to see how much of the current y can be explained by past values of y and then to see whether adding lagged values of x can improve the explanation. y is said to be Granger-caused by x if x helps in the prediction of y, or equivalently if the coefficients on the lagged ’s x’s are statistically significant. Note that two-way causation is frequently the case; x Granger causes y and y Granger causes x. It is important to note that the statement x Granger causes y does not imply that y is the effect or the result of x. Granger causality measures precedence and information content but does not by itself indicate causality in the more common use of the term. In general, it is better to use more rather than fewer lags, since the theory is couched in terms of the relevance of all past information.
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Bivariate regressions of the form: yt = a0 + a1 yt-1 + …+ al yt – l + b1 xt-l + …........ + bl x–l + et xt = a0 + a1 xt-1 + …+ al xt – l + b1 yt-l + …........... + bl y–l + ut for all possible pairs (x,y) of series in the group. The reported F-statistics are the Wald statistics for the joint hypothesis: b1 = b2 = … = bl = 0 for each equation. The null hypothesis is that x does not Granger-cause y in the first regression and that y does not Granger-cause x in the second regression. RESULTS AND DISCUSSION Nearly 81 percent farmers in the country belong to the small and marginal category. For them, better prices are the best incentive to remain in the farming. The NSSO study (Anonymous, 2003) on situational assessment survey of farmers revealed that almost 60 percent farmers were willing to relinquish their profession owing to non-remunerative returns and instability in prices due to market upheavals. Keeping this in mind, the government decision to open up the futures market operation in castor is investigated. Future-spot Price Linkages The descriptive statistics of spot and futures prices of castor has been given in Table1. The table depicts that the mean of spot price for castor was `2804.68 per quintal and for futures price; it was `2826.44 per quintal. Both spot and futures prices were positively skewed with a skewness coefficient of 0.43 and 0.46 and platykurtic, as their kurtosis values were Table 1: Descriptive statistics of spot and futures prices of castor Items Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis Range CV%
Spot prices 2804.68 2736.75 6145.7 1386.5 1030.79 0.4298 -0.7481 4759.2 36.75
less than three (-0.75 and -0.72). The distribution of spot prices is comparatively less skewed than futures prices. As the standard deviation of futures price was greater than spot price, it resulted in the greater variability of futures price as compared to spot price. Coefficient of variation for spot and futures prices was 36.75 and 36.92 percent. Low liquidity might be a potential reason for higher instability in futures prices. The interdependence among prices (spot and futures prices) was probed through multivariate co-integration technique. The integration tests are pre-requisite for cointegration. The order of integration (existence or absence of non-stationarity) in the series was examined through the ADF test. The perusal of Table 2 contains the result of ADF test implying the existence of unit root in the price series. Akaike’s information criterion (AIC) was used to determine the lag length. To conduct ADF, a lag length of five was sufficient to remove autocorrelation. The ADF test at the series levels [integrated of order 0,I(0)] supported the null hypothesis of unit root (non-stationary) at 99 percent level of significance for the spot and futures prices of castor crop. The ADF test statistics of spot and futures prices have fallen within the confidence interval, indicating all price series exhibited random walk or levels of series were non-stationary. The first difference of all these non-stationary time series of spot and futures price of castor was then tested using t-test. The first difference or integrated of order 1 denoted as I(1) of all price series was found to be significant or stationary. Thus, the price
Futures prices 2826.44 2728 5962.5 1346 1043.55 0.4636 -0.7199 4616.5 36.92
Table 2: ADF unit root test for spot and futures prices of castor Lag Length: 5 (Automatic - based on AIC, maxlag=5)
ADF-unit root (t test) Level First difference **
Spot price
Futures price
-0.742 (0.834) -28.421** (0.000)
-0.908 (0.786) -18.827** (0.000)
significant at 1 percent level Figures in parentheses indicate Mackinnon (1996) p-values
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series of spot and futures markets have a unit root. The occurrence of unit root in the price data generation process of this commodity gave a preliminary indication of shocks which may have permanent or long-lasting effect. Rangasamy and Elumalai (2010) also found similar results in case of pepper and they concluded that spot and futures prices were found to be stationary at first difference. It can be seen from Figure 1 that the auto correlation function (ACF) declined very slowly and as many ACFs were significantly different from zero and fell outside the 99 percent confidence interval, the spot and futures price of castor was non-stationary. The partial auto correlation function (PACF) declined rapidly after the fourth lag period, which also indicated the non-stationarity of the spot and futures price series. It was corrected through appropriate differencing of the data and in Figure 2 ACF and PACF declining sharply, which shows stationarity among spot and futures prices. Since differencing was carried out only once to arrive at stationary series. Since it is established through ADF test that both the series have long run relationship, co-integration was tested using Johansen’s
maximum likelihood procedure. The cointegration of spot and futures markets could be established and the same order of integration for both spot and futures prices reveals that there exists a long run price equilibrium relationship between these prices. The trace test for co-integration vectors of the two markets with the assumption that there existed a linear deterministic trend in the data was carried out at the lag interval of five. The results of Johansen co-integration test is given in Table 3 which revealed that trace statistics value of 98.41 was greater than the critical value of 15.49 at five percent level. So, we reject the null hypothesis, which indicates the existence of at least one co-integrating equation(s) at five percent level of significance, and thereby price transmission takes place as it helps in Table 3: Johanse n's co-integration te st results for castor Unrestricted Co-integration Rank Test (Trace) Hypothesized Eigen Trace 0.05 critical Prob.** No. of CE(s) value statistics value None* 0.041987 98.4137 15.4947 0.0001 At most 1 0.000270 0.61508 3.84146 0.4329 Trace test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level ** MacKinnon-Haug-Michelis (1999) p-values
Figure 1: At level series ACF and PACF of spot and future price of castor
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the process of price discovery. This further indicates that one of the price series could be predicted from the other price series. Hence it was concluded that the model variables had a long-run equilibrium/comovement among the spot and futures price series during the period under study. The existence of co-integration is necessary for long-term market efficiency. It helps to determine whether spot prices are affected by the futures prices or not. That means there is price discovery process in the spot and futures market as co-integration is necessary condition for price discovery. Elumalai et al. (2009) also conducted a study on the price discovery mechanism in pepper, guar seed and gram and obtained similar results. Long-run equilibrium relationships between spot and futures prices were also observed, even though there can be disequilibrium in the short run. For this, the error term can be treated as equilibrium error and also the intertwined relationship in the short run giving way to a long run association. The error correction mechanism was used to estimate the acceleration speed of the short run deviation to the long run equilibrium. The advantage of ECM is that it allows for the short run dynamics as well as an assessment for the
degree towards the long run relation as shown by co-integration. Results of vector error correction model are given in Table 4. The coefficient of error equilibrium was found to be -0.025 in the futures market equation. This indicated that when the average futures price of castor was too high, it immediately fell back towards spot price. That is, the futures price corrects to its previous period’s disequilibrium by 2.5 percent. These results revealed that there was long run relationship between futures and spot prices and the adjustment towards equilibrium was made by futures prices. The error correction coefficient suggests that a sustainable longterm equilibrium is achieved by closing the gap between futures and spot prices. The error correction coefficient in spot and futures is 0.1198 and -0.0254, respectively. This measures how quickly the dependent variables, such as, spot and futures prices absorb and adjust themselves for last period disequilibrium errors. In other words, it measures the ability of dependent variable, such as, spot and futures prices to incorporate shocks or news in its prices. Result suggests that presence of futures market leads marginally price discovery process. As regard short run causality, that is
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Table 4: Ve ctor e rror corre ction mode l (VECM) estimates for Castor Co-integrating eq. FF(-1) SS(-1)
C Error Correction Cointegration Equation-1
D(FF(-1))
D(FF(-2))
D(SS(-1))
D(SS(-2))
C
R2
Cointegration Equation-1 1 -1.011195 (0.00746) [-135.466] 9.792824 D(FF)
D(SS)
-0.025428
0.119811
(0.01301) [-1.95489] 0.167528 (0.02549) [ 6.57118] 0.025233 (0.02694) [0.93676] -0.009749 (0.02306) [ -0.42267] -0.049861 (0.02070) [-2.40855] 1.035918 (1.10947) [ 0.93371] 0.026
(0.01379) [8.69107] 0.551359 (0.02702) [ 20.4061] 0.010702 (0.02855) [ 0.37486] -0.271688 (0.02444) [ -11.1146] -0.138557 (0.02194) [-6.31535] 1.0064 (1.17583) [ 0.85591] 0.252294
Figures in parentheses () are standard errors and [] are t-statistics
changes in futures (spot) prices with respect to lagged changes in spot (futures). In the spot price model of castor, the coefficient of one day lagged futures price was positive (0.551). It implies that price discovery was occurred futures market and was transmitted to spot market. The coefficient of its own (spot) one day lagged spot price was negative (-0.272) and two day’s lagged spot price was negative (-0.138). It means that the spot market was influenced by its own price too. However, in the futures model, the coefficient of two day’s lagged spot price was negative (-0.050) and did not seem to affect futures prices. The coefficient of its own (futures) one day lagged (0.167) and two day’s lagged (0.025) a spot price was positive. It means futures market was influenced by its
own price too. This showed that the causality was unidirectional, means better discovery of prices at castor futures market from where the information flows to spot market. They also exhibited the convergence in spot and futures prices. This phenomenon of price convergence for castor clearly depicts that farmers and exporters would be able to mitigate the price risk by taking opposite position in the markets. It is a good option for hedging in the Indian futures market for the farmers growing this crop. Singh et al. (2009) also found similar results while studying the efficiency of futures market operations in mitigating price risk in wheat, chana (chickpea), urad (black gram), masoor (lentil), refined soya oil, potato, jeera (cumin) and kapas (cotton) and they found cointegration/ long run relationship between spot and futures prices. They concluded that futures contract behave in an expected manner and have a positive impact on the short run changes in the spot prices except wheat and potato. Granger Causality Granger causality test was employed to examine the lead-lag relationship between markets. Granger-causality is a necessary condition for causality, but not a sufficient condition. Granger, x causes y if the past value of x can be used to predict y more accurately than simply using the past values of y. In other words, if past values of x statistically improve the prediction of y, then we can conclude that x Granger-causes y. The test itself is just an Ftest of the joint significance of the other variables in a regression that includes lags of the dependent variable. The result of Granger test given in Table 5 detects unidirectional Granger Causality from futures to spot markets. In case of castor at lag one to five, null hypothesis of no granger causality from futures prices to spot prices is rejected as indicated by low p values and high F-values, but this is not true for the null hypothesis of Granger causality from spot to futures prices.
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Table 5: Granger Causality test for spot and futures prices of Castor Lag Null hypothesis 1 Spot does not Granger cause futures Futures does not Granger cause spot 2 Spot does not Granger cause futures Futures does not Granger cause spot 3 Spot does not Granger cause futures Futures does not Granger cause spot 4 Spot does not Granger cause futures Futures does not Granger cause spot 5 Spot does not Granger cause futures Futures does not Granger cause spot
Observations 2284 2283 2282 2281 2280
This further indicates that futures market is more efficient in castor and unidirectional flow of information occurs from futures to spot market. This indicates that the castor futures influenced the spot prices indicating its better hedging efficiency for farmers and exporters to mitigate the price risk by taking opposite position in the markets. Sen and Paul (2010) also found similar result in case of chana, refined soya oil, potato and wheat. CONCLUSIONS In this study an attempt has been made to look into the mechanism of movement of spot and futures prices of castor in India. The mean value of spot prices was found less than those of the future prices for castor crop. The distribution of spot prices is comparatively less skewed than futures prices. The price series of castor crop in spot and futures market showed that level data was non-stationary but at their first differences it became stationary (implying the presence of unit-root in the series). The occurrence of unit root in the price data generation process of this commodity gave a preliminary indication of shocks which may have permanent or long-lasting effect. The co-integration of spot and futures markets could be established and the same order of integration for both spot and futures prices reveals that there exists a long run price equilibrium relationship between these prices. However, in the short run there may be disequilibrium between these two. Short run changes in the future price series have a positive impact on the short run-changes in
F-statistics 0.01049 267.67 1.5932 341.354 2.9675 242.529 2.1016 187.728 3.38449 153.224
Probabilities 0.9184 6.00E-57 0.2035 2.00E-130 0.0308 2.00E-136 0.0781 4.00E-139 0.0048 2.00E-140
the spot price. This phenomenon of price convergence shows that futures market behaves in an expected manner. The result of Granger test detects unidirectional Granger Causality from futures to spot markets. This broadly indicates that the castor futures influenced the spot prices indicating its better hedging efficiency for farmers and exporters to mitigate the price risk by taking opposite position in the markets. This study suggests that there is a need to broaden the coverage by effectively popularizing the futures trading among farmers, traders and exporters in commodity trading domain and convincing the policy makers about the effectiveness of the futures trading. REFERENCES Akaike, H. 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control. 19 (6): 716-723. Anonymous. 2003. Situational Assessment Survey of Farmers Report No. 496 (39/33/3, NSS 59th Round (Jan-Dec2003). NSSO. Ministry of Statistics and Programme Implementation. Government of India, New Delhi. Dickey, D. and Fuller, W.A. 1979. Distribution of the estimators for autoregressive time series regressions with unit roots. Journal of American Statistical Association. 74: 427-431. Elumalai, K., Rangasamy, N. and Sharma, R.K. 2009. Price discovery in India’s agricultural commodity markets. Indian Journal of Agricultural Economics. 64 (3) (Conf. spcl.): 315-323. Granger, C.W.J. 1969. Investigating causal relations by econometric models and cross-spectral methods. Econometrica. 37 (3): 424-438. Johansen, S. 1988. Statistical analysis of co-
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Received: January 20, 2015 Accepted: February 13, 2015
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