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Impact of Futures Trading on Commodity Prices

The article attempts to explore the effect of the introduction of futures trading on spot prices of pulses. It finds that volatility in urad as well as pulses prices was higher during the period of futures trading than in the period prior to its introduction as well as after the ban of futures contracts.


Impact of Futures Trading on Commodity Prices

Golaka C Nath, Tulsi Lingareddy

The article attempts to explore the effect of the introduction of futures trading on spot prices of pulses. It finds that volatility in urad as well as pulses prices was higher during the period of futures trading than in the period prior to its introduction as well as after the ban of futures contracts.

Golaka C Nath ( and Tulsi Lingareddy ( are both at the Clearing Corporation of India, Mumbai. The views expressed here are the personal views of the authors.

well-developed and effective commodity futures market facilitates price discovery and thereby, helps in minimising the price risk associated with seasonal variations in the demand and supply of commodities. Globally, different market players participate in buying and selling activities in the futures market, based on diverse domestic as well as international market information, such as price, demand and supply, climatic conditions, etc. All these factors put together result in efficient price discovery as a result of a large number of buyers and sellers transacting in the futures markets.

In India, commodity derivatives trading has a long history of more than a century but was often subjected to government interventions as agricultural commodities, in which most of the contracts traded, are price sensitive in nature. The government decision to allow the setting up of modern national commodity exchanges helped revive the futures markets after nearly 40 years. The government has permitted futures trade in more than 100 commodities under various groups inclu ding agricultural commodities, metals and energy products.

Following this, the department of consumer affairs granted recognition to three national exchanges – the National Multi-Commodity Exchange of India, Ahmedabad (NMCE), which started functio ning in November 2002, the Multi-Commodity Exchange of India (MCX), Mumbai and National Commodity and Derivatives Exchange (NCDEX), Mumbai which became operational in November 2003. Apart from the three national multi-commodity exchanges, there are 21 regional exchanges recognised for trading commodity futures. Of the total 103 commodities allowed for futures trading, 94 commodities are currently traded across various exchanges.

Soon after the establishment of national exchanges, volumes gathered momentum rather quickly (Table 1) in 2004-05 and extended further to 2005-06. Although growth has persisted in the subsequent period, it has apparently decelerated to about 55 per cent in 2006-07. Besides this, the functioning of futures markets has also come under scrutiny during 2006-07. The government ordered delisting of futures contracts in urad, tur, wheat and rice during January and February 2007 suspecting that futures trading in these commodities has been contributing to the increase in prices of these essential

Table 1: Trends in Volume Trade on Futures Exchanges

2002-03 2003-04 2004-05 2005-06 2006-07

(Rs crore) 66,530 1,29,363 5,71,759 21,34,471 33,27,633
(%) 92.8 94.4 342.0 273.3 55.9

Source: Annual Reports, Ministry of Food and Consumer Affairs, Delhi.

items. In view of this, an attempt was made to study the impact of futures trading on agricultural commodity prices.

Review of Relevant Literature

Kamara (1982) found that the introduction of commodity futures trading generally reduced or at least did not increase cash price volatility. The study compared cash market volatility before and after the introduction of futures trading; thus, impli citly focused on the paradigm of introducing futures trading. Singh (2000) investigated the Hessian cash (spot) price variability before and after the introduction of futures trading (1988-97). Results of a multiplicative dummy variable model indicated that the futures market has reduced the price volatility in the Hessian cash market.

On the other hand, Dasgupta (2004) found that there is a co-movement among futures prices, production decisions and inventory decisions. The results showed that the futures price elasticity of inventory is inversely related to the carrying cost. Therefore, an unnecessary hoarding will increase the carrying cost, leading to a lower responsiveness of inven tory to futures prices. This paper also determines the effect of expected production shocks on the futures price elasticity of supply.

Yang et al (2005) examined the lead-lag relationship between futures trading activity and cash price volatility for

january 19, 2008 Economic & Political Weekly


major agricultural commodities. Granger introduction of futures trading in wheat, Data series on Comdex, a comprehensive causality tests and generalised forecast turmeric, sugar, cotton, raw jute and index on all groups of commodities on error variance decompositions showed soy oil. However, a weak destabilising MCX was collected from the web site of MCX. that an unexpected and unidirectional effect was found from futures to spot in Data were analysed using various

the case of wheat and raw methods including simple percentages,

Table 2: Commodity-wise Trends in Volume Traded

jute. Further, results of percentage variations, correlations, regres

2005 Share 2006 Share 2007 Share Growth January-% January-% January-% in 2006

Granger causality tests sion analysis and the Granger causality test.

December December March

indicated that the unex-

Precious metals: 4,87,333 32.1 16,81,220 49.7 3,86,523 42.7 245

pected increase in fu-Linear Regression: The following lin-

Gold 2,06,398 13.6 10,45,573 30.9 2,04,261 22.6 407

tures activity in terms of ear regression was used to study factors

Silver 2,80,935 18.5 6,35,647 18.8 1,82,263 20.1 126 Base metals: 3916 0.3 2,33,449 6.9 1,73,353 19.1 5,861 increases in volumes influencing urad prices: Aluminium 31 5,072 0.2 3,966 0.4 16,181 and open interest has urad=α+β1*uradt–1+β2* gram+β3* pulses

t t t

Copper 3,544 0.2 1,76,941 5.2 99,909 11.0 4,893 led to an increase in cash + β4* allcommo +β5* foodgrains


Lead 337 1,110 0.1 price volatility in all the + β6*dummy ...(1) Nickel 230 946 11,996 1.3 311 commodities listed. The gram= α + β1* gram t-1 + β2* urad

t t

Tin 111 193 335 0.0 74

study has confirmed the + β3*pulses + β4* allcommo


Zinc 49,960 1.5 56,037 6.2

conception of the desta-+ β5 * foodgrains + β6 * dummy ...(2)


Ferrous 5,099 0.3 7,947 0.2 1,043 0.1 56

bilising effect of futures The prices in their first differentials have

Energy: 1,44,888 9.5 1,72,362 5.1 98,978 10.9 19

trading on agricultural been used for the study. We strongly be-

Crude oil 1,44,288 9.5 1,37,372 4.1 87,181 9.6 -5

commodity prices. lieve that there is an economic rationality

Natural gas 32,625 1.0 11,793 1.3

Thus, recent studies for establishing a relationship between

Polymers 489 1,229 -151 Agri: 8,79,149 57.8 12,85,372 38.0 2,45,426 27.1 46 have shown mixed results the price of urad and other pulses. Th


Cereal 10,664 0.7 34,543 1.0 2,749 0.3 224 indicating that futures clusion of prices of foodgrain as well as all Fibre 9,327 0.6 7,602 0.2 2,401 0.3 -18 trading has either driv-commodities in the regression is to under-Oil complex 1,04,548 6.9 1,28,854 3.8 46,971 5.2 23 en up or brought down stand the effect of a general price rise in Pulses19.8 5,16,137 5.0 volatilities in spot prices foodgrains and other components of the

3,00,699 15.3 45,713 72 Plantations 4,828 0.3 6,944 0.2 4,005 0.4 44

depending on the com-WPI. The price of commodity like urad is

Spices 27,606 1.8 1,46,482 4.3 82,972 9.2 431

modities and underlying dependent on the price of other substitute

Others 4,21,477 27.7 4,44,808 13.2 60,615 6.7 6

market conditions. items such as pulses and chana as well

Total 15,20,385 100 33,80,350 100 9,05,323 100 122

as its own previous prices. The price rise

Source: Forward Markets Commission, NCDEX and MCX, Mumbai.

Data and may be a general rise due to increases in

Table 3: Trends in Turnover of Agricultural Commodities (Rs crore)

Methodology the price of other food items and other

January-Share January-Share January-Share December 2005 (%) December 2006 (%) March 2007 (%)

Spot price data for the commodities. Since we have used weekly Agriculture 8,79,149.1 100.0 12,85,372.0 100.0 2,45,426 100.0 analysis of trends in pre-prices, we have used the previous week’s Guarseed 3,37,844.9 38.4 3,26,344.4 25.4 35,766 14.6

and post-futures trading price of the commodity in the regression

Gram 1,66,587.5 18.9 3,41,035.7 26.5 40,145 16.4

were not available from equation. The dummy variable is used to

Urad 1,06,012.3 12.1 1,45,333.9 11.3 3,004 1.2

any authenticated and find out whether the event of introducing

Mentha oil 19,354.3 2.2 63,041.6 4.9 11,241 4.6

reliable sources, particu-futures contract had any impact on the

Tur 24,055.8 2.7 25,696.7 2.0 2,529 1.0

larly for the period prior price movements of the commodities.

Soy oil 67,204.2 7.6 85,861.6 6.7 28,331 11.5

to futures trading. Hence, The dummy variable will take the value

Guargum 35,301.8 4.0 15,980.5 1.2 1,458 0.6 Soy seed 14,493.9 1.6 22,145.4 1.7 8,620 3.5

the wholesale price “0” or “1” corresponding to the period of Pepper 9,213.0 1.0 60,905.8 4.7 31,891 13.0 index (WPI) series, com-presence or absence of futures trading Jeera 10,879.8 1.2 33,124.5 2.6 38,241 15.6 piled and published by respectively.

Wheat 9,072.7 1.0 28,828.8 2.2 1,409 0.6 the Central Statistical R chillies 3,431.3 0.4 35,432.6 2.8 6,805 2.8 Organisation were taken Granger Causality Test: Testing causal

Source: Market Review, FMC (

for the commodities un-relations between two stationary series X


increase in futures trading volume der study covering a period from Janu-and Y (in bi-variate case) can be based on


causes an increase in cash price volatility. ary 2001 to August 2007. the following two equations:


Further, they found a weak causal asso-Apart from prices, commodity-wise Y = α ...(3)

to +Σk=1 α k Y t-k + Σk=1 βkXt-k +ut

ciation between open interest and cash futures volumes were collected from the

p p

X= ϕX t–k + v...(4)

price volatility. web sites of the respective exchanges t o +Σk=1ϕkYt–k + Σk=1økt

Sahi (2006) studied the impact of intro-and the Forward Markets Commission where p is a suitably chosen positive ducing futures contracts on the volatility (FMC). Data on indices of various financial integer; αk‘s and βk‘s, k = 0, 1, …, p are of the underlying commodity in India. markets along with commodity futures constants; and u and v are usual distur


Empirical results suggest that the nature were collected from secondary sources bance terms with zero means and finite of volatility has not changed with the such as web sites of stock exchanges, etc. variances. The null hypothesis that X


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does not Granger-cause Y is not accepted


if the βk’s, k>0 in equation (2) are jointly and significantly different from zero using a standard joint test (for example, an Ftest). Similarly, Y Granger-causes X if the


s, k>0 coefficients in equation (3) are


jointly different from zero [Nath 2003].

This test will help determine if there is a bi-directional impact flowing from one to other prices and vice versa. Apart from prices, the test is also used for understanding the relationship between volumes and prices of urad and gram.

Overview of Commodity Futures

This section provides an overview of commodity prices.

Exchanges: The multi-commodity exchanges in India are not specialised as those in the US and Europe and offer futures contracts uniformly in all commodities. Nevertheless, the turnover on the domestic exchanges has been concentrated in a few commodities exclusive to each exchange and thereby, made them appear specialised. The turnover of agricultural commodities futures is dominant on the NCDEX and NMCE, while that of non- agricultural commodities is prevalent on the MCX as presented in Appendix Table 1 (p 23).

Commodities: At present, futures contracts are available for about 94 commodities across the three national exchanges. The total number of commodities traded on futures exchanges are categorised into two major groups’, viz, agricultural and non-agricultural commodities. Non- agricultural commodities are further

Chart 1: Trends in Prices and Futures Volumes

categorised into bullion/precious metals, base metals, energy and polymer products. Agricultural commodities are further categorised into cereals, oils and oilseeds, pulses, fibres, plantations, spices and others that include guarseed, mentha oil, potato, sugar, etc. Nevertheless, of all the contracts available, only a few have been traded actively and gained major volumes including gold, silver, copper, crude oil, guar seed, gram, urad and mentha oil.

Trends in volumes during the past two and half years presented in Table 2 (p 19), indicate that volumes in the agricultural commodities group dominated the total trade in 2005 contributing for about 58 per cent followed by bullion with about 30 per cent share. However, the trends were reversed in 2006, with a fall in the share of agricultural commodities to 38 per cent and an increase in the share of bullion to

Table 4: Average Changes and Volatility in Prices

Urad Gram Pulses Foodgrains Commodities

Average change in prices P-I -0.168 -0.054 -0.012 0.023 0.093

P-II 0.463 0.390 0.303 0.140 0.079

P-III -0.296 -0.450 -0.211 0.026 0.073

Standard deviation (volatility) P-I 1.716 1.226 0.827 0.389 0.202

P-II 2.544 1.306 1.174 0.349 0.215

P-III 1.756 1.284 0.784 0.336 0.157

about 50 per cent. The share of agricultural commodities has further fallen to 27 per cent in 2007 (April-March) while that of base metals reached 19 per cent. The share of energy futures has also gone up to 10 per cent whereas the share of bullion has declined to 43 per cent in 2007.

Trading in Agricultural Commodity Futures: Indian commodity exchanges have
0 2500 5000 7500 10000 12500 15000 17500 20000 50 100 150 200 250 300 350 400 450 500 1/01 5/01 9/01 1/02 5/02 9/02 1/03 5/03 9/03 1/04 5/04 8/04 12/04 4/05 8/05 12/05 4/06 8/06 12/06 4/07 8/07 Price indexVolumes (000) MT Gram Pulses Urad Gram volume Urad volume

the largest number of futures contracts in agricultural commodities compared to any other exchange in the world. Among a large number of agricultural commodities traded on futures exchanges, major volumes have been contributed by only four to five commodities including guar, gram, urad and to some extent soya oil. Further, based on the data available from January 2005, it is evident that only volumes of guar seed, gram and to some extent, soya oil were persistent throughout the period, while that of other largely traded commodities including urad, mentha oil, pepper and jeera were shifting from one to another following the regulatory measures such as additional and special margins, positions limits, compulsory delivery, etc. On the other hand, among foodgrains, wheat and tur gained about 3 per cent of total volumes in agricultural category and that too for only a short period.

Thus, urad and gram have contributed towards a major portion of volumes among foodgrains. Considering the large share contributing to futures volumes and in the light of the ban on futures trade in foodgrains (urad, tur, wheat and rice), urad and gram were selected as cases for detailed study.

Influence on Spot Prices

The impact of futures trading on spot prices has been studied in terms of trends and volatilities in prices during the periods before and after the introduction of futures trading. In this section, the results of a series of statistical and econometric tests conducted to check the influence of futures on spot are presented.

Trends in Spot Prices: Trends in spot prices during pre- and post-futures trading periods were studied in order to determine whether futures trading has any influence on the spot prices of urad and gram. Prices (WPI) of the selected commodities were juxtaposed with volumes traded on futures as depicted in Chart 1.

Though the urad futures contract was introduced in July 2004, it started actively trading from January 2005 onwards. However, there was a spurt in futures trading volumes after September 2005. Coinciding with this, there was a distinct

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rise in the prices of urad and consequently, fluctuations. However, spot prices of gram Thus, it is evident from Chart 1 that that of pulses as a whole. But no significant continued to rise steadily until November there was a distinct increase in urad prices change in the production of urad was 2006 and started declining thereafter. in the period of futures trading. Further, noticed in the corresponding period Table 5: Results of Two Sample T-tests the steep increase in urad prices has also

(Appendix Table 2, p 23).

Nevertheless, the volumes dipped sharply after April 2006 on account of the regulatory measures taken by the exchanges under the directions of the FMC.

P I & P II P II & P III P I & P III F-stat t-stat F-stat t-stat F-stat t-stat

Urad 0.457* 0.000 -2.434* 0.008 0.476** -1.910** 0.961 0.013 0.031 0.416 0.371 0.356
Gram 0.886 0.226 -3.061* 0.001 0.967 0.480 3.131* 0.916 1.606** 0.001 0.353 0.055
Pulses 0.499* 0.000 -2.601* 0.005 0.446*0.008 -2.861* 0.8930.003 0.376 0.663 0.254

pushed prices of total pulses. Further, the spurt in spot prices was observed in the post-futures trading period even in the case of gram though less distinct compared to urad. However, we do not know if the observed increase in urad and gram prices was precisely due to the intro duction of futures contracts or not. In order to test the statistical significance of the apparent trends, correlation and regression analyses were carried out and the results are presented in the next section.

Price Changes and Volatilities: In order to find the impact of futures trading on price volatilities, the entire period was divided into three, viz, period I (PI) – covers The measure included a raise in margins to an extent of about 45 per cent in the form of additional, special and initial margins. Subsequently, in April 2006 the FMC directed the exchanges to stop introducing fresh contracts of the existing urad futures that allow trade exclusively in imported (Burmese) variety of urad.

However, acting on the directions of the FMC, the exchanges have once again introduced the modified contracts of urad on July 14, 2006. The modified urad futures allowed trade in the desi as well as imported varieties. Consequently, the volumes have once again moved up moderately in the subsequent months. However, before the volumes could pick up further momentum, rumours of the ban turned the market participants apprehensive and cautious and lead to a

(1) Two sample t-tests of unequal variances were conducted when the F

turned statistically significant or else two sample t-tests of equal variance were conducted. (2) Figures in italics indicate the ‘p’ values.

Table 6: Results of Regression for Urad

Variables Coefficient Std Error t-statistic Probability Significance

C -0.145 0.085 -1.709 0.088

Urad (-1) -0.093 0.031 -3.006 0.003 *

Gram -0.615 0.063 -9.793 0.000 *

Pulses 2.076 0.088 23.563 0.000 *

Foodgrains 0.218 0.193 1.130 0.259

All commodities -0.556 0.318 -1.749 0.081

D urad 0.278 0.136 2.041 0.042 **

R-squared 0.688

Adjusted R-squared 0.682

Durbin-Watson statistic 2.187

N 345

* and ** indicate significant at 1% and 5% level

Table 7: Regression Results for Gram

Coefficients t-stat P-value Significance

prior to futures trading (January 2001 to September 2004), period II (PII) – covers active futures trading in urad (October 2004 to January 2007) and period III (PIII)

– covers period after ban of urad futures (February 2007 to August 2007). The mean, standard deviation and coefficient

moderate fall in volumes during Novem-of variation in the three periods for all the

Intercept -0.081 -1.112 0.267 ber and December 2006. Nevertheless, D-gram 0.112 1.116 0.265 variable were calculated.

the ban came into effect from January 23, Gram(-1) 0.028 0.501 0.617 The results as presented in Table 4

2007. Incidentally, urad prices have also Pulses 1.331 16.047 0.000 * (p 20) indicate that the average change in

posted a declining trend from November 2006 onwards.

On the other hand, futures contracts of gram were introduced in April 2004 but gained considerable volumes only after September 2004. Similar to the case of urad, a spurt in volumes was noticed in the case of gram June 2005 onwards with a corre-

FoodAll commodities 0.163 -0.084 1.085 -0.340 0.279 0.734
Urad -0.359 -9.671 0.000 *
R-square 0.526
Adjusted R-square 0.512
Observations 345

* and ** indicate significant at 1% and 5% level.

Table 8: Granger Causality Tests between Volume and Prices

the prices of urad, gram and pulses was negative prior to futures trading and became positive uniformly across the three variables in PII but once again turned negative in PIII. This suggests that the prices of uard, gram and pulses have increased in the period of futures trading in urad and declined in the other two peri

sponding but moderate increase in spot prices though there was no significant change in production (Appendix Table 2). The WPI of gram has crossed the 150 mark in July 2005 after a gap of nearly three years (November 2002) and continued to rise thereafter, though at a slow pace. However, the volumes have shown wide fluctuations corresponding to the regulatory measures. The FMC has started directing the exchanges from early 2006 to impose regulatory measures such imposition of position limits, margins (additional and special), reducing the daily price variation limits, etc, in order to control extreme price

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Null Hypothesis F-statistic Probability Significance

Volume (val) of urad does not Granger cause spot price 3.427 0.002 *

Spot price of urad does not Granger cause volume (val) 0.927 0.475

Volume (Q) of urad does not Granger cause spot price 3.255 0.004 *

Spot price of urad does not Granger cause volume (Q) 0.810 0.563

Volume (val) of gram does not Granger cause spot price 0.714 0.638

Spot price of gram does not Granger cause volume (val) 2.328 0.031 **

Volume (Q) of gram does not Granger cause spot price 0.718 0.635

Spot price of gram does not Granger cause volume (Q) 1.412 0.207

* and ** indicate significant at 1% and 5% level.

ods of pre-futures trading and post-ban of futures trading in urad. On the other hand, the standard deviation of price changes have also increased in PII and declined in PIII, across the three variables and more prominently in case of urad indicating the increase in volatilities.

Further, the observed differences in the means of price changes during the three periods were tested for statistical significance by using two-sample t-tests with equal or unequal variances depending on the results of the F-tests. The results indicated that the average price change in P II was found significantly different from PI as


well as P III in case of urad, gram and pulses. On the other hand, in the case of gram, average change in prices during P III was found significantly different from the first period also.

It is evident that the mean price levels as well as volatilities of urad, gram and pulses were significantly higher in PII where futures trade in urad was present compared to that in other two periods.

Spot Price Variation

This section discusses futures activity and spot price variation.

Detection of Association: Linear regression analysis was carried out to test the statistical significance of the apparent impact of futures trading on the spot prices of urad and gram. In view of the significant associations noticed in correlation analysis, regressions with all the variables including their lags were run. A dummy was introduced to indicate the period of futures trading. Results of the best fit are presented below.

Of all the regressions tried, the following equation turned out to be the best fit for urad. The results (Table 6, p 21) indicate that the coefficients included in the fitted regression equation explained about 68 per cent of the variation in the dependent variable-urad prices. The coefficients of urad with one lag, prices of gram and pulses were found to be significant at the 1 per cent level. However, the negative sign of the

Chart 2: Pattern of Volatility in Prices

coefficient of urad with one lag needs further probe for a precise explanation. One possible reason could be the high volatilities in urad prices as the variables considered were changes and not the actual values.

On the other hand, the dummy variable turned out to be statistically significant at 5 per cent level suggesting that there was a moderate impact on spot prices of urad during the period of futures trading in urad. Thus, the null hypothesis is rejected and the alternate hypothesis that the trading in futures has a moderate influence on spot prices of urad is accepted.

Regression results of gram on the other hand, indicated that only 52 per cent of variation in gram prices was explained by the fitted regression (Table 7, p 21). Further, only the coefficients of urad and pulses were found to be statistically significant at 1 per cent level. The dummy variable bifurcating the pre- and post-futures trading turned out to be statistically in significant suggesting that there was no significant direct impact of futures trading on spot price changes of gram. The apparent increase in prices in the post-futures trading period could be on account of other reasons such as a mismatch in demand and supply.

Thus, the regression analysis gives some clear hints about the influence of futures on spot prices, particularly of urad. However, the signs of the coefficients especially the lag-variables, need further explanation as they have not turned out to be along the expected lines.

Testing Causal Relations: It is evident from the results of the Granger causality

tests that futures volumes had a significant causal impact on spot prices and not vice versa. However, in the case of gram, the causal relation from volumes to prices was not found significant while spot prices found to have a mild causal effect on volumes of gram.

Further, to test the causality among gram, urad, pulses and foodgrains, pair-wise Granger causality tests were conducted on both price changes as well as volatilities.

The results showed that a change in urad has a significant influence on total pulses’ prices and vice versa while that of gram has significant causal influence on urad as well as on pulses. Thus, when there was a steep rise in urad prices during the post-futures trading period, the prices of pulses also went up correspondingly though at a lower pace.

Thus, futures activity in terms of volumes has a positive and significant causal effect

Table 9: Granger Causality Tests for Price Changes

Null Hypothesis F-Prob Signi- statistic ability ficance

Δ Pulses does not Granger cause Δ gram 0.660 0.6196

Δ Gram does not Granger cause Δ pulses 2.721 0.0296 ** Δ Urad does not Granger cause Δ gram 1.367 0.2449

Δ Gam does not Granger cause Δ urad 4.073 0.0031 * Δ Urad does not Granger cause Δ pulses 2.534 0.0401 ** Δ Pulses does not Granger cause Δ urad 5.424 0.0003 *

* and ** indicates significant at 1% and 5% level.

Table 10: Descriptive Statistics and Correlation Coefficients of Volatilities

N Mean Std Dev Minimum Maximum

Vola urad 345 1.99 0.70 0.85 4.01

Vola_gram 345 1.23 0.36 0.58 2.31

Vola_pulses 345 0.94 0.29 0.53 1.93

Vola_foodgrains 345 0.38 0.08 0.19 0.53

Vola_commo 345 0.21 0.05 0.13 0.34

Vola_ Vola_ Vola_ Vola_ Vola_ urad gram pulses food commo

Vola_urad 1

Vola_gram 0.071 1

Vola_pulses 0.803* 0.509* 1

Vola_foodgrains 0.529* 0.292* 0.602*

Vola_all-commo -0.155* -0.498* -0.271* -0.254*

* Indicates significant at 1% level. Vola indicates volatility.

Null Hypothesis F-stat Probability Significance

Table 11: Granger Causality Tests for Price Volatilities

Vola_urad does not Granger
cause Vola_food 2.407 0.0923 ***
Vola_food does not Granger
cause Vola_urad 0.201 0.8179
Vola_pulses does not Granger
cause Vola_gram 1.565 0.2112
Vola_gram does not Granger
cause Vola_pulses 3.440 0.0337 **
Vola_urad does not Granger
cause Vola_pulses 3.191 0.0429 **
Vola_pulses does not Granger
cause Vola_urad 1.002 0.3684

** and *** indicate significant at 5% and 10% level. Vola indicates volatility .

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00.511.522.533.544.51/4/20034/4/20037/4/200310/4/20031/4/20044/4/20047/4/200410/4/20041/4/20054/4/20057/4/200510/4/20051/4/20064/4/20067/4/200610/4/20061/4/20074/4/20077/4/2007 Urad Pulses Gram Food Commodity


on volatilities in spot prices of urad while the same could not be established in case of gram. On the other hand, price changes in urad were caused by changes in both gram and pulses prices, whereas urad prices did not have causal impact on gram prices.

Spillover of Volatilities: Correlations among price volatilities of urad, gram, pulses, foodgrains and all-commodities were studied to check the spillover of volatilities. The volatilities were estimated using an IGARCH method with the decay factor (lambda) of 0.94 and plotted in Chart 2 (p 22), the scale on the x-axis indicates number of weeks starting from the first week of January 2001 to August 2007.

Urad prices have shown significant volatility followed by gram compared to other prices in our study. As apparent in Chart 2, the volatility was higher during the period of futures trading. The same fell after the futures were banned.

Correlation of volatilities indicated that there was a significant spillover of volatilities among pulses and foodgrains. The flow is found to be strong and significant from urad to pulses, pulses to foodgrains, urad to foodgrains and from gram to pulses as presented in Table 10 (p 22).

Results of Granger causality tests of volatilities among the selected variables indicated that there was a spillover of volatilities. The causality tests were found statistically significant from volatilities of urad to foodgrains, gram to pulses and urad to pulses.

Appendix Table 1: Trends in Exchange-wise Turnover

Thus, a significant causal relation from urad to pulses, pulses to gram and gram to urad existed during the period of study, while the correlation of volatilities indicated a mild flow of volatility from urad to foodgrains and gram to pulses prices but a relatively strong spillover from urad to pulses and from pulses to foodgrains.


Only a few commodities among a large number of agricultural commodities listed on domestic multi-commodity futures exchanges have contributed towards a major chunk of volumes. Most of these largely traded commodities such as guar, urad, mentha oil, jeera, etc, have a relatively small physical market and the trading activity in these commodities has been shifting from one to the other following the imposition of regulatory measures.

A series of tests indicated that spot prices of urad and their volatilities have posted significant increases during the period of futures trading. A corresponding but relatively slow increase in the prices of total pulses was observed as a result of the significant causal association existed between urad and pulses. Although gram prices too have posted a moderate increase in the post-futures trading period, the impact was not found to be statistically significant. Futures activity has a significant and direct causal influence on urad prices and volatilities whereas the same has not been statistically significant in the case of gram. Nevertheless, the average price changes and volatilities have increased during the period of futures trading in case of urad, gram and total pulses.

Thus, the argument of futures activity causing an increase in price volatilities is found to be true in the case of urad though enough statistical evidence to that extent could not be found in case of gram. Although there was a mild spillover of volatilities from urad to foodgrains, the flow did not seem to extend to all commodities. Hence, the proposition of futures trading contributing to an increase in inflation (WPI) appears to have no merit in the present context, considering the absence of a direct causal relationship between prices of pulses (urad and gram) and all commodities.


Dasgupta, Basab (2004): ‘Role of Commodity Futures Market in Spot Price Stabilisation: Production and Inventory Decisions with Reference to India’, Indian Economic Review, Vol 39, No 2.

FMC (2007): ‘Market Review’, Forward Markets Commission,

Kamara, A (1982), ‘Issues in Futures Markets: A Survey’, Journal of Futures Markets, Vol 2, pp 261-94.

MCX (2007): ‘Market Data, Multi-Commodity Exchange of India Limited’,

Nath, Golaka C (2003): ‘Inter-linkages among Global Equity Markets – A Cointegration Approach’, Decision, Vol 30, No 2, pp 77-108.

NCDEX (2007): ‘Market Data’,

Sahi, Gurpreet S (2006): ‘Influence of Commodity Derivatives on Volatility of Underlying’, available at SSRN: .

Singh, Jatinder Bir (2000): ‘Futures Markets and Price Stabilisation – Evidence from Indian Hessian Market’, http://www. se/EASAS papers/8Jatinder Singh.pdf

Yang Jian, R Brian Balyeat and David J Leatham (2005): ‘Futures Trading Activity and Commodity Cash Price Volatility’, Journal of Business Finance Accounting, Vol 32, Nos 1 and 2, pp 297-323.

AppendixTable 2: Trends in Production of

Value (Rs Crore) Share (%) Urad and Chana (million tonnes)
2005 2006 2007 2005 2006 2007 Urad Gram
(January-March) (January-March) Kharif Rabi Total Rabi
MCX (market share) 41.7 59.9 74.1 1995-96 0.9 0.4 1.3 5.0
Metals 3,91,693 16,87,759 5,30,345 61.8 83.3 79.0 1996-97 0.9 0.4 1.4 5.6
Energy 1,41,327 1,68,319.8 97,653 22.3 8.3 14.6 1997-98 0.9 0.4 1.4 6.1
Agriculture 1,00,303 1,69,589.7 43,027 15.8 8.4 6.4 1998-99 1.0 0.4 1.4 6.8
Total 6,33,324 20,25,668 6,71,027 100.0 100.0 100.0 1999-2000 0.9 0.4 1.3 5.1
NCDEX (market share) 57.5 36.8 24.7 2000-01 0.8 0.5 1.3 3.9
Metals 1,04,654 2,26,741 26,884 12.0 18.2 12.0 2001-02 1.0 0.5 1.5 5.5
Energy 3,560 4,042.3 1,323 0.4 0.3 0.6 2002-03 1.0 0.5 1.5 4.2
Agriculture 7,66,712 10,12,555 1,95,018 87.6 81.4 87.4 2003-04 1.2 0.3 1.5 5.7
Total 8,74,927 12,43,339 2,23,226 100.0 100.0 100.0
NMCE (market share) 0.8 3.3 1.2 2004-05 1.0 0.4 1.3 5.5
Metals 8,116 3,689 7.3 33.3 2005-06 0.9 0.4 1.3 5.6
Agriculture 12,133 1,03,226 7,379 100.0 92.7 66.7 2006-07 1.0 0.5 1.5 6.3
Total 12,133 1,11,343 11,068 100.0 100.0 100.0 2007-08 (AE) 1.1
AE = advanced estimates.
Grand total 15,20,385 33,80,350 9,05,322.7 Source: Advanced estimates released by Department of Economics and
Source: Market Review, Forward Markets Commission, Mumbai ( Statistics, Ministry of Agriculture, GoI.
Economic & Political Weekly january 19, 2008 23

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