COMMODITY FUTURES MARKETS IN INDIA: ROLE, PERFORMANCE AND POLICY PARADIGMS
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Date
2011-06
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jau,junagadh
Abstract
The agricultural products’ prices are highly volatile. There is considerable time lag between the time of initial spending and procuring of receipts from the final farm produce. A farmer is highly susceptible to price fluctuations of both farm produce as well as farm inputs. Traditionally, this risk is borne mainly by the producer (sometimes by the government) more than the consumer for a variety of reasons. This has made farmers look for alternatives to mitigate the risk. Futures market is one such option. This 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 problems that are holding back commodity futures from becoming popular amongst the small traders and farming community need to be studied. Research in this area is still in a very nascent stage in the country. Keeping all this in view, the present study has been undertaken, so that the findings would help the policymakers in better decision-making; hence benefiting the market participants at the grassroots level.
Time series data on the wholesale price indices (WPI) from 1999-2009 of selected agricultural commodities were obtained from the website of the Office of Economic Advisor, Ministry of Commerce and Industry, Government of India, New Delhi. The daily spot and futures price data of major agricultural commodities were obtained from the website of Multi Commodity Exchange of India Ltd. (MCX), Mumbai. Four commodities viz. cotton (long staple), cumin, soya oil and tur were studied for a period of five years as per the availability of data. Compound Growth Rate (CGR), Auto Regressive Conditional Heteroscedasticity (ARCH) and Generalised Auto Regressive Conditional Heteroscedasticity (GARCH), Augmented Dickey-Fuller (ADF) unit root test, Johansen’s co-integration test, Vector Error Correction Mechanism (VECM) and Auto Regressive Integrated Moving Average (ARIMA) model were used to achieve the objectives of the study.
Major findings of the study revealed that, the average CGRs for all the commodities viz. cotton (long staple), soya oil and tur except cumin were negative during the pre-futures period and it was positive for all the commodities in the post-futures period. However, it was statistically non-significant during both the periods which proved that introduction of futures trading was not responsible for the increase in growth rate during the post-futures period. The spot and futures price series of cotton (long staple) and soya oil were significantly volatile. While, that of tur was found to be highly volatile and significant. In case of cumin, the spot and futures price series were found to be non-significant and hence, stable.
The results of the Augmented Dickey-Fuller (ADF) unit root test for all the commodities viz. cotton (long staple), cumin, soya oil and tur showed that the level data were non-stationary but their first differences were stationary. This implies the presence of unit root in the spot and futures price series of all the commodities. Hence, both the series were integrated of the order 1 i.e. I(1). Further, the Johansen’s co-integration test revealed that the spot and futures prices series of cotton (long staple), cumin and soya oil were co-integrated while, there was no co-integration in case of tur. The results of vector error correction mechanism (VECM) showed that the causality in case of cotton (long staple) and cumin was bi-directional i.e. both spot and futures prices influenced each other equally. In case of soya oil the spot and futures price series showed divergence, and there was no causality in case of tur as the price series were not co-integrated. It was observed that the following ARIMA models were the most appropriate for forecasting the spot and futures prices of selected agricultural commodities viz. (1,1,0) and (0,1,1) for cotton (long staple); (0,1,1) and (2,1,2) for cumin; (0,1,2) and (1,1,0) for soya oil; and (0,1,0) for both spot as well as futures price of tur.
Expansion of warehousing facilities, strengthening the institutional credit facilities, lifting the ban on futures trading in cereals and pulses, spreading awareness regarding futures trading and its various functions amongst the farming community, dissemination of prices of futures markets and other spot mandis on a larger scale and improvement in the regulatory framework of futures markets in order to bring in more transparency and curb illegal trading are the major suggestions of the study.
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economics