ifs final project by mam

8/8/2019 Ifs Final Project by Mam

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PRELIMINARYDRAFT TO BEQUOTED WITHPERMISSIONFROM AUTHORS

Commodity Derivative Market and its Impact on Spot MarketGolaka C Nath and Thulasamma LingareddyAbstractNation wide trading in commodities futures in India was introduced for manyagriculturalcommodities and later trading in futures was banned for few commoditiesdue to pressureon spot prices of these commodities. The paper studies the impact of futurestrading inthree important commodities which were banned by the government fromtrading in

futures and their impact on spot prices. The study uses simple linearregressions,correlations, Granger Causality tests to find the answer. The study also triesto find if theseasonal/cyclical fluctuations in these commodities prices have beenaffected by theintroduction of futures in those commodities. Hodrick-Prescott filter is usedtodifferentiate the general trend and seasonal/cyclical fluctuations in prices.The finds thatin India future trading in the selected commodities had apparently led to

increase inprices of commodities like urad but the same may not be statisticallytrue for othercommodities. However the study finds that introduction of futures inselevctedcommodities have not helped in reducing seasonal/cyclical fluctuations inprices. It alsofinds that futures have increased the volatilities in the spot market for someof thecommodities.This Draft: January 2008

Contact: [email protected]** [email protected]) The views expressed in this paper are personal views ofauthors and notnecessarily the views of the organization where they work.

Commodity Derivative Market and its Impact on Spot MarketA well developed spot commodities market with pure commercial interesthelps in


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discovering the true price of commodities. An effective spot market is abarometer forefficient pricing mechanism as it is the market which is made of directparticipation fromfarmers/producers, intermediaries, wholesalers, consumers, investors, etc.

However, spotmarket will heavily depend on physical market infrastructure as well as costof movinggoods from one place to another, tax rate applicable to the particularcommodity, etc.Traditionally agricultural commodities in India are in the domain of federalstates andeach state has its own tax and relief package for the commodities.Agricultural marketinghas also been in the domain of federal Sates. Organised marketing ofagricultural

commodities has been promoted in the country through a network ofregulated markets.Most of the federal States have enacted APMC Act to provide for regulationofagricultural produce markets. While by the end of 1950, there were 286regulatedmarkets in the country, the same is 7,521 as of 31.3.2005. Besides, thecountry has27,294 rural periodical markets, about 15 per cent of which function underthe ambit ofregulation. These regulated markets have helped in mitigating some of the

problems, butthe rural markets by and large remained out of its developmental ambit.Agriculturesector needs well functioning markets to drive growth, employment andeconomicprosperity in rural areas of the country. In order to provide dynamism andefficiency intothe marketing system, large investments are required for the developmentof post harvest

and cold chain infrastructure nearer to the farmers field(reason whycyclical prices volatility has not reduced). Enabling policies need to be putin place to encourage procurement of agricultural commodities directly fromfarmers toestablish linkage between the farm production and the retail chain and foodprocessingindustries. Amendment to the State APMC Act for deregulation of marketingsystem in


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the country is required to promote investment in marketing infrastructureand to facilitatea national integrated market. However, at present, the spot market remainsfragmentedand State specific and rural markets which contribute to the lion share of

physicalmarketing infrastructure are projected as a market that enables movementof agriculturalproduct directly to consumers from producers.In the back drop of this, commodities derivative markets with nation-wideconnectivitywere introduced in India in 2003. Commodities derivatives market is not newin India butthe scope was limited in older version of commodities derivatives market.The oldercommodities derivatives markets were restricted to few traders and had very

littleactivity. The new generation commodities derivative markets brought newtechnologythrough which trading was conducted for various commodities includingagriculturalcommodities. The new commodities derivative markets started with theoption of cashand physical deliveries but brokers have opted for cash settlement andhence physicaldelivery is negligible. This fact supports the theory of limited physical marketinfrastructure to support cost effective physical deliveries. Further, majority

of farmers inIndia traditionally produce mainly for consumption and hence farmers havenot generallyparticipated in commodities derivative markets. Today, commoditiesderivative marketsin India are dominated by speculating traders and brokers.A well-developed and effective commodity derivatives market facilitatesprice discoveryand thereby reduces price risk associated with extreme seasonal variationsin demand andsupply of commodities. Futures prices are generally referred as predictors of

future spotprices (Samuelson, 1965) and tend to provide direction to spot pricesthereby helping inprice discovery as well as minimization of price fluctuations. Hence, pricedeterminationin derivatives markets becomes crucial as it sends signals to spot markets ofthe


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underlying commodities and the efficiency of a futures exchange dependsupon theirability to ensure that the prices of the contracts traded on the exchangereflect supply anddemand (World Bank, 1996). If the futures prices do not reflect the

prevailing demandsupplysituation due to any reason then they may tend to disseminate wrong signalsto thespot markets and thereby lead to increase in price risk. In addition, increasein price riskcould be witnessed in the conditions of deficit supply of commodities. Asimilar concernwas expressed by the Khusro Committee in its reportwhen everyone is expecting a price rise, both trend wise and seasonally, itmaybe thought that there are no dissenting opinions. All opinions would seem to

converge over a price rise. It is thought that under these circumstances ifspeculators enter the futures market, they would also be buyers rather thansellersand their buying activity may further aggravate the price rise. The futurespriceswill then stand above the spot prices and would be rising over time (GoI,1980).Govt. of India decided to suspend the futures trading in urad, turand wheatin early 2007when the spot prices spurted significantly after introduction of futurescontracts in these

agricultural products. It was suspected that the futures trading in thesecommodities hadled to increase in price risk of the underlying commodities and therebycontributing toinflationary pressure.Commodity Derivatives in IndiaCommodity derivatives markets in India had a long history of more than aCentury sincethe inception of Bombay Cotton Trade Association Ltd in 1875. Although theyflourishedafter the independence particularly in the early 1960s, the shortages

cropped up in themid 1960s due to the war in 1965 and natural calamities, has led to ban offutures tradingin 1966 in most of the commodities except pepper and turmeric.Subsequently, based onthe recommendation of the A. M. Khusro Committee (1980) futures trading insome


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commodities like gur, potatoes and castorseed was permitted in the early1980s.Following this, the Kabra Committee (1993) recommended to permit futuresin 17commodities and unanimously opined against granting permission for

futures in wheat,pulses, nonbasmati rice, tea, coffee, dry chillies, maize, vanaspati and sugar,on the basisof a case-by-case review of the suitability of each commodity in the light ofits presentand likely position in the coming years (Kabra, 2007).Nevertheless, the government issued notification on 1.4.2003 stating thatFutures tradingcan be conducted in any commodity subject to the approval /recognition oftheGovernment of India. However, 91 commodities are in the regulated list and

they havebeen notified under section 15 of the Forward Contracts (Regulation) Act.Forwardtrading in these commodities can be conducted only between, with, orthrough membersof recognized associations. The commodities other than those areconventionally referredto as 'Free' commodities and forward trading in these commodities can beorganized byany association after obtaining a certificate of Registration from ForwardMarkets

Commission. But, options and other derivative instruments are still notpermitted fortrade in Indian commodity markets.Further, the government has also granted permission to set up modernnationalcommodity exchanges in 2002. This step has led to the revival of futuresmarkets afternearly 40 years and the national exchanges equipped with moderntechnology helped intaking futures markets to many targeted participants which were possiblyoutside the

domain in the earlier era.As a result, three national level and 21 regional futures exchanges havebecomeoperational providing trading platform for commodity futures. Totalcommodities tradedon futures exchanges can be categorized into two major groups viz.,agricultural and


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non-agricultural commodities. Non-agricultural commodities can be furthercategorizedinto bullion/ precious metals, base metals, energy and polymer productswhileagricultural commodities can be grouped as cereals, oils & oilseeds, pulses,

fibres,plantations, spices and others that include guarseed, mentha oil, potato,sugar etc.,Table 1: Trends in volume trade on futures exchanges2002-03 2003-04 2004-05 2005-06 2006-07

Turnover(Rs. crore)66,530 129363 571759 2134471 3327633Growth(per cent)92.8 94.4 342.0 273.3 55.9Source:Annual Reports, Ministry of Food and Consumer Affairs, Delhi

Volumes on the national exchanges have picked momentum rather quickly(Table 1) andalmost tripled consistently for two years in 2004-05 and 2005-06. Althoughthe growthpersisted in the subsequent period, it has apparently decelerated to about55 per cent in2006-07. Besides this, the functioning of futures markets has also comeunder scrutinyduring 2006-07 due to some aberrations noted in trading of agriculturalcommodities.Subsequently, the government has ordered for delisting of futures contracts

in urad, tur,wheat and rice during January and February 2007 with the suspicion thatfutures tradingin these commodities has been contributing for the rise in prices of theseessential items.Considering these developments in Indian commodities futures market, anattempt ismade to investigate whether the futures trading in the selected commoditieshas led to riseor fall in spot prices and volatilities. Further, the study also tries tounderstand as to

whether introduction of futures in agricultural commodities like wheat, gramand uradhas removed seasonal/cyclical fluctuations in India.The study is presented in four sections. First section provides overview ofIndianCommodities Market, second section provides review of relevant literatureand the third


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one gives the methodology used in the study and the fourth one discussesabout results.I. INDIAN COMMODITIES DERIVATIVES MARKETAll the committees (including Khusro Committee-1980, Kabra-1993 and theWorld

Bank-UNCTAD-1996) that studied the prospects of Indian commodityderivativesmarkets have strongly opposed the introduction of futures trading infoodgrains andsugar in view of the existing government controls and inadequate domesticsupplysituation in case of pulses. The presence of substantial government controlson cerealsmarkets and inadequate domestic supply particularly in case of pulsesrestrict the freemarket movements and thereby hamper price discovery process in those

commodities.Further, there are control price mechanism exists for sugar, rice and wheatwhich aremade available to consumers through public distribution system. Further,there isminimum support price mechanism for procurement in case of the abovefood grainsalong with minimum support price (MSP) for pulses (procurement in case ofpulses isvirtually nil which may be due to lack of adequate supply that forces themarket price to

be higher than the MSP). There are regulatory limitations on imports andexports of thesecommodities. In case of food grains, most of the imports are routed throughGovernmentagencies. Sugar is also covered under free-sale quota declared bygovernment on regularbasis.On the other hand, although the government has allowed futures trading inallcommodities notifying a specific list of 91 as regulated and the rest asfree

commodities, the suitability of a commodity for trading in futures wasclearly describedby the Forward Markets Commission as follows (www.fmc.gov.in/faq).

The market for commodity should be competitive, i.e., there should belargedemand for and supply of the commodity - no individual or group of personsacting in concert should be in a position to influence the demand or supply,and


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consequently the price substantially.

The market for the commodity should be free from substantialgovernmentcontrol. The government intervention may adversely affect the pricediscovery

process. The commodity should have long shelf-life and be capable ofstandardization andgradation.In the light of these arguments, the economic rationale behind theintroduction of futurestrading in food grains, including wheat, rice, gram, urad and tur, wasanalysed andpresented in the following paragraphs.Market size:The first and essential characteristic of a commodity for trading in futures

markets is thesufficiently large market size so that prices cannot be influenced throughpurposivemanipulation of demand and supply of the underlying commodity. Thesupply anddemand data for the selected food grains are presented in Table 2. Sincethere were noprecise estimates for demand, National Sample Survey Organization (NSSO)estimates ofconsumption were used to derive rough estimates of consumption demand.The

consumption estimates were used to understand the balance of demand andsupply.An overview of domestic production of the selected food grains indicatedthat only riceand wheat have sizeable and competitive markets while the markets forpulses appear tobe relatively small with a deficit in domestic supply. The total market size ofurad and turduring the lean season comes to about Rs 50 and Rs 100 crore respectivelyacross all themarkets in the country. In addition, the domestic production of urad and tur

is grosslyinadequate to meet the consumption demand and the imports account forabout 35 percent and 16 per cent of domestic supply respectively. Majority of theseimports comefrom Myanmar on which no authenticated data for demand and supply areavailable.


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Initially, the futures contracts in urad and tur were offered exclusively for theimportedvariety but later modified to allow domestic varities. The total size of themarkets, as canbe seen from Table 2, is still very small.

Table 2: Trends in supply of major pulsesValue of output(TE 2005-06)Production(TE 2005-06)Imports Stocks inoff-season(25%)Per capitaavailability

Per capitaconsumption(2004-05)(Rs Crore) % MMT MMT MMT Kg/year Kg/yearUrad 2089 0.45 1.4 0.5 0.4 0.93 1.2Tur 3995 0.86 2.5 0.4 0.6 2.7 3.6Gram 8612 1.85 5.6 0.3 1.4 3.5 3.1Wheat 46808 10.1 74.9 5.5 18.7 54.1 53.3Rice 74861 16.1 92.7 - 23.2 78.2 68.5Notes:1. Value of output is taken at current prices and the share indicates the proportion ofindividual commodityto the total agricultural output2. TE 2005-06 indicates the triennium ending 2005-06 as the data were taken as averages ofrecent threeyears (2003-04, 2004-05 and 2005-06)Source: Central Statistical Organisation, Department of agriculture and Cooperation, DG ofForeign Trade,GoI and Foreign Agricultural Service, USDA.

Government interventionTraditionally food grains supply and demand has been regulated by thegovernment. TheRBI has a strong control over banks on food credit. However, slowly someof thecommodities are coming out of controls. The controls had been implementedto supportthe farming of the commodities. Minimum Support Price mechanism is invogue for along time which gives the price at which Government will procure food grainsfrom


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farmers. Government has been intervening to a large extent in the wheat,rice and sugarmarkets through procurement and open market sale operations whenever itfelt necessaryas these three commodities are backbone of Public Distribution System in

the country. Aconcern was expressed in the report of the World BankIndian agricultural policies for rice, wheat and sugar do not satisfy any oftheminimum rules, making futures markets impossible. For other agriculturalcommodities, notably cotton, oilseeds and their derived products, severalminimum conditions are satisfied. In their case, spot and futures markets canbeallowed to develop in synergy (World Bank, 1996).Grading & standardizationFacilities of grading and standardization are prerequisite for futures trading

primarily sothat uniformity in the quality of the commodity exists across all the marketsand therebyfacilitating price discovery and delivery processes. Apart from theinfrastructurerequirement for grading and standardization, existence of a large number ofvarietiescreates problems for futures trading as it happened in the case of non-basmati rice.Futures trading in rice could not gain any significant volumes due to thepresence of a

large number of region specific varieties which dont have a commonbenchmark varietybased on which the rest of the varieties can be priced with either premium ordiscount.Further, similar problem but at a lower extent could be noticed in case ofpulses as wellas wheat that creates problems during the physical delivery of thesecommodities.Thus, it is evident from the above mentioned facts that there is littleeconomic rationalefor futures trading in the food grains, more specifically wheat, rice, urad, tur

and chana(gram).Trends in agricultural futures tradingIndian commodity exchanges have the largest number of futures contracts inagriculturalcommodities compared to any other exchange in the world. Among a largenumber of


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agricultural commodities traded on futures exchanges, major volume hasbeencontributed by only four to five commodities including guar, gram, urad andto someextent soya oil. Further, based on the data available from January 2005 it is

evident thatonly volumes of guar seed, gram and to some extent soya oil were persistentthroughoutthe period while that of other largely traded commodities including urad,mentha oil,pepper and jeera were shifting from one to other following the regulatorymeasures suchas additional & special margins, positions limits, compulsory delivery etc.,Table 3: Trends in turnover of agricultural commodities(Rs crore)Jan-Dec 2005 share Jan-Dec 2006 share Jan-Mar 2007 share

Agri 879149.1 100.0 1285372.0 100.0 245426 100.0Guarseed 337844.9 38.4 326344.4 25.4 35766 14.6Gram 166587.5 18.9 341035.7 26.5 40145 16.4Urad 106012.3 12.1 145333.9 11.3 3004 1.2Mentha Oil 19354.3 2.2 63041.6 4.9 11241 4.6Tur All 24055.8 2.7 25696.7 2.0 2529 1.0Soy Oil 67204.2 7.6 85861.6 6.7 28331 11.5Guargum 35301.8 4.0 15980.5 1.2 1458 0.6Soyseed 14493.9 1.6 22145.4 1.7 8620 3.5Pepper 9213.0 1.0 60905.8 4.7 31891 13.0Jeera 10879.8 1.2 33124.5 2.6 38241 15.6

Wheat 9072.7 1.0 28828.8 2.2 1409 0.6R Chillies 3431.3 0.4 35432.6 2.8 6805 2.8Source: Market Review, FMC (www.fmc.gov.in)

On the other hand, wheat and tur gained only about 2-3 per cent of totalvolumes inagricultural category and that too for only a short period. Thus, urad andgram havecontributed for a major portion of volumes among foodgrains.II. LITERATURE REVIEWSurvey of relevant literature on the impact of future trading on spot pricesindicated that

majority of them compared spot market volatility before and after theintroduction offutures trading while some of them have investigated the impact of futuresactivity onspot volatilities.Kamara (1982) compared cash market volatility before and after theintroduction of


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futures trading and found that the introduction of commodity futures tradinggenerallyreduced or at least did not increase cash price volatility.Further, Singh (2000) investigated the hessian cash (spot) price variabilitybefore and

after the introduction of futures trading (1988-1997) in Indian markets usingthemultiplicative dummy variable model and concluded that futures trading hasreduced theprice volatility in the hessian cash market.Slade and Thille (2004) assessed the levels and volatilities (means andstandarddeviations) of the spot prices of the six commodities that were traded on theLondonMetal Exchange in the 1990s. The theories that they examined could begrouped into four

classes. The first considered how productmarket structure and forwardmarket tradingjointly affect the spotmarket game, the second explored the links betweenproductmarket structure and spotprice stability, the third assessed whether forwardtradingdestabilizes spot prices, and the last related the arrival of new information topricevolatility and the volume of trade. They found support for traditional marketstructuremodels of the price level but not of price stability. In addition, increased

forward tradingwas associated with lower prices. Further, although they found a positiverelationshipbetween increased trading and price instability, the link appeared to beindirect via acommon causal factor.Worthington and Helen (2004) examines the relationship between futuresand spotelectricity prices for two of the Australian electricity regions in the NationalElectricityMarket (NEM): namely, New South Wales and Victoria. A generalized

autoregressiveconditional heteroskedasticity (GARCH) model is used to identify themagnitude andsignificance of mean and volatility spillovers from the futures market to thespot market.The results indicated the presence of positive mean spillovers in the NSWmarket for


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peak and off-peak (base load) futures contracts and mean spillovers for theoff-peakVictorian futures market. The large number of significant innovation andvolatilityspillovers between the futures and spot markets indicates the presence of

strong ARCHand GARCH effects. Contrary to evidence from studies in North Americanelectricitymarkets, the results also indicate that Australian electricity spot and futuresprices arestationaryOn the other hand, Yang et al (2005) examined the lead-lag relationshipbetween futurestrading activity and cash price volatility for major agricultural commodities.Grangercausality tests and generalized forecast error variance decompositions

showed that anunexpected and unidirectional increase in futures trading volume drove cashpricevolatility up. Further, a weak causal association between open interest andcash pricevolatility was also established.However, Nitesh (2005) studied the implications of soy oil futures in Indianmarketsusing simple volatility measures and concluded that the futures trading waseffective inreducing seasonal price volatilities but did not brought down daily price

volatilitiessignificantly.Sahi (2006) also studied the impact of introducing futures contracts on thevolatility ofthe underlying commodities in India. Empirical results suggested that thenature ofvolatility did not change with the introduction of futures trading in wheat,turmeric,sugar, cotton, raw jute and soy oil. Nevertheless, a weak destabilizing effectof futures onspot prices was found in case of wheat and raw jute. Further, results of

granger causalitytests indicated that unexpected increase in futures activity in terms of rise involumes andopen interest has caused increase in cash price volatilities in all thecommodities listed.The study has confirmed the notion of destabilizing effect of futures tradingon spotprices of commodity.


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Sahi and Raizad (2006) studied the impact of commodity futures on welfareand inflationin the economy. They have estimated the efficiency of futures using theJohansensCointegration for different forecasting frequencies. Their results suggested

that the wheatfutures were not efficient even in the short term. Further, they concludedthat the pricediscovery was poor and the higher volumes in futures markets had asignificant causalimpact on inflation.III. DATA AND METHODOLOGYThe study covers three important commodities: urad, gram and wheat. Allthesecommodities are used are staple diet in all parts of India though notproduced in all parts

of the country. Spot price data for the analysis of trends in pre and post-futures tradingwere not available from any authenticated and reliable sources particularlyfor the periodprior to futures trading. Hence, the Wholesale Price Index (WPI) series,compiled andpublished by the Central Statistical Organisation (CSO), were taken for thecommoditiesunder study covering a period from January 2001 to August 2007. Apart fromprices,commodity-wise futures volumes were collected from the websites of the

respectiveexchanges and the forward Markets Commission (FMC).Linear RegressionThe following linear regression was used to study factors influencing the spotprices.allcommo foodgrains dummyurad urad gram pulsest tttt t

* * ** * *4 5 6

1 1 2 3

+ +

= + + + + ---1allcommo foodgrains dummygram gram urad pulses


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t tt t tt

* * ** * *4 5 61 2 3 1

+ +

= + + + + ---2allcommo foodgrains dummywheat wheat rice cerealst tt t tt

* * ** * *4 5 6

1 2 3 1

+ +

= + + + + ---3

The prices in their first differentials have been used for the study. Westrongly believethat there is an economic rationality for establishing a relationship betweenthe price ofurad and the price of other pulses. The inclusion of prices of foodgrain aswell as allagricultural commodities in the regression is to understand the effect fromgeneral pricerise in foodgrains and other agricultural commodities. The price ofcommodity like uradis dependent on price of other substitute items like pulses and chana (gram)as well as itsown previous prices. Similarly, the price of wheat is expected to beinfluenced by the

price of rice (a close substitute) and other cereals(like systematic

and unsystematic risks are taken same way the risin prices can be done by increase in prices in allcommodities and also cyclic and seasonal changesdo affect prices also so co-efficient ofdetermination can also be used ). The price rise may be ageneral rise


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due to increase in price of other food items and other commodities. Since wehave usedweekly prices, we have taken the previous weeks price of the commodityinto theregression equation. The dummy variable is used to find out if the event of

introducingfutures contract had any impact on the price movements of thecommodities. The dummyvariable will take the value 0 or 1 corresponding to the period ofpresence or absenceof futures trading respectively.Granger Causality testTesting of causal relations between two stationary series Xt and Yt (in bi-variate case) canbe based on the following two equationst k tp

t k k kp

t k kY= + Y+ X+ u = = _ _0 1 1 t k tpt k k kp

t k kX= + Y+ X+ = = _ _0 1 1Where p is a suitably chosen positive integer; ks and ks, k = 0, 1, , pare constants;and u t and vt are usual disturbance terms with zero means and finitevariances. The null

hypothesis that Xt does not Granger-cause Yt is not accepted if the ks, k>0in equation(2) are jointly and significantly different from zero using a standard joint test(eg. an F

test). Similarly, Yt Granger-causes Xt if the ks, k>0 coefficients in equation(3) arejointly different from zero (Nath, 2003).This test will help to understand if there is a bi-directional impact flowingfrom one toother prices and vice versa. Apart from prices, the test is also used forunderstanding the

relation between volumes and prices of urad, gram and wheat.III. DISCUSSIONS AND RESULTSGeneral Trend and Cyclical and Seasonal Variations of theCommoditiesThe prices of commodities under our study have moved in upward anddownwarddirections depending on many factors the most important being the supplyand demand


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factors for respective commodities. All the three commodities are generallyconsumed inthe final form but a small part of the total production is diverted to makeother foodproducts for commercial use. Since the scope of derived products for

commercial usefrom these three commodities is limited, we strongly believe that thedemand and supplyis purely consumption driven. Further, these three commodities are generallynotexported from India.Like any agricultural product, the commodities under our study faceseasonal/cyclicalfluctuations. The Hodrick-Prescott Filter methodology was used to filter outthe transitorycomponents of the fundamental series. This is used to decompose the

variables in theprices into trend and stationary components, which are respectively inducedby real andnominal shocks. The technique suggests that the real shocks causepermanent changes inprices whereas nominal shocks only cause temporary effects on the realprices. It is thussupposed that if one can observe the values of a series yt through yT and it ispossible to

decompose the series into a trend (t) and a stationary component yt - t,onecan solve a

minimisation problem for the deviation of yt from t. Hence with the followingsum ofsquares, for instance;the problem is selecting the { t } sequence which minimises this sum ofsquares. In the

minimisation problem, is an arbitrary constant reflecting the penalty ofincorporating

fluctuations into the trend. Increasing the value of acts to smooth out thetrend. With

=0, the sum of squares is minimised when yt = t, i.e. the trend is equal toyt itself. Andas yt _ _, then the trend approaches a linear time trend. Intuitively, for largervalues of

, the Hodrick-Prescott decomposition forces the change in the trend i.e.

( t + t) tobe as small as possible.Chart-1 gives the trend component arrived using Hodrick-Prescottdecomposition along


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with the spot prices. The seasonal/cyclical component arrived using Hodrick-Prescottdecomposition is given in Chart-2.125175225275325

37542547513-01-2001 13-01-2002 13-01-2003 13-01-2004 13-01-2005 13-01-2006 13-01-2007Axis Title

Chart - 1: Commodities Prices - Trend Component and Spot Pricesurad gram Pulses food commoTrenUrad TrendGram TrendPulses Trendfoodgrains TrendCommodities

-35-15525456513-01-2001 13-01-2002 13-01-2003 13-01-2004 13-01-2005 13-01-2006 13-01-2007Axis Title

Chart-2 Seasonal /Cyclical Component of Commodity PricesCyclurad Cyclgram Cyclpulses Cyclfood Cyclcommo150

16017018019020021022023024013-01-2001 13-01-2002 13-01-2003 13-01-2004 13-01-2005 13-01-2006 13-01-2007

Spot PricesChart 3 - Trend of Spot Price of food coomoditiesWheat rice cereals food commo

TrenW TrendR TrendC Trendf TrendCom

-12-7-23

81313-01-2001 13-01-2002 13-01-2003 13-01-2004 13-01-2005 13-01-2006 13-01-2007Axis Title

Chart 4 : Seasonal/Cyclical fluctuations in food commoditiesCyWheat Cyrice Cycereals Cyfood Cycommo

Trends in spot prices during pre- and post-futures trading periodsIn order to find the impact of futures trading on price volatilities, the periodof study wasdivided in to three distinct phases:

PI-covers prior to futures trading (Jan 2001 to Sept 2004),

PII-covers futures trading in all the three commodities (Oct 2004 to Jan

2007) and PIII- covers post-ban period (Feb 2007 to Oct 2007).Trends in spot prices during pre- and post-futures trading periods werestudied in order tofind whether the futures trading has any influence on spot prices of urad andgram. Prices(WPI) of the selected commodities were juxtaposed with volumes traded onfutures as


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depicted in Chart 5.Urad: Though urad futures contract was introduced in July 2004 it startedtradingactively from January 2005 onwards. However, there was a spurt in futurestrading

volumes after September 2005. Coinciding this, there was a distinct sharprise in prices ofurad and consequently that of pulses as a whole. But, no significant changein productionof urad was noticed in the corresponding period. There is no data to supportanyabnormal increase in consumption demand for the same.Nevertheless, the volumes dipped sharply from April 2006 on account of themeasurestaken by the exchanges under the directions of FMC. The measures includedthe hike in

margins to an extent of about 45 per cent in the form of additional, specialand initialmargins. Subsequently, the FMC has directed the exchanges in April 2006 tostopintroducing fresh contracts of the existing urad futures that allow tradeexclusively inimported (Burmese) variety of urad.However, on the directions of the FMC, the exchanges once again introducedthemodified contracts of urad on July 14, 2006. The modified urad futuresallowed the trade

in both desi as well as imported varieties. Consequently, the volumes haveonce againmoved moderately up in the subsequent months. However, before thevolumes couldpickup further momentum, rumors of ban turned the market participantsapprehensiveand cautious and lead to a moderate fall in volumes during November andDecember2006. Nevertheless, the ban came into effect from January 23, 2007.Incidentally, uradprices have also posted a declining trend from November 2006 onwards.

Gram: On the other hand, futures contracts of gram were introduced in April2004 butgained considerable volumes only after September 2004. Similar to the caseof urad, aspurt in volumes was noticed in the case of gram as well from June 2005onwards with acorresponding but moderate rise in spot prices though there was nosignificant change in


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production. The WPI of gram has crossed 150 mark in July 2005 after a gapof nearlythree years (November 2002) and continued to rise thereafter though at aslow pace. TheFMC directed the exchanges from the early 2006 to impose regulatory

measures suchimposition of position limits, margins (additional & special), reducing thedaily pricevariation limits etc., in order to control extreme price fluctuations. However,the volumesshowed wide fluctuations corresponding to the regulatory measures. Thespot prices ofgram continued to rise steadily until November 2006 and started decliningthereafter.0

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Chart - 5: Trend in Prices and Futures Volume in Urad and Gramurad gram Pulses commo gram vol urad vol

Wheat: in the case of wheat futures, co movement of futures volumes and

spot price risewas noticed for a very brief period. Wheat contracts were started trading inJuly 2004 butthe volumes remained at about one million MT a month until the later half of2005.However, a spurt in volumes was seen in January 2006 (off-season) with acorrespondingrise in prices. The volumes continued to grow until May 2006 and declinedthereafterwhile the prices receded in April with start of wheat marketing season.Nevertheless, the

volumes of futures trading declined thereafter rather steeply andconsistently despite apersistent rise in prices. Although the spurt in futures volumes was coincidedwith rise inprices for a brief period, the fall in wheat production consistently for twoyears (2004-05and 2005-06 to about 68 million tones from 71 million tones) could havecontributed for


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increase in wheat prices. However, empirical evidence to that extent wasexplored andpresented in the subsequent sections.Tur and Rice: although futures contracts for tur and rice were introduced in2004, no

significant trading activity was noticed and hence detailed analysis could notcarried outfor the same.Nevertheless, it is evident from Chart-5 that there was a distinct rise in uradprices in theperiod of futures trading. Further, the steep rise in urad prices has alsopushed prices ofpulses. Further, the spurt in spot prices was observed in post futures tradingperiod evenin the case of gram though less distinct compared to that in urad. No specificpattern of

association between wheat prices and futures volumes was noticed from thetrendsplotted in Chart 6. In order to test the significance of the apparent trends,furtherstatistical tests such as correlation, regression and granger causality testswere carried outand the results are presented in the following sections.0

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May-07Volume (000 MT)WPIChart 6: Futures volumes vs spot prices-cerealswheat cereals rice commo wheat vol

Spot price variations in the presence and absence of futuresThe results as presented in Table 4 indicated that the average change inprices of urad,gram and pulses was negative prior to futures trading and became positiveuniformlyacross the three variables in PII but once again turned negative in PIII. Thisapparentlysuggests that the prices of uard, gram and pulses have increased in theperiod whenfutures trading were allowed in these commodities and declined in the othertwo periodsof pre-futures trading and post-ban of futures trading in urad. Similar resultswere foundin case of wheat. The average change was distinctly higher during the periodof futurestrading (PII) than that in PI and PIII. The standard deviation of price changeshave alsogone up in PII and declined in PIII, across the three variables and moreprominently incase of urad indicating the increase in volatilities.Table 4: Average changes and volatilities in pricesPeriod Urad Gram Pulses Wheat Cereals Food

grainsCommoditiesAverage change in pricesPanel I P-I -0.168 -0.054 -0.012 0.023 0.027 0.023 0.093Panel II P-II 0.463 0.39 0.303 0.179 0.114 0.14 0.079Panel III P-III -0.296 -0.45 -0.211 -0.019 0.083 0.026 0.073Standard Deviation (volatility)Panel I P-I 1.716 1.226 0.827 0.641 0.404 0.389 0.202Panel II P-II 2.544 1.306 1.174 0.847 0.347 0.349 0.215Panel III P-III 1.756 1.284 0.784 0.775 0.3 0.336 0.157PI-Jan 2001 to Sept 2004

PII-Oct 2004 to Jan 2007PIII-Feb 2007 to Oct 2007Further, the results of sample variances (F) tests and two sample t-testsindicated that theobserved increase in average price changes and volatilities in the secondperiod (P-II)compared to the first (P-I) as well as the third period (P-III) were found to bestatistically


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significant in case of urad, gram, wheat, pulses and to some extent cereals.Table 5: Results of two-sample t-testsP-I & PII P-II & P-III P-I & P-IIIF-stat t-Statistic F-stat t-Statistic F-stat t-StatisticUrad 0.457* -2.434* 0.476** -1.910** 0.961 0.371

0.000 0.008 0.013 0.031 0.416 0.356Gram 0.886 -3.061* 0.967 3.131* 0.916 1.6060.226 0.001 0.480 0.001 0.353 0.055Pulses 0.499* -2.601* 0.446* -2.861* 0.893 0.6630.000 0.005 0.008 0.003 0.376 0.254Wheat 0.572* -1.773** 0.837 1.303 1.463** 0.3200.000 0.039 0.267 0.097 0.052 0.375Cereals 1.352** -2.058** 0.745 0.510 0.551** 0.9880.033 0.020 0.148 0.305 0.016 0.1631. Two sample t-tests of unequal variances were conducted when the Fturned

statistically significant or else two sample t-tests of equal variance wereconducted2. * and ** indicates significant at 1% and 5% level.3. Figures in italics indicate the _ valuesThus, the average price levels as well as volatilities of urad, gram, wheat andconsequently pulses and cereals were significantly higher in the periodwhere futurestrade in all the three commodities was allowed.Impact of Futures on Spot pricesLinear regression analysis was carried out to test the statistical significanceof the

apparent impact of futures trading on spot prices of urad, wheat and gram.In view of thesignificant associations noticed in correlation analysis, regressions were triedwith all thevariables including their lags. A dummy was introduced to indicate the periodof futurestrading. Results of the best fit are presented belowUrad:The coefficients of urad with one lag, prices of gram and pulses werefound to besignificant at one per cent level (Table 6). However, the negative sign ofthe coefficient

of urad with one lag needs further probe for a precise explanation. Onepossible reasoncould be the high volatilities in urad prices as the variables considered werechanges andnot the actual values.Table 6: Results of regression for uradVariables Coefficient Std. Error t-Statistic Prob. SignificanceIntercept -0.145 0.085 -1.709 0.088


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* indicates significant at 1% levelR-squared 0.900166Adjusted R-squared 0.898383Durbin-Watsonstatistic 1.370383

n 344We find that the seasonal/cyclical fluctuations is not affected by theintroduction futuresi.e. futures have not helped the cyclical/seasonal fluctuations in urad (Table-8). The resultis similar in case of gram (Table-9).Table 9: Results of regression for Seasonal/Cyclical Fluctuations inGramVariables Coefficient Std.Errort-

StatisticProb. SignificanceC -0.01646 0.141126 -0.11663 0.9072LAGCYGRAM 0.78274 0.022895 34.18777 0 *CYURAD -0.10247 0.01933 -5.3013 0 *CYFOOD 0.084549 0.072121 1.172323 0.2419

CYPULSES 0.461531 0.060997 7.566408 0 *CYCOMMO 0.135523 0.109558 1.236991 0.217DUMMY -0.16131 0.2067 -0.7804 0.4357

R-squared 0.953421Adjusted R-squared 0.952589

Durbin-Watsonstatistic 1.492494n 344We also did the similar exercise for wheat and found that the results aresimilar to gramand urad (Table 10).Table 10: Results of regression for Seasonal/Cyclical Fluctuations inWheatVariables Coefficient Std.Errort-

StatisticProb. SignificanceC 0.029333 0.0862 0.340286 0.7339

LAGW 0.591375 0.030487 19.39787 0 *CYFOOD 0.527844 0.110018 4.7978 0 *CYCOMMO -0.18311 0.074073 -2.47206 0.0139CYCEREALS 0.659783 0.113346 5.82097 0 *CYRICE -0.60923 0.056219 -10.8368 0 *


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DWHEAT -0.10525 0.126168 -0.8342 0.4048

* indicates significant in 1%R-squared 0.947148Adjusted R-squared 0.946205Durbin-Watson

statistic 1.022307n 344Results of Granger causality testsFutures activity-Spot Prices: It is evident from the results of Grangercausality tests(Table-11) that futures volumes had a significant causal impact on spotprices in case ofwheat and urad. However, in case of gram the causal relation from volumesto prices wasnot found significant while spot prices found to have a mild causal effect onvolumes of

gram.Table 11: Results Granger causality tests between volumes and pricesNull Hypothesis: F-Statistic Prob. significanceVolume of URAD ----->Spot price 3.427 0.002 *Spot price of URAD ----->Volume 0.927 0.475Volume of Gram -----> Spot price 0.714 0.638Spot price of Gram ----->Volume 2.328 0.031 **Spot price of wheat ----->Volume 3.928 0.000 *Volume of wheat ----->Spot price 1.789 0.027 *** and ** indicates significant at 1% and 5% level

Further, to test the causality among gram, urad, pulses and foodgrains, pair-

wise Grangercausality tests were conducted on both price changes as well as volatilities.The results showed (Table 12) that change in urad has a significantinfluence on totalpulses prices and vice-versa while that of gram has significant causalinfluence on urad aswell as on pulses. Thus, when there was a steep rise in urad prices during thepost-futurestrading period, prices of pulses also went up correspondingly though at alower pace.Table 12: Granger causality results for price changes

Null Hypothesis: F-Statistic Prob. Significance_PULSES ----->_GRAM 0.660 0.6196_GRAM ----->_PULSES 2.721 0.0296 **_URAD ----->_GRAM 1.367 0.2449_GRAM ----->_URAD 4.073 0.0031 *_URAD ----->_PULSES 2.534 0.0401 **_PULSES ----->_URAD 5.424 0.0003 *_ wheat ----->_ cereals 0.455 0.841


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_ cereals ----->_ wheat 0.774 0.590* and ** indicates significant at 1% and 5% level

Thus, futures activity in terms of volumes has a positive and significantcausal effect onvolatilities in spot prices of urad and wheat while the same could not be

established incase of gram. On the other hand, price changes in urad were caused bychanges in bothgram and pulses prices whereas urad prices did not have causal impact ongram prices.Spillover of VolatilitiesCorrelations among price volatilities of urad, gram, pulses, foodgrains andallcommoditieswere studied to check the spillover of volatilities. The volatilities wereestimated using an IGARCH method with the decay factor (Lambda) of 0.94and plotted

in Chart 3, the scale on X-axis indicates number of weeks starting from thefirst week ofJanuary 2001 to August 2007.Urad prices have shown significant volatility followed by gram compared toother pricesin our study. As apparent in Chart 7 below, the volatility was higher duringthe period offutures trading. The same came down after the futures were banned.00.511.522.5

33.544.506-01-2001 06-01-2002 06-01-2003 06-01-2004 06-01-2005 06-01-2006 06-01-2007Volatiliyu (%)

Chart 7 : Volatility of Commodities PricesVola_urad Vola_gram Vola_pulses Vola_food Vola_commo

Correlation of volatilities indicated that there was a significant spillover ofvolatilitiesamong pulses and foodgrains. Flow found to be strong and significant fromurad topulses, pulses to foodgrains, urad to foodgrains and from gram to pulses aspresented in

Table 13.Table 13: Descriptive Statistics and Correlation Coefficients of VolatilitiesN Mean Std Minimum MaximumVolatility urad 345 1.99 0.70 0.85 4.01Volatility gram 345 1.23 0.36 0.58 2.31Volatility pulses 345 0.94 0.29 0.53 1.93Volatility foodgrains 345 0.38 0.08 0.19 0.53Volatility commo 345 0.21 0.05 0.13 0.34


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Vola_urad Vola_gram Vola_pulses Vola_food Vola_commoVolatility urad 1Volatility gram 0.071 1Volatility pulses 0.803* 0.509* 1Volatility foodgrains 0.529* 0.292* 0.602* 1

Volatility all-commo -0.155* -0.498* -0.271* -0.254* 1* Indicates significant at one per cent levelVola : indicates volatility

Results of granger causality tests (Table 14) of volatilities among theselected variableindicated that there was a spillover of volatilities. The causality tests werefoundstatistically significant from volatilities of gram to pulses and urad to pulsesbut urad tofoodgrains showed a mild short term relationship.Table 14: Granger causality results for price volatilitiesNull Hypothesis: F-Statistic Prob. significanceVOLA_URAD ----->VOLA_FOOD 2.407 0.0923 ***VOLA_FOOD ----->VOLA_URAD 0.201 0.8179VOLA_PULSES ----->VOLA_GRAM 1.565 0.2112VOLA_GRAM ----->VOLA_PULSES 3.440 0.0337 **VOLA_URAD ----->VOLA_PULSES 3.191 0.0429 **VOLA_PULSES ----->VOLA_URAD 1.002 0.3684** and *** indicates significant at 5% and 10% levelVola : indicates volatility

CONCLUSIONSUsing dummy variables, the study finds that the introduction of futuretrading in theselected commodities had apparently led to increase in price of commoditylike urad butthe same is not true for wheat and gram. The spot prices of all threecommodities understudy have increased in the post futures period though except for urad, thedummyvariables are not found statistically significant. The spot prices of thesecommoditiesdeclined after the ban on futures trading was introduced. However, the pricevolatilityincreased significantly during the period when futures were allowed. Therehas been asharp fall in volatility after the ban of futures in these commodities. Althoughgram pricestoo have posted a moderate rise in the post-futures trading period, theimpact was notfound statistically significant. Although a similar increase was observed incase of wheat,


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steep fall in supply coinciding the same period thus bringing ambiguity in theinference.The study also finds that the introduction of futures in commodities underour study hasnot affected the seasonal/cyclical fluctuations of the commodities under our

study.REFERENCESDasgupta Basab (2004) Role of Commodity Futures Market in Spot PriceStabilization,Production and Inventory Decisions with Reference to India, Indian EconomicReview, Vol. 39, No. 2, 2004FMC (2007) Market Review, Forward Markets Commission, www.fmc.gov.inGoI, Ministry of Civil Supplies (1980): Report of the Committee on ForwardMarkets,(Chairman: A M Khusro), DelhiHodrick, Robert, and Edward C. Prescott (1997), "Postwar U.S. Business

Cycles: AnEmpirical Investigation,"Journal of Money, Credit, and BankingKabra Kamal Nayan (2007) Commodity Futures in India, Economic andPoliticalWeekly, Vol , No , pp 1163-1170Kamara, A. (1982), Issues in Futures Markets: A Survey,Journal of FuturesMarkets,Vol. 2, pp. 26194Margaret E. Slade and Henry Thille (2004) Commodity Spot Prices: An ExploratoryAssessmentof MarketStructure and ForwardTrading Effects,Journal of Economic Literature,

September 7, 2004MCX (2007) Market data, Multi Commodity Exchange of India Limited,www.mcxindia.comNath, Golaka C (2003): Inter-linkages Among Global Equity Markets ACointegrationApproach, Decision, Volume 30, No 2, pp 77-108NCDEX (2007) Market data, National Commodities and Derivatives ExchangeIndiaLimited, www.ncdex.comNitesh Ranjan (2005) Role Of Commodity Exchanges, Futures & Options - ACase Study

On Soya Oil, Occasional paper 46, Department of Economic analysis andresearch,NABARD.Sahi, Gurpreet S. (2006) Influence of Commodity Derivatives on Volatility ofUnderlying" (December 2006). Available atSSRN:http://ssrn.com/abstract=953594Sahi, Gurpreet S. and Raizada, Gaurav, "Commodity Futures MarketEfficiency in India


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and Effect on Inflation" . Available at SSRN: http://ssrn.com/abstract=949161Samuelson, P.A. (1965) "Proof that Properly Anticipated Prices MoveRandomly,"Industrial Management Review, Vol. 6, pp. 41-49.Singh Jatinder Bir (2000) Futures Markets and Price Stabilization - Evidence

from IndianHessian Market, http://www.sasnet.lu.se/EASASpapers/8JatinderSingh.pdfWorld Bank (1996) India Managing Price Risks in India's LiberalizedAgriculture: CanFutures Markets Help?" Report No. 15453-IN, Agriculture and WaterOperationsDivision Country Department II, South Asia Region, World Bank andCommodityDivision, United Nations Conference on Trade and DevelopmentWorthington, Andrew and Higgs, Helen (2004) The Relationship BetweenEnergy Spot

And Futures Prices: Evidence From The Australian Electricity Market . ICFAIJournal of Applied Economics 3(4):pp. 65-82Yang Jian, Brian Balyeat R and David J. Leatham (2005) Futures TradingActivity andCommodity Cash Price Volatility, Journal of Business Finance Accounting, Vol32,Nos 1 & 2, pp. 297-323

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