Farmers' Participation in Indian E-commodity D Paul_2011

8/6/2019 Farmers' Participation in Indian E-commodity D Paul_2011

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FARMERS PARTICIPATION IN INDIAN

AGRICULTURAL COMMODITY MARKETS

-FEW AVENUES FOR A BETTER PRICE!

1. INTRODUCTION

Recent years have witnessed significant growth in Indian agricultural commodity markets. This

market, in effect, has notched up phenomenal growth in terms of number of products on offer,

participation, spatial distribution, and volume of trade (Sen et al., 2008). Despite this Indian

farmers have been occupied with a little share in this market. Undeniably, farmers participation

is a central theme to discussion in this article. An attempt has been made to explore a few

avenues to encourage the farmers engagement in Indian commodity markets. Thin literature

base with respect to this has so far been available and there is hardly any comprehensive

framework depicting the modalities and possibilities of participation of Indian farmers,

especially, for a large pool of small and marginal farmers having less than a hectare or two of

operational landholdings. Hence, this article will provide a roadmap to explore the nuances

involved in farmers participation in Indian agricultural commodity markets.

In India, futures market was, albeit, present in some crude form until 19 th century. Bombay

Cotton Trade Association (BCTA) had received paramount importance by virtue of being a

regional exchange in Indian soil in the year 1875. Off and on, many regional commodity

exchanges had established like India Pepper and Spice Trade Association (IPSTA), Kochi, Vijay

Beopar Chambers Ltd.(VBCL), Muzaffaranagar, Kanpur Commodity Exchange (KCE), East

India Jute Association Ltd.(EIJAL), Kolkata to name a few in between 1900s to 1950s. A study

by Naik and Jain in early 21st century seems to be a good attempt as their study meticulously

tried to gauge the performance of regional level commodity exchanges until 2002. In late 2002,commodity futures market underwent a rebirth following the establishment of countrys first

national level demutualised commodity exchange, the National Multi Commodity Exchange

(NMCE, 26th November, 2002). It is noteworthy to mention that recent years have witnessed

significant growth despite the fact that intermittent ban on a few commodities led to arrest the

pace of growth, in essence, trade volume and participation of agents on electronic futures

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platform. Nair (2004) pointed out that the major stumbling block for the development of futures

market is due to the fragmented and unorganised physical or cash markets. Since 2006, efforts

have been channelised to achieve integration between futures and spot commodity markets to a

greater extent. Three national level spot exchanges, the National Spot Exchange (NSPOT)

promoted by the National Commodity and Derivative Exchange (NCDEX), the National Spot

Exchange of India (NSEIL) overseen by the Financial Technology Group (FTG), National

Agricultural Co-operative Marketing Federation (NAFED), and National Agricultural Produce

Marketing Company (NAPMCL) and innovations at futures platform through Exchange of

Futures for Physicals (EFP) and Alternate Settlement Mechanism for Futures (ASMF) are some

of the good precedence perceived to be outcomes of man-made innovations at several occasions

in the country, which augurs well for better price discovery and risk management mechanisms.

Up until now commodity futures market has augmented with significant growth in terms of

product on offer, trade volume which is about Rs. 112 lakh crore, spatial distribution covering

more than 800-900 sub-urban or metros via more than 20,000 terminals, and participation of

members including professional clearing or/and professional clearing-cum-trading members and

institutional clearing members (more than 3000) reported by a study under the chairmanship of

Dr. Abhijit Sen Committee in the year 2008.

In India, significant and relevant literature base on commodity futures have been delimited the

depth of research in commodity futures markets, in general and agricultural commodity futures

markets in particular (Kolamkar, 2003; Kumar and Pandey, 2009; Ramaswami and Singh, 2007;

Raipuria, 2002; Roy, 2008; Thomas, 2003). Several committees had constituted to monitor,

control, and regulate this market at several occasions at the behest of Government of India,

namely, A.D. Shroff Committee (1950), M. L. Dantwala Committee (1966), A.M. Khusro

Committee (1979), K.N. Kabra Committee (1993), Shankarlal Guru Committee (2001),

Habibullah Committee (2003), and lastly Sen Committee (2008). More or less, those

committees recommendations, inevitably, stand out few indicatives with respect to measuring

the efficacy of Indian commodity futures markets, assertions behind low degree of participation

or on contrary, excessive speculation, and implications behind impositions of ban on several

commodities relating to economic fundamentals driven by those commodities and their

underlying policy issues (FTGKMC, 2011).

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Newbery and Stiglitz (1981) argue that futures market provides a partial insurance mechanism to

the farmers' produces as this market is considered to mitigate risk of price of output only rather

than that of value of total produces. Producers are not homogenous in nature and their

expectations are largely varied with respect to nature of produces, floated futures contract

specifications, delivery month, margin money, and over and above, efficiency of the market.

Intuitively, it can be inferred that market integration has improved the process of price formation

and transmission following the establishment of national level commodity exchanges in the post

2003 and until 2010. However, the progress is on anvil to curtail the magnitude of information

asymmetry. Price stabilisation is a matter of concern in this regard, which warrants a critical

review and appraisal with respect to various forms of their merits, say, nonlinearities, disturbance

form, risk response, surplus, partial stabilisation, export earnings, optimization, simulation, and

others (Labys, 1980). At microstructure level, moral hazard and adverse selection have been

mitigated to some extent because of the prudent governance mechanisms, risk compliances, and

transparent reporting systems (Ghosh, 2009). These are, probably, few accepted elucidations of

technology mediated innovations in Indian commodity markets (Bhattacharjee, 2007). The major

issue, yet challenging and persisting, has not been addressed in full-fledged manner. Few

exchanges, namely, the MCX and the NCDEX have showcased few successful cases which have

implemented aggregation models to encourage the farmers participation on futures as well as on

electronic spot platforms (Berg, 2007; Fernandes and Mor, 2009). Still, these initiatives are in

nascent stages. Quite logically, benefits of these innovations have not fully permeated at the

producers or the growers level, who are directly or actively engaged in agriculture. Why,

what, and how-these can be subject to further discussions with special emphasis on producers

participation in electronic-commodity ore-commodity markets. Models will try to narrate the

probable avenues that how these can help to rendering services at producers or farmers level

and to what extent models will be scalable.

The remainder of this article proceeds as follows. Section one illustrates few concepts which are

useful to operationalise the proposed avenues. Section two discusses the model as a case of

triangle by incorporating the principal as the farmer, the exchange, and other agencies including

private players, co-operatives, collateral management agencies (CMAs). Last section concludes.

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SECTION-I

2. FUTURES MARKET: ITS MECHANICS AND INSTRUMENTS

Mechanics of commodity trading has largely been adopted by almost all national level

exchanges, which has been at par with the best practices being reflected on the platform of global

commodity exchanges up until now. Trading, settlement, and delivery-these three integral

processes have hitherto largely been performed by any national level commodity exchange at the

behest of the regulator in India. Novation comes to play here. Contract design, margin money,

mark-to-market, settlement pattern, etc. are parameters underlying principles of market

microstructure which usually provide a performance guarantee being monitored by the exchange

for both the buyer and the seller (Dey and Maitra, 2011). These are put in place for ensuring

liquidity, leverage, and transparency (Kaul, 2007). Few arguments have also illustrated

empirically against the benefits of electronic markets over open outcry practices (Pavaskar,

2008). Market microstructure issues have also being discussed in the realm of Indian commodity

futures markets in particular by Ghosh (2009) and Pavaskar (2009).

2.1. MARGIN MONEY AND PAYOFF

Margin money is an important pre-requisite by providing a gate pass to enter into this market.

For a poor farmer, arrangement of atleast initial margin is difficult, which constitutes about 4%

to 5% of total value of the contract traded on exchange platform. Additional, special,

incremental-these are being charged by the exchange based on trading frequency, contract size,

maintenance of spread (bid-ask) gap, volatility in the market etc. By considering all these

requisites, this is quite impossible for a farmer to reap the benefits by leveraging on futures

markets. Arbitraging or short selling can be an alternative. Aggregators (who aggregate

produce by pooling) can make it possible by participating on behalf of a group of farmers on

exchange platform. From financial angle, margin money raised through collateral has a direct

impact on the number of contracts being purchased. On contrary, margin raised via debt or loan

is inversely proportional to purchased contracts (Bailey, 2005: p.13-15).

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The following equations are written below to show the two probable conditions with their

different likelihoods.

( / ), , ,... 0

[( / ) 1]

1

( / ), , ,... 0

[1 ( / )]

1

. 0

max( 1,0)

min(1 ,0)

m c pn pn i j

m c pn

m k

m pn l pn i j

m l pn

m s

obj

k

s

=

=

=

=

(1), (2), (3)

Where m is amount of margin, c and lare collateral and loan respectively. p is price per contract

and n is the number of contract purchased. (c/pn) is considered as k whereas (l/pn) taken ass.

Objective is to maximize either collateral or minimise loan amount to increase pnamount and

increase the gap between collateral and loan relative to loan amount to calculate actual margin

requirement (expressed in percentage). Similarly, rate of return (%) is calculated on the

difference between payoff and price paid relative to price, which is considered as an opportunity

cost. Maxima and minima functions are presented with respect to the objective function

parameter (3). More formally, as p varies, so does m. Ifp falls, m may fall so low that the

member/broker demands funds from the investor (aggregator in this case) to reduce land raise m.

It is common to require that the actual margin be restored to its initial value (a common practice

in short-selling); although it is possible that investor may be obliged to restore it only to the

maintenance margin threshold. The precise requirement depends on the terms of agreement

between the parties to the transaction and the exchange authorities. Through equation (2), it can

be derived that m varies withp. Ifp increases, m may fall so low that the broker demands funds

from the investor to increase the collateral, c, and thus raise m. Margin money has an implication

on daily settlement mechanism or mark-to-market process. In case of long futures, margin call

indicates a low or less than the previous settled/close price whereas margin call for short

futures indicates an opposite direction, i.e., high or more than the previous close price (through

tick-by-tick basis).

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Telser (1981) argued that initial margin requirements do have a cost attached which is liquidity

cost. Once the margin money is deposited it is no more available to the hedger for any further

purposes and now it renders participant less liquid than before he/she bought or sold futures

contracts. Kalavathi and Shanker (1991) also examined that there is negative impact of initial

margin requirements upon the demand for futures contracts by hedger. The cost of initial margin

is the spread between hedgers borrowing and lending rates. Since a substantial portion of liquid

cash has been deposited for investing any other high yielding assets or purposes, the person has

to borrow again. Thus, the cost of margin is an opportunity cost here. In order to meet liquidity

needs and also to make the opportunity of investing being happened in the presence of margin

money, the hedger has to borrow. This has also been mathematically expressed by Kalavathi and

Shanker (1991). It is assumed that a hedger holds Cunit of spot commodity (longat spot) and

he/she wants to maximise the excess return per unit of risk of the hedged portfolio and

accordingly choosesN, the number of futures contracts to be hedged at the futures by selling N

futures contracts in the commodity exchange (shortfutures). Now,

CP

CS

MFFNSPPC

R

m

h

).'()'( +

=(4)

Where Rh= return from the hedged portfolio over the hedge period; C=number of units of the

spot commodity held; P=price of commodity at spot after the hedge period; P=price of

commodity at spot at the beginning of hedge period; N=number of futures contracts in the

hedged portfolio; S=size of each futures contract F= futures price of futures contracts at the

beginning hedge period; M=initial margin requirement per futures contract and Cm=cost per

rupee of margin money requirement, this could be liquidity or opportunity cost. F=futures price

of futures contracts at the end hedged- period; the cost per rupee of margin requirement is critical

for determining the return from the hedged portfolio of the spot and futures instrument. As Cm is

critically important, it becomes obvious that an increase in margin requirement or high margin

requirements would adversely affect the demand of futures contracts.

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TABLE 1: Contract-wise margin requirement

Source: compiled from the MCX and the NCDEX as on 10 May 2011, data with respect to margin money are

expressed in percentage (%). Bold figures indicate total margin money in %, which shows a sharp contract in termsof margin imposition between two exchanges.

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MCX NCDEX

SymbolExpiry

Date

Initial

Margin

Tender

Margin

Total

Margi

n

SymbolExpiry

Date

Initial

Margin

Exposure

Margin

Total

Margin

ALMOND 30-Jun 5 0 5 BADAM 20-Jun 3.5 1.5 5

BARLEY 20-Jun 5 0 5 BARLEYJPR 20-Jun 5.96 3.25 9.21

BARLEY 20-May 5 0 5 BARLEYJPR 20-May 6.06 3.25 9.31

GUARSEED 20-Jun 5 0 5 GARSEDJDR 20-Jun 5.41 2.5 7.91

GUARSEED 20-May 5 0 5 GARSEDJDR 20-May 5.46 2.5 7.96

MAIZE 20-Jul 5 0 5 MAIZE 20-Jul 3.9 1.5 5.4

MAIZE 20-Jun 5 0 5 MAIZE 20-Jun 3.96 1.5 5.46

MAIZE 20-May 5 0 5 MAIZE 20-May 3.99 1.5 5.49

MENTHAOIL 30-Jun 5 0 5 MENTHAOIL 30-Jun 9.78 2.75 12.53

MENTHAOIL 31-May 5.83 0 5.83 MENTHAOIL 31-May 9.23 2.75 11.98

POTATO 15-Jul 6.53 0 6.53 POTATO 20-Jul 9.51 1 10.51

POTATO 15-Jun 6.73 0 6.73 POTATO 20-Jun 8.39 1 9.39

POTATO 14-May 5.11 5 10.11 POTATO 20-May 7.31 1 8.31

RUBBER 15-Jun 5 0 5 RBRRS4KOC 20-Jun 4.98 1.5 6.48

RUBBER 14-May 5 3 8 RBRRS4KOC 20-May 5.08 1.5 6.58

SOYABEAN 20-Jun 5 0 5 SYBEANIDR 20-Jun 2.78 2.25 5.03

SOYABEAN 20-May 5 0 5 SYBEANIDR 20-May 2.81 2.25 5.06

SUGARMKO

L 20-Jul 5 0 5 SUGARS150 20-Jul 3.5 1.5 5

SUGARMKO

L 20-Jun 5 0 5 SUGARS150 20-Jun 3.5 1.5 5

SUGARMKOL 20-May 5 0 5 SUGARS150 20-May 3.5 1.5 5

WHEAT 19-Aug 5 0 5

WHTSMQDE

LI 19-Aug 3.57 1.5 5.07

WHEAT 20-Jul 5 0 5

WHTSMQDE

LI 20-Jul 3.61 1.5 5.11

WHEAT 20-Jun 5 0 5

WHTSMQDE

LI 20-Jun 3.64 1.5 5.14

WHEAT 20-May 5 0 5

WHTSMQDE

LI 20-May 3.68 1.5 5.18


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2.2. MECHANICS OF HEDGING USING FUTURES INSTRUMENT

The exchanges trading futures in any given commodity are indicated followed by a mention of

the contracts that have matured during the period. The predominant pattern of the hedge market,

namely, contango orbackwardation, is indicated. The hedge is said to be in backwardation when

current supplies are scarce leading to exhaustion of producers inventories (stock out condition)

and opposite phenomenon holds in case ofcontango. We can describe these two commonly used

terminologies in hedging strategy using futures. A situation called contango is said to arise when

the futures prices rise over the life of the contract following hedgers and speculators desire to

be net long and net short, respectively. Conversely, a falling price where spot price of asset is

greater than the futures price of the underlying asset is referred to as backwardation. In case of

normal contango and normal backwardation, futures price will be above the expected future spot

price and expected spot price will be above the futures price, respectively. Future trade receives

impetus from increased volatility in the spot market of the underlying commodity. Thus,

precisely, both the markets need to be watched and in this context, basis assumes significance.

Basis reflects cost of marketing the commodity, which is storage costs (cost of carry model)

forming an important component; and thus, ought to be less variable than the spot. Basis

variation or basis risk also implies operational risk, credit risk, and market risk. Economic

fundamentals (production, import, export, carryover stock, and consumption) of the asset,

liquidity, and return on assets (Roll, 1984) largely affect basis variation either in a positive or

negative manner. Basis variation, in turn, decides the magnitude of hedge-effectiveness or degree

of variance minimising hedge-ratio (Roy, 2008).

Lets1,s2 be spot prices andf1,f2be futures priceswith time variable t1, t2and the basis (b1, b2) are

denoted by

b1 = s1-f1; b2 = s2-f2 (5)f1 +b2 ors2 (f2-f1), (6)

Hence, we can get payoff equation after considering effective realization on financial asset orcommodity, i.e., payoff through hedging mechanism,

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= [f1 + (s2-f2) + (s2-s1)]n or (7)

[s2 + (s2-s1) - (f2-f1)]n where n is contract size

Where h = nf/nA, nf is the number of contracts for futures and nA is the number of contracts of

underlying assets or commodities at spot. Basis occasionally remains constant because local

supply and demand conditions continually change through time. Changes in basis are known as

basis patterns or basis variations. In order to use basis to select among marketing alternatives,

basis theory and associated concepts must be understood, and basis patterns are one of the

important concepts. An improving basis changes from weak to strong. Logically, if basis

strengthens unexpectedly then this improves a short hedger position. On contrary, if basis

weakens unexpectedly, the situation worsens a long hedger position (Hull, 2007).

If we know the value ofs2 and nA then we can calculate mimimum variance hedge ratio. The

equations are written below.

= snA - fnf

= snA - fhnf (8)

Now, variance (2v) of the hedged-position can be calculated

(s hf)2

(2s - 2hsf + h22f), taking the first-order differentiation of variance with respect to h

(dv/dh) and equating with 0, we get

dv/dh = d/dh (2s - 2hsf + h22f) = 0

dv/dh = 0 - 2sf+ 2h2f = 0

h = s/f (9)

d2v/dh2 = 22f 0

Second-order derivative satisfies the minima condition and first-order derivative holds the

condition of minimum-variance hedge ratio. In alternative way, we can calculate minimum-

variance hedge ratio (h) by considering covariance of change in spot and futures price relative to

variance of change in futures price [s,f/2f] or [* s/f] where s, f, are standard deviation of

change in spot price, standard deviation of change in futures price, and correlation coefficient

between change in spot and futures price, respectively. Some literature show that simple ordinary

least square (OLS) technique is inferior over the GARCH (Generalized Autoregressive

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Conditional Heteroscedasticity) estimated dynamic hedge-ratio (Gupta and Singh 2009). Still,

OLS (static hedge ratio) outperforms GARCH models in most of the cases for calculating

variance minimising hedge-ratio.

Formally, the basis is defined here as the difference between the spot and futures market price

for a commodity, which is negative in contango market orvice versa. It is a signal of market

forces at work and will change over time as the cash market price and futures market price

converge. The simple efficiency hypothesis of futures market postulates that the future price is

simply the expected and technically called the unbiased predictor of the future spot price

implying that spot and futures prices share a one to one long-run equilibrium. The expectations

hypothesis treats the future prices as the consensus (indicative) forecast of the future spot price.

To a great extent, this is dependent upon the method of collection of spot quotes by the

exchanges. Biased collection procedures present distorted patterns in the spot quotes and, for this

reason, the two do not seem to converge at the expiration of contract. To solve the issue of

settlement, a due date rate (DDR) is fixed by exchanges which is simple average of spot prices

during delivery period, which should take place within 11 days after settlement at the exchange-

notified warehouses (FMC, 2002).

Key factors affecting the basis are, inter alia, distortions/opacities in the underlying spot market,

weather, imports/exports, government aid packages, trade disputes, disease outbreaks,

anticipated short term supply and/or demand, work holidays etc. As agents face more basis risk,

they reduce their exposure by reducing inventory level. Hence, the storage level is adversely

affected.

A fairly good and necessarily positive correlation (>0.5 or 0.5-0.80) and relatively low deviation

between the spot and future prices would posit certain degree of integration of two markets. The

movement of futures and spots prices in tandem is a necessary condition for manageably low

basis risk. Wherever the basis is not zero, the local supply and demand factors are different from

those prevailing in the futures market. A negative basis indicates the local spot price is less than

futures price implying the local supply is greater than local demand, a situation called contango.

Conversely, related concepts are that of a normal market where the futures trade at a premium

over the nearer one and the opposite in the case of an inverted market. When the basis is positive,

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i.e., the cash price greater than the futures price and the cash market is trading at a premium over

the futures indicating that the local demand is greater than local supply, a situation called

backwardation.

In India, a study conducted under the chairmanship of Abhijit Sen (2008) illustrates

comprehensively that the magnitude of basis pattern forwheatvaries from high to low followed

by moderate to weak for chana and for others, variation seems to be moderate. Hedge-

effectiveness (HE) is found relatively high in case of pulses, namely, chana, 36%), tur, 44%, and

urad, 43%. In case of guar seed, sugar and wheat, this is 58%, 32%, and 15% respectively. These

numbers implicitly throw some lights on suspension of trading of commodities, namely, sugar,

tur, urad (although ban on sugar had lifted few months before).

SECTION-II

3. EXCHANGE, FARMER, AND OTHER AGENCIES: A CASE OF

TRIANGLE

Exchange usually provides fairly an improved and sophisticated platform for price discovery

processes and price risk management. But nuances involved in trading are complex ones, if not,

being understood by agents or investors properly. Same is also applicable to Indian farmers. Tinyland holdings, poor productivity, exorbitant interest rates on informal credit, lack of access to

formal credit, and lack of marketing acumen are few impediments which impound them from

realising better price rather than experiencing good yield. This is true for almost 80 % of Indian

peasants. Other obstacles could be market driven. Poor infrastructure relating to market yards,

(Agriculture Produce Market Committee Act, 2003), poor trade practices (auctioning), limited

initiatives for increasing awareness about commodity futures markets, spot markets across

regional centers and so on have been delimited the growth of commodity markets. Thanks to the

mediation of few private agencies or a sort of private-public-partnership projects, which have

been initiated in the recent past to augment the liquidity in this trade like Institute for Financial

Management and Research (IFMR), Adani facilitated aggregation model to encourage the

farmers participation in Reliance e-Mandi trade, NCDEX-Haryana State Cooperative Supply

and Marketing Federation (HAFED) collaboration for hedging of wheat on behalf of farmers,

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and MCX- Aga Khan Rural Support Programme (AKRSP-I)-Cardinal Edge (CE)-a case of

triangle to name a few.

Case-I: Guetemalan Coffee Growers Association (ANACAFE), a non-government

organisation had set precedence by introducing a credit system for small coffee growers in

1980s. By linking the farmers with banks for credit, ANACAFE made it prerequisite to use risk

management instrument in order to hedge the risk. So, farmers received only loan amount after

assuring banks that they had proper risk management tools like forward price agreement,

hedging through futures markets, etc. In this process banks also sanctioned loan amount with

lower interest rate which eventually brought about savings for farmers of more than 10% of loan-

to- value. Both banks and farmers had become successful to minimise the risk.

Case-II: In 1994, Agricultural Products Option Programme (APOP) was introduced in

Mxico in cotton which was further extended to wheat, corn etc. Here, Support and Services

for Agricultural Trading, (ASERCA), a decentralized administrative part of the Ministry of

Agriculture, Livestock, Rural Development, Fisheries and Alimentary, acted as an intermediary

between producers and exchange, e.g. Chicago Board of Trade and New York Cotton Exchange.

ASERCA helped the farmers to participate in the exchanges by buying put option through

grouping their production to meet minimum size requirement for which ASERCA contributed 50

% of total option premium. But farmers ought to deposit the same amount in one fund calledFINCA. The cost appeared to the farmers was 5-8 % of the strike price of the option . As a result

of which APOP covered 11 % of the total wheat production of Mexico.

Case-III: In Surendranagarof Gujarat state, the MCX in collaboration with Cardinal Edge for

administrative support and Aga Khan Rural Support Programme (AKRSP-I) initiated one

programme to make cotton growers aware of the futures markets and its complex operational

nuances during 2007. For funding purposes, they sought the help of NABARD and opening of

trading accounts was accomplished by Kotak Securities.

Case-IV: Centre for Micro Finance (CMF), Self-Employed Womens Association (SEWA)

and the NCDEX partnered in 2007. A randomised controlled trial (RCT) had been conducted to

examine the impact of providing commodity futures prices to farmers at 108 villages in four

districts in Gujarat and its impact on price expectation and sowing decision. The programme

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seemed to be found with significant impact on formation of price expectation but at the same

time, it had not been found with any significant effect on selecting crops or areas cultivated.

Source: OECD (2000) report on Income Risk Management in Agriculture (case-I to Case-III) and Cole, S (2009),

Futures Price Information for Farmers paper presented at the conference on Risk Mitigation inAgriculture organised by IFMR, Chennai, SEWA Bank Ahmedabad, Gujarat, 2009,August (case-IV).

In 1999, International Task Force on Commodity Risk Management of World Bank (WB)

recommended the establishment of one international intermediary which would fill the gap

between price insurance providers like banks, brokers or traders, etc. and the service seekers like

producers organisations, agribusiness organisations, co-operatives, etc. It would perform three

types of functions, (a) facilitation by providing partial guarantees to mitigate risk involved in the

transaction, (b) intermediation between service providers and users, and (c) provision of core

services and technical assistance-in particular, market information and support to local

transmission mechanisms (WB, 1999).

Agriculture and agribusiness both are highly influenced by the vagaries of nature. Hence, the

risks attendant cannot be avoided or wished away. Financing in agriculture requires a long-range

planning. At the same time, relatively stable cashflows would ease out the financing process.

Seasonality is a major bottleneck which results in a conservative outlook towards credit rationing

in agriculture. Pledge financing is an age-old financing technique adopted by most nationalized

banks, a few private sector banks and non banking finance companies (NBFCs). This is usually

accomplished on the basis of collateral being produced by the borrower to/before the lender.

Technically, collateral is an asset, say, a marketable property, either in the form of physical or

financial, which can be pledged or physically transferred by a borrower to a lender; the borrower

retains the right to any earnings from the asset, and the lender can only dispose of the asset when

the borrower defaults on his payment obligations (UNCTAD, 1996). So, the loan amount is

being obtained through collateral, which should bring rights ascribed to the title of

ownership. Warehouse receipt (WR) is such example that can be considered as a negotiableinstrument under the directives prescribed by the Warehouse Development (Regulation) Act,

2007. Of late, this kind of financing has been changed to a different nomenclature, which is,

collateral (commodity) based structured financing (CBSF).This type of financing, typically,

helps to assure predictable cashflows that can be isolated from its originator in order to secure

credit on part of the borrower and to mitigate risks on part of the lender. Collateral management

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agency (CMA) is a third party to ensure a guarantee for both parties with respect to physical risk,

market risk, and operational risk like National Collateral Management Services Limited,

National Bulk Handling Corporation limited, Arya collateral, Star Agri, India Commodities to

name a few . In turn, the agency usually charges a commission or service fee, which is linked

with performance guarantee for the collateral overseen by the CMA under its lock-and-key

arrangement.

Role of CMAs is well described. Since collateral management is still being in nascent stage,

there is hardly any data or information on the exact quantum of agricultural produces and

industrial assets financed under collateral management structures. Avanthakrishnan (2011) puts

forward

It would be of interest to note that against a gross bank credit of Rs. 3,38,656 crore as at the endof March, 2009, to the agriculture sector, the extent of finance secured by collateral management

structures is only 10,000 crore, clearly indicating that there is tremendous scope for such

financing in the days to come (Avanthakrishnan, 2009: p. 135).

On contrary, commodity futures or derivative markets have witnessed exponential growth in

recent times. Evidently, total turnover and value of futures trading of agro commodities were

approximately 291.0 million tonnes and Rs.9.02 lakh crores in the year 2009-10 respectively

(Economic Survey of India, 2009-10) despite the invocations of ban at several occasions on

many commodities. Research with the help of exchange-level proprietary data helps to uncover

many issues behind those decisions, which could be pertinent to practitioners also with varied

magnitudes, namely, the presence of causality, effect of net convenience yield (i.e., convenience

yield minus cost of carry) under two market situations, contango and backwardation, modelling

of volatility spillover in both futures and spot markets, determination of optimal hedge-ratio, etc.

It is evident from a relevant literature base in the same domain that futures and spot prices of

commodities co-move with a profound lead-lag relationship. They go or move in tandem and

their relationships over a particular point of time seem to establish a long-run equilibrium

through short-range dynamics. Hence, order of integration between futures and spot series can be

achieved, which is subject to the differenced stationary processes. However, futures-and spot-

return series do not usually stick to or follow the independent and identical distribution (IID)

pattern. BDS (Broch et al., 1996) test can be employed to check this hypothesis as a dependence

test of nonlinearity under some distributional assumption, which may be parametric or semi

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parametric in nature. This implies that non-linearity and non-normality are inherent in both

futures-and spot-series. It is a typical characteristic of any time-series data whatever the case

may be, either for financial asset or for a commodity. Incorporation of suitable or right

methodology and some prudent tools and techniques can ensure the pattern and magnitude of

price transmission and thus, would help anticipate the future expectations of agents about spot

prices of commodities at different time interval or at different lag length. Futures also serves as a

barometer or a price messenger in a manner that to what extent futures and spot markets are

(co)integrated, which is often referred to as co-integration under a suitable econometric

framework. Volatility measures also reinstates that how agents can derive benefits from the price

volatility of two markets and eventually, risk-return optimisation through price instability

(Waugh, 1944). Labys (1980) put a counter argument that consumers or producers gain or loss

depends upon: the source of the price instability, say, transition in supply as compared demand;

the additive or multiplicative nature of random disturbances which are functional in nature and

their autoregressive properties; the nature of producer response including the formation of

expectations and risk etc. Granularity or concavity effect due to the presence of non-linearity

structure brings about a varied degree of outcomes with the value of position (buy or sell) on

asset for agents, which could be effected through a nave strategy-buy low and sell high

considering an appropriate reference frame or time horizon of trading or through exercising on

the respective positions.

Research through secondary sources of data definitely provides some roadmap for strategy

formulation at the grass root level, which is nothing but a top-down approach. Crop selection,

staggered planting, cropping intensity, inputs, and credit requirement-all these decisions are

being influenced by the direction or dimension of causality between futures and spot prices.

Apparently, futures prices provide the forward looking arrangement for agents or market

participants and adjustment of disturbances or shocks in both price-series largely depend on

different forms of market efficiency. Phenomena including contango or forwardation and

backwardation are reflections of price discovery mechanism and prevailing form of market

efficiency in commodity markets. Thus, appropriate and timely inputs from research-desk

undoubtedly promotes the speed of price dissemination and would meet out the expectations of

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11th Five Year Plan (2007-12)-at the behest of the FMC under the aegis of the Ministry of

Consumer Affairs, Food, and Public Distribution.

Adequate research and peer-reviewed journals in Indian commodity markets have been limited to

few although there is huge scope and untapped potential for unveiling the uncovered issues.

Researchers opined at several occasions that Indian futures markets are ill-developed as the

major stumbling block for the development is due to the fragmented and unorganised

underlying physical or spot markets (Nair, 2004). Merely, seven years data of futures-spot prices

series are not effective enough to provide a signpost on futuristic trend. As commodity is

distinctively different from a financial asset, primary level research should be conducted at the

producers level. Theory of storage, convenience yield, and risk premium or liquidity preference

theory succinctly warrant some action-research being conducted at participatory level in this

market.

It is important to leave a scope for discussions, which may evolve around the debate on farmers

participation in commodity futures markets. Indian farmers are mostly indebted to middlemen

and tiny land holdings make them handicapped from realising marketable surplus. Farmers

usually grow or take one to two crop(s) in a year as mono-cropping or rice-wheat cycle has

been in vogue in India. An alternative may be aggregation of produce on lot basis in order to

fulfill the criteria of contract specifications directed by the exchanges as self-regulatory

organisations (SROs). Aggregator model should be implemented in a manner that some subject

matter specialists can intervene in the network of aggregator and exchange. This will help to

achieve backward and forward integration and thereby, economies of integration. Cooperatives

or NGOs who have been engaging for the last couple of years in the present structure of the

model, awareness and some hands-on-exposure should be rendered at their disposal by those

national level commodity exchanges. Contract specifications like lot size, margining system, and

delivery process should be understood by the aggregator properly. The role of middlemen should

be recognized as they know the trade better. Quality of the produce at e-auction or on futures-

platform should be examined with right deployment of entities, say of middlemen or/and

exchanges employed product managers. The following avenues can be evaluated based on their

merits for implementation. Few are in offing at the behest of the Government of India and others

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can be explored which will seek to achieve concordance among policymakers and industry

professionals. However, support at the policy level cannot be avoided or wished away. The

following avenues can be explored to encourage farmers participation in this market:

4.1. Aggregators can adopt short selling strategy. In that case, margin money and payoff

should be calculated meticulously. Stop loss strategy can also be adopted by the agents in

absence of arbitraging mechanism followed by a reverse cash and carry model (for more details,

see, Bailey, 2005).

4.2. Hedging is not as effective as theoretically illustrated in the first section of this article. Basis

risk is the most important risk element inter alia, which warrants a vigil on day-to-day price

movements at both futures and its underlying markets. It is always better to have tailed hedging

than untailed hedging from economic perspective as in tailed hedging the difference between the

time future gains or losses and the time the gains or losses from spot markets position realized

are considered. Thus, it well considers the cost of financing or returns due to variation in margin

settlement. In this case, every day the hedge ration is multiplied by the daily spot to futures price

ratio. If options could be introduced, then there could be some possibilities for adopting delta

hedging strategy under risk-neutral environment. Aggregators could go with a positive theta

(time variable), a positive gamma (first order derivative of delta or second order derivative of

option premium with respect to underlying) by neutralising delta in order to optimise returns or

minimise losses in either of two options, say, call or put.

4.3. Collateral Management Agencies (CMAs) should be cautious and industrious enough to

protect both the exchanges and aggregators by mitigating physical, operational, and market risks

with differential impacts of their respective risks elements. CMAs should test, validate, and

certify the stocks kept in the bonded or accredited warehouses with the help of quality control

department (which may be in-house or outsourced). First, CMA can keep the produce of

aggregators into accredited warehouses and mark a lien on warehouse receipt (WR) which can be

produced before a banker to raise the credit. In addition to, CMAs can take a call to dispose of

the produce either at spot or futures platform based on prevailing market prices and other

conditions on behalf of aggregators (as per bye-laws, which should be prescribed under the

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WDRA, 2007 to act accordingly). Hence, CMAs roles are very crucial in making the whole

value chain effective and efficient. CMAs can look at seasonal calendar and historical prices of

traded commodities so that they can deliver end-to-end solutions to aggregators, in turn, the latter

can pass on the information to producers. This will help immensely to end users of futures or

spot exchanges for choosing or selecting the crop-portfolio and modes of marketing the

produces.

4.4. Government agencies including the Food Corporation of India (FCI), Food and Civil

Supplies Departments, State Agricultural Marketing Boards (SAMBs) can directly procure from

the farmers or traders, or/and aggregators at current market prices (besides entering into Price

Support Scheme, i.e., Minimum Support Price (MSP) in collaboration with spot exchanges

promoted by commodity exchanges in the year 2006. FCI, State and Central Warehousing

Corporations (SWCs, CWCs) can store the procured produces after issuing warehouse receipt

(WR)-a negotiable instrument as per directives prescribed by the Warehouse Development

(Regulation) Act, 2007 (for more details, see, Avanthakrishnan, 2011: p. 135-139). Two kind of

strategy can be formulated. If aggregators want to store the harvested produces at exchanges

notified warehouses at respective locations, then WR can act as a shield by arranging the credit

from financial institutions at cheap or at moderate interest rates (differential rate of interest-DRI

can also be provisioned). In alternative manner, FCI, SWCs, CWCs can issue commodity

bonds (like potato) which can provide an assured stream of returns (as same as coupon or

coupon striping) in a stipulated time period to avoid the distress sales or crop failure. What

exchanges can do that they can open alternative delivery centers in nearby FCI or SWCs/CWCs

depots to expedite delivery processes during the post-settlement period. In that case, brokerage

fee can be fixed upto a certain limit so that aggregators can earn reasonably a fair amount of

commission based on the value of trade executed. Apart from these, RBI (2005) advocated that

banks can offer non-standard contracts to the farmers and cover them in the commodity futures

trade as for farmers it is difficult to take positions directly in futures markets. In view of this,

RBI decided that banks can offer tailor made products to the farmers like Non-Transferable

Specific Delivery (NTSD) and Transferable Specific Delivery (TSD) which are only allowed

under FCRA Act, 1952. Although there are significant development happened in commodity

futures markets so far, still more changes need to be required at different levels. Recently, price

dissemination of both spot and futures prices of agricultural commodities has been identified as

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one of important activities by Planning Commission in its XIth Five Year Plan. The initiative had

undertaken by the FMC in collaboration with Ministry Agriculture, five national level exchanges

(NMCE, NCDEX, MCX, India Commodity Exchange (ICEX) and Ace Derivatives and

Commodity Exchange (ACDE)) and 18 regional exchanges where futures and spot prices of

commodities of national exchanges and spot prices of Agricultural Marketing board

(AGMARKNET) would be operated and shown on the ticker board installed in APMCs

networks under the aegis of AGMARKNET and the National Informatics Centre (NIC). By

disseminating a spectrum of instantly observable prices these exchanges transferred the pricing

power to the farming community and enhanced institutional development like grading,

warehouse receipt etc., supply chain integration and farm credit facilitation (FAO, 2007).

4.5. FMC can promote options for certain commodities, which have users-specific demands and

have relatively high asset-specificity in the industry, namely, rubber, black pepper, crude palm

oil, cotton, guar seed, etc. These commodities have their regional importance as productions are

being limited to few states, but consumptions are being observed profoundly round the year.

Aggregators can enter into an option contract with exchanges by buying a put option (long

put) whereas the FCI and other private agencies, viz., ITC, Ruchi, Reliance, Glencore, Australian

Wheat Board, Louis Dreyfus, and Cargill etc. can exercise the option by selling a put (short

put). Aggregators would pay the premium and can delimit the potential losses in a way that

option holders can hold the produce until the maturity or till the contract expiration (Fernandes

and Mor, 2009). European option would be better in order to avoid the temporal risk or liquidity

risk between the time value of option price/ premium and intrinsic or theoretical value of the

option. Thus, arbitrage opportunity, if any, exists during the time period of option contract until

the maturity can be avoided to some extent. Hence, call-put parity will hold. In that case,

exchanges should devise some indices for agricultural commodities which can minimise

excessive spikes or volatilities for both the spot-futures prices of the notified commodities (like

MCX-AGRI, NCDEX FUTEX) and indices should incorporate the changes in prices being

reflected on tick-by-tick basis by improvising some robust mechanisms with international

exchanges indices, say, Goldman Sachs Commodity Index (GSCI). In alternate of MSP, option

can be introduced as several merits of this have been discussed by Fernandes and Mor (2009).

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SECTION-III

4. CONNECTION AND CONCLUSION

It is well known fact that agriculture has been a mainstay of the Indian economy. This is also

evident as major portion of the economy has been harping on agriculture since the first Five Year

Plan (1950-51-1955-56) with respect to procurement of raw materials, processing, and finished

products. Several risks are also associated with this activity, namely, physical, credit,

operational, and market risks.

Risk quantification is a daunting task because of complex methodological issues. Covariance-

variance matrix is a simpler one to understand risk metrices. Correlation comes to play here. It

implies strength of the linear relationship among variables. But it cannot capture nonlinearity, if

any, exists in the variables. Statistically, it is a ratio of covariance to the product of standard

deviations of atleast two random variables; one may be dependent and other being independent.

Correlation, intuitively, tells about the movement of atleast two variables either in a positive or a

negative direction. A recent estimate by Sabnavis (2011) shows that correlation coefficient

between domestic and international prices of commodities are quite high, for instance,

correlation of sugar prices projects 0.92, followed by soybean, 0.80, then cotton, 0.71, soy oil,

0.68, and lastly, wheat, 0.64. Authors estimation considers International Monetary Fund (IMF)

data for international prices and Wholesale Price Index (WPI) for domestic prices. What does

this reflect? How does it help to increase producers share in consumers rupee? Is it only an

indication of openness to trade (differential of export and import relative to the Gross Domestic

Product (GDP) of the country during a particular point of time) and smoothening of trade?

One needs to be careful while interpreting these numbers. The estimation could be erroneous, if

not, competitive advantage with respect to few coefficients, nominal protection coefficient

(NPC), effective protection coefficient (EPC), and effective subsidy coefficient (ESC) could

have been taken into consideration for exploring the status of Indian agriculture in international

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markets (for more details, see Gulati, 2002). Since these estimates have several implications at

macro as well as at micro level, price discovery and market efficiency in agricultural commodity

markets have become a debatable issue (Ghosh, 2009). Linking to this, integration of Indian

agriculture into world markets has thrown some signposts for future research, namely, volatility

of commodity prices, export-import parity in agriculture, role of tariffs on agriculture trade

liberalization, etc.

Moreover, India has been found losing out its comparative advantage in export of some of the

agricultural commodities, tea, coffee, spices, and marine products to other Asian countries during

1991-2004 after economic reforms (Shinoj and Mathur, 2008). Arguably, simple correlation

coefficient estimates hardly reflects any true picture of Indian agriculture trade in general and

price realisation by farmers in particular. At this juncture, we may consider that technological

mediations resulting in improvising markets could be a reason for the structural changes, which

have been taken place in Indian commodity markets, especially in derivative markets, in the post

2002-03. Contrary to this, Bhalla and Singh (2009) counter that the post-reform period; 1990-93

to 2003-06 has been characterised by decreasing trend in the growth rate of crop yields and total

agricultural outputs in most states of India. Commodity futures exchanges, e-auction centers or

e-mandis, terminal markets, spot exchanges are few examples of technological innovations,

which have been percolated at three layers-products, processes, and business models being

adopted by exchanges and other agents.

The fundamental focus of this article was to present the existing debate on farmers participation

in commodity markets, as also to present a tentative framework on how the mechanism of

participation can be drawn with some proposed avenues. While we have illustrated few concepts,

this does not mean that the debate should end here. For application of such concepts, one needs

to take into consideration knowledge of the physical and derivative markets, as the methodology

should ideally be commodity-specific. Every commodity has its own market characteristic, and

hence, the propositions narrated here should not be adopted blindly. A primary survey is most

important in this regard. It is only by understanding the commodity and its associated market that

the right framework or model can be adopted.

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No doubt, however, price formation and its transmission are some of the most discussed matter

nowadays. While, commodity futures markets are expected to play the two important roles of

risk management and price discovery, their utilities in provisioning of these two services have

come under criticism from various corners. It is further argued that availability and effective

dissemination of information from the futures market helps to stabilise and decrease spot price

volatility (Dey and Maitra, 2011). However, all these present testable hypotheses. While

ambivalence on the utility of the futures markets still exists at the policy levels, probably the

endeavour will help in expanding the literature base by furthering the debate on farmers ground.

Nonetheless, stated alternatives presented in the above are a few avenues which can

accommodate farmers to some extent in the present structure of commodity futures markets. If

option would be allowed, then possibility might lie in and around to validate the propositions

empirically. Policy should weave its strength with industrys inputs to make the implementation

more effective and efficient. Thereby, this article would hold its position as a precursor for the

reality check in ground.

Note

___________________________________

[a] Moral hazard and adverse selection: Moral hazard refers to risk or hazard that arises when a party

enters into a transaction without good faith and has given false information about his assets, liabilities etc.

because of incentives attached in doing so. It sometimes leads to take unusual risk by one party to earn

more profit before agreement expires. Adverse selection is wrong selection of a product. It refers to the

market process which gives rise to bad results because of information asymmetry present at both sides i.e.

sellers and buyers

[b] Market microstructure: Exchange structure studies would stimulate some thoughts for conductingresearch on market microstructure level. It will further help to understand and provide explanation for

price discovery, market efficiency, adequate methods to stimulate new futures contracts have been most

effective (Rutten, 2009). Market microstructure may be defined broadly as the process by which

investors desires and preferences are translated into financial market transactions (Madhavan, 2000;Harris, 2002; Brooks, 2008). The dynamic nature of market microstructure has been put in place because

the movement of the stock/commodity prices largely depends on time-varying variances, past news,

market information and macroeconomic variables (Pradhan, 2009). This actually asserts that the

efficiency level of the market seems to have an impact for determining the actual return (rather than

expected) to the assets being invested by the investors (Black, 1971; French, 1980; Rogalski, 1984; Roll,

1984; Kyle, 1985; French and Roll, 1986; Jegadeesh and Titman, 1993).

[c] Co-integration and Causality: Stationarity condition of time-series is, indeed, helpful to satisfy the

prerequisite for modelling. ConsideringXt and Yt are two non-stationary series, we would expect that acombination ofXt and Yt is also non-stationary. However, a particular combination may be stationary

(Gujarati, 2006). If such a combination exists, we say that Xt and Yt are cointegrated. Two cointegratedseries will thus drift or float far apart overtime, say, future and spot price-series. Johansen and Juselius

(1990) test is mostly employed for testing the cointegration. Engle-Granger causality, and paired Granger

causality tests to confirm the short run causality dynamics in order to achieve long-run equilibrium

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relationship among bivariate or multivariate time-series (Engle and Granger, 1987; Hamilton, 1994;

Enders, 1995).

[d] Independently and Identically distributed variable underlying Random Walk (RW) hypothesis

and dependence test for nonlinear time-series: Random walk is the simplest version of independently

and identically distributed increments case in which asset price is influenced by previous close price andthe disturbance form or error component follows an IID path which means mean 0 and variance 2.

Sometimes, it is assumed that if error component follows IID pattern, then it is referred to as arithmeticBrownian motion. BDS test is used to find out the presence of non-linearity in the in the time series

provided linear dependency has been removed from the data.

[e] Hedging through options and option Greeks, delta, gamma and theta: Option pricing,

especially implied option pricing works well employing Black, Scholes (1973) and Merton (1973b)

model, which makes the following assumptions: There are no market imperfections, say, taxes, short sales

constraint, transaction costs, excessive bid-ask spread gap, and trading is synchronous, continuous, and

frictionless. There is unlimited riskless borrowing and lending at the continuously intermittent

compounded interest rate (r). Option is sensitive to few variables, namely, asset price, strike price, time tomaturity, interest rate, dividend or convenience yield (for commodities), and volatility. Delta,

gamma, and theta are option Greeks or coefficients which give sensitivities to option pricing. Delta is

defined as the change in option price at time trelative to change in underlying asset price, this implies

that number of assets one needs to purchase to offset any risk arising out of a number of options (call orput) being sold. If this condition holds, then a portfolio is referred to as perfectly hedged portfolio under

risk-neutral or arbitrage-free environment (Campbell, Lo and MacKinlay, 2007). Gamma is the first-

order derivative of delta and theta is defined as the change in option price relative to the change in time

to maturity of the option contract. Hence, effective realisation on assets or portfolio can be ascertainedgiven a continuously compounded interest rate, r.

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