Groundnut In Indian Commodity Markets

―Detailed study of groundnut and groundnut oil as commodities in major commodity exchanges in India‖

Prepared by: Partha Ghosh MBA-SYMBIOSIS INTERNATIONAL UNIVERSITY

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Contents 1.

Introduction: ........................................................................................................................6

2.

Literature Review: ...............................................................................................................7 History of Commodity Exchanges: ..........................................................................................7 Chicago Board of Trade:..........................................................................................................8 Dalian Commodity Exchange: ............................................................................................... 20 NYSE EURONEXT: ............................................................................................................. 25 NCDEX:................................................................................................................................ 27 MCX: .................................................................................................................................... 29 COMMODITY MARKET:.................................................................................................... 31 Overview of commodities exchanges in India: ....................................................................... 48 General information on groundnut as a commodity: ............................................................... 50 Description: ....................................................................................................................... 50 Overview:.......................................................................................................................... 50 History: ............................................................................................................................. 52 Cultivation pattern: ............................................................................................................ 53 Varieties of groundnut: ...................................................................................................... 53 Groundnut producing countries: ........................................................................................ 54 Production of groundnut in India ....................................................................................... 56 Indian groundnut market: .................................................................................................. 58 Market Influencing Factors................................................................................................ 58 Major trading centers of groundnut: ................................................................................... 59

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Objectives: ........................................................................................................................ 60

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Methodology: .................................................................................................................... 61

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Analysis of Data and Findings: .......................................................................................... 66 Objective b: To study the factors affecting the spot prices of these commodities in the exchanges. ............................................................................................................................. 66 Table: 5.1 Correlation between NCDEX spot price of groundnut (2007) and CPI (IW) of groundnut oil in Junagarh (2007): ...................................................................................... 66 Table: 5.2 Correlation between NCDEX spot price of groundnut (2007) and CPI (IW) of mustard oil in Junagarh (2007): ......................................................................................... 67

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Table: 5.3 Correlation between NCDEX spot price of groundnut (2007) and CPI (IW) of vanaspati in Junagarh (2007): ............................................................................................ 68 Objective c: To find the similarity/ dissimilarity in spot price trends of these commodities in two major commodity exchanges in India (NCDEX and MCX): ............................................ 71 Table 5.5: Descriptive statistics of the two samples of price data for the year 2007: ........... 71 Table 5.6: Results for the two samples independent t-test: ................................................. 71 Objective d: To suggest a pricing model based on trend and fundamental analysis of prices of these two commodities: ......................................................................................................... 72 Table 5.7: detailed information about the regression model with time as an independent variable (X) and prices of groundnut oil futures contract as the dependent variable (Y): .... 73 Table 5.8: detailed information about the regression model with time as an independent variable (X) and prices of groundnut seed futures contract as the dependent variable (Y): . 74 Table 5.9: detailed information about the regression model with groundnut oil spot prices as independent variable (X) and prices of groundnut oil futures contract as the dependent variable from January 2007 and December 2007 at NCDEX(Y): ....................................... 76 Table 5.10: detailed information about the regression model with groundnut oil spot prices as independent variable (X) and prices of groundnut oil futures contract as the dependent variable from January 2007 and December 2007 at MCX(Y):............................................ 77 Objective e: To study the correlation of the futures prices of these commodities with major futures indices in these markets. ............................................................................................ 79 Table 5.11: correlation analysis of prices of grounut oil futures prices with FUTEXAGRI (the futures prices based index of NCDEX). ...................................................................... 79 Objective f: To have a qualitative view of the illiquidity of groundnut oil as a commodity in the exchanges. ....................................................................................................................... 80 Objective g: To measure the accuracy achieved in price matching (spot and futures) of these two commodities by the stock exchanges. .............................................................................. 80 Table 5.12: correlation results for spot and futures prices of groundnut oil in NCDEX. ..... 80 6.

Implications of Results and Findings: ................................................................................ 82 Results and conclusion pertaining to objective b: ................................................................... 82 Results and conclusion pertaining to objective c: ................................................................... 82 Results and conclusion pertaining to objective d: ................................................................... 82 Results and conclusion pertaining to objective e: ................................................................... 83 Results and conclusion pertaining to objective f: .................................................................... 83 Results and conclusion pertaining to objective g: ................................................................... 83 3

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Conclusion: ....................................................................................................................... 84

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References: ........................................................................................................................ 84

ANNEXURE I .......................................................................................................................... 85 Tables displaying F test results and ANOVA results for the regression models: ..................... 85 Table 3.4: F test and ANOVA results for model ................................................................ 85 Table 3.7: F test and ANOVA results for model: ............................................................... 87 Y = β0.X10+ β1.X9+ β2.X8+ β3.X7+ β4.X6+ β5.X5+ β6.X4+ β7.X3+ β8.X2+ β9.X+ β10 ............ 87 Table 3.8: F test and ANOVA results for model: ............................................................... 90 Y = β0.X10+ β1.X9+ β2.X8+ β3.X7+ β4.X6+ β5.X5+ β6.X4+ β7.X3+ β8.X2+ β9.X+ β10 ............ 90 Table 3.9: F test and ANOVA results for model: ............................................................... 93 Y = β0.X8+ β1.X7+ β2.X6+ β3.X5+ β4.X4+ β5.X3+ β6.X2+ β7.X+ β8 ...................................... 93 Table 3.10: F test and ANOVA results for model: ............................................................. 96 Y = β0.X9+ β1.X8+ β2.X7+ β3.X6+ β4.X5+ β5.X4+ β6.X3+ β7.X2+ β8.X+ β9 .......................... 96 ANNEXURE 2 ......................................................................................................................... 99 Groundnut (in shell) Product Note ......................................................................................... 99 Authority ........................................................................................................................... 99 Unit of Trading .................................................................................................................. 99 Months Traded In .............................................................................................................. 99 Tick Size ........................................................................................................................... 99 Basis Price......................................................................................................................... 99 Unit for Price Quotation .................................................................................................... 99 Hours of Trading ............................................................................................................... 99 Mark to Market ................................................................................................................. 99 Position limits ................................................................................................................. 100 Margin Requirements ...................................................................................................... 100 Delivery Default Penalty ................................................................................................. 101 Arbitration ....................................................................................................................... 101 Unit of Delivery .............................................................................................................. 101 Delivery Size ................................................................................................................... 101 Delivery Requests ........................................................................................................... 101 Delivery Allocation ......................................................................................................... 102 4

Actual Delivery ............................................................................................................... 102 Accredited Warehouse ..................................................................................................... 102 Quality Standards ............................................................................................................ 102 Packaging ........................................................................................................................ 102 Standard Allowances ....................................................................................................... 103

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1. Introduction: Groundnut is an important crop both for oil and food. It is grown in over 100 countries in the world and plays an important role in the economy of several countries. About two thirds of the crop produced in the world is crushed to extract oil and one-third is used to make other edible products. India accounts for 40 per cent of the world area and 30 per cent of world output of groundnut. On the other hand groundnut and groundnut oil is the most illiquid commodity in the commodity exchanges in India. Therefore the focus of the study is to understand the trade volume of groundnut as a commodity in commodity exchanges all over the world and gain knowledge about various factors governing supply, demand and price of these commodities in Indian market. The major objective of this project is to understand the functioning of commodity markets and how derivatives based on underlying (groundnut kernel and groundnut oil in this case) behave in their price fluctuations depending on certain fundamental factors. Throughout the report certain features of derivative trading like cross hedging using two different commodities having high correlation would also be explained using the selected underlying and major agricultural indices of the markets. Thus the project is a gateway into the world of commodity using two very unique commodities.

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2. Literature Review: Various web based literatures have been referred to for gaining an understanding of the following: Which are the major commodity exchanges in the world? And what is their nature of operation? Which are the major commodity exchanges in India? What is their modus operandi? What are the essential features of groundnut as a crop and as a commodity? The first section of the literature review would concentrate the major commodity exchanges in world, brief history, nature of derivative market. The second part of the literature review would concentrate on groundnut as a commodity, its cropping pattern, production, major markets and its significance as a commodity traded in exchange.

History of Commodity Exchanges: Link: http://www.business.headlinesindia.com/commodity/ Markets for futures trading were developed initially to help agricultural producers and consumers manage the price risks they faced harvesting, marketing and processing food crops each year. Today, futures exist not only on agricultural products, but also a wide array of financial, stock and forex markets. The world's oldest established futures exchange, the Chicago Board of Trade, was founded in 1848 by 82 Chicago merchants. The first of what were then called "to arrive" contracts were flour, timothy seed and hay, which came into use in 1849. "Forward" contracts on corn came into use in 1851 and gained popularity among merchants and food processors. Meanwhile, what is now the nation's largest futures exchange, the Chicago Mercantile Exchange, was founded as the Chicago Butter and Egg Board in 1898. At that time, trading was offered in – you guessed it – butter and eggs.

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In 2007, CME and CBOT officially merged, and are now collectively known as CME group Inc., the world's largest and most diverse derivatives exchange. Other prominent U.S. commodities exchanges were formed before or just after the turn of the century, and also had their roots in agriculture. At one time, you could trade on the National Metal Exchange, the Rubber Exchange of New York, the National Raw Silk Exchange, and the New York Hide Exchange. Small exchanges like these ultimately merged to become the exchanges we have today. In the 21st century, online commodity trading has become increasingly popular, and commodity brokers offer front-end interfaces to trade these electronic-based markets. A commodities broker may also continue to offer access to the traditional pit-traded, or open-outcry, markets that established the commodity exchanges. The major commodity exchanges in world are as follows:

Chicago Board of Trade: Link: http://www.cbot.com/cbot/pub/page/0,3181,942,00.html#1848 History: 1848 On April 3, 1848, the Chicago Board of Trade (CBOT) was officially founded by 83 merchants at 101 South Water Street. Thomas Dyer is elected the first president of the CBOT. 1849-50 "To arrive" contracts come into use for future delivery of flour, timothy seed and hay. 1851 The earliest "forward" contract for 3,000 bushels of corn is recorded. Forward contracts gain popularity among merchants and processors. 1852 The Exchange moves to Clark and South Water streets.

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1856 Rising from an 1851 total of 38 members, 122 new members are admitted in 1856. The Exchange moves to South Water and LaSalle streets. 1859 Exchange receives charter from State of Illinois. Exchange is mandated to set standards of quality, product uniformity and routine inspections of grain. 1861 The Civil War begins. CBOT finances formation of three regiments and an artillery battery for the Union Army. CBOT adopts gold coin as its standard of value. 1865 CBOT formalizes grain trading by developing standardized agreements called "futures contracts." CBOT also begins requiring performance bonds called "margin" to be posted by buyers and sellers in its grain markets. CBOT moves to its first permanent home, Chamber of Commerce Building, on corner of LaSalle and Washington streets. 1866 First trans-Atlantic cable laid, facilitates communication between Chicago and foreign markets; transmission of message cut from three days to three hours. 1868 CBOT Board of Directors states any members engaging in a transaction to corner a market would be expelled from trading. 1870 Early version of octagonal trading pit introduced; present type patented in 1878.

1871-1872 The Great Chicago Fire destroys CBOT's first building and with it all records therein. The Exchange closed October 9-10, opens two weeks after fire. A 90-ft by 90-ft, wigwam at Washington and Market streets becomes Exchange's temporary quarters.

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1873 During the Financial Panic of 1873, the CBOT remains open despite large financial institution failures.

1875 Chicago newspapers voice CBOT's sentiment against state grain inspection, favor Board of Trade inspection; Exchange felt that state politicians, unfamiliar with grain business and antagonistic toward grain dealers, were diverting grain from Chicago by misgrading.

1876 First appearance of a "bucketshop" in Chicago (such a shop was dishonest, executing orders and anticipating profit from market price changes adverse to the customer's interest.)

1877 Futures trading becomes more formalized and "speculators" enter the picture. 1885 As a result of the explosive growth of futures, the CBOT erects a new building at LaSalle Street and Jackson Boulevard, Chicago's tallest building at the time. It's the city's first commercial structure with electrical lighting. 1893 The Exchange galleries are opened to the public for first time in honor of World's Columbian Exposition in Chicago. 1897 Wheat prices rise from 60 cents a bushel to $1.00 and William Jennings Bryan states higher prices due to crop shortages in India and Europe are not political events. 1909 The CBOT organizes the largest meeting ever of grain exchanges, some 20 exchanges meet at the Board of Trade in the U.S.

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1912 Under auspices of the CBOT Grain Inspection Committee, improvements are made in inspection and grading, including civil service examinations.

1914 World War I begins. 1916 War grinds on; corn reaches $1.05 per bushel, highest since Civil War. 1917-20 War makes the market unstable; wheat trades at $3.25 per bushel, highest ever paid for a future delivery. Grain trade and railroads nationalized during and for a period after the end of World War I on November 11, 1918.

1922 The federal government establishes the Grain Futures Administration to regulate grain trading.

1923 The U.S. Supreme Court upholds the Capper-Tincher Act (Grain Futures Act), to eliminate price manipulation and other trade abuses.

1924 The U.S. government discusses assuming power to set daily trading limits; called by Exchange President Frank L. Carey a deplorable, unhealthy restriction of supply-demand freedom. 1925 The CBOT Board of Directors is given authority to declare an emergency situation and establish daily price limits. CBOT has one of its most successful years; 26.9 billion bushels of grain traded.

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1926 Board of Trade Clearing Corporation founded to guarantee trades made on the CBOT.

1929 Exchange outgrows its building, temporarily relocates to quarters at Clark and Van Buren streets while new building is erected at the LaSalle and Jackson site.

CBOT seat sells for $62,500, a record at the time.

1930 CBOT moves into 45-story new home at LaSalle and Jackson; building was tallest in Chicago, dominating skyline. 1936 CBOT launches Soybean futures contracts.

1940 During World War II, Paris falls to the German army and open wheat futures shrink 37 million bushels in six days of liquidation and prices decline.

1950-51: The CBOT completes the Soybean complex with the introduction of Soybean Oil and Soybean Meal futures.

1952 The Exchange joins American Red Cross to obtain blood donors for American forces in Korea.

1956 The CBOT hires its first paid, non-member president. Robert C. Liebenow, 34. He is the youngest person to hold the post of CBOT president. At the same time, Julius Mayer is elected the first CBOT Chairman.

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CBOT introduces the industry's first examination for commodity brokers. 1963 CBOT closes for funeral of President Kennedy on November 25. 1966 CBOT introduces the first examination in the futures industry for commission house representatives.

1967 New, fast, automatic electronic price display boards are installed on the walls above the trading floors, replacing chalkboard markers and Morse code telegraph clicks. Price reporting time is cut to seconds.

1968 Iced broilers, the CBOT's first non-grain related commodity, begins trading. CBOT names its first public directors: John Hopkins University President Milton S. Eisenhower; former Bureau of the Budget Director Charles L. Schultze; and Inland Steel Chairman Joseph I. Block. 1969 CBOT begins trade in first non-grain product, with a Silver futures contract. On July 29, 1969, Carol J. Ovitz, Assistant Vice President of Mitchell Hutchins & Company, becomes the first woman member of the Exchange.

1973 Members of the CBOT start Chicago Board Options Exchange (CBOE), the world's first stock options exchange. The government establishes the Commodity Futures Trading Commission to regulate the futures industry.

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1974 CBOT starts trade in 3 Kilo Gold futures on December 31.

1975 CBOT launches first interest rate futures contract, Government National Mortgage Association futures; sets stage for a huge increase in trading volume, a new era of growth and trading instruments for futures exchanges around the world.

1977 CBOT launches the U.S. Treasury Bond futures contract; becomes most actively traded contract in the world. October 1977, The Prince of Wales visits the CBOT. 1979 CBOT begins trade in 100 troy oz Gold futures on February 20.

1980 CBOT closes Jan 7-8 by CFTC order; to suspend trading after President Carter places embargo on grain shipments to Soviet Union.

1982 CBOT launches first options on futures contract, for U.S. Treasury Bond futures on October 1.

CBOT completes its annex to the original 1930 building, which houses a new agricultural trading floor, then the world's largest at 32,000 sq. feet. Exchange launches 10-Year Treasury Note futures contracts on May 3. 1984 CBOT launches trading in Soybean futures-options.

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1986 The CBOT saluted Vietnam war veterans during their parade down LaSalle Street. CBOT trade volume tops 100 million contracts for the first time, sets world record. 1987 CBOT markets remain open throughout October stock market crash. The CBOT is the only major exchange in the world to operate without interruption during the financial crisis. 1988 Fed Fund futures begin trading May 3. 1991 CBOT welcomes President George Bush; first U.S. President to visit the Exchange. Peggy A. Ogorek is the first woman elected CBOT Director. 1992 CBOT closed April 13-14, due to the Chicago River tunnel flood. Soviet President Mikhail Gorbachov visit the CBOT on May 7, 1992. 1993 CBOT administers first cash SO2 emission allowance auction for the Environmental Protection Agency. 1994 CBOT launches Project A, its after-hours electronic trading system for futures and futuresoptions. 1995 Members approve trading of agricultural products on Project A during off-exchange hours. CBOT launches its Internet site. 1996 Vice President Gore is special guest at Exchange's Democratic Senatorial Campaign reception during 1996 Democratic National Convention in Chicago. 1997 CBOT launches the CBOT Dow Jones Industrial Average Index® futures and options on futures contracts. 15

CBOT opens the world's largest trading floor, 60,000 sq. ft. for financial futures and futuresoptions on February 18, 1997. 1998 On April 3, the CBOT celebrates its 150th Anniversary. On September 28, the Board of Directors establishes side-by-side open outcry and electronic trading for financial contracts, providing trading opportunity for those members and firms who wished to trade on the CBOT‘s electronic trading system during the day. 2001 Chicago's four financial exchanges close on Wednesday, September 12 in recognition of the tragic events of September 11. CBOT launches 10-Year Interest Rate Swap futures. 2003 On November 25, 2003, the CBOT transitions to its new electronic trading platform, powered by LIFFE Connect and its agreement with the Chicago Mercantile Exchange (CME) to provide clearing and related services for all CBOT products 2005 On March 23, the CBOT successfully launched its Ethanol futures contract. On April 14, the CBOT announced that an overwhelming 99 percent of the votes were cast in favor of the CBOT‘s restructuring proposal, which includes the demutualization of the Exchange into a for-profit, stock-based holding company and for-profit, membership exchange subsidiary. On June 9, the CBOT celebrated the 75th anniversary of the Exchange‘s landmark building; CBOT rededicates two statues that once stood at the entrance of the original CBOT building completed in 1885. On October 19, CBOT Holdings, Inc. has its Class A common stock listed on the New York Stock Exchange at a price of $54.00 per share.

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2006 The CBOT announced that the Exchange achieved the highest yearly total volume recorded in its history, with more than 674 million contracts traded in 2005. On January 6, 2006, U.S. President George W. Bush toured the CBOT agricultural trading floor, becoming the second U.S. Chief Executive to visit the Exchange. On April 26, 2006, the CBOT announced that it will increase global access to its benchmark Agricultural products by offering trading of CBOT full-sized, physically delivered Agricultural futures contracts on its electronic trading platform during daytime trading hours. Trading is expected to begin on August 1, 2006. August 1, 2006, marked the CBOT's historic launch of electronic agricultural futures trading side-by-side with the open auction market during daytime trading hours. On September 26, Singapore Exchange and Chicago Board of Trade commenced the first day of trade for the Joint Asian Derivatives Exchange (JADE). JADE a market division of SGX Derivatives Trading Ltd., launched its debut in commodities futures trading with TSR 20 Rubber futures contract. On October 17, 2006, the Chicago Mercantile Exchange Holdings Inc. and the CBOT Holdings, Inc. announced that they signed a definitive agreement to merge the two organizations to create the most extensive and diverse global derivatives exchange. On November 7, 2006, the CBOT announced that it successfully launched open auction trading of its options on Full-sized Gold (100 oz.) and Silver (5,000 oz.) futures contracts.

On December 18, 2006, the CBOT announced that it has successfully launched clearing services for two new over-the-counter (OTC) Ethanol Calendar Swap contracts with the clearing of 60 contracts last week. On December 21, 2006, CBOT Holdings and CME Holdings have filed a preliminary joint proxy and registration statement on Forms S-4 with the U.S. Securities and Exchange Commission relating to the proposed merger of the two companies.

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2007 On Janaury 2, 2007, the CBOT announced that it set a new record for annual trading volume, with 805,884,413 contracts. CBOT Holdings, Inc. announced its best year in company history, with revenue of $169.3 million for the fourth quarter 2006 on January 31, 2007. On February 5, 2007, the CBOT launched the electronically-traded DJUSRE Index futures contract designed to allow market participants to capitalize on changes in the real estate sector of the stock market. On February 26, 2007, CBOT Holdings, Inc. announces that it will hold special meetings for the CME merger vote on April 4. On March 1, 2007, CBOT Holdings, Inc. filed a brief challenging CBOE's attempt to terminate Exercise Rights. On March 15, 2007, CBOT Holdings, Inc. received unsolicited, non-binding proposal letter from Intercontinental Exchange to merge with CBOT Holdings. On March 19, 2007, CBOT Board of Directors authorized the company to begin discussions with Intercontinental Exchange, Inc., relating to ICE's announced proposal. On March 31, 2007, the CBOT postpones the Special Meeting for the CME merger to give Board of Directors sufficient time to complete their review of ICE's proposal. On May 1, 2007, CBOT Holdings, Inc., following its annual meeting of stockholders, announced that Charles P. Carey was re-elected as a director and reappointed to serve a third two-year term as Chairman of the Board. On May 11, 2007, Chicago Mercantile Exchange Holdings Inc. (NYSE, NASDAQ: CME) and CBOT Holdings, Inc. (NYSE: BOT) announced that they have revised the terms of their definitive merger agreement and recommendation that CBOT Holdings shareholders vote in favor of the merger agreement with CME. On May 15, 2007, CBOT Holdings sets record date for Jul 9 meeting to vote on CME Merger. 18

On June 11, 2007, CME and CBOT received Department of Justice Clearance to proceed with merger. On June 14, 2007, CBOT Holdings, Inc. (NYSE: BOT) announced that its Board of Directors had carefully reviewed the revised proposal from IntercontinentalExchange, Inc. (ICE) and concluded that it is not superior to the revised CME merger agreement. On June 14, 2007, the CME, CBOT revised the merger agreement to provide increased value and delivers one-time cash dividend to all CBOT shareholders and guarantee for holders of CBOE exercise rights. On July 9, 2007, the Chicago Mercantile Exchange Holdings Inc. (NYSE/Nasdaq: CME) and Chicago Board of Trade Holdings, Inc. (NYSE: BOT) completed the merger of their companies, creating the world's largest and most diverse exchange. Organizational Profile: The Chicago Board of Trade (CBOT®), established in 1848, is a leading futures and futuresoptions exchange. More than 3,600 CBOT member/stockholders trade 50 different futures and options products at the CBOT by open auction and electronically. Volume at the Exchange in 2006 surpassed 805 million contracts, the highest yearly total recorded in its history. In its early history, the CBOT traded only agricultural commodities such as corn, wheat, oats and soybeans. Futures contracts at the Exchange evolved over the years to include non-storable agricultural commodities and non-agricultural products. In October 2005, the CBOT marked the 30th anniversary of the the Exchange's first financial futures contract, based on Government National Mortgage Association mortgage-backed certificates. Since that introduction, futures trading has been initiated in many financial instruments, including U.S. Treasury bonds and notes, 30-Day Federal Funds, stock indexes, and swaps, to name but a few. Another market innovation, options on futures, was introduced in 1982. The CBOT added a new category to its diverse product mix in 2001 with the launch of 100 percent electronic Gold and Silver futures contracts. Additionally, South American Soybean futures and Ethanol futures, the Exchange‘s newest products, were introduced in 2005 in response to shifting trends in the global agricultural economy. For decades, the primary method of trading at the CBOT was open auction, which involved 19

traders meeting face-to-face in trading pits to buy and sell futures contracts. But to better meet the needs of a growing global economy, the CBOT successfully launched its first electronic trading system in 1994. During the last decade, as the use of electronic trading has become more prevalent, the Exchange has upgraded its electronic trading system several times. Most recently, on October 12, 2005, the CBOT successfully launched its newly enhanced electronic trading platform, e-cbot, powered by LIFFE CONNECT®, by introducing a major API upgrade. Whether trading futures and options on futures through an electronic platform or open auction, the CBOT‘s primary role is to provide transparent and liquid contract markets for its member/stockholders and customers to use for price discovery, risk management and investment purposes. These futures markets also allow speculators throughout the world to interpret economic data, news and other information and use that information to make decisions about price and enter the futures markets as investors. Speculators bridge the gap between hedgers‘ bids and offers, thereby making the market more liquid and cost effective.

Dalian Commodity Exchange: Link: www.dce.com.cn Structure and function: The exchange has the deepest liquidity pool among all Chinese Commodity Futures Exchanges. According to the Futures Industry Association, the bourse has been the largest mainland futures exchange by volume for eight years, half the domestic market share in 2007, and captures roughly 2% of global futures market share (including financial futures). A near-tripling in volumes of its benchmark corn future in 2006 saw the contract leapfrog the DCE soy complex to become the single-largest product, with the 65m traded, trailing only Nymex WTI Crude in the global commodity rankings. According to the Futures Industry Association, DCE is the second largest agricultural futures bourse in the world, with a 29% market share. [1] In 2007, total trading volume and turnover reached 371 million contracts and RMB 11.97 trillion (1.67 trillion USD). As of November 2007, the exchange had 194 members – including 180 brokers, with a reach of more than 160,000 investors. Louis Dreyfus became the first foreign member in June 2006.

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At present, soybeans, soy meal,soy oil corn, palm oil, linear low density polyethene (LLDPE) futures are traded on the DCE. The introduction of LLDPE in 2007 also marks the first petrochemical futures contract in the country. On August 20, 2007, China officially announced the Northeast Area Revitalization Plan(a national-level development strategy). In this Plan, the Dalian Commodities Exchange was named as a key player in developing the fourth economic region in China. The northeast area is a relatively untapped market space and is traditionally associated with an edge in natural resources such as crude oil, agricultural land, electricity and coal mining. Shipbuilding, port logistics & distribution networks, utilities and agriculture are the most notable sectors in the region.

Development: From the company news release, the leadership will launch the hog/pork belly futures within the first half of the year 2008, and then, coking coal futures, rice futures within the year. In addition, DCE intends to increase its support to industries and develop corporate and institutional client group. As a Deputy to the 11th National People's Congress, Mr. Liu Xingqiang was calling on the government to allow the establishment of commodity futures funds in an attempt to draw more institutional investors balance into the country's burgeoning futures market. Currently Only 5 percent of investors in China's commodity futures markets are commodity producers and consumers, while the remaining 95 percent are private investors. He also is encouraging the government to let companies use money they borrow from banks for hedging in the future market, though not to allow those funds for speculation. DCE shall provide assistance to members in technology upgrading so as so as to improve their technical trading system. The exchange shall intensify its efforts in following areas: a more efficient new commodity futures approval mechanism, promote the integration of futures transaction and cash transaction; increase the support of futures market to industries, and strengthen the link between futures 21

market industries; conduct research on and develop option and commodity index futures products to promote the integration of commodities and financial products. History: Dalian Commodity Exchange (DCE) was established on February 28, 1993. Since the establishment, it has been an important player in the production and circulation of mainland soybeans. Over the next decade of market ratification, DCE earned a reputation among investors for its financial integrity with prudent risk management and great market functionality in international price correlation, transparency and liquidity. In the first few years after the introduction of commodity markets, new exchanges opened with wild abandon, and speculative volume ballooned. Soon a directive titled The Notice of Firmly Curbing the Blind Development of the Futures Market was launched. In October 1994, the State Council rectified over 50 futures exchanges down to 15 futures exchanges, delisted 20 futures contracts (leaving 35), began issuing licenses to futures commission merchants for the first time while lopping their number by over 70%, restricted trading on foreign futures exchanges, introduced new rules and regulations, and shifted the control of the exchanges from local governments to regulatory authorities. DCE's market share then ranked No. 9 in China with Dry kelp as a pilot product.[5] DCE traded soybeans and corn back then. Continued abuse in the market brought forth the Second Rectification in 1998, most of the surviving 15 futures exchanges were restructured, and subsequently closed. Three national level future exchanges emerged: Shanghai Metal Exchange, Dalian Commodity Exchange, Zhengzhou Commodity Exchange. The number of futures contracts was cut back further to 12 from 35, and more brokers were closed, leaving just 175 standing from the early 1990s peak of 1,000. Margins were standardized and regulations further toughened. Trading on foreign futures exchanges was further restricted to a small number of large, global entities. Soybeans, soy meal and beer barley were traded at DCE. The post-rectification Chinese futures exchanges are financially independent of any government body. On the one hand, that means they have to make do without the public subsidies of the 22

hyper-competitive pre-rectification days (in fact they had to pay back investments made by the local governments), but on the other hand rising volumes and the more rationalized industry structure has kept revenues quite healthy. On July 17, 2000, DCE restarted trading soy meal, the first product listed since the last tumultuous rectification of China's futures exchanges. Until 2004, soy meal futures had been one of the most rapidly developing futures contract at China's futures market. On March 15, 2002, DCE started trading No.1 soybeans futures (Non-GMO soybeans). It quickly became the largest agricultural futures contract in China and the largest Non-GMO soybeans futures contract in the world half a year later. According to the Futures Industry Association, Dalian's soybean futures volume quickly became the second largest in the world. A cointegration relationship exists for Dalian Commodity Exchange and Chicago Board of Trade (CBOT) soybean futures prices. On September 22, 2004, DCE started trading corn futures. On December 22, DCE started trading No.2 soybeans futures. According to FIA statistics of volume in 2004, DCE ranks No.8 among international futures exchanges. On January 9, 2006, DCE started trading soybean oil futures. On July 31, 2007, linear low density polyethene (LLDPE) futures are traded on the DCE. The introduction of LLDPE in 2007 marks the first petrochemical futures contract in the country. On October 29, 2007, RBD palm oil futures are launched on the DCE to complement the current edible oil futures structure. China's economy more than doubled in size in the past decade, turning the country into the world's top user of commodities such as copper, soy and rice. Though the government says it wants more financial instruments to help companies hedge risks, regulators aim to avoid a repeat of the 1990s, when speculation caused prices to soar and some contracts to fail. According to Wang Xue Qin, a noted expert on the Chinese futures market and also the vice general economist of Zhengzhou Commodities Exchange, in theory, a new contract can be listed upon approval by the CSRC. In practice, the CSRC won‘t approve a product unless a consensus 23

has been formed by the State Council and almost any ministry or commission that has some interest in the product. For some products that means over 10 ministries and commissions have to weigh in before a new contract gets a green light. Another aspect of the approval process that makes for cautious approval, if one were needed, is that regulators and others with some tie to the product demand from the exchanges a virtual guarantee of success. Unlike the western system where the exchanges are free to fail or look foolish, failure could mean loss of face and career risk for too many parties in China‘s hybrid system. According to the management, there will be more new contracts, pending from the favorable development in terms of types of products, market awareness and quality of participation over the coming few years, as futures are a key risk hedging component to an economy that is becoming more market-oriented and subject to global trade. Realized Price and Predicted Price: Futures trading at work: Commodity Futures form an advanced clearing function for the physical commodity clearing. Each Futures contract would generate a particular pattern of cash flow and cash commitment at a given price between the counterparties. In a Futures contract, payments are being made all along the life of the contract, whenever the Futures price changes. This is called "mark to market". Concretely, these payments involve additions and subtractions from "margin accounts" held at the Futures clearinghouse. It is significant that both the long and short side have to put up margin, because at the moment the contract is entered, both are in a sense equally likely to lose and so equally likely to have to make a payment to the other side. By means of Margin Calls, Commodity Futures shifts future imbalances between cash inflows and outflows into the present. Financial crisis in the present can also arise when these future imbalances get so large that they disrupt the present. At any moment, a particular pattern of cash flows and cash commitments resolves itself into a particular pattern of clearing and settlement. Deficit Agents in the trade will need to borrow cash from banks today to delay settlement of that Commodity Futures. Of course, banks will not hold this risk unless they are compensated by an expectation of profit. But by means of credit, current imbalances are pushed into the future where, hopefully, they can be offset against a pattern of 24

imbalances going the other way. And the elastic availability of such promises to pay are the essential source of elasticity in the payment system. In some sense, the futures market works just the opposite from the credit market. The credit market operates to postpone settlement until a future date or dates, while the futures market operates to accelerate settlement to a present date or dates. It is important to emphasize that Futures contracts, like debt contracts, are in zero net supply in the aggregate economy. One person's long contract is another person's short contract. Further, the quantity of outstanding contracts, called the open interest, has no tight relation to the quantity of the underlying. It's an approximate measure of the elasticity of uncertainty relative to the convergence of price.

NYSE EURONEXT: Link: http://www.nyse.com/about/1088808971270.htmlOverview NYSE completed its acquisition of Archipelago Holdings via reverse takeover on March 7, 2006 in a 10 billion USD deal to create the NYSE Group. The NYSE Group became a for-profit corporation and began trading publicly on its own stock exchange on March 8, 2006 under the NYX ticker. Owners of the 1,366 NYSE seats received 80,177 shares of NYSE Group stock plus US$300,000 in cash and US$70,571 in dividends. NYSE Group merged with Euronext on April 4, 2007 to form the first global equities exchange.

Merger of EURONEXT and NYSE Group: Due to apparent moves by NASDAQ to acquire the London Stock Exchange, NYSE Group offered 8 billion euros in cash and shares for Euronext on May 22, 2006, outbidding a rival offer for the European Stock exchange operator from Germany's Deutsche Börse, the German stock market.[1] Contrary to statements that it would not raise its bid, on May 23, 2006, Deutsche Börse unveiled a merger bid for Euronext, valuing the pan-European exchange at US$11 billion

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(€8.6bn), €600 million over NYSE Group's initial bid. [2] Despite this, NYSE Group and Euronext penned a merger agreement, subject to shareholder vote and regulatory approval. The initial regulatory response by SEC chief Christopher Cox (who was coordinating heavily with European counterparts) was positive, with an expected approval by the end of 2007.[3] The new firm, tentatively dubbed NYSE Euronext, would be headquartered in New York City, with European operations and its trading platform run out of Paris. NYSE CEO John Thain, who would head NYSE Euronext, intends to use the combination to form the world's first global stock market, with continuous trading of stocks and derivatives over a 21-hour time span. In addition, the two exchanges hoped to add Borsa Italiana (the Milan stock exchange) into the grouping. On June 23, 2007, the Borsa Italiana was however sold to the London Stock Exchange. Deutsche Börse dropped out of the bidding for Euronext on November 15, 2006, removing the last major hurdle for the NYSE Euronext transaction. A run-up of NYSE Group's stock price in late 2006 made the offering far more attractive to Euronext's shareholders. On December 19, 2006, Euronext shareholders approved the transaction by a 98.2% margin. The remainder voted in favor of the Deutsche Börse offer. Jean-Francois Theodore, the Chief Executive Officer of Euronext, stated that they expected the transaction to close within three or four months.[6] Some of the regulatory agencies with jurisdiction over the merger had already given approval. NYSE Group shareholders gave their approval on December 20, 2006. The NYSE consummated its US$11 billion takeover of Paris-based exchange operator Euronext NV at ceremonies in the U.S. and Europe on April 4, 2007.

Locations: Below is a list of major NYSE Euronext locations: •

Brussels, Belgium — Euronext Brussels

•

Paris, France — Euronext Paris

•

Amsterdam, Netherlands — Euronext Amsterdam 26

•

Lisbon, Portugal — Euronext Lisbon

•

London, United Kingdom — Euronext.liffe

•

Chicago, Illinois, United States of America — NYSE Arca (formerly Archipelago)

•

New York City, New York, United States of America — NYSE, Headquarters

•

New York City, New York, United States of America — AMEX (To be relocated to

NYSE headquarters) •

San Francisco, California, United States of America — NYSE Arca (formerly Pacific

Exchange) •

Belfast, Northern Ireland part of the NYSE EURONEXT Technologies branch following

the acquisition of Wombat Financial Software which had a Centre of Excellence based in the City. NYSE EURONEXT also owns 25% of the Doha Securities Market.

NCDEX: Profile: National Commodity & Derivatives Exchange Limited (NCDEX) is a professionally managed on-line multi commodity exchange. The shareholders are: Promoter shareholders: Life Insurance Corporation of India (LIC), National Bank for Agriculture and Rural Development (NABARD) and National Stock Exchange of India Limited (NSE) . Other shareholders: Canara Bank, CRISIL Limited (formerly the Credit Rating Information Services of India Limited), Goldman Sachs, Intercontinental Exchange (ICE), Indian Farmers Fertiliser Cooperative Limited (IFFCO) and Punjab National Bank (PNB). NCDEX is the only commodity exchange in the country promoted by national level institutions. This unique parentage enables it to offer a bouquet of benefits, which are currently in short supply in the commodity markets. The institutional promoters and shareholders of NCDEX are 27

prominent players in their respective fields and bring with them institutional building experience, trust, nationwide reach, technology and risk management skills. NCDEX is a public limited company incorporated on April 23, 2003 under the Companies Act, 1956. It obtained its Certificate for Commencement of Business on May 9, 2003. It commenced its operations on December 15, 2003. NCDEX is a nation-level, technology driven de-mutualised on-line commodity exchange with an independent Board of Directors and professional management - both not having any vested interest in commodity markets. It is committed to provide a world-class commodity exchange platform for market participants to trade in a wide spectrum of commodity derivatives driven by best global practices, professionalism and transparency. NCDEX is regulated by Forward Markets Commission. NCDEX is subjected to various laws of the land like the Forward Contracts (Regulation) Act, Companies Act, Stamp Act, Contract Act and various other legislations. NCDEX is located in Mumbai and offers facilities to its members about 550 centres throughout India. The reach will gradually be expanded to more centres. NCDEX currently facilitates trading of 57 commodities Agriculture: Barley, Cashew, Castor Seed, Chana, Chilli, Coffee - Arabica, Coffee - Robusta, Crude Palm Oil, Cotton Seed Oilcake, Expeller Mustard Oil, Groundnut (in shell), Groundnut Expeller Oil, Guar gum, Guar Seeds, Gur, Jeera, Jute sacking bags, Indian Parboiled Rice, Indian Pusa Basmati Rice, Indian Traditional Basmati Rice, Indian Raw Rice, Indian 28.5 mm Cotton, Indian 31 mm Cotton, Masoor Grain Bold, Medium Staple Cotton, Mentha Oil, Mulberry Green Cocoons, Mulberry Raw Silk, Mustard Seed, Pepper, Potato, Raw Jute, Rapeseed-Mustard Seed Oilcake, RBD Palmolein, Refined Soy Oil, Rubber, Sesame Seeds, Soybean, Sugar, Yellow Soybean Meal, Tur, Turmeric, Urad, V-797 Kapas, Wheat, Yellow Peas, Yellow Red Maize.

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Metals: Aluminum, Electrolytic Copper Cathode, Gold, Mild Steel Ingots, Nickel Cathode, Silver, Sponge Iron, Zinc Ingot. Energy: Brent Crude Oil, Furnace Oil. At subsequent phases trading in more commodities would be facilitated.

MCX: Overview Headquartered in the financial capital of India, Mumbai, MCX (www.mcxindia.com) is a demutualised nationwide electronic multi commodity futures exchange set up by Financial Technologies with permanent recognition from Government of India for facilitating online trading, clearing & settlement operations for futures market across the country. The exchange started operations in November 2003.

Apart from being accredited with ISO 9001:2000 for quality standards, MCX offers futures trading in 55 commodities as on December 31, 2007, defined in terms of the type of contracts offered, from various market segments including bullion, energy, ferrous and non-ferrous metals, oils and oil seeds, cereals, pulses, plantations, spices, plastics and fibres. The exchange strives to be at the forefront of developments in the commodities futures industry and has forged ten strategic alliances across the world, including with Tokyo Commodity Exchange, Chicago Climate Exchange, London Metal Exchange, New York Mercantile Exchange, New York Board of Trade and Bursa Malaysia Derivatives, Berhad. Key shareholders: The Key shareholders in MCX are: State Bank of India and its associates (SBI), National Bank for Agriculture and Rural Development (NABARD), National Stock Exchange of India Ltd. (NSE), SBI Life Insurance Co. Ltd., Bank of India (BoI) , Bank of Baroda ( BoB ), Union Bank

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of India, Corporation Bank, Canara Bank, HDFC Bank,Benett Coleman & Company Limited , Fid Fund (Mauritius) Ltd. - an affiliate of Fidelity International, ICICI Trusteeship Service Limited, IL&FS Trust Company Limited, Kotak group, Citibank Strategic Holdings Mauritius Limited, Merrill Lynch Holdings (Mauritius) and Financial Technologies of India Ltd Trading: MCX employs state-of-the-art, new generation integrated trading platform that permits faster and efficient operations in a cost effective manner. The Exchange Central System is located in Mumbai, and maintains the Central Order Book, which matches the trades on a pre-defined matching algorithm, and confirms the execution of trades to the members on an online real-time basis. It has an integrated Surveillance and Settlement System. Exchange members located across the country are connected to the central system through VSAT, Leased line, Internet or any other mode of communication as permitted by the Exchange. The Exchange also has a Disaster Recovery Site

Risk Management: The central objective of MCX's Risk Management System is to assess and manage the risk of the market in an expeditious manner to ensure smooth and timely pay-in/ pay-out process of the Exchange. Some of the basic functions of Risk Management are as follows – Real-time Margining System at client level Monitoring of position limits (Quantity) Capital adequacy norms Daily price limits Initial margins Special margins

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Marked-to-market margin Delivery period margin Clearing and Settlement: The Clearing and Settlement System of the Exchange is system driven and rule based. The Exchange has its own in-house clearing house, which undertakes to clear each and every trade and is counter-party for all trades; thus offering novation (zero counter-party risk) to each and every trade executed on the Exchange

Clearing Bank Interface: Exchange maintains electronic interface with its Clearing Banks. All members of the exchange have their Settlement and Client Accounts for exchange operations with the Clearing Bank. All debits and credits are conducted electronically through Settlement account only. Delivery and Final Settlement: All contracts on maturity are for delivery. MCX specifies tender and delivery periods. For example, such periods can be from the 8th working day till the 15th day of the month - where 15th is the last trading day of the contract month - as tender and/ or delivery period. A seller or a short open position holder in that contract may tender documents to the exchange expressing his intention to deliver the underlying commodity. The exchange would then select the buyer from the long open position holder for the tendered quantity. Once the buyer is identified, seller has to initiate the delivery process and the buyer has to take delivery according to the delivery schedule prescribed by the exchange.

COMMODITY MARKET: The gradual evolution of commodity market in India has been of great significance for our country's economic prosperity. In the Indian commodity market there are so many verities of products including agricultural products like rice, wheat, cattle etc; energy products like coal, 31

petroleum, kerosene, gasoline; metals like copper, gold, silver, aluminum and many more. There are some delicate commodities also such as sugar, cocoa, and coffee, which is perishable, so cannot be put in stock for long time. The commodity futures exchanges were evolved in 1800 with the sole objective of meeting the demand of exchangeable contracts for trading agricultural commodities. Nowadays a wide range of agricultural products, energy products, delicate commodities and metals can be sold under standardized contracts on exchanges prevailing across the globe. Commodities have gained importance with the development of commodity futures indexes along with the mobilization of more resources in the commodity market. Commodity Futures Markets in India: Agriculture sector in India has always been a major field of government intervention since long back. Government tries to protect the interests of the poor Indian farmers by procuring crops at remunerative prices directly from the farmers without involving middlemen in between. This way Government maintains sufficient buffer stocks and at the same time provides the farmers safeguard against the fluctuating food crop prices. But government at the same time has restricted this traditional sector by fixing prices of crops at a particular level and also by imposing several other restrictions on export and import of agricultural commodities. All these restrictions prevented this sector to move out its traditional features. So according to many economists liberalization of this traditional agricultural sector could have been of great benefit to our economy. But questions will naturally come up about the maintenance of buffer stocks and provisions of remunerative prices to the farmers. In absence of government's intervention farmers will not be getting any prior information about the future markets of their products. Naturally a sudden price crash of food crops will have devastating effects on farmers. Here comes the significant role of futures market. If the buyers in the commodity market anticipate shortage of a particular crop in the coming season, future price of that crop will increase now and this will act as a signal to the farmers who will accordingly plan their seeding decisions for the next season. In the same way, an increase in future demand of food crops will be reflected in the today's price in futures market. In this way the system of futures market can be of great help to the Indian farmers preventing them from being directly exposed to the unexpected price changes all of a sudden. It also helps towards evolving a better cropping pattern in our country.

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If the peasants are farming some crop now and are very much concerned that price will crash by the time the crop comes in, then if there is futures market, they will have the option to sell their products in it. Price in the future markets being fixed; by selling products in future markets they get rid of their worries about the about the unexpected price fall. This helps them to take the risk of innovations, by using new high yielding varieties of seeds, fertilizers and new techniques of cultivation. Futures Market will act as a smoothing agent between the present and future commodity market. If the price, which is going to prevail in future, is high compared to what is it now, then the arbitragers would like to buy the commodities now to sell those in future. The reverse process is also true. So the existence of a futures market is always good for any economy. It opens up a new opportunity to people to protect themselves from unexpected risks. Derivatives Market in India: Derivative markets can broadly be classified as commodity derivative market and financial derivatives markets. As the name suggest, commodity derivatives markets trade contracts for which the underlying asset is a commodity. It can be an agricultural commodity like wheat, soybeans, rapeseed, cotton, etc or precious metals like gold, silver, etc. Financial derivatives markets trade contracts that have a financial asset or variable as the underlying. The most popular financial derivatives are those, which have equity, interest rates and exchange rates as the underlying. Financial derivatives are used to hedge the exposure to market risk. The commodity derivatives differ from the financial derivatives mainly in the following two aspects: Firstly, due to the bulky nature of the underlying assets, physical settlement in commodity derivatives creates the need for warehousing. Secondly, in the case of commodities, the quality of the asset underlying a contract can vary largely. Some of the major market players in commodities market are: Hedgers, Speculators, Investors, Arbitragers Producers - Farmers Consumers - refiners, food processing companies, jewelers, textile mills, exporters & importers

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Commodities Market in India: India has a long history of futures trading in commodities. In India, trading in commodity futures has been in existence from the nineteenth century with organised trading in cotton, through the establishment of Bombay Cotton Trade Association Ltd. in 1875. Over a period of time, other commodities were permitted to be traded in futures exchanges. Spot trading in India occurs mostly in regional mandis and unorganised markets, which are fragmented and isolated. There were booming activities in this market and at one time as many as 110 exchanges were conducting forward trade in various commodities in the country. The securities market was a poor cousin of this market as there were not many papers to be traded at that time. The era of widespread shortages in many essential commodities resulting in inflationary pressures and the tilt towards socialist policy, in which the role of market forces for resource allocation got diminished, saw the decline of this market since the mid-1960s. This coupled with the regulatory constraints in 1960s, resulted in virtual dismantling of the commodities future markets. It is only in the last decade that commodity future exchanges have been actively encouraged. However, the markets have been thin with poor liquidity and have not grown to any significant level. Indian Policy makers have traditionally coped with the uncertainty and risks associated with price volatility by resorting to policy instruments which attempted to minimize or eliminate price volatility - a virtually closed external trade regime, price control, pervasive government controls on private sector activities extensive market interventions and crop insurance. Liberalization of Indian economy since 1991 recognised the role of market and private initiative for the development of the economy. The much maligned market instruments such as the futures trading were also given due recognition. After some halting efforts since 1994 when Prof. Kabra Committee submitted its report, the late 1990s spilling into the new millennium, saw some bold initiatives in the commodity market. A three-pronged approach has been adopted to revive and revitalise this market. Firstly, on policy front many legal and administrative hurdles in the functioning of the market have been removed. Forward trading was permitted in cotton and jute goods in 1998, followed by some 34

oilseeds and their derivatives, such as groundnut, mustard seed, sesame, cottonseed etc. in 1999. A statement in the first ever National Agriculture Policy, issued in July, 2000 by the government that futures trading will be encouraged in increasing number of agricultural commodities was indicative of welcome change in the government policy towards forward trading. Secondly, strengthening of infrastructure and institutional capabilities of the regulator and the existing exchanges received priority. Thirdly, as the existing exchanges are slow to adopt reforms due to legacy or lack of resources, new promoters with resources and professional approach were being attracted with a clear mandate to set up demutualised, technology driven exchanges with nationwide reach and adopting best international practices. The year 2003 marked the real turning point in the policy framework for commodity market when the government issued notifications for withdrawing all prohibitions and opening up forward trading in all the commodities. This period also witnessed other reforms, such as, amendments to the Essential Commodities Act, Securities (Contract) Rules, which have reduced bottlenecks in the development and growth of commodity markets. Of the country's total GDP, commodities related (and dependent) industries constitute about roughly 50-60 %, which itself cannot be ignored. Most of the existing Indian commodity exchanges are single commodity platforms; are regional in nature, run mainly by entities which trade on them resulting in substantial conflict of interests, opaque in their functioning and have not used technology to scale up their operations and reach to bring down their costs. But with the strong emergence of: National Multi-commodity Exchange Ltd., Ahmedabad (NMCE), Multi Commodity Exchange Ltd., Mumbai (MCX), National Commodities and Derivatives Exchange, Mumbai (NCDEX), and National Board of Trade, Indore (NBOT), all these shortcomings will be addressed rapidly. These exchanges are expected to be role model to other exchanges and are likely to compete for trade not only among themselves but also with the existing exchanges. The recent policy changes and upbeat sentiments about the economy, particularly agriculture, have created lot of interest and euphoria about the commodity markets. Even though a large number of the traditional exchanges are showing flat volume, this has not weakened excitement among new participants. Many of these exchanges have been permitted with a view to extend the

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culture and tradition of forward trading to new areas and commodities and also to introduce new technology and practices. The current mindset of the people in India is that the Commodity exchanges are speculative (due to non delivery) and are not meant for actual users. One major reason being that the awareness is lacking amongst actual users. In India, Interest rate risks, exchange rate risks are actively managed, but the same does not hold true for the commodity risks. Some additional impediments are centered around the safety, transparency and taxation issues. Which Commodities are suitable for Future Trading? The following are some of the key factors, which decide the suitability of the commodities for future trading: The commodity should be competitive, i.e., there should be large demand for and supply of the commodity - no individual or group of persons acting in concert should be in a position to influence the demand or supply, and consequently the price substantially. There should be fluctuations in price. The market for the commodity should be free from substantial government control. The commodity should have long shelf life and be capable of standardization and gradation. Need For Futures Trading In Commodities: Commodity Futures, which forms an essential component of Commodity Exchange, can be broadly classified into precious metals, agriculture, energy and other metals. Current futures volumes are miniscule compared to underlying spot market volumes and thus have a tremendous potential in the near future. Futures trading in commodities results in transparent and fair price discovery on account of large-scale participations of entities associated with different value chains. It reflects views and expectations of a wider section of people related to a particular commodity. It also provides effective platform for price risk management for all segments of players ranging from producers, traders and processors to exporters/importers and end-users of a commodity. 36

It also helps in improving the cropping pattern for the farmers, thus minimizing the losses to the farmers. It acts as a smart investment choice by providing hedging, trading and arbitrage opportunities to market players. Historically, pricing in commodities futures has been less volatile compared with equity and bonds, thus providing an efficient portfolio diversification option. Raw materials form the most key element of most of the industries. The significance of raw materials can further be strengthened by the fact that the "increase in raw material cost means reduction in share prices". In other words "Share prices mimic the commodity price movements". Industry in India today runs the raw material price risk; hence going forward the industry can hedge this risk by trading in the commodities market. Regulatory Body: The Forward Markets Commission (FMC) is the regulatory body for commodity futures/forward trade in India. The commission was set up under the Forward Contracts (Regulation) Act of 1952. It is responsible for regulating and promoting futures/forward trade in commodities. The FMC is headquartered in Mumbai while its regional office is located in Kolkata. Curbing the illegal activities of the diehard traders who continued to trade illegally is the major role of the Forward Markets Commission. Why Commodities Market? India has very large agriculture production in number of agri-commodities, which needs use of futures and derivatives as price-risk management system. Fundamentally price you pay for goods and services depend greatly on how well business handle risk. By using effectively futures and derivatives, businesses can minimize risks, thus lowering cost of doing business. Commodity players use it as a hedge mechanism as well as a means of making money. For e.g. in the bullion markets, players hedge their risks by using futures Euro-Dollar fluctuations and the international prices affecting it.

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For an agricultural country like India, with plethora of mandis, trading in over 100 crops, the issues in price dissemination, standards, certification and warehousing are bound to occur. Commodity Market will serve as a suitable alternative to tackle all these problems efficiently. Problems faced by Commodities Markets in India: Institutional issues have resulted in very few deliveries so far. Currently, there are a lot of hassles such as octroi duty, logistics. If there is a broker in Mumbai and a broker in Kolkata, transportation costs, octroi duty, logistical problems prevent trading to take place. Exchanges are used only to hedge price risk on spot transactions carried out in the local markets. Also multiple restrictions exist on inter-state movement and warehousing of commodities. Risks associated with Commodities Markets: No risk can be eliminated, but the same can be transferred to someone who can handle it better or to someone who has the appetite for risk. Commodity enterprises primarily face the following classes of risks, namely: the price risk, the quantity risk, the yield/output risk and the political risk. Talking about the nationwide commodity exchanges, the risk of the counter party (trading member, client, vendors etc) not fulfilling his obligations on due date or at any time thereafter is the most common risk.

This risk is mitigated by collection of the following margins: Initial Margins Exposure margins Market to market of positions on a daily basis Position Limits and Intra-day price limits Surveillance Commodity price risks include: Increase in purchase cost vis-à-vis commitment on sales price 38

Change in value of inventory Counter party risk translating into commodity price risk Key Factors For Success Of Commodities Market: The following are some of the key factors for the success of the commodities markets: How one can make the business grow? How many products are covered? How many people participate on the platform? Strategy, method of execution, background of promoters, credibility of the institution, transparency of platforms, scalable technology, robustness of settlement structures, wider participation of Hedgers, Speculators and Arbitrageurs, acceptable clearing mechanism, financial soundness and capability, covering a wide range of commodities, size of the trade guarantee fund, reach of the organisation and adding value on the ground. In addition to this, if the Indian Commodity Exchange needs to be competitive in the Global Market, then it should be backed with proper "Capital Account Convertibility". The interests of Indian consumers, households and producers are most important, as these are the people who are exposed to risk and price fluctuations.

Key Expectations of Commodities Exchanges: The following are some of the key expectations of the investors‘ trading in any commodity exchange: To get in place the right regulatory structure to even out the differences that may exist in various fields. Proper Product Conceptualization and Design. 39

Fair and Transparent Price Discovery & Dissemination. Robust Trading & Settlement systems. Effective Management of Counter party Credit Risk. Self-Regulation to ensure: Overview of Trading and Surveillance, Audit and review of Members, Enforcement of Exchange rules. Future Prospects: With the gradual withdrawal of the government from various sectors in the post-liberalization era, the need has been felt that various operators in the commodities market be provided with a mechanism to hedge and transfer their risks. India's obligation under WTO to open agriculture sector to world trade would require futures trade in a wide variety of primary commodities and their products to enable diverse market functionaries to cope with the price volatility prevailing in the world markets. Government subsidy may go down as a result of WTO. The MSP programme will not be sustainable in such a scenario. The farmer will have to look at ways of being in a position to trade on commodity exchanges in future. Also, corporates will feel the pressure to hedge their price risk once the frontiers open up for free trade. Indian markets have recently thrown open a new avenue for retail investors and traders to participate: commodity derivatives. For those who want to diversify their portfolios beyond shares, bonds and real estate, commodity is the best option. Following are some of the applications, which can utilize the power of the commodity markets and create a win-win situation for all the involved parties: Regulatory Approval / Permission to FII's for trading in the Commodity Markets FII's are currently not allowed nor disallowed under any law. As, they have added depth to the equity markets; they will add depth to the commodities markets, since they globally know the commodities. Active Involvement of Mutual Fund Industry in India

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Currently Mutual Funds are prohibited from not using derivatives apart from hedging. Mutual Funds as investors can invest in gold and get returns as they get from debt instruments, equity markets. AMFI & SEBI need to collectively work towards the same. Launch of the "Commodity Funds", by the Mutual Funds in India, can serve as a newer investment avenue for investors. Permission to Banks for acting as Aggregators and Traders If institutions join this market, confidence of the investor increases. If a bank like SBI decides to invest in the market, the confidence of investors in the markets goes up. Banks can on behalf of the farmer's hedge their risks, and get a fee income. Banks can have limits, which can be set up by the RBI. This requires a change in the Banking Regulation Act, which will take a long time. This way it can be a win-win-win situation for the market, the banks, and the farmers. Active Involvement of Small Regional Stock Exchanges The existing regional stock exchanges (RSE), which have good trading infrastructure in place but are having tough time due to tiny volumes, should be used for trading commodities. The skills and infrastructure of these RSE's will be very useful to get a jump ahead in terms of market development at low cost. Newer Avenues for trading in Foreign Derivatives Exchanges Millions of people in India use gold as a financial asset and are constantly exposed to fluctuations in the price of gold. Hence from the viewpoint of India's securities industry, it would be great to trade gold futures globally on foreign derivative exchanges - it would yield higher revenues as well as raise sophistication. Steady Transition towards Electronic Warehouse Receipts Commodity Exchanges in India are expected to contribute significantly in strengthening Indian Economy to face the challenges of globalisation. Indian markets are poised to witness further development in the areas of "Electronic Warehouse Receipts" (similar to Demat Shares), which would facilitate seamless nationwide spot market for commodities.

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Impact of WTO Regime: India being a signatory to WTO may open up the agricultural and other commodity markets more to the global competition. India's uniqueness as a major consumption market is an invitation to the world to explore the Indian market. Indian producers and traders too would have the opportunity to explore the global markets. Price risk management and quality consciousness are two important factors to succeed in the global competition. Indian companies are allowed to participate in the international commodity exchanges to hedge their price risk, resultant from export and import activities of such companies. But due to the compliance issues and international exchange rules, 90 % of the commodity traders and producers are not in a position to participate in the international exchanges. Convergence of Various Markets In the near future the integration of the international equity, commodities, forex and debt would enhance the business opportunities. It will also create specialized treasuries and fund houses that would offer a gamut of services, thus providing comprehensive risk management solutions to India's corporate and trade community. Amendments in the Commodities Act and Implementation of VAT Amendments to Essential Commodities Act and implementation of Value-Added-tax would enable movement of across states and more unified tax regime, which would facilitate easier trading in commodities. Introduction of Options Contract Options contracts in commodities are being considered and this would again boost the commodity risk management markets in the country. Thus, Commodity derivatives as an industry is poised to take-off, which may provide the numerous investors in this country with another opportunity to invest and diversify their portfolio. Strong Emergence of the Yellow Metal - GOLD

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Interestingly, gold turned out to be the strongest major currency last fiscal (2002-03) as it outperformed the other major currencies by between 9 and 25 % over the year. The yellow metal outperformed the major stock indices too. Over the course of the year, gold outperformed the dollar by 25 %, the yen by 14 % and the pound sterling by 13 %, the euro by 9 %and the Swiss franc by 7 %. India being the largest buyer of Gold in International market can be the market leader in respect of Price discovery and Price formation in International market, if a transparent Gold Exchange at national level is set up with widespread participation. Structured Commodity Financing: (Asset-backed Financing) In Structured Finance, collateral is assigned and an automatic reimbursement procedure is devised. It can be made available as receivable-backed financing and inventory financing. The clients retain the economic benefits and risks of ownership by transacting a swap. For successful implementation of Structured financing in India, the rural banking infrastructure should be made strong, the government should come across with strong policies and lastly there should be increased awareness and training amongst common masses. Launch of "Weather Derivatives", through the commodity exchanges in the near future. Globally, it is done on a temperature basis, whereas in India it could be done on rainfall basis. The Road Ahead: The following 4-step action plan sums up the road ahead for commodity markets in India: 1. Seeking changes in Banking Regulation Banking Regulation Act to be modified. Accreditation of warehouses and quality of products furthers bank lending against commodities. Change in Priority sector norms. Lending against commodities to be considered priority sector lending. Banks to lend against bullion. 43

Need for hallmarking. 2. Changing the face of Warehousing Electronic warehouse receipt (EWR) to be legally recognized. EWR to be a negotiable instrument. Depositories Act to be amended to cover commodities. SEBI to notify that FMC is regulatory body for dematerialized commodities. 3. Inducing Policy changes Introduction of options will ably substitute the MSP programme of government. Permit weather derivatives. Redefining commodities. Goods not covered under 'securities'. Products not covered under SCRA. Physical delivery not to be mandatory. 4. Budget proposals relating to Commodities Integration of commodity market with securities market. Service tax on forward contracts to affect commodity brokers. Significant omissions in light of other announcements. Weather derivatives. Bank investment in commodities. Action Plan: (As suggested by Dr. Kewal Ram - Chairman FMC)

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The following steps need to be taken by the exchanges, regulator and the government in order that this market develops in a robust manner and the benefits flow to the ultimate beneficiaries like the consumers, processors, exporters and farmers etc. To mount a massive awareness programme among the potential beneficiaries about the benefits and risks of futures trading. Disseminate futures prices widely so that stakeholders can take informed decisions. Develop other allied activities such as warehousing, standardisation and gradation, collateral financing linked to futures markets. Reforms in physical market to develop efficient and integrated national market. Make necessary amendments in the FC(R) Act for permitting futures in intangible commodities and options trading, which are at present prohibited. Allow participation of mutual funds and financial institutions in the commodity market. Coordination with other segments of the financial market such as banking, debt and capital market. Above all, to upgrade and empower regulator to provide effective and efficient leadership for the development and regulation of the market. Comparative Analysis Of Commodity And Equity Markets: Factors

Commodity Markets

Equity Markets

Percentage

Gold gives 10-15 % returns on the Returns in the range of 15-20 %

Returns

conservative basis.

on annual basis.

Initial Margins

Lower in the range of 4-5-6%

Higher in the range of 25-40%

Arbitrage

Exists on 1-2 month contracts.

Significant Arbitrage

Opportunities

There is a small difference in

Opportunities exists.

prices, but in case of commodities, 45

which is in large tonnage it makes a huge difference.

Price Movements

Price movements are purely based on the supply and demand.

Price changes are due to policy Price Changes

changes, changes in tariff and duties.

Prices movements based on the expectation of future performance. Price changes can also be due to Corporate actions, Dividend announcements, Bonus shares / Stock splits.

Predictability of future prices is Future

not in the control due to factors

Predictability

like Failure of Monsoon and Formation of El-ninos at Pacific.

Predictability of futures performance is reasonably high, which is supplemented by the History of management performance.

Volatility

Lower Volatility

Higher Volatility

Securities

Securities Transaction Act is not

Securities Transaction Act is

Transaction Act

applicable to commodity futures

applicable to equity markets

Application

trading.

trading.

Some Interesting Facts: Commodities in which future contracts are successful are commodities those are not protected through government policies; (Example: Gold/ Silver/ Cotton/ Jute) and trade constituents of these commodities are not complaining too. This should act as an eye-opener to the policy makers to leave pricing and price risk management to the market forces rather than to administered mechanisms alone. Any economy grows when the constituents willingly accept the

46

risk for better returns; if risks are not compensated with adequate or more returns, economic activity will come into a standstill. Worldwide, Derivatives volumes of non-US exchanges in the last decade, has been increasing as compared to the US Exchanges. Commodities are less volatile compared to equity market, but more volatile as compared to GSec's. The basic idea of Commodity markets is to encourage farmers to choose cropping pattern based on future and not past prices. Industry in India runs the raw material price risk, going forward they can hedge this risk. Commodities Exchanges are working with banks to provide liquidity to retail investors against holdings such as bullion, cotton or any edible oil, much like loan against shares. Conclusion: The commodity market is poised to play an important role of price discovery and risk management for the development of agriculture and other sectors in the supply chain. New issues and problems will surface as the market evolves. The government, regulator and other stakeholders will need to be proactive and quick in their responses to new developments. The globalisation of markets under the WTO regime makes it all the more urgent to develop these markets to enable our economy, especially agriculture, to meet the challenges of new regime and benefit from the opportunities unfolding before us. With risks not being absorbed any more, the idea is to transfer it. As the focus is shifting to "Manage price change rather than change prices", the commodity markets will play a key role for the same.

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Overview of commodities exchanges in India: Link: http://sify.com/finance/commodities/fullstory.php?id=13428587

Forward Markets Commission (FMC) headquartered at Mumbai is a regulatory authority, which is overseen by the Ministry of Consumer Affairs and Public Distribution, Govt. of India. It is a statutory body set up in 1953 under the Forward Contracts (Regulation) Act, 1952. The Act Provides, that the Commission shall consist of not less than two but not exceeding four members appointed by the Central Government out of them being nominated by the Central Government to be the Chairman thereof Currently Commission comprises three members among whom Dr. Kewal Ram, IES, is acting as Chairman and Smt. Padma Swaminathan, CSS and Dr. (Smt.) Jayashree Gupta, CSS, are the Members of the Commission. The functions of the Forward Markets Commission are as follows: (a) To advise the Central Government in respect of the recognition or the withdrawal of recognition from any association or in respect of any other matter arising out of the administration of the Forward Contracts (Regulation) Act 1952. (b) To keep forward markets under observation and to take such action in relation to them, as it may consider necessary, in exercise of the powers assigned to it by or under the Act. (c) To collect and whenever the Commission thinks it necessary, to publish information regarding the trading conditions in respect of goods to which any of the provisions of the act is made applicable, including information regarding supply, demand and prices, and to submit to the Central Government, periodical reports on the working of forward markets relating to such goods; (d) To make recommendations generally with a view to improving the organization and working of forward markets;

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(e) To undertake the inspection of the accounts and other documents of any recognized association or registered association or any member of such association whenever it considerers it necessary. The list of exchanges that has been allowed to trade in commodities are 1. Bhatinda Om & Oil Exchange Ltd., Batinda. 2. The Bombay Commodity Exchange Ltd.Mumbai 3. The Rajkot Seeds oil & Bullion Merchants` Association Ltd 4. The Kanpur Commodity Exchange Ltd., Kanpur 5. The Meerut Agro Commodities Exchange Co. Ltd., Meerut 6. The Spices and Oilseeds Exchange Ltd. 7. Ahmedabad Commodity Exchange Ltd. 8. Vijay Beopar Chamber Ltd.,Muzaffarnagar 9. India Pepper & Spice Trade Association. Kochi 10. Rajdhani Oils and Oilseeds Exchange Ltd. , Delhi 11. National Board of Trade. Indore. 12. The Chamber Of Commerce, Hapur 13. The East India Cotton Association Mumbai. 14. The Central India Commercial Exchange Ltd, Gwaliar 15. The East India Jute & Hessian Exchange Ltd, 16. First Commodity Exchange of India Ltd, Kochi 17. Bikaner Commodity Exchange Ltd., Bikaner 18. The Coffee Futures Exchange India Ltd, Bangalore. 49

19. Esugarindia Limited. 20. National Multi Commodity Exchange of India Limited. 21. Surendranagar Cotton oil & Oilseeds Association Ltd, 22. Multi Commodity Exchange of India Ltd 23. National Commodity & Derivatives Exchange Ltd. 24. Haryana Commodities Ltd., Hissar 25. e-Commodities Ltd. Out of these 25 commodities the MCX, NCDEX and NMCE are large exchanges and MCX is the biggest among them.

General information on groundnut as a commodity: Description: Groundnut is an oilseed derived from the fruit of the groundnut plant. It is referred to as a nut in general terms but it is not a nut exactly in actual terms, it is a seed rather and is also known by the name of peanut. The groundnut plant is an annual plant herb that comes from the pea family of Fabaceae. The plant has feather type leaves; yellow flowers and grows a legume shaped fruit that has 2 to 3 seeds which develops inside the earth. Also, oil is obtained from the groundnut seeds that is an excellent source of vitamin E, various fatty acids and carbohydrates and is largely used as a cooking medium, lighting fuel and food constituent. Overview: Groundnut is considered to be the one of the most important oilseed crops in the world. It is grown in over 100 countries of the world and plays a crucial role in the world economy. The seeds are a good source of edible oil and proteins present in the groundnut oil cake. The percentage of oil and protein are extracted from the seed are approximately 55% and 28% respectively. The oil cake meal left after the extraction of the oil is used as an animal fodder and fertilizer. The peanut oil is primarily needed as a cooking agent but it also has some industrial uses like in paint, varnish, lubricating oil, soap, furniture polish etc. the peanut seeds are also consumed directly in roasted form, as butter, in brittle and candies etc .

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Groundnut production has reached the mark of around 34 million tons. China followed by India is the largest producer of this oilseed crop in the world. The groundnut oil production hovers around 8 million tons annually. These two countries are also responsible for the highest consumption of groundnut. The list depicting the major groundnut consuming countries is given below China India Nigeria United States European Union Except European Union, all the countries lie in the list of major groundnut producing countries as well. European Union countries are the largest consumer of groundnuts where the crop is not produced. The major demand i.e. around 75% comes from the food sector and the rest from other sectors. As European Union is the largest consumer of the oilseed where it is not produced, it has to rely on imports and that makes the countries in the union the largest importers of groundnut. The trade done in the world in the context of groundnuts is estimated to about 1 lakh tons per year. The leading groundnut exporting countries are Argentina Senegal Nigeria India United States China Vietnam South Africa Gambia

51

The countries mentioned above contribute to about 90% of the world exports. Argentina makes the largest groundnut exporter to the world. The major countries that satisfy their domestic consumption demand by importing groundnuts are Belgium France Germany Ireland Italy Netherlands United States United Kingdom Sweden Indonesia Canada Malaysia Singapore Philippines Japan History: The groundnut plant is believed to have originated in the South American continent, primarily in the tropical areas of Peru as the archeological records assume. The climate there helped the plant to grow and develop as a wild plant. But no evidence has been found out regarding the confirmation of the above assumption. But the domestication of this plant was done in the valleys of Paraguay only. Groundnut was being cultivated in the new world countries since 2500BC and this was the place where a diversity of the species of groundnut was cultivated. When Columbus found America, groundnut came in to the contact of the rest of the world. The Spaniards who explored the southern America encountered with this nut like seed and soon after, the different varieties of groundnut started to get spread around the world. A type of variety named ‗Virginia‘ was taken to Mexico first and then to West Africa. Then it moved to the North America courtesy the West Indies and West Africa in the 17th century. The Peruvian variety was 52

taken to the southeastern Asian regions of China and Philippines by the Spanish ships and ultimately these foreign beans spread all over Asia. The Spanish variety was taken into Africa directly by the Portuguese explorers and there it got mixed with the Virginia variety and was finally introduced into Spain in the 18th century. The latest groundnut variety i.e. Valencia was taken to the country of Spain from Argentina in the 19th century and then it spread over all the Europe later. Cultivation pattern: Groundnut plant is a tropical plantation, as it requires a hot and humid climate to grow. The basic factor that affects the performance of the crop is rain or extent of irrigation. It is an essential for a good yield of groundnut to have high level of rainfall. Groundnut prospers well in a light, sandy loam soil. The pod needs duration of 4-5 months to ripen. The time at which the crop is to be harvested also requires proper attention. The groundnut fruits are to be harvested exactly when they started to get ripe and not anytime else. At the time of harvesting, the whole plant, even the roots are totally removed from the soil. The planting season in India starts from May and stays till August. It largely depends on the time of the arrival of monsoon in the country. The harvesting season starts from September to January. In India, 2/3rds of the crop is cultivated as a kharif crop and the rest is produced as rabi crop. Varieties of groundnut: There are vast varieties of groundnut that are grown in the world. but the most popular varieties are mentioned below. All these varieties have different backgrounds, different characteristics and various other features that differentiate them from each other 1. Spanish group – Small seeded Mostly cultivated in South Africa and southeastern and southwestern America Includes - Dixie Spanish, Improved Spanish 2B, GFA Spanish, Argentine, Spantex, Spanette, Shaffers Spanish, Natal Common (Spanish), White 53

Kernel Varieties, Starr, Comet, Florispan, Spanhoma, Spancross and Wilco I. 2. Runner group – Better flavor, better roasting characteristics and higher yield Grown in Georgia, Alabama, Florida, and South Carolina Includes - Southeastern Runner 56-15, Dixie Runner, Early Runner, Virginia Bunch 67, Bradford Runner, Egyptian Giant, Rhodesian Spanish Bunch, North Carolina Runner 56-15, Florunner and Shulamit. 3. Virginia group – Large seeded, high quality Found in Virginia, North Carolina, Tennessee and Georgia Includes - NC 7, NC 9, NC 10C, NC-V 11, VA 93B, NC 12C, VA-C 92R, Gregory, VA 98R, Perry, Wilson, Georgia Green 4. Valencia group – Coarse, reddish stem and large foliage Largely cultivated in New Mexico 5. Tennessee red and tennessee white – Same as Valencia group except the color of the seed. Rough pods, irregular pods and having a small number of kernels. Groundnut producing countries: Groundnut is one of the vastly produced oilseed crop in the world as it is cultivated in more than 100 countries in the world and that is why it is referred to as a universal crop. The areas in the tropical belt of the earth enjoy the major share in this groundnut crop production as these type of weather conditions suit well to it. It is estimated that around 65% of the crop produced in the world is crushed to extract groundnut oil and the rest is used in making other edible products. 54

The world production of groundnut seeds hovers around 34 million tons per year in the current scenario. The groundnut oil is produced to an extent of around 8 million tons. The major producers of groundnut in 2005 along with their production figures are China (14408500 tons) India (5900000 tons) Nigeria(2937000 tons) United States of America (2112700 tons) Indonesia (1469000 tons) Sudan (1200000 tons) Senegal (820569 tons) Myanmar (715000 tons) Argentina (593000 tons) Vietnam (453000 tons) Chad (450000 tons) Ghana (389649 tons) Congo (368110 tons) Guinea (300000 tons) Brazil (291966 tons) Burkina Faso (245307 tons) Cameroon (225000 tons) Egypt (190000 tons) Mali (163900 tons) Malawi (161162 tons) In context of the production of groundnut oil, China again tops the chart with a production of around 2.5 million tons India following with the production of around 2 million tons. The other regions where groundnut oil is produced includes sub-Saharan African countries and central and southern America. The maximum area that is used for the production of this oilseed is bagged by India with around 8 million hectares that accounts up to 30% share in the total area of around 26.5 million hectares. The country that gets maximum yield from the groundnut crop is USA

55

which has a yield of approximately 3540 Kg/ hectare. The world production has been in the uptrend since last decade and still, it is rising steadily. Production of groundnut in India India has been producing groundnut since it has been introduced in Asia in the 16th century. The weather in the Indian subcontinent suited well to the crop and India transformed into an important contributor to the world production. The country ranks 2nd in the world groundnut production scenario with an annual groundnut seed production of 5.9 million tons and annual groundnut oil production of 1.5 million tons in 2005. Also, India has the maximum area covered under groundnut cultivation. The major states in India that are indulged in the production of this crop along with their production figures are Gujarat (2.5 million tons) Tamil Nadu (1 million tons) Andhra Pradesh (1 million tons) Karnataka (0.5 million tons) Maharashtra (0.5 million tons) Madhya Pradesh Orissa Rajasthan The Indian production and area covered is largely concentrated in the above-mentioned states. Today, groundnut has a share of approximately 25% in the total Indian oilseed production. But this share is constantly reducing since India got independent, as it was around 70% in 1950s. A quick summary of area under cultivation, production and yield of groundnut crop is provided in the tables below:

56

1950-51 1953-54 1956-57 1959-60 1962-63 1965-66 1968-69 1971-72 1974-75 1977-78 1980-81 1983-84 1986-87 1989-90 1992-93 1995-96 1998-99 2001-02 2004-05 2007-08*

production in Million Tonnes

1950-51 1953-54 1956-57 1959-60 1962-63 1965-66 1968-69 1971-72 1974-75 1977-78 1980-81 1983-84 1986-87 1989-90 1992-93 1995-96 1998-99 2001-02 2004-05 2007-08*

Area in million hactares

Table no: 2.1

Year Wise Area Under Cultivation of Groundnut in India

10.00 9.00 8.00 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00 Area

Table no: 2.2

Year Wise Production of Groundnut in India

12.00

10.00

8.00

6.00

4.00 production

2.00

0.00

57

The inferences from the above data sets are that: a. The average production per year had been to the tune of 5.95 million tones. b. Nevertheless, the variation in average production has been high with a co-efficient of variation of almost 27%. Indian groundnut market: India has been a land where oilseeds have much importance than any other crop. Groundnut constitutes one of the major oil seed in India. As already mentioned, it accounts to about 25% share in the total oilseeds production in India. The country produces around 6 million tons of groundnuts annually; Gujarat being the leader in the production of the crop producing over 40% of the crop produced India. The groundnut oil production in India hovers around 1.5 million tons per year. The production of groundnut seed in the country has shown fluctuations quite often largely due to the monsoon behavior. India places 2nd in the world groundnut consumption list. The country actually consumes all most all of its groundnut yield produced that is around 30% of the world‘s total consumption. The main demand of groundnut and derivatives generate from the western and southern parts of the country. This consumption pattern of the country contracts the Indian groundnut export size and does not allow it to gain dominance over the world market even though a high production level. Earlier it was an important exporter of groundnut and its by-products in 1970s. But with time it lost all its importance due to its high prices and increasing competitiveness. The Indian exports of groundnut oil to the world showed a fluctuating trend in the last decade. In 2003-04 India exported around 1 lakh tons of groundnut oil due to a crop failure in Argentina and Senegal. The imports in the country are second to none as the production level is quite sufficient for the domestic demand level in the country. Market Influencing Factors Weather conditions in major groundnut producing regions. Monsoon status in the country. Price fluctuations of the other competitive edible oils. International price movements. 58

High consumption in festive seasons and celebrations. Major trading centers of groundnut: The major trading centers of groundnut and derivatives in India are Rajkot (Gujarat) Ahmedabad (Gujarat) Gondal (Gujarat) Junagarh (Gujarat) Mumbai (Maharashtra) Indore (Madhya Pradesh) Delhi Adoni (Andhra Pradesh) Also, groundnut is traded in Indian commodity exchanges namely, NCDEX, MCX, NMCE etc.

59

3. Objectives: 1. To have an overview of various commodity exchanges in India and world with focus on groundnut as a commodity. 2. To study the factors affecting the spot prices of these commodities in the exchanges. 3. To find the similarity/ dissimilarity in spot price trends of these commodities in two major commodity exchanges in India (NCDEX and MCX). 4. To suggest a pricing model based on trend and fundamental analysis of prices of these two commodities. 5. To study the correlation of the futures prices of these commodities with major futures indices in these markets. 6. To have a qualitative view of the illiquidity of groundnut oil as a commodity in the exchanges. 7. To measure the accuracy achieved in price matching (spot and futures) of these two commodities by the stock exchanges.

60

4. Methodology: Different methodologies were adopted for meeting different objectives. Thus the methodology adopted for each objective has been separately discussed The first objective of the project is to have an overview of various commodity exchanges in world with special focus on agricultural commodities. For reaching this objective, secondary data and information was referred from various web based sources. The scope of this objective is to introduce the reader with basic idea about the history and origin of commodity exchanges, the various commodity exchanges in world and their contribution towards augmenting agricultural price risk management. This objective also looks at various specifications of contract as far as groundnut kernel and groundnut oil is considered. The details of the contract specifications of various exchanges are appended with the document separately in appendix I.

The second objective is to study the factors affecting the spot prices of these commodities in the exchanges. For completion of this objective data on spot prices of groundnut oil was collected from database released by MCX. The data was average annual spot price of groundnut oil in Junagarh, Gujarat. Data of average annual production was also collected from website of department of agriculture, government of India (agri.nic.in). Data on groundnut oil export was also collected from ministry of commerce‘s sources. The following table represents the data collected: Table: 4.1 Spot prices (GNO) Year

as released by MCX

Groundnut production

Groundnut oil export

(MMT)

(MT)

(per 10 Kg) 2008

676.48

9.36

2125.22

2007

487.92

4.86

4301.73

2006

690.40

7.99

727.56

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The number of data points was very low to generate any model with high degree of accuracy. A primary model was developed based on groundnut production as independent variable and spot prices (MCX) as dependent variables. The reason for selecting groundnut production and not groundnut oil export as an independent variable is the high degree of positive correlation of groundnut oil spot prices (MCX) with groundnut production. The following bi-variate correlation matrix shows that the spot prices of groundnut bear very strong positive correlation with groundnut production. The single tailed p value is 0.116 which indicates that the ―t‖ value is significant at a significance level of 88.4% and the level of significance chosen is 95% thus the null hypotheses i.e there is no correlation is accepted. Thus the limitations of the sample size of 3 proved to be a major constraint. Nevertheless, for academic purposes we will consider positive correlation and move ahead with groundnut production as independent variable. Table 4.2: Correlation between MCX spot prices of groundnut oil and groundnut production:

Correlations

MCX groundnut oil spot prices(per 10 kgs) groundnut production (MMT)

Pearson Correlation Sig. (1-tailed) N Pearson Correlation Sig. (1-tailed) N

62

MCX groundnut oil spot prices(per 10 kgs) 1.000 . 3 .935 .116 3

groundnut production (MMT) .935 .116 3 1.000 . 3

The primary model that was developed using the data sets of these two variables is as follows:

Y= β1 + β2 × e (-x) where Y = spot prices of groundnut oil (MCX) and β1, β2 are the coefficients for the given equation. The details of this model are described below: Number of observations = 3 Sum of Residuals = -3.41060513164848E-13 Average Residual = -1.13686837721616E-13 Residual Sum of Squares (Absolute) = 209.443969618803 Residual Sum of Squares (Relative) = 209.443969618803 Standard Error of the Estimate = 14.4721791592974 Coefficient of Multiple Determination (R^2) = 0.9918129227 Proportion of Variance Explained = 99.18129227% Adjusted coefficient of multiple determination (Ra^2) = 0.9836258453 Durbin-Watson statistic = 1.00168476119442

Regression Variable Results Variable

Value

Standard Error

t-ratio

Probability (t)

a

688.76496135474

10.5280762728814

65.42172981

0.00973

b

-25869.61084682

2350.3874582744

-11.00653033

0.05768

Variance Analysis Source

DF

Sum of Squares

Mean Square

F Ratio

Regression

1

25372.8194970479

25372.8194970479

121.1437

Error

1

209.443969618803

209.443969618803

Total

2

25582.2634666667

63

Probability (F) 0.05768

Based on this model; Y=

β1 + β2 × e (-x), a data series was generated using Monte

Carlo simulation techniques. The steps involved in the simulation are as follows: 1. Random values of X (groundnut production) were generated. 2. These random values were fitted into the above equation. 3. Values of Y were thus obtained. 4. The new sets of X and Y were plotted against each other to develop a new model. Thus the objective of this exercise to develop a model to understand the effect of a fundamental factor like production on prices was reached. The findings of which will be documented in the next part of the report under the section: findings. The third objective of this project is to find the similarity/ dissimilarity in spot price trends of these commodities in two major commodity exchanges in India (NCDEX and MCX). To meet this objective data was collected from sources of NCDEX and MCX. These data sets were then plotted against each other to find the level of correlation between them. The findings of which will be documented in the next part of the report under the section: findings. The fourth objective is to suggest a pricing model based on trend and fundamental analysis of prices of these two commodities. Theory suggests that the price of futures price of any derivative is reached at using the following equation: F0 = S0erT this model is based on the assumption that the futures price is dependent on the cost of making the investment, which is the major compounding factor. It also assumes that the compounding of the initial principal is done on a continuous basis. Here in this model, F0 is the futures price and S0 is the spot price on that given day. Now the interesting question is how this compounding factor is arrived at. To answer this question a models would be developed using trend analysis and fundamental analysis of futures prices. These models help us look into deep into the relation between the spot prices and the futures prices apart from the general trend of the futures prices. This will help in prediction of futures prices based on some fundamental model. This is important for risk analysis.

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The methodology adopted is the same as the previous one. The data sets were plotted against each other and a non linear model was developed. The findings of which will be documented in the next part of the report under the section: findings. The fifth objective is to study the correlation of the futures prices of these commodities with major futures indices in these markets. This objective utilizes the concept of correlation analysis. The methodology being the bi-variate correlation analysis of data on futures prices of groundnut oil and groundnut kernel in MCX and NCDEX. The scope of this analysis is to understand the relation of movement of price in these futures with the movement of prices of major indices. The scope of this analysis is to help traders in cross-hedging. For example, a trader wants to take position in groundnut kernel which is a highly illiquid commodity in the market, instead of taking position in groundnut seed he can take positions in indices which has high correlation with groundnut kernel prices (returns). This is thus a quantitative tool to utilize any possibility of cross hedging if possible. The findings regarding this objective are presented under the section: findings. The sixth objective is to have a qualitative view of the illiquidity of groundnut oil as a commodity in the exchanges. The methodology adopted to achieve this objective is qualitative views expressed by traders. In depth interview techniques would be adopted. The views of the interviewee would be expressed in a qualitative manner. The seventh and final objective is to measure the accuracy achieved in price matching (spot and futures) of these two commodities by the stock exchanges. The theoretical background of this objective is to estimate the possibility of arbitrage in case of contracts specific to these commodities. We know that there is a possibility of arbitrage if there is mismatch between spot and futures prices. Thus on the delivery day the price of the futures must match the price of the spot. The efficiency of the markets is determined by its ability to check arbitrage. The results of these analyses have been reported in the findings section. 65

5. Analysis of Data and Findings: The findings and conclusion pertains to specific objectives which were undertaken.

Objective b: To study the factors affecting the spot prices of these commodities in the exchanges. Law of demand says that the price of a commodity is directly prportional to its quantity demanded keeping other factors constant. Thus the obvious factors of spot price fluctuations are the fluctuations in demand of the commodities (groundnut oil and seed). Now to understand the quanititative measure as to how the fluctutations in domestic prices of these products affect the spot prices correlation of consumer price index (IW) data of groundnut oil, mustard oil and vanaspati for past 12 months in the year of 2007 have been collected. The results of the correlation analyses are presented in the following table. *prices are expressed as Rs per 10 Kgs. Table: 5.1 Correlation between NCDEX spot price of groundnut (2007) and CPI (IW) of groundnut oil in Junagarh (2007): From the following table it can be seen that the positive correlation between NCDEX spot price of groundnut (2007) and CPI (IW) of groundnut oil in Junagarh (2007) is low at 0.582. (p=0.047 being less than 0.05, the H0 is rejected and the alternate hypotheses for positive correlation is accepted).

Correlations

NCDEX spot groundnut oil CPI groundnut oil (Junagarh)

Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N

66

NCDEX spot groundnut oil 1.000 . 12 .582 .047 12

CPI groundnut oil (Junagarh) .582 .047 12 1.000 . 12

Table: 5.2 Correlation between NCDEX spot price of groundnut (2007) and CPI (IW) of mustard oil in Junagarh (2007): The correlation between NCDEX spot price of groundnut (2007) and CPI (IW) of mustard oil in Junagarh (2007) is non significant. (p=0.6 is much higher than 0.05 level of significance). Thus the mustard oil prices doesnot prove to be very significantly related to spot prices of groundnut oil. Correlations

NCDEX spot groundnut oil CPI mustatrd oil (Junagarh)

Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N

67

NCDEX spot groundnut oil 1.000 . 12 -.169 .600 12

CPI mustatrd oil (Junagarh) -.169 .600 12 1.000 . 12

Table: 5.3 Correlation between NCDEX spot price of groundnut (2007) and CPI (IW) of vanaspati in Junagarh (2007): High positive correlation between NCDEX spot price of groundnut (2007) and CPI (IW) of vanaspati in Junagarh (2007) is shown by the analysis of data. The p value of 0.029 also suggests that the alternate hypothesis of positive correlation between the variables is proved.

Correlations

NCDEX spot groundnut oil CPI vanaspati (Junagarh)

Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N

68

NCDEX spot groundnut oil 1.000 . 12 .628 .029 12

CPI vanaspati (Junagarh) .628 .029 12 1.000 . 12

Law of demand also derives that the price of a commodity is inversely proportional to its supply. Thus the production of groundnut must also be considered to create a model which will give us the expected annual average value of groundnut oil spot prices given a particular quantity of groundnut produced in that year. As discussed in the methodology section the primary regression model that was developed using annual production data (million metric tonnes) versus average annual spot price of groundnut oil (-x) (per 10Kg) was: Y= β1 + β2 × e where Y = spot prices of groundnut oil (MCX) and β1, β2 are the co-efficients for the given equation. X is the annual production in MMT. Based on this primary model the secondary model that was developed is as follows:

Y= β0 + β1 × e (x) + β2 × e (-x) Table 3.4: Details of the regression model for groundnut production versus groundnut spot prices in NCDEX: The following table gives us most striking result as the coefficient of determination is 100%. Thus the dependence of spot prices of groundnut on the production is being empirically established. And a Durbin-Watson statistic value of 2.08 suggest insignificant amount of autocorrelation.

69

Number of observations = 150 Number of missing observations = 0 Solver type: Nonlinear Nonlinear iteration limit = 250 Diverging nonlinear iteration limit =10 Number of nonlinear iterations performed = 16 Residual tolerance = 0.0000000001 Sum of Residuals = -8.64019966684282E-12 Average Residual = -5.76013311122855E-14 Residual Sum of Squares (Absolute) = 1.22054490982467E-13 Residual Sum of Squares (Relative) = 1.22054490982467E-13 Standard Error of the Estimate = 2.88149728395616E-08 Coefficient of Multiple Determination (R^2) = 1.0 Proportion of Variance Explained = 100.0% Adjusted coefficient of multiple determination (Ra^2) = 1.0 Durbin-Watson statistic = 2.08773762641957

The graph of the above model is presented below:

70

Objective c: To find the similarity/ dissimilarity in spot price trends of these commodities in two major commodity exchanges in India (NCDEX and MCX): The commodity exchanges are supposed to give full proof market data to their customers. This service of the commodity exchange benefits the traders in the exchange to determine the prices of contracts. Thus the spot price data released by the two exchanges must match with each other to give customers the confidence to rely upon the figures and determine their bid and ask rate. The similarity/ dissimilarity in the spot prices released by MCX and NCDEX were put to two sample independent t-test. The significance level was kept at 10%. The results show that the two samples that were drawn from spot price data of year 2007 have the following descriptive characteristics. Table 5.5: Descriptive statistics of the two samples of price data for the year 2007:

Group Statistics

Spot prices

Exchanges NCDEX MCX

N 297 297

Mean 686.6410 688.2357

Std. Deviation 65.4810 67.8664

Std. Error Mean 3.7996 3.9380

The result obtained from the two sample independent t-test indicates that the two samples have equal variances. (Levene‘s test for equality of variances assumes a significance level of 5%. Here the probability at which the F value is significant is 30.3%, thus the H 0 of equal variances of the two samples can be accepted). For the second part of the test, that is equality of means we shall thus assume equality of variances and accept the t statistice value of 0.291 (d.f =592) at a significance level of 77%. Thus the H0, that the means of the two samples is equal is accepted. Table 5.6: Results for the two samples independent t-test: Independent Samples Test Levene's Test for Equality of Variances

F Spot prices

Equal variances assumed Equal variances not assumed

1.061

Sig. .303

t-test for Equality of Means

t

df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference Lower Upper

-.291

592

.771

-1.5947

5.4722

-12.3420

9.1526

-.291

591.244

.771

-1.5947

5.4722

-12.3420

9.1526

71

Scatter plot of spot prices of groundnut released by NCDEX and MCX in year 2007 900

Spot prices in Rs per 10 Kgs

800 700 600 500

NCDEX

400

MCX

300

Linear (NCDEX)

200

Linear (MCX)

100

0 0

50

100

150

200

250

300

350

Number of price samples = 297

Objective d: To suggest a pricing model based on trend and fundamental analysis of prices of these two commodities: Theory suggests that the price of futures price of any derivative is reached at using the following equation: F0 = S0erT this model is based on the assumption that the futures price is dependent on the cost of making the investment (r), which is the major compounding factor. It also assumes that the compounding of the initial principal is done on a continuous basis. Here in this model, F0 is the futures price and S0 is the spot price on that given day. Now the interesting question is how this compounding factor is arrived at. To answer this question mathametical models have been developed using trend analysis and fundamental analysis of futures prices. These models help us look deep into the relation between the spot prices and the futures prices apart from the general trend of the futures prices. This will help in prediction of futures prices based on some fundamental model. This is important for risk analysis.

72

The first regression model is obtained when a trend analysis was conducted on the futures price data of groundnut oil at NCDEX. The model (tenth order polynomial) that is obtained is presented below:

Y = β0.X10+ β1.X9+ β2.X8+ β3.X7+ β4.X6+ β5.X5+ β6.X4+ β7.X3+ β8.X2+ β9.X+ β10 Table 5.7: detailed information about the regression model with time as an independent variable (X) and prices of groundnut oil futures contract as the dependent variable (Y): From the following table it can be inferred that the prportion of variance explained by time on the prices of futures contract of groundnut oil is as high as 90.27%. Nevertheless, the DurbinWatson statistic value of 0.12 suggests significant autocorrelation. This lays the scope for development of much more sophisticated model.

Number of observations = 255 Number of missing observations = 0 Solver type: Nonlinear Nonlinear iteration limit = 250 Diverging nonlinear iteration limit =10 Number of nonlinear iterations performed = 7 Residual tolerance = 0.0000000001 Sum of Residuals = -1.01181285572238E-11 Average Residual = -3.96789355185248E-14 Residual Sum of Squares (Absolute) = 119845.287891774 Residual Sum of Squares (Relative) = 119845.287891774 Standard Error of the Estimate = 22.162337707724 Coefficient of Multiple Determination (R^2) = 0.9027139048 Proportion of Variance Explained = 90.27139048% Adjusted coefficient of multiple determination (Ra^2) = 0.8987267698 Durbin-Watson statistic = 0.121269477764985

73

The next model is based on the trend analysis of prices of futures contract of groundnut seed in NCDEX for the contract ending at January 2007 to December 2007. The model that is obtained is also a tenth order polynomial.

Y = β0.X10+ β1.X9+ β2.X8+ β3.X7+ β4.X6+ β5.X5+ β6.X4+ β7.X3+ β8.X2+ β9.X+ β10

Table 5.8: detailed information about the regression model with time as an independent variable (X) and prices of groundnut seed futures contract as the dependent variable (Y): The following table reveals that the co-efficient of determination achieved using this model is 97.24%. This is quite a good amount of variation that is explained by the independent variable. Similarly, a Durbin-Watson statistic value of 1.94 indicates minimum interference in the data due to error variables.

74

Number of observations = 37 Number of missing observations = 0 Solver type: Linear Sum of Residuals = 1.37904222015095E-05 Average Residual = 3.72714113554311E-07 Residual Sum of Squares (Absolute) = 2039.70262032826 Residual Sum of Squares (Relative) = 2039.70262032826 Standard Error of the Estimate = 8.85720614990167 Coefficient of Multiple Determination (R^2) = 0.9724706721 Proportion of Variance Explained = 97.24706721% Adjusted coefficient of multiple determination (Ra^2) = 0.9618824691 Durbin-Watson statistic = 1.94374064718371

75

The second part of the objective is to develop a model based on fundamental factors which influence the price of futures contract. As we have seen earlier, the futures price of an agricultural commodity is equal to spot price and a compounding factor which compounds exponentially with the rate (cost of investment) of investment and the cost of carry since agricultural commodities are perishable products. Therefore, the following models are developed for analysing the dependence of spot prices on the value of futures contract for groundnut oil and seed in NCDEX as well as in MCX. The first of the fundamental analysis involved plotting the groundnut oil spot prices with respective futures prices (NCDEX price). The regression equation that was obtained was an eight order polynomial.

Y = β0.X8+ β1.X7+ β2.X6+ β3.X5+ β4.X4+ β5.X3+ β6.X2+ β7.X+ β8 Table 5.9: detailed information about the regression model with groundnut oil spot prices as independent variable (X) and prices of groundnut oil futures contract as the dependent variable from January 2007 and December 2007 at NCDEX(Y): The following table reveals that the co-efficient of determination achieved using this model is 98.76%. This is quite a good amount of variation that is explained by the independent variable. Similarly, a Durbin-Watson statistic value of 1.05 indicates large interference in the data due to error variables. Number of observations = 247 Number of missing observations = 0 Solver type: Nonlinear Nonlinear iteration limit = 250 Diverging nonlinear iteration limit =10 Number of nonlinear iterations performed = 116 Residual tolerance = 0.0000000001 Sum of Residuals = 1.04613596931813E-05 Average Residual = 4.23536829683453E-08 Residual Sum of Squares (Absolute) = 14773.214930324 Residual Sum of Squares (Relative) = 14773.214930324 Standard Error of the Estimate = 7.87859959891456 Coefficient of Multiple Determination (R^2) = 0.9876960429 Proportion of Variance Explained = 98.76960429% Adjusted coefficient of multiple determination (Ra^2) = 0.9872824645 Durbin-Watson statistic = 1.05196930346445

76

The next model involves spot prices of groundnut oil and the corresponding prices of futures contract that were released for groundnut oil contracts ending January 2007 to December 2007 at MCX . The model that was obtained is a ninth order polynomial.

Y = β0.X9+ β1.X8+ β2.X7+ β3.X6+ β4.X5+ β5.X4+ β6.X3+ β7.X2+ β8.X+ β9 Table 5.10: detailed information about the regression model with groundnut oil spot prices as independent variable (X) and prices of groundnut oil futures contract as the dependent variable from January 2007 and December 2007 at MCX(Y):

The following table reveals that the co-efficient of determination achieved using this model is 99.96%. This is quite a good amount of variation that is explained by the independent variable.

77

Similarly, a Durbin-Watson statistic value of 1.65 indicates medium but significant interference in the data due to error variables.

Number of observations = 103 Number of missing observations = 0 Solver type: Nonlinear Nonlinear iteration limit = 250 Diverging nonlinear iteration limit =10 Number of nonlinear iterations performed = 20 Residual tolerance = 0.0000000001 Sum of Residuals = -7.27119982002478E-08 Average Residual = -7.05941730099493E-10 Residual Sum of Squares (Absolute) = 197.855663112337 Residual Sum of Squares (Relative) = 197.855663112337 Standard Error of the Estimate = 1.45858844384554 Coefficient of Multiple Determination (R^2) = 0.9996752626 Proportion of Variance Explained = 99.96752626% Adjusted coefficient of multiple determination (Ra^2) = 0.9996438364 Durbin-Watson statistic = 1.65658618213462

78

Objective e: To study the correlation of the futures prices of these commodities with major futures indices in these markets. Table 5.11: correlation analysis of prices of grounut oil futures prices with FUTEXAGRI (the futures prices based index of NCDEX). The following correlation matrix shows that there is medium level of negetive correlation between groundnut oil futures prices (NCDEX) and a major market index at NCDEX called the FUTEXAGRI (This index is constructed using the gross value of futures contract of major agricultural commodities). The two tailed significance level of 0.000 suggest that the t-statistic for testing the validity of the bivariate correlation co-efficient (r) is significant at a significance level of 0.01, which indicates that the null hypothesis of no correlation is rejected. Thus, there is some correlation between the groundnut oil futures prices and FUTEXAGRI.

Descriptive Statistics

GNO FUTURES FUTEXAGRI

Mean 690.4039 1496.5569

Std. Deviation 69.9814 62.3122

N 243 241

Correlations

GNO FUTURES

FUTEXAGRI

Pearson Correlation Sig. (2-tailed) Sum of Squares and Cross-products Covariance N Pearson Correlation Sig. (2-tailed) Sum of Squares and Cross-products Covariance N

GNO FUTURES 1.000 . 1185171.4 4897.403 243 -.526** .000

-552020.727 -2300.086 241 1.000 .

-552020.7

931874.203

-2300.086 241

3882.809 241

**. Correlation is significant at the 0.01 level (2-tailed).

79

FUTEXAGRI -.526** .000

Objective f: To have a qualitative view of the illiquidity of groundnut oil as a commodity in the exchanges. The major reasons sited by most of the experts for the groundnut oil futures not being traded in NCDEX as well MCX are as follows: 1. Low variability in spot prices of the commodity is one of the major reasons that make both hedgers and speculators undertake position in commodities which has higher variability thus higher risk to hedge or higher profits to make from speculative activity. The empirical data collected for the year 2007 support this fact. The co-efficient of variations in spot prices released by NCDEX and MCX are very low, 9.53 and 9.86 respectively. 2. The next important reason is the uncertainity of availability of contracts caused by irrate policies of the government. Whenever there is a dearth in production government prohibits speculative activities in essential commodities which frequently includes groundnut and sumflower oils. 3. Apart from the major factors, the other factors like absense of pan India delivery centres and high warehouse charges resulted in fewer volumes of groundnut oil and seed futures contracts being traded at the commodity exchanges.

Objective g: To measure the accuracy achieved in price matching (spot and futures) of these two commodities by the stock exchanges. Matching of prices is a very important phenomenon in any derivative market. The spot prices must match its near future prices, to avoid any arbitrage. Table 5.12: correlation results for spot and futures prices of groundnut oil in NCDEX. The results of the correlation analysis show that the price matching achived by NCDEX is very high with a high positive correlation of 0.993 and a significance level of 0.01 indicates rejection of null hypothesis of no correlation.

80

Correlations

NCDEX GNO futures

NCDEX GNO spot

Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N

NCDEX NCDEX GNO futures GNO spot 1.000 .993** . .000 247 247 .993** 1.000 .000 . 247 247

**. Correlation is significant at the 0.01 level (2-tailed).

81

6. Implications of Results and Findings: Results and conclusion pertaining to objective b: The second objective which was to establish the relation between various factors affecting the spot prices of groundnut. Empirical evidence was sought for. The correlation results indicate the following; 1. Mustard oil consumers consume very little groundnut oil to have a significant bearing on phenomenon like cross elasticity of demand. 2. Vanaspati consumers sometimes complement their oil consumption with groundnut oil. These two can be seen used as a complementary product in states like Gujarat. The empirical data proves this with high positive correlation between prices of groundnut oil and vanaspati prices in Junagarh (Gujarat). Which means the demand for one type of oil grows with the demand of the other. Another most important factor affecting the spot prices of groundnut oil is the production of the crop itself. This is quite obvious, what is not is how to make predictions about prices looking at the production data. The price model [Y= β0 + β1 × e (x) + β2 × e (-x)] with 100% R2 value and very little or insignificant auto-correlation would give a scope for determining the future spot prices based on advanced estimates of crop production released by NSSO ans CSO. This hopefully can be a valuable tool for further research.

Results and conclusion pertaining to objective c: As discussed earlier, the commodity exchanges are supposed to give full proof market data to their customers. This service of the commodity exchange benefits the traders in the exchange to determine the prices of contracts. Thus the spot price data released by the two exchanges must match with each other to give customers the confidence to rely upon the figures and determine their bid and ask rate. The result of the two samples independent t-test proved that the prices released by both the exchanges do match. This result would allow an amateur into the derivative market trade without bothering the reliability of the price figures.

Results and conclusion pertaining to objective d: The regression models that were developed keeping spot prices of groundnut oil and seed as the independent variable and futures prices of these commodities as dependent variables have achieved a certain degree of accuracy. The accuracy of the models is expressed by certain determinants these are the R2 values of the model the Durbin-Watson* statistic value and the F test values. Though the models seem to generate a good amount of fit in terms of R 2 values the usefulness of the models can only be judged looking at the Durbin-Watson statistic values. This is the statistic which shows the presence of any auto correlation between the error variables. The gretaer the auto correlation the lesser is the reliability of the model. 82

Thus looking at the information tables of the models redears are requested to interpret the usefulness of the models. Nevertheless, these kinds of models would certainly help readers to understand the kind of quantitative relationship that exists between the spot prices and futures prices. These models can be used predict the values of futures prices or better still, determine the rate of compounding.

Results and conclusion pertaining to objective e: The result shows negetive correlation between groundnut oil futures prices and FUTEXAGRI which is not a tradable derivative contract in NCDEX. The objective of this study is to empirically prove that if this index is introduced for derivative trading, is there a possibility for groundnut oil traders to take position in this index to perform cross hedging. The findings of this empirical test nullify any such possibility.

Results and conclusion pertaining to objective f: The findings pertaining to the objective are self explanatory.

Results and conclusion pertaining to objective g: The implication of very high positive correlation shows that the price matching is highly accurate in our commodity derivative markets. The implication as stated earlier is to negate possibility of any price arbitrage. This is not due to any control by the exchange itself. This is a normal market phenomena where the existence of any such price arbitrage possibility itself increases the demand of such contract and the futures prices shoot up to match the spot prices and vice versa. The role of the exchange is limited to create enough liquidity in the market to allow a perfect market condition and help in price discovery. This is the very essence of trading a forward at exchanges.

83

7. Conclusion: The following conclusion can be derived out this entire project. Though groundnut is a major oilseed crop in India, several factors prevent it from being traded in the futures platform and groundnut farmers are thus not being able to harness the full fledged opportunity that these futures exchanges offer. Better price discovery, price hedging, easy credit these are advantages that an exchnage can offer to groundnut farmers. Thus a more liquid groundnut oil and seed deriavtive market will help these poor farmers. The next motive for which this project was undertaken was the application of econometric modelling for determining factors responsible for the movement of prices on these exchanges. These models can be used by readers to predict prices of futures contract and predict the behaviour of spot prices as well. Risk mitigation and cross hedging are some of the issues that will be addressed through these findings. Thus this project and its findings along with its substantial amount of empirical model building would find its purpose served.

8. References: 1. 2. 3. 4. 5. 6. 7.

www.wikipedia.com www.ncdex.com www.mcxindia.com www.investopedia.com www.nmce.com www.ftkmc.com Options, Futures and Other Derivatives (John.C.Hull)

84

ANNEXURE I Tables displaying F test results and ANOVA results for the regression models: Table 3.4: F test and ANOVA results for model Y= Variable

Value

a

688.764961362425

b

7.86518689922018E13

c

-25869.6109009708

β0 + β1 × e (x) + β2 × e (-x)

Standard Error 1.79319019131964E08

t-ratio

Prob(t)

38410033955

0.0

1.55991008904492E12

-0.504207708

0.61487

3.8991588517952E05

-663466452.2

0.0

68% Confidence Intervals Variable

Value

68% (+/-)

Lower Limit

Upper Limit

a

688.764961362425

1.78924517289874E08

688.764961344533

688.764961380318

b

7.86518689922018E13

1.55647828684902E12

2.34299697677104E12

7.69959596927005E13

c

-25869.6109009708

3.89058070232125E05

-25869.6109398766

-25869.6108620649

90% Confidence Intervals Variable

Value

90% (+/-)

Lower Limit

Upper Limit

a

688.764961362425

2.96826772369141E08

688.764961332743

688.764961392108

b

7.86518689922018E13

2.58211917039606E12

3.36863786031808E12

1.79560048047404E12

c

-25869.6109009708

6.45427764737659E05

-25869.6109655135

-25869.610836428

85

95% Confidence Intervals Variable

Value

a

688.764961362425

b

7.86518689922018E13

c

95% (+/-) 3.54370245608588E08

Lower Limit

Upper Limit

688.764961326988

688.764961397862

3.08269431797057E12

3.86921300789259E12

2.29617562804856E12

-25869.6109009708

7.70551772291767E05

-25869.6109780259

-25869.6108239156

Variable

Value

99% (+/-)

Lower Limit

Upper Limit

a

688.764961362425

4.67968844228687E08

688.764961315629

688.764961409222

b

7.86518689922018E13

4.07089735938053E12

4.85741604930255E12

3.28437866945852E12

c

-25869.6109009708

1.01756348555299E04

-25869.6110027271

-25869.6107992144

99% Confidence Intervals

ANOVA table

Source

DF

Sum of Squares

Mean Square

F Ratio

Prob(F)

Regression

2

1707.55578331296

853.777891656478

1.02827E+18

0

Error

147

1.22054490982467E13

8.30302659744672E16

Total

149

1707.55578331296

86

Table 3.7: F test and ANOVA results for model:

Y = β0.X10+ β1.X9+ β2.X8+ β3.X7+ β4.X6+ β5.X5+ β6.X4+ β7.X3+ β8.X2+ β9.X+ β10 Regression Variable Results Variable Value a b c

8.82588878610492E-19 -3.39828841205586E16 -4.90300101311694E14

d

9.53242675638456E-12

e

8.78996501187075E-10 -5.71445231628646E08 -5.15224261870306E06 -2.46240901155467E04 -8.84070634780857E03 2.06350359972982 758.89140304866

f g h i j k

68% Confidence Intervals Variable Value a b c

8.82588878610492E-19 -3.39828841205586E16 -4.90300101311694E14

d

9.53242675638456E-12

e

8.78996501187075E-10

f g h i j k

-5.71445231628646E08 -5.15224261870306E06 -2.46240901155467E04 -8.84070634780857E03 2.06350359972982 758.89140304866

Standard Error 1.01376713672228E18 6.4658815476919E17 3.92310463036417E14 2.24058375005284E12 5.370967701659E-10 2.62594953553362E08 3.08461970213242E06 1.18860861744625E04 6.72353418053984E03 0.170194307278203 3.75760525501393

t-ratio

Prob(t)

0.870603166

0.38483

-5.255723271

0.0

-1.249775745

0.21258

4.254438941

0.00003

1.636570075

0.10301

-2.176147043

0.0305

-1.670300755

0.09614

-2.071673531

0.03935

-1.314889775

0.18978

12.12439848 201.9614493

0.0 0.0

68% (+/-) 1.01021895174375E18 6.44325096227498E17 3.9093737641579E14 2.23274170692766E12 5.3521693147032E10 2.61675871215925E08 3.07382353317496E06 1.18444848728519E04 6.70000181090795E03 0.169598627202729 3.74445363662138

Lower Limit -1.27630073133259E19 -4.04261350828336E16 -8.81237477727484E14 7.2996850494569E12 3.43779569716756E10 -8.33121102844571E08 -8.22606615187802E06 -3.64685749883986E04 -1.55407081587165E02 1.89390497252709 755.146949412038

Upper Limit 1.89280783035424E18 -2.75396331582836E16 -9.93627248959037E15 1.17651684633122E11 1.4142134326574E09 -3.09769360412721E08 -2.0784190855281E06 -1.27796052426948E04 -2.14070453690061E03 2.23310222693255 762.635856685281

87

90% Confidence Intervals Variable Value a b c

8.82588878610492E-19 -3.39828841205586E16 -4.90300101311694E14

d

9.53242675638456E-12

e

8.78996501187075E-10

f g h i j k

-5.71445231628646E08 -5.15224261870306E06 -2.46240901155467E04 -8.84070634780857E03 2.06350359972982 758.89140304866

95% Confidence Intervals Variable Value a b c

8.82588878610492E-19 -3.39828841205586E16 -4.90300101311694E14

d

9.53242675638456E-12

e

8.78996501187075E-10

f g h i j k

-5.71445231628646E08 -5.15224261870306E06 -2.46240901155467E04 -8.84070634780857E03 2.06350359972982 758.89140304866

90% (+/-) 1.67383091944216E18 1.06758170233941E16 6.47743805519429E14 3.69942782971225E12 8.86800477220918E10 4.33570527811956E08 5.09301559019085E06 1.96251168826551E04 1.11012272854893E02 0.281007820747041 6.20418203655349

Lower Limit -7.91242040831663E19 -4.46587011439527E16 -1.13804390683112E13 5.83299892667231E12 -7.80397603384242E12 -1.0050157594406E07 -1.02452582088939E05 -4.42492069982017E04 -1.99419336332979E02 1.78249577898278 752.687221012106

Upper Limit 2.55641979805265E18 -2.33070670971645E16 1.57443704207735E14 1.32318545860968E11 1.76579697840799E09 -1.3787470381669E08 -5.92270285122106E08 -4.99897323289158E05 2.26052093768076E03 2.34451142047686 765.095585085213

95% (+/-) 1.99681712920187E18 1.27358468844887E16 7.72733919042831E14 4.41327781247908E12 1.05791950819577E09 5.17233280014057E08 6.07577542729024E06 2.34120239378388E04 1.32433452754093E02 0.335231727045876 7.40135507080093

Lower Limit -1.11422825059138E18 -4.67187310050473E16 -1.26303402035453E13 5.11914894390548E12 -1.78923007008698E10 -1.0886785116427E07 -1.12280180459933E05 -4.80361140533855E04 -2.20840516232179E02 1.72827187268395 751.490047977859

Upper Limit 2.87940600781237E18 -2.12470372360698E16 2.82433817731138E14 1.39457045688636E11 1.93691600938285E09 -5.4211951614589E09 9.23532808587179E07 -1.21206617770782E05 4.40263892760076E03 2.3987353267757 766.292758119461

88

99% Confidence Intervals Variable Value a

8.82588878610492E-19 -3.39828841205586E16 -4.90300101311694E14

b c d

9.53242675638456E-12

e

8.78996501187075E-10 -5.71445231628646E08 -5.15224261870306E06 -2.46240901155467E04 -8.84070634780857E03 2.06350359972982 758.89140304866

f g h i j k

99% (+/-) 2.63184086364471E18 1.67860750859629E16 1.01847719308884E13 5.81677947351218E12 1.39435692502769E09 6.81722758919883E08 8.00798120870599E06 3.08574683175222E04 1.74549670860995E02 0.441841441124942 9.75511900254165

Lower Limit -1.74925198503422E18 -5.07689592065215E16 -1.50877729440054E13 3.71564728287238E12 -5.15360423840618E10 -1.25316799054853E07 -1.3160223827409E05 -5.54815584330688E04 -0.026295673433908 1.62166215860488 749.136284046118

Upper Limit 3.5144297422552E18 -1.71968090345956E16 5.28177091777149E14 1.53492062298967E11 2.27335342621477E09 1.10277527291237E08 2.85573859000293E06 6.2333782019755E05 8.61426073829091E03 2.50534504085477 768.646522051201

ANOVA results Source

DF

Sum of Squares

Mean Square

F Ratio

Prob(F)

Regression

10

1112039.77910823

111203.977910823

226.4066538

0

Error

244

119845.287891774

491.169212671205

Total

254

1231885.067

89

Table 3.8: F test and ANOVA results for model:

Y = β0.X10+ β1.X9+ β2.X8+ β3.X7+ β4.X6+ β5.X5+ β6.X4+ β7.X3+ β8.X2+ β9.X+ β10 Regression Variable Results Variable Value -7.10946905109278Ea 10

Standard Error 2.96128829472893E10 2.64056038128886E09 2.35917364639297E07 1.89199149596528E06 6.63525672049825E05 4.57553845123374E04 7.81206473519442E03 4.26477761287686E02 0.348341330710335 1.25489533554914 3.97420861501665

t-ratio

Prob(t)

-2.40080274

0.02381

2.036623608

0.05199

2.560789154

0.0166

-2.325910686

0.02809

-2.804739253

0.0094

2.812876118

0.00922

3.221891939

0.00341

-3.84785917

0.00069

-4.006738601 9.306159263 126.7112201

0.00046 0.0 0.0

68% (+/-)

Lower Limit

-7.10946905109278E10

3.00215407319619E10

-1.0111623124289E09

5.37782760982766E09 6.04134628692341E07

2.67700011455064E09 2.39173024271319E07

d

-4.40060323801972E06

1.9181009786096E06

e

-1.86101649754805E04

6.72682326324112E05

1.28704228370136E03 2.51696283969861E02

4.63868088186076E04 7.9198712285401E03 4.32363154393456E02 0.353148441074138 1.27221289117972 4.02905269390388

2.70082749527701E09 3.64961604421021E07 6.31870421662932E06 2.53369882387216E04 8.23174195515283E04

Upper Limit 4.10731497789659E10 8.0548277243783E09 8.4330765296366E07 2.48250225941011E06 1.18833417122394E04 1.75091037188744E03 3.30894996255262E02

b

5.37782760982766E-09

c

6.04134628692341E-07

d e

-4.40060323801972E06 -1.86101649754805E04

f

1.28704228370136E-03

g

2.51696283969861E-02

h

-0.164102636452476

i j k

-1.3957126560415 11.6782558512385 503.576822377472

68% Confidence Intervals Variable Value a b c

f g h

-0.164102636452476

i j k

-1.3957126560415 11.6782558512385 503.576822377472

90

0.017249757168446 -0.207338951891822

-0.120866321013131

-1.74886109711564 10.4060429600588 499.547769683569

-1.04256421496736 12.9504687424182 507.605875071376

90% Confidence Intervals Variable Value -7.10946905109278Ea 10 b

5.37782760982766E-09

c

6.04134628692341E-07

d e

-4.40060323801972E06 -1.86101649754805E04

f

1.28704228370136E-03

g

2.51696283969861E-02

h

-0.164102636452476

i j k

-1.3957126560415 11.6782558512385 503.576822377472

95% Confidence Intervals Variable Value -7.10946905109278Ea 10 b

5.37782760982766E-09

c

6.04134628692341E-07

d e

-4.40060323801972E06 -1.86101649754805E04

f

1.28704228370136E-03

g

2.51696283969861E-02

h

-0.164102636452476

i j k

-1.3957126560415 11.6782558512385 503.576822377472

90% (+/-) 5.05077331548967E10 4.50373978632627E09 4.02380657128785E07 3.22698069551838E06 1.13170938624818E04 7.80403838242426E04 1.33242576123476E02 7.27400469652277E02 0.594130973659547 2.14034948431262 6.77841021377239

Lower Limit -1.21602423665824E09 8.74087823501384E10 2.01753971563556E07 -7.6275839335381E06 -2.99272588379623E04 5.06638445458933E04 1.18453707846385E02

95% (+/-) 6.08692808981532E10 5.42767186373924E09 4.84928143016075E07 3.88898851995663E06 1.36387701889841E04 9.40501928651094E04 1.60576990631921E02 8.76625038326838E02 0.716015605275093 2.57943736222126 8.16898580816672

Lower Limit -1.31963971409081E09 -4.9844253911587E11 1.19206485676266E07 -8.28959175797635E06 -3.22489351644646E04 3.46540355050265E04

91

-0.236842683417704 -1.98984362970105 9.53790636692591 496.7984121637

0.009111929333794 -0.25176514028516 -2.11172826131659 9.09881848901726 495.407836569306

Upper Limit -2.05869573560311E10 9.88156739615393E09 1.00651528582113E06 -1.17362254250133E06 -7.29307111299868E05 2.06744612194379E03 3.84938860093337E02 -9.13625894872484E02 -0.801581682381954 13.8186053355511 510.355232591245

Upper Limit -1.02254096127746E10 1.08054994735669E08 1.08906277170842E06 -5.11614718063083E07 -4.97139478649634E05 2.22754421235245E03 4.12273274601783E02 -7.64401326197923E02 -0.679697050766408 14.2576932134598 511.745808185639

99% Confidence Intervals Variable

Value

99% (+/-)

a

-7.10946905109278E10

8.22853178456328E10

b

5.37782760982766E09

7.33732513148735E09

c

6.04134628692341E07

6.55543581123215E07

d

-4.40060323801972E06

5.25727676983872E06

-1.86101649754805E04 1.28704228370136E03 2.51696283969861E02

1.84373878492485E04 1.27140486944432E03 2.17073842796847E02

Lower Limit 1.53380008356561E09 1.95949752165969E09 5.14089524308738E08 9.65788000785844E06 -3.7047552824729E04 1.56374142570408E05 3.4622441173014E03

h

-0.164102636452476

0.118505375529009

-0.282608011981485

i

-1.3957126560415

0.967936055644808

-2.36364871168631

-1.7277712623201E06 2.55844715314568E03 4.68770126766709E02 4.55972609234669E02 -0.427776600396693

j

11.6782558512385

3.4869776688904

8.19127818234812

15.1652335201289

k

503.576822377472

11.0431334785468

492.533688898926

514.619955856019

e f g

Upper Limit 1.1190627334705E10 1.2715152741315E08 1.25967820981556E06 8.56673531819008E07

ANOVA results

Source

DF

Sum of Squares

Mean Square

F Ratio

Prob(F)

Regression

10

72052.2849472393

7205.22849472393

91.84473217

0

Error

26

2039.70262032826

78.450100781856

Total

36

74091.9875675676

92

Table 3.9: F test and ANOVA results for model:

Y = β0.X8+ β1.X7+ β2.X6+ β3.X5+ β4.X4+ β5.X3+ β6.X2+ β7.X+ β8 Regression Variable Results Variable Value -5.59902351357938Ea 16 b

2.9814772370934E-12

c

-6.89090603157755E09

d

9.02320050902173E-06

e f g h i

-7.31596813379059E03 3.75749512036985 -1192.39014309209 213421.257580854 -16461206.0226317

68% Confidence Intervals Variable Value -5.59902351357938Ea 16 b

2.9814772370934E-12

c

-6.89090603157755E09

d

9.02320050902173E-06

e

-7.31596813379059E03

f

3.75749512036985

g h i

-1192.39014309209 213421.257580854 -16461206.0226317

Standard Error 1.63217827011855E17 5.86407703589864E14 4.3947570698227E11 1.18391849351733E07 1.07816847314385E04 0.021005606958521 26.586499267132 8442.64321902946 977678.569013618

t-ratio

Prob(t)

-34.30399495

0.0

50.84307759

0.0

-156.7983377

0.0

76.21471037

0.0

-67.85551902

0.0

178.8805783 -44.84946029 25.27896206 -16.83703269

0.0 0.0 0.0 0.0

68% (+/-) 1.62646564617313E17 5.84355276627299E14 4.37937542007832E11 1.17977477879002E07 1.07439488348785E04 2.09320873341662E02 26.493446519697 8413.09396776285 974256.69402207

Lower Limit -5.76167007819669E16 2.92304170943067E12 -6.93469978577834E09 8.90522303114273E06 -7.42340762213938E03

Upper Limit -5.43637694896206E16 3.03991276475613E12 -6.84711227737677E09 9.14117798690074E06 -7.20852864544181E03

3.73656303303569

3.77842720770402

-1218.88358961179 205008.163613091 -17435462.7166537

-1165.89669657239 221834.351548617 -15486949.3286096

93

90% Confidence Intervals Variable Value -5.59902351357938Ea 16 b

2.9814772370934E-12

c

-6.89090603157755E09

d

9.02320050902173E-06

e

-7.31596813379059E03

f

3.75749512036985

g h i

-1192.39014309209 213421.257580854 -16461206.0226317

95% Confidence Intervals Variable Value -5.59902351357938Ea 16 b

2.9814772370934E-12

c

-6.89090603157755E09

d

9.02320050902173E-06

e

-7.31596813379059E03

f

3.75749512036985

g h i

-1192.39014309209 213421.257580854 -16461206.0226317

90% (+/-) 2.69521597744675E17 9.68335040937942E14 7.25706234939823E11 1.95500460834517E07 1.78037959970244E04 3.46865587706058E02 43.902286239815 13941.3367475833 1614440.62101219

Lower Limit -5.86854511132405E16 2.88464373299961E12 -6.96347665507154E09 8.82770004818722E06 -7.49400609376084E03

Upper Limit -5.3295019158347E16 3.07831074118719E12 -6.81833540808357E09 9.21870096985625E06 -7.13793017382035E03

3.72280856159925

3.79218167914046

-1236.2924293319 199479.920833271 -18075646.6436438

-1148.48785685227 227362.594328437 -14846765.4016195

95% (+/-) 3.21539119213353E17 1.15522317607203E13 8.65767142755072E11 2.33231943222914E07 2.12399189209339E04 4.13810457082864E02 52.37540355625 16632.007141488 1926026.78095683

Lower Limit -5.92056263279273E16 2.8659549194862E12 -6.97748274585306E09 8.78996856579882E06 -7.52836732299993E03

Upper Limit -5.27748439436602E16 3.0969995547006E12 -6.80432931730205E09 9.25643245224465E06 -7.10356894458125E03

3.71611407466157

3.79887616607814

-1244.76554664834 196789.250439366 -18387232.8035885

-1140.01473953584 230053.264722342 -14535179.2416748

94

99% Confidence Intervals

Variable

Value

99% (+/-)

Lower Limit

Upper Limit

a

-5.59902351357938E16

4.23811409618981E17

6.02283492319836E16

5.17521210396039E16

b

2.9814772370934E-12

1.52266624314144E13

2.82921061277926E12

3.13374386140754E12

c

-6.89090603157755E09

1.14114262075016E10

7.00502029365257E09

6.77679176950254E09

d

9.02320050902173E-06

3.0741627602671E07

8.71578423299502E06

9.33061678504844E06

e

-7.31596813379059E03

2.79957225736532E04

7.59592535952713E03

7.03601090805406E03

f

3.75749512036985

5.45431590284957E02

3.70295196134136

3.81203827939835

g

-1192.39014309209

69.0345039970348

-1261.42464708912

-1123.35563909505

h

213421.257580854

21922.1673825319

191499.090198322

235343.424963386

i

-16461206.0226317

2538640.17230076

-18999846.1949324

-13922565.8503309

ANOVA results Source

DF

Sum of Squares

Mean Square

F Ratio

Prob(F)

Regression

8

1185914.89310611

148239.361638264

2388.171311

0

Error

238

14773.214930324

62.0723316400166

Total

246

1200688.10803644

95

Table 3.10: F test and ANOVA results for model:

Y = β0.X9+ β1.X8+ β2.X7+ β3.X6+ β4.X5+ β5.X4+ β6.X3+ β7.X2+ β8.X+ β9 Regression Variable Results Variable

Value

Standard Error

t-ratio

Prob(t)

a

-1.88604785750052E03

2.46681515546585E03

-0.764567971

0.44646

b

0.120920535145288

0.147375045027757

0.820495323

0.41403

c

-3.40909138627248

3.90457031655549

-0.873102828

0.38486

d

55.4456949747358

60.3683781582228

0.918455931

0.36076

e

-572.792246564859

601.946486143803

-0.951566725

0.34378

f

3891.03589853482

4024.72137300907

0.966783918

0.33616

g

-17321.6951392161

18075.7892652729

-0.958281538

0.34041

h

48392.8641188205

52603.0408147461

0.919963245

0.35997

i

-75850.6822768342

89905.4345289805

-0.843671828

0.40102

j

49681.8479662316

68596.8832058927

0.724258095

0.47073

68% Confidence Intervals Variable

Value

68% (+/-)

Lower Limit

Upper Limit

a

-1.88604785750052E03

2.46632179243476E03

5.80273934934241E04

b

0.120920535145288

0.147345570018752

-4.35236964993528E03 -2.64250348734634E02

c

-3.40909138627248

3.90378940249218

-7.31288078876466

0.494698016219692

d

55.4456949747358

60.3563044825912

-4.91060950785539

115.801999457327

e

-572.792246564859

601.826096846575

-1174.61834341143

29.0338502817154

f

3891.03589853482

4023.91642873447

-132.880530199649

7914.9523272693

g

-17321.6951392161

18072.1741074199

-35393.869246636

750.478968203748

h

48392.8641188205

52592.5202065831

-4199.6560877626

100985.384325404

i

-75850.6822768342

89887.4534420747

-165738.135718909

14036.7711652405

j

49681.8479662316

68583.1638292515

-18901.31586302

118265.011795483

96

0.26826610516404

90% Confidence Intervals Variable

Value

90% (+/-)

Lower Limit

Upper Limit

a

-1.88604785750052E03

4.09836669929097E03

-5.98441455679148E03

2.21231884179045E03

b

0.120920535145288

0.244848899809116

-0.123928364663828

0.365769434954404

c

-3.40909138627248

6.48705312392529

-9.89614451019777

3.0779617376528

d

55.4456949747358

100.296023472071

-44.8503284973356

155.741718446807

e

-572.792246564859

1000.07389207931

-1572.86613864417

427.281645514456

f

3891.03589853482

6686.67208911728

-2795.63619058245

10577.7079876521

g

-17321.6951392161

30031.1162853244

-47352.8114245406

12709.4211461083

h

48392.8641188205

87394.6920096191

-39001.8278907986

135787.55612844

i

-75850.6822768342

149368.888926448

-225219.571203282

73518.206649614

j

49681.8479662316

113966.86175827

-64285.0137920386

163648.709724502

95% Confidence Intervals Variable

Value

95% (+/-)

Lower Limit

Upper Limit

a

-1.88604785750052E03

4.89860153572409E03

-6.78464939322461E03

3.01255367822357E03

b

0.120920535145288

0.29265736441612

-0.171736829270832

0.413577899561409

c

-3.40909138627248

7.75369573461589

-11.1627871208884

4.3446043483434

d

55.4456949747358

119.879525346599

-64.4338303718631

175.325220321335

e

-572.792246564859

1195.34533218436

-1768.13757874922

622.553085619506

f

3891.03589853482

7992.29170252142

-4101.2558039866

11883.3276010562

g

-17321.6951392161

35894.902322979

-53216.5974621951

18573.2071837629

h

48392.8641188205

104459.118449923

-56066.2543311022

152851.982568743

i

-75850.6822768342

178534.211887649

-254384.894164484

102683.529610815

j

49681.8479662316

136219.690670262

-86537.8427040302

185901.538636493

97

99% Confidence Intervals Variable

Value

99% (+/-)

Lower Limit

Upper Limit

a

-1.88604785750052E03

6.48698381432855E03

-8.37303167182907E03

4.60093595682803E03

b

0.120920535145288

0.387552155909493

-0.266631620764205

0.508472691054782

c

-3.40909138627248

10.267848561446

-13.6769399477184

6.85875717517348

d

55.4456949747358

158.750724042678

-103.305029067943

214.196419017414

e

-572.792246564859

1582.93867461236

-2155.73092117722

1010.1464280475

f

3891.03589853482

10583.809794602

-6692.77389606714

14474.8456931368

g

-17321.6951392161

47533.9030308882

-64855.5981701043

30212.2078916721

h

48392.8641188205

138330.216430538

-89937.3523117172

186723.080549358

i

-75850.6822768342

236424.32118086

-312275.003457694

160573.638904026

j

49681.8479662316

180389.223766536

-130707.375800305

230071.071732768

ANOVA results

Source

DF

Sum of Squares

Mean Square

F Ratio

Prob(F)

Regression

9

609081.066919412

67675.6741021569

31810.2479

0

Error

93

197.855663112337

2.12748024851976

Total

102

609278.922582524

98

ANNEXURE 2 Groundnut (in shell) Product Note Authority Trading of Groundnut (in shell) futures may be conducted under such terms and conditions as specified in the Rules, Byelaws & Regulations and directions of the Exchange issued from time to time. Unit of Trading The unit of trading shall be 10 MT. Bids and offers may be accepted in lots of 10 MT or multiples thereof. Months Traded In Trading in Groundnut (in shell) futures shall be conducted in the months as specified by the Exchange from time to time. Tick Size The tick size of the price of Groundnut (in shell) shall be Re 0.05 (5 Paisa). Basis Price The basis price of Groundnut (in shell) shall be Ex-Warehouse Junagadh (Gujarat), exclusive of Sales tax/VAT. Unit for Price Quotation The unit of price quotation for Groundnut (in shell) shall be in Rupees per 20 kg. The basis for Groundnut (in shell) traded as Groundnut (in shell) is basis Junagadh (Gujarat), exclusive of Sales tax/VAT. Hours of Trading The hours of trading for futures in Groundnut (in shell) shall be as follows: Mondays through Fridays – 10.00 AM to 5.00 PM Saturdays – 10.00 AM to 2.00 PM Or as determined by the Exchange from time to time. All timings are as per Indian Standard Timings (IST) Last Day of Trading. Last day of trading shall be 20th calendar day of contract month, if 20th happens to be a holiday or a Saturday, then the previous working day. Mark to Market The outstanding positions in futures contract in Groundnut (in shell) would be marked to market daily based on the Daily Settlement Price (DSP) as determined by the Exchange. 99

Position limits Member Level-: 9,000 MT or 15% of Market OI whichever is higher Client-Level: 3,000 MT The above limits will not apply to bona fide hedgers as determined by the Exchange. For bona fide hedgers, the Exchange will, on a case to case basis, decide the hedge limits. For near month contracts: The following limits would be applicable from 28 days prior to expiry date of a contract Member: Maximum of 1,800 MT or 15 % of Market open interest, whichever is higher Client: Maximum of 600 MT Both position limits will be subject to NCDEX Regulations and directions from time to time. Margin Requirements

NCDEX will use Value at Risk (VaR) based margin calculated at 99% confidence interval for one day time horizon. NCDEX reserves the right to change, reduce or levy any additional margins including any mark up margin. Special Margin Special margin of 5% of the value of the contract will be levied whenever the rise or fall in price exceeds 20% of the 90 days prior settlement price. The margin will be payable by buyer or seller depending on whether price rises or falls respectively. The margins shall stay in force so long as price stays beyond the 20% limit and will be withdrawn as soon as the price is within the 20% band. Pre-Expiry Additional Margin There will be an additional margin imposed for the last 5 trading days, including the expiry date of the Groundnut (in shell) contract. The additional margin will be added to the normal exposure margin and will be increased by 3% everyday for the last 5 trading days of the contract.

Delivery Margins In case of open positions materializing into physical delivery, delivery margins as may be determined by the Exchange from time to time will be charged. The delivery margins will be 100

calculated based on the number of days required for completing the physical delivery settlement (the look-ahead period and the risks arising thereof). Delivery Default Penalty The penalty structure for failure to meet delivery obligations will be as per circular no. NCDEX/TRADING-091/2007/235 dated October 4, 2007 Arbitration Disputes between the members of the Exchange inter-se and between members and constituents, arising out of or pertaining to trades done on NCDEX shall be settled through arbitration. The arbitration proceedings and appointment of arbitrators shall be as governed by the Bye-laws and Regulations of the Exchange. Unit of Delivery The unit of delivery for Groundnut (in shell) shall be 10 MT. Delivery Size Delivery is to be offered and accepted in lots of 10 MT Net or multiples thereof. A quantity variation of +/-3 % is permitted as per contract specification. Delivery Requests The procedure for Groundnut (in shell) delivery is based on the contract specifications as per Exhibit 1. During five trading days prior to expiry of the contract (including the date of expiry), sellers having open positions would be required to indicate delivery information for giving delivery. Accordingly, the window for acceptance of delivery requests will be open for 3 working Days. Members giving delivery requests for the commodities are not permitted to square off their open positions. A penalty of 5% of final settlement price on the position squared off will be levied on the embers violating the same. NCDEX would thereafter complete the matching process based on the location and by random, keeping in view the storage capacity of warehouse and Groundnut (in shell) already deposited / dematerialized for delivery or any other factor(s) that the Exchange deems appropriate for completion of the matching process. It may be noted that upon expiry of the contract, if any seller having open position desires to give physical delivery at a specified delivery center, then the buyer with corresponding open position as matched by the process put in place by the Exchange, shall be bound to settle by taking physical delivery. All open positions of those sellers who do not provide required information for physical delivery shall be settled in cash with penalties. For Groundnut (in shell), applicable cash settlement penalties currently, is 0.5 % of the Final Settlement Price.

101

Ten percent (10%) of such penalty amount shall be retained by the Exchange and the balance ninety percent (90%) shall be paid to the buyers to whom the deliveries could not be made. Delivery Allocation The Exchange would then compile all open positions of the members on the last trading day, as specified in Chapter 1 above. The buyers / sellers who have to receive / give delivery would be notified on the same day after the close of trading hours. Delivery of Groundnut (in shell) is to be accepted by buyers at the accredited warehouse where the seller affects delivery in accordance with the contract specifications. Actual Delivery Where Groundnut (in shell) is sold for delivery in a specified month, the seller must have requisite electronic credit of such Groundnut (in shell) holding in his Clearing Member‘s Pool Account before the scheduled date of pay in. On settlement the buyer‘s Clearing Member‘s Pool Account would be credited with the said delivery quantity on pay out. The Clearing Member is expected to transfer the same to the buyer‘s depository account. However, the buyer must take actual physical delivery of Groundnut (in shell) before expiry of the validity date as indicated in the quality test report/Assayer‘s Certificate of the Assayer or get the same revalidated. Accredited Warehouse NCDEX has accredited warehouses for receipt and delivery of Groundnut (in shell). Groundnut (in shell) will be received and delivered only from the NCDEX accredited warehouse. The details of the NCDEX accredited warehouses are as per Exhibit 2. The Groundnut (in shell) received at the NCDEX accredited warehouse will be tested and certified by NCDEX accredited Assayer before acceptance as good delivery in the warehouse. Likewise, Groundnut (in shell) delivered to buyers will be from the accredited warehouse only. Quality Standards The contract quality for delivery of Groundnut (in shell) futures contracts made under NCDEX Regulations shall be Groundnut (in shell) conforming to the quality specification indicated in the contract. No lower grade/quality shall be accepted in satisfaction of futures contracts for delivery except as and to the extent provided in the contract specifications. Delivery of higher grade would be accepted with premium. Packaging Groundnut (in shell) shall be delivered in 35 kg gross basis with a permissible range of +/-1.5 kg in clean, dry, sound, single, unmended Jute bags in merchantable condition or any other accepted industry standard material with the mouth of the bag stitched disallowing sweating/spilling.

102

Standard Allowances Sample weight per validation of quality allowed will be 0.2% on account of sample testing. At the time of deposit: The quantity credited will be the actual quantity delivered at the tested moisture level, after providing for standard allowances on account of sampling. Weight The quantity of Groundnut (in shell) received and / or delivered at the NCDEX designated warehouse would be determined / calculated by the weighbridge / weigh scale at the premises of the designated warehouse or such other.

103