critical analysis of commodity futures -...

International Journal of Advanced Research Foundation

Website: www.ijarf.com (ISSN: 2394-3394, Volume 3, Issue 8, August 2016)

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Critical Analysis of Commodity Futures (With Special Reference to National Multi Commodity Exchange of India-NMCE)

Dr. K.Kanaka Raju Department of Commerce and Management Studies

Andhra University Campus, Tadepalligudem, West Godavari District-534101. [email protected]

Abstract—The main objectives of this paper to extract the more

favorable response towards the total commodity futures in terms of the tonnes, lots and the value along with the identification of possible strategies to enhance the trading activity of the commodity market. The data acquired from the website of NMCE and the period of study was 2010-11 to 2014-15 and the multiple regression model was applied through the SPSS 16.0 version. The study found that futures of agriculture was the more favorable response towards the total volume of commodity futures and it was followed by the metals and the bullion, but in terms of lots bullion commodity future was dominated than the metals and the agricultural commodity futures where as in terms of value metals futures were dominated rather than the agriculture and bullion commodity futures. It is suggested that to include the further products in commodity exchanges to expand the volume, size and value of the commodity futures market.

Index Terms— Agriculture, Metals, Bullion, Tonnes, Lots and Value.

I. INTRODUCTION

The advantages of futures market sector, revelation of cost and administration of value danger. NMCE takes after the previous framework. It is not bolster any business sector creator. Merchants submit orders and the approaching requests are coordinated against the current requests in the request book. Exchanges are cleared and settled through NMCE's in-house Clearing and Settlement House, which is associated with every one of its Members and the Clearing Banks. Conveyance of the hidden items is allowed just through a Central Warehousing Corporation (CWC) receipt, which meets most noteworthy contemporary global measures. Namelessness of exchanging members and viable danger administration framework fortifies the trust of the members in the exchanging framework, which is a precondition for improving broadness and profundity of the business sector. Exchanging rights on the Exchange can be procured by Individuals, Registered Firms, Corporate bodies and Companies (as characterized in the Companies Act 1956) by conforming to the affirmation standards. It is one who has the privilege to execute exchanges notwithstanding a privilege to clear its exchanges in contracts executed at NMCE either all alone sake or for other Trading Members.

II. REVIEW OF LITERATURE

Ollermann and Farris(1995) opined that futures market may increase the efficiency of the commodity markets. Garbade and Silber(1983) exhibit the relationship between

futures price and cash commodity prices and opined that influence on the cash transactions. Figlewski(1984) points out that variability can reduce due to the liquidity contributed by the speculators and the possible adverse price fluctuations may be compensated. Bhattacharya et al(1986) calculated the weekly variations in series for future and spot prices for GNMAS and confirmed that there was no variability spot price since futures trading had taken place. Edwards(1988) argued that changes in the futures leads to changes in the spot market and demonstrated the futures markets react more fastly to information. Yushang-Wu(2001) concluded that risk avoidance of growers is one and half to two times higher than that of the local traders. Zant Wouter(2001) concluded that risk avoidance of growers is one and half to two times higher than that of the local traders. Yang Jianet.al (2005) study the lead -leg relationship between futures trading activity to cash price volatility for relevance of major agricultural commodities and found that enhances of futures trading uni-directionally results an increase in changes in cash price for majority of the commodities. Lien Donald and Yang Li(2007) observed the correlation and variance of futures and spot market returns and its influence on dynamic futures of strategies of hedging in copmmodity markets. Alizadeh AH et al (2008) calculated the constant and dynamic hedge ratios and found regime shifts between the futures price and the spot price. Gallo Giampiero M and Otranto Edoardo(2008) found that changes in aparticular market reacts to innovations in other markets due to the financial integration. Geman Helyette and Ohanasteve (2008) pointed out that specific risks based on a utility form certainly equilence with time considerable and also pointed out that problem of storage of commodities in the form of physical positions and physical assets, Liu Peng and Tang Ke (2010) examined the three factor model comprises of interest rate, convenience yield and captured log spot price and identified that separation of the storage cost and convenience yield produces a lesser probability violation of the non-negative issue.Paschke Raphael and Prokopc Zuk Marcel(2010) generated a model of time factor of continuous time auto regressive moving average (CARMA) and concluded that substantially enhances the pricing of long-horizon contracts.Sakthivel P and Kamiah B(2010) concluded that the inception of derivatives contracts in commodities enhance the market efficiency and reduce the asymmetric information. Verma Ashutosh and Kumar Vijaya C.V.R.S(2010) focused on the maturity effect and explain by the negative co-variance between net carry cost and the spot price. Debasis S.S(2011)



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concluded that initiation of Nifty Index futures trading in India is coincide with both reduction in spot price volatility and decresing the trading efficiency in the concerned stock market. Silvero Renan and Szklo Alexandere (2012) applied the technique of Kalman filter and concluded that recent development of financial markets have influenced the futures oil market so as to enhance its contribution to the price discovering process of the spot market.

III. OBJECTIVES OF THE STUDY

1.To know the more favorable response towards the total commodity futures in terms of the tonnes, lots and the value.

2.To identify the possible strategies to enhance the trading activity of commodity market.

IV. RESEARCH METHODOLOGY

Source of Data The data acquired from the website of NMCE of

commodity futures of agricultural, metals and the bullion, in addition to that required data obtained from the published journals and magazines and confirmed that most of the information acquired through the secondary source rather than the primary source.

Period of Study

The study period was confined to during the year 2010-11 to 2014-15, because of there was irregular and unavailable pattern of information before the 2010. Hence the study period was selected only for the above period to have homogeneity in the pattern of data.

Techniques

The multiple regression model was applied through the SPSS 16.0 version.

Research Models

Model-1 Total Volume of tonnes of NMCE Commodity futures = a

+ b1 x T of agriculture + b2 x T of metals + b3 x Value of bullion + error term

a = constant, T = Tonnes b1, b2, b3 are the un standardized coefficients of ?

Model-2

Total Volume of Lots of NMCE = a + b1 x Lots of agriculture + b2 x Lots of metals + b3 x Lots of bullion market + error term

Model-3

Total Volume of Crores Commodity futures = a + b1 x Value of agriculture + b2 x Value of metals + b3 x Value of bullion market + error term

Table-1

This table describes the volume in terms of tonnes of commodity futures at NMCE regarding the agriculture, metals and the bullion.

Trends in Commodity Futures at NMCE-Volume in

(000tonnes) Year Agriculture Metals Bullion Total

2010-11 27683 4666 1 32350 2011-12 27852 6965 0 34817 2012-13 21016 3918 0 24934 2013-14 30255 827 0 31082 2014-15 8334 0 0 8334

Source:NMCE

Table-2

This table denotes the variables entered as an independent variables of volume of bullion, metals and the agriculture.

Test of Variables Entered or Removed

Model Variables Entered Variables Removed

Method

1 Bullion-Volume-Tonnes-NMCE, Metals-Volume-Tonnes-NMCE, Agriculture-Volume-Tonnes-NMCEa

. Enter

a. All requested variables entered. b. Dependent Variable: Total-Volume-Tonnes-NMCE Source : SPSS

Table-3

This table shows that 100 percent of variation in total volume of commodity futures was explained through the bullion, metals and agriculture in terms of the tonnes.

Test of Variability in Total Volume (In Tonnes) of Bullion

Market through the Various Independent Variables.

Model R R

Square Adjusted R Square

Std. Error of the

Estimate

1 1.000a 1.000 1.000 .00023 a. Predictors: (Constant), Bullion-Volume-Tonnes-NMCE,

Metals-Volume-Tonnes-NMCE, Agriculture-Volume-Tonnes-NMCE, Source : SPSS

Table-4

This table suggests that proposed research Model-1 was fit for regression analysis, where sum of squares of regression value was much more than that of the sum of squares of residual value at df was 4 and the significance value was the 0.000, it was suggested that the proposed research model was fit for regression analysis.


Model Particulars Sum of Squares

df Mean

Square F Sig.

1 Regression 4.567E8 3 1.522E8 . .000a Residual .000 1 .000 Total 4.567E8 4



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a. Predictors: (Constant), Bullion-Volume-Tonnes-NMCE, Metals-Volume-Tonnes-NMCE, Agriculture-Volume-Tonnes-NMCE, Source : SPSS

b. Dependent Variable: Total-Volume-Tonnes-NMCE

Table-5

This table shows the more favorable response towards the total volume of commodity futures of NMCE in terms of tonnes. This table depicted that agriculture commodity future was more favorable response towards the total volume of commodity futures of NMCE and it was followed by the metals and the bullion.

Table-6

This table exhibits that trends in commodity futures at NMCE - volume in terms of lots from the year 2010-11 to 2014-15 regarding agriculture, metals, bullion and the total volume.

Trends in Commodity Futures at NMCE-Volume(in Lots)

Year Agriculture Metals Bullion Total 2010-11 5316742 1737243 2182231 9236216 2011-12 4262296 2709065 4878460 11849821 2012-13 4564610 1554469 1032090 7151169 2013-14 5776429 372385 1249561 7398375 2014-15 1576654 0 0 1576654

Source:NMCE

Table-7

This table shows the variables entered as an independent variables like bullion, agriculture and the metals of NMCE.



Method

1

Bullion-Volume-Lots-NMCE, Agriculture-Volume-Lots-NMCE, Metals-Volume-Lots-NMCEa

. Enter

a. All requested variables entered.

b. Dependent Variable: Total -Volume-Lots-NMCE,

Source : SPSS

Table-8

This table explains the 100 percent variation in commodity future of total volume in terms of lots of NMCE was explained through the various independent variables.

Test of Variability in Total Volume(In Lots) of Bul lion Market through the Various Independent Variables.

Model R R


Std. Error of

the Estimate

1 1.000a 1.000 1.000 .00023 a. Predictors: (Constant), Bullion-Volume-Lots-NMCE, Agriculture-Volume-Lots-NMCE, Metals-Volume-Lots-NMCE, Source : SPSS

Table-9

This table confirm the application of research Model-2 fit for regression analysis, where the sum of squares of regression was only witnessed but the significance value was the 0.000, hence, it can be inferred that proposed research model-2 was fit for regression analysis.

Test of Difference between Volume of the Bullion Market (In Lots) through the Various Independent

Variables and Test of Suitability of the Regression Model.

Model Particulars Sum of Squares df

Mean Square F Sig.

1 Regression 5.714E13 3 1.905E13 . .000a Residual .000 1 .000 Total 5.714E13 4

Test of More Favorable Response towards the Total Volume of the Bullion Market through the Various Independent Variables.

Unstandardized Coefficients Standardized Coefficients

t

Sig Model Particulars B Std. Error Beta

1 (Constant) 3.944E-13 .000 .000 1.000 Agriculture-Volume-Tonnes-NMCE

1.000 .000 .833 5.814E7 .000

Metals-Volume-Tonnes-NMCE

1.000 .000 .268 1.878E7 .000

Bullion-Volume-Tonnes-NMCE

1.000 .000 .000 3.301E3 .000

a. Dependent Variable: Total-Volume-Tonnes-NMCE, Source : SPSS



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a. Predictors: (Constant), Bullion-Volume-Lots-NMCE, Agriculture-Volume-Lots-NMCE, Metals-Volume-Lots-NMCE b. Dependent Variable: Total -Volume-Lots-NMCE, Source : SPSS

Table-10

This table shows the more favorable response towards the total volume of commodity futures in terms of lots. This table exhibited that lots of bullion commodity future was the more favorable response towards the total lots of commodity futures and it was followed by the agriculture and the metals.

Table-11

This table shows the commodity futures of NMCE in terms of value during the year 2010-11 to 2014-15 of agriculture, metals, bullion and the total.

Trends in Commodity Futures at NMCE-Volume

(in Rs Crore) Year Agriculture Metals Bullion Total

2010-11 129431 72372 18608 218411 2011-12 133636 111318 23396 268351 2012-13 107012 63940 6182 177134 2013-14 132447 13927 6445 152819 2014-15 36040 0 0 36040 Source:NMCE Notes:Natural Gas Volumes are in mmBTU,CFI volumes are in tons of CFI units and not included for computing the total volume. Conversion Factors:Cotton(1 Bale=170kgs), crude oil(1 tonne=7.33barrels),Heating Oil(42 Gallons=100 barrels,1 tonne=7,5 barrels),Gasoline(42 Gallons=100 barrels, 1tonne=8.45 barrels),ATF(1tonne=7.8 barrels)

Table-12

This table shows the variable entered as an independent variables namely bullion, agriculture and the metals and the dependent variable was the total value of NMCE.



Method

1 Bullion-Volume-Turnover(Value)-NMCE, Agriculture-Volume-Turnover(Value)-NMCE, Metals-Volume-Turnover(Value)-NMCEa

. Enter

a. All requested variables entered., Source : SPSS b. Dependent Variable: Total-Volune-Turnover Value NMCE

Table-13

This table shows the 100 percent of variation in total value of commodity futures was explained through the independent variables of bullion, agriculture and metals commodity futures.

Test of Variability in Total Value (In Value) of Bullion

Market through the Various Independent Variables.

Model R R


Std. Error of the

Estimate

1 1.000a 1.000 1.000 .00023 a. Predictors: (Constant), Bullion-Volume-Turnover-NMCE, Agriculture-Volume-Turnover-NMCE, Metals-Volume-Turnover Value –NMCE, Source : SPSS

Table-14

This table shows that the proposed regression model-3 was fit for regression analysis, where the sum of the squares of the residual value was much more than that of the sum of the squares of the regression value at df was 4 with F value was 4.597 at significance value was the 0.011, hence it was confirmed that the proposed regression model fit for the analysis.

Table-10 : Test of More Favorable Response towards the Total Volume of the Bullion Market (In Lots) through the Various Independent Variables.

Unstandardized Coefficients Standardized Coefficients

t

Sig Model Particulars B

Std. Error

Beta

1 (Constant) 1.261E-10 .078 .000 1.000 Agriculture-Volume-Lots-NMCE

1.000 .000 .433 5.414E7 .000

Metals-Volume-Lots-NMCE

1.000 .000 .289 1.802E7 .000

Bullion-Volume-Lots-NMCE

1.000 .000 .490 3.082E7 .000

a. Dependent Variable: Total -Volume-Lots-NMCE, Source : SPSS



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Table-15

This table shows the more favorable response towards the total value of commodity futures and it exhibited that value of metals commodity futures were the more favorable response towards the total value and it was followed by the agriculture and the bullion market.

V. FINDINGS OF THE STUDY

• The study found that agricultural commodity futures were the more favorable response towards total commodity futures in terms of tonnes, followed by the metals and the bullion market.

• The study also identified that bullion commodity futures were the more favorable response towards the total value of commodity futures in terms of lots and it was followed by the agriculture and the metals.

• It was observed that commodity futures of metals were the more favorable response towards the total value of commodity futures in terms of the value and it was followed by the agriculture and the bullion.

VI. SUGGESTIONS

• It was observed that a few commodity futures were traded in National Multi Commodity Exchange, hence it is required to introduce more commodity futures along with the existing commodity futures.

• It was noticed that futures of bullion was comparatively

lesser than the other commodity futures, hence it is required to enhance the trading volume of bullion market on par with the other stock exchanges.

VII. CONCLUSION

Finally, it can be concluded that the agriculture commodity futures were predominant than the metals and the bullion in terms of the tonnes, where as in terms of bullion commodity predominated, where as in terms of value future of metal was predominated.

REFERENCES [1] Alizadeh, Amir H., Nomikos, Nikos K.and Pouliassis,Panos,K.,(2008).

A Markow regime switching approach for hedging energy commodities, Journal of Banking and Finance,32(9),1970-1983.

[2] Bhattacharya, A.K,.A.Ramjee and B.Ramjee (1986).The Conditional Relationship between Futures Price Volatility and the Cash Price Volatility of GNMA Securities Journal of Futures Markets.6(1),29-39.

[3] Debasish S.S.,(2011). Spot and Futures : Market Relative Volatility, SCMS Journal of Indian Management,8(3),94-105.

Table -14: Test of Difference between Volume of the Bullion Market (In Value ) through the Various Independent Variables and Test of Suitability of the Regression Model.

Model Particulars Sum of

Squares Df Mean

Square F Sig.

1 Regression 3.030E10 3 1.010E10 4.597E3 .011a Residual 2197530.757 1 2197530.757 Total 3.031E10 4

a. Predictors: (Constant), Bullion-Volume-Turnover-NMCE, Agriculture-Volume-Turnover-NMCE, Metals-Volume-Turnover-NMCE b. Dependent Variable: Total-Volune-Turnover-NMCE, Source : SPSS

Table-15 :Test of More Favorable Response towards the Total Volume of the Bullion Market (In Value ) through the Various Independent Variables.

Unstandardized Coefficients

Standardized Coefficients

t

Sig

Model Particulars B

Std. Error Beta

1 (Constant) -190.471 2216.070 -.086 .945 Agriculture-Volume-Turnover-NMCE

1.002 .026 .478 38.665 .016

Metals-Volume-Turnover-NMCE

1.015 .037 .529 27.457 .023

Bullion-Volume-Turnover-NMCE

.890 .194 .099 4.582 .137

a. Dependent Variable: Total-Volume-Turnover Value-NMCE, Source : SPSS



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[4] Edwards(1988). Futures Trading and Cash Market Volatility: Stock Index and Interest Rate Futures, Journal of Futures Markets,8(4),421-440.

[5] Figlewski,S,(1984). Futures Trading and Volatility in the GNMA Market, Journal of Finance,36(2),445-456.

[6] Gallo,Giampiero,M and Edoardo, Otranto,(2008). Volatility Spillovers, Interdependence and Co-Movements: A Markov Switching Approach, Computational Statistics and Data Analysis,52(6),3011-3026.

[7] Garbade,K.D. and W.L.Silber,(1983). Price Movements and Price Discovery in Futures and Cash Markets, Review of Economics and Statistics,65(1),289-297.

[8] Geman, Helyette and Ohana,Steve.,(2008). Time Consistency in Managing a Commodity Portfolio: A Dynamic Risk Measure Approach,Journal of Banking and Finance,32(10),1991-2005.

[9] Lien,Donald and Yang,Li(2007). Asymmetric Effect of Basis on Dynamic Futures Hedging: Empirical Evidence from Commodity Markets,. Journal` of Banking and Finance32(1),187-198.

[10] Liu,Peng(Peter) and Tang,Ke.,(2010). No-Arbitrage Conditions for Storable Commodities and Modeling of Futures Term Structures, Journal of Banking and Finance,34(7),1675-1687.

[11] Ollermann,C.M and Farris,P.L(1985). Futures or Cash: Which Market Leeds Beef CattlePrices? .The Journal of Futures Markets,529-538.

[12] Paschke,Raphael and Prokopczuk,Marcel.,(2010). Commodity Derivatives Valuation with Autoregressive and Moving Average Components in the Price Dynamics, Journal of Banking and Finance,34(11),2742-2752.

[13] Sakthivel,P.and Kamaiah,B.,(2010). Price Discovery and Volatility Spillover Between Spot and Futures Markets: Evidence from India IUP Journal of Applied Economics,9(2),81-97.

[14] Silverio,Renan and Szklo,Alexandre.,(2012). The Effect of the Financial Sector on the Evolution of Oil Prices: Analysis of the Contribution of the Futures Market to the Price Discovery Process in the WTI spot market,Energy Economics 32(6),1799-1808.

[15] Verma,Ashutosh and Kumar,C.V.R.S.Vijay,(2010). An Examination of the Maturrity Effect in the Indian Commodity Futures Market, Agricultural Economics Research Review,23(2),335-342.

[16] Yang,Jian,Balyeat,Brain R. and Leatham,DavidJ.,(2005).Futures Trading Activity and Commodity Cash Price Volatility, Journal of Business Finance and Accounting,32(1-2)297-323.

[17] Yu, Shang-WU.,(2001). Index Futures Trading and Spot Price Volatility Applied Economics Letters,8(3),183-186.

[18] Zant,Wouter.,(2001).Hedging Price Risks of Farmers by Commodity Boards: A Simulation Applied to the Indian Natural Rubber Market, World Development,29(4),691-710.

About the author

Dr. K. Kanaka Raju was awarded the Best Business Academics of the Year Award (BBAY)-2015, by the All India Commerce Association in the field of Social Media at the 68 th All India Commerce Conference held at Hazaribagh- Vinobha Bhave University in Nov 2015 and also he received Distinguished Teacher Award from the MTC-Global for the year 2016. He is an Assistant Professor in the department of Commerce and Management Studies (Andhra University Campus, Tadepalligudem) and he has more than 10 years of teaching experience in Accounting and Finance. He obtained Post Graduation as well as Doctoral Degree, Ph.D from the Andhra University and he was also qualified for UGC-NET and SET from Management as well as Commerce. A prolific and internationally renowned author Dr. K. Kanaka Raju authored Seven Books and over 83 research papers published in national and international journals to his credit, along with the number of papers presented in various national and international seminars. He is the Life Member of the Indian Accounting Association (IAA), India Commerce Association (ICA), MTC and Member of the Indian Academicians and Research Association (IARA) and Member of Dr. J. K. Research Foundation. His current areas of teaching and research interest are Financial Management, Security Analysis and Portfolio Management, Strategic Financial Management, Financial Derivatives and Accounting for Managers. His other books are Investment Management (Analysis of Securities and Management of Portfolio), Risk Management (Futures, Options and Swaps), Working Capital Management, Human Resource Accounting, Trade Unionism and UGC-JRF/NET/SET- Management and Financial Management.

critical analysis of commodity futures -...

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