Overview of the Indian Commodity Market Finance Essay

Published: 2021-06-28 03:55:04
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In India market for futures are from a very long time back , it was there in early 1800s. After Independence, the Forward Contracts (Regulation) Act, 1952 (FCRA, 1952) was passed to promote and regulate this market with Forward Markets Commission (FMC) being set up in 1953 in Mumbai as the regulator. "Commodity derivatives were banned in the late ’60s, but were revived again in the ’80s.After the successful equity market reforms of the ’90s, the Government of India tried to replicate similar reforms for the commodity derivatives markets and in 1999 suggested that the Minimum Support Price (MSP)as a price-hedging instrument could be replaced with derivatives markets. National-level multi-commodity exchanges were permitted to be set up on conditions of being backed by internationally prevailing best practices of trading, clearing and settlement. The national commodity exchanges follow electronic, transparent trading and clearing with novation, similar to the equity market [See Box 2]. At present, 105 commodities have been approved for trading out of which 95 commodities are actively traded. The development of the commodity derivatives market in India like many other countries has been hindered by policy reversals on concerns regarding its effect on prices and supplies of essential commodities." This apart, integration of spot and futures market is cited as a critical factor for further growth of commodity futures in India. According to Nair (2004), the major stumbling block for the development of commodity futures markets in India is the fragmented physical/spot market with government laws and various taxes that hinder the free movement of commodities critique draws attention to the prevalence of bilateral deals in local exchanges, the lack of price transparency both in the (fragmented) futures and spot markets for many commodities and the absence of certified warehouses.
At present 22 Exchanges are recognised/registered for forward/futures trading in commodities. Most of the commodity exchanges in India are single commodity platforms and cater mainly to the regional requirements. However, three national-level multi-commodity exchanges have been set up in the country to overcome the problem of fragmentation. These exchanges are:
1. National Multi Commodity Exchange of India (NMCE)
2. Multi Commodity Exchange of India (MCX)
3. National Commodity & Derivatives Exchange of India (NCDEX)
NMCE (National Multi Commodity Exchange of India) – It is the first state of the demutualized multi-commodity Exchange, National Multi Commodity Exchange of India Ltd. (NMCE) was promoted by commodity-relevant public institutions, viz., Central Warehousing Corporation (CWC), National Agricultural Cooperative Marketing Federation of India (NAFED), Gujarat Agro-Industries Corporation Limited (GAICL), Gujarat State Agricultural Marketing Board (GSAMB), National Institute of Agricultural Marketing (NIAM), and Neptune Overseas Limited (NOL). While various integral aspects of commodity economy, viz., warehousing, cooperatives, private and public sector marketing of agricultural commodities, research and training were adequately addressed in structuring the Exchange, finance was still a vital missing link. Punjab National Bank (PNB) took equity of the Exchange to establish that linkage. Even today, NMCE is the only Exchange in India to have such investment and technical support from the commodity relevant institutions. These institutions are represented on the Board of Directors of the Exchange and also on various committees set up by the Exchange to ensure good corporate governance.. NMCE commenced futures trading in 24 commodities on 26th November, 2002 on a national scale and the basket of commodities has grown substantially since then to include cash crops, food grains, plantations, spices, oil seeds, metals & bullion among others..
MCX( Multi Commodity Exchange of India ) :- Headquartered in Mumbai, Multi Commodity Exchange of India Ltd (MCX) is a state-of-the-art electronic commodity futures exchange. The demutualised Exchange has permanent recognition from the Government of India to facilitate online trading, and clearing and settlement operations for commodity futures across the country. Having started operations in November 2003, today, MCX holds a market share of over 85%* (as on March 31, 2012 MCX had a market share of 86%) of the Indian commodity futures market. The Exchange has more than 2,170 registered members operating through over 3,46,000 including CTCL trading terminals spread over 1,577 cities and towns across India. MCX was the third largest commodity futures exchange in the world, in terms of the number of contracts traded in CY2011
4.2 PURPOSE OF THE STUDY
In India, as derivatives only futures trading in commodities is allowed. These trading are delineated into agriculture and non agriculture commodities. There has to be a systematic approach to reap benefits from commodity futures. These markets are driven by news, rumors, data release aspects and many more .These all things sometimes results in market volatility so assessing the risk mitigation possibility through use of futures is necessary and also the use of other derivative instruments like options. These complicated aspects make the research desk inevitable in brokerage firm. Hence the study is useful to understand the future market trends , analyze the hedging effectiveness of commodity futures and the need of options in commodity market.
4.3 SIGNIFICANCE OF THE STUDY
To get to know the hedging effectiveness of the futures as it is many times talked that the futures increases speculative trading rather than hedging tool and to find out wheather price discovery mechanism is there and how efficient is it in hedging price risk, It also covers the need of commodity option in the market and its use as an hedging tool.
4.4 LIMITATION OF STUDY
The study is limited to Kolkata and Bangalore brokerage houses only.
Limited number of respondents i.e.50 as it was not easy to get data from the corporate.
Time constraint was a major limitation.
Not covered the industrialist and farmers who trade in commodity market to hedge the production risk..

5.1 INTRODUCTION
This part of the research paper shows the detailed analysis and related interpretation of the collected data in a detailed manner . This analysis and interpretation is based on the research methodology mentioned in Chapter 3 and the micro analysis of the Indian commodity market .In this Chapter initially the analysis of the respondents is done and then it moves on to analyse the the primary data and further to conclude a secondary research is also done from the data sources as earlier mentioned in Chapter 3.
5.2 RESPONDENTS PROFILE
The number of respondents taken in for this research is 50. Basically the respondents are those people who trade in commodity market. Majority of respondents are from Kolkata and Bangalore, respondents are from all the age groups . Out of 50 , 20% of respondents are professional people like Chartered Accountants, Company Secretary, and MBA who trade in commodity and other financial instruments and rest are the people who trade in commodity market, respondents also includes the people orking in for brokerage houses who deals in commodity.
5.3 ANALYSIS OF DATA
The analysis is done on both primary data and Secondary Data.
Exchange in which respondents trade.
Chart 5.1: Preference for Commodity Exchange
Source: Primary Data
60% of the respondents prefer MCX as the commodity exchange for their investment, whereas NCDEX is preferred by 29% of the sample, and rest of the respondents prefer options other than MCX and NCDEX.
Preference for the type of Commodity among Investors.
Chart 5.2 : Type of Commodity trade
Source: Primary Data
Bullion, Metals, Agricultural Products are the most preferred commodity types by 49%, 18%, and 33% of the respondents respectively.
Opinion about Indian Market Commodity market volatility.
Chart 5.3 : Opinion about Indian commodity market volatility
48% of the respondents are of the opinion that Commodity Market in India is highly volatile, and 43% percentage of respondents feel that the volatility of the Indian Commodity Market is moderate, whereas it is thought to be low volatile by 9% of the respondents.
Level of participation in commodity market.
Chart 5.4: Level of participation in commodity market
Most of the respondents are very active at trading in Commodity Market, whereas 31 % of the respondents are found to be less active in the Commodity Market.
5.3.4 Relationship between Mitigation of risk and level of participation by an investor
Table 5.1 :- Cross tabulation of level of investement participation and mitigation of risk.
How_Active * Mittigation of risk Crosstabulation
Count
Mittigation of risk
Total
Least Priority
Less Priority
Neutral
High Priority
Highest Priority
How_Active
Very Active
1
1
0
4
10
16
Moderate
1
4
8
3
5
21
Less Active
2
2
7
2
1
14
Total
4
7
15
9
16
51
Chart 5.5: Cross tabulation of level of investment participation and mitigation of risk
Interpretation: – Cross tabulation analysis shows that activeness of a trader in a market have a important role in taking up mitigation of risk. Active trader gives highest priority to Mitigation of risk on the other hand moderate and less active traders are not motivated towards risk mitigation.
5.3.5 Cross tabulation Relationship between investors level of participation and
Pure investment Purpose
Table 5.2 : Cross tabulation relationship between investement level of participation and
investement purpose
How_Active * Pure_Investement_Purpose Crosstabulation
Count
Pure_Investement_Purpose
Total
Least Priority
Less Priority
Neutral
High Priority
Highest Priority
How_Active
Very Active
3
3
6
3
1
16
Moderate
3
5
5
5
3
21
Less Active
1
2
1
3
7
14
Total
7
10
12
11
11
51
Chart 5.6: Cross tabulation relationship between investement level of participation and
investement purpose
Interpretation :- Through this cross tabulation we get to know that investors who are less active they make investement in futures with pure investement purpose . On the other hand very active and moderate investors are more neutral towards pure investement purpose.
Factor analysis :
Factors taken into account are Risk mitigation, Parking of excess cash , Pure investement purpose and To offset price volatility.
Table 5.3 : Factor analysis of Risk mitigation, Parking of excess cash , Pure investement
purpose
KMO and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
.615
Bartlett’s Test of Sphericity
Approx. Chi-Square
14.924
Df
6
Sig.
.41
Interpretation :-Kaiser Meyer Olkin Values range between 0 and 1. A value near to 1 indicates a high correlation and suggests that the data is reliable whereas a value near 0 indicates that correlation is less than partial correlation.
KMO value should be greater than 0.5, otherwise we need to collect more data or some of the factors should be eliminated. There are Four categories of value on the basis of KMO value. (0.5-0.7 is mediocre, 0.7-0.8 is good, 0.8-0.9 is great, greater than 0.9 is superb)
In this case, the KMO value is .615, so the data falls in the category of ‘mediocre’. It means that the data are fair enough to carry on the factor analysis.
Further, there should be some relationship between the variables, if we need to carry out Factor analysis. So the significance value should be less than 0.5.
In this case the sig value is 0.04 which shows that the data are related to each other, so we can carry out Factor Analysis
Communalities
Initial
Extraction
Mittigation of risk
1.000
.432
Pure_Investement_Purpose
1.000
.687
Parking_Excess_Cash
1.000
.774
To_offset price volatility
1.000
.710
Extraction Method: Principal Component Analysis.
In this case we assume that the initial variance is common, so the communalities initially are 1.But in the extraction column, the variance has changed for every factor. For Example, variance of .710 signifies that variable 1 is 71% common to variance of other factor
Component Matrixa
Component
1
2
Mittigation of risk
.187
.630
Pure_Investement_Purpose
.818
.135
Parking_Excess_Cash
-.863
.170
To_offset price volatility
.098
-.837
Extraction Method: Principal Component Analysis.
a. 2 components extracted.
Testing of Hypothesis
5.4.1 Regression Analysis
On the primary data collected for determining the impact of various independent variables like Easy money, Speculators , Demand ,Supply, Expiry date on dependent variable i.e. price.
Table 5.4 : Regression analysis of independent variables like Easy money, Speculators Demand ,Supply, Expiry date on dependent variable i.e. price.
Variables Entered/Removed
Model
Variables Entered
Variables Removed
Method
1
Easy Money, Speculators, Demand, Supply, Expiry_Datea
.
Enter
a. All requested variables entered.
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.761a
.679
.531
.74737
a. Predictors: (Constant), Easy_Money, Speculators, Demand, Supply, Expiry_Date
ANOVAb
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
33.743
5
6.749
12.082
.000a
Residual
24.577
44
.559
Total
58.320
49
a. Predictors: (Constant), Easy_Money, Speculators, Demand, Supply, Expiry_Date
b. Dependent Variable: Price
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
-.980
.791
-1.238
.222
Demand
.511
.122
.436
4.202
.000
Speculators
.487
.100
.496
4.872
.000
Supply
.037
.150
.027
.248
.805
Expiry_Date
.146
.151
.107
.966
.339
Easy_Money
.170
.123
.151
1.380
.175
a. Dependent Variable: Price
Interpretation : The second table of interest is the Model Summary table. This table provides the R and R2 value. The R2 value is 0.679, which represents the simple correlation and, therefore, indicates a degree of correlation. The R2 value indicates how much of the dependent variable, Price of the commodity , can be explained by the independent variables, Demand , speculator , Supply, Easy money , Expiry date . In this case, 67.9% is the effect which is fair enough.
3. The next table is the ANOVA table. This table indicates that the regression model predicts the outcome variable significantly well. In the "Regression" row go to the Sig. column. This indicates the statistical significance of the regression model that was applied. Here, P < 0.000 which is less than 0.05 and indicates that, overall, the model applied is significantly good enough in predicting the outcome variable.
4. The next table is Coefficients which provides us with information on each predictor variable. This provides us with the information necessary to predict Price from , Demand , speculator , Supply, Easy money , Expiry date . It is seen that Demand and Speculators contribute significantly to the model (by looking at the Sig. column).
5. By looking at the B column under the Unstandardized Coefficients column we can present the regression equation as:
Price = -.980 + 0.511(Demand) + .487 (Speculators)
5.4.2 Regression Analysis
An analysis of investors perception about Indian commodity market and there participation in the market as independent variable. And its impact on risk mitigation strategy as a dependent
Table 5.5 : Regression analysis on factors determining mitigation of risk
Interpretation :-
The second table Model Summary table. This table provides the R and R2 value. The R2 value is 0.669, which represents the simple correlation and, therefore, indicates a degree of correlation. The R2 value indicates how much of the dependent variable, Price of the commodity , can be explained by the independent variables, How active an investor is there in the commodity market and opinion about volatility in the commodity market. In this case, 67.9% is the effect which is fair enough.
3. The next table is the ANOVA table. This table indicates that the regression model predicts the outcome variable significantly well. In the "Regression" row go to the Sig. column. This indicates the statistical significance of the regression model that was applied. Here, P < 0.000 which is less than 0.05 and indicates that, overall, the model applied is significantly good enough in predicting the outcome variable.
4. The next table is Coefficients which provides us with information on each predictor variable. This provides us with the information necessary to predict Price from ,How Active an investor participate and his opinion of Indian commodity market . It is seen that contribute significantly to the model (by looking at the Sig. column).
5. By looking at the B column under the Unstandardized Coefficients column we can present the regression equation as:
Price = 6.128 + 0.767(How Active) + .548 (Volatility)
5.4.4 About price discovery mechanism in future market
Chart 5.6 : Future market price discovery process
Interpretation :- As the Price discovery mechanism is concerned 28% of the respondents say that it depends on the future and spot relationship, 25% says its reflected first on spot, 23% on futures, 24% says market is efficient. So with this conclusion we can say that it is not very clear which way the price is discovered.
5.4.5 Factor Analysis :- Of the factors which can help in determining wheather price discovery mechanism exist in the commodity market factors taken into consideration are efficient market, price reflects first on the spot market, information first reflects first on the futures market and their exist a relationship between futures and spot prices
KMO and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
.557
Bartlett’s Test of Sphericity
Approx. Chi-Square
19.164
df
6
Sig.
.004
Interpretation :-Kaiser Meyer Olkin Values range between 0 and 1. A value near to 1 indicates a high correlation and suggests that the data is reliable whereas a value near 0 indicates that correlation is less than partial correlation.
KMO value should be greater than 0.5, otherwise we need to collect more data or some of the factors should be eliminated. There are Four categories of value on the basis of KMO value. (0.5-0.7 is mediocre, 0.7-0.8 is good, 0.8-0.9 is great, greater than 0.9 is superb)
In this case, the KMO value is .557, so the data falls in the category of ‘mediocre’. It means that the data are fair enough to carry on the factor analysis.
Further, there should be some relationship between the variables, if we need to carry out Factor analysis. So the significance value should be less than 0.5.
In this case the sig value is 0.04 which shows that the data are related to each other, so we can carry out Factor Analysis
Correlation Matrix
Efficient
Futures_Market
Spot_Market
Closely_Related
Sig. (1-tailed)
Efficient
.003
.147
.014
Futures_Market
.003
.009
.218
Spot_Market
.147
.009
.074
Closely_Related
.014
.218
.074
5.4.6 Cross Tabulation
To check the need of commodity options and to find out particularly in which commodity the financial instrument ids much needed.
Importance * Agriculture Products Crosstabulation
Count
Agriculture Products
Total
No
Yes
Importance
Very Important
9
14
23
Not Important
9
2
11
Indifferent
3
8
11
Total
21
24
45
Importance * Bullion Cross tabulation
Count
Bullion
Total
No
Yes
Importance
Very Important
17
6
23
Not Important
3
8
11
Indifferent
6
5
11
Total
26
19
45
Importance * Metals Cross tabulation
Count
Metals
Total
No
Yes
Importance
Very Important
20
3
23
Not Important
10
1
11
Indifferent
10
1
11
Total
40
5
45
Importance * Fossil/Energy Crosstabulation
Count
Fossil/Energy
Total
No
Yes
Importance
Very Important
21
2
23
Not Important
9
2
11
Indifferent
11
0
11
Total
41
4
45
Interpretation :-
This cross tabulation analysis is done to determine the need of commodity options in the market and also to know in which commodity product option is needed. So as per the data collected it shows that the commodity option is needed in the market it is much need derivative tool in the agriculture commodity.
5.4.7 Purpose for which commodity option can be used in the market
Chart 5.6 : Purpose for which commodity option can be used in the market
Interpretation :- As the chart shows that the investment in commodity option will be more for making profit through movement in the price and also it will be used as a risk mitigation tool.
5.4.8 Research based on Secondary Research Comdex index futures and spot
Correlation :- This analysis is done in order to check the movement of futures and spot . As per the analysis correlation shows negative correlation .31. Which denotes movement in price of one effect the other inversely.
Conclusion :- This shows that the movement in the price of one effect the other so there exist a price discovery mechanism between these two prices.
6.1 FINDINGS
From the research we got to know that on the basis of an individual participation level in the commodity futures market determines his needs for investment. It is found that an active investor invest in futures market to mitigate the risk so it can be considered that the futures are being considered as a hedging tool.
On the other hand non active investors in the commodity futures main motive is either making a pure investment or offsetting the price volatility in the market.
Factors like mitigation of risk, parking of excess cash, offsetting price volatility and pure investment are the main reasons for trading in commodity futures.
It is found that the factors like Supply , Demand , Speculators , Easy Money , Expiry date all have a direct effect on the price of the commodity and there exist a correlation among them.
It is also found that there exist a relationship between individual perception of commodity market and there investment decision. As per the research done it is found that investor who view commodity market as volatile and are also active in market they in commodity futures for risk mitigation i.e. they consider futures as a hedging tool.
On other hand who feels commodity market is not that volatile it is moderate on less volatile and they are too active in market there investment purpose is more as pure investment or offsetting price volatility.
It is found that factors which help in price discovery mechanism like market efficiency, reflection of market information first on the spot market, effect of market information first on the futures market and that spot market and futures market are interrelated and helps in determining.
As per the secondary research done on the data’s collected of 3 index traded fund on MCX of India both their daily spot and future prices of past one year daily historic price it shows a coo relation, which means that they are closely interrelated so it can be said that market information are reflected on both the prices so futures can be said to discover price because of co orelation.
As the research area also analyse the need of commodity option in the market it comes out with a finding that commodity option is much needed item especially in the agri commodity sector
6.2 CONCLUSION :-
To conclude presently the Indian commodity market is getting very popular the volume of trade has increased so this has resulted in increasing the efficiency level of the market . But as India being a agriculture driven country and agri market mainly depends on the monsoon which is very uncertain so hedging against the price risk through commodity futures can be good idea and it has proved effective too in past and in other commodity too like energy, bullion, metals commodity futures can be a effective tool. Apart from futures in order to encourage commodity market commodity option too is about to be introduced in the market and as per the research investors are really looking forward for it and that too especially it will be great help in the agricultural sector.
6.3 SUGGESTION
Commodity market is a good place too invest in it can help in mitigation of risk like price in risk in today’s inflation effected scenario. Commodity market can help in securing a certain price level through the use of futures. Through the use of relation between futures and spot price discovery can be done so this can be used for arbitraging and speculating future trends in prices. With introduction of commodity option possibility in near future investement in commodity market will be emcouraged as it will provide a better hedge mechanism . So it can be said that it is worthwhile making an investement in commodity market now in order to hedge against the price risk, inflation risk and production risk .
6.4 SUGGESTION FOR FURTHER STUDY
As my research is limited to areas like Kolkata and Bangalore soa further research can be conducted covering major cities.
Sample size can be increased for getting a more clesr result . Respondents can include farmers and industrialist who trade in commodity market to hedge against their production and price risk
More variables can be added which affect the commodity futures.

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