International Journal of Advancements in Research & Technology, Volume 1, Issue 5, October-2012
ISSN 2278-7763
1
A STUDY ON MARKET EFFICIENCY OF SELECTED
COMMODITY DERIVATIVES TRADED ON NCDEX DURING 2011
N.SAJIPRIYA
Department of management studies,
Manonmaniam Sundaranar University,
Tirunelveli, India.
Email:sajipriyans@gmail.com
ABSTRACT
The study aims at testing the weak form of Efficient Market Hypothesis in the context of an
emerging commodity market - National Commodity Derivatives Exchange (NCDEX), which is
considered as the prime commodity derivatives market in India. The study considered daily spot
and futures prices of five selected commodities traded on NCDEX over 12 month period (the
futures contracts originating and expiring during the period January 2011 to December 2011)
The five commodities chosen are Pepper, Crude palm Oil, steel silver and Chana as they account
for almost two-thirds of the value of agricultural commodity derivatives traded on NCDEX. The
results of Run test indicate that both spot and futures prices are weak form efficient
Keywords: Commodity Market, Market Efficiency
1. INTRODUCTION
in cotton, through the establishment of
Bombay Cotton Association Ltd. In 1875
India has a long history of Future
Over a period of time, other commodities
Trading in Commodities. In India, trading in
were permitted to be traded in future
Commodity Futures has been in existence
Exchanges. Spot trading in India occurs
from the 19th century with organized trading
mostly in regional mantis and unorganized
Copyright © 2012 SciResPub.
IJOART
International Journal of Advancements in Research & Technology, Volume 1, Issue 5, October-2012
ISSN 2278-7763
2
market, which are fragmented and isolated.
The 100 unorganized exchanges were
conducting
forward
trade
in
various
However, the success of these
markets
in
performing
the
stabilizing
commodities. The securities market was a
function critically depends on whether they
poor competitor of this market as there were
are “efficient”. Moreover the “dual price
not many papers to be traded at that time.
system” under which different prices for
India, commodity based economy
where
two-third
billion
of a part of market for the commodity by
agricultural,
the government is expected to give rise to
commodities, surprisingly has an under
inefficiency . In an emerging market
developed commodity market. Unlike the
context like India, the growth of commodity
physical market futures markets trades in
future market would depend on efficiency
commodity
of the future market.
population
of
depend
are
the
one
same commodities exist, the administration
on
largely
used
as
risk
management (hedging) mechanism on either
physical commodity itself or open positions
Commodity
in commodity stock.
A commodity may be defined as an
article, a product or material that is bought
Since commodity “futures” trading
was permitted in 2003, the commodity
derivative market in India has witnessed
phenomenal growth. Trading of commodity
derivatives on exchange platforms helps to
achieve
price
discovery.
Price
risk
management besides helping economy with
better resource allocation. Though
volume
of
commodity
future
the
trade
increased exponentially since its launch in
2003, the
functioning
of
the
futures
market came under scrutiny during 200082009 due to price rise.
Copyright © 2012 SciResPub.
and sold. It can be classified as every kind of
movable
property,
except
Actionable
Claims, Money & Securities. Commodities
actually offer immense potential to become
a separate asset class for market-savvy
investors,
arbitrageurs
and
speculators.
Retail investors, who claim to understand
the equity markets, may find commodities
an unfathomable market. But commodities
are
easy
to
understand
as
far
as
fundamentals of demand and supply are
concerned.
Retail
investors
should
understand the risks and advantages of
IJOART
International Journal of Advancements in Research & Technology, Volume 1, Issue 5, October-2012
ISSN 2278-7763
3
trading in commodities futures before taking
that have a financial assets or variable as the
a leap. Historically, pricing in commodities
underlying. The most financial derivatives
futures has been less volatile compared with
are those, which have equity, interest rate
equity and bonds, thus providing an efficient
and exchange rate as the underlying.
portfolio diversification option.
Financial derivatives are used to hedge the
exposure to market risk. The commodity
Commodities actually offer immense
potential to become a separate asset class for
market-savvy investors, arbitrageurs and
speculators. Retail investors, who claim to
understand the equity markets, may find
commodities an unfathomable market. But
commodities are easy to understand as far as
fundamentals of demand and supply are
concerned.
Retail
investors
derivatives
trading in commodities futures before taking
futures has been less volatile compared with
equity and bonds, thus providing an efficient
portfoliodiversification option.
Derivative Market can broadly be
classified as commodity derivative market
and financial derivative market. As the name
trade
contracts for which the underlying assets is a
commodity like, wheat, soya bean, cotton
etc or precious metal like Gold and Silver.
Financial derivatives markets trade contract
Copyright © 2012 SciResPub.
financial
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.
National
Commodity
Derivatives
Exchange Ltd (NCDEX)
The
National
Commodity
and
Derivatives Exchange Ltd , is a national
level,
technology
online
commodity
independent
Commodity Derivatives Market
derivatives
the
derivatives mainly in the following two
a leap. Historically, pricing in commodities
commodity
from
should
understand the risks and advantages of
suggest,
differ
Board
driven
demutualized
exchange
of
with
Directors
an
and
professional management. NCDEX is a
public limited company incorporated on
April 23,2003 under the companies act
1956.It
obtained
commencement
of
its
certificate
business
on
for
May
9,2003.It commenced its operations on
December
15,2003,NCDEX
is
located
Mumbai
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International Journal of Advancements in Research & Technology, Volume 1, Issue 5, October-2012
ISSN 2278-7763
4
1. To analyze market efficiency of
MARKET EFFICIENCY
There
are
different
selected
types
of
commodities
(Chana,
Pepper, Steel, Silver, Crude oil).
market
efficiency that are defined in terms of the
2. To study the market efficiency of
available information. Fama (1970) has
NCDEX in India by considering all
categorized market efficiency into three
commodity indices in future and
forms.
spot markets
3. RESEARCH METHODOLOGY OF
(1) Weak form efficiency
(2) Semi –strong form efficiency
(3) Strong form efficiency
THE STUDY
Selection of the Sample
Weak form efficiency
Prices reflect all information found in the
While selecting the sample indices, all the
record of past prices and volumes market is
NCDEX indices, the sample indices consist
called weak form efficient
of Futures and Spot Prices
if the current
price fully reflects all available information
in the historic
series of price, it is also
known as the “ test of return predictability”.
Semi – strong form efficiency
Prices reflect not only all information
found in the record of prices and volumes
but
also
all
other
publicly
available
information.
Strong form efficiency
Prices reflect all available
information public as well as private.
Sources and Collection of Data
The present study was mainly based on
secondary data which were collected from
the
NCDEX
websitewww.ncdex.com.
Further, the available secondary data were
collected from the various published books,
Articles, Journals.
Period of the Study
This study was mainly intended to examine
Market Efficiency of Futures and Spot
2. Objectives of the study
market indices of National Commodity
Derivatives Exchange (NCDEX) in India.
Copyright © 2012 SciResPub.
IJOART
International Journal of Advancements in Research & Technology, Volume 1, Issue 5, October-2012
ISSN 2278-7763
All the indices which were under NCDEX,
from January 1
th
2011 to December 31
st
5
caused by over speculation and government
intervention.
2011, were considered as the sample indices
“Efficiency and Future Trading- Price
Nexus in Indian Commodity Future
4. REVIEW OF LETERATURE
Market” By Pravakar Sahoo(2009).The
“Market Efficiency in agricultural future
markets” By Andrew M.Mckenzie and
Mathew
.F.Hold
(2002).This
study
examined and future trading price Nexus for
five selected commodities (gold, copper,
petroleum, soyaoil, and chana). Results
suggested that the market is efficient for all
examined in four agricultural commodity
futures
(Livecattle,
hogs,
five commodities.
corn,
In India, NCDEX is considered as prime
soybean).Results
indicate
that
livecattle,hogs, corn and soy bean meal
futures markets are both efficient, and
national level commodity exchange for
agricultural
and
non
agricultural
commodities and hence selected for the
study and the time frame chosen for the
biased in the long run, also suggest short run
time varying risk premium in cattle and hogs
study is the future contracts originating and
expiring during the period January 1th 2011
to December 31st 2011. Five selected
futures markets.
commodities
“Efficiency
Test
of
agricultural
commodity futures markets in China”By
Wang Hong and Bingfanke (2005).This
result suggest a long term equilibrium
relationship between the future price and
cash price for soybean and weak short term
efficiency in the soy bean market .The study
namely
Steel,Silver,Chana , and
–
Pepper,
Crude palm oil
have been selected for this study.The future
Open and Close and Spot Close prices on all
trading days during the period were obtained
from
home
page
of
NCDEX
(www.ncdex.com).
5.Results and Discussions
also highlighted in efficient future market
wheat an suggested that it may have been
Copyright © 2012 SciResPub.
Run Test
IJOART
International Journal of Advancements in Research & Technology, Volume 1, Issue 5, October-2012
ISSN 2278-7763
The Run test is a non
6
observed number of runs in the series should
parametric test that is designed to examine
be close to the expected number of runs.
whether successive price changes are
independent. The test based on the premise
that if a series of a data is random, the
Table 1: Results of Run Test for Chana
Period
January
F(O)
F(C)
S(C)
February
F(O)
F(C)
S(C)
March
F(O)
F(C)
S(C)
April
F(O)
F(C)
S(C)
May
F(O)
F(C)
S(C)
June
Total No. of Test
Observations Value
No. of
Observations
less than
Test Value
No. of
No. of
Observations Runs
Equal to or
Greater than
Test Value
ZStatistics
25
25
73
2609.12
2611.64
2556.64
14
13
42
11
12
31
4
4
4
-3.659
-3.670
-7.883
24
24
68
2822.75
2816.04
2628.25
9
9
37
15
15
31
3
3
4
-3.908
-3.908
-7.570
27
27
74
2583.25
2681.18
2456.77
4
12
29
23
15
45
4
6
2
-2.685
-3.115
-8.422
24
24
65
2400.33
2505.00
2240.58
1
11
39
23
13
26
3
2
8
0.0
-4.380
-6.307
26
26
71
2440.42
2545.03
2337.60
1
13
35
25
13
36
3
4
4
0.0
-3.803
-7.770
Copyright © 2012 SciResPub.
IJOART
International Journal of Advancements in Research & Technology, Volume 1, Issue 5, October-2012
ISSN 2278-7763
F(O)
F(C)
S(C)
July
F(O)
F(C)
S(C)
August
F(O)
F(C)
S(C)
September
F(O)
F(C)
S(C)
October
F(O)
F(C)
S(C)
November
F(O)
F(C)
S(C)
December
F(O)
F(C)
S(C)
7
26
26
74
2654.61
2660.38
2535.55
18
17
51
8
9
23
2
2
2
-4.532
-4.561
-8.408
26
26
79
3148.92
3159.30
2841.38
8
8
43
18
18
36
2
2
4
-4.532
-4.532
-7.877
26
26
71
3257.15
3389.96
3036.30
1
13
46
25
13
25
2
5
5
-1.588
-3.403
-7.449
26
26
71
3508.65
3639.69
3460.44
2
15
34
24
11
37
2
3
3
-3.477
-4.183
-8.008
26
26
62
3249.03
3371.92
3274.74
5
14
37
21
12
25
9
4
4
0.00
-3.796
-7.146
26
26
68
3475.92
3459.46
3462.01
10
11
25
16
15
43
6
6
8
-2.885
-2.952
-6.476
17
17
29
3153.94
3162.47
34865.10
9
9
15
8
8
14
6
4
5
-1.494
-2.499
-3.780
Table 2: Results of Run Test for Crude Palm Oil
Period
January
F(O)
F(C)
Total No.of
Test
Observations Value
No.of
Observations
less than
Test Value
No.of
No.Of
Observations Runs
Equal to or
Greater than
Test Value
Z-Statistics
25
25
21
9
4
16
-0.264
-4.014
89.03
557.31
Copyright © 2012 SciResPub.
6
3
IJOART
International Journal of Advancements in Research & Technology, Volume 1, Issue 5, October-2012
ISSN 2278-7763
S(C)
February
F(O)
F(C)
S(C)
March
F(O)
F(C)
S(C)
April
F(O)
F(C)
S(C)
May
F(O)
F(C)
S(C)
June
F(O)
F(C)
S(C)
July
F(O)
F(C)
S(C)
August
F(O)
F(C)
S(C)
September
F(O)
F(C)
S(C)
October
F(O)
F(C)
S(C)
November
F(O)
8
43
557.15
17
26
5
-5.190
24
24
44
0
569.42
567.74
0
10
18
24
14
26
1
4
2
0
-3.511
-6.24
27
27
48
0
526.75
524.01
0
15
28
0
12
20
1
4
4
0
-3.910
-5.956
0
0
48
0
0
516.92
0
0
19
0
0
29
0
0
7
0
0
-5.025
0
0
46
0
0
525.25
0
0
24
0
0
22
0
0
2
0
0
-6.411
26
26
48
0
504.78
503.25
0
13
24
26
13
24
1
2
2
0
-4.604
-6.565
10
10
47
0
490.66
479.48
0
5
22
10
5
25
1
6
4
0
0
-5.898
25
25
45
0
490.51
489.09
0
11
14
25
14
31
1
6
5
0
-2.829
-5.223
20
20
46
0
505.06
497.55
0
7
23
20
13
23
1
3
3
0
-3.350
-6.113
26
26
41
0
474.04
468.07
0
14
22
26
12
19
1
5
5
0
-3.393
-5.054
26
0
0
26
1
0
Copyright © 2012 SciResPub.
IJOART
International Journal of Advancements in Research & Technology, Volume 1, Issue 5, October-2012
ISSN 2278-7763
F(C)
S(C)
December
F(O)
F(C)
S(C)
Table 3:
Period
January
F(O)
F(C)
S(C)
February
F(O)
F(C)
S(C)
March
F(O)
F(C)
S(C)
April
F(O)
F(C)
S(C)
May
F(O)
F(C)
S(C)
June
F(O)
F(C)
S(C)
9
26
44
474.04
500.70
14
18
12
26
5
2
-3.393
-6.244
0
0
49
0
0
519.69
0
0
35
0
0
14
0
0
2
0
0
-6.575
Results of Run Test for Pepper
Total No.of
Test
Observations Value
No.of
Observations
less than
Test Value
No.of
No.Of
Observations Runs
Equal to or
Greater than
Test Value
ZStatistics
25
25
25
23790.04
23778.84
23778.84
10
10
10
15
15
15
9
8
8
-1.492
-1.919
-1.919
24
24
24
9934.42
24477.5
29533.17
14
14
14
10
10
10
6
3
7
-2.651
-3.966
-2.221
27
27
27
12824.33
24424.04
20723.04
13
16
13
14
11
14
27
4
11
-0.000
-3.882
-1.172
24
24
24
26259.79
28786.5
36651.08
3
13
14
21
11
10
6
4
12
0.00
-3.539
-0.072
26
26
26
29599.96
30827.19
36113.15
4
11
14
22
15
12
6
6
11
-1.016
-2.952
-0.976
26
26
26
24893.84
29372.92
25510.77
4
12
14
22
14
12
7
9
13
-0.215
-1.782
-0.170
Copyright © 2012 SciResPub.
IJOART
International Journal of Advancements in Research & Technology, Volume 1, Issue 5, October-2012
ISSN 2278-7763
July
F(O)
F(C)
S(C)
August
F(O)
F(C)
S(C)
September
F(O)
F(C)
S(C)
October
F(O)
F(C)
S(C)
November
F(O)
F(C)
S(C)
December
F(O)
F(C)
S(C)
10
26
26
26
27382.08
29602.31
20024.35
2
13
14
24
13
12
2
4
13
-3.477
-3.803
-0.170
26
26
26
30890.08
32317.96
26539.42
10
12
13
16
14
13
3
2
10
-4.157
-4.601
-1.401
26
26
26
34550.85
35976.42
24663.00
7
14
12
19
12
14
4
4
11
-3.463
-3.796
-0.976
26
26
26
35101.35
36414.04
19729.5
7
13
14
19
13
12
4
3
12
-3.463
-4.203
-0.573
26
26
26
31927.69
34528.65
14471.00
2
9
11
24
17
15
5
5
13
0
-3.228
-0.019
17
17
27
36111.47
36249.71
16535.67
17
10
15
6
7
12
2
2
17
-3.458
-3.490
0.862
Total No.of
Test
Observations Value
No.of
Observations
less than
Test Value
No.of
No.Of
Observations Runs
Equal to or
Greater than
Test Value
ZStatistics
25
25
46
5128.00
44899.92
44211.29
22
10
22
3
15
24
6
4
6
0.00
-3.624
-5.216
23
23
2959.08
47498.73
9
13
14
10
6
2
-2.450
-4.261
Table 4: Results of Run Test for Silver
Period
January
F(O)
F(C)
S(C)
February
F(O)
F(C)
Copyright © 2012 SciResPub.
IJOART
International Journal of Advancements in Research & Technology, Volume 1, Issue 5, October-2012
ISSN 2278-7763
S(C)
March
F(O)
F(C)
S(C)
April
F(O)
F(C)
S(C)
May
F(O)
F(C)
S(C)
June
F(O)
F(C)
S(C)
July
F(O)
F(C)
S(C)
August
F(O)
F(C)
S(C)
September
F(O)
F(C)
S(C)
October
F(O)
F(C)
S(C)
November
F(O)
F(C)
S(C)
December
F(O)
11
44
46767.74
28
16
2
-6.229
27
27
47
53696.74
53765.51
54040.98
12
11
18
15
16
29
6
6
8
-3.115
-3.068
-4.597
24
24
40
24142.79
64664.29
63896.95
14
11
21
10
13
19
2
2
4
-4.371
-4.380
-5.284
25
25
46
000
57477.12
56691.73
0
15
25
25
10
21
1
5
7
0
-3.198
-4.907
26
26
48
8328.07
55457.15
54014.05
22
11
25
4
25
23
6
4
8
-1.016
-3.773
-4.811
26
26
47
38600.46
57880.96
56170.17
9
10
20
17
16
27
7
2
2
-2.430
-4.581
-6.482
26
26
41
45548.78
62168.46
60858.10
7
15
24
19
11
17
9
4
4
-0.891
-3.773
-5.348
27
27
46
61478.89
60864.40
61520.23
7
8
13
20
19
33
2
2
2
-4.586
-4.630
-6.341
26
26
39
53353.73
53521.26
53204.93
16
19
19
10
7
20
8
4
12
-2.038
-3.463
-2.594
26
26
44
56130.42
56184.88
56469.46
12
13
26
14
13
18
6
5
3
-2.990
-3.403
-5.928
4
41528.25
1
3
3
0
Copyright © 2012 SciResPub.
IJOART
International Journal of Advancements in Research & Technology, Volume 1, Issue 5, October-2012
ISSN 2278-7763
F(C)
S(C)
4
46
55156.25
53787.05
12
2
26
2
20
2
2
-0.612
-6.406
Total No. of Test
Observations Value
No. of
Observations
less than
Test Value
No. of
No. of
Observations Runs
Equal to or
Greater than
Test Value
ZStatistics
17
17
45
28310.00
28030.58
30028.00
8
6
19
9
11
26
6
4
5
-1.494
-2.354
-5.398
16
16
44
27627.50
27510.62
29220.68
8
9
21
8
7
23
2
2
5
-3.364
-3.356
-5.336
16
16
48
27206.25
27195.62
29750.00
8
9
30
8
7
18
2
2
2
-3.364
-3.356
-6.546
16
16
38
28290.00
28270.62
30452.63
8
7
11
8
9
27
8
4
3
-0.259
-2.303
-5.282
17
17
47
28710.58
28758.82
30791.48
9
9
21
8
8
26
5
4
8
-1.997
-2.499
-4.695
Table 5: Results of Run Test for Steel
Period
January
F(O)
F(C)
S(C)
February
F(O)
F(C)
S(C)
March
F(O)
F(C)
S(C)
April
F(O)
F(C)
S(C)
May
F(O)
F(C)
S(C)
June
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International Journal of Advancements in Research & Technology, Volume 1, Issue 5, October-2012
ISSN 2278-7763
F(O)
F(C)
S(C)
July
F(O)
F(C)
S(C)
August
F(O)
F(C)
S(C)
September
F(O)
F(C)
S(C)
October
F(O)
F(C)
S(C)
November
F(O)
F(C)
S(C)
December
F(O)
F(C)
S(C)
13
17
17
48
30595.88
30734.70
30792.91
7
7
19
10
10
29
4
4
9
-2.454
-2.454
-4.415
17
17
47
30284.17
30267.64
30759.57
6
5
23
11
12
24
3
6
10
-2.906
-0.953
-4.128
16
16
42
29980.00
29846.25
30830.95
8
7
24
8
9
18
4
2
6
-2.329
-3.356
-4.810
17
17
43
29508.23
29522.35
31459.30
7
8
17
10
9
26
7
7
2
-0.899
-0.991
-6.160
17
17
39
29662.35
29576.47
33364.10
5
3
19
12
14
20
3
2
6
-2.788
-3.119
-4.543
16
16
43
32656.87
32720.00
32670.93
5
4
31
11
12
12
2
2
2
-3.276
-3.182
-6.098
17
17
49
31706.47
31706.47
33689.79
9
9
19
8
8
30
6
6
2
-1.494
-1.494
-6.625
Run Test Analysis
number of observations below mean, while
The result of run test indicates that both
number of observations equal to or greater
future and spot price for all select selected
than the test value indicates the number
commodities are weak form efficient. The
greater than or equal to the mean.
total numbers of runs indicate dependence
information regarding the yesterday prices
between
of
are effectively absorbed by today’s price.
observations less than test value denotes the
The significant negative Z value for prices
observations.
Number
Copyright © 2012 SciResPub.
The
IJOART
International Journal of Advancements in Research & Technology, Volume 1, Issue 5, October-2012
ISSN 2278-7763
14
indicate that the actual number of runs fall
short of the expected number of runs.
Statistics For Management –Chand
II. Articles
“Market Efficiency in agricultural future
6.Conclusions
markets” By Andrew M.Mckenzie and
Theoretical basis of the weak-form efficient
Mathew .F.Hold (2002).
hypothesis states that the successive prices
“Efficiency Test of agricultural
are independent and past prices have no
commodity futures markets in China”By
predictive content to forecast commodity
Wang Hong and Bingfanke (2005).
price. The non parametric run test for the
“Efficiency and Future Trading- Price
full sample period indicated that both future
Nexus in Indian Commodity Future
and spot price for all selected commodities
Market” By PravakarSahoo(2009).
are efficient in weak form.
Bibliography
III. Website
 www.googlefinance.com
I. Books
 www.ncdex.com
Commodity and Financial Derivatives –
S.Kevin
 www.wikipedia.com
 www.ssrn.com
Finanmcial Management – Prasanna
Chandra
Copyright © 2012 SciResPub.
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