Modern Economy, 2011, 2, 213-227
doi:10.4236/me.2011.23027 Published Online July 2011 (http://www.SciRP.org/journal/me)
International Linkages of the Indian Commodity
Futures Markets
Brajesh Kumar1, Ajay Pandey2
1Jindal Global Business School, O P Jindal Global University, New Delhi, India
2Finance and Accounting Area, Indian Institute of Management Ahmedabad, Ahmedabad, India
Received January 6, 2011; revised March 2, 2011; accepted March 22, 2011
Abstract
This paper investigates the cross market linkages of Indian commodity futures for nine commodities with
futures markets outside India. These commodities range from highly tradable commodities to less tradable
agricultural commodities. We analyze the cross market linkages in terms of return and volatility spillovers.
The nine commodities consist of two agricultural commodities: Soybean, and Corn, three metals: Aluminum,
Copper and Zinc, two precious metals: Gold and Silver, and two energy commodities: Crude oil and Natural
gas. Return spillover is investigated through Johansen’s cointegration test, error correction model, Granger
causality test and variance decomposition techniques. We apply Bivariate GARCH model (BEKK) to invest-
tigate volatility spillover between India and other World markets. We find that futures prices of agricultural
commodities traded at National Commodity Derivatives Exchange, India (NCDEX) and Chicago Board of
Trade (CBOT), prices of precious metals traded at Multi Commodity Exchange, India (MCX) and NYMEX,
prices of industrial metals traded at MCX and the London Metal Exchange (LME) and prices of energy
commodities traded at MCX and NYMEX are cointegrated. In case of commodities, it is found that world
markets have bigger (unidirectional) impact on Indian markets. In bivariate model, we found bi-directional
return spillover between MCX and LME markets. However, effect of LME on MCX is stronger than the ef-
fect of MCX on LME. Results of return and volatility spillovers indicate that the Indian commodity futures
markets function as a satellite market and assimilate information from the world market.
Keywords: International Linkages, Commodity Futures Markets, Return Spillover, Volatility Spillover,
Variance Decomposition Techniques, BEKK
1. Introduction
Risk management and price discovery are two of the
most important functions of futures market [1-2]. Futures
markets perform risk allocation function whereby futures
contracts can be used to lock-in prices instead of relying
on uncertain spot price movements. Price discovery is
the process by which information is assimilated in a
market and price converges towards the efficient price of
the underlying asset. In financial economic literature, the
price discovery function of futures market has been stud-
ied in two broad contexts a) return and volatility spill-
over between spot and futures of an asset, and b) interna-
tional link-ages or return and volatility spillover across
different futures markets (across countries). This paper
focuses on the latter, studying the return and volatility
spillover between Indian and international commodity
futures markets. Another interesting prospective on un-
derstanding market linkages has its origin in the efficient
market hypothesis which says that all markets incorpo-
rate any new information simultaneously and there does
not exist any lead-lag relationship across these markets.
However, frictions in markets, in terms of transaction
costs and information asymmetry, may lead to return and
volatility spillovers between markets. Moreover, all the
markets do not trade simultaneously for many assets and
commodeties. Besides being of academic interest, under-
standing information flow across markets is also impor-
tant for hedge funds, portfolio managers and hedgers for
hedging and devising cross-market investment strategies.

B. KUMAR ET AL. 214
Empirical literature on price discovery in futures mar-
kets mostly covers the relationship between futures and
underlying spot prices. In equity markets, price discovery
function of futures markets has been extensively studied
[3-11]. In commodity futures market, price discovery
function of futures markets has also been investigated
[12-16]. However, these studies are mostly from devel-
oped markets like US and UK. Most of the studies in
equity and commodity spot-futures markets linkages
confirm the leading role of futures markets in informa-
tion transmission and in fore- casting future spot prices.
Surprisingly, very few studies have sought to investigate
the information transmission through futures prices on
the same underlying, traded across different markets. In
emerging commodity futures market context, interna-
tional linkages of commodity futures market with devel-
oped futures markets have been even less explored.
Since the inception of the organized commodity de-
rivatives markets in India in 2003, Indian futures markets
have grown rapidly. In 2003, three national level multi
commodity exchanges, National Multi Commodity Ex-
change (NMCE), Multi Commodity Exchange (MCX)
and National Commodities and Derivatives Exchange
(NCDEX), were setup. At present, commodity futures
are traded on three national exchanges, and 20 other re-
gional exchanges, which have been in existence for
longer time. Currently, the futures contracts of around
103 commodities are traded on three national exchanges.
In terms of volume, Copper, Gold, Silver and Crude fu-
tures traded on Multi Commodity Exchange (MCX),
India has been ranked within the top 10 most actively
traded futures contracts1 in the world. However, the
commodity futures markets in India are subject to many
regulations and many a times have been criticized for
speculative trading activity as well as for causing an in-
crease in spot price volatility [17]. Emerging commodity
markets are generally criticized for speculative activity
and destabilizing role of derivatives on spot market
through increased price volatility [16,18,19].
Most of the studies on Indian commodity futures mar-
kets are limited to policy related issues. Some of the ma-
jor issues identified and investigated in Indian commod-
ity futures are: the role of spot markets integration and
friction (high transaction cost), proper contract design,
identification of delivery location, importance of ware-
housing facilities and policy issues like restriction on
cross-border movement of commodities, different kind of
taxes etc [20-22]. The literature on price discovery on
Indian com- modity futures markets is limited to regional
exchanges, dated/small sample form the period prior to
setting up of national exchanges, or to very fewer com-
modities traded on national exchanges [23-26]. The In-
dian commodity futures markets have since then matured
and have started playing a significant role in price dis-
covery and risk management in the recent period, if in-
creased volume of trading is any indicator. Trade and
financial liberalization in the country and rest of the
world may also have led to strong integration of Indian
markets with their world counterparts. However, the re-
lationship between the Indian and world commod- ity
futures markets has not been explored adequately and
hence there is a case for investigating the linkages of
Indian commodity futures markets with the counterparts
elsewhere in the world trading the futures contracts on
the same underlying.
2. Literature Review
The research on international linkages across markets has
been mainly on the financial asset markets [27-34]. Eun
and Shim [23] found the dominance of US equity market
in information dissemination to rest of the world. They
found that any innovations in the US equity futures mar-
ket are rapidly transmitted to other markets, whereas no
single foreign market can significantly explain US mar-
ket movements. Koutmos and Booth [30] investigated
the dynamic interaction across three major stock markets
New York (S&P 500), London (FTSE 100) and Tokyo
(Nikkie 225) and found significant price spillovers from
New York to London and Tokyo and from Tokyo to
London market. Susmel and Engle [29] investigated the
return and volatility spillovers between US and UK eq-
uity markets but did not find strong evidence of return
and volatility spillovers between these two while the
studies cited above examined cross-market linkage
where the underlying differed. Tse [23] investigated the
Eurodollar futures markets in Chicago, Singapore, and
London and found that all these markets are cointegrated
by a common factor. Booth et al. [31] found that Nikkei
225 Index futures that are traded in Singapore, London
and Chicago are cointegrated.
In commodity futures context, Booth and Ciner [35]
investigated the return and volatility spillovers of corn
futures between the CBOT and the Tokyo Grain Ex-
change (TGE). They found significant return and volatil-
ity spillovers between the two markets. Booth, Brockman,
and Tse [33] studied the wheat futures traded on the Chi-
cago Board of Trade (CBOT) of US and the Winnipeg
Commodities Exchange (WCE) of Canada and found one
way information spillover from CBOT to WCE. Low,
Muthuswamy, and Webb [36] examined the futures
1Leading commodity futures contracts in terms of volume are Gold,
Crude, Natural gas, and Silver futures traded at NYMEX in US, Alu-
minum, Copper, and Zinc futures traded at LME, London, and Corn,
Soybean contracts at CBOT in US. (Details:
http://www.futuresindustry.org/files/pdf/Jul-Aug_FIM/Jul-Aug_Volum
e.pdf)

B. KUMAR ET AL.215
prices for storable commodities, soybeans and sugar,
which are traded on the TGE and the Manila Interna-
tional Futures Exchange (MIFE), and found no co-inte-
gration between these two markets. Lin and Tamvakis
[37] examined the information transmission mechanism
and price discovery process in crude oil and refined oil
products traded on the New York Mercantile Exchange
(NYMEX); and London’s International Petroleum Ex-
change (IPE). They found substantial spillover effects
between two markets where IPE morning prices seem to
be considerably affected by the closing price of the pre-
vious day on NYMEX. Holder, Pace and Tomas III [38]
investigated the market linkage between US and Japan
for Corn and Soybean futures. They considered Corn and
Soybean futures traded on the Chicago Board of Trade
(CBOT) in US and the Tokyo Grain Exchange (TGE),
and the Kanmon Commodity Exchange (KCE) in Japan.
They found that trading at CBOT had very little effect on
the Japanese contract volumes. Xu and Fung [39] inves-
tigated the crossmarket linkages between US and Japan
for precious metals futures: Gold, platinum, and Silver.
They applied bivariate asymmetric ARMA-GARCH
model to estimate the return and volatility spillovers be-
tween these two markets and found that there was a
strong linkage between these markets with US market
playing a leading role in return spillover. They, however,
found bidirec- tional volatility spillover between the two
markets. Kao and Wan [40] studied the price discovery
process in spot and futures markets for Natural gas in the
US and UK using a quadvariate VAR model. They found
that all spot prices and futures prices were driven by one
common factor. They found that the US futures market
dominated over UK futures market and acted as the cen-
ter for price discovery. They also concluded that the spot
markets in the US and UK were less efficient than their
corresponding futures market.
In the emerging markets context, Fung, Leung and Xu
[41] examined the information spillover between US
futures markets and the emerging commodity futures
market in China for three commodity futures: Copper,
Soybean, and Wheat. They used VECM-GARCH model
and found that for Copper and Soybean, US futures
market played a dominant role in transmitting informa-
tion to the Chinese market. However, in the case of
Wheat, which is highly regulated and subsidized in
China, both markets were highly segmented. Hua and
Chen [42] investigated the international linkages of Chi-
nese commodity futures markets. Commodities con-
sidered in the analysis were: Aluminium, Copper, Soy-
bean and Wheat. Aluminum and Copper futures traded
on LME and Soybean and Wheat futures traded on
CBOT were analyzed. They applied Johansen’s cointe-
gration test, error correction model, and Granger causal-
ity test and impulse response analyses to understand the
relationship. They found that Aluminum, Copper and
Soybean futures prices are integrated with spot prices but
did not find such cointegration for wheat spot and futures
prices. They concluded that LME had a bigger impact on
Shanghai Copper and Aluminium futures and CBOT had
a bigger impact on Dalian Soybean futures. Ge, Wang
and Ahn [43] investigated the linkages between Chi-
nese and US cotton futures market. They considered the
futures prices of contracts trading on New York Board of
Trade (NYBOT) in US and the Zhengzhou Commodity
Exchange (CZCE) in China. They found that these mar-
kets were cointegrated and that there was bidirectional
causality in returns between these markets.
To summarize, most studies on international linkages
across futures markets of the same underlying suggest
that there are stronger international market linkages in
highly traded commodities as compared to relatively less
traded commodities. Moreover the developed markets (in
terms of volume and number of derivatives products)
play dominant role in price discovery process. Given
limited research on international linkages of futures
markets in emerging markets, which are characterized by
low liquidity, and exhibit higher price variability and poor
information processing [44,45], this paper is an attempt to
investigate the cross-market link- ages of Indian com-
modity futures market with developed world futures
markets for both high tradable (precious metals) and less
tradable (agricultural) commodities.
In order to fill the research gap, this paper investigates
the cross market linkages of Indian commodity futures
market with their world counterparts. The commodities
considered in our analysis range from agricultural com-
modities (Soybean and Corn), to industrial metals (Alu-
minium, Copper and Zinc), precious metals (Gold and
Silver) and energy commodities (Brent Crude oil and
Natural gas).We chose commodities which are highly
traded (and have less tariff barriers/transportation costs)
as well as more regulated and less traded agricultural
commodities to understand and examine potential market
linkage differences across commodities. We use Gold,
Silver, Brent crude oil, and Natural gas futures contract
traded on New York Mercantile Exchange (NYMEX),
Aluminium Copper and Zinc futures contracts traded on
the London Metals Exchange (LME), and Soybean and
Corn futures contracts traded on the Chicago Board of
Trade (CBOT). In agricultural commodities, India is the
fifth largest producer of Soybean and Corn. In case of
precious metals, industrial metals and energy commode-
ties, India is net importer. India’s gold consumption is
around 20% - 25% of world’s total gold production and
it is also a dominant consumer of silver (10% - 15%).
India is a major consumer country of crude oil after US,

B. KUMAR ET AL. 216
China, Japan, Germany and Russia. India is among top
20 major producers as well as consumers of Aluminium,
Copper and Zinc.
Given this background, firstly we test that whether In-
dian commodity futures market is cointegrated with rest
of the world in the long run and if so for which com-
modities (tradable/less tradable)? We expect that because
of the importance of Indian market in the world and also
due to world trade liberalization, Indian markets should
be cointegrated with rest of the world for industrial met-
als, precious metals and energy commodities. It may be
possible that prices are cointegrated in the long run but
deviate in the short run. Hence, we further investigate
whether there is any lead-lag relationship between Indian
market and their world counterpart in terms of return
spillover. Further, we examine the direction and speed of
information transmission between the markets through
return spillover. We also investigate whether there are
any differences across commodities as far as return
spillover is concerned. The information spillover or the
market linkages are also examined by examining the
second moment or volatility spillover across markets
with the objective of investigation being similar to return
spillover.
3. Data and Time Series Characteristics of
Returns
To examine the international linkages of Indian com-
modity futures markets, we use data set consisting of two
agricultural commodities: Soybean, and Corn, three met-
als: Aluminum, Copper and Zinc, two precious metals:
Gold and Silver, and two energy commodities: Crude oil
and Natural gas. For agricultural commodities daily
prices of near month futures contracts from NCDEX and
for non-agricultural commodities daily prices of near
month futures contracts traded on MCX are used. The
selection of a particular Indian exchange is based on
trading volume of the commodity futures contract. We
chose Gold, Silver, Brent crude oil, and Natural gas fu-
tures price traded on New York Mercantile Exchange
(NYMEX), Aluminium, Copper and Zinc futures con-
tracts traded on the London Metals Exchange (LME),
Soybean and Corn futures contracts traded on the Chi-
cago Board of Trade (CBOT) as the counterpart markets
for Indian futures markets. These are the leading ex-
changes for the respective commodity futures contracts
in terms of volume traded. Details of the data period and
source of data are given in Table 1.
We construct the continuous futures price series using
daily closing futures prices of near month futures con-
tracts for all commodities. For consistency, we converted
all data into USD2/unit. The Gold price is converted into
USD/10grams3, Silver, Aluminium, Copper and Zinc
into USD/kg Soybean and Corn into USD/100kg, Crude
into USD/Barrel and Natural gas into USD/mmBtu. The
daily futures returns are constructed from the futures
price data as log(Ps,t/Ps,t-1), where Ps,t is the futures price
at time t. Standard unit root test is performed on log prices
and returns series. The augmented Dickey-Fuller (ADF)4
indicates that the log prices for all commodities and in all
markets have unit root and returns series are stationary. It
indicates that the log prices follow an I(1) process, which
is a prerequisite for the cointegration analysis.
Table 1. Details of Commodity, Data Period and Source.
Commodities Data-Period World Futures Market Indian Futures Market
Soy Bean 9/1/2004 to 1/11/2008 CBOT, US NCDEX
Agricultural
Corn 1/5/2005 to 1/11/2008 CBOT, US NCDEX
Gold 5/5/2005 to 4/7/2008 COMEX, US MCX
Bullion
Silver 5/5/2005 to 4/7/2008 COMEX, US MCX
Aluminium 2/2/2006 to 7/31/2007 LME, UK MCX
Copper 7/4/2005 to 7/31/2007 LME, UK MCX Metals
Zinc 8/1/2006 to 7/31/2007 LME, UK MCX
Crude Oil 5/5/2005 to 4/7/2008 NYMEX, US MCX
Energy
Natural Gas 8/7/2006 to 4/7/2008 NYMEX, US MCX
2We used the daily exchange rate to convert Indian currency Rupees into US. The exchange rate data for the required period is collected from Reserve
Bank of India (Federal).
3We used the conversion factor 1 ounce = 31.1034 gm and for Soybean, 1 Tonne = 36.744 Bushels and for Corn, 1 Tonne = 39.368 Bushels.
4Results of ADF test can be obtained from authors on request.

B. KUMAR ET AL. 217
t
4. Long-Run and Short-Run Relationship in
Futures Prices Traded on Indian
Commodity Futures Markets and their
World Counterparts
4.1. Johansen Cointegration Test
As a first step to understand relationship between Indian
commodity futures markets with their world counterparts,
we test co-integration between Indian commodity futures
market and international futures market. Cointegration
theory suggests that two non-stationary series having
same stochastic trend, tend to move together over the
long run [46]. However, deviation from long run equilib-
rium can occur in the short run. The Johansen full infor-
mation multivariate cointegrating procedure [47,48] is
widely used to perform the cointegration analy- sis. It
can only be performed between the series having same
degree of integration. Johansen Cointegration test can be
conducted through the kth order vector error correc- tion
model (VECM) represented as
1
11
1
k
tt it
i
YY Y





 
 (1)
Where, Yt is (n × 1) vector to be examined for cointegra-
tion, ΔYt = Yt – Yt-1, ν is the vector of deterministic term
or trend (intercept, seasonal dummies or trend), П and Ѓ
are coefficient matrix. The lag length k is selected on
minimum value of an information criterion5. The exis-
tence of cointegration between endogenous variable is
tested by examining the rank of coefficient matrix П. If
the rank of the matrix П is zero, no cointegration exists
between the variables. If П is the full rank (n variables)
matrix then variables in vector Yt are stationary. If the
rank lies between zero and p, cointegration exists be-
tween the variables under investigation. Two likelihood
ratio tests are used to test the long run relationship [44].
a) The null hypothesis of at most r cointegrating vec-
tors against a general alternative hypothesis of more than
r cointegrating vectors is tested by trace Statistics.
Trace statistics is given by


1
traceln1
n
i
ir
T



 
 (2)
where T is the number of observations and λ is the ei-
genvalues.
b) The null hypothesis of r cointegrating vector
against the alternative of r + 1 is tested by Maximum
Eigen value statistic
Maximum Eigen Value is given by

1
maxln 1r T 


In our test for the cointegration between Indian com-
modity futures market and their world counterpart, n = 2
and null hypothesis would be rank = 0 and rank = 1. If
rank = 0 is rejected and r = 1 is not rejected, we conclude
that the two series are cointegrated. However, if rank = 0
is not rejected, we conclude that the two series are not
cointegrated.
Since all the time series of logged futures prices are
I(1) series, we test the cointegration between futures
prices of contracts traded in Indian commodity market
and their counterpart futures exchanges elsewhere in the
world. Both λtrace and λmax statistic are used to test the
cointegration. The results of the cointegration test are
presented in Table 2. It is found that all commodities
traded on Indian commodity futures market are cointe-
grated with their world counterparts. We reject the null
hypothesis of rank = 0 and can not reject the null hy-
pothesis of rank = 1 for all commodities under investiga-
tion at 5% significance level. It is interesting to note that
futures prices of agricultural commodities (Soybean and
Corn) traded on India commodity futures exchanges are
cointegarted with CBOT futures prices. Hua and Chen
[38] investigated the similar relationship for Chinese
commodity futures market and found the long run coin-
tegration with world futures market for Aluminium, Cop-
per and Soybean but did not find cointegration for Wheat
futures traded on CBOT and the Chinese com- modity
futures exchange.
4.2. Weak Exogeneity Test
The weak exogeneity test measures the speed of adjust-
ment of prices towards the long run equilibrium rela-
tionship. If the two price series are cointegrated in long-
run, then the coefficient matrix П (explained in Equation
1) can be decomposed as П = αβ′, where β contains
cointegrating vectors and α measures the average speed
of adjustment towards the long-run equilibrium. The
larger the value of α, the faster is the response of prices
towards the long-run equilibrium. If prices do not react
to a shock or value of α is zero for that series, it is said to
be weakly exogenous. We test the weak exogeneity of
Indian commodity futures prices and world futures prices
for each commodity. It is tested through likelihood-ratio
test statistics with null hypothesis as αi = 0. The results of
this test are presented in Table 3.
The results of weak exogeneity test of Indian and the
world commodity futures prices indicate that in most of
the commodities, except Copper, Zinc and Natural gas,
Indian commodity futures prices respond to any price
discrepancies from long run equilibrium whereas the
world futures prices are exogenous to the system. In case
of Copper, both LME and Indian futures prices are ex-

(3)
5We use Akaike Information Criterion (AIC) to select the lags in the
cointegration equations.

B. KUMAR ET AL. 218
ogenous to the system. In case of Zinc and Natural gas,
LME and NYMEX market respond to the error correct-
ing terms to restore long run equilibrium whereas the
Indian market is exogenous. Our results that the response
of Indian commodity futures markets not to deviate too
far from the long-run equilibrium relationship and the
weak exogeneity of world prices for most of the com-
modities, indicate the leading role of world market in
price discovery and satellite nature of Indian commodity
futures markets.
4.3. Short Run Cointegration
After examining the long run integration between Indian
and world markets, we also analyze the short-run inte-
gration or return spillover between these markets. The
short run integration between Indian futures prices and
their world counterparts is investigated through VECM
model as these prices are cointegrated in the long run.
The Granger causality test is also applied to examine the
lead-lag relationship between Indian and the World
counterpart. We apply forecast error variance decompo-
sition for each returns series to understand the economic
importance of one market on the other.
Vector Error Correction Model (VECM)
Since futures prices traded in Indian market and their
Table 2. Johansen cointegration test results.
Commodity Lag length Cointegration Rank Test Using
Maximum Eigenvalue Cointegration Rank Test Using Trace
H0: rank = 0 Vs
H1: rank = 1
H0: rank = 1 Vs
H1: rank = 2
H0: rank = 0 Vs
H1: rank = 1
H0: rank = 1 Vs
H1: rank = 2
Soy Bean 3 31.7744* 2.4626 30.546* 2.1009
Agricultural
Corn 1 19.7004* 2.2961 21.0072* 2.507
Gold 4 17.8351* 5.3996 23.4629* 4.9913
Bullion
Silver 3 14.7067* 4.6723 19.379* 4.6723
Aluminium 5 24.4747* 3.8698 28.3445* 3.8698
Copper 4 13.1998* 4.9158 21.7399* 5.4253 Metals
Zinc 4 20.5895* 2.6857 29.6307* 2.5211
Crude Oil 3 17.586* 3.2128 23.2074* 3.5273
Energy
Natural Gas 3 22.6747* 2.7698 34.1376* 4.7614
* denotes rejection of null at 5% level.
Table 3. Results of weak exogeneity test.
World Prices Indian Prices
Commodity Chi-Square Chi-Square
Agricultural Soy Bean 0.87 27.65**
Maize 0.1 17.13**
Bullion Gold 0.78 3.87*
Silver 1.45 4.39*
Metals Aluminium 0.24 17.64**
Copper 1.98 1.16
Zinc 4.03* 1.64
Energy Crude Oil 2.7 3.64*
Natural Gas 23.76** 0.06
*
* and * denote rejection of null at 1% (5%) level.

B. KUMAR ET AL. 219
,1
,1
world counterparts are cointegrated, short run relation-
ship (return spillover) can be examined through error
correction model. Vector error correction model specifi-
cations allow a long-run equilibrium error correction in
prices in the conditional mean equations [46]. Similar
approach has been used to model short run relationship
of cointegrated variables [44-51]. The VECM specifica-
tion for Indian futures prices and the world futures prices
can be represented by
,,,1,
,,
2
,, ,
2
,,,1,
,,
2
,, ,
2
WF tWFWFECWF tINECIN t
k
WF iWF ti
i
l
INjIN tjWF t
j
IN tININECINtWF ECWF t
k
IN iIN ti
i
l
WFjWF tjIN t
j
PC PP
P
P
PC PP
P
P
















 


 






(4)
Where, PIN,t is the log price in the Indian commodity
futures market and PWF,t is the log futures prices in the
World market. The error correction term,,1IN ECINt P



,,1WFECWF t
P

 or ,,1,,1WFECWF tINECIN t
PP




(П = αβ′
representation) represents the speed of adjustment to-
wards long run equilibrium. The short run parameter
estimates χIN, χWF, γIN and γWF measure the short run inte-
gration or return spillover. The significance and value of
these parameters measures the short run spillover be-
tween these markets. We performed the Granger causal-
ity test to find the lead-lag relationship between Indian
commodity futures prices and the World counterparts. It
tests whether, one endogenous variable (say PIN,t) is sig-
nificantly explained by other variable (say PWF,t). More
specifically, we say that PWF,t Granger causes PIN,t if
some of the γWF (i) coefficients () are non-
zero and/or γWF,EC is significant at conventional levels.
Similarly PIN,t Granger causes PIN,t t if some of the γIN (i)
coefficients () are nonzero and/or γIN,EC is
significant at conventional levels.
2, 3,i
2, ,ip
,p

Table 4 represents the results of VECM for Indian
commodity futures prices and their world counterpart for
all commodities. As mentioned earlier, we used Akaike
Information Criterion (AIC) to select the lags in the
VECM. We found that error correcting terms
,,1,,1
I
NECIN tWFECWFt
PP


 in the equation of Indian
futures prices are significant at 5% level for all com-
modities except Copper, Zinc and Natural gas. In case of
Copper, error correcting term in the equation of the
world futures returns are not significant. These terms are
however significant only for Zinc and Natural gas. These
findings are consistent with the results of weak exogene-
ity tests. It can be concluded that even though Indian
futures market are cointegrated with the world futures
prices for Copper, Zinc and Natural gas, in the short run
Indian futures markets do not respond to the error cor-
recting term. However, world prices (LME and NYMEX)
returns respond to the error correcting term.
These results may be biased because of small sample
size for Zinc and Natural gas contracts, as these have
been traded only since August 2006 in Indian market.
Further, it is not clear that whether results are due to fric-
tions in the Indian commodity futures markets for these
commodities, or dues to high transaction cost or the
leading role of Indian markets in price discovery. This is
beyond the scope of the paper and further research is
required to address this issue. The short run coefficients
γWF (i), which measure the return spillover from world
market to Indian futures market are also significant for
Gold, Silver, crude, and Zinc. However, short run coeffi-
cients γIN (i), which measures the return spillover from
Indian market to the World markets, are significant only
for metals. The results of the VECM indicate bi-direc-
tional causality between Indian market and their world
counterparts for industrial metals. We estimated the Chi
square statistics for Granger causality test to understand
the lead-lag relationship between Indian commodity fu-
tures returns and their world counterpart. Results of the
Granger causality test are reported in the Table 5.
The results of Granger causality test indicate that for
Soybean, Corn, Gold, and Silver, world futures prices
lead the Indian market and affect the Indian futures re-
turns. The weak exogeneity test and results of error cor-
rection model also indicate the same for these commode-
ties. World futures price lead Indian markets in price
discovery process and Indian market respond to long run
as well as short run deviations in the prices. After com-
bining the results of cointegration test and VECM model,
it can be concluded that for Soybean, Corn, Gold, and
Silver, the world markets affect Indian futures prices
both in the long and short run.
In case of metals, we find bidirectional causality be-
tween MCX, India and LME, London for Aluminium
futures prices. It is very surprising to note that in case of
Copper and Zinc, Indian futures returns Granger cause
(lead) the LME returns. These results could be due to the
difference in the timing of closing hours and the effect of
other important markets in the price discovery process. It
is possible that a market, which closes after another
market in the same underlying, is likely to impound more
information from others markets, which are open at that
time and the lead-lag relation, therefore, would be biased
towards the market which closes later. In case of metals,
Indian futures markets close after the LME and hence

B. KUMAR ET AL. 220
Table 4. Parameter estimates of VECM.
A. Indian Futures Prices
Commodity CIN γWF,EC χIN,EC γWF,1 γWF,2 γWF,3 χWF,4 χIN,1 χIN,2 χIN,3 χIN,4
Soy Bean 0.0910** 0.0442** –0.0553** 0.0730 0.0481 –0.0467–0.0117
Maize 0.1487** 0.0320** –0.0530**
Gold 0.0347* 0.1897* –0.1931* 0.5358** 0.2814 –0.1374 –0.5616** –0.3062 0.2033
Silver 0.0459* 0.1190* –0.1230* 0.2774* 0.2514 –0.2996* –0.3568*
Aluminium 0.3530 0.2998** –0.3732** –0.1104–0.0390–0.04070.0469 0.2208* 0.0551 0.0401–0.0614
Copper –0.0232 0.0937 –0.0892 –0.0356–0.1621–0.0884 0.0343 0.0794 0.2541*
Zinc 0.0677 0.3076 –0.3206 –0.3452–0.3615*–0.2671* 0.5251* 0.2055 0.3801*
Crude Oil –0.0154 0.0646* –0.0624* –0.0260–0.0817 –0.01380.0597
Natural Gas 0.0011 –0.00620.0060 –0.0167–0.0070 0.0558 –0.1307*
B. World Futures Prices
Commodity CWF χWF,EC γIN,EC χWF,1 χWF,2 χWF,3 χWF,4 γIN,1 γIN,2 γIN,3 γIN,4
Soy Bean 0.0188 0.0086 –0.0108 –0.02050.0075 0.0004 0.0367
Maize –0.0148 –0.00360.0059
Gold 0.0182 0.0946 –0.0962 0.1239 0.0965 –0.2536–0.1250–0.10780.2936
Silver 0.0300 0.0753 –0.0779 –0.11040.1439 0.0893 –0.2419
Aluminium 0.0328 0.0277 –0.0345 –0.3393** –0.2279** –0.0934 0.3878** 0.1446 0.0601 0.0369–0.1342*
Copper 0.0313 –0.11140.1061 –0.6304** –0.4394** –0.1985** 0.6881** 0.5084** 0.3961**
Zinc –0.0956* –0.4314*0.4496* –0.5170** –0.4100** –0.3027** 0.7920** 0.3641* 0.4998**
Crude Oil 0.0192 –0.06750.0652 –0.4925** –0.3081** 0.3663**0.2194**
Natural Gas 0.0603** –0.4525** 0.4387** –0.6546** –0.3420** 0.3285 0.3447
** and * denote significance of parameter at 1% (5%) level.
Table 5. Results of granger causality test.
International → India India → International
Agricultural Soy Bean 40.12** 1.08
Maize 16.36** 0.06
Bullion Gold 26.98** 6.43
Silver 14.02** 5.53
Metals Aluminium 21.6** 51.24**
Copper 3.83 117.79**
Zinc 6.37 152.69**
Energy Crude Oil 6.12 39.3**
Natural Gas 2.63 33.04**
*
* denotes rejection of null at 1% level.

B. KUMAR ET AL. 221
they may assimilate information from US markets, which
are open at that time. Our result may be reflective of this
fact6. In case of energy commodities (Crude and Natural
gas), results of Granger causality test indicate that Indian
futures prices lead the NYMEX prices. This again is sur-
prising.
Sims [52,53] and Abdullah and Rangazas [54] sug-
gested that the variance decomposition of the forecast
error is advisable while analyzing the dynamic relation-
ship between variables because it may be misleading to
rely solely on the statistical significance of economic
variables as determined by VAR model or Granger cau-
sality test. Therefore, we also estimate the variance de-
composition of the forecast error of each endogenous
variable in order to further investigate the relationship
between Indian and the world commodity futures markets.
4.4. Variance Decomposition
The variance decomposition of the forecast error gives
the percentage of variation in each variable (e.g. Indian
commodity futures returns) that is explained by the other
variables (futures returns of markets elsewhere on the
same underlying). We estimated the orthogonal variance
decomposition of forecast error up to 20 lags from the
VECM (Equation 4). Results of the variance decomposi-
tion for Indian commodity futures returns and their world
counterparts are shown in Table 6. Panel-A of Table 6
explains the percentage of variation in futures price
traded on world market explained by its own lagged re-
turns and futures returns traded on Indian market
whereas Panel-B of Table 6 represents the percentage of
variation in Indian commodity futures returns explained
by its own lagged returns and their world counterparts.
As shown in Table 6, it is found that in the case of Soy-
bean, Corn, Gold and Silver, variation in world futures
returns are explained by their own lagged returns,
whereas Indian futures returns explain 0% - 1% variation
in the futures returns of the market elsewhere.
On the other hand, in case of precious metals (Gold
and Silver), variation in Indian futures returns are mostly
explained by NYMEX returns (more than 90%) and its
own lagged returns explain only 10% variation. In case
of agricultural commodities (Soybean and Corn), CBOT
returns are able to explain more than 20% [Soybean
more than 20% and Corn more than 50%] of variation in
Indian futures returns. Results of agricultural commode-
ties and precious metals are consistent with the results of
error correction model results and Granger causality test.
In case of industrial metals, it is found that LME returns
are able to explain more than 70% variation in Indian
metals futures [Aluminium, Copper and Zinc > 70%]
whereas Indian returns are able to explain more than 5%
(Aluminium 5% and Copper and Zinc 20%) in LME
metals futures returns. This result is not consistent with
the results of Granger causality test especially results of
Copper and Zinc where we find that Indian returns
Granger cause LME returns. Thus, combining the evi-
dence from both tests, it can be concluded that there may
be bidirectional causality between Indian and LME re-
turn for metals but the effect of LME on the Indian prices
is stronger than the effect of Indian prices on LME prices.
In case of crude, NYMEX returns are mostly explained
by their own lagged returns, however Indian futures re-
Table 6. Forecast error variance decompositions.
A. World Futures return explained by B. Indian Futures return explained by
World Returns India Returns World Returns India Returns
1 5 10 15 20 1 5 101520 1 5 101520 1 5 10 1520
Soy Bean 100% 100% 100% 100% 100% 0% 0%0%1%1%1%4%11%19% 28% 99% 96% 89% 81%72%
Maize 100% 100% 100% 100% 100% 0% 0%1%2%2%11% 24%35% 45% 54% 89% 76% 65% 55%46%
Gold 100% 100% 100% 99% 99% 0% 0%0%0%0%92%98%99%99%99% 8% 2% 1%1%1%
Silver 100% 100% 99% 98% 98% 0% 0%0%0%0%89%96%98%99%99% 11% 4% 2%1% 1%
Aluminium 100% 93% 96% 97% 97% 0% 7% 4%3%3%23%47%63%71%76% 77% 53% 37%29%24%
Copper 100% 80% 79% 80% 80% 0% 20%21% 20% 20% 59% 60% 64%68% 70% 41% 40% 36% 32%30%
Zinc 100% 72% 77% 80% 81% 0% 28%23%20%19%63%65%73%77%79% 37% 35% 27%23%21%
Crude Oil 100% 92% 91% 90% 89% 0% 8%9% 10%11% 6%4%4%4%4% 94% 96% 96%96%96%
Natural Gas 100% 90% 78% 69% 60% 0% 10% 22% 31% 40% 45% 49% 55% 60% 64% 55% 51% 45% 40%36%
6We later analyze this issue by using trivariate VAR model in which other than MCX and LME prices, we include COMEX prices for industrial met-
als.

B. KUMAR ET AL. 222

turns are able to explain only 8% - 10% variation in NY-
MEX returns. Further, NYMEX crude returns are able to
explain only 4% - 6% variation in Indian returns. In case
of Natural gas, Indian returns are able to explain 30% -
40% variation in NYMEX returns and NYMEX returns
explains 50% - 60% variation in Indian returns. We may
conclude that in case of energy commodities bidirec-
tional causality exist between MCX, India and NYMEX,
US. However, effect of NYMEX market on Indian mar-
ket is stronger than the effect of Indian market on NY-
MEX.
In order to shed more light into bidirectional causality
between LME and MCX for industrial metals, we intro-
duce a variable, COMEX, US, prices (Copper)7, in
VECM model as another endogenous variable. As ex-
plained earlier, results of bivariate models with LME and
MCX prices may be misleading because of extended
trading period in Indian market and closing timing dif-
ference between LME and Indian market. The Indian
market closes around two hours after the LME market
and at that time COMEX market is trading. It is likely
that the information is coming from COMEX market and
is affecting LME market through MCX. Trading timings
of LME, India and COMEX market are given in Table 7.
First, we test the cointegration8 between LME, MCX
and COMEX Copper prices and it is found that these
prices are cointegrated with single stochastic term, which
indicates that the Copper prices are driven by a common
factor. Results of Granger Causality test indicate that,
LME prices are affected by both MCX and COMEX
prices. We do not find any Granger causality between
COMEX and MCX Copper futures prices. Results of
Granger causality test of Copper is reported in Table 8.
We also estimate the variance decomposition from
VECM (3), which explains the percentage of variation in
each variable (e.g. LME copper futures returns) that is
explained by other variables (COMEX copper futures
returns and MCX copper futures returns) in the system.
The results are shown in Table 9.
It is clear from the variance decomposition results that
the LME returns’ variance is mostly explained by its own
lags (65%) and COMEX returns (35%). Indian market is
not able to explain any variation in the LME returns or
COMEX returns. It is also interesting to see that MCX
Copper return variance is mostly explained by LME
(55%) return variance and COMEX return variance
(38%). It indicates that even in case of metals, Indian
market gets information from world markets; LME and
COMEX, and Indian market does not affect LME market.
This negates the results of bivariate case wherein bidi-
rectional causality between LME and Indian futures
prices is found. To sum up, it can be concluded that for
all commodities, price discovery takes place in the world
market and Indian futures market assimilate information
through return spillover.
The VECM, Granger Causality test and variance de-
composition examine the information transmission be-
tween markets by investigating first moment (mean re-
turn). However, the information transmission is better
tested by examining the second moment or volatility
spillover across markets. Ross [55] demonstrated that the
rate of information transmission is critically linked to
volatility.
4.5. Volatility Spillover: A BEKK Model
Approach
After the seminal work of Engle, Ito and Lin [56], who
applied multivariate GARCH model in estimating vola-
tility spillover between US and Japanese foreign ex-
change markets, multivariate GARCH model has been
widely applied to equity, exchange, bond and commodity
markets etc. In this paper we apply multivariate GARCH
model, BEKK (developed by Baba, Engle, Kraft and
Kroner, 1991), to investigate volatility spillover between
Indian commodity futures prices and their world coun-
terpart. The residuals t

12
,
tt

  from VECM
(Equation 4), which has conditional multivariate normal
distribution the, are used in the following bivariate
Table 7. Trading timings of LME, India and COMEX ex-
changes.
Exchange Timings
LME MCX COMEX
Winter 17:20 p.m. -
22.30 p.m.
10:00 p.m.-
23.55 p. m
18:40 p.m. -
23.30 p.m.
Summer 16:20 p.m. -
21:30 p.m.
10:00 p.m. -
23.00 p. m
17:40 p.m. -
22:30 p.m.
Table 8. Results of granger causality test of copper from
VECM (3).
Variables Causality Chi-Square
LME → MCX 1.61
LME and MCX
MCX → LME 43.53**
COMEX → MCX 4.33
COMEX and MCX
MCX → COMEX 2.95
LME → COMEX 4.56
LME and COMEX
COMEX → LME 55.48**
7We are not able to get the data of other two industrial metals. However
results of Copper can be extended to other industrial metals.
8Results of Cointegration and weak exogeneity test are not presented
here and the same can be obtained from author on request. *
* denotes rejection of null at 1% level.

B. KUMAR ET AL. 223
Table 9. Forecast error variance decompositions of copper returns from VECM (3).
LME returns COMEX Return MCX returns
1 5 10 15 20 1 5 10 15 20 1 5 10 1520
LME returns 100% 79% 70% 65% 63%0% 21%29% 33% 36%0% 1% 1% 1%1%
COMEX Return 63% 59% 59% 58% 58% 37% 41% 41% 41% 42% 0% 0% 0% 0%0%
MCX returns 69% 60% 56% 53% 52% 23% 33% 38% 40% 41%8% 7% 7% 7%7%
BEKK (p, q) model.
The BEKK(p,q) representation of the variance of error
term Ht
00
11
qp
ttitiii
ii
tii
H
CCAAGH G 
 

 


(5)
Where, Ai and Gi are k × k parameter matrix and C0 is k ×
k upper trangular matrix. Bivariate VAR(k) BEKK (1,1)
model can be written as
2
11 121, 12, 11, 1
02
21 222, 11,12,1
111211121112
1
21 2221222122
ttt
tt t
T
aa
CC aa
aa gggg
H
aa gggg

 

 




 

 


 

 
 







t
tt




t

(6)
Or simply,


22 22
111111,111211,12, 1212, 1
22 22
1111,111 2112, 12122, 1
2
12211121,1211211221,12, 1
2
21222,11112 11,1
211211 2212,121 2222,
2
2
tttt
ttt
t
tt
tt
Hhcaaaa
ghggh gh
hc aaaaaa
aa ggh
gggg hggh








 
 
 

 1
22
223121, 112221,12, 1
22 222
222, 11211, 1122212, 12222, 1
2
2
ttt
tt t
hca aa
aghgghgh




 
 
 
(7)
In the BEKK representation of volatility, the parame-
ter, 21 is the volatility spillover from market 2 to
market 1, and 12 indicates the spillover from market 1
to market 2. Hence, the statistical significance of these
parameters tells about the volatility spillover between
markets. In the BEKK representation, we assume a con-
ditional time invariant covariance, namely constant con-
ditional correlation (CCC) assumption between futures
returns traded in Indian market and futures prices traded
outside India.
a
a
Tse [57] explained that the two-step approach of first
estimating the residuals from VECM (Equation 4) and
then estimating bivatiate BEKK models (Equation 7), is
efficient and equivalent to joint estimation of the two
steps. The two step estimation method also reduces the
problem of estimating large number of parameters in-
volved in the process. Following Engel and Ng [58],
Kroner and Ng [59], Tse [57], so Tse [60] and Kao and
Wang [40], we perform the two step estimation process
to investigate the volatility spillover between Indian and
their world counterparts for each of the nine commodi-
ties.
We estimated the parameters of BEKK (1,1) for each
commodity separately. Parameters estimate are presented
in Table 10. As explained in Equation 7, h11 estimates
the conditional volatility of world futures and the pa-
rameter 21 is the volatility spillover from India to the
world futures market. Similarly h22 estimates the condi-
tional volatility of Indian commodity futures and the
parameter 12 measures the volatility spillover from the
world market to India. These two parameters measure the
volatility spillover between Indian futures market and
markets abroad.
a
a
In case of agricultural commodities, it is found that the
volatility of futures returns traded in India and CBOT is
highly autoregressive. It is interesting to note that for
Soybean, volatility spills over from Indian market to
CBOT. The parameter is significant at 5% level. Also,
for Corn, bidirectional volatility spillover is found. The
parameters 21 and 12 are significant at 1% signify-
cant level. As explained earlier, the results of VECM
model indicate that CBOT market play a leading role in
price discovery for Soybean and Corn. However, results
of volatility spillover indicate that Indian futures market
also affect the CBOT futures market.
a a
In case of precious metals, we find bidirectional vola-
tility spillover between Indian market and NYMEX for
Gold only. In Silver market, there is no significant vola-
tileity spillover between the markets. The volatility spill-
over between Indian futures market and LME is investi-
gated for Aluminium, Copper and Zinc. In case of Alu-
minium, both parameters 21 and 12 are insignificant,
for Copper both parameters 21 and 12 are signifi-
cant at 5% significant level and for Zinc only 12 is
significant at 1% significant level. These results indicate
that there is significant information spillover from LME
market to India through volatility for Copper and Zinc.
Indian market affects LME volatility for Copper only.
The BEKK results of energy commodities indicate that
volatility spillover is mainly taking place from NYMEX
a a
a a
a

B. KUMAR ET AL. 224
Table 10. Parameters estimates of BEKK (1,1) model.
Soybean Corn Gold Silver
Parameters Estimates Tstat Estimates Tstat Estimates Tstat Estimates Tstat
c1 0.0002 1.413 0.0060 2.696 0.0091 0.703 0.0075 1.132
c2 –0.0001 –0.600 –0.0020 –1.900 0.0057 0.380 0.0024 0.449
c3 –0.0011 –2.157 0.0001 0.786 0.0013 0.183 0.0000 0.013
a11 0.0032 0.207 –0.0328 –0.304 –0.5948 –1.313 –0.3874 –0.608
a21 –0.0518 –2.448 –0.1666 –2.725 –0.9056 –2.761 –0.9241 –1.750
a12 –0.0382 –1.003 0.2208 3.602 1.0324 2.005 0.6839 1.000
a22 0.1293 3.842 0.2595 3.016 1.3896 3.600 1.2613 2.131
g11 0.9922 374.106 0.9654 41.580 –1.5547 –0.539 0.9708 1.539
g21 –0.0076 –2.494 0.0953 5.705 –1.7578 –0.748 0.3967 0.972
g12 0.0276 4.480 –0.2138 –3.591 1.1451 0.438 –0.0712 –0.107
g22 0.9926 186.905 0.9117 28.533 1.4553 0.658 0.4715 0.989
Aluminium Copper Zinc Crude Natural gas
Parameters Estimates Tstat Estimates Tstat Estimates Tstat Estimates Tstat Estimates Tstat
c1 0.0000 –0.116 0.0008 0.192 0.0105 2.900 0.0049 1.570 0.0262 6.912
c2 0.0000 –0.413 –0.0007 –0.353 0.0055 2.240 –0.0027 –1.732 0.0091 2.287
c3 0.0000 0.030 0.0023 0.976 0.0000 0.018 0.0001 0.503 0.0000 0.054
a11 0.0281 0.299 0.2317 3.192 0.5266 3.721 0.4102 3.816 0.7065 5.562
a21 –0.1587 –1.629 0.1560 1.961 0.1431 1.139 0.0598 0.676 0.0247 0.335
a12 0.1500 1.383 0.0812 2.035 –0.2731 –3.158 –0.3588 –1.812 –0.6485 –3.587
a22 0.1369 2.764 –0.0129 –0.2910.0570 0.412 0.0998 0.981 0.2729 2.417
g11 0.9774 76.450 0.8540 31.698–0.5522 –4.170 0.7987 7.692 0.7161 8.415
g21 0.0029 0.253 –0.1570 –5.067–1.3982 –11.200 –0.0236 –0.384 0.1479 1.808
g12 0.0120 1.191 0.1261 7.413 0.9435 4.450 0.2157 2.347 –0.0698 –0.447
g22 0.9864 72.429 1.0641 41.518 1.1952 7.245 0.9961 17.843 0.7802 8.339
futures market to Indian market; 12 parameter is sig-
nificant at 10% and 1% significance level for crude and
Natural gas respectively.
a
5. Conclusions
Since the inception of modern electronic trading platform,
combined with establishment of three national commod-
ity exchanges, India has become one of the fastest grow-
ing commodity futures markets in the world. Like other
emerging markets, Indian commodity futures are of re-
cent origin, suggesting that Indian markets may respond
to global markets. On the contrary, it can be argued that,
given the size of the economy, Indian market may also
influence global markets. This issue has interesting im-
plications to gain insight on the directionality of infor-
mation generation and assimilation in the commodities
markets. The purpose of the study reported in this paper
is to investigate the relationship between Indian com-
modity futures with their world counterparts.
The results of long run relationship between Indian
futures prices and their world counterparts indicate that
for all the nine commodities studied, the Indian markets
are cointegrated with the world markets. The weak exo-

B. KUMAR ET AL.225
geneity test indicates that for most of the commodities
Indian futures prices adjust to any discrepancy from long
run equilibrium whereas the world prices are exogenous
to the system. The Granger Causality test results and
variance decomposition of forecast error of VECM
model indicate that there exists one-way causality from
world markets to Indian market in most of the commodi-
ties. The impact of CBOT on Indian agricultural futures
market is unidirectional and approximately 30% - 40%
variations in returns of Indian commodity futures are
explained by CBOT futures prices. In case of precious
metals, NYMEX market unidirectionally affects Indian
futures prices and it explains around 98 - 99% variation
in Indian futures returns. In case of industrial metals also,
we find unidirectional information spillover through re-
turns. For industrial metals, Indian market is extensively
influenced by LME and other developed markets with
LME having stronger impact on Indian prices while In-
dian market having no impact on LME or other futures
markets. For energy commodities, Brent crude oil and
Natural gas, both Indian and NYMEX market influence
each other but, NYMEX has stronger impact on Indian
prices. However, in case of energy commodities, the ef-
fect of world prices is not as strong as in case of precious
metals and industrial metals. This may be because of
higher governmental control (tariff barriers/subsidy) in
crude oil and natural gas or because of difference in in-
ventory and transportation costs.
Volatility spillover analysis indicates similar results,
but it is interesting to note that for agricultural commodi-
ties, volatility spillover also takes place from Indian fu-
tures to CBOT futures. Bidirectional volatility spillover
between Indian and NYMEX is also observed for Gold
futures. In case of industrial metal futures, volatility
spills from LME to Indian market except for Copper fu-
tures whereas Indian market also affects LME futures. In
case of Crude oil and Natural gas, unidirectional volatil-
ity spillover from NYMEX futures to Indian futures is
found. To sum up, we find the US market plays an im-
portant leading role in information transmission to the
Indian market for Soybean, Corn, Gold, Silver, Crude
and Natural gas and LME leads the indian markets for
industrial metals. Overall, we also find that the Indian
futures markets are cointegrated with the world markets
and are working as a satellite market. They are able to
assimilate information through return and volatility
spillovers from world markets.
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