Description
This paper aims to examine the destabilization effect in the case of India’s agricultural
commodity market for the sample period of 01 January 2009 to 31 May 2013.
Journal of Financial Economic Policy
The investigation of destabilization effect in India’s agriculture commodity
futures market: An alternative viewpoint
Wasim Ahmad Sanjay Sehgal
Article information:
To cite this document:
Wasim Ahmad Sanjay Sehgal , (2015),"The investigation of destabilization effect in India’s agriculture
commodity futures market", J ournal of Financial Economic Policy, Vol. 7 Iss 2 pp. 122 - 139
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Mantu Kumar Mahalik, Debashis Acharya, M. Suresh Babu, (2014),"Price discovery and volatility
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The investigation of
destabilization effect in India’s
agriculture commodity futures
market
An alternative viewpoint
Wasim Ahmad and Sanjay Sehgal
Department of Financial Studies, University of Delhi, New Delhi, India
Abstract
Purpose – This paper aims to examine the destabilization effect in the case of India’s agricultural
commodity market for the sample period of 01 January 2009 to 31 May 2013.
Design/methodology/approach – The daily data of eight agricultural commodities traded on the
National Commodity & Derivatives Exchange, viz., barley, castor seed, chana (chickpea), chilli, potato,
pepper, refned soya and soybean, have been used in this study. At the frst stage of the empirical
analysis, the study estimates the time-varying spot market volatility by using the exponential
generalized autoregressive conditional heteroscedasticity model and applies three different high and
band-pass flters, viz., the two-sided linear band-pass flter by Hodrick and Prescott (1997), the
fxed-length symmetric band-pass flter by Baxter and King (1999) and the asymmetric band-pass flter
by Christiano and Fitzgerald (2003), to calculate the unexpected liquidity of sample commodities. At the
second stage of the empirical analysis, the study applies linear Granger causality and recently
developed non-linear causality given by Diks and Panchenko (2006) to examine the cause and effect
between time-varying volatility of spot market and futures market liquidity of sample commodities.
Findings – The linear and non-linear causality results suggest the destabilizing effect of commodity
futures on the underlying spot market for chana, chilli and pepper. The empirical fndings are in
contrast with the recommendations of Abhijit Sen’s committee and provide important direction for
further policy research.
Research limitations/implications – The study has a limitation in that it is based on the daily data.
The use of intra-day data would have been more suitable for such type of analysis.
Practical implications – The study has strong policy implications from a fnancial policy
perspective, as there is already disagreement among researchers and policy makers with regard to the
functioning of commodity derivatives markets in India. There have been many occasions when
commodity market regulators have to undertake decisions of suspension of trading of many
commodities. The study also provides new directions of policy research with regards to the
restructuring of the commodity derivatives market in India.
Social implications – The fndings of this study may further help the regulators and policy makers
to undertake decisions about howto provide an alternative platformfor farmers to sell their agricultural
produce more effciently. This will certainly have some impact on the socioeconomic set-up of the
country, as India is primarily an agriculture-dominated country.
Originality/value – So far not many studies have investigated the destabilization hypothesis in the
case of emerging markets. This study is a novel attempt to fll the gap. In the case of emerging markets
and especially in the case of India’s commodity derivatives market, this is the frst study that examines
JEL classifcation – C32, G10, G14, G15
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1757-6385.htm
JFEP
7,2
122
Received11 February2014
Revised27 July2014
Accepted2 October 2014
Journal of Financial Economic
Policy
Vol. 7 No. 2, 2015
pp. 122-139
©Emerald Group Publishing Limited
1757-6385
DOI 10.1108/JFEP-02-2014-0008
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the destabilization hypothesis in the case of India by applying new methods of high and band-pass
flters and non-linear causality.
Keywords Government policy and regulation, Financial economics, Financing policy,
Time-series models, Contingent pricing
Paper type Research paper
1. Introduction
Since its inception, the role of commodity futures in India’s commodity futures markets
has been the subject of considerable disagreement among academia, regulators and
various stakeholders. The main issue of debate appears to be about whether
introduction of futures in agricultural products has destabilized the underlying spot
market? In other words, are futures trading increasing the volatility in the underlying
spot market? In the literature on the futures–spot market relationship, not many studies
have analysed such a phenomenon, particularly in the case of emerging markets. The
examination of the destabilization effect is considered important because studies have
shown that the presence of uninformed investors in the commodity derivatives market
often induces noise in the price discovery process and also obstructs the information
transmission process (Newbery, 1987). As a result, the spot market exhibits a higher
level of volatility compared to the situation without a futures market (Cox, 1976; Cagan,
1981; Figlewski, 1981; Stein, 1987; Hart and Kreps, 1986; Bessembinder and Seguin,
1993). Considering this as a special issue with regard to the development of the
agri-commodity futures market in the case of India, this study attempts to examine the
role of the commodity futures market on the spot markets of eight agriculture
commodities. More specifcally, we try to examine whether futures trading destabilizes
the underlying spot market by increasing its volatility in the case of India’s agriculture
commodity market. The statistics reveal that in the frst fve-year, the agriculture
commodity market witnessed the spectacular growth rate of more than 119 per cent
(FMC, 2013). But in recent years, due to overplay by the speculators and arbitragers,
there has been continuous increase in spot market volatility, leading to suspension in the
trading of many important agricultural commodities. There is also widespread
apprehension that the development of the futures market has led to excess volatility in
the spot market. On some occasions, these apprehensions appeared to be true because
high volatility in commodity prices, mainly food products, has led to suspension of
several traded commodities. According to the regulator of the commodity market in
India, i.e. the Forward Market Commission (FMC), in 2011 and 2012, the futures trading
of tur, urad, rice, guar gumand guar seed has been suspended for speculative reasons[1].
Despite these apprehensions and speculations about futures market development, the
proponents of futures markets argue that futures markets often play a constructive role
in stabilizing the volatility in the underlying spot market. Futures markets help in the
assimilation of new market information more quickly than the underlying spot market,
thereby providing better opportunities for fair price discovery of traded commodities,
enhancing market effciency, augmenting market liquidity and, hence, contributing to
the market completion. The introduction of futures trading also helps in reducing the
excess volatility of the underlying spot market (Powers, 1970; Danthine, 1978; Bray,
1981; Kyle, 1985; Stoll and Whaley, 1988; Bessembinder and Seguin, 1992, 1993;
Guesnerie and Rochet, 1993; Jochumand Kodres, 1998; Bohl et al., 2011; Lee et al., 2014).
123
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Summarizing, given that the above-mentioned issues are critically linked with the
commodity market development in India, empirical investigations seemto be necessary
to gain additional insight into the impact of futures trading on the volatility of the
underlying spot market. While there is very limited theoretical work on this subject,
there are some empirical studies that have examined the issue of destabilization effect to
understand the direction of crucial linkage between futures market liquidity and spot
return volatility. For the sake of simplicity, we divide the destabilization hypothesis into
two different streams. The frst streamof literature focusses on analysing the impact of
introduction of futures markets on underlying spot market volatility by utilizing the
generalized autoregressive conditional heteroscedasticity-type models augmented by
dummy variables. The dummy variables are mainly introduced in the model to
discriminate between the pre- and the post-futures periods and thus to analyze the effect
that the introduction of futures market has on spot market volatility. A large of
proportion of existing studies have focused their attention on developed stock markets
by considering institutional investors as predominant traders. Most studies in this
stream focus on developed markets, and literature on emerging markets like India is
almost missing (Antoniou et al., 1998, 2005; Gulen and Mayhew, 2000; McKenzie et al.,
2001). Second stream highlights the role of well-informed investors in futures market
which is inherently linked with the destabilization hypothesis according to which
uninformed investors disrupt the smooth price discovery process and lower the
information transmission process (Lee et al., 1999; Cohen et al., 2002; Barber and Odean,
2008; Kaniel et al., 2008). There is very limited number of studies on this particular
stream, and so far, studies have mainly covered the developed market.
In this paper, we attempt to test the destabilization hypothesis in the case of the
Indian agricultural commodity market and try to answer a fundamental question which
is strongly linked with the introduction of commodity futures markets. That is, does
commodity futures market trading destabilize the underlying spot market? We mainly
focus on agricultural commodities because of the ongoing debate on the speculative role
of the futures market in augmenting the infationary pressure. The present study is also
motivated to re-examine the recommendations made by Sen’s (2008) committee on the
passive role of the futures market in increasing the spot market volatility. For this
purpose, we cover eight agricultural commodities traded on the platform of National
Commodity &Derivatives Exchange (NCDEX). The list of sample commodities includes
barley, castor seed, chana, chilli, pepper, potato, soybean and refned soya oil.
The study is organized as follows: in Section 2, the study data are outlined and the
methodology is discussed, followed by empirical results in Section 3. Finally, Section 4
provides conclusion and outlines policy directions.
2. Data and methodology
The sample data for the daily volume and spot prices are retrieved from NCDEX’s
website. Based on the availability of data, the sample period of each commodity has been
decided as: barley, chana and refned soya (January 01, 2009, to May 31, 2013; 1,177;
1,211; and 1,295 observations, respectively); soybean (January 01, 2009, to May 27, 2013;
1,291 observations); castor seed (August 25, 2009 to May 31, 2013; 899 observations);
chilli (January 01, 2009, to May 10, 2013; 914 observations); pepper (January 01, 2009 to
May 20, 2013; 1,158 observations); and potato (March 09, 2009 to May 31, 2013; 745
observations). Further, sample commodities have been selected based on the criteria
JFEP
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that they should have continuous trading records. Frequent price breaks can pose an
estimation problem, and hence, thinly traded commodities have been excluded. For
estimation purposes, sample series are further converted into natural logarithms and
returns, wherever required.
We have applied various models to confrm the relevance of the destabilization
hypothesis in the case of India. Before, we begin analysing the data, the study applies the
unit root test to confrm whether the sample series achieve stationarity at their frst
difference or not. This basically highlights whether a series contains outliers or not. For
this purpose, the study applies two unit root tests, viz., Augmented Dickey-Fuller (ADF)
and Phillips and Perron (PP), on the returns series of futures and spot and logarithmic
values of trading volume and open interest. The null hypotheses of ADF and PP are the
same, i.e. there is unit root in the examined series and alternative that there is no unit root
in the series. At the second stage of the empirical analysis, the study uses three different
high and band-pass flters to extract the unexpected liquidity in the futures market. For
this purpose, the study uses futures market’s traded volume data of all sample
commodities. To compute the unexpected liquidity, the two-sided linear band-pass flter
by Hodrick and Prescott (henceforth HP, 1997), the fxed-length symmetric band-pass
by Baxter and King (hereafter BK, 1999) and the asymmetric band-pass flter by
Christiano and Fitzgerald (henceforth CF, 2003) are used[2]. Despite several limitations
of these high and band-pass flters, we mainly apply these three models to compare
the results of unexpected liquidity. According to Mink et al. (2007), among these flters,
the CF flter has the advantage that it does not lead to the loss of observations at the
beginning and the end of the sample period. Finally, the study applies techniques of
linear and non-linear causality to check the causal directions.
To calculate the spot market volatility, we use the exponential generalized
autoregressive conditional heteroscedasticity (EGARCH) model on the spot return
series of sample commodities. EGARCH specifcation has mainly been utilized to
capture the possible asymmetries in the data. The model is specifed as follows:
Mean equation:
r
t
? ?
t
? ?
1
r
t?1
? ?
t
(1)
Where r
t
?(r
1, t
, r
2, t
, . . . , r
n, t
)’, n ?8; ?
t
?(?
1, t
, ?
2, t
, . . .?
n, t
)’; ?
?
?
t?1
?N(0, H
t
)
Variance equation:
log(h
t
) ? ? ?
?
j?1
q
?
j
?
u
t?j
?
h
t?j
?
?
?
j?1
q
?
j
u
t?j
?
h
t?j
?
?
i?1
p
?
i
log(h
t?i
) (2)
where r
t
is the return series of each sample commodity and ?
1
in the mean equation, and
?, ?s, ?s and ?s in the variance equation are parameters to be estimated. The left-side
variable is the log of the variance series. As it is well-known, the EGARCH model is
usually applied to decipher the asymmetries in a series (Enders, 2010).
After calculating unexpected liquidity and spot market volatility, the study uses the
Granger causality test to confrm the direction of the causal relationship between
unexpected liquidity of the futures market and time-varying volatility of underlying
spot returns of sample commodities. In general, the Granger causality test is normally
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used to examine the linear causality by estimating as reduced for vector autoregression
(VAR). In our case, the general form of Granger causality can be specifed as:
X
t
? ?
0
? ?
i ?
i?1
l
X
t?i
? ?
j ?
j?1
m
Y
t?j
? u
t
(3)
Y
t
? ?
=
0
? ?
=
?
i?1
m
X
t?i
? ?
=
j ?
j?1
n
Y
t?j
? v
t
(4)
Where l and mare lag orders, and Xand Yare stationary time-series. Xdenotes the spot
market volatility and Y shows the unexpected liquidity. The regression residuals, ?u
t
?
and ?v
t
?, are assumed to be i.i.d. The standard F-test or chi-square test is used to establish
the direction of the causal relationship. The null hypothesis of this test is that Ydoes not
Granger-cause X. It is rejected when the coeffcients of the lag values Y in equation (2)
are jointly different from zero, i.e. ?
1
?
2
. . . ?
n
0.
To substantiate the linear Granger causality test, we also use the modifed
non-parametric, non-linear causality test given by Diks and Panchenko (2006). This
particular model is a substantiation of Hiemstra and Jones’ (1994) non-linear causality
test.
Unlike the conventional Granger causality test, which often does not account for
non-linear causal relationships between variables, especially when analysing the
high-frequency data, it becomes crucial to re-confrm the causal direction by applying
non-linear parametric or non-parametric methods of causality. In the literature, several
studies have applied the recently developed test of causality, which is also a modifed
form of Hiemstra and Jones’s (1994) non-linear causality test. Tracing the history, in an
early attempt, Baek and Brock (1992) propose a non-parametric statistical method for
revealing the non-linear relationship by using the correlation integral between time
series. In Baek and Brock’s test, the time series are assumed to be mutually and
individually independent and identically distributed. Moreover, Hiemstra and Jones
(1994) argue that the linear regression of the traditional Granger causality test and Baek
and Brock (1992) tests are too strict. So, by relaxing this strict assumption, Hiemstra and
Jones (1994) develop a modifed test statistic for the non-linear causality, which allows
each series to display the short-term temporal dependence and does not depend on the
specifc form of the test equation.
However, Diks and Panchenko (2005) suggest that the approach of the Hiemstra and
Jones’ (1994) test is not completely in accordance with the defnition of Granger causality
and may result in wrong rejection of the null hypothesis especially in the case of
increasing sample size, as it ignores the possible variations in conditional distributions.
In a recent study, Diks and Panchenko (2006), hereafter (D-P), develop a new
non-parametric test for Granger causality that overcomes the over-rejection problem in
the Hiemstra and Jones (1994) test. This non-parametric test can be described as follows:
Testing Granger causality from one time series X to another Y is based on the null
hypothesis that Xdoes not contain additional information about Y
t?1
, which is specifed
as:
H
0
? Y
t?
(X
t
lx
;Y
t
ly
)
?
Y
t
ly
(5)
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where, X
t
lx
? (X
t?l?1
, . . . , X
t
) and Y
t
ly
? (Y
t?l?1
, . . . , Y
t
), (l
x
, l
y
? 1), is a subseries of,
l
x
and l
y
, which denote the past observations (i.e. lag length) of Xand Y, respectively. For
two stationary time series, and by assuming Z
t
?Y
t?1
and by dropping time index and
lags for simplifcation in equation (3), the conditional distribution of Z, given (X, Y) ?
(x, y), is consistent with that of Z, given Y?y, under the null hypothesis. Therefore, the
joint probability density function f
X, Y, Z
(x, y, z) and its marginal distribution must satisfy
the following equation:
?f
X, Y, Z
(x, y, z)
f
Y
?
f
X, Y
(x, y)
f
Y
?
f
Y, Z
(y, z)
f
Y
(6)
Equation (4) explicitly states that X and Z are independently conditional on Y ? y for
each fxed value of y. Diks and Panchenko (2006) then re-specify the null hypothesis of
no non-linear Granger causality as follows:
q ? E ?f
X, Y, Z
(X, Y, Z)f
Y
(Y) ? f
X, Y
(X, Y)f
Y, Z
(Y, Z)? ? 0 (7)
Where f
ˆ
w
(W
i
) is a local density estimator of a d
w
- variate randomvector Wat W
i
defned
by:
f
ˆ
w
(W
i
) ? (2?
n
)
?d
w
(n ? 1)
?1
?
j . j1
I
ij
W
where, I
ij
W
? I(?W
i
? W
j
? ? ?
n
), I(•) is an indicator function and ?
n
is the pre-setting
bandwidth depending on the sample size n. Given this estimator, the test statistic, which
is a scaled sample version of q in equation (7), is developed as:
T
n
(?
n
) ?
n ? 1
n(n ? 2)
?
i
(f
ˆ
X, Y, Z
(X
i
, Y
i
, Z
i
)f
ˆ
Y
(Y
i
) ? f
ˆ
X, Y
(X
i
, Y
i
)f
ˆ
Y, Z
(Y
i
, Z
i
)).
For l
x
?l
y
?1, if ?
n
?C
n
(
C?0, 1 / 4 ???1 / 3
)
, Diks and Panchenko (2006) prove that
this statistic follows the asymptotic distribution of the form:
?n
T
n
(?
n
) ? q
S
n
¡
D
N(0, 1) (8)
where, ¡
D
denotes convergence of distribution and S
n
is an estimator of the asymptotic
variance T
n
(•). Accordingly, the D-P test statistic in equation (7) for non-linear causality
is asymptotically distributed as standard normal and diverges to positive infnity under
the alternative hypothesis.
The direction of causal relationship can be interpreted as follows:
• The unidirectional causal relationship moving from unexpected liquidity to spot
market return volatility implies that there is a destabilization effect of the futures
market on the spot market. This further implies that there is strong participation
of uninformed investors and speculators in futures markets. It may also point out
at the informational ineffciency in the case of the disorganized spot market.
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• The unilateral causal relationship moving from spot return volatility to
unexpected liquidity in the futures market may create excess trading interest in
futures markets either by speculators or by arbitragers who adopt multi-asset/
market trading strategies based on return volatility.
• The bidirectional causal relationship between unexpected liquidity and spot
market volatility indicates that there is strong fow of information between the
two markets.
• The absence of any signifcant causal relationship implies a weak information
transmission process between the two markets.
3. Empirical results
In this section, we discuss the empirical results. Descriptive statistics of the sample
series are shown in Table I. All spot series have positive daily mean returns. Among
sample commodities, none of the markets shows negative returns. The standard
deviation (often referred as standard measure of volatility) of return series can be
considered high when compared to their respective means. Skewness and kurtosis
statistics are clearly revealing the possible asymmetry in the data. The p-values of the
Jarque–Bera test of normality further confrm the non-normality characteristics of the
data. The estimated results of the unit root test show that all the sample series are
stationary at their level. However, it may here be relevant to note that the unit root
results of futures and open interest have been mentioned for the sake of comparison. The
unit root results indicate that all variables are stationary at their level, indicating that
there is no outlier in the data set (Table II). After this, we analyse the estimated results
of Granger causality between unexpected liquidity (UNEXP_LIQ) of the futures market
computed using three flters, viz., HP, BK and CF, and spot market volatility computed
from the EGARCH model. Table III exhibits the results of Granger causality using
unexpected liquidity calculated from HP, BK and CF flters; the linear causality results
indicate unilateral causality moving from unexpected liquidity of futures markets to
spot market volatility in the cases of barley, castor seed and chilli, while other fve
commodities indicate no causal relationship. Similarly, the causality results based on
the unexpected liquidity calculated using the BK flter also substantiate the causality
results of the HP flter (Table III). The results show that among sample commodities,
barley, castor seed, chana and pepper exhibit the unidirectional causality from
unexpected liquidity to spot volatility. Notably, chana exhibits bilateral causality
moving fromunexpected liquidity of the futures market and spot market volatility. The
causality results based on the CF flter also report unidirectional causality moving from
unexpected liquidity to spot market volatility in the case of barley, castor seed, chilli and
pepper (Table III).
Summarizing, the results of linear causality indicate that the unexpected liquidity
calculated from three high and band-pass flters provide, by and large, causal direction
in the case of barley, castor seed and chilli. While, BK and CF flter-based causality
results indicate a causal relationship fromunexpected liquidity to spot market volatility
in the case of chana and pepper. The surprising result of the linear causality test could
be the case of chana in the case of the BK flter that exhibits bilateral causality between
both variables. Based on the linear causality results, it can be concluded that among
eight sample commodities, there are destabilization effects in the case of barley, castor
seed, chilli and pepper. Before analysing these results from the perspective of
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Table I.
Descriptive statistics
Commodities Mean SD Skewness Kurtosis Jarque–Bera
Barley
Spot 0.040 1.135 0.353 24.053 0.000**
Futures 0.034 1.512 0.008 6.018 0.000**
Volume 3,489 5,387 3.489 19.573 0.000**
Open interest 9,318 8,783 1.389 4.033 0.000**
Castor seed
Spot 0.016 1.711 ?0.128 5.212 0.000**
Futures 0.023 1.788 0.296 13.448 0.000**
Volume 19,015 33,095 2.160 7.738 0.000**
Open interest 32,738 47,351 1.478 3.697 0.000**
Chana
Spot 0.012 1.415 4.529 54.880 0.000**
Futures 0.025 2.350 0.213 10.930 0.000**
Volume 2,633 2,680 1.739 6.696 0.000**
Open interest 5,476 4,063 1.500 5.708 0.000**
Chilli
Spot 0.041 1.401 0.224 4.378 0.000**
Futures 0.037 1.495 0.103 5.900 0.000**
Volume 92,186 58,062 1.557 7.057 0.000**
Open interest 103,015 57,837 1.723 6.745 0.000**
Pepper
Spot 0.103 1.058 2.276 32.188 0.000**
Futures 0.103 1.605 0.437 10.445 0.000**
Volume 4,264 4,068 2.063 8.689 0.000**
Open interest 6,008 3,375 0.517 2.681 0.000**
Potato
Spot 0.055 4.130 9.405 332.562 0.000**
Futures 0.044 4.497 ?3.311 148.360 0.000**
Volume 8,582 14,047 3.792 21.674 0.000**
Open interest 22,229 33,958 2.969 11.513 0.000**
Refned soya
Spot 0.029 0.805 0.102 8.571 0.000**
Futures 0.034 1.075 ?0.369 5.917 0.000**
Volume 99,636 62,933 0.865 3.491 0.000**
Open interest 95,732 31,062 0.043 2.672 0.000**
Soybean
Spot 0.055 1.254 ?2.458 33.205 0.000**
Futures 0.053 1.423 ?0.445 8.234 0.000**
Volume 83,450 48,909 1.205 4.434 0.000**
Open interest 130,560 57,750 0.791 3.272 0.000**
Notes: **Shows level of signifcance at 5% or better; only p-values of Jarque–Bera are shown in the
table; volume and open interest series are converted into logarithmic formbefore implementation of the
unit root test; the spot and futures series are expressed in percentage returns
129
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Table II.
Unit root results
Commodities
ADF PP
Level First diff. Level First diff.
Barley
Spot ?26.026** ?21.195** ?25.775** ?252.548**
Futures ?31.073** ?19.422** ?31.063** ?288.352**
Volume ?4.596** ?23.688** ?10.995** ?64.721**
Open interest ?4.180** ?32.689** ?4.535** ?32.688**
Castor seed
Spot ?26.084** ?18.449** ?25.937** ?160.214**
Futures ?22.328** ?18.157** ?26.320** ?269.841**
Volume ?4.523** ?17.903** ?12.765** ?95.571**
Open interest ?4.625** ?29.211** ?4.811** ?29.202**
Chana
Spot ?26.367** ?17.178** ?33.029** ?261.092**
Futures ?33.582** ?18.668** ?33.572** ?217.937**
Volume ?6.628** ?21.615** ?15.094** ?80.128**
Open interest ?5.022** ?33.202** ?5.114** ?33.197**
Chilli
Spot ?21.810** ?15.078** ?21.621** ?210.629**
Futures ?26.994** ?15.948** ?27.064** ?409.307**
Volume ?5.926** ?25.711** ?13.904** ?75.900**
Open interest ?4.562** ?30.622** ?4.679** ?31.707**
Pepper
Spot ?29.631** ?19.227** ?30.356** ?319.015**
Futures ?32.826** ?17.382** ?32.815** ?255.367**
Volume ?5.474** ?15.592** ?12.191** ?55.001**
Open interest ?3.212* ?32.238** ?3.999** ?32.315**
Potato
Spot ?25.487** ?14.429** ?25.518** ?450.161**
Futures ?26.433** ?13.747** ?26.454** ?324.052**
Volume ?3.501** ?16.966** ?7.301** ?51.220**
Open interest ?3.500** ?25.970** ?3.500** ?25.941**
Refned soya
Spot ?28.049** ?16.221** ?28.482** ?272.380**
Futures ?34.768** ?16.120** ?34.927** ?499.386**
Volume ?5.684** ?19.490** ?22.323** ?85.243**
Open interest ?5.597** ?34.830** ?5.834** ?38.984**
Soybean
Spot ?28.857** ?16.006** ?29.574** ?215.866**
Futures ?33.075** ?16.170** ?33.186** ?514.198**
Volume ?6.110** ?16.224** ?25.055** ?98.187**
Open interest ?4.558** ?36.142** ?4.290** ?40.469**
Notes: **and *showthe level of signifcance at 5 and 10%and better, respectively; the unit root tests
used are Augmented Dickey–Fuller (ADF) and Phillips and Perron (PP)
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commodity futures market development, we further reconfrm these causal fndings by
applying the non-linear causality test. As suggested by Baek and Brock (1992), we apply
the non-linear causality test after removing the linear dependence using the VARmodel.
We also test for the presence of non-linear dependence by applying the BDS (Brock et al.,
1996) test. The BDS test results indicate the presence of non-linear dependence in the
case of all sample series, implying that there is non-linearity in the data. After
confrming this, we implement Diks and Panchenko’s (2005) test on all sample
commodities. As above-mentioned, we apply this test based on the residuals of the VAR
model. The lag lengths Lx and Ly are attempted from1 to 5 with the epsilon value of 1.2,
as suggested by Diks and Panchenko (2006)[3]. The results of the non-linear causality
test show that among sample commodities, chana, chilli and pepper exhibit
unidirectional non-linear causality running from unexpected liquidity to spot market
volatility (Tables IV-VII), indicating the existence of the destabilization effect.
Analysing individually, the results of non-linear causality based only on the HP flter
indicate the presence of unidirectional causality moving from unexpected liquidity to
spot market volatility for chana, chilli and pepper. If we compare the results of linear and
non-linear causality as exhibited in Table VII, we fnd that the results of non-linear
causality are at variance for some commodities with linear causality. Like for example,
linear causality reports the destabilization effect in the case of barley and castor seed,
while non-linear causality results do not fnd such evidence. But in the case of chana,
chilli and pepper, the results of non-linear causality are in agreement with linear
causality results. The application of non-linear causality appears to be appropriate in
this case, as some of its results are at variance with causality results. Like for example,
Table III.
Linear causality
results
Variables
Causality inference
based on HP flter
Causality inference
based on BK flter
Causality inference
based on CF flter
SBARLEY UNEXP_LIQ – – –
UNEXP_LIQ SBARLEY ** ** **
SCASTOR UNEXP_LIQ – – –
UNEXP_LIQ SCASTOR ** ** **
SCHANA UNEXP_LIQ – ** –
UNEXP_LIQ SCHANA – ** **
SCHILLI UNEXP_LIQ – ** –
UNEXP_LIQ SCHILLI ** – **
SPEPPER UNEXP_LIQ – – –
UNEXP_LIQ SPEPPER – ** **
SPOTATO UNEXP_LIQ – – –
UNEXP_LIQ SPOTATO ** – –
SREFINED UNEXP_LIQ – – –
UNEXP_LIQ SREFINED – – –
SSOYA UNEXP_LIQ – – –
UNEXP_LIQ SSOYA – – –
Notes: **Denotes the level of signifcance at 5% and better; SBARLEY, SCASTOR, SCHANA,
SCHILLI, SPEPPER, SPOTATO, SREFINED and SSOYA are denoted as time-varying volatilities of
spot markets of barley, castor seed, chana, chilli, pepper, potato, refned soya and soybean, respectively;
UNEXP_LIQ indicates unexpected liquidity of sample commodities
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Table IV.
Non-linear Granger
causality results
based on HP flter
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Table V.
Non-linear Granger
causality results
based on BK flter
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f
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t
1
0
%
133
India’s
agriculture
commodity
futures market
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
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R
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Y
A
t
2
1
:
5
1
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Table VI.
Non-linear Granger
causality results
based on CF flter
P
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3
N
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s
:
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,
S
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A
S
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R
,
S
C
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A
N
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,
S
C
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L
L
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,
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P
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P
P
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,
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,
S
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d
S
S
O
Y
A
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r
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d
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d
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v
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p
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m
a
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k
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s
o
f
b
a
r
l
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y
,
c
a
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d
,
c
h
a
n
a
,
c
h
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l
l
i
,
p
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p
p
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r
,
p
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t
a
t
o
,
r
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f
n
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d
s
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y
a
,
a
n
d
s
o
y
b
e
a
n
,
r
e
s
p
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c
t
i
v
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l
y
;
U
N
E
X
P
_
L
I
Q
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n
d
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c
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l
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q
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d
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t
y
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f
s
a
m
p
l
e
c
o
m
m
o
d
i
t
i
e
s
;
*
d
e
n
o
t
e
t
h
e
l
e
v
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l
o
f
s
i
g
n
i
f
c
a
n
c
e
a
t
1
0
%
JFEP
7,2
134
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
5
1
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Table VII.
Summary of the
linear and non-linear
Granger causality
tests
P
a
i
r
o
f
v
a
r
i
a
b
l
e
s
M
a
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y
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y
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l
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Y
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P
P
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R
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the relevance of the destabilization effect in the case of barley and castor seed is not
supported by the results of non-linearity. This strongly justifes the argument of why
important causal relationships should be substantiated by applying the non-linear
causality test. The overall causality results (both linear and non-linear) suggest that for
some commodities, there are visible patterns of causality movements from futures
market liquidity to spot market volatility. This also confrms the presence of suffcient
amount of market players and uninformed investors in the Indian commodity futures
market.
4. Conclusion
In this study, we attempt to answer a fundamental question related with the
introduction of commodity futures in India’s agricultural commodity market. That is,
does futures market trading destabilize the underlying spot market? In the literature, a
very limited amount of studies have shown their interest in examining such a
phenomenon, and considering the investigation of the destabilization hypothesis in the
case of India’s commodity futures markets as crucial, this study provides an alternative
viewpoint to the literature on the futures and spot relationship regarding examination of
the destabilization effect in the case of India’s commodity market. For this purpose, this
study uses the trading volume and spot returns data of eight agricultural commodities.
Following Bessembinder and Seguin (1992), the study uses three different high and
band-pass flters to extract the unexpected components of futures market liquidity. The
conventional EGARCH model has been used to estimate the time-varying volatility of
the spot market. To confrmthe destabilization effect, the study applies linear (Granger
causality) and non-linear (Diks and Panchenko) causality tests. The estimated results of
both causality tests indicate strong causality movement fromfutures market liquidity to
spot market volatility. Though non-linear causality results are at variance with the
linear causality results, both tests report the evidence of destabilization effects in the
case of chana, chilli and pepper. This also confrms the presence of large number of
players and uninformed investors in the Indian commodity futures market. The
fndings of this study are in line with Bessembinder and Seguin (1993) and Sehgal et al.
(2012) in the Indian case. From a policy implication perspective, the causality results
further confrm the destabilization effect of the futures market on the spot market. The
results reveal that there is strong fow of information between the futures and the spot
market. The possible explanations could be provided in two ways. First, the
introduction of large number of commodity futures often creates opportunities for
collusive behaviour as a speculator. This appears to be relevant due to the suspension of
trading of major agricultural commodities in the Indian commodity market. Second
possible explanation could also be due to unorganized spot exchanges. In India, spot
exchanges are relatively disorganized, as they are basically physical in nature and are
not electronically traded markets as futures. Further, there may be several spot markets
known as Mandis that are scattered geographically and are very less integrated.
National spot exchange, which is an electronically traded spot market, is of recent origin
and deals with few commodities. Therefore, from a policy perspective, it can be
suggested that the policy focus of the fnancial economic policy of India should be on
developing an organized spot market. Suspension of trading is not a solution, and there
should be provision of better hedging options and introduction of commodity options in
this regard appears to be an appropriate instrument to gauge the risk. For the welfare of
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farmers, policy measures must be undertaken to link warehousing receipt with trading,
so that there should not be black hoarding of essential commodities. Besides this, there
should be introduction of commodity funds in the Indian commodity market. An
umbrella agency can also be introduced which can invest on behalf of small farmers in
derivatives market. The farmers can pay a small premiumto the agency for its services.
Finally, the major implication of this study is that it reveals the negative impact of
over-dominance of the futures market on spot market volatility. Therefore, it is urgently
required to set-up dedicated fnancial architecture that can gauge and manage the risk of
high spot market volatility. Especially in the context of emerging markets, this study is
important because it outlines numerous measures to contain the spot market volatility.
In the case of India, future studies may further investigate as to why futures trading of
chana, chilli and pepper is having a destabilization effect on spot market volatility.
Notes
1. For further details about suspension of agricultural commodities. Please refer
www.ncdex.com/MarketData/FuturePrices.aspx
2. Ewing and Thompson (2007) provide suffcient overview about these three flters. Interested
readers may source this paper.
3. As Diks and Panchenko (2006) suggest, the value of epsilon depends on the length of the time
series. For our empirical analysis, we use the epsilon value of 1.2.
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Corresponding author
Wasim Ahmad can be contacted at: [email protected]
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doc_126561169.pdf
This paper aims to examine the destabilization effect in the case of India’s agricultural
commodity market for the sample period of 01 January 2009 to 31 May 2013.
Journal of Financial Economic Policy
The investigation of destabilization effect in India’s agriculture commodity
futures market: An alternative viewpoint
Wasim Ahmad Sanjay Sehgal
Article information:
To cite this document:
Wasim Ahmad Sanjay Sehgal , (2015),"The investigation of destabilization effect in India’s agriculture
commodity futures market", J ournal of Financial Economic Policy, Vol. 7 Iss 2 pp. 122 - 139
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The investigation of
destabilization effect in India’s
agriculture commodity futures
market
An alternative viewpoint
Wasim Ahmad and Sanjay Sehgal
Department of Financial Studies, University of Delhi, New Delhi, India
Abstract
Purpose – This paper aims to examine the destabilization effect in the case of India’s agricultural
commodity market for the sample period of 01 January 2009 to 31 May 2013.
Design/methodology/approach – The daily data of eight agricultural commodities traded on the
National Commodity & Derivatives Exchange, viz., barley, castor seed, chana (chickpea), chilli, potato,
pepper, refned soya and soybean, have been used in this study. At the frst stage of the empirical
analysis, the study estimates the time-varying spot market volatility by using the exponential
generalized autoregressive conditional heteroscedasticity model and applies three different high and
band-pass flters, viz., the two-sided linear band-pass flter by Hodrick and Prescott (1997), the
fxed-length symmetric band-pass flter by Baxter and King (1999) and the asymmetric band-pass flter
by Christiano and Fitzgerald (2003), to calculate the unexpected liquidity of sample commodities. At the
second stage of the empirical analysis, the study applies linear Granger causality and recently
developed non-linear causality given by Diks and Panchenko (2006) to examine the cause and effect
between time-varying volatility of spot market and futures market liquidity of sample commodities.
Findings – The linear and non-linear causality results suggest the destabilizing effect of commodity
futures on the underlying spot market for chana, chilli and pepper. The empirical fndings are in
contrast with the recommendations of Abhijit Sen’s committee and provide important direction for
further policy research.
Research limitations/implications – The study has a limitation in that it is based on the daily data.
The use of intra-day data would have been more suitable for such type of analysis.
Practical implications – The study has strong policy implications from a fnancial policy
perspective, as there is already disagreement among researchers and policy makers with regard to the
functioning of commodity derivatives markets in India. There have been many occasions when
commodity market regulators have to undertake decisions of suspension of trading of many
commodities. The study also provides new directions of policy research with regards to the
restructuring of the commodity derivatives market in India.
Social implications – The fndings of this study may further help the regulators and policy makers
to undertake decisions about howto provide an alternative platformfor farmers to sell their agricultural
produce more effciently. This will certainly have some impact on the socioeconomic set-up of the
country, as India is primarily an agriculture-dominated country.
Originality/value – So far not many studies have investigated the destabilization hypothesis in the
case of emerging markets. This study is a novel attempt to fll the gap. In the case of emerging markets
and especially in the case of India’s commodity derivatives market, this is the frst study that examines
JEL classifcation – C32, G10, G14, G15
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1757-6385.htm
JFEP
7,2
122
Received11 February2014
Revised27 July2014
Accepted2 October 2014
Journal of Financial Economic
Policy
Vol. 7 No. 2, 2015
pp. 122-139
©Emerald Group Publishing Limited
1757-6385
DOI 10.1108/JFEP-02-2014-0008
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the destabilization hypothesis in the case of India by applying new methods of high and band-pass
flters and non-linear causality.
Keywords Government policy and regulation, Financial economics, Financing policy,
Time-series models, Contingent pricing
Paper type Research paper
1. Introduction
Since its inception, the role of commodity futures in India’s commodity futures markets
has been the subject of considerable disagreement among academia, regulators and
various stakeholders. The main issue of debate appears to be about whether
introduction of futures in agricultural products has destabilized the underlying spot
market? In other words, are futures trading increasing the volatility in the underlying
spot market? In the literature on the futures–spot market relationship, not many studies
have analysed such a phenomenon, particularly in the case of emerging markets. The
examination of the destabilization effect is considered important because studies have
shown that the presence of uninformed investors in the commodity derivatives market
often induces noise in the price discovery process and also obstructs the information
transmission process (Newbery, 1987). As a result, the spot market exhibits a higher
level of volatility compared to the situation without a futures market (Cox, 1976; Cagan,
1981; Figlewski, 1981; Stein, 1987; Hart and Kreps, 1986; Bessembinder and Seguin,
1993). Considering this as a special issue with regard to the development of the
agri-commodity futures market in the case of India, this study attempts to examine the
role of the commodity futures market on the spot markets of eight agriculture
commodities. More specifcally, we try to examine whether futures trading destabilizes
the underlying spot market by increasing its volatility in the case of India’s agriculture
commodity market. The statistics reveal that in the frst fve-year, the agriculture
commodity market witnessed the spectacular growth rate of more than 119 per cent
(FMC, 2013). But in recent years, due to overplay by the speculators and arbitragers,
there has been continuous increase in spot market volatility, leading to suspension in the
trading of many important agricultural commodities. There is also widespread
apprehension that the development of the futures market has led to excess volatility in
the spot market. On some occasions, these apprehensions appeared to be true because
high volatility in commodity prices, mainly food products, has led to suspension of
several traded commodities. According to the regulator of the commodity market in
India, i.e. the Forward Market Commission (FMC), in 2011 and 2012, the futures trading
of tur, urad, rice, guar gumand guar seed has been suspended for speculative reasons[1].
Despite these apprehensions and speculations about futures market development, the
proponents of futures markets argue that futures markets often play a constructive role
in stabilizing the volatility in the underlying spot market. Futures markets help in the
assimilation of new market information more quickly than the underlying spot market,
thereby providing better opportunities for fair price discovery of traded commodities,
enhancing market effciency, augmenting market liquidity and, hence, contributing to
the market completion. The introduction of futures trading also helps in reducing the
excess volatility of the underlying spot market (Powers, 1970; Danthine, 1978; Bray,
1981; Kyle, 1985; Stoll and Whaley, 1988; Bessembinder and Seguin, 1992, 1993;
Guesnerie and Rochet, 1993; Jochumand Kodres, 1998; Bohl et al., 2011; Lee et al., 2014).
123
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Summarizing, given that the above-mentioned issues are critically linked with the
commodity market development in India, empirical investigations seemto be necessary
to gain additional insight into the impact of futures trading on the volatility of the
underlying spot market. While there is very limited theoretical work on this subject,
there are some empirical studies that have examined the issue of destabilization effect to
understand the direction of crucial linkage between futures market liquidity and spot
return volatility. For the sake of simplicity, we divide the destabilization hypothesis into
two different streams. The frst streamof literature focusses on analysing the impact of
introduction of futures markets on underlying spot market volatility by utilizing the
generalized autoregressive conditional heteroscedasticity-type models augmented by
dummy variables. The dummy variables are mainly introduced in the model to
discriminate between the pre- and the post-futures periods and thus to analyze the effect
that the introduction of futures market has on spot market volatility. A large of
proportion of existing studies have focused their attention on developed stock markets
by considering institutional investors as predominant traders. Most studies in this
stream focus on developed markets, and literature on emerging markets like India is
almost missing (Antoniou et al., 1998, 2005; Gulen and Mayhew, 2000; McKenzie et al.,
2001). Second stream highlights the role of well-informed investors in futures market
which is inherently linked with the destabilization hypothesis according to which
uninformed investors disrupt the smooth price discovery process and lower the
information transmission process (Lee et al., 1999; Cohen et al., 2002; Barber and Odean,
2008; Kaniel et al., 2008). There is very limited number of studies on this particular
stream, and so far, studies have mainly covered the developed market.
In this paper, we attempt to test the destabilization hypothesis in the case of the
Indian agricultural commodity market and try to answer a fundamental question which
is strongly linked with the introduction of commodity futures markets. That is, does
commodity futures market trading destabilize the underlying spot market? We mainly
focus on agricultural commodities because of the ongoing debate on the speculative role
of the futures market in augmenting the infationary pressure. The present study is also
motivated to re-examine the recommendations made by Sen’s (2008) committee on the
passive role of the futures market in increasing the spot market volatility. For this
purpose, we cover eight agricultural commodities traded on the platform of National
Commodity &Derivatives Exchange (NCDEX). The list of sample commodities includes
barley, castor seed, chana, chilli, pepper, potato, soybean and refned soya oil.
The study is organized as follows: in Section 2, the study data are outlined and the
methodology is discussed, followed by empirical results in Section 3. Finally, Section 4
provides conclusion and outlines policy directions.
2. Data and methodology
The sample data for the daily volume and spot prices are retrieved from NCDEX’s
website. Based on the availability of data, the sample period of each commodity has been
decided as: barley, chana and refned soya (January 01, 2009, to May 31, 2013; 1,177;
1,211; and 1,295 observations, respectively); soybean (January 01, 2009, to May 27, 2013;
1,291 observations); castor seed (August 25, 2009 to May 31, 2013; 899 observations);
chilli (January 01, 2009, to May 10, 2013; 914 observations); pepper (January 01, 2009 to
May 20, 2013; 1,158 observations); and potato (March 09, 2009 to May 31, 2013; 745
observations). Further, sample commodities have been selected based on the criteria
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that they should have continuous trading records. Frequent price breaks can pose an
estimation problem, and hence, thinly traded commodities have been excluded. For
estimation purposes, sample series are further converted into natural logarithms and
returns, wherever required.
We have applied various models to confrm the relevance of the destabilization
hypothesis in the case of India. Before, we begin analysing the data, the study applies the
unit root test to confrm whether the sample series achieve stationarity at their frst
difference or not. This basically highlights whether a series contains outliers or not. For
this purpose, the study applies two unit root tests, viz., Augmented Dickey-Fuller (ADF)
and Phillips and Perron (PP), on the returns series of futures and spot and logarithmic
values of trading volume and open interest. The null hypotheses of ADF and PP are the
same, i.e. there is unit root in the examined series and alternative that there is no unit root
in the series. At the second stage of the empirical analysis, the study uses three different
high and band-pass flters to extract the unexpected liquidity in the futures market. For
this purpose, the study uses futures market’s traded volume data of all sample
commodities. To compute the unexpected liquidity, the two-sided linear band-pass flter
by Hodrick and Prescott (henceforth HP, 1997), the fxed-length symmetric band-pass
by Baxter and King (hereafter BK, 1999) and the asymmetric band-pass flter by
Christiano and Fitzgerald (henceforth CF, 2003) are used[2]. Despite several limitations
of these high and band-pass flters, we mainly apply these three models to compare
the results of unexpected liquidity. According to Mink et al. (2007), among these flters,
the CF flter has the advantage that it does not lead to the loss of observations at the
beginning and the end of the sample period. Finally, the study applies techniques of
linear and non-linear causality to check the causal directions.
To calculate the spot market volatility, we use the exponential generalized
autoregressive conditional heteroscedasticity (EGARCH) model on the spot return
series of sample commodities. EGARCH specifcation has mainly been utilized to
capture the possible asymmetries in the data. The model is specifed as follows:
Mean equation:
r
t
? ?
t
? ?
1
r
t?1
? ?
t
(1)
Where r
t
?(r
1, t
, r
2, t
, . . . , r
n, t
)’, n ?8; ?
t
?(?
1, t
, ?
2, t
, . . .?
n, t
)’; ?
?
?
t?1
?N(0, H
t
)
Variance equation:
log(h
t
) ? ? ?
?
j?1
q
?
j
?
u
t?j
?
h
t?j
?
?
?
j?1
q
?
j
u
t?j
?
h
t?j
?
?
i?1
p
?
i
log(h
t?i
) (2)
where r
t
is the return series of each sample commodity and ?
1
in the mean equation, and
?, ?s, ?s and ?s in the variance equation are parameters to be estimated. The left-side
variable is the log of the variance series. As it is well-known, the EGARCH model is
usually applied to decipher the asymmetries in a series (Enders, 2010).
After calculating unexpected liquidity and spot market volatility, the study uses the
Granger causality test to confrm the direction of the causal relationship between
unexpected liquidity of the futures market and time-varying volatility of underlying
spot returns of sample commodities. In general, the Granger causality test is normally
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used to examine the linear causality by estimating as reduced for vector autoregression
(VAR). In our case, the general form of Granger causality can be specifed as:
X
t
? ?
0
? ?
i ?
i?1
l
X
t?i
? ?
j ?
j?1
m
Y
t?j
? u
t
(3)
Y
t
? ?
=
0
? ?
=
?
i?1
m
X
t?i
? ?
=
j ?
j?1
n
Y
t?j
? v
t
(4)
Where l and mare lag orders, and Xand Yare stationary time-series. Xdenotes the spot
market volatility and Y shows the unexpected liquidity. The regression residuals, ?u
t
?
and ?v
t
?, are assumed to be i.i.d. The standard F-test or chi-square test is used to establish
the direction of the causal relationship. The null hypothesis of this test is that Ydoes not
Granger-cause X. It is rejected when the coeffcients of the lag values Y in equation (2)
are jointly different from zero, i.e. ?
1
?
2
. . . ?
n
0.
To substantiate the linear Granger causality test, we also use the modifed
non-parametric, non-linear causality test given by Diks and Panchenko (2006). This
particular model is a substantiation of Hiemstra and Jones’ (1994) non-linear causality
test.
Unlike the conventional Granger causality test, which often does not account for
non-linear causal relationships between variables, especially when analysing the
high-frequency data, it becomes crucial to re-confrm the causal direction by applying
non-linear parametric or non-parametric methods of causality. In the literature, several
studies have applied the recently developed test of causality, which is also a modifed
form of Hiemstra and Jones’s (1994) non-linear causality test. Tracing the history, in an
early attempt, Baek and Brock (1992) propose a non-parametric statistical method for
revealing the non-linear relationship by using the correlation integral between time
series. In Baek and Brock’s test, the time series are assumed to be mutually and
individually independent and identically distributed. Moreover, Hiemstra and Jones
(1994) argue that the linear regression of the traditional Granger causality test and Baek
and Brock (1992) tests are too strict. So, by relaxing this strict assumption, Hiemstra and
Jones (1994) develop a modifed test statistic for the non-linear causality, which allows
each series to display the short-term temporal dependence and does not depend on the
specifc form of the test equation.
However, Diks and Panchenko (2005) suggest that the approach of the Hiemstra and
Jones’ (1994) test is not completely in accordance with the defnition of Granger causality
and may result in wrong rejection of the null hypothesis especially in the case of
increasing sample size, as it ignores the possible variations in conditional distributions.
In a recent study, Diks and Panchenko (2006), hereafter (D-P), develop a new
non-parametric test for Granger causality that overcomes the over-rejection problem in
the Hiemstra and Jones (1994) test. This non-parametric test can be described as follows:
Testing Granger causality from one time series X to another Y is based on the null
hypothesis that Xdoes not contain additional information about Y
t?1
, which is specifed
as:
H
0
? Y
t?
(X
t
lx
;Y
t
ly
)
?
Y
t
ly
(5)
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where, X
t
lx
? (X
t?l?1
, . . . , X
t
) and Y
t
ly
? (Y
t?l?1
, . . . , Y
t
), (l
x
, l
y
? 1), is a subseries of,
l
x
and l
y
, which denote the past observations (i.e. lag length) of Xand Y, respectively. For
two stationary time series, and by assuming Z
t
?Y
t?1
and by dropping time index and
lags for simplifcation in equation (3), the conditional distribution of Z, given (X, Y) ?
(x, y), is consistent with that of Z, given Y?y, under the null hypothesis. Therefore, the
joint probability density function f
X, Y, Z
(x, y, z) and its marginal distribution must satisfy
the following equation:
?f
X, Y, Z
(x, y, z)
f
Y
?
f
X, Y
(x, y)
f
Y
?
f
Y, Z
(y, z)
f
Y
(6)
Equation (4) explicitly states that X and Z are independently conditional on Y ? y for
each fxed value of y. Diks and Panchenko (2006) then re-specify the null hypothesis of
no non-linear Granger causality as follows:
q ? E ?f
X, Y, Z
(X, Y, Z)f
Y
(Y) ? f
X, Y
(X, Y)f
Y, Z
(Y, Z)? ? 0 (7)
Where f
ˆ
w
(W
i
) is a local density estimator of a d
w
- variate randomvector Wat W
i
defned
by:
f
ˆ
w
(W
i
) ? (2?
n
)
?d
w
(n ? 1)
?1
?
j . j1
I
ij
W
where, I
ij
W
? I(?W
i
? W
j
? ? ?
n
), I(•) is an indicator function and ?
n
is the pre-setting
bandwidth depending on the sample size n. Given this estimator, the test statistic, which
is a scaled sample version of q in equation (7), is developed as:
T
n
(?
n
) ?
n ? 1
n(n ? 2)
?
i
(f
ˆ
X, Y, Z
(X
i
, Y
i
, Z
i
)f
ˆ
Y
(Y
i
) ? f
ˆ
X, Y
(X
i
, Y
i
)f
ˆ
Y, Z
(Y
i
, Z
i
)).
For l
x
?l
y
?1, if ?
n
?C
n
(
C?0, 1 / 4 ???1 / 3
)
, Diks and Panchenko (2006) prove that
this statistic follows the asymptotic distribution of the form:
?n
T
n
(?
n
) ? q
S
n
¡
D
N(0, 1) (8)
where, ¡
D
denotes convergence of distribution and S
n
is an estimator of the asymptotic
variance T
n
(•). Accordingly, the D-P test statistic in equation (7) for non-linear causality
is asymptotically distributed as standard normal and diverges to positive infnity under
the alternative hypothesis.
The direction of causal relationship can be interpreted as follows:
• The unidirectional causal relationship moving from unexpected liquidity to spot
market return volatility implies that there is a destabilization effect of the futures
market on the spot market. This further implies that there is strong participation
of uninformed investors and speculators in futures markets. It may also point out
at the informational ineffciency in the case of the disorganized spot market.
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• The unilateral causal relationship moving from spot return volatility to
unexpected liquidity in the futures market may create excess trading interest in
futures markets either by speculators or by arbitragers who adopt multi-asset/
market trading strategies based on return volatility.
• The bidirectional causal relationship between unexpected liquidity and spot
market volatility indicates that there is strong fow of information between the
two markets.
• The absence of any signifcant causal relationship implies a weak information
transmission process between the two markets.
3. Empirical results
In this section, we discuss the empirical results. Descriptive statistics of the sample
series are shown in Table I. All spot series have positive daily mean returns. Among
sample commodities, none of the markets shows negative returns. The standard
deviation (often referred as standard measure of volatility) of return series can be
considered high when compared to their respective means. Skewness and kurtosis
statistics are clearly revealing the possible asymmetry in the data. The p-values of the
Jarque–Bera test of normality further confrm the non-normality characteristics of the
data. The estimated results of the unit root test show that all the sample series are
stationary at their level. However, it may here be relevant to note that the unit root
results of futures and open interest have been mentioned for the sake of comparison. The
unit root results indicate that all variables are stationary at their level, indicating that
there is no outlier in the data set (Table II). After this, we analyse the estimated results
of Granger causality between unexpected liquidity (UNEXP_LIQ) of the futures market
computed using three flters, viz., HP, BK and CF, and spot market volatility computed
from the EGARCH model. Table III exhibits the results of Granger causality using
unexpected liquidity calculated from HP, BK and CF flters; the linear causality results
indicate unilateral causality moving from unexpected liquidity of futures markets to
spot market volatility in the cases of barley, castor seed and chilli, while other fve
commodities indicate no causal relationship. Similarly, the causality results based on
the unexpected liquidity calculated using the BK flter also substantiate the causality
results of the HP flter (Table III). The results show that among sample commodities,
barley, castor seed, chana and pepper exhibit the unidirectional causality from
unexpected liquidity to spot volatility. Notably, chana exhibits bilateral causality
moving fromunexpected liquidity of the futures market and spot market volatility. The
causality results based on the CF flter also report unidirectional causality moving from
unexpected liquidity to spot market volatility in the case of barley, castor seed, chilli and
pepper (Table III).
Summarizing, the results of linear causality indicate that the unexpected liquidity
calculated from three high and band-pass flters provide, by and large, causal direction
in the case of barley, castor seed and chilli. While, BK and CF flter-based causality
results indicate a causal relationship fromunexpected liquidity to spot market volatility
in the case of chana and pepper. The surprising result of the linear causality test could
be the case of chana in the case of the BK flter that exhibits bilateral causality between
both variables. Based on the linear causality results, it can be concluded that among
eight sample commodities, there are destabilization effects in the case of barley, castor
seed, chilli and pepper. Before analysing these results from the perspective of
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Table I.
Descriptive statistics
Commodities Mean SD Skewness Kurtosis Jarque–Bera
Barley
Spot 0.040 1.135 0.353 24.053 0.000**
Futures 0.034 1.512 0.008 6.018 0.000**
Volume 3,489 5,387 3.489 19.573 0.000**
Open interest 9,318 8,783 1.389 4.033 0.000**
Castor seed
Spot 0.016 1.711 ?0.128 5.212 0.000**
Futures 0.023 1.788 0.296 13.448 0.000**
Volume 19,015 33,095 2.160 7.738 0.000**
Open interest 32,738 47,351 1.478 3.697 0.000**
Chana
Spot 0.012 1.415 4.529 54.880 0.000**
Futures 0.025 2.350 0.213 10.930 0.000**
Volume 2,633 2,680 1.739 6.696 0.000**
Open interest 5,476 4,063 1.500 5.708 0.000**
Chilli
Spot 0.041 1.401 0.224 4.378 0.000**
Futures 0.037 1.495 0.103 5.900 0.000**
Volume 92,186 58,062 1.557 7.057 0.000**
Open interest 103,015 57,837 1.723 6.745 0.000**
Pepper
Spot 0.103 1.058 2.276 32.188 0.000**
Futures 0.103 1.605 0.437 10.445 0.000**
Volume 4,264 4,068 2.063 8.689 0.000**
Open interest 6,008 3,375 0.517 2.681 0.000**
Potato
Spot 0.055 4.130 9.405 332.562 0.000**
Futures 0.044 4.497 ?3.311 148.360 0.000**
Volume 8,582 14,047 3.792 21.674 0.000**
Open interest 22,229 33,958 2.969 11.513 0.000**
Refned soya
Spot 0.029 0.805 0.102 8.571 0.000**
Futures 0.034 1.075 ?0.369 5.917 0.000**
Volume 99,636 62,933 0.865 3.491 0.000**
Open interest 95,732 31,062 0.043 2.672 0.000**
Soybean
Spot 0.055 1.254 ?2.458 33.205 0.000**
Futures 0.053 1.423 ?0.445 8.234 0.000**
Volume 83,450 48,909 1.205 4.434 0.000**
Open interest 130,560 57,750 0.791 3.272 0.000**
Notes: **Shows level of signifcance at 5% or better; only p-values of Jarque–Bera are shown in the
table; volume and open interest series are converted into logarithmic formbefore implementation of the
unit root test; the spot and futures series are expressed in percentage returns
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Table II.
Unit root results
Commodities
ADF PP
Level First diff. Level First diff.
Barley
Spot ?26.026** ?21.195** ?25.775** ?252.548**
Futures ?31.073** ?19.422** ?31.063** ?288.352**
Volume ?4.596** ?23.688** ?10.995** ?64.721**
Open interest ?4.180** ?32.689** ?4.535** ?32.688**
Castor seed
Spot ?26.084** ?18.449** ?25.937** ?160.214**
Futures ?22.328** ?18.157** ?26.320** ?269.841**
Volume ?4.523** ?17.903** ?12.765** ?95.571**
Open interest ?4.625** ?29.211** ?4.811** ?29.202**
Chana
Spot ?26.367** ?17.178** ?33.029** ?261.092**
Futures ?33.582** ?18.668** ?33.572** ?217.937**
Volume ?6.628** ?21.615** ?15.094** ?80.128**
Open interest ?5.022** ?33.202** ?5.114** ?33.197**
Chilli
Spot ?21.810** ?15.078** ?21.621** ?210.629**
Futures ?26.994** ?15.948** ?27.064** ?409.307**
Volume ?5.926** ?25.711** ?13.904** ?75.900**
Open interest ?4.562** ?30.622** ?4.679** ?31.707**
Pepper
Spot ?29.631** ?19.227** ?30.356** ?319.015**
Futures ?32.826** ?17.382** ?32.815** ?255.367**
Volume ?5.474** ?15.592** ?12.191** ?55.001**
Open interest ?3.212* ?32.238** ?3.999** ?32.315**
Potato
Spot ?25.487** ?14.429** ?25.518** ?450.161**
Futures ?26.433** ?13.747** ?26.454** ?324.052**
Volume ?3.501** ?16.966** ?7.301** ?51.220**
Open interest ?3.500** ?25.970** ?3.500** ?25.941**
Refned soya
Spot ?28.049** ?16.221** ?28.482** ?272.380**
Futures ?34.768** ?16.120** ?34.927** ?499.386**
Volume ?5.684** ?19.490** ?22.323** ?85.243**
Open interest ?5.597** ?34.830** ?5.834** ?38.984**
Soybean
Spot ?28.857** ?16.006** ?29.574** ?215.866**
Futures ?33.075** ?16.170** ?33.186** ?514.198**
Volume ?6.110** ?16.224** ?25.055** ?98.187**
Open interest ?4.558** ?36.142** ?4.290** ?40.469**
Notes: **and *showthe level of signifcance at 5 and 10%and better, respectively; the unit root tests
used are Augmented Dickey–Fuller (ADF) and Phillips and Perron (PP)
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commodity futures market development, we further reconfrm these causal fndings by
applying the non-linear causality test. As suggested by Baek and Brock (1992), we apply
the non-linear causality test after removing the linear dependence using the VARmodel.
We also test for the presence of non-linear dependence by applying the BDS (Brock et al.,
1996) test. The BDS test results indicate the presence of non-linear dependence in the
case of all sample series, implying that there is non-linearity in the data. After
confrming this, we implement Diks and Panchenko’s (2005) test on all sample
commodities. As above-mentioned, we apply this test based on the residuals of the VAR
model. The lag lengths Lx and Ly are attempted from1 to 5 with the epsilon value of 1.2,
as suggested by Diks and Panchenko (2006)[3]. The results of the non-linear causality
test show that among sample commodities, chana, chilli and pepper exhibit
unidirectional non-linear causality running from unexpected liquidity to spot market
volatility (Tables IV-VII), indicating the existence of the destabilization effect.
Analysing individually, the results of non-linear causality based only on the HP flter
indicate the presence of unidirectional causality moving from unexpected liquidity to
spot market volatility for chana, chilli and pepper. If we compare the results of linear and
non-linear causality as exhibited in Table VII, we fnd that the results of non-linear
causality are at variance for some commodities with linear causality. Like for example,
linear causality reports the destabilization effect in the case of barley and castor seed,
while non-linear causality results do not fnd such evidence. But in the case of chana,
chilli and pepper, the results of non-linear causality are in agreement with linear
causality results. The application of non-linear causality appears to be appropriate in
this case, as some of its results are at variance with causality results. Like for example,
Table III.
Linear causality
results
Variables
Causality inference
based on HP flter
Causality inference
based on BK flter
Causality inference
based on CF flter
SBARLEY UNEXP_LIQ – – –
UNEXP_LIQ SBARLEY ** ** **
SCASTOR UNEXP_LIQ – – –
UNEXP_LIQ SCASTOR ** ** **
SCHANA UNEXP_LIQ – ** –
UNEXP_LIQ SCHANA – ** **
SCHILLI UNEXP_LIQ – ** –
UNEXP_LIQ SCHILLI ** – **
SPEPPER UNEXP_LIQ – – –
UNEXP_LIQ SPEPPER – ** **
SPOTATO UNEXP_LIQ – – –
UNEXP_LIQ SPOTATO ** – –
SREFINED UNEXP_LIQ – – –
UNEXP_LIQ SREFINED – – –
SSOYA UNEXP_LIQ – – –
UNEXP_LIQ SSOYA – – –
Notes: **Denotes the level of signifcance at 5% and better; SBARLEY, SCASTOR, SCHANA,
SCHILLI, SPEPPER, SPOTATO, SREFINED and SSOYA are denoted as time-varying volatilities of
spot markets of barley, castor seed, chana, chilli, pepper, potato, refned soya and soybean, respectively;
UNEXP_LIQ indicates unexpected liquidity of sample commodities
131
India’s
agriculture
commodity
futures market
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
5
1
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Table IV.
Non-linear Granger
causality results
based on HP flter
P
a
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:
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r
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s
h
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w
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r
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;
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n
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a
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-
v
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r
s
a
JFEP
7,2
132
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
5
1
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Table V.
Non-linear Granger
causality results
based on BK flter
P
a
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s
:
S
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,
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t
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U
N
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Q
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s
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c
o
m
m
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d
i
t
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s
;
*
d
e
n
o
t
e
t
h
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l
e
v
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l
o
f
s
i
g
n
i
f
c
a
n
c
e
a
t
1
0
%
133
India’s
agriculture
commodity
futures market
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
5
1
2
4
J
a
n
u
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r
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6
(
P
T
)
Table VI.
Non-linear Granger
causality results
based on CF flter
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JFEP
7,2
134
D
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2
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1
6
(
P
T
)
Table VII.
Summary of the
linear and non-linear
Granger causality
tests
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135
India’s
agriculture
commodity
futures market
D
o
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n
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o
a
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e
d
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y
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O
N
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2
1
:
5
1
2
4
J
a
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u
a
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y
2
0
1
6
(
P
T
)
the relevance of the destabilization effect in the case of barley and castor seed is not
supported by the results of non-linearity. This strongly justifes the argument of why
important causal relationships should be substantiated by applying the non-linear
causality test. The overall causality results (both linear and non-linear) suggest that for
some commodities, there are visible patterns of causality movements from futures
market liquidity to spot market volatility. This also confrms the presence of suffcient
amount of market players and uninformed investors in the Indian commodity futures
market.
4. Conclusion
In this study, we attempt to answer a fundamental question related with the
introduction of commodity futures in India’s agricultural commodity market. That is,
does futures market trading destabilize the underlying spot market? In the literature, a
very limited amount of studies have shown their interest in examining such a
phenomenon, and considering the investigation of the destabilization hypothesis in the
case of India’s commodity futures markets as crucial, this study provides an alternative
viewpoint to the literature on the futures and spot relationship regarding examination of
the destabilization effect in the case of India’s commodity market. For this purpose, this
study uses the trading volume and spot returns data of eight agricultural commodities.
Following Bessembinder and Seguin (1992), the study uses three different high and
band-pass flters to extract the unexpected components of futures market liquidity. The
conventional EGARCH model has been used to estimate the time-varying volatility of
the spot market. To confrmthe destabilization effect, the study applies linear (Granger
causality) and non-linear (Diks and Panchenko) causality tests. The estimated results of
both causality tests indicate strong causality movement fromfutures market liquidity to
spot market volatility. Though non-linear causality results are at variance with the
linear causality results, both tests report the evidence of destabilization effects in the
case of chana, chilli and pepper. This also confrms the presence of large number of
players and uninformed investors in the Indian commodity futures market. The
fndings of this study are in line with Bessembinder and Seguin (1993) and Sehgal et al.
(2012) in the Indian case. From a policy implication perspective, the causality results
further confrm the destabilization effect of the futures market on the spot market. The
results reveal that there is strong fow of information between the futures and the spot
market. The possible explanations could be provided in two ways. First, the
introduction of large number of commodity futures often creates opportunities for
collusive behaviour as a speculator. This appears to be relevant due to the suspension of
trading of major agricultural commodities in the Indian commodity market. Second
possible explanation could also be due to unorganized spot exchanges. In India, spot
exchanges are relatively disorganized, as they are basically physical in nature and are
not electronically traded markets as futures. Further, there may be several spot markets
known as Mandis that are scattered geographically and are very less integrated.
National spot exchange, which is an electronically traded spot market, is of recent origin
and deals with few commodities. Therefore, from a policy perspective, it can be
suggested that the policy focus of the fnancial economic policy of India should be on
developing an organized spot market. Suspension of trading is not a solution, and there
should be provision of better hedging options and introduction of commodity options in
this regard appears to be an appropriate instrument to gauge the risk. For the welfare of
JFEP
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:
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(
P
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)
farmers, policy measures must be undertaken to link warehousing receipt with trading,
so that there should not be black hoarding of essential commodities. Besides this, there
should be introduction of commodity funds in the Indian commodity market. An
umbrella agency can also be introduced which can invest on behalf of small farmers in
derivatives market. The farmers can pay a small premiumto the agency for its services.
Finally, the major implication of this study is that it reveals the negative impact of
over-dominance of the futures market on spot market volatility. Therefore, it is urgently
required to set-up dedicated fnancial architecture that can gauge and manage the risk of
high spot market volatility. Especially in the context of emerging markets, this study is
important because it outlines numerous measures to contain the spot market volatility.
In the case of India, future studies may further investigate as to why futures trading of
chana, chilli and pepper is having a destabilization effect on spot market volatility.
Notes
1. For further details about suspension of agricultural commodities. Please refer
www.ncdex.com/MarketData/FuturePrices.aspx
2. Ewing and Thompson (2007) provide suffcient overview about these three flters. Interested
readers may source this paper.
3. As Diks and Panchenko (2006) suggest, the value of epsilon depends on the length of the time
series. For our empirical analysis, we use the epsilon value of 1.2.
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India’s
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Corresponding author
Wasim Ahmad can be contacted at: [email protected]
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