An
Empirical Analysis of Price Discovery in Spot and Futures Market of Gold in
India
Author 1:
Name: JOSHY.K.J
Designation: Assistant
Professor
Address: Department
of Economics
Christ University
Bangalore560029
Email: joshy.kj@christuniversity.in
Phone: 08040129718
(Office), 9916990522 (Mobile).
Author 2:
Name: GANESH.L
Designation: Associate
Professor
Address: Institute
of Management
Christ University
Bangalore560029
Email: ganesh.l@christuniversity.in
Phone: 08040129522
(Office), 9900733882 (Mobile).
Abstract
The study aims at analyzing the price discovery
process of gold in Indian commodity market. It also examines the long run
dynamic relationship between spot and futures markets and looks into the
volatility impact of futures price on spot price as well as the volatility
impact of spot price on futures price. The study is based on the secondary data
obtained from the official website of NCDEX during 2008 to 2012. In order to test the hypotheses different econometric tools
are employed. The Johansen cointegration reveals the dynamic relationship
between spot and futures markets. The results of VECM indicate that the
spot market is dominant in the price discovery process. It implies the
information efficiency of spot market. The findings of GARCH model highlight
the significant price volatility impact of both futures and spot market prices
on the returns. The findings are very significant for
investors, marketers and policy makers since it provides a reliable forecast of
spot prices in the future which allow them to effectively manage their risks in
the production and marketing process. The unprecedented uptrend and high
volatility in the gold prices in recent years has very significant social
implications too.
GEL
Classification: G12, G13, G14
Key words: Price
discovery, GARCH, VECM, Price volatility, Market efficiency.
Introduction
The broadest classification of the Indian financial market can be made in
terms of commodity market, stock and security markets and foreign exchange
market. Commodity markets are markets in which primary products are exchanged
on regulated commodity exchanges through standardized contracts. The
transformation of Indian economy with globalization move has made the commodity
markets more structured and systematic. The introduction of gold futures
trading has brought the integration of demand and supply of market
participants. The three major commodity exchanges operating in India are
MultiCommodity Exchange (MCX), National Commodities and Derivatives Exchange
(NCDEX) and National MultiCommodity Exchange of India Ltd. (NMCEIL). The
commodities and the futures market in the country is regulated by Forward
Markets Commission (FMC). India is the largest
consumer of gold in the world accounting for nearly 25 percent of the total
gold consumption which is mainly in the form of jewellery and investment
demand. Indian gold demand is supported by cultural and religious traditions
which are not directly linked to global economic trends. Thus demand remains
steady even during high prices. The steadily rising prices of gold reinforce
the inherent value of gold, an intrinsic part of its desirability and also as a
means of investment.
It
is often said that gold price is a good indicator of the health of the economy.
When the price of gold is on a rise, it indicates adverse health for the
economy. That means the investors flock to gold in order to protect their
investments from either a crisis or inflation. When gold prices drop, that
usually signals the economy is healthy because investors leave gold for other,
more lucrative, investments like bonds or real estate. On the demand side,
factors like rise in investor demand, Jewellery demand, technological demand,
geopolitical concerns, US dollar movement against other currencies, Indian
rupee movement against the US dollar and Central Banks diversifying into
bullion are very significant. On the supply front factors like slowing Central
Bank sales, massive dehedging, gold mine production are important. The main
sources of supply of gold are mine production and recycling of (used) gold.
Price
Discovery and Volatility
Price
discovery is a mechanism to determine the price of an asset or a commodity in
the market place through the interactions of buyers and sellers. It helps in
identifying whether the spot market dominates the futures market or the futures
market dominates the spot market. It involves buyers and sellers arriving at a
transaction price for a specific item at a given time.
In a dynamic market, the price discovery takes place continuously. It helps in
finding the exact price for a commodity or a share based on inputs regarding
specific market information, the demand and supply equilibrium, weather
forecasts, expert views and comments, inflation rates, government policies,
market dynamics like hopes and fears, futures trading and so on. This
transforms in to continuous price discovery mechanism. Spot contract or spot
transaction is a contract of buying or selling a commodity, security or
currency for settlement (payment and delivery) on the spot date, which is
normally two business days after the trade date. The settlement prices are
called spot price. Commodity futures contract refers to an agreement to buy or
sell a set amount of a commodity at a predetermined price and date. Unlike cash
markets a futures market is based on buying (or selling) commodity contracts at
a fixed price for potential physical delivery at some future date.
Volatility measures the variation of
price of a financial instrument over time and is used to
quantify the risk of the financial instrument. It is normally expressed in
annualized terms, and it may either be an absolute number ($5) or a fraction of
the mean (5 percent). It refers to the relative rate
at which the price of a security moves up and down. It is found by calculating
the annualized standard deviation of daily change in price. It can either
be measured through the standard deviation or variance between
returns from that same security or market index. It is influenced by
factors such as investor confidence, direct or implied government intervention,
uncertainty, booming and bursting bubbles and noise trading. There exists a direct relationship between volatility and
risk from a particular security. Higher the volatility, the riskier the
security is. Correspondingly, there is an inverse relationship between
volatility and return from a financial asset since there is a positive
relationship between risk and return. Positive trends are said to be
existed when the futures prices are greater than spot price. Generally
investors take decisions based on positive trends. Negative trends signal
higher amount of volatility and irrational behaviour of investors in the
markets.
Literature
Review
Kushankur.
D and Debasish. M (2012) inferred that unidirectional causality from futures to
spot prices has been observed in the Indian pepper futures market and the
adjustment of innovations or shocks in the futures market is relatively faster
than that of the spot market. Srinivasan P (2012) observed that there is a flow
of information from spot to futures commodity markets and bidirectional
volatility spillover persists between the markets. Sarkar A.K, and Shailesh
Rastogi (2011) found that the introduction of gold and silver futures in India
has increased the depth of the market and has helped in the price discovery in the
spot market but without impacting price volatility. Pantisa Pavabutr and
Piyamas Chaihetphon (2010) examined the price discovery concluding that futures
prices of both standard and mini contracts lead spot price. Vishwanathan Iyer
and Archana Pillai (2010) found evidence for price discovery in the futures
market in five out of six commodities including gold. Biswat Pratap
Chandra (2009) concluded that futures and spot markets are co integrated and
sharing a long run relationship with a causality flow from futures markets to
spot markets indicating information flow from futures to spot markets. R.Salvadi
Easwaran and P. Ramasundaram (2008) indicated that the futures and spot markets
are not integrated in agricultural commodities and the market volume and depth
are not significantly influenced by the return and volatility of futures and
spot markets. Golaka.C.Nath and Tulsi Lingareddy (2012) explored the effect of
the introduction of futures trading on spot prices of pulses and found that
futures volumes had a significant causal impact on spot prices and not vice
versa. Sahi G.S (2006) analysed the impact of introducing futures contracts on
the volatility of the underlying commodities and observed the destabilizing
effect of futures trading on spot prices of commodities. Yang J, Balyeat B, and
Leatham D. J (2005) indicated that an expected and unidirectional increase in
futures trading volume drove cash price volatility up. Fu and Quing (2006)
found the long term equilibrium relationships and significant bidirectional
information flows between spot and futures markets, with futures being
dominant. Zhong, Maosen, Ali F. Darrat and Rafael Otero (2004) concluded that
the futures price index was a useful price discovery vehicle and futures
trading had been a source of instability for the spot market. M.T. Raju and
Kiran Karande (2003) found that the futures market responds fast to deviations
from equilibrium and the price discovery occurs in both the futures and the
spot market.
Significance
of the Study
The
recent global meltdown has witnessed large fluctuations in the prices of most
of the commodities traded across different commodity markets in the world. It
is quite interesting to state that gold is the one of the commodities showing
the greatest resistance in terms of price stability. However the period from
the beginning of 2008 witnessed large fluctuations in the price of gold across
the world. It has made this commodity as one of the most preferred assets of
investment. It further enhances the speculation over the price of gold in
future. Though there has been an uptrend in the price of gold in general, it
has undergone serious price fluctuations over the years. Hence, it is quite
significant to examine the price discovery process during the periods of high
volatility and how the price discovery is affected by volatility in spot and
futures markets. Since the study covers the volatility impact of futures prices
and spot prices on their returns, it has very high significance from the policy
perspective. Besides, it supports all the different parties dealing with
investment decisions pertaining to commodity markets. The price discovery
analysis is of greater significance in the context of faster growth of Indian commodity
market and increasing global integration among financial markets.
Statement
of the Problem
The transformation that happened to the commodity market brought
in a number of changes like the arrival of new forms of trading and inclusion
of new items of commodities which seem to have produced consequences as
relative fluctuations in price, higher margins of profits to the participants
and brokers involved in trading and so on. The price discovery process has been
a matter of wide discussion among researchers and scholars. Though recent global
meltdown witnessed large fluctuations in the prices of most of the commodities,
gold was an exception with a strong
resistance in terms of price stability. However, the period starting from 2008
witnessed large fluctuations in the price of
gold across the world. The same trend has been continuing even now. At present the price of gold has gone up to record
breaking a height which has paved the way for huge speculation in its trading.
Thus, it is very important to observe the nature of relationship between spot
and futures markets. Also it is a key aspect to examine the price discovery process
during price fluctuations in the market. Further, this scenario offers a
significant area of research as to see the volatility effect of spot and
futures market prices on the returns.
Objectives of the Study
To analyse the price discovery process
of gold in Indian commodity market
To examine the long run dynamic
relationship between spot and futures markets
To observe the volatility effect of spot
and futures market prices on returns
Data
and Methodology
This empirical study
is based on the secondary data collected from the official website of National
Commodities Derivatives Exchange of India (NCDEX). The data analysis is carried
out through appropriate statistical and econometric techniques. In order to
make the time series data stationary, the Augmented Dickey Fuller test is used.
The long run dynamic relationship between the futures and spot prices is
examined through the Johansen cointegration technique. The analysis of price
discovery is carried out with the help of Vector Error Correction Model (VECM)
model. Further, the volatility impact of futures and spot prices on the
returns, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH)
model is used.
The
Trend of Spot and Future Price Movements
The
spot and futures price of gold exhibit moderate volatility during the period of
study. It shows that both of them follow the path of movement more or less in
the same direction and magnitude. Thus it can be assumed that there is a good
deal of interlink between these two markets. (Appendix: Figure 1)
Descriptive Statistics
Table
No.1: Descriptive Statistics of Spot and Futures Markets
FUTURE


SPOT


Mean

0.000761

Mean

0.000768

Median

0.000744

Median

0.000509

Maximum

0.029472

Maximum

0.029167

Minimum

0.041514

Minimum

0.030697

Std.
Dev

0.00858

Std.
Dev.

0.008157

Kurtosis

5.505045

Kurtosis

4.513161

JarqueBera

131.1043

JarqueBera

46.13633

Probability

0

Probability

0

The descriptive
statistics above shows that the sample means of spot and futures market returns
are positive and the standard deviation ranges from 0.00858 (spot) to 0.00815
(futures). The values of skewness and excess kurtosis indicate that the
distributions of spot and futures market returns are negatively skewed and
leptokurtic relative to the normal distribution. The JarqueBera test statistic
rejects normality at one per cent level of statistical significance in both
cases.
Stationarity
of Data through Augmented Dickey Fuller (ADF) Test
The
augmented DickeyFuller test (ADF) is the extension of the DickeyFuller (DF)
test that removes all the structural effects (autocorrelation) in the time
series data and then tests using the same procedure. ADF is a test for a unit
root in a time series sample. It is an augmented version of the Dickey–Fuller
test for a larger and more complicated set of time series models. The augmented
Dickey–Fuller (ADF) statistic, used in the test, is a negative number. The more
negative it is, the stronger the rejections of the hypothesis that there is a
unit root at some level of confidence. Regressing nonstationary variables on
each other leads to potentially misleading inferences about the estimated
parameters and the degree of association. Therefore, before testing for cointegration,
the order of integration of price series must be determined. To identify
whether the series are I (1), the augmented DickeyFuller (ADF) test
(Dickey and Fuller, 1979) is employed.
Augmented
DickeyFuller Regression:
Δ
X_{t} = ρ0 + ρX_{t1 }+ δiΔX_{ti
}+ εt (1)
Where X_{t}= the log
price series, ρ0 = a constant or drift, ρ = (α1),
Δ = the first difference operator, εt= a pure white noise
error term and ΔX_{t1} = (X_{t1 }X_{t2} ), ΔX_{t2
}= ΔX_{t2}  ΔX_{t3}, etc.,
i=1 to n is number of lagged difference terms which is determined
empirically to remove any autocorrelation in error term εt. The
null hypothesis is to test that ρ =0. If ρ =0, then α =1, that
is, there is a unit root, meaning the time series under consideration is nonstationary.
But for stationarity, α must be less than one and hence ρ must be
negative.
Augmented DickeyFuller regression:
t
Where=
the log price series, ρ0 = a constant or drift, ρ =
(α1), Δ = the first difference operator, ε=
a pure white noise error term and ΔXt_{1} = (X_{t1}
X_{t2 }), ΔX_{t2} = ΔX_{t2 }
ΔX_{t3}, and so on, i=1 to n is number
of lagged difference terms which is determined empirically to remove any
autocorrelation in error term ε_{t}_{.} The null
hypothesis is to test that ρ =0. If ρ =0, then α =1, that is,
there is a unit root, meaning the time series under consideration is nonstationary.
But for stationarity, α must be less than one and hence ρ must be
negative.
Table No.2: ADF Test
Result
Sl. No.

Variables

Intercept

Intercept & trend

I. Level

1

Future

2.8661
(0.9297)

3.417190
(0.4776)

2

Spot

2.8661
(0.8199)

3.4172
(0.1526)

II. First Difference

1

Future

3.417190*
0

3.417737*
0

2

Spot

2.866401*
(0.0000)

3.417737*
0

Note:
* – indicates significance at one per cent level.
The
results reveal that both the data series are not stationary at the level but
stationary after the first difference. It reveals that the time series has
random walk effect which means there is information asymmetry in the market.
It signifies that the market is a weakly efficient one. There are at least
two advantages when using the first difference data series to explain the
impulse response function. First, it focuses more on the increase or the
decrease trend rather than the actual change. Second, it captures more
information on the shocks of gold prices, because the first difference data
shows the changes in the past two days while the level data shows the changes
in one day in impulse response function.
The long run dynamics between spot and
futures markets of gold
The
Johansen cointegration test examines the cointegration
of several time series. It examines the number of
independent linear combinations (k) for an m time series variables set that
yields a stationary process. It permits more than one
cointegrating relationship. It has two forms, with trace or
with maximum eigen value. It assumes the presence of common nonstationary
(that is)
processes underlying the input time series variables.

The
number of independent linear combinations (k) is related to the assumed number
of common nonstationary underlying processes (p) as .
The three plausible outcomes are given below.
1.
k=0,
In
this case, time series variables are not cointegrated.
2.
0
< k < m, 0 < p < m. In this case, the time series variables are cointegrated.
3.
k=m,
All
timeseries variables are stationary to
start with. Cointegration is not relevant here.
Table No.3: Johansen Cointegration Test
Hypothesized


Trace

0.05


No. of
CE(s)

Eigen value

Statistic

Critical
Value

Prob.**

None *

0.253228

232.2015

15.49471

0.0001

At most 1
*

0.180382

94.08767

3.841466

0.1832

Trace
test indicates 2 cointegrating equations at the 0.05 level

*
denotes rejection of the hypothesis at the 0.05 level

**MacKinnonHaugMichelis
(1999) pvalues


Maximum Eigen value and Trace test
statistics indicate the presence of one cointegrating vector between the spot and
futures market prices at five per cent level. This shows that spot and futures
prices of gold market are cointegrated and there exists utmost one
cointegrating relationship between them. In other words, spot and futures
prices share common longrun information.
Analysis of Price Discovery Process
The price discovery process is analysed
through a vector error correction model (VECM). It adds error correction
features to a multifactor model such as a Vector Autoregression (VAR) model.
In a frictionless market, security prices on the same underlying asset price
should be perfectly correlated and that no leadlag relationship would exist. When
the price of one commodity leads the price of another commodity, we say that
price is discovered in the first commodity. It implies that the first commodity
responds fast to new information. Moreover, the price should be cointegrated,
meaning despite short term deviations from each other, market forces will
restore them in the longrun because the random walk component in their
efficient prices are driven by the same fundamentals.
Engle and Granger (1987) showed the VECM
representation for two cointegrated series as given below:
Where Δ is the differencing
operator, such that
Table No.4: The Vector Error Correction
Model
Cointegrating
Variables

Cointegrating
equation


S(1)

1.000000


F(1)

0.977552



(0.01316)



[74.3037]


C

428.2496


Error
Correction:

D(S)

D(F)

Cointegrating
Equation 1

0.124142

0.024935


(0.03551)

(0.04322)


[3.49626]

[0.57696]

D(S(1))

0.600438

0.202733


(0.05984)

(0.07284)


[10.0333]

[
2.78327]

D(S(2))

0.224046

0.150587


(0.05113)

(0.06223)


[4.38201]

[
2.41982]

D(F(1))

0.773375

0.107667


(0.05439)

(0.06620)


[
14.2192]

[1.62640]

D(F(2))

0.239519

0.267761


(0.06154)

(0.07490)


[
3.89217]

[3.57486]

C

11.66847

9.845516


(7.65916)

(9.32229)


[
1.52347]

[
1.05613]

In the vector error
correction estimates given above, the probability value is highly significant
at 1 percent level. Also the cointegrating equation value is less than one for
futures market. It signifies that spot market is dominant in the price
discovery process. It is a clear indication that spot market of gold is
information efficient in India. This could be attributed to the efficient
transmission of information among traders in spot market as they tend to trade
more frequently. The coefficients (DS and DF) of the error correction terms
provide some insight into the adjustment process of spot and futures prices towards
equilibrium in all types of contracts which means that the error correction
terms represent a meanreverting price process.
Checking the Volatility Impact
The volatility
impact refers to the impact of spot and futures price fluctuations on spot and
futures market returns. Financial time series often exhibit the phenomenon of
volatility clustering, that is periods in which their prices show wide swings
for an extended time period followed by periods in which there is relative
calm. One of the important characteristics of financial time series is that in
their level form they are random walks, that is they are nonstationary and in
the first difference form they are generally stationary. But these first
differences often exhibit wide swings, or volatility, suggesting that the
variance of financial time series varies over time. In order to model these
‘varying variance’ the Genaralised Autoregressive Conditional
Heteroscedasticity (GARCH) model is used. The GARCH equation can be written as
follows:
When dealing with time series data, heteroscedasticity implies to test for ARCH errors
and GARCH errors.
Table No.5: The GARCH (1,1) Result
Variable

Mean
Equation

Variance
Equation


C
AR(1)

µ α β


10.087780.997822

0.000 0.045848 0.908893


10.24164 0.998267

0.000 0.053470.914588

The table
clearly shows the GARCH effect in both spot and futures market. The coefficient
value is significant at 5% level. Since the probability value is zero, the null
hypothesis is rejected which signifies the market is highly volatile. The value
of variance of residual and the variance of GARCH equation clearly indicate
that the volatility in independent variable (spot market) influences the
dependent variable (futures market). It implies that both futures as well as
spot do have significant impact in the price volatility of gold in India.
Policy
Implications and Suggestions
A major reason for the dominance of spot
market in India can be the unprecedented volatility in the price of gold in
recent years. This can be well connected with important investment premises. Investors
and speculators are normally guided by expectations about future returns. The futures
price will be determined by current price level along with future expectations.
Investors may use the spot market to discover the prices applicable to futures
contracts, which may be transmitted to the futures market. Greater efficiency
of price discovery of spot market may help investors with more efficient
strategies for hedging and speculating over the current price fluctuations. The
insights from this would help the brokers and middlemen in reframing their
future moves. It also guides in providing various customer services such as
investment advice, consultancy and so on. For marketers, it provides a reliable
forecast of spot prices in the future to effectively manage their risks in the
production or marketing. They can evolve their long run strategies in the
context of changing cost conditions and marketing strategies. Also it helps in
evolving various strategies of hedging and arbitraging. The insights of the
study can provide a strong support to international exporters and importers in
evolving and modifying the strategies related to international operations.
From the policy front, the unprecedented
uptrend in the price of gold and high volatility in recent years provides room
for some important social implications. In India, jewellery demand plays a key role
in the social life of people which in turn affects the consumption and saving
of weaker sections of the society. It reminds the need of government
intervention in safeguarding the interests of weaker sections. Further, a
better understanding of the interconnectedness of the markets is useful for
policy makers. Since the cointegration results uphold the long run dynamic
relationship between futures and spot markets, any policy measure that affects
the spot or futures market in particular will have a long run impact on the
other. It implies that any policy change, directly or indirectly affects the
financial market in general, will have its impact on both the markets. From the
government policy point, it clearly signals for a better alternative to market
intervention such as imposing price stabilization policies. It also indicates
the need of proper control over insiders and noise traders from manipulating
the commodity prices.
The study
offers room for further research by extending the period under consideration so
that clubbing the periods of normal and abnormal price increase will be
possible which will help in reading the long run spotfutures relationship in a
better way. It is also possible to look into other macroeconomic variables,
such as exchange rate, changes in price level, changes in government policies
and external sector influences on the volatility of gold prices. Since the
jewellery demand is very huge, it is very apt to study the social implications
or consequences of this abnormal price rise and volatility. Further, it is also
interesting to look at some other commodities for which there are similar
trends and see whether the results are in line with the results of gold.
References
Day
Kushankur, & Maitra Debasish. (2012). Price discovery in Indian commodity
futures market: An empirical exercise. International Journal of Trade and
Global Markets, 5(1), 6887.
Golaka.C.Nath, & Tulsi Lingareddy.
(2012). Impact of futures trading on commodity prices.Economic and Political
Weekly, 43(3),1823.
Srinivasan, P. (2012). Price discovery and
volatility spillovers in Indian spotfutures commodity market.The IUP
Journal of Behavioral Finance, 9(1), 2138.
Sarkar, A. K.,
&ShaileshRastogi. (2011). Impact of gold and silver futures on the spot
rate volatility: An Indian perspective. Nice Journal of Business, 6,
2328.
Pantisa Pavabutr., & Piyamas Chaihetphon. (2010).
Price discovery in Indian gold futures market. Journal of Economics and
Finance, 5,455467.
VishwanathanIyer., & Archana Pillai. (2010). Price
discovery and convergence in the Indian commodities market. Indian Growth
and Development Review, 3(1), 5361.
Biswat Pratap Chandra. (2009). Price discovery
in futures and spot commodity markets in India. AlBarkaat Journal of
Finance & Management, 1(1), 2144.
Salvadi Easwaran, R. & Ramasundaram, P. (2008).
Whether commodity futures market in agriculture is efficient in price
discovery? An econometric analysis. Agricultural Economics Research Review,
21, 337344.
Sahi, G. S. (2006). Influence of
commodity derivatives on volatility of underlying; Working Paper, Indian
Institute of Management (Lucknow), India.
Yang, J., Balyeat, B., & Leatham, D. J. (2005).
Futures trading activity and commodity cash price volatility. Journal of
Business Finance and Accounting, 32, 297323.
Zhong, Maosen, Ali F. Darrat.,
& Rafael Otero. (2004). Price discovery and volatility spillovers in index
futures markets: Some evidence from Mexico. Journal of Banking and Finance,
28, 3037 3054.
M.
T. Raju., & Kiran Karande. (2003). Price discovery and volatility on NSE
futures market. NSE Working Paper Series, No.7.
Damodar. N. Gujarati, Dawn. C. Porter & Sangeeta
Gunasekar. (2012). Basic Econometrics. Fifth Edition, New York: Tata
McGrawHill.
Christopher, D. (2007). Introduction to
Econometrics. Third Edition, New York: Oxford University Press.
Appendix:
Figure 1: The
spot and future price movement: