This article examines empirically the impact of futures on spot
market volatility in India. A generalised auto regression conditional
hetroscedasticity (GARCH) model is selected to measure the spot return
volatility in the present stud),. The study also employed the vector
autoregression (VAR) model to investigate the relationship between spot
return volatility and futures market. The daily data from 12th, June,
2000 through 28th, December, 2006 has been considered for the analysis
which has been retrieved from National Stock Exchange (NSE). The results
indicate that the volatility in the spot market has been declined after
the introduction of futures market.
Since the introduction of derivatives market in India almost six
years ago, the appreciable spread of derivatives trading activity makes
a great interest of academic research on the impact of derivatives
trading on the underlying market. The trading in futures markets
commenced from 12, June, 2000, which is an important instruments of
derivatives. It provides the function of price discovery to help market
efficiency and also transfers risk through hedging. The introduction of
futures market makes a significant influence on spot market. The
movements of the prices of spot market have been hugely influenced by
the speculation, hedging and arbitrage activity of futures markets.
Therefore, research on the relationship between futures trading and spot
market volatility has been important issues to generate for
academicians, regulators and investors alike.
From a theoretical stand of view, the impact of futures trading
activity on the volatility of the underlying market provides quite mixed
evidence. One view is that derivatives securities increase volatility in
the spot market due to more highly leveraged and speculative
participants in the futures market. Conversely, the derivatives markets
reduce spot market volatility by providing low cost contingent
strategies and enabling investors to minimise portfolio risk by
transferring speculation from spot markets to futures markets. The low
margin, low transaction costs and the stabilised contracts and trading
conditions attract risk taking speculators to futures. Hence, futures
are expected to have stabilising influence as it attracts more informed
traders to the spot market and making it more liquid. Hence, it is less
volatile. Cox (1976) defines that the transaction costs in the
derivatives market are lower than those in the spot market; new
information may be transmitted to the spot market more quickly.
After the theoretical discussion, let us examine the earlier
literature pertaining to the study areas which will be immensely useful
to identify the gaps of the study. The study by Edward (1988), Harris
(1989), Antoniou and Holms (1995), Kyriacou and Sarno (1999), Gulen and
Mayhen (2000) and Vipul (2006) supported that the volatility of spot
market has decreased after the introduction of futures trading. Besides,
the study concluded that due to the higher degree of leverage, futures
markets tend to attract uniformed speculative investors and thus
destabilise cash markets by increasing volatility. Yu (2001) and IIIneca
and Lafuente (2003) did not get any significant changes in the
volatility on spot market and it is attributed to macroeconomic factors
and structures of the markets.
However, James (1993), Perieli and Koutomos (1997), Tenmozhi
(2001), Raju & Karande (2003) and Nath (2003), Bae, et al. (2004)
found that the volatility of spot market has been declined after
introduction of futures markets. It has been pointed out that futures
markets increase the overall market depth and informativeness. These are
important for price discovery, allow the transfer risk and it reduces
However, most of the studies mentioned the above were related to
the international level. But its relevance to a developing economy like
India is limited. At the national level, the introduction of S&P CNX
Nifty Index futures market started from 2000. It is important to examine
the spot market volatility after the introduction of futures market in
India. Most of the studies have examined the futures market by comparing
the unconditional variance of returns before and after the introduction
of futures market. The present study investigates the relationship
between spot market volatility and futures trading activities (FTA) such
as: open interest and volume by considering post-futures period. Though,
open interest and volumes are the important variables for futures
market, it can give well clarification on impact on spot market
volatility. For finding spot volatility, the study can employ GARCH
techniques because GARCH is expected to explain sufficiently the time
varying volatility of spot market. Also, vector autorgression (VAR) can
be taken for this analysis which can investigate the relationship
between spot volatility and FTA. By this context, it is worth mentioning
that VAR model can better reveal the underlying process and that
simultaneous equation model (SEM) can be misleading and may yield
unreliable inferences (Chan and Chung, 1995).
On the above background, the present article investigates the spot
market volatility after the introduction of futures market in India. The
rest of article is as follows: After the brief introduction of the
subject, Section-II presents the data and methodology of the study.
Empirical results and discussions are presented in Section-III. Finally,
concluding remarks are presented in Section IV.
All the required data information for the study has been retrieved
from the National Stock Exchange (NSE) website. Daily closing value of
the S&P CNX Nifty spot index and data on futures volume and open
interest have been employed for the study. The data on futures are
collected for near-month contracts as they are most heavily traded. The
study has been considered daily data from 12th, June, 2000 through 28th,
December, 2006. Returns are calculated as log of ratio of present
day's price to previous day's price. The measure for futures
trading activity is denoted by FTA. Therefore, the daily volume of
futures is standardised by open interest. [FTA.sub.t] are constructed as
[FTA.sub.t] = V[(FUT).sub.t] / OI[(FUT).sub.t] (1)
Where, V (FUT) and OI (FUT) denote daily volume and open interest
A generalised auto regression conditional heteroscedasticity
(GARCH) model is selected as the most adequate measure of spot market
volatility in the present study. Hence, a natural way to capture the
time varying nature of volatility is to model the conditional variance
as a GARCH process (Engle, 1982 and Bollerslev, 1986). A volatility
proxy is constructed using the conditional variance of returns and
[h.sub.t] retrieved from the maximum likelihood estimation of a GARCH
(1, 1) of the form:
[R.sub.t] = [[beta].sub.0] + [[beta].sub.1] [R.sub.t-1] +
[[epsilon].sub.t], [[epsilon].sub.t] | ([[epsilon].sub.t-1],
[[epsilon].sub.t-2], ....) ~ N (o, [h.sub.t]) (2)
[h.sub.t] = ([[alpha].sub.o] + [[alpha].sub.1]
[[epsilon].sup.2.sub.t-1] + [[alpha].sub.2] [h.sub.t-1] (3)
Where, equation (2) and (3) denote the conditional mean equation
and the conditional variance equation respectively; [[alpha].sub.1] and
[[alpha].sub.2] are nonnegative, and [[epsilon].sub.1] is an error term.
The dynamic relationship between the spot return volatility and
futures trading activity (FTA) is examined in the framework of a vector
auto-regression (VAR) models for volatility and futures trading. Before
running the VAR model, it is necessary to test the stationary of the
The augmented Dickey-Fuller (1979) and Phillips-Perron (1988) test
are employed to infer the stationary of the series. If the variables are
stationary, it is not to proceed since standard time-series methods
apply to stationary variables. All the variables included in the model
should be stationary for the VAR estimation.
Then the study employed the vector autoregression (VAR) model to
investigate the relationship between spot return volatility and FTAs.
VAR model is the most appropriate model to examine the study in that all
the variables are considered to be endogenous. However, each endogenous
variable is explained by its lagged and the lagged values of all other
endogenous variables included in the model. Usually, there are no
exogenous variables in the model. Thus, by avoiding the imposition of
priori restrictions on the model, the VAR adds significantly to
flexibility of the model. In other words, a VAR system consists of a set
of regression equations, each of which has an adjustment mechanism such
that even small changes in one variable component in the system may be
accounted automatically by possible adjustments in the rest of the
variables. Furthermore, by incorporating the lagged terra of the
variables, the VAR becomes useful in capturing the empirical
regularities embedded in the data. Now the model can be written as:
[[sigma].sub.t] = [[alpha].sub.1] + [n.summation over (i=1)]
[[beta].sub.11] [[sigma].sub.t-i] + [n.summation over (i=1)]
[[beta].sub.12] FT [A.sub.t-i] + [[epsilon].sub.1t] (4)
[FTA.sub.t-] = [[alpha].sub.2] + [n.summation over (i=1)]
[[beta].sub.21] [[sigma].sub.t-i] + [n.summation over (i=1)]
[[beta].sub.22] FT [A.sub.t-i] + [[epsilon].sub.2t] (5)
where [sigma] denotes the spot market volatility measure employed;
[[beta].sub.11], [[beta].sub.12], [[beta].sub.21], and [[beta].sub.22]
are parameters; n is chosen on standard statistical grounds; and
[[epsilon].sub.1t] and [[epsilon].sub.2t] are the stochastic error term.
The lagged values of the right-hand side variables in the equation
(4) and (5) of VAR are estimated by ordinary least squares (OLS). It
executes Granger (1969) causality tests by testing for zero restrictions
on subsets of lagged parameters in each equation of the VAR in order to
investigate the relationship between spot return and FTAs. The lag
length of n is selected using the multi-variate generalizations of
akaike information criteria (AIC) and schwarz's criteria (SC) due
to fact that the results of the test are quite sensitive to the lag
III. EMPIRICAL RESULTS AND DISCUSSIONS
The volatility of Nifty spot returns are estimated by GARCH (1, 1)
model, where volatility is modeled as a GARCH (1, 1) process. These are
considered as statistically reliable and consistent. The advantage of a
GARCH model is that it captures the tendency in financial data for
volatility clustering. The GARCH (1, 1) estimated series of Nifty
returns volatility have been used for further analysis.
The tests of stationary developed by Dickey and Fuller (1979),
Phillips and Perron (1988) have been performed for the series. Before
conducting the ADF and PP tests, the optimal lag number of each
differenced series should be tested by using the Akaike's
Information Criteria (AIC) and Schwarz Criteria (SC). According to AIC
and SC, five lags for the ADF test, and seven lags for the PP test have
been selected for spot return volatility and FTAs.
The unit root test was conducted for Nifty spot return volatility
and futures trading activities (FTAs) for near month contracts
separately for determining stationary. The estimates of the ADF and PP
tests at the levels of the series are given in table (1) and it reveals
that Nifty spot return volatility and futures trading activities (FTA)
are stationary at their levels at one percentage. Therefore, the
stationary of the series in level justify the use of VAR model.
The VAR estimation results are presented in table (2). The results
reveal that the futures trading activities (FTAs) influence to the Nifty
spot volatility returns. In the three and four lags of FTA, the
coefficient is statistically significant at ten percent and five percent
level simultaneously. The significantly negative estimated coefficients
on the lag values of FTA suggest that greater futures trading in
previous days reduce volatility of Nifty spot return. The high F-stat of
the table signifies the overall significance of VAR model.
The paper examined the relationship between Nifty spot volatility
and futures trading activity. It empirically evaluated the impact of
introduction of futures trading on spot market volatility. The results
of the empirical analysis provide strong evidence that spot market
volatility is time-varying and well characterized by a GARCH process.
The relationship between the spot market volatility and futures market
are determined in a vector autoregression (VAR) models. The results
indicate that the volatility in the spot market has been declined after
the introduction of futures market which also supported the earlier
study by Indian authors. The results concluded that futures market
increase the over market depth, increased liquidity and informativeness.
It also plays an important function of price discovery and allows
transfer risk through hedging. Therefore, it generates to reduce spot
The result found that volatility has been reduced after the
introduction of Index futures. The following implications may be
suggested to further improve efficiency, liquidity and reduce
volatility: (a) the more number of futures contracts on the stock
indices can be introduced (b) more institutional participation is needed
in the total turnover to enhance in derivatives participants and to
improve the derivatives market and (c) right now institutional
participation appear to be negligible in the total turnover, therefore,
efforts should be made to enhance their role in derivations
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KAILASH CHANDRA PRADHAN
National Council of Applied Economic Research (NCAER), New Delhi
K. SHAM BHAT
Department of Economics, Pondicherry University, Pondicherry
Unit Root Test
Constraint ADF PP
Panel-A: Futures Trading
Intercept and trend -7.8298 * -15.6694 *
Intercept -5.7542 * -11.5800 *
Panel-B: Spot Market Volatility
Intercept and trend -10.1335 * -9.7798 *
Intercept -10.1356 * -9.7821 *
Note: * Significant at one percent level.
Vector Autoregression (VAR) Models
VAR estimation results
(Volatility measure is GARCH)
[[sigma].sub.t-1] 1.233099 * -577.2791
[[sigma].sub.t-2] -0.433648 * 2352.105
[[sigma].sub.t-3] 0.080898 ** -1563.335
[[sigma].sub.t-4] -0.002452 472.487
[FTA.sub.t-1] 2.71E-07 0.664910 *
[FTA.sub.t-2] 1.51E-07 0.025870
[FTA.sub.t-3] -9.21E-07 *** 0.018829
[FTA.sub.t-4] -9.19E-07 ** 0.182534 *
3.84E-06 * 0.220337 *
F-statistic 1064.904 * 482.5310*
Note: t-Statistic in parenthesis
* Null rejected at one percent level
** Null rejected at five percent level
*** Null rejected at ten percent level