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Market Efficiency and Volatility
Spill over in Spot & Futures Currency Market (w.r.t $ and
Dr.M.Sriram^{1}, Dr.M.Senthil^{2}
^{1}Associate Professor, D.J. Academy for Managerial Excellence, Coimbatore
sriram.m@djacademy.ac.in
^{2 }Dean (College Development Council), Alagappa University, Karaikudi
drmsenthil@gmail.com
Abstract The present study has analysed the long term
relationship between spot and future prices of currency (dollar) for the period
of study between 01/06/2009 and 10/07/2013. The spot and future prices of the currency is found to have long
term relationship which is supported by the existence of an error correction
mechanism called arbitrage. The error correction mechanism restores the
equilibrium relationship whenever disequilibrium takes place between the two
markets. It is the spot price which
corrects the disequilibrium in the market. The study also finds the presence of
unidirectional causality in the currency market wherein the spot causes the
future. Using Impulse response function,it
is found that significant and higher response of future price to the spot price
shocks of dollar and also Volatility spill over was from the spot price to the
future price whereas, there was no evidence of volatility spill over from
future to spot price.
Keywords arbitrage; volatility spill over; error correction
mechanism; impulse response.
Globalization and financial sector reforms in India have ushered in a
sea change in the financial architecture of the economy. In the contemporary
scenario, the activities in the financial markets and their relationships with
the real sector have assumed significant importance. Since the inception of the
financial sector reforms in the beginning of 1990’s through liberalisation,
privatisation and globalisation, the implementation of various reform measures have
brought in a dramatic change in the functioning of the financial sector of the
economy. Floating exchange rate was implemented in India in 1991 and the
implementation of the same facilitated greater volume of trade thus resulting
in high volatility in equity as well as forex market. This increased the
markets’ exposure to economic and financial risks. The regulator’s decision to
permit Foreign Institutional Investors to invest in stock market and permitting
FDI (Foreign Direct Investments) gave a boost to the inflow of foreign exchange
and increased volatility in the stock markets. Also, the movement away from pegged exchange rate
regime to partially floated in 1992 and fully floated in 1993 was instrumental
in developing a marketdetermined exchange rate of the rupee and was a
significant step in the progress towards total current account convertibility.
The country is also moving towards full convertibility of rupee on capital
account and it is imperative that a foreign exchange derivative market is set up
for better price discovery of the underlying asset (i.e, currency) and curb
volatility in the movement of a pair of currency. Currency futures trading in
INRUS$ started on August 29, 2008. Till January2010, exchange rate futures
were available only for US $ visàvis Indian Rupee. Exchangetraded currency
futures have now been expanded to the euro, pound and yen pairing. At the time
of introduction of currency futures in India, it was thought that the currency
futures market in India would make a notable contribution towards improving the
menu of options available for currency risk management. International
experience of the emerging markets with the introduction of currency futures is
a mixed one. In several cases, the volatility is found to be reduced following
the constitution of currency futures market, though empirical evidence to the
contrary also exists. The transaction volumes in currency futures in these
countries have remained too small to put any significant upward pressure on
exchange rate volatility. Also, there is no clear evidence to prove that
futures contracts traded on exchanges result in increased volatility in the
prices for the underlying asset. The rupee has
already depreciated by 20% in the last one year thanks to the policies of the
U.S in terms of easing out quantitative restrictions thus making it attractive
for the investors’ to invest in the U.S market. The volume and the open
interest position in the currency derivative market have been low since its
introduction in India and it has become important to understand the
relationship between spot and futures market.
The
following are the review of earlier studies
Chatrath et al., (1996) explicitly examined the relationship between level
of currency futures trading and the volatility in the spot rates of the British
pound, Canadian dollar, Japanese yen, Swiss franc and Deutsche mark. The
researchers provide strong evidence on the causality between futures trading
volume exchange rate volatility, as it is found out that the trading activity
in futures has a positive impact on conditional volatility in the exchange rate
changes, with a weaker feedback from the exchange rate fluctuations to the
futures volatility.
Bhargava et al., (2007) focused on trading in futures on four currencies over
the time period of 19822000. The authors found evidence that day traders and
speculators destabilize the market for futures. According to them, it is
inconclusive whether hedgers stabilize or destabilize the market
Gallo Giampiero et al., (2008) examined the volatility spillovers,
interdependence and co – movements between markets and found that volatility in
one market reacts to innovations in other markets as a result of financial
integration. The research study employed Multi – Chain Markov Switching Model
(MCMS, Otarnto 2005) to study the market characterizations by relying on the
definitions of spillover, interdependence and co – movements. The model is
estimated on the weekly high – low range of five Asian markets assuming a central
(but not necessarily dominant) role for Hong Kong. The results showed plausible
market characterizations over the long run with a spillover from Hong Kong to
Korea and Thailand, interdependence with Malaysia and co – movement with
Singapore.
Rosenberg et al.,
(2009) studied the relationship between intraday
exchange rate movements in cash and futures are examined during 1996 and 2006
in Chicago Mercantile Exchange. Interestingly, in 1996, futures price changes
mostly led those in the cash market; while in 2006, the direction of influence
was largely reversed. The authors believe this change primarily reflects
increased transparency of the cash FX market.
Guru (2010). Exchange traded currency
futures in the Indian RupeeDollar currency pair, have recently been introduced
in the country last year. The paper empirically tested the impact of currency
futures trading on volatility and returns of underlying spot exchange rates.
The informational advantage in exchange traded currency futures contracts
relative to OTC contract is also tested. Results indicate that both speculative
and hedging activities in the futures market for currency have no influence on
the volatility in the underlying exchange markets. The returns in futures
markets are seen to be driving the returns in spot markets, indicating the
information advantage of the futures markets. Further, results indicate that
the information content of futures markets is higher than that of forward
markets.
Sakthivel
et al., (2010) investigated the impact of
introduction of index futures trading on volatility of Nifty. The study
employed GARCH (1, 1) model to capture the time varying nature of the
volatility and volatility clustering phenomena using daily closing price of the
Nifty. The results showed that after introduction of the futures trading
reduced stock market volatility, due to increase market efficiency. The study
also examined futures trading changes structure of spot market volatility using
GARCH model. The study observed that there is a changes structure in spot
market volatility after introduction futures trading. Specifically, there is
evidence that the increased impact on recent news and reduced effect of the
uncertainty originating from the old news. The study concluded that the
introduction of the derivatives contract improved the market efficiency and
reduced the asymmetric information.
Debasish
S. S. (2011) investigated the effect of futures
trading on the volatility and operating efficiency of the underlying Indian
stock market by taking a sample of selected individual stocks. The study
examined whether the index futures trading in India has caused a significant
change in spot price volatility of the underlying stocks and how the index
futures trading has affected market/trading efficiency in the Indian futures
and stock markets. The study employed event study approach to test whether the
introduction of index futures trading has resulted in significant change in
volatility and efficiency of the stock returns. The study indicated that the
introduction of Nifty index futures trading in India is associated with both
reduction in spot price volatility and reduced trading efficiency in the
underlying stock market. The study suggested that there is a tradeoff between
gains and costs associated with the introduction of derivatives trading at least
on a shortterm perspective.
Nair. (2011) examined
the impact of introduction of derivatives trading on the underlying spot market
volatility of seventy two scrips using symmetric and asymmetric GARCH methods.
The research study indicated the existence of asymmetric response to new
information. Further, the results indicate an increase in the efficiency of
processing new information. Overall, the study found that there is a strong
evidence of a reduction of volatility after the introduction of derivatives
trading.
Sharma (2011).This
paper has focussed at the relation between volatility in the exchange rate in
the spot market and trading activity in the currency futures. The results show
that there is a twoway causality between the volatility in the spot exchange
rate and the trading activity in the currency futures market.
Sahu (2012)
in his paper aimed at examining the impact of currency futures on exchange rate
volatility of EURO after the introduction of currency futures trading in India.
The data used in this paper comprises of daily exchange rate of EURO in terms
of Indian rupees for the sample period January 02, 2008 to December 31, 2011.
To explore the time series properties, Unit Root Test and ARCH LM test have
been employed and to study the impact on underlying volatility, GJR GARCH (1,
1) model has been employed. The results indicate that the introduction of
currency futures trading has had no impact on the spot exchange rate volatility
of the foreign exchange market in India. Further, the results are also
indicative of the fact that the importance of recent news on spot market
volatility has increased and the persistence effect of old news has declined
with the introduction of currency futures trading.
The
objective of the study is to analyse the leadlag relationship between the
level of spot and futures trading in the currency market. The study also
analyses the volatility spill over effect between spot and futures market.
4. DATA AND METHODOLOGY
The
study is based on the secondary data. The data for spot and futures exchange
rate details are collected from www.mcxindia.com. The period of study is from
01062009 to 01072013.
4.1
Statistical Tools
The following statistical tools
are employed for the present study. The tests namely JB test, , Unit root test,
Johansen’s Co integration test, Block Exogeneity Test(Wald Test) and GARCH(1,1)
test were conducted using Eviews
software (version 7). A brief explanation about various statistical tools are given
below
4.2 Normality
Test
The JarqueBera (JB)
test [Gujarati (2003)] is used to test whether stock returns and exchange rates
individually follow the normal probability distribution. The JB test of
normality is an asymptotic, or largesample, test. This test computes the
skewness and kurtosis measures and uses the following test statistic:
JB = n [S2 /6 + (K3)2 /24]
Where n = sample size, S = skewness
coefficient, and K = kurtosis coefficient. For a normally distributed variable,
S = 0 and K = 3. Therefore, the JB test of normality is a test of the joint
hypothesis that S and K are 0 and 3 respectively.
To
analyse the pattern of distribution of data skeweness and kurtosis have been
calculated. Zero skewness implies symmetry in the distribution whereas kurtosis
indicates the extent to which probability is concentrated in the centre and
especially at the tail of the distribution. Kurtosis measures the peakedness of
a distribution relative to the normal distribution. A distribution with equal
kurtosis as normal distribution is called ‘mesokurtic’; a distribution with
small tails is called ‘platykurtic’ and a distribution with a large tail is
called ‘leptokurtic’.
4.3 Unit
Root Test (Stationarity Test)
Empirical work based on time series data
assumes that the underlying time series is stationary. Broadly speaking a data
series is said to be stationary if its mean and variance are constant
(nonchanging) over time and the value of covariance between two time periods
depends only on the distance or lag between the two time periods and not on the
actual time at which the covariance is computed [Gujarati (2003)].A unit root
test has been applied to check whether a series is stationary or not.
Stationarity condition has been tested using Augmented Dickey Fuller (ADF).
4.4 Augmented
Dickey–Fuller (ADF) Test
Augmented DickeyFuller (ADF) test has been
carried out which is the modified version of DickeyFuller (DF) test. ADF makes
a parametric correction in the original DF test for higherorder correlation by
assuming that the series follows an AR (p) process. The ADF approach controls
for higherorder correlation by adding lagged difference terms of the dependent
variable to the righthand side of the regression. The Augmented DickeyFuller
test specification used here is as follows:
Yt = b_{0} + β∆ Yt1 + μ1 ∆Yt1 + μ2
∆Yt2 +….. + μp ∆Ytp + et
Yt represents time series to be tested, b_{0}
is the intercept term, β is the coefficient of interest in the unit root
test, μi is the parameter of the augmented lagged first difference of Yt
to represent the pthorder autoregressive process, and et is the white noise
error term.
4.5
The Johansen’s Co
integration test is used to test the presence of long term equilibrium
relationship between the spot and future market of the currencies. The Vector
Error Correction Model (VECM) is used to analyse the whether error correction
mechanism takes place if some disturbance comes in the equilibrium
relationship. The Block Exogeneity test is applied to analyse the short term
causality relationship between spot and futures market of the currencies.
4.6
The volatility spillover between the
spot and future prices of the currency is analyzed using GARCH (1,1) method.
GARCH (1,1) technique were developed independently by Bollerslev
(1986) & Taylor (1986). GARCH model allows the conditional variance to be
dependent upon previous own lags, so that the conditional variance eq in
simplest case is now
This is a GARCH (1,1) model. σ^{2}_{t }is
known as conditional variance since it is a one period ahead estimate
for the variance calculated based on any past info thought relevant. In the
above mentioned eq 2, one more exogenous variable is included, the square of
the lagged error terms of other variable, estimated with the help of ARMA
forecasting models. The new equation can be represented as
Where, the last term represents the square of the lagged error
terms of other variable.
5. ANALYSIS AND INTERPRETATION
Table I
Descriptive Statistics
Particulars 
Spot
Price 
Future
Price 
Mean 
49.06 
49.60 
Median 
47.00 
48.09 
Maximum 
60.588 
60.57 
Minimum 
43.94 
44.27 
Standard Deviation 
4.23 
4.11 
Skewness 
0.63 
0.49 
Kurtosis 
1.95 
1.83 
Jarque Bera 
103.72 
89.30 
Probability 
0.00 
0.00 
Table I shows the descriptive statistics of
daily closing spot and future price of dollar in terms of rupees for the period
selected for the study. It can be seen that the closing price in case of spot
price varies from 43.94 to 60.58 thereby stating that there is wide fluctuation
in the daily closing spot price. Similarly, the closing price of futures varies
from 44.27 to 60.57 respectively. The mean return for the entire period is
49.06 for spot and 49.60 for the future.. Skewness is positive (0.636) for spot
and future (0.49) indicating a relatively long right tail compared to the left
one. Kurtosis with 1.93 for spot and 1.83 for futures indicates short tails and
the distribution is platykurtic’. The findings are similar to the existing
literature and with a high JarqueBera statistic, it can be confirmed that the
returns series is not normally distributed.
It is a fact that many financial time series
data are random walk or nonstationary time series and contains unit root. Test
of unit root in the spot and future currency prices of dollar is essential as
the presence of unit root may give invalid inferences in the analysis. ADF
(Augmented DickeyFuller Test) is the popular test for unit root testing of
time series.
Table II shows the results of ADF test and
the results indicate that both (spot and future) series are non stationary at
level but becomes stationary at their first difference and is statistically significant.
Table II
ADF Unit Root Test for Spot
Price (SP) and Future Price (FP)
Particulars 
‘t’ Value (SP) 
Probability(SP) 
‘t’ Value (FP) 
Probability (FP) 
At level 
0.3056 
0.978 
0.1374 
0.968 
At first difference 
29.456 
0.00 
33.702 
0.00 
In
derivatives market the future prices of the currency can be derived from the
spot prices, due to which a theoretical relationship is supposed to exist
between the spot and future prices of the currency. This existence of long term equilibrium relationship between the
spot and future prices of the currency can be tested using co integration test.
The
co integration test was introduced by Granger (1981, 1983) and Engle and
Granger (1987) to explain stationary equilibrium relationship among the
nonstationary variables. The co
integration test is useful in analyzing the presence of a stationary linear
combination among the nonstationary variables of the same order. If such
combination is found, an equilibrium relationship said to exists between the
variables. The Johansen co integration test is applied in the research study
between the spot and future closing prices of the currency .The result of the
Johansen’s CoIntegration Test are shown in table III. The trace statistics for
the calculated Eigen value is more than the table value and hence the null
hypothesis of no co integration is rejected. The results are similar for the
future prices of dollar and hence the result indicates the presence of long
term relationship between the spot and future closing prices of dollar. Hence
the long term equilibrium relationship also exists between the spot and future
closing prices of the currency.
Table III
Johansen’s
CoIntegration Test on spot and future prices of Dollar
Co integration
Between 
Lag length
selected 
Co integration
test using 
No. of Co
integrating Equations (CEs) 
Eigen Value 
Statistic 
Critical value
at 5% 
Probability** 
Daily Spot
Closing and Daily Future
Closing of Dollar 
1 to 4 ( in
first difference of 2 series) 
Trace test 
H_{0}:
r=0 (None) H_{1}:
r ≤ 1 (At most 1) 
0.222 0.000 
50.328 0.973 
15.495 3.841 
0.000 0.324 
MaxEigen
Value test 
H_{0}:
r=0 (None) H_{1}:
r ≤ 1 (At most 1) 
0.022 0.000 
49.354 0.973 
14.265 3.841 
0.000 0.324 
Trace test indicates 1 Co integrating equation
at 5% level of significance
Maxeigen test indicates 1 Co
integrating equation at 5% level of significance
Denotes rejection of null hypothesis at
5% level of significance
**Mackinnon et.al.(1999) estimated
p values
The
equilibrium relationship between the nonstationary variables is used to
construct an Error Correction Model (ECM). An error correction model is a
statistical specification of economic dynamics through which the pull and push
forces restore the equilibrium relationship whenever a disequilibrium takes
place. In currency market, the future prices can be estimated using
deterministic models. According to these models the future prices of the
currency should be equal to the spot prices plus cost of carry. Any difference
between the theoretical and actual prices of the currency may lead to arbitrage
opportunities in the market. These arbitrage opportunities help in correcting
the disequilibrium between the spot and future prices of the currency in the
market. The results of the Error Correction model are shown in table IV for
both the commodities
Table IV
Error Correction
Model Result for Future and Spot price of Dollar
Exchanges 
Variables 
∆(Spot) 
∆(Future) 

Coefficient 
t value 
Coefficient 
t value 

Dollar 
Equilibrium
Error 
0.0316 
4.34 
0.002 
0.304 
∆Spot(1) 
0.00073 
0.002 
0.125 
3.28 

∆Future(1) 
0.05 
1.73 
0.014 
3.28 

Constant 
0.013 
1.34 
0.015 
1.33 
The
results indicate that the correction of equilibrium error is higher in the case
of spot price and is statistically significant when compared to the future
price. It shows that the spot price corrects the disequilibrium between the
spot and the future prices. Also, the change in the future value of currency is
determined by the lagged value of spot price and future price. However, the
influence is positive in the case of lagged value of spot price.
Table V
represents the results of the Block Exogeneity Wald Test in vector error
correction model for the currency dollar. The
Block Exogeneity test is applied to analyse the short term causality
relationship between spot and futures market of the currencies. The results
indicate unidirectional causality (from spot to future) in the case of dollar.
Table V
VEC Grangers Causality/ Block Exogeneity
Wald Test for Dollar
Dependent
Variable 
Excluded 
Dollar 

Chi Square
Statistic 
P Value 

∆(Spot) 
∆(Future) 
4.315 
0.113 
∆(Future) 
∆(Spot) 
10.9266 
0.0042 
Variance decomposition explains
the percentage of forecasting error that can be explained with the help of
variances in its previous behavior as well as the behavior of other series. The
results of variance decomposition of spot and future prices of the currency
dollar for ten lags are shown in table VI. The results indicate that the
forecasting error in spot price is mainly explained by the lagged values of the
spot series whereas the forecasting error of future price is explained by the
lagged values of spot price series. The rate of variance decomposition is
higher in the case of future series when compared to the spot series.
Therefore, it can be concluded that the spot series is exogenous in nature
.Table VI
Variance
Decomposition Index for Spot Price and Future Price

Dollar 

Period 
Variance
Decomposition of Spot 
Variance
Decomposition of Future 


SP 
FP 
SP 
FP 
1 
100 
0.00 
4.89 
95.10 
2 
99.51 
0.43 
7.71 
92.28 
3 
98.79 
1.2 
8.17 
91.82 
4 
98.29 
1.7 
8.31 
91.74 
5 
97.79 
2.2 
8.25 
91.68 
6 
97.26 
2.73 
8.31 
91.67 
7 
96.68 
3.33 
8.32 
91.69 
8 
96.06 
3.39 
8.30 
91.73 
9 
95.44 
4.55 
8.22 
91.77 
10 
94.77 
5.22 
8.16 
91.83 
The
impulse response explains the responsiveness of the endogenous variable in the
system to shocks to each of the other endogenous variables. So, for each
endogenous variable in the system, a unit shock is applied to the error, and
the effects over time are noted. Figure I shows the pair wise impulse response
relations between the spot and future prices of dollar. The results indicate
the significant and higher response of future price to the spot price shocks of
dollar.
Figure I
Impulse Response Function of Dollar
The future market of the currencies is
featured with low volume of trade with better liquidity and high margin
requirements and the presence of reasonable number of participants including
traders, speculators and arbitrageurs. Due to this reason the future markets of
the currencies are supposed to be less efficient as compared to spot market.
Hence when new information about the currency comes into the market, the
participants responds to the information and involves in rebalancing their
positions in the portfolios according to their perception about the current
implications of the news. In such a case the spot market is very fast to
respond to the news as compared to the future market. In this system the
volatility lying in the currency prices is also a major concern for the
participants. The purpose of this study is also to analyze the impact of
volatility in one series on the future volatility in other series. This impact
is known as volatility spillover in the literature.
Table
VII shows the volatility spill over effect from spot to future market. The
results indicate that the existence of volatility spill over in case of dollar
from spot price to future price as indicated by the z statistics (2.24) in
table VII.
Table VII
Volatility Spill over effect from Spot Market to
Future Market
Dependent Variable: Future Price Method: ML – ARCH (Marquardt)  Normal distribution 

Mean
Equation 


Coefficient 
Std. Error 
zStatistic 
Prob. 
C 
50.57 
15.30 
3.30 
0.009 
AR(1) 
0.995 
0.07 
57.5 
0.00 
MA(1) 
0.011 
0.62 
0.018 
0.985 
Variance Equation 

C 
0.761 
0.34 
2.192 
0.028 
Residual Term 
0.77 
0.039 
1.94 
.051 
GARCH Term 
0.02 
.004 
5.91 
0.00 
Squared lagged
residual in spot price of dollar 
0.529 
0.023 
2.24 
0.024 
Table
VIII shows the volatility spill over effect from future to spot market. The
results indicate that there is no existence of volatility spill over in case of
dollar from future price to spot price as indicated by the z statistics (0.0986)
in table VIII.
Table VIII
Volatility Spill over effect from Future Market to
Spot Market
Dependent Variable: Spot
Price Method: ML – ARCH (Marquardt)  Normal distribution 

Mean
Equation 


Coefficient 
Std. Error 
zStatistic 
Prob. 
C 
48.77 
19.81 
2.46 
0.013 
AR(1) 
0.96 
0.15 
6.45 
0.00 
MA(1) 
0.32 
1.02 
0.31 
0.75 
Variance Equation 

C 
9.21 
5.39 
1.70 
0.08 
Residual Term 
0.49 
0.21 
2.29 
0.021 
GARCH Term 
0.35 
0.79 
0.44 
0.65 
Squared lagged
residual in future price of dollar 
0.83 
0.85 
0.98 
0.32 
6. CONCLUSION
The
spot and future prices of the currency is found to have long term relationship
which is supported by the existence of an error correction mechanism called
arbitrage. The error correction mechanism restores the equilibrium relationship
whenever disequilibrium takes place between the two markets. In the present
study, it is the spot price which corrects the disequilibrium in the market.
The study also finds the presence of unidirectional causality in the currency
market wherein the spot causes the future. Variance decomposition explains the
percentage of forecasting error that can be explained with the help of
variances in its previous behavior as well as the behavior of other series. The
results indicate that the forecasting error in spot price is mainly explained
by the lagged values of the spot series whereas the forecasting error of future
price is explained by the lagged values of spot price series. The rate of
variance decomposition is higher in the case of future series when compared to
the spot series. The spot price of the currency was found to be exogenous in
nature. The study also found significant and higher response of future price to
the spot price shocks of dollar. The future market of the currencies is featured with low volume of trade
with better liquidity and high margin requirements and the presence of
reasonable number of participants including traders, speculators and
arbitrageurs. Due to this reason the future markets of the currencies are
supposed to be less efficient as compared to spot market. Hence when new
information about the currency comes into the market, the participants responds
to the information and involves in rebalancing their positions in the
portfolios according to their perception about the current implications of the news.
In such a case the spot market is very fast to respond to the news as compared
to the future market. Volatility spill over was from the spot price to
the future price whereas, there was no evidence of volatility spill over from
future to spot price.
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Dr.M.Sriram 
Dr.M.Sriram has done his M.Com, M.B.A, FCMA (Fellow of The Institute of Cost Accountants of India), M.Phil and Ph D. He has 13 years of PG teaching experience and has written articles in leading management Journals. He has also conducted training programmes for executives and practising managers in the areas of accounting, finance and capital markets. 
Dr.M.Senthil 
Dr.M.Senthil has done his B.E, MBA, PhD. He is presently Professor of Management and Dean (College Development Council) at Alagappa University and has 25 years of teaching and industrial experience in management and technology. Handled many funded research projects and guided many scholars for Ph.D. and M.Phil research degrees. 
IJME introduces peerreview from its first Edition onwards. The researchers submitting their papers for publication should review atleast one technical paper from their domain. The manuscript also undergoes mandatory procedural review with IJMES review and scholar panel.