This document summarizes a research paper that examines the impact of currency futures trading on exchange rate volatility of the euro in India after currency futures were introduced in 2010. The paper uses daily exchange rate data from 2008 to 2011 and unit root and ARCH LM tests to analyze time series properties. It then employs a GJR GARCH model to study the impact on underlying volatility. The results indicate that currency futures trading had no impact on spot exchange rate volatility in India's foreign exchange market. It also found that recent news has a greater impact on spot market volatility while the influence of older news has declined since futures trading began.
Asymmetric Analysis of Exchange Rates Volatility: Evidence from Emerging EconomyIOSRJBM
The primary objective of this study is to empirically establish the level of volatility persistence and ascertain the presence of asymmetric effect on the three segment of the Nigerian foreign exchange market (Inter-bank Foreign Exchange Market (IFEM), bureau de change (BDC) and Wholesale Dutch Auction System (WDAS)). Asymmetric Threshold Generalized Authoregressive Conditional Heteroscadasticity (TGARCH) approach was adopted in the research methodology for the empirical analysis to capture the simultaneous estimation of the mean and the conditional variance in 1,262 sample observations. Generally, this study produced some interesting findings: first, it reveals that naira to US dollar nominal exchange rate volatilities were found to be persistent in all the market segments. Second, the exchange rate volatility in the interbank is persistent and explosive; while the volatilities in the BDC and WDAS market are high and moderate, respectively. This means that the BDC segment of the Nigerian foreign exchange market is less volatile than the interbank market segment even when the interbank segment of the market is more funded with foreign exchange from autonomous and official sources. Additionally, it is evident that interbank segment reacts more to past shocks of the foreign exchange market. Finally, the study also confirms the existence of asymmetric effect in the Nigerian foreign exchange market. The practical implication of these findings is that it raises a policy concerns for the regulators of interbank foreign exchange transaction because the finding of this study signals liquidity squeeze in the market and it is a disincentive to international investors and market players. This is not unconnected to trend seeking and round tripping behavior.
THE EFFECT OF FINANCIAL CRISES ON THE ENTROPY EVOLUTION OF FOREIGN EXCHANGE R...ijitjournal
This study investigates the possible effect of financial crises on foreign exchange markets, where entropy
(using the time-dependent block entropy method) for different exchange rates is measured. Results suggest
that financial crises are associated with significant increase of exchange rate entropy especially in US and
Hongkong currencies, reflecting instability in FX market dynamics. Moreover, for most of the currencies
studied, increase of exchange rate entropy was observed after a period of financial crisis. In addition,
empirical results show that periods of economic uncertainty are led by periods of low entropy values,
which might serve as indicator for anticipating the inception of financial crises.
This paper empirically examines the role of uncertainty occurred by ‘news’ in Japanese financial markets. A GARCH-MIDAS model is used for estimation. It finds that news-based implied volatility performs well in predicting long-term aggregate market volatilities. A subsample analysis provides that the predictive power of news-based volatility is continuing, as most of the coefficients are positive and significant. So, in general, the news based implied volatility model is associated with high market volatility. Moreover, stock market prices go on rising, different effects that appeared in each subsample period. On the recent period, when Abenomics was conducted, the effect decreased. Also, the effect of exchange rates decrease in short time. When stock prices decrease, volatilities of the stock prices in the past period increase. There is some possibility that markets were too unstable about the movements because of the low prices. Also, the volatility of long-term interest rates increases when the interest rate declines in the recent period under Abenomics. Although interest rates have been quite low in both sample periods, the Bank of Japan (BOJ) started to manage long-term interest rates in the recent period, so market participants seem to begin noticing the movements.
Asymmetric Analysis of Exchange Rates Volatility: Evidence from Emerging EconomyIOSRJBM
The primary objective of this study is to empirically establish the level of volatility persistence and ascertain the presence of asymmetric effect on the three segment of the Nigerian foreign exchange market (Inter-bank Foreign Exchange Market (IFEM), bureau de change (BDC) and Wholesale Dutch Auction System (WDAS)). Asymmetric Threshold Generalized Authoregressive Conditional Heteroscadasticity (TGARCH) approach was adopted in the research methodology for the empirical analysis to capture the simultaneous estimation of the mean and the conditional variance in 1,262 sample observations. Generally, this study produced some interesting findings: first, it reveals that naira to US dollar nominal exchange rate volatilities were found to be persistent in all the market segments. Second, the exchange rate volatility in the interbank is persistent and explosive; while the volatilities in the BDC and WDAS market are high and moderate, respectively. This means that the BDC segment of the Nigerian foreign exchange market is less volatile than the interbank market segment even when the interbank segment of the market is more funded with foreign exchange from autonomous and official sources. Additionally, it is evident that interbank segment reacts more to past shocks of the foreign exchange market. Finally, the study also confirms the existence of asymmetric effect in the Nigerian foreign exchange market. The practical implication of these findings is that it raises a policy concerns for the regulators of interbank foreign exchange transaction because the finding of this study signals liquidity squeeze in the market and it is a disincentive to international investors and market players. This is not unconnected to trend seeking and round tripping behavior.
THE EFFECT OF FINANCIAL CRISES ON THE ENTROPY EVOLUTION OF FOREIGN EXCHANGE R...ijitjournal
This study investigates the possible effect of financial crises on foreign exchange markets, where entropy
(using the time-dependent block entropy method) for different exchange rates is measured. Results suggest
that financial crises are associated with significant increase of exchange rate entropy especially in US and
Hongkong currencies, reflecting instability in FX market dynamics. Moreover, for most of the currencies
studied, increase of exchange rate entropy was observed after a period of financial crisis. In addition,
empirical results show that periods of economic uncertainty are led by periods of low entropy values,
which might serve as indicator for anticipating the inception of financial crises.
This paper empirically examines the role of uncertainty occurred by ‘news’ in Japanese financial markets. A GARCH-MIDAS model is used for estimation. It finds that news-based implied volatility performs well in predicting long-term aggregate market volatilities. A subsample analysis provides that the predictive power of news-based volatility is continuing, as most of the coefficients are positive and significant. So, in general, the news based implied volatility model is associated with high market volatility. Moreover, stock market prices go on rising, different effects that appeared in each subsample period. On the recent period, when Abenomics was conducted, the effect decreased. Also, the effect of exchange rates decrease in short time. When stock prices decrease, volatilities of the stock prices in the past period increase. There is some possibility that markets were too unstable about the movements because of the low prices. Also, the volatility of long-term interest rates increases when the interest rate declines in the recent period under Abenomics. Although interest rates have been quite low in both sample periods, the Bank of Japan (BOJ) started to manage long-term interest rates in the recent period, so market participants seem to begin noticing the movements.
This study investigates the impact of the introduction of index options on emerging market volatility in the context of Malaysia. Company specific daily closing prices for 29 listed companies were examined to determine the conditional volatility shifts before and after the introduction of index options. Multiple window periods are examined to avoid year-end effects.The exponential generalized autoregressive conditional heteroskedasticity (EGARCH) (1.1) model is used to determine the conditional volatility shift before and after the introduction of index options in Malaysia. The findings of this study suggest that the introduction of index options reduced market volatility in the Malaysia equity market at the 0.01 level of statistical significance. Further, this study contributed to extant literature because it uses company-specific daily equity price data and no such previous study exists on the impact of index options for this important emerging market. The study will be useful for academics, researchers, domestic and foreign investors and policy-makers, among others.
Is the Saudi Stock Market Efficient? A case of Weak-form EfficiencyHamad Alzeera
Stock Market Efficiency, Weak-form market efficiency, Efficient Market Hypothesis, Random Walk Hypothesis, unit root test, auto correlation, runs test, Kingdom of Saudi Arabia
Links: http://www.iiste.org/Journals/index.php/RJFA/article/view/5647
http://www.iiste.org/Journals/index.php/RJFA/article/view/5647/5759
The research studies the impact of the exchange rate fluctuations of the local currency on the share dividends exchanged in the stock market, and stating whether there is a trace of the fluctuations occurring in the exchange rate on the fluctuations reflected on the stock returns in the stock market – during the political and economic crisis in Syria. The descriptive analytical approach was adopted to indicate whether there is any direct or indirect impact of fluctuations in the exchange rate of the pound (Lira) against the dollar on the exchange value of the Damascus Securities Exchange Index. The study community consists of all stock companies listed in Damascus Securities Exchange. It covers the total of 23 listed companies. It relied on the period from 1/7/2011 through 12/31/2013 to study the impact of exchange rate fluctuations on stock returns, where the crisis began on 18/03/2011, but reflections on economic life began to appear in mid-2011 when the severe fluctuations in the exchange rate and returns began as a result of lack of stability and economic siege Syria has been witnessing and the study stretched until the year 2013. The data is a sort of daily observations of each of the dependent and independent variable sending with 381 observations. The study reached the many results some of which include that there is an inverse weak between the Syrian pound exchange rate and Damascus Securities Exchange Index returns. The inefficiency of Damascus Securities Exchange Index on the weak level, where, as we have seen, this index is not subject to normal distribution and it is auto-correlated of the third degree and does not settle at the first level; instead, it settles at the first change.
This study investigates the impact of the introduction of index options on emerging market volatility in the context of Malaysia. Company specific daily closing prices for 29 listed companies were examined to determine the conditional volatility shifts before and after the introduction of index options. Multiple window periods are examined to avoid year-end effects.The exponential generalized autoregressive conditional heteroskedasticity (EGARCH) (1.1) model is used to determine the conditional volatility shift before and after the introduction of index options in Malaysia. The findings of this study suggest that the introduction of index options reduced market volatility in the Malaysia equity market at the 0.01 level of statistical significance. Further, this study contributed to extant literature because it uses company-specific daily equity price data and no such previous study exists on the impact of index options for this important emerging market. The study will be useful for academics, researchers, domestic and foreign investors and policy-makers, among others.
Is the Saudi Stock Market Efficient? A case of Weak-form EfficiencyHamad Alzeera
Stock Market Efficiency, Weak-form market efficiency, Efficient Market Hypothesis, Random Walk Hypothesis, unit root test, auto correlation, runs test, Kingdom of Saudi Arabia
Links: http://www.iiste.org/Journals/index.php/RJFA/article/view/5647
http://www.iiste.org/Journals/index.php/RJFA/article/view/5647/5759
The research studies the impact of the exchange rate fluctuations of the local currency on the share dividends exchanged in the stock market, and stating whether there is a trace of the fluctuations occurring in the exchange rate on the fluctuations reflected on the stock returns in the stock market – during the political and economic crisis in Syria. The descriptive analytical approach was adopted to indicate whether there is any direct or indirect impact of fluctuations in the exchange rate of the pound (Lira) against the dollar on the exchange value of the Damascus Securities Exchange Index. The study community consists of all stock companies listed in Damascus Securities Exchange. It covers the total of 23 listed companies. It relied on the period from 1/7/2011 through 12/31/2013 to study the impact of exchange rate fluctuations on stock returns, where the crisis began on 18/03/2011, but reflections on economic life began to appear in mid-2011 when the severe fluctuations in the exchange rate and returns began as a result of lack of stability and economic siege Syria has been witnessing and the study stretched until the year 2013. The data is a sort of daily observations of each of the dependent and independent variable sending with 381 observations. The study reached the many results some of which include that there is an inverse weak between the Syrian pound exchange rate and Damascus Securities Exchange Index returns. The inefficiency of Damascus Securities Exchange Index on the weak level, where, as we have seen, this index is not subject to normal distribution and it is auto-correlated of the third degree and does not settle at the first level; instead, it settles at the first change.
The Impact of Policy Announcement on Stock Market Volatility: Evidence from C...IOSRJBM
The aim of the current empirical paper is to investigate the impact of major political events and its impact on stock market with special reference to BSE Sensex, Nifty fifty and BSE100 index. History has exhibited that stock market plays a major role in any economy. Stock markets have been impacted by various macro and micro economic factors. Therefore, the main objective of this empirical paper is to investigate the pricing behaviour of the chosen benchmark indices (Sensex, Nifty and BSE100) with respect to a major political event in India (demonetisation of currency) and its implications on regulators, researchers and market participants. For the purpose of the study the data has been collected from 26-10-2015 to 30-11-2016. The collected data has been tested for stationarity by applying ADF test statistics. The event study methodology has been employed to determine the impact of demonetisation on India bench mark indices. In order to capture the historical volatility the standard deviation of the abnormal returns of the selected indices has been computed. GARCH (1,1) model has been employed to ascertain the existence of ARCH/GARCH effect in the indices. We found a significant impact of currency demonetisation on the chosen indices on the event day. Nobody knows the actual impact of demonetisation on the economy in the long run. Bulk of the studies and opinions of experts on the demonetisation is mixed. Some experts opine that the impact on the economy would be significant and adverse. However, another bunch of experts opine that the shock on the economy would be smaller, although no extensive macroeconomic assessment has been published.
Does Economic Growth Affect Capital Market Development In Nigeria? 1985 – 2016AJHSSR Journal
The goal of this paper is to assess the impact of economic growth affects capital market
development in Nigeria using annualised data from 1986-2016. We employed that Johansen cointegration
technique to determine if our variables are cointegrated. The error correction model (ECM) was employed to
estimate our dynamic short and long-run model. Various diagnostic tests were also conducted to confirm the
validity of our results. The results indicate that there is a long-run relationship between economic growth and
capital market development. The baseline estimator further showed that economic growth has significant
positive influence on capital market development. We also found that inflation has significant negative impact
on the capital market while money supply was found to have insignificant effect on the dependent variable. The
error correction term showed evidence of slow speed of adjustment toward long-run equilibrium, with deviation
from equilibrium corrected at the speed of 2.3 percent on annual basis. We conclude that economic growth
indeed drives capital market development in Nigeria. And were recommend that policies aimed at facilitating
economic activities should be pursued by the monetary authorities, the government and policymakers to further
enhance the development of Nigerian capital market
A study on the impact of global currency fluctuations with a special focus to...Aman Vij
The paper discusses about the factors influencing and impact of currency fluctuations on global economy. Then we shift our focus to Indian rupees factors which causes the Rupee fluctuations has been discussed. In the end we discuss about the steps taken by the RBI and the government and what else can be done by investors to lessen the impact of Global currency fluctuations and what can be done to prevent Indian Rupee fluctuation.
Like developed countries, developing countries have established stock markets in view of achieving their
economic growth. This study sought to investigate the impact of stock exchange market to the economic growth
in Tanzania over a period of 1998 - 1992. A simple regression model using the 1998-2012 annual data sets was
employed. The empirical findings show that the market size has a negative impact on economic growth, which
suggests that the stock market in Tanzania is still infant and thus does not have a significant impact on
economic growth. The findings also show that the market liquidity has a positive impact on the economic
growth, which suggests that that despite the size of the stock market, the market is very active.
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Dynamics of currency futures trading and underlying exchange rate volatility in india
1. Research Journal of Finance and Accounting www.iiste.org
ISSN 2222-1697 (Paper) ISSN 2222-2847 (Online)
Vol 3, No 7, 2012
Dynamics of Currency Futures Trading and Underlying
Exchange rate Volatility in India
Dhananjay Sahu
Associate Professor, Faculty of Commerce, Banaras Hindu University, Varanasi, India
Tel. 91-9451890836, E mail: sahudj@gmail.com
Abstract
The paper is 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.
Keywords: Exchange Rate, Currency Futures, Forex Market Volatility, GARCH.
1. Introduction
Financial deleveraging and abrupt reversal of foreign capital flows due to the systemic risk emanating from
manifold international occurrences has magnified the quantum of currency exposure in India and making the
currency exposure to reach to an alarming state which need to be addressed meticulously in order to counter the
evil effects of currency exposure on the economy. In consonance with the international practice of using
currency derivatives, market regulators in India introduced derivatives trading and initiated the trading of
currency futures in INR-EURO pair of currency in February, 2010 at National Stock Exchange. With the belief
that currency derivatives would be able to provide a mechanism to alleviate currency exposure and strengthen
the microstructure of Indian forex market, market participants started to apply currency futures in the process of
risk management and the turnover in currency futures has magnified substantially.
However, the impact of derivatives trading on spot market is a polemic issue and the financial literature is
evidencing varied and contradictory opinions both in theoretical and empirical orientations. In general, derivative
markets have been criticized for bringing destabilizing force. It is argued thatthe inflow of and existence of
speculators in derivatives market may produce destabilizing forces, which among other things create undesirable
“bubbles”. Further, transactions in derivative markets bring excess volatility into the underlying spot market due
to the presence of uninformed noise and speculative trades induced by low transactions costs (Figlewski, 1981;
Stein, 1987; Ross, 1989). On the contrary, it is argued that the introduction of derivatives trading leads to more
complete market; enhances information flow and there by improves the investment choices facing investors.
Market-wide information may be more efficiently impounded in the derivatives market with its low transaction
costs which in turn leads to a reduced price disparity and low cash market volatility (Danthine, 1978;
Butterworth 2000; Bologna and Cavallo, 2002). Moreover, derivatives markets play an important role within the
price discovery process of underlying assets and currency futures have relatively lower transaction costs and
capital requirement. Further, the arrival of external information is quickly incorporated into exchange rate as
participant’s expectations are updated and providing a phillip to market efficiency.
The issue of what impact derivatives trading would have on underlying cash market has been extensively
explored in equity markets (e.g., Edwards, 1988; Harris, 1989; Bansal et. al., 1989; Bessembinder and Seguin,
1992; Antoniou et. al., 1998; Kyriacou and Sarno, 1999; Gulen and Mayhew, 2000; Bologna and Cavallo, 2002;
Ryoo and Smith, 2003; Spyrou, 2005; Alexakis, 2007among many others). However, the same issue has not
been studied extensively in the context of currency markets. Some of the early studies pertaining to the
introduction of currency futures in developed and emerging markets and their impact on spot exchange rate
volatility are far from any consensus. Several studies evidenced a decline in spot exchange rate volatility with
the introduction of currency derivatives whereas contradictory conclusions of magnification in exchange rate
volatility were also noticed in the context of developed and emerging markets. The aforementioned fact has
provided impetus to explore the influence of currency derivatives in Indian context. The present paper is aimed
at analyzing the impact of the introduction of currency futures in INR-EURO pair on the spot exchange rate
volatility. The rest of the paper is as follows: Section two discusses the existing literature; Section three specifies
the data used; Section four deliberates on methodological issues; Section five analyses the data and interprets the
result of analysis followed by Section six where conclusions and possible implications have been documented.
15
2. Research Journal of Finance and Accounting www.iiste.org
ISSN 2222-1697 (Paper) ISSN 2222-2847 (Online)
Vol 3, No 7, 2012
2. Review of Literature
2.1 Theoretical Literature
Figlewaski (1981) argued that speculation in the derivatives market is transmitted to the underlying spot markets.
The speculation produces a net loss with some speculators gaining (and others loosing), thereby destabilize the
market. Uninformed speculative traders increase price volatility by interjecting noise to a market with limited
liquidity. The inflow and existence of the speculators in the derivatives market produces destabilization forces,
which creates undesirable bubbles. Stein (1987) developed a model in which prices are determined by the
interaction between hedgers and informed speculators. In this model, opening a futures market has two effects;
(1) the futures market improves risk sharing and therefore reduces price volatility, and (2) if the speculators
observe a noisy but informative signal, the hedgers react to the noise in the speculative trades, producing an
increase in volatility. Ross (1989) assumed that there exist economies that are devoid of arbitrage and proceeds
to provide a condition under which the no arbitrage situation will be sustained. It implies that the variance of the
price change will be equal to the rate of information flow. The implication of this is that the volatility of the asset
price will increase as the rate of information flow increases. Thus, if derivatives market increases the flow of
information, the volatility of the spot price must change in the absence of arbitrage opportunity.
In contrast, the model developed by Danthine (1978) argued that the futures markets improve market depth
and reduce volatility because the cost to informed traders of responding to mispricing is reduced. Butterworth
(2000) also argued that introduction of the derivatives trading leads to more complete market enhancing the
information flow. Derivatives market allows for new positions and expanded investment sets and enables to take
position at lower cost. Derivatives trading bring more information to the market and allows for quicker
disseminations of the information. The transfer of the speculative activity from spot to futures market decreases
the spot market volatility. Bologna and Cavallo (2002) argued that the speculation in the derivatives market also
leads to stabilization of the spot prices. Since derivatives are characterized by high degree informational
efficiency, the effect of the stabilization permits to the spot market. The profitable speculation stabilizes the spot
price because informed speculators tend to buy when the price is low pushing it up and sell when the price is
high causing it to fall. These opposing forces constantly check the price swings and guide the price towards to
the mean level. Uninformed speculators are not successful and are eliminated from the market. This profitable
speculation in the derivatives market leads to a decrease in spot price volatility.
2.2 Empirical Literature
Clifton (1985) found a strong positive correlation between futures trading and exchange rate volatility measured
by the spread between the daily high and low exchange rates for Deutsche marks, Swiss franc, Canadian dollars,
and Japanese yen. Grammatikos and Saunders (1986) investigated British pound, Canadian dollar, Japanese yen,
Swiss franc and Deutsche mark foreign currency futures traded on the International Monetary Market over the
period of 1978-1983 and found that there exists a bidirectional causal relationship between volume and price
variability in futures market transactions.
Kumar and Seppi (1992) and Jarrow (1992) studied the impact of currency derivatives on spot market
volatility and found that speculative trading executed by big players in the derivatives market increases the
volatility in the spot exchange rate. Hence, currency futures trading increases the spot market volatility.Glen and
Jorion (1993) examined the usefulness of currency futures/forwards and concluded that currency risk can be
minimized through futures/forward hedging. Chatrath, Ramchander and Song (1996) analyzed the impact of
currency futures trading on spot exchange rate volatility by establishing 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. They concluded that there exists a causal relationship between currency futures trading
volume and exchange rate volatility and also found that the trading activity in currency futures has a positive
impact on conditional volatility in the exchange rate changes. Further, futures trading activity has declined on the
day following increased volatility in spot exchange rates.
Shastri, Sultan and Tandon (1996) investigate the effect of the introduction of options on the volatility of
currency markets and conclude that options contracts complete and stabilize the spot currency markets. Jochum
and Kodres (1998) examine the impact of the introduction of the futures market to the spot currency markets,
and report varying results depending on the market they studied. For Mexico, they find that the introduction of
currency futures help reduce the volatility of the spot currency market, while for Brazil and Hungary, they find
no discernable impacts. Adrangi and Chatrath (1998) studied the impact of currency futures commitments and
found that the overall growth in currency futures commitments has not caused exchange rates to be more
volatile. However, increase in the participation of large speculators and small traders do destabilize the markets.
They concluded that margin requirements that “penalize” speculators and small traders may serve to promote
stability in the market. Chang and Wong (2003) examined the usefulness of currency futures/forwards and
concluded that currency risk can be minimized through futures/forward hedging. Röthig (2004) reported a strong
16
3. Research Journal of Finance and Accounting www.iiste.org
ISSN 2222-1697 (Paper) ISSN 2222-2847 (Online)
Vol 3, No 7, 2012
causal relationship between the futures trading volume and GARCH-based exchange rate volatility for different
currencies.
Bhargava and Malhotra (2007) analyzed futures trading on four currencies over the time period of
1982-2000 and found the evidence that day traders and speculators destabilize the market for futures but it is not
clear whether hedgers stabilize or destabilize the market. Exchange rate movements affect expected future cash
flow by changing the home currency value of foreign cash inflows and outflows and the terms of trade and
competition. Consequently, the use of currency derivatives for hedging the unexpected movement of currency
becomes more sensitive and essential. Sharma (2011) investigated the impact of currency futures trading in India
by establishing 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 two-way causality between the volatility in the spot exchange
rate and the trading activity in the currency futures market.
A synthesis of the empirical literature on the impact of currency futures trading on underlying market
volatility purported that majority of studies are in the context of developed markets and in most of the cases, the
exchange rates of currencies in US dollar have been used. The literature in the context of emerging markets is
scanty except few studies and EURO has not been considered in any study since its introduction. Further, the
outcomes of various studies asserted that the impact of introduction of currency futures trading has been
different in different markets with respect to different span of time and it is difficult to arrive at a consensus with
respect to the impact of currency futures introduction on the volatility of spot exchange rate. Again, looking at
the typical characteristics of emerging economies emanating from structural and institutional changes, the
exchange rate of domestic currency is witnessing unusual behavior in terms of volatility against other currencies.
The aforementioned fact has provided impetus to explore the influence of currency derivatives in the context of
emerging markets which in turn, necessitates further empirical investigation on the impact of currency futures
trading on spot exchange rate volatility.
3. Data
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 December31, 2011. The time series data have been collected from the data warehouse
of Reserve Bank of India. In order to explore the impact of currency futures trading, the window period has been
divided into pre introduction period (January 02, 2008 - January31, 2010) and post introduction period (February
01, 2010- December 31, 2011). In addition, daily close prices of CNX Nifty Index have also been used. Daily
close prices for the period have been collected from the NSE website.
4. Methodological Issues
The empirical literature documented two different methodologies to analyze the impact of derivatives trading on
cash market volatility. One way to analyze the impact is by comparing the cash market volatility before and after
the introduction of derivatives trading as adopted in studies (Edwards, 1988 and Bologna and Cavallo, 2002; for
different equity markets; and Shastri, Sultan, and Tandon, 1996 and Jochum and Kodres, 1998; for different
currency markets). The other way to study the impact of derivatives trading is by comparing the underlying
market volatility and derivatives trading activity variables as adopted in studies (Bessembinder and Seguin,
1992; Gulen and Mayhew, 2000; for different equity markets, and Clifton,1985; Chatrath, Ramchander, and
Song, 1996; Rothig, 2004; Adrangi and Chatrath, 1998; and Bhargava and Malhotra, 2007; for different currency
markets).
The present study is based on the first methodology of analyzing the impact of currency futures trading on
underlying currency market volatility in India by comparing the underlying volatility before and after the
introduction of currency futures in INR-EURO pair of currency. The data used in the study are essentially time
series and it becomes necessary to unfold the statistical properties of the time series. Natural logarithm
transformation is commonly used transformation techniques whereas ADF test is applied for observing the
characteristics of the data series under study.
Under the study, the exchange rate series is transformed into its natural logarithm rate series. In view of the
inherent heteroscedasticity of changes in exchange rates, it is considered advisable to transform it into log rate
changes. Log transformation is likely to render the exchange rate changes to be homoscedastic and thereby make
the series stationary. To smooth the changes in exchange rate, this transformation is done as it depicts the rate of
change rather than actual change. The first difference of log exchange rates referred to as log returns have been
used throughout the study. The logarithmic return has been applied in all the empirical tests in the study. Unless
otherwise specified, the returns used from now are logarithmic returns.
In order to have a ready reference, descriptive statistics such as skewness, Kurtosis and Jarque-Bera have
been calculated which provides basic albeit, elementary evidence about changes in the time series behavior and
explains the fact that exchange rate distribution of currency for the pre-period, post-period &full period are not
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normally distributed which is a well-documented fact in financial literature. Given the fact that, the presence of a
stochastic trend or deterministic trend in a financial time series or its stationary/non-stationary in levels is a
prerequisite for conducting any test, the study begins with the testing of exchange rate series for a unit root using
Augmented Dickey Fuller (ADF) test. A stationary time series is one for which the mean and variance are
constant over time; they depend only on the distance or lag between the two time periods and not on the actual
time at which they are computed. The presence of a unit root indicates that the given series has become unstable
or non-stationary; showing an uneven movement. The time series variables considered in this paper is daily
exchange rate of INR-EURO and the ADF unit root test is performed by using the following equations:
ΔY௧ ൌ ߙଵ ܻ௧ିଵ ∑ୀଵ ߛ ΔY௧ି ߝ௧ Eq.1
ΔY௧ ൌ ߙ ߙଵ ܻ௧ିଵ ∑ୀଵ ߛ ΔY௧ି ߝ௧ Eq.2
ΔY௧ ൌ ߙ ߙଵ ܻ௧ିଵ ߙଶ ݐ ∑ୀଵ ߛ ΔY௧ି ߝ௧ Eq.3
Another characteristic of time series that needs attention is the heteroscedasticity. The Lagrange Multiplier (LM)
test is used to reject the null hypothesis of no ARCH effect, which is indicative of the fact that time series is
heteroscedastic. Such heteroscedasticity causes the ordinary least square estimates to be inefficient as OLS
regression assume constant error variance. Models that take into account the changing variance can make more
efficient use of data. Property of heteroscedasticity in time series is well documented (Fama, 1965 & Bollerslev,
1986). The presence of heteroscedasticity in the time series calls for the use of ARCH family of models to study
volatility.
The standard GARCH (p, q) model introduced by Bollerslev (1986) suggests that conditional variance of
returns is a linear function of lagged conditional variance and past squared error terms. A model with errors that
follow the standard GARCH (1, 1) model can be expressed as follows:
ܴ௧ ൌ ܿ ߝ௧ ߝ ,݁ݎ݄݁ݓ௧ ⁄߰௧ିଵ ~ܰ ሺ0, ݄௧ ሻ Eq.4
ଶ
݄ܽ݊݀௧ ൌ ߙ ߙଵ ߝ௧ିଵ ߙଶ ݄௧ିଵ Eq.5
The underlying issue being the exchange tare, the term ܴ௧ is replaced by ܴ,௧ in the mean equation. Further, the
impact of introduction of currency futures trading on foreign exchange market volatility can be isolated by
removing from the time series, any predictability associated with other factors contributing to the volatility. CNX
Nifty has been used as the independent variable in mean return equation to isolate market wide factors other than
those which are associated with the introduction of currency futures trading. The mean equation to be estimated
is as follows:
ܴ,௧ ൌ ߛ ߛଵ ܴ௧௬,௧ ߝ௧ Eq.6
However, the standard GARCH models assume symmetry in the response of volatility to information. In other
words, the models assume that the response of volatility, to ‘bad’ news as well as ‘good’ news, is similar. If the
response is asymmetric, then the standard GARCH models will end up mis specifying the relationship and
further, inferences based on this model may be misleading. However, the standard GARCH model can be easily
extended to include asymmetric effects (Glosten, Jagannathan and Runkle, 1993). In the model, the asymmetric
response of conditional volatility to information is captured by including, along with the standard GARCH
variables, squared values of ߝ௧ିଵ when ߝ௧ିଵ is negative. In other words, the model allows for asymmetries by
augmenting the standard GARCH model with a squared error term following ‘bad’ news. In doing so, it allows
the negative return shocks to generate greater volatility than positive return shock. Hence, equation (5) is
extended as follows:
ଶ ି ଶ
݄௧ ൌ ߙ ߙଵ ߝ௧ିଵ ߙଶ ݄௧ିଵ ߙଷ ܵ௧ିଵ ߝ௧ିଵ Eq.7
ି
Where ܵ௧ିଵ ൌ 1 if ߝ௧ିଵ ൏ 1
In studying the impact of currency derivatives, firstly, the existence of asymmetric response is tested for
exchange rate for all the three periods. Test of asymmetry in the period pre and post introduction of derivatives,
reveals the impact that introduction of derivatives trading has had on the response of volatility to new
information generated. The test of asymmetric response for the full period helps in identifying the GARCH
model to be specified while analyzing the impact of currency futures trading on spot market volatility. For this
purpose, a dummy variable is added while specifying the volatility dynamics with the dummy taking a value of
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zero before introduction of currency futures trading and one for the period after introduction. Capturing the
asymmetric response for the full period of analysis, the GJR model along with a dummy is specified as follows:
ଶ ି ଶ
݄௧ ൌ ߙ ߙଵ ߝ௧ିଵ ߙଶ ݄௧ିଵ ߙଷ ܵ௧ିଵ ߝ௧ିଵ ߙସ ܦ,௧ Eq.8
where, ܦ,௧ is a dummy variable and ߙସ is the coefficient of the dummy variable. If ߙସ is statistically
significant, it can be said that the existence of currency futures trading has had an impact on spot exchange rate
volatility. Further, a significant positive value for ߙସ would indicate that introduction of currency futures
trading increases the volatility of the spot exchange rate.
5. Empirical Results
The descriptive statistics in Table 1 indicate that there is an increase in standard deviation of exchange rate return
from 0.008244to 0.016081 with the onset of currency futures trading. This may lead to the fact that there has been
a marginal increase in volatility after the introduction of currency futures trading in the Indian foreign exchange
market. However, at this stage, it is difficult to say that the increase in volatility after the introduction of currency
futures trading is due to currency futures and not because of other factors that influence market wide movements.
To make any significant inferences, one needs to further analyze the behavior of exchange rate returns and account
for any predictability associated with other factors that may be having an impact on the volatility of the time series.
The Jarque-Bera test statistics of exchange rate returns as shown in Table 1 for the total period is 1513459 and
statistically significant as well as the time series have excess kurtosis (197.5158). The computation of descriptive
statistics such as skewness, Kurtosis and Jarque-Bera during the period under study provides elementary
evidence about the fact that the distribution of exchange rates are not normally distributed which is in
consonance with the documented financial literature.
Owing to the aforesaid fact, it is imperative to analyze whether there is the presence of unit root in the
exchange rate series. The ADF test has been conducted at level and at first difference for different periods and
the result is documented in Table 2. The ADF coefficients of exchange rate series at level for the total period, pre
and post period are -2.413791, -2.64546and -1.028839respectively and statistically insignificant which indicates
the presence of unit root and the exchange rate series is non-stationary. But, the ADF coefficients of exchange rate
series at first difference for the total period, pre and post period are -28.40158, -22.57939and
-21.82535respectively and statistically significant which indicates absence of unit root and the exchange rate series
is stationary. The outputs of ADF test are in consonance with the already documented fact about time series that
most of the time series data are non-stationary at level but stationary at first difference. Another characteristic of
time series that needs attention is the heteroscedasticity. The Lagrange Multiplier (LM) test is used to reject the
null hypothesis of no ARCH effect, which is indicative of the fact that time series is heteroscedastic. The
Lagrange Multiplier (LM) test for no ARCH effect of exchange rate returns is having the F-statistics of
211.4946and statistically significant with a zero probability, implying that there is a significant ARCH effect in
exchange rate returns. All these results indicate that exchange rate returns series is heteroscedastic. The presence
of heteroscedasticity in the exchange rate series calls for the use of ARCH family of models to study volatility.
The ARCH family of models is exclusively designed to address the heteroscedastic behavior of financial time
series data.
Before applying the ARCH model, it is essential to specify the model. The standard GARCH models
assume symmetry in the response of volatility to information, which may not be the case always. Hence, the
study first tests for existence of asymmetric response by specifying the GJR GARCH (1, 1) specification of
volatility dynamics. The results of the asymmetric response analysis of exchange rate returns for the pre and post
periods are reported in Table 3 and Table 4 respectively. The coefficients of asymmetric response for the pre
period, post period and total period are -0.086927, 2.772952 and 1.600506 respectively and statistically
significant which implies that the response of volatility to ‘bad’ news and ‘good’ news are different. The
aforesaid issue has specified the use of GJR GARCH model to analyze the impact of currency futures on spot
exchange rate volatility.
In the specified model, ߙଵ (ARCH 1) is the “news” coefficient; with a higher value implying that recent news
has a greater impact on exchange rate changes. It relates to the impact of yesterday’s news on today’s exchange
rate changes. In contrast, ߙଶ (GARCH 1) reflects the impact of “old news” on exchange rate changes. It indicates
the level of persistence in information and its effect on volatility. After dividing the study period into before and
after the introduction of currency futures trading, it is found that the ߙଵ ൌ 0.102167 and ߙଶ ൌ 0.933412 before
the introduction of futures and ߙଵ ൌ 0.312947 and ߙଶ ൌ െ0.000309after the introduction of futures trading as
presented in Table 3 and Table 4. The aforementioned changes in the coefficients of ARCH and GARCH are
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. This implies that after the
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introduction of currency futures trading, the spot market has become more efficient owing to the diminishing
importance of old news and faster incorporation of recent news in exchange rates.
In the model, the value of ߙଵ in the conditional variance equation is 0.338964 and more than the value of
ߙଶ as shown in Table 5. This seems to suggest that past conditional variance has a lesser impact on volatility of
exchange rate than recent news announcements. A high ߙଶ shows the persistence of volatility due to old news.
The log likelihood value in respect of exchange rate series is high, which is an indication that GJR GARCH model
is a good fit.
Finally, the impact of introduction of currency futures on the conditional volatility is analyzed by
introducing a dummy in the variance equation. The dummy would take a value ‘zero’ in the pre introduction
period and ‘one’ in the post introduction period. The results of the analysis have been documented in Table 5.
The coefficient of the currency futures dummyߙସ is positive (4.63E-07) and there seems to have an increase in
volatility but the coefficient is statistically insignificant. The result of the analysis implies that the spot exchange
rate volatility is not influenced by the introduction of currency futures trading in INR-EURO pair of currency.
6. Conclusion
The magnification of cross-border transactions as a consequence to the structural and regulatory reforms has
intensified the issue of currency exposure around the globe. Particularly, emerging economies being the favored
destinations to undertake economic activities are experiencing substantial capital inflows through FDI and FII
routes by relaxing regulatory norms and resultantly making their economies more vulnerable to international
dynamism with respect to economic and financial issues. The growing currency exposure experienced by
emerging economies at the back drop of financial deleveraging and abrupt reversal in foreign capital flows has
instigated regulators and policy makers to introduce currency derivatives trading on currency through designated
stock/currency exchanges to provide a mechanism to hedge currency exposure. However, such currency
derivatives are capable of influencing the extent of volatility in the underlying spot exchange rate. Hence, it is
imperative to explore the impact of currency futures trading on the volatility of spot exchange rate.
The objective of the present study is to examine the impact of currency futures trading on spot exchange rate
volatility of the foreign exchange market in India. To explore the objective, daily exchange rate of EURO in terms
of Indian rupees for the sample period January 02, 2008 to December 31, 2011have been used. The time series
data have been collected from the data warehouse of Reserve Bank of India. In order to explore the impact of
currency futures trading, the window period has been divided into pre introduction period (January 02, 2008 -
January 31, 2010) and post introduction period (February 01, 2010- December 31, 2011). In addition, daily close
prices of CNX Nifty Index have also been used. Daily close prices for the period have been collected from the
NSE website. To test the hypothesis, GJR GARCH model capable of capturing the asymmetric response has
been employed.
The results indicate that the coefficient of the dummy variable is positive but statistically insignificant.
Thus, it can be concluded that the introduction of currency futures trading has had no impact on the spot
exchange rate volatility of the foreign exchange market in India. The implication of the result is that both hedging
and speculative activities executed in currency futures market tend to offset the net effect of each other on the
volatility of spot currency market. Further, the results of ARCH and GARCH coefficients before and after the
onset of currency future trading are 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. This implies that after the introduction of currency futures trading, the spot market has become
more efficient owing to the diminishing importance of old news and faster incorporation of recent news in
exchange rates. However, the impact of currency futures trading on spot currency can be further refined with the
availability of data pertaining to different groups of traders in the foreign exchange market.
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Table 1: Descriptive Statistics of Daily Exchange Rate Return
Descriptive Statistics of Exchange Rate Return
Pre Period Post Period Total Period
Mean 0.000234 0.00014 0.000189
Std. Dev. 0.008244 0.016081 0.012631
Skewness -0.272203 -0.095024 -0.14007
Kurtosis 5.107721 156.1769 197.5158
Jarque-Bera 98.33142 451666.4 1513459
Probability 0 0 0
Source: Computed Output
Table 2: Results of Unit Root Test
Augmented Dickey-Fuller test Statistics
Period Exchange Rate at Levels Exchange Rate at First Difference
t-Statistics Probability t-Statistics Probability
Total Period -2.413791 0.1381 -28.40158 0
Pre Period -2.64546 0.0846 -22.57939 0
Post Period -1.028839 0.7442 -21.82535 0
Source: Computed Output
Table 3: Results of GJR GARCH and Asymmetric Response for the Pre Period
Estimates of GJR GARCH Model for the Pre Period
Variables Description Co-efficient Standard Error Z-Statistics Probability
γ0 Intercept 0.000243 0.000319 0.762637 0.4457
γ1 Nifty (R) -0.025474 0.009737 -2.61629 0.0089
α0 Constant 6.69E-07 3.76E-07 1.777529 0.0755
α1 ARCH 0.102167 0.029521 3.460864 0.0005
α2 GARCH 0.933412 0.020393 45.77121 0
α3 Asymmetric response -0.086927 0.029181 -2.978858 0.0029
Source: Computed Output
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Table 4: Results of GJR GARCH and Asymmetric Response for the Post Period
Estimates of GJR GARCH Model for the Post Period
Variables Description Co-efficient Standard Error Z-Statistics Probability
γ0 Intercept 0.000672 0.000351 1.913114 0.0557
γ1 Nifty (R) 0.082478 0.018709 4.408445 0
α0 Constant 3.99E-05 3.50E-06 11.39544 0
α1 ARCH 0.312947 0.135269 2.313524 0.0207
α2 GARCH -0.000309 0.006029 -0.051277 0.9591
α3 Asymmetric response 2.772952 0.327482 8.46749 0
Source: Computed Output
Table 5: Results of GJR GARCH Model for the Total Period
Estimates of GJR GARCH Model for the Total Period
Variables Description Co-efficient Standard Error Z-Statistics Probability
γ0 Intercept 0.000361 0.000237 1.522196 0.128
γ1 Nifty (R) -0.023285 0.008715 -2.67192 0.0075
α0 Constant 3.92E-05 3.53E-06 11.11867 0
α1 ARCH 0.338964 0.077423 4.378079 0
α2 GARCH -0.000337 0.020367 -0.016546 0.9868
α3 Asymmetric response 1.600506 0.159639 10.02577 0
α4 DUMMY 4.63E-07 4.12E-06 0.11254 0.9104
Source: Computed Output
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