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The purpose of this paper is to examine the presence of the day-of-the-week (DOW) and January effect in the Indian currency market for selected currency pairs; USD-(Indian rupee) INR, EUR-INR, GBP-INR and JPY-INR, from January, 1999 to December, 2014.

Ordinary least square regression analysis is used to examine the presence of DOW and January effect to test the efficiency of the Indian currency market. The sample period is later divided into two sub-periods, that is, pre- and post-2008 to capture the behavior of returns before and after the 2008 financial crisis. Further, the authors also use the non-parametric technique, the Kruskal-Wallis test, to provide robustness check for the results.

The results indicate that the returns during Monday to Wednesday are positive and higher than the returns on Thursday and Friday which show negative returns. The returns during January are found to be higher than the returns during rest of the year. Further, all currencies exhibit significant DOW and January effects in pre-crisis period, however, post-crisis; these effects disappear for all currencies indicating that the markets have become more efficient in the later time. The findings can be further attributed to the increased intervention in the forex markets by the Reserve Bank of India after the crisis.

The results have important implications for both traders and investors. The findings suggest that the investors might not be able to earn excess profits by timing their positions in some particular currencies taking the advantage of DOW or January effect which in turn indicates that the currency markets have become more efficient with time. The results are in conformity with those reported for the developed markets. The results might be appealing to the practitioners as well in a way that they can consider the state of financial market for financial decision making.

The authors provide the first study to examine the calendar anomalies (DOW and January effect) across a range of emerging currencies using 16 years of data from January, 1999 to December, 2014. To the best of the authors’ knowledge, no study has yet examined these calendar anomalies in the currency markets using data which covers two important periods, pre-2008 and post-2008.

Corporate treasurers manage the currency risk of their organization by hedging through futures contracts. The purpose of this paper is to evaluate the effectiveness of hedging by US currency futures contracts by taking into account the efficiency of the currency market.

The static models for calculating hedge ratio are as popular as dynamic models. But the main disadvantage with the static models is that they do not consider important properties of time series like autocorrelation and heteroskedasticity of the residuals and also ignore the cointegration of the market variables which indicate short-run market disequilibrium. The present study, therefore, measures the hedging effectiveness in the US currency futures market using two dynamic models – constant conditional correlation multivariate generalized ARCH (CCC-MGARCH) and dynamic conditional correlation multivariate GARCH (DCC-MGARCH).

The study finds that both the dynamic models used in the study provide similar results. The relative comparison of CCC-MGARCH and DCC-MGARCH models shows that CCC-MGARCH provides better hedging effectiveness result, and thus, should be preferred over the other model.

The findings of the study are important for the company treasurers since the new updated Indian accounting standards (Ind-AS), applicable from the financial year 2016–2017, make it mandatory for the companies to evaluate the effectiveness of hedges. These standards do not specify a quantitative method of evaluation but provide the flexibility to the companies in choosing an appropriate method which justifies their risk management objective. These results are also useful for the policy makers as they can specify and list the appropriate methods for evaluating the hedge effectiveness in the currency market.

Majorly, the studies on Indian financial market limit themselves to either examining the efficiency of that market or to evaluate the effectiveness of the hedges undertaken. Moreover, most of such works focus on the stock market or the commodity market in India. This is one of the first studies which bring together the concepts of efficiency of the market and effectiveness of the hedges in the Indian currency futures market.

The purpose of this paper is to examine the contemporaneous and causal relationship between returns (volatility) and trading volume in the Indian currency futures market for selected currency pairs; USD-INR, EUR-INR, GBP-INR and JPY-INR, from August 2008 to December 2014.

The data for all the currency futures series has been taken from National Stock Exchange of India Limited which represents the daily settlement prices along with trading volume. The contemporaneous returns-volume relation is tested using the generalized method of moments, and Granger-causality framework impulse response function is used to test the predictive ability of returns (volatility) and volume for each other.

The author reports a positive contemporaneous relationship between futures returns and trading volume which persists even after controlling for heteroskedasticity providing support to mixture of distribution hypothesis. The results show a unidirectional Granger causality from futures returns to volume. However, there is a significant bidirectional Granger causality between returns volatility and volume lending support to sequential arrival of information hypothesis. Next, the results for cross-currencies show significant influence of US dollar on the volume and returns of all other currencies. Overall, the author suggests that the short- to medium-term movements in the currency markets are dominated by market microstructure and not by fundamentals.

The findings of this paper are very important for the participants in the market and regulators. The participants in the market require alternatives to diversify their risk. The significant relationship between futures returns (volatility) and trading volume implies that the current trading volume help predict the futures prices and should lead to creation of more reliable hedging strategies for investment purposes. Further, it may interest the regulators who need to decide upon the appropriateness of their policies in the currency futures market. Based on returns-volume relation, they need to set forth market restrictions such as daily price movement and position limits.

To the best of the knowledge, no study has yet investigated the forecast ability of trading volume to price changes and their volatility in the Indian currency futures market. Given that currency futures market is one of the largest markets in the world, and Indian rupee has seen wide fluctuations in the recent years, it seems exciting to explore the price-volume relationship in the Indian currency futures market.

The purpose of this paper is to examine the presence of the turn-of-month effect in the Indian currency market for selected currency pairs: USD-INR, EUR-INR, GBP-INR and JPY-INR, from January 1999 to April 2014.

Ordinary least square regression analysis is used to examine the presence of the turn-of-month effect and to test the efficiency of the Indian currency market. The characteristics of the returns during the turn-of-month days are compared with that of the non-turn-of-month trading days. The sample period is later divided into two sub-periods, that is, pre- and post-2008 to capture the behavior of returns before and after the 2008 financial crisis.

The results indicate the existence of pricing patterns which are unique to individual currencies. For the entire sample period, USD and JPY exhibit turn-of-month effect and the returns in turn-of-month trading days are significantly lower than the returns during non-turn-of-month trading days. For the sub-period before 2008, all the currencies exhibit significant turn-of-month effects and the returns in the turn-of-month trading days are significantly lower than those in the non-turn-of-month trading days. However, post-2008; this effect vanishes for all the currencies except for USD.

The results have important implications for both traders and investors. The findings suggest that the investors might not be able to earn excess profits by timing their positions in some particular currencies taking the advantage of turn-of-month effect which in turn indicates that the currency markets have become more efficient with time. The results are in conformity with those reported for the developed markets.

To the best of the author’s knowledge, no study has yet examined these calendar anomalies in the currency markets using data which covers two important periods, pre-2008 and post-2008. Therefore, we provide a pioneer study in which we analyze the calendar anomalies in an emerging currency market (India) by segregating the data before and after 2008 financial crisis.

The purpose of this paper is to examine the linear and nonlinear relations between returns volatility and trading volume for the Indian currency futures market.

To examine the contemporaneous relation between returns volatility and volume, the author uses the generalized method of moment estimator. For the linear causal relation, the author makes use of Granger (1969) bivariate vector autoregression model. The author tests for nonlinear Granger causality between returns volatility and trading volume based on a modified version of the Baek and Brock (1992) nonparametric technique developed by Hiemstra and Jones (1994).

The results indicate a negative contemporaneous relation between returns volatility and trading volume; therefore, the mixture of distribution hypothesis is not supported. The results of both linear and nonlinear Granger causality between futures returns volatility and trading volume indicate a significant bidirectional relation between the two variables lending support to the sequential arrival of information hypothesis. The results are robust to divergence of opinions as proxied by open interest.

The findings of this paper are important for the participants in the market and regulators. The participants in the market require alternatives to diversify their risk. The significant causal relation between returns volatility and trading volume implies that trading volume helps predict the futures prices and should lead to creation of more reliable hedging strategies for investment purposes. Furthermore, it may interest the regulators who need to decide upon the appropriateness of their policies in the currency futures market.

To the best of the author’s knowledge, there is no study that investigates the forecast ability of trading volume to futures returns volatility in an emerging currency futures market. Given that currency futures market is one of the largest markets in the world, and Indian rupee has seen wide fluctuations in the recent years, it seems exciting to explore the price–volume relation in the Indian currency futures market.

The purpose of this paper is to study the lead-lag relationship between the futures and spot foreign exchange (FX) market in India to understand the price discovery mechanism and the relationship between these two markets.

The estimation of lead-lag relationship is realized in three steps. First unit root and stationarity tests (Augmented Dickey-Fuller, Phillips-Perron, and Kwiatkowski-Phillips-Schmidt-Shin) are applied to check the stationarity of the data. Second, cointegration tests (Engle and Granger’s residual based approach and Johansen’s cointegration test) are applied to determine long run relationship between the markets. Third, error correction estimation is carried out by applying Vector Error Correction Model (VECM) to determine the leading market.

The study finds that there is a long run relationship between the futures and spot market where the futures market has emerged as the leading market for the four currencies studied in the paper.

Majorly, the studies on Indian FX market limit themselves to identifying the efficiency of the market and the studies which talk about the lead-lag relationship focus on the Indian stock market. This paper enhances the existing literature on Indian FX market by exploring the less explored subject of the lead-lag relationship between futures and spot FX market in India.

The purpose of this paper is to measure the effectiveness of the hedging with futures currency contracts. Measuring the effectiveness of hedging has become mandatory for Indian companies as the new Indian accounting standards, Ind-AS, specify that the effectiveness of hedges taken by the companies should be evaluated using quantitative methods but leaves it to the company to choose a method of evaluation.

The paper compares three models for evaluating the effectiveness of hedge – ordinary least square (OLS), vector error correction model (VECM) and dynamic conditional correlation multivariate GARCH (DCC-MGARCH) model. The OLS and VECM are the static models, whereas DCC-MGARCH is a dynamic model.

The overall results of the study show that dynamic model (DCC-MGARCH) is a better model for calculating the hedge effectiveness as it outperforms OLS and VECM models.

The new Indian accounting standards (Ind-AS) mandates the calculation of hedge effectiveness. The results of this study are useful for the treasurers in identifying appropriate method for evaluation of hedge effectiveness. Similarly, policymakers and auditors are benefitted as the study provides clarity on different methods of evaluation of hedging effectiveness.

Many previous studies have evaluated the efficiency of the Indian currency futures market, but with rising importance of hedging in the Indian companies, Reserve Bank of India’s initiatives and encouragement for the use of futures for hedging the currency risk and now the mandatory accounting requirement for measuring hedging effectiveness, it has become more relevant to evaluate the effectiveness of hedge. To the authors’ best knowledge, this is one of the first few papers which evaluate the effectiveness of the currency future hedging.

The purpose of this paper is to examine the price discovery and volatility spillovers in spot and futures prices of four currencies (namely, USD/INR, EURO/INR, GBP/INR and JPY/INR) and between futures prices of both stock exchanges namely, Multi-Commodity Stock Exchange (MCX-SX) and National Stock Exchange (NSE) in India.

The study applies cointegration test of Johansen’s along with VECM to investigate the price discovery. GARCH-BEKK model is used to examine the volatility spillover between spot and futures and between futures prices. The other two models namely, constant conditional correlation and dynamic conditional correlation are used to demonstrate the constant and time-varying correlations. In order to confirm the volatility spillover results, the study also applies test of directional spillovers suggested by Diebold and Yilmaz (2009, 2012).

The results of the study show that there is long-term equilibrium relationship between spot and futures and between futures markets. Between futures and spot prices, futures price appears to lead the spot price in the short-run. Volatility spillover results indicate that the movement of volatility spillover takes place from futures to spot in the short-run while spot to futures found in the long-run. However, the results of between futures markets exhibit the dominance of MCX-SX over NSE in terms of volatility spillovers. By and large, the findings of the study indicate the important role of futures market in price discovery as well as volatility spillovers in India’s currency market.

The results highlight the role of futures market in the information transmission process as it appears to assimilate new information quicker than spot market. Hence, policymakers in emerging markets such as India should focus on the development of necessary institutional and fiscal architecture, as well as regulatory reforms, so that the currency market trading platforms can achieve greater liquidity and efficiency.

Due to recent development of currency futures market, there is dearth of literature on this subject. With the apparent importance of currency market in recent time, this study attempts to study the efficient behavior of currency market by way of examining the price discovery and volatility spillovers between spot and futures and between futures prices of four currencies traded on two platforms. The study has strong implications for India’s stock market especially at the time when its currency is under great strain owing to the adverse impact of global financial crisis.

This paper aims to propose the implied volatility index for the US dollar–Indian rupee pair (INRVIX). The study seeks to examine whether INRVIX truly reflects future USDINR (US Dollar-Indian rupee) volatility and signals profitable currency trading strategies.

Two measures of INRVIX are constructed and compared: a model-free version based on the methodology adopted by the Chicago Board of Options Exchange (CBOE) and a model-dependent version constructed from Black–Scholes–Merton-implied volatility. The proposed INRVIX is computed by tweaking some parameters of the CBOE methodology to ensure compatibility with the microstructure of the Indian currency derivatives market. The volatility forecasting ability of INRVIX is compared to that of a generalized autoregressive conditional heteroscedasticity (1,1) model. Ordinary least squares regression is used to examine the relationship between n-day-ahead USDINR returns and different quantiles of INRVIX.

Results indicate that INRVIX based on the model-free approach reflects ex post volatility in a better manner than its model-dependent counterpart, although neither measure is found to be an unbiased and efficient forecast. Subsample analysis across tranquil and turbulent periods corroborates the results. The volatility forecasting performance of INRVIX is found to be better than that of forecasts based on historical time-series. These results are consistent with similar studies of developed market currencies. The study does not find any significant relationship between extreme levels of INRVIX and the profitability of trading strategies based on such levels, which is contrary to results from the equity options market.

Foreign exchange volatility affects the costs of international trade and the external sector competitiveness of Indian multinationals. It is a significant risk factor for financial institutions and traders in the financial markets. An implied VIX for the USDINR could serve as an indicator of expected foreign exchange risk. It could thus provide a signal for a possible intervention in the forex market by the regulator. Regulators could introduce volatility derivative contracts based on the INRVIX. Such contracts would enable hedging of the pure volatility risk of dollar–rupee exposure. Thus, the study has practical implications for investors, hedgers, regulators and academicians alike.

To the author’s knowledge, this is one of a few studies to construct an implied VIX for an emerging currency like the rupee. The study is based on up-to-date sample data that includes the recent COVID-19 market crash. A novel contribution of this paper is that in addition to examining whether INRVIX contains information about future USDINR volatility, and it also examines the signalling power of INRVIX for currency trading strategies.

This study aims to examine the presence of the day-of-the-week (DOW), January and turn-of-month (TOM) effect in 20 currency pairs against the US dollar, from January, 1995 to December, 2014.

Ordinary least square with GARCH (1,1) framework is used to examine the presence of DOW, January and TOM effect to test the efficiency of the currency markets. The sample period is later divided into two sub-periods of equal length, that is, from 1995 to 2004 and 2005 to 2014, to explore the time-varying behavior of the calendar anomalies. Further, the authors also use the non-parametric technique, the Kruskal–Wallis test, to provide robustness check for the results.

For the DOW effect, the results indicate that the returns on Monday and Wednesday are negative and lower than the returns on Thursday and Friday which show positive and higher returns. The returns of all the currencies are higher (lower) in January (TOM trading days) and lower (higher) during rest of the year (non-TOM trading days). However, these calendar anomalies seem to have disappeared for almost all currencies during 2005 to 2014 and indicate that the markets have achieved a higher degree of efficiency in the later part of the sample.

The results have important implications for both traders and investors. The findings suggest that the investors might not be able to earn excess profits by timing their positions in some particular currencies taking the advantage of DOW, January or TOM effect, which in turn indicates that the currency markets have become more efficient with time. The results might be appealing to the practitioners as well in a way that they can consider the state of financial market for financial decision-making.

The findings of lower returns on Monday and Wednesday and high returns during Thursday and Friday for all the currencies indicate that the foreign investors can take the advantage by going short on Monday and Wednesday and long on Thursday and Friday. Similarly, the returns of all the currencies are higher (lower) in January (TOM trading days) and lower (higher) during rest of the year (non-TOM trading days). During this period, investors in the currency markets could benefit themselves by taking long (short) positions in January (TOM trading days) and short (long) positions during rest of the year (non-TOM trading days).

The author provides a pioneer study on the presence of calendar anomalies (DOW, TOM and the January effect) across a wide range of currencies using 20 years of data from January 1995 to December 2014. To the best of the author’s knowledge, no study has examined the presence of January effect in the currency market; therefore, the author provides the first study in which January effect in a number of currencies is investigated.