Hedging Effectiveness of Currency Futures

Transcription

1 Hedging Effectiveness of Currency Futures Tulsi Lingareddy, India ABSTRACT India s foreign exchange market has been witnessing extreme volatility trends for the past three years. In this context, foreign exchange derivatives become important tools for market risk management. While the over the counter (OTC) derivatives, forwards, are widely used in the post liberalisation period, exchange traded currency futures are also available for trading since Aug 008. An attempt is made to study the hedging effectiveness of exchange traded futures and OTC traded forwards. The results indicated that the hedging effectiveness of forwards is significantly higher than that of exchange traded futures. This could be one of the reasons for the less popularity of the futures among hedgers compared to OTC forwards. Introduction India s foreign exchange market has been witnessing extreme volatility trends for the past three years. Initially due to unstable foreign investment flows into and out of the country and more recently due to changing global as well as domestic economic scenarios. These high volatilities in Rupee particularly against US Dollar pronounce the strong need for appropriate market risk management measures. In this context, foreign exchange derivatives become important tools for market risk management. While the over the counter (OTC) derivatives (forwards) are widely used in the post liberalisation period, exchange traded currency futures are also available for trading since Aug 008. On the other hand, the OTC markets for currency are still largely controlled by the RBI. Following the extreme volatilities, the RBI has brought some more regulations on the booking and cancellation of OTC forwards, the hedging has become slightly costlier especially under the wide fluctuations in the exchange rate. As a result, corporates have apparently become slightly reluctant to hedge and their uncovered exposure to exchange rate risk has increased significantly in the past one year. In this context, exchange traded derivatives like futures and options can be used as alternative tools for hedging in currency risk management. Trading in exchange traded products has gained significant volumes during the past four years. The trade volumes of futures have gone up from Rs.3.07 lakh crore in 008 to Rs.86.7 lakh crore in though moderated to Rs.7. lakh crore in Apart from futures, options were also introduced for USD/INR in Oct 01. The notional trade value of options also increased from Rs.1.7 lakh crore in to Rs.13.3 lakh crore in and to Rs.18.5 lakh crore in Considering the rise in liquidity in futures markets, it is useful to study the characteristics of trading in this exchange traded derivatives. > RJEBS: Volume: 0, Number: 11, September-013 Page 1

2 Table 1: Trends in trade volumes in various segments (Rs cr) OTC traded Exchange traded Cash Tom Spot Forward Total Futures Options Source: Collated and compiled from the websites of CCIL, NSE, USE and MCX-SX Unlike the OTC forwards, there seems to be no extensive use of currency futures for hedging purpose and the currency futures reported to have been used mostly for speculative and arbitrage purposes (Lingareddy, 009). However, the OTC derivative markets are dominant and preferred instruments in currency markets world over compared to the exchange traded derivatives. Nonetheless, in view of the regulatory requirements and restrictions on the OTC products, exchange traded futures can be a suitable alternative for OTC forwards towards. But in order to be acceptable for hedging purpose, the derivatives should move in close connection with the underlying spot market. One way to understand the close connection between the derivative and the underlying spot is measuring the hedging effectiveness of the derivative instrument. Thus, an attempt is made to study the hedging effectiveness of exchange traded futures in comparison with that of OTC forwards The paper has been presented in five sections. First part includes introduction while second and third sections cover review of literature and Data & methodology. Fourth section discusses the results of various econometric and statistical analyses conducted. Fifth section concludes the findings of the study. II. REVIEW OF RELEVANT LITERATURE Unlike studies on volatility, not many studies have focused on studying the hedging effectiveness of currency futures and especially in the Indian context. A few empirical studies have explored the stock futures and calculated hedge ratios in emerging market economies such as Bhaduri and Durai (008) while others like Roy and Kumar (007) focused especially on commodity futures. Bhaduri and Durai (008) analysed the effectiveness of hedge ratio through mean return and variance reduction between hedge and unhedged position for various horizon periods of NSE Stock Index Futures using GARCH model. Nonetheless, the yobserved that simple OLS-based strategy also performed well at shorter time horizons. Roy and Kumar (007) studied hedging effectiveness of wheat futures in India using least square method and found that hedging effectiveness provided by futures markets was as low as 15%. A more recent study by Kumar et al (008) also tried to explore hedging effectiveness of futures contract on a financial asset and commodities in Indian markets. They estimated dynamic and constant hedge ratio for S&P CNX Nifty index futures, Gold futures and Soybean futures. Various models (OLS, VAR, and VECM) were used to estimate constant hedge ratio. To estimate dynamic hedge ratios, they used VAR-MGARCH. Results showed that futures and spots prices are found to be cointegrated in the long run. Among constant hedge ratio models, in most of the cases, VECM performs better than OLS and VAR models whereas the time varying hedge ratio derived from VAR-MGARCH model provides highest variance reduction as compared to the other methods in both in-sample as well as out-of sample period for all contracts. > RJEBS: Volume: 0, Number: 11, September-013 Page

3 III. DATA & METHODOLOGY Data: Daily time series data futures and spot exchange rates were compiled from the start of the trading in currency futures on exchanges. That is the period from Sep 008 to Sep 01 was considered. In view of the liquidity and to have continuity in futures data, two contracts are selected. Near month contract and next-month contracts near-month contract tentatively starts at around 5 th of each month with around 30days residual maturity and ends days before maturity. The period coincides with the highest liquid contract of all the 1 contracts traded. Similarly, the next-month contract starts 5 th of the previous month with around 60 day residual maturity till 31 days residual maturity. Thus, two series of current and next month futures contract series are compiled. Apart from this, trade volumes of futures were compiled corresponding to the daily exchange rate data of exchange rates. Methodology The study has mainly used least squares regression analysis for testing hedging effectiveness. The details of various techniques and models used in the analysis were described below. Hedge Ratio and Hedging Effectiveness: Theoretically, hedge ratio is assumed to be equal to unity. However, in reality, it may not be true as the derivative instrument prices rarely move in tandem with the underlying assets. Hence, in the real world the hedge ratio is different from one. To define, the hedge ratio measures degrees of the linear relationship between the exposed assets and derivative instruments used to provide protection for the underlying asset (Homaifar, 004). s h = f Where ρ is the coefficient of correlation between change in spot and futures prices σ s is standard deviation of changes in spot rates σ f is standard deviation of changes in futures exchange rates Alternatively, return series of spot price is regressed on the return series of futures prices. The slope coefficient of the OLS regression is the Minimum-Variance Hedge Ratio. S t = c + hf t +ε t Where, S t and F t are spot and futures returns, h is the optimal hedge ratio and ε t is the error term in the OLS equation. This hedge ratio, estimated from the best fit of regression, is termed as the minimum variance hedge ratio and no other hedge ratio produces a hedge with a smaller variance. Hence, the minimum variance hedge ratio is also called optimal hedge ratio. Hedging effectiveness, on the other hand, can be defined as the proportion of variance eliminated by hedging (Hull, 005). The R-square of the specified regression model indicates the hedging effectiveness. Alternatively, as described by Hull (005), hedging effectiveness is also measured as follows. h f s > RJEBS: Volume: 0, Number: 11, September-013 Page 3

4 IV. RESULTS Characteristics of data series Time series data has some inherent characteristics such as serial correlation and heteroscedasticity that may sometime give rise to misleading or false interpretation of results. Hence it becomes important to first scrutinise the data for the presence of such characteristics and then find out an alternate way to use the data through transformation while preserving the basic information available in the series. In view of this, the following tests are formed to understand characteristics of futures and spot exchange rates. Table 1: Results of Augmented Dickey-Fuller test statistic Augmented Dickey-Fuller test statistic (t-statistic) Probability Spot I(1) * Near-month futures I(1) * Next-month futures I(1) * Futures volumes I(0) * * Denotes rejection of the hypothesis at the 0.01 level Stationarity test: Prior to do any detailed analysis, it is important to ensure that the data series are stationary so that the results of any further analysis will be reliable and not spurious. In order to test whether the series is stationary or not, tests for the presence of unit root are conducted on both the exchange rate series. The results of Augmented Dickey-Fuller tests indicated that the original exchange rate series of both spot and futures were having unit roots and hence non-stationary. But their return series, I(1)- process, didn t have a unit root and are stationary as presented in Table below. The exchange rate series have one degree of integration. Cointegration: Further, the co-integration between spot and futures prices is tested by Johansen s (1991) maximum likelihood method. The results of co-integration are presented in Table. It can be observed that the I(1) series of spot and futures prices have more than one co-integrating vectors at the 1% level of significance. Table : Johansen co-integration tests of spot and futures prices I(1) series Hypothesized number of CEs Eigen value Trace Statistic Critical Value Probability Near-month futures None * At most 1 * Next-month futures None * At most 1 * Futures Volumes None * At most 1 * * Denotes rejection of the hypothesis at the 0.01 level Hedge ratio and hedging effectiveness Once it was ensured that the data series are stationary then optimal hedge ratios and hedging effectiveness were calculated through OLS regression models. The optimal hedge ratio, as defined by Hull (005), is the product of coefficient of correlation between change in spot and futures prices and the ratio of the standard deviations of change spot and futures prices. In other words, the optimal hedge ratio is the slope coefficient of the best fit regression line when the return series of spot prices are > RJEBS: Volume: 0, Number: 11, September-013 Page 4

5 regressed against the return series of futures prices. Hedging effectiveness on the other hand is the proportion of the variance, which is eliminated by hedging. The optimal hedge ratio and the hedging effectiveness calculated from regression models indicated similar results with nearly equal numbers. The optimal hedge ratio is about 0.64 for the near-month futures contracts and 0.64 to 0.65 for the next month futures. Hedging effectiveness on the other hand is only about 37% in both the futures with both the tests. Hedging effectiveness of forwards has also been calculated using the RBI s polled data as well as the trade reported data from Reuters platform. Understandably, the polled data from the RBI and traded data from the Reuters are slightly different from each other. Hence, the hedging ratios were calculated separately for both series using the same methodology after making sure that the data series are stationary at I(1). The hedging effectiveness was found to about 99%. Table 4: Results of OLS regression OLS Regression Near-month futures Optimal Hedge ratio * Hedging Effectiveness * Next-month futures Hedge ratio * Hedging Effectiveness * OTC forwards Hedge ratio * Hedging Effectiveness * * Denotes rejection of the hypothesis at the 0.01 level Thus, the hedging effectiveness was found to be very high in OTC forwards compared to that in futures market. Thus, the extent of variation in spot rates explained and/or eliminated if hedged using futures is significantly lower than the variance eliminated using the OTC forwards. This was on account of the relatively high correlation between OTC forward and spot exchange rates at 0.99 compared to the correlation coefficient of 0.61 between futures and spot exchange rates. VI. CONCLUSIONS Thus, from the results and discussion it can be concluded that currency futures in Indian markets have relatively low hedging effectiveness, as the futures and spot rates are only moderately correlated and not at a higher degree. On the other hand, the forwards have a very strong correlation with spot markets and hence their hedging effectiveness is relatively high. A further investigation particularly taking into the views and opinions of market participants may throw some light on the factors responsible for the apparent trends. REFERENCES Baum, C.F. and Barkoulas, J., Time-varying risk premia in the foreign currency futures basis, Journal of Futures Markets, 1996, 16(7): Bhaduri, S. N., and Durai, S. N. S., Optimal hedge ratio and hedging effectiveness of stock index futures: evidence from India, Macroeconomics and Finance in Emerging Market Economies, 1, 008, Fama, E.F., Forward and spot exchange rates, Journal of Monetary Economics 14, 1984, > RJEBS: Volume: 0, Number: 11, September-013 Page 5

6 Homaifar A. Ghassem., Managing Global Financial and Foreign Exchange Rate Risk, John Wiley & Sons, Inc., Hoboken, New Jersey, 004 Hull, J.C. Options, Futures, and other Derivatives.New Jersey: Prentice Hall. Homaifar A. Ghassem (004) Managing Global Financial and Foreign Exchange Rate Risk, John Wiley & Sons, Inc., Hoboken, New Jersey, 006. Inci, A.C. and Lu, B., Currency futures-spot basis and risk premium. Journal of International Financial Markets, Institutions and Money, 007, 17(): Kumar Brajesh, Priyanka Singh and Ajay Pandey., Hedging Effectiveness of Constant and Time Varying Hedge Ratio in Indian Stock and Commodity Futures Markets, W.P. No , Indian Institute of Management, Ahmedabad, June 008. Lingareddy, Tulsi., Foreign Exchange Derivative Markets in India: Futures Vs Forwards, Rakshitra, August, 009. Roy, A., and Kumar, B., Castor seed futures trading: Seasonality in Return of Spot and Futures Market, Paper presented at the 4th International Conference of Asia-pacific Association of Derivatives (APAD), Gurgaon, India, Jun > RJEBS: Volume: 0, Number: 11, September-013 Page 6