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ARE currency forwards effective in volatile market conditions? An emerging market perspective.

1. Introduction

Volatility in exchange rates has been a characteristic of crises. The subprime crisis that transformed into a global crisis only emphasised this point. Emerging markets, that had erstwhile faced a surge of capital flows seeking higher returns, were suddenly subject to 'reversal' as Dollar's role as a 'safe haven' gained ground. A number of firms were caught on the wrong foot with many facing bankruptcy. Forward currency contracts have been widely used by importers/exporters to hedge against currency risk. Their ability to safeguard firms against currency volatility however, has been found wanting. The paper explores the reasons for the same using the Indian experience as a backdrop.

An importer faces the risk of domestic currency depreciation (US $ appreciation). A fall in value of the same necessitates larger payment in local currency terms for buying the same amount of foreign currency. In the early years, when the domestic currencies in developing countries were, in general, under pressure to depreciate, forward contracts were extensively used by importers to minimize currency risk. For an exporter on the other hand, the risk is that of local currency appreciation (US $ depreciation) resulting in lower realization in domestic currency terms. In the years preceding the global crisis, emerging market economies faced huge capital inflows, leading to appreciation of domestic exchange rates. Exporters faced the risk of loss due to lower realization on account of appreciating local currency.

Forwards, being single period contracts however could not provide long-term hedge in the face of sustained volatility. In a scenario of continuously appreciating/depreciating domestic exchange rates, forward contacts failed to mitigate long term exchange risk since the forward rate is a function of continuously appreciating/depreciating domestic spot rate. Against these developments, the paper seeks to explore and test the hypothesis that forward rates failed to hedge currency risks of an exporter in an environment of high and sustained local currency volatility.

Section II highlights derivatives usage in emerging markets to gauge the volume of activity and reasons out the dominance of foreign exchange derivatives in emerging markets. While Section III underpins the basic premise of illiquid long-term contracts, Section IV provides evidence on the existence of volatility clusters and analyses the behaviour of forward premia during periods of high volatility. Section V looks at the earnings volatility in case of multi-period exposure when using forwards. Finally, Section VI suggests for the way forward including possible measures to negate the shortcomings of the currency forwards.

2. The Predominance of Derivatives in Emerging Economies

The use of derivatives in emerging economies has increased significantly in recent years. The most common usage has been for hedging against currency risk. Unlike advanced countries, wherein interest rate derivatives dominate, accounting for more than 75 per cent of the derivative market, currency derivatives account for nearly 80 per cent of the derivative market in emerging economies. (1) The share of emerging economies in the global derivatives market had also increased to nearly 12 per cent by 2007 with the growth rate of 24 per cent per annum during 2004-07. Besides, the bulk of emerging economy foreign currency derivative activity is concentrated in Emerging Asia, with Singapore and Hong Kong accounting for the lion's share and India being the distant third. The average daily turnover of Over-the-Counter (OTC) derivative activity in 2007 was US $516 billion with foreign currency derivatives accounting for US $423 billion (Table 1).
Table 1 Geographical Distribution of Daily Average Turnover of
Reported OTC Derivatives Market Activity (US $ in Billions)

                   Total              Foreign               Interest
                                      exchange                rate
             2001  2004   2007  2001    2004    2007  2001    2004

Emerging      137    207   438   130       183   355     6        24

China         ...    ...     1   ...       ...     1   ...       ...

Hong Kong      52     82   160    49        70   143     3        11

India           2      4    27     2         3    24     0         1

Indonesia       1      1     1     1         1     1     0         0

Korea           4     11    23     4        10    18     0         1

Malaysia        1      1     2     1         1     2     0         0

Philippines     1      0     1     1         0     1     0         0

Singapore      73    100   210    69        91   153     3         9

Taiwan          2      6     8     2         5     7     0         2

Thailand        1      2     5     1         2     5     0         0

Latin           8      9    18     7         7    15     0         2

Central         5     10    19     4         8    16     1         2

Others         10     14    20    10        11    16     1         3

Total         160    248   516   151       217   423     8        31


Emerging       83


Hong Kong      17

India           3

Indonesia       0

Korea           5

Malaysia        0

Philippines     0

Singapore      57

Taiwan          1

Thailand        0

Latin           3

Central         5

Others          4

Total          95

Source: Triennial Central Bank Survey, Bank of International
Settlement 2007

The predominance of foreign exchange in the emerging economy derivative markets is due to varied reasons. First, many countries have moved away from fixed exchange rates and adopted flexible exchange rate regimes; those exposed to greater currency risks. Second, structural reforms and trade liberalization has ushered in greater financial and trade integration with the global economy, increasing currency risk exposure of market participants. Third, with the gradual liberalisation of foreign exchange and capital controls by many emerging economies, the ever increasing magnitude of capital flows and their sudden reversal for any development in the global capital markets have brought in along with it inherent currency risks. Fourth, there is increasing investor interest in the emerging economies leading to higher investment flows. Foreign exchange derivatives encourage such flows, as they allow hedging against currency risk. Fifth, exporters/importers find derivative instruments like forwards useful for hedging against currency volatility, at least, for hedging against currency fluctuations in the shortrun. Sixth, foreign currency debt holders can use currency swap market to change the currency configuration of debt portfolio to diversify and build natural hedges.

The forward rate is generally determined by the interest rate differential between the two currencies. The higher interest currency is at a discount to avoid covered interest arbitrage opportunity. In emerging economies with capital account restrictions, the forward rates are determined by the supply of and demand for forward contacts, with interest rate differentials acting as a proxy and spot volatility influencing forward premia.

3. Illiquid Long-Term Contracts

Notwithstanding the growth in use of currency forwards, forward contracts fail to provide protection in situations of sustained long-term appreciation/ depreciation of domestic currency by being short-term in nature. An Exporter/ Importer faces problems because forward contracts are not available for the long-term, which could have enabled currency risk hedging over a 2-3 year time horizon.

Liquidity in longer term forward contracts in emerging economies is generally low because of high interest differential between advanced economy and emerging economy interest rates. This translates into high forward premium for the low interest advanced economy currency, which increases with the duration of the forward contract via the covered interest parity condition. Currency hedging for longer term at such high premium therefore may not be attractive for a potential hedger. On the contrary, when a forward contract is established between two advanced economy currencies, the lower interest differential will allow longer term forward contracts, as the premium would be low. (2) Therefore, liquidity in higher maturity contracts in developed economies is expected to be higher.

In conditions of low volatility, however short-term currency forwards can be rolled over to hedge exposures of desired duration without implications for hedge effectiveness. The presence of long periods of high volatility, however, poses a few problems.

4. Volatility Clusters and Forward Premiums

Currency markets are marked by the presence of volatility clusters. They are of concern since short term contracts that expire during a period of high volatility have to be rolled-over at very high cost (premium) providing a limited hedge. As a result, when a contract is renewed/rolled over during a volatility cluster, the risk due to higher volatility is often reflected in higher forward premium, which makes hedging unattractive.


To test the above hypothesis, we may look at the volatility behavior of the Indian Rupee (INR) over the past many years. Volatility clusters can be observed for time series data of INR-US $. The standard deviation of logarithmic returns over a year (252 trading days) has been used. (3) The "rolling" method of calculating volatility has been used throughout the paper. (4) Modulus of logarithmic returns gave significant and gradually decreasing autocorrelation. (5) This indicates the presence of clusters. The month-end annual volatilities confirm this test.

A 'volatility cluster' was determined by the number of months where volatility exceeded a stipulated benchmark, e.g. if five per cent is the benchmark, a period of four months with volatilities in excess of the benchmark would constitute a four month long volatility cluster. Table 3 indicates that even when using a conservative benchmark, the average length of volatility clusters extended well beyond the longest expiry period of available liquid forward contracts; the maturity of existing contracts is insufficient to steer clear of turbulent periods.

The likelihood that the forward contract would need to be rolled over in the midst of volatility clusters is thus very high. With higher premiums entering the picture, such roll-overs would therefore fail to provide the desired long-term hedge.

Following the evidence of the presence of volatility clusters, we need to look at forward premium movement during such periods. Monthly volatility was used for this purpose. (7) A period covering January 2005 to December 2008 is considered, keeping in view sharp movement of the rupee (appreciation followed by sharp depreciation). In addition, we consider the absolute deviation of forward premium from its mean; over the period January 2001 to December 2009; a period characterized by stable as well as volatile phases.

Figure 2 illustrates the results. We notice that forward premium deviation in all cases (for one, three and six month forwards) was in tandem with volatility figures for the month. (8) In other words, increase in volatility was matched by higher premium/discount in the forward currency market. A possible reason for this could be sharp changes in demand and supply by firms when confronted with exchange rate volatility. (9) The higher demand/ supply would lead to changes in forward premia.


The paper finds evidence for the presence of volatility clusters in the foreign exchange market in India. Additionally, a relationship between forward premia and volatility exists having implications for the use of forward contracts as an effective hedging instrument.

5. Methodology for Testing Volatility for Multi-period Exposure

Forward contracts have long been cited as derivatives offering reduced volatility and stable earnings. This conclusion holds true when an entity wishes to hedge a single exposure in the future. When faced with recurrent exposures however, this conclusion may be debated.


The rolling method of calculating volatility has been used. The data for spot and forward INR-US $ exchange rate covers the period from January 1993 to January 2009. The period was selected keeping in view the postliberalization era when India moved towards a flexible/managed exchange rate system and the impact on exchange rates. A secondary consideration was the availability of forward premia for USD-INR. Income streams translated from dollar to rupee have been considered. The earnings volatility was calculated in two different ways.

First, is the conventional measure which incorporates reduced volatility on account of 'locked-in' price. To arrive at this measure, earnings volatility using forward contracts were compared to those without, one at a time. For volatility in earnings using forward rates, the standard deviation of returns in earnings was calculated for each year.

Standard Deviation ([X.sub.i]) X sqrt(12)


[X.sub.i] = Ln (Fwd [Rate.sub.ij] / Spot Ratei)

where i represent the current date and j the contract type (1M, 3M, 6M); The resultant volatility figures were compared with the 'spot' measures. Here [X.sub.i] takes the form:

[X.sub.i] = Ln (Spot [Rate.sub.i + t]/ Spot [Rate.sub.j]

where t represents the period of the forward contract being compared.

The test produced expected results. Forward contracts offered markedly reduced volatility. Figure 3 illustrates this result.

This however, does not reveal the complete picture. Such an analysis, shows that forward contracts allow the hedger to lock-in the price. It however, fails to account for changing forward rates when hedging successive income/expense streams. Measuring for volatility in earnings must account for such changes. X.sub.i] mentioned earlier now takes the form:

[X.sub.i] = Ln (Fwd [Rate.sub.(i - t)j] / Fwd [Rate.sub.(i - t - l)j])

The corresponding figure for spot rates took the form of

[X.sub.i] = Ln (Spot [Rate.sub.i] / Spot [Rate.sub.i - l])

Here i refers to the current date, t refers to the contract period and j refers to the contract type. The numerator within the logarithmic function refers to the forward rate applicable for the month, as determined by the contract entered into a few months earlier. Likewise, the denominator refers to the forward rate of the month prior to the one for which the numerator indicates the forward rate.

This volatility figure accounts for the fact that forward contracts, although allow locking -in of currency exposure at a particular level, fail to hedge currency risk when faced recurrent foreign exchange exposures hedged using new currency forwards at the beginning of each period. The resultant volatilities are illustrated in Figure 4.


The atypical graph could be explained by the behaviour of forward rates. A firm which has continuous/multiple exposure in the foreign exchange market would not do any better by entering into a forward contract. Attempts to hedge by entering into a new currency forward at the beginning of each period would provide a limited hedge. (10)

A time of extreme volatility would reflect in earnings (through forward contracts) at a later date, since the contract rate is realized at the future date. Similarly, periods of low volatility in spot market would also be realized at a later period. This is because forward rates today reflect movement in the underlying. Hence, volatility in spot prices would eventually reflect in forward volatility a few months down the line.

6. A Suggestive Way Forward

Currency forwards in emerging markets display shortcomings. Large volatility clusters present the risk of higher costs on roll-over of contracts. Multiple exposures hedged by buying a single period contract at the outset is inadequate. This is because spot volatility gets transferred to future earnings by means of derived forward rates.

We could begin by looking at how to stretch the existing derivative tool-kit to get maximum advantage. To avoid the risk of encountering a volatility cluster, exporters/ importers could consider multi-period contracts (one/three/six/nine months) during normal times. It is important to consider such multi-period forward contracts because volatility clusters are often difficult to predict. This would involve hedging exposure in foreign currency by entering into multiple currency forwards of increasing maturity at the onset. Swap contracts present such an alternative. In principle, Swaps are series of forward contracts with different maturities. As such, they present an ideal means to hedge the mismatch described above. Such swaps are extensively used in some countries as a means of acquiring domestic currency, as foreign investors do not often have access to domestic money markets for raising short-term currency resources (Saxena and Villar, 2009). The use of currency swaps involving exchange of two streams of contractual payment is less common. Currency swap is generally used with debt contracts and involves simultaneous exchange of principal and interest streams. In some countries, where domestic capital markets are sufficiently developed and liquid, the currency swap market is also used for swapping foreign currency debt into domestic currency liability and vice versa. Such contracts would need to be customized depending on the payment/receipt schedule and would allow the party to hedge future cash flows, without worrying about spot induced volatility in forward market.

The non- availability of higher maturity forward contracts (12 months and more) is another major constraint. Their availability would have been useful in hedging single exposures in the distant future. The maturity of these contracts is however limited by the consideration of higher interest differential between advanced and emerging economy currencies, resulting in higher forward premiums/discounts, making hedging less attractive.

It may be the case however, that exporters have to look for approaches beyond the existing tool-kit of derivatives to hedge currency risks, especially in times of volatile market conditions. A way forward could be natural hedges. This essentially means that, to the extent possible, asset and liabilities of the corporate are in the same currency, so that gains and losses offset each other. For instance, if export receipts are in US Dollar, a borrowing in US Dollar would mean that lower export receipts in local currency due to domestic currency appreciation are offset, to some extent, by lower debt service payments in local currency terms.

The exporter could also swap the foreign currency debt liability through entering into a currency swap arrangement. For example, when the debt liability is denominated in euro, sterling, yen etc., whereas the currency of export receipt is US Dollar, the existing liability could be swapped into US Dollar to develop the natural hedge. Even when the debt liability is in local currency, an exporter with US Dollar receivables not catering to the domestic market could swap the local currency liability into US Dollar to develop the natural hedge between asset and liabilities.

An example worth mentioning in the context of Natural Hedges is that of Japanese companies and the shifting of operations outside the country to counter the fast appreciating Yen in the 1980s. As a result, overseas comprised 37.2 percent of total output of Japanese manufactures at the end of 2002 (Hussain Khan, 2003) With domestic currencies heading northward in the long run for a number of emerging economies, notably India and China, this option might prove to be the most beneficial over the coming decades.

Lastly, the nature of volatility cluster is also important. When a crisis is brewing and domestic currency is depreciating, the importers need to worry and not the exporters, who stand to gain from declining domestic currency and vice versa. However, an uncertain situation where the domestic currency is experiencing upswing/downswing at short intervals could be a cause for concern for both exporters/importers. A careful monitoring of global markets and the domestic economic situations, nevertheless, could provide exporters/ importers some indication of the risk of volatility clusters in the near future, enabling some pre-emptive measures.

RELATED ARTICLE: Box: The use of speculative instruments

The losses due to local currency appreciation compelled many exporters to look for alternatives. Some of these were in the nature of speculative bets on currency movement, which provided an upfront premium to the exporter and helped lower the extent of losses. These positions however amounted to writing exotic options. Instead of modest gains, many exporters incurred huge losses as these positions backfired in volatile currency market situations following the onset of global crisis. Court cases and litigations followed in many cases.

In India, an exotic instrument sold was 'knock-in' barrier option, which provided an upfront premium to exporters, which partly compensated against the loss of export income due to Rupee appreciation. For example, a binary option required an exporter to write an option to sell US $ at the then prevailing spot exchange rate vis-a-vis Swiss Franc, for instance, at US $ 1 = Swiss Franc 1.3, when the US $ would fall to US $ 1 = Swiss Franc 1, which was a highly unlikely situation. However, due to the global crisis, the US $ depreciated to US $ 1 = Swiss Franc 1 in the international market, requiring exporters to make a huge payout, which significantly dented their balance sheets. Similar, exotic options were written for US $ - Japanese Yen exchange rates.

The use of exotic options to lower the currency losses was not restricted to India. These instruments were commonly used in South Korea, China, Indonesia, Poland, Brazil, Sri Lanka, etc. Different variants including that directly linked US $ - domestic currency exchange rates were used. Further, though most users of exotic options were from the private corporate sector, there are examples of public sector losses, for instance, Sri Lanka's publicly owned Ceylon Petroleum Company lost US $ 600 million and China's Citic Pacific suffered a loss of US $ 2.4 billion (Randall Dodd, 2009) due to the use of derivatives.


(1.) See Saxena and Miller (2009).

(2.) For example, the interest differentials between US dollar and Euro are small, implying lower absolute premium for contracts stretching up to two years and beyond.

(3.) This number is multiplied by root of 252 to attain annual volatility.

(4.) The results and arguments presented in the paper will stand and not be significantly affected with the use of other available measures.

(5.) See appendix for correlogram.

(6.) Benchmark Volatility figures pertain to Simple Averages.

(7.) As opposed to the use of annual volatility when testing for the presence of volatility clusters. This was done to ensure that forward premia are compared against the volatilities of only the period in question.

(8.) In some cases, the premium was higher than volatility, stressing the point being made.

(9.) The paper does not attempt to prove this point, instead only offers a possible reason

(10.) Hedging multiple exposures at the outset is difficult owing to the lack of liquidity in contracts with longer expiry.


Aggarwal, Raj, and Demaskey, Andrea L. (1997), "Using Derivatives in Major Currencies for Cross-Hedging Currency Risks," Journal of Futures Markets 17(7): 781-796.

Chiang, Yi-Chein, and Lin, Hui-Ju (2005), "The Use of Foreign Currency Derivatives and Foreign-Denominated Debts to Reduce Exposure to Exchange Rate Fluctuations", International Journal of Management 22(4): 598-604.

Dodd, Randall (2009), "Playing with Fire," Finance and Development 46(2): 40-42.

Fung, Hung-Gay, and Leung, Wai K. (1991), "The Use of Forward Contracts for Hedging Currency Risk," Journal of International Financial Management and Accounting 3(1): 78-92.

Herman Kamil, Bennett W. Sutton, and Chris Walker (2009), "A Hedge, Not a Bet Finance and Development," International Monetary Fund 46(2).

Lee, Yoolim (2009), "Korean Corporations Court Bankruptcy with Suicidal KIKO Options",, October 8.

Moguillansky, Graciela (2003), "Corporate Risk Management and Exchange Rate Volatility in Latin America", Office of the Executive Secretary, Chile.

Reserve Bank of India, http:\\

Ripple, Ronald D., and Moosa, Imad A. (2005), "Futures Maturity and Hedging Effectiveness: The Case of Oil Futures," Working Paper 513, Macquarie University.

Saxena, Sweta, and Villar, Agustin (2007), "Hedging Instruments in Emerging Market Economies," Bank for International Settlements Papers, No. 44.
Table 2 Length of the Volatility Cluster (6)

Benchmark Volatility  Average Length of Volatility Cluster
(in Per Cent)                     (in Months)

4.60                                                  14.6

5.00                                                  12.6

6.00                                                   7.6


I. First Measure of Volatility

Date         1M Fwd     Spot     3M Fwd     Spot     3M Fwd     Spot

1993-12-01   No Data            0.024958   0.00985  0.044664  0.009757

1994-12-01                      0.009255  0.007329  0.016161  0.008079

1995-12-01  0.020424  0.071822  0.035097  0.136172  0.058674  0.197146

1996-12-01   0.02638  0.071716  0.052453  0.083955  0.076946  0.065398

1997-12-01  0.005856  0.059862  0.015212  0.105755  0.024313  0.134576

1998-12-01  0.013665  0.051361  0.029627  0.103837  0.038086  0.127305

1999-12-01  0.002825  0.010538  0.008185  0.020744  0.016784  0.032176

2000-12-01  0.003312  0.024969  0.007691  0.050764  0.013015  0.074924

2001-12-01  0.002592  0.014676  0.006773  0.019753  0.012161  0.019302

2002-12-01  0.003436  0.012194  0.009828  0.026356  0.016703  0.036367

2003-12-01    0.0049  0.014309  0.012561   0.02772  0.022034  0.046802

2004-12-01  0.004777  0.053576  0.011467  0.104792  0.018313  0.124964

2005-12-01  0.002104   0.04433  0.005371  0.083313  0.008979  0.064524

2006-12-01  0.003573  0.034549  0.007913   0.09298   0.01087  0.182345

2007-12-01  0.006873  0.052855  0.015944   0.11186  0.025612  0.195241

2008-12-01  0.007073  0.088182  0.014762  0.144193  0.021723  0.226126

II. Second Measure of Volatility

Date            Spot         1M Fwd       3M Fwd       6M Fwd

1995-12-01  0.026540067  0.088718187  0.054358048  0.014755206

1996-12-01   0.01325915  0.074069711  0.093546225  0.107245271

1997-12-01   0.02323943  0.033938925  0.024593552   0.01797745

1998-12-01  0.006091914  0.076898069  0.084858671  0.086604059

1999-12-01  0.018741733  0.011230012  0.013259358  0.020770587

2000-12-01  0.006431707  0.025027248  0.029290211  0.022333846

2001-12-01  0.004964958  0.015315504  0.017240069  0.024869024

2002-12-01  0.001121143  0.014339836  0.016513556  0.018363783

2003-12-01  0.002528941  0.013794829  0.010555779  0.012609761

2004-12-01  0.014756946  0.048443018  0.048136646  0.046356726

2005-12-01  0.009753704  0.042195262  0.031953628  0.041010601

2006-12-01  0.001364545  0.044244832  0.045393996  0.041901811

2007-12-01  0.007643223  0.053369684  0.049575394  0.050008945

2008-12-01  0.014855994  0.085339886  0.074436173  0.063587097

III Volatility Clustering

Date: 02/28/10 Time: 11:01
Sample: 1 3578
Included observations: 3577

Autocorrelation  Partial            AC       PAC     Q-Stat  Prob

                              1   0.449   0.449  722 39  0.000
                              2   0.409   0.260  1321.4  0.000
                              3   0.330   0.104  1712.1  0.000
                              4   0.384   0.197  2241.8  0.000
                              5   0.328   0.073  2626.6  0.000
                              6   0.344   0.103  3051.9  0.000
                              7   0.291   0.029  3354.5  0.000
                              8   0.289   0.037  3654.2  0.000
                              9   0.275   0.042  3925 5  0.000
                              10  0.304   0.076  4256.2  0.000
                              11  0.254   0.000  4487.7  0.000
                              12  0.253   0.020  4718.4  0.000
                              13  0.278   0.077  4995.2  0.000
                              14  0.253   0.007  5225.9  0.000
                              15  0.241   0.014  5435.2  0.000
                              16  0.244   0.030  5649.2  0.000
                              17  0.265   0.057  5901.8  0.000
                              13  0.261   0.041  6147.6  0.000
                              19  0.255   0.024  6382.4  0.000
                              20  0.238   0.007  6586.6  0.000
                              21  0.240   0.020  6793.6  0.000
                              22  0.26G   0.060  7048.8  0.000
                              23  0.266   0.031  7303.0  0.000
                              24  0.222  -0.026  7480.8  0.000

[c]Aditya Bisen, D. Tripati Rao

ADITYA BISEN PGDM 2010, Indian Institute of Management Lucknow

D. TRIPATI RAO Indian Institute of Management Lucknow
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Publication:Economics, Management, and Financial Markets
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Date:Jun 1, 2012
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