Increasingly, even companies who have business contracts of shorter horizons, but who have longstanding customer relationships, so they are able to estimate their sales into the future, are looking to shift their risk horizons to more than 12 months.
With the rupee having been stuck in a narrow (63.50 to 65.50 per $) range since early 2017, and despite the multiple scares—oil prices, FC debt falling due next year, increase in demand for imports since LOUs are not permitted, etc—many exporters are beginning to look at extending their hedge horizons, fearing continued rupee steadiness (or even strength) and wanting to capture more premium. In point of fact, the tenor of risk identification should be a key element of any risk management framework, and the primary principle should be to identify (and manage) risk out to the longest tenor to which you have effective visibility—to know your break-even exchange rate. Some companies, particularly in IT and some engineering businesses, have very long tenor contracts and it is a good idea for them to link their hedging horizon to their business contracts. Many of them have been using 24-month (and, in some cases, even longer) horizons for some time.
Increasingly, even companies who have business contracts of shorter horizons, but who have longstanding customer relationships, so they are able to estimate their sales into the future, are looking to shift their risk horizons to more than 12 months. Given the “bird in the hand” principle—premium today is better than possible depreciation tomorrow—we would go along with this idea, except, of course, for highly commoditised businesses. In all cases, it is important to be conservative in forecasting amounts in the longer tenors. When hedging to longer tenors, you have to balance the increased premium earning with the possible opportunity loss if the rupee falls more than the premium—obviously, the longer the tenor, the greater is the probability that the rupee could fall sharply. To get a fix on this, we analysed historic data from 2007 to 2017 (to cover a wide range of market trends), which showed that over this period, a fully hedged 24-month export exposure earning today’s 8% premium would have lost money only 11.5% of the time, as compared to when staying unhedged.
On a first pass, this would seem to suggest that an 88.5% hedge would be appropriate. Of course, you would then lose out on the occasional sharp depreciation, but, what is worse, the unhedged 11.5%, if we assume it was settled at the average 24-month return, would actually return a negative 7.7% (to rupee appreciation) for an overall yield of only 6.2%. [This is because, although the rupee depreciated from 44 to 65 between 2007 and 2017, the pattern of depreciation showed that over the period, the rupee would fall sharply quickly and then recover strength on a longer-term basis. As a result, on a 24-month basis, the rupee actually appreciated 72% of the time point to point (and depreciated only 28% of the time, 11.5% of which the depreciation was more than 8%).]
Clearly, a 100% hedge would, on average, produce a better return than this but few companies, if any, would hedge 100% to 24-months for fear of losing out if/when the rupee fell sharply. Many companies who have been running 24-month books for some time use a ladder approach, which, in its simplest form would involve hedging 25% at 24 months, a further 25% at 18 months, another 25% at 12 months and the balance at 6 months, the idea being they would hope to benefit from rupee depreciation in excess of the 2% premium give-up in each interim period. However, we found that, on average, using the same test period, this strategy would lead to even worse results. This is because over any 6-month horizon, although the rupee depreciated more often (but still only 44% of the time), the average return was negative 1.7% (rupee strengthened). Thus, at each interval, the company was giving up a 2% gain and on average getting a 1.7% loss (on spot). Thus, the calculated return on this approach is well below the return in either of the other two approaches discussed.
I hasten to add that these results are based on averages over a long period and actual results could vary significantly. First of all, markets do not repeat themselves exactly and, of course, you could get lucky and hit a period of sharp depreciation during one of the periods—the highest 6-month deprecation seen over the test period was 9.5%. We have used this research to tweak our hedge model (the Mecklai Hedge Program, or MHP) for 24-month exports. Between January 2009 and December 2017, which reflects the same history we have used so far, MHP delivered an average gain of 20.4% over the Day 1 forward premium; at today’s 8% premium, this works out to 9.63%, far better than all the models described above.
MHP is able to generate such good returns because it has a healthy initial hedge and, while it doesn’t carry any market view, it hedges at fixed intervals when the market moves favourably. It has, of course, a stop loss to prevent traumatic loss. Given the long tenor of the exposure, the stop loss was triggered 84% of the time, often after some lock-ins were hit so that only 22% of the exposures were hedged under stop loss. Despite this, the excellent average performance reflects some huge returns when the rupee falls sharply—addressing a major reason why people do not hedge—balanced by slightly sub-Day 1 forward performance for a little more than half the time.
CEO, Mecklai Financial.
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