How this model can significantly reduce risk of companies by identifying rupee’s behaviour in advance

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Published: June 14, 2019 12:06:04 AM

On balance, the model produces significant savings in cost with contained risk; importantly, it can also enable companies to increase their tenor of risk identification

Digging deeper into the averages, we found that on a 6-month basis, the ` fell less than the premium (including appreciating significantly many times) as often as 58% of the timeDigging deeper into the averages, we found that on a 6-month basis, the rupee fell less than the premium (including appreciating significantly many times) as often as 58% of the time

…If you knew exactly when the ` was going to fall sharply? Or, at least, when it was going to fall more than the forward premiums? Then, as an importer you would have a very easy life—you would know exactly when to hedge at inception and when to stay unhedged (and, perhaps, go to the beach). Indeed, if you had such a magic wand you might also start identifying risk more in line with your business model that simply on a 3-month basis, which many companies do because they feel that the further premiums are too high. While there are, indeed, companies, who import mostly commodities, whose business contracts call for 3-month risk identification, most companies actually have business contracts, or certainly import visibility that is much longer—to 6 months or even more.

We continue to work at finding this elusive model—turning lead to gold, in a sense—and we have recently come up with a model that, in our estimation, performs very well. It provides a binary on-off signal that enables you to enjoy ` strength half or a little more than half of the time, while exiting at a small cost the rest of the time. And so, even though there will be several occasions when you pay more than Day 1, the overall impact is strongly positive, generating cost savings of 3-4.5% pa. The accompanied graphic shows the starting point of the model. As is clear, over tenors of both 6 months and 3 months, the average decline in spot ` is lower than the prevailing premium, which suggests that staying unhedged on imports would be profitable on average. Of course, the risk would be huge—the worst 6-month decline was >13% and at 3 months, the worst decline was ~8.5%. Nobody could live with these numbers.

Digging deeper into the averages, we found that on a 6-month basis, the rupee fell less than the premium (including appreciating significantly many times) as often as 58% of the time; on a 3-month basis, the number was 56%. Now, if we did have the magic wand, our portfolio value would (on average) have been 0.54% better than Day 1 spot for 6-month exposures, and 0.66% for 3-month exposures. Thus, as compared to hedging on Day 1, our performance would have resulted in savings of 2.54% [0.54% + 2.00% forward premium]—for 6-month exposures, this translates to 5.08% pa saving of funding costs. For 3-month exposures, the savings would have been 1.68% [0.66% + 1.02% forward premium], or 6.72% pa. Both are quite fantastic. Of course, this is based on magic. Our model doesn’t quite get to such extraordinary levels, but it has been able to capture between 45% and 85% of these magic savings. The accompanied graphic shows how it worked for three clients.

The wide variation in results was because each client had different specific exposure dates over different periods; nonetheless, these are excellent savings. The model is designed to set a stop loss based on the tenor at the start of the exposure; this number is empirically determined based on ongoing research. The forward rate is monitored and after 30 days, the stop loss is adjusted downwards based on the then—forward rate—again, to a level empirically determined. This continues, the stop loss coming down every 30 days. If the last stop loss is not hit, you pay at spot. Thus, you either hedge at the stop loss or pay at spot.

The downside is that the stop loss gets hit a large number of times—recall, the spot fell by less than the premium 58% of the time, which means it fell by more than the premium 42% of the time—so, you do end up paying more than the Day 1 forward quite often. The losses are contained by the stop loss—the worst case in the above analyses was an increase of nearly 3% from Day1; albeit, the gains are substantial—the best gain we say was 9.9% better than Day 1 reflecting a significant negative cost of funding.

On balance, the model produces significant savings in cost with contained risk; importantly, it can also enable companies to increase their tenor of risk identification, which would lower the overall risk on their business plan.

The author is CEO, Mecklai Financial. (Views are personal)

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