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FX Proxy Hedging Using Machine Learning

How Ridge or Bayesian machine learning libraries can improve proxy accuracy and robustness.

By Paul Stafford | October 10, 2019 at 01:29 PM

Foreign exchange (FX) risk generated in first-world countries is readily managed by tapping into the deep and liquid hedging options that are available. However, organizations doing business in emerging markets—whether companies, charities, or investment funds—face a significant and double-edged challenge. First, emerging-market currencies are typically more volatile than major currencies. And second, hedging instruments may not exist for these currencies. Where hedging instruments do exist, they are often prohibitively expensive, due to forward points.

If an organization needs to manage FX risk for an emerging-market currency, the only choice may be to use a proxy. Proxy hedging is not uncommon. One good example is the way that airlines hedge jet fuel using heating oil futures. Fuel costs account for about 25 percent of airlines’ operating costs, and price variation has a large effect on profitability, so hedging fuel costs can be very helpful. Until recently, there were no futures contracts for jet fuel. However, because fuel oil prices closely track (for the most part!) with jet fuel prices, fuel oil futures form a reasonable proxy hedge.

FX risk managers have used proxy hedging for years, too. Before the advent of the euro, companies and traders with exposure to the illiquid Swiss franc (SFr) often hedged that exposure with the more liquid Deutschmark. The use of a proxy currency to hedge FX risk mimics a non-deliverable forward contract (NDF). For example, a U.K.-based treasurer with exposure to the SFr might have executed a hedge denominated in Deutschmarks. At the end of the hedge tenor, he would still exchange SFr for British pounds (GBP); the hedge gain/loss would be similar to a direct hedge gain/loss.

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