Currency Pulse #12 - Spread Analysis

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Bi-weekly backtest: FX-pricing at a Swiss seeds company (*)

A medium-to-large Swiss company produces and sells vegetable seeds in more than 80 countries. Headquarters (HQ) sell to their commercial subsidiaries and distributors either in their own currencies or directly in CHF.

The firm’s ad hoc pricing policy lacks consistency and results in excessive markups. These markups often stem from using the spot rate instead of the forward rate when pricing in currencies like MXN or ZAR, which trade at a forward discount to CHF (4.5% and 6.8% respectively in one-year forwards).

With data from 2020 to 2023, we backtested a market-based pricing policy based on HQ providing price lists in CHF that are then translated into local currencies. HQ updates the FX rates used in pricing by creating a corridor around an initial rate. That rate is updated whenever exchange rates move beyond the downward/upward limits of the corridor.

The solution also sets automated markups at a centralised level. Several possibilities were tested, including:

• A range for the FX rate of -12.5%/+12.5%, but a 5% markup compensates HQ for the pricing risk it takes.

• No markup from HQ, but an automatic FX rate update if the market moves beyond a -7.5%/+7.5% range.

The first arrangement results, as expected, in less frequent FX rate updates and pricing changes. This is considered especially useful both by headquarters and subsidiaries. All in all, the solution helps the company identify the desired pricing strategy per currency pair and entity.

Now let the hedging begin!

(*) Every two weeks, Currency Pulse discusses real-life cases. No names are mentioned, and absolute values are changed. We use simulation tools to backtest, with historical data, our proposed hedging programs.

Spread analysis

When assessing a company’s FX cash flow hedging strategy, we pay special attention to whether the firm’s principal objectives are met: protecting the budget rate, achieving a smooth hedge rate over time, or protecting the dynamic FX rate in every transaction.

Among the company’s secondary objectives, forward points optimisation may play an important role, depending on the currency pair involved. Spread analysis can be part of the assessment too. Let us see an example.

On 19/X/2023 at 3:46 PM, a Spanish exporter sold, at a 10-month forward rate of EUR-BRL 5.5125, the amount of BRL 75,000,000 against a notional amount of €13,605,442. What was the cost of this Non-Deliverable Forward (NDF) transaction?

To calculate it, we look back at what the mid-market rate was at exactly the minute the transaction was executed, for the same value date: EUR-BRL 5.4428. At that rate, the initial BRL amount would be worth €13,779,672. The spread was thus 1.28% = (13,779,672 - 13,605,442) / 13,605,442. 

With the help of some macros, spread analysis can be easily carried out over extensive datasets. By helping treasurers calculate the cost of FX transactions, spread analysis can be used to assess, for example, the advantages of connecting with a Multi-Dealer Trading platform such as 360T.  

Book review: can risk managers take advantage of ‘Black Swans’?

Håkan Jankensgård. The Black Swan Problem: Risk Management Strategies for a World of Wild Uncertainty (John Wiley & Sons, 2022) [see].

Ancient Greeks thought that communities were held together by fear. Likewise, managers’ fear of failure acts as a key motivation in the business world. In his new book, Håkan Jankensgård, risk management specialist and co-author of the excellent Corporate Foreign Exchange Risk Management (Currency Pulse #6), deals with the ‘Black Swan problem’.

Made popular by Nicholas Nassim Taleb in 2008, Black Swans are highly improbable, highly consequential events. Mr. Jankensgård argues that corporate Black Swans —measured as a year-on-year drop in revenue between 30% and 90%— are on the rise, and that companies should do well to prepare for periods of “wild uncertainty” ahead.

But the book ends on a rather optimistic note: firms can ‘tame’ and ‘ride’ Black Swans by turning them into calculated tail risk or ‘Grey Swans’. Well-prepared, antifragile firms can even profit from them. To do that, they need to centralise risk management and prioritise liquidity-related topics. 

• Centralisation and risk management. By pooling resources, centralisation reduces risk. Companies should avoid ‘siloed’ risk management—a point that was discussed in detail in the 2020 book in the context of ‘in-house’ banks and hedging net FX-denominated exposures. Treasury surveys increasingly adhere to this position: see PwC 2023 Treasury Survey and Journeys to Treasury 2023-24.

• Liquidity is king. Cost-cutting does not necessarily create antifragile businesses, i.e., firms that can benefit from shocks. Rather, liquidity management must take centre stage. Taking a page from bank regulators, Mr. Jankensgård argues that firms should conduct periodic stress tests to assess the ‘risk capital’ at their disposal under worst-case scenarios, including the proportion of liquid assets to total assets. 

The Black Swan Problem is full of useful indications about how companies can cope with low probability/high impact events. This includes, among others, a corporate governance structure that allows board members to keep power-seeking managers in check in normal times, while giving them more latitude during Black Swan-type of events. 

“Cash-at-hand, equity and operating flexibility all serve to strengthen a company’s defences. To this list we can add the ability to generate cash internally, or the ‘cash margin’, defined as revenue less operating costs” — Håkan Jankensgård.

From the FX risk point of view, it’s worth noting that many concerns expressed in the book can be tackled with Currency Management Automation solutions that use, for example, conditional orders to ensure at least a worst-case scenario FX rate when protecting the budget rate.

Refresher training: historical FX volatility

The simplest measure of exchange rate volatility is the standard deviation of past or ‘historical’ exchange rate returns. These are typically calculated as the percentage change in the exchange rate over a specific period.

The formula for calculating the standard deviation of exchange rate returns, or σ, is σ = √[Σ(Rt - R̄)² / (n - 1)], where:

Where:

• Rt represents the return at time t (usually expressed as a percentage change in the exchange rate).
• R̄ represents the mean return over the given period.
• n represents the number of observations (daily, weekly, or monthly returns).

For example, when we type VOLATILITY_60D for the GBP-USD rate on the Bloomberg terminal, the result (6.378) signifies a 6.378% volatility or standard deviation over the last year. 

As treasurers watch volatility in GBP-USD collapse from 19.7% a year ago to 6.4% today, does that mean that they should reduce the buffer between the spot rate at the start of the budget period and the rate they wish to ‘defend’ during the campaign?

We can only say: beware of recency bias and other behavioural biases. 

Five Useful Links

1. Spanish banks’ hidden FX fees. On X (formerly Twitter), Kristo Käärman (@kaarman), founder and CEO of Wise, posts a thread on Spanish banks’ hidden FX fees. The thread includes a ‘transparency’ rating. See our LinkedIn post. 
2. CurrencyCast on FX hedging and corporate governance. In the last episode of CurrencyCast, we discuss some cases of weak corporate governance and selective currency hedging—and how to protect the firm from ‘narcissistic’ managers. 
3. The days of the single ERP are over. ATEL has a piece by Gary Conroy of TransferMate: “The day when one single ERP was managing everything is over. Corporate treasurers can now benefit from a wider range of platforms doing niche jobs”. 
4. More on treasury centralisation. At the Treasury Corner podcast, Mike Richards talks to Jonathan Leon, Senior VP and Corporate Treasurer at Owens & Minor. “You need the big picture to make the best decisions for the enterprise—and that requires centralisation”.
5. CFOs & Technology. McKinsey interviews Staircase Ventures' Janet Bannister on the role of the modern CFO: “The average lifespan of a company listed on the S&P 500 was 61 years in 1958. Today, it is less than 18 years. Technology is the driver of this change”.

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