The world is changing as a result of COVID-19 in ways which we do not yet fully comprehend. This round-table discussion focused on commodity-intensive corporations and the challenges they face in a volatile market. It may be worth defining what we mean by a commodity-intensive company: these are typically companies that purchase large quantities of commodities and transform them into finished products. However, many of the strategies discussed apply equally to producers and traders.
The COVID-19 related lockdown has resulted in the greatest global economic downturn since the Great Depression, with the global gross domestic product (GDP) expected to contract by 5.2%. In this webinar, we focus on commodity risk management areas that have been most affected by changes in today’s business environment, including:
- Hedging strategies for coping with fluctuating sales forecasts
- Automation of trade execution
- Strategies for coping with increased price volatility and rapidly changing order-books
- Changes in trade finance, supply chain financing and credit lines
- Accelerating trend towards software as a service (SaaS) in treasury and commodity management systems
- Measuring commodity exposure across a diversified product line
The importance of pre-trade analysis
What steps would you recommend that risk managers take to make better use of their systems pre-trade?
FIS provides commodity trading risk management (CTRM) systems to many corporations, but they are not used extensively for pre-trade analysis. Pre-trade analysis is fundamental to a successful hedging strategy. Advanced scenario analysis based on changes in FX, interest rates, and forward prices identifies and quantifies potential risks, some of which you may overlook.
Pre-trade analysis requires a lot of data. Though new data is becoming available all the time, not all of it is useful. Data providers have an incentive to sell their data, but this data is not always very useful in pre-trade analysis. A competitive advantage often comes from non-standard sources, while low-quality data can be worse than having no data at all. When a company uses non-standard data, whether it is ‘big data’ or non-time series data, you must be able to drill down into the data for any given task.
Interpreting how non-standard data might affect your company’s commodity exposure requires building additional price curves, including physical premiums, to see the cash impact on your portfolio. You need to consider price and volume changes when building your curves to ensure sufficient cash is available and to meet the requirements of agreed credit facilities.
The trend towards automated hedge execution
Has the current crisis accelerated the trend towards automated hedge execution of FX and commodity trades?
Automatic hedge execution is more important than ever before because we are seeing more and bigger price shocks than in the past. For example, in April 2020, the price of the CME WTI future turned negative on the day before expiration of the May contract, losing $55 per barrel in a single day due to a shortage of storage facilities. At the end of March 2020, the spread on the exchange of futures for physical (EFP) trade (the EFP in gold is an exchange for physical, comparing the future price traded on the CME vs. the spot price in London) jumped from a few dollars to $70. When you have price shocks of this magnitude, you need to automate your hedge execution strategy. If you are a commodity buyer who cannot continuously monitor market conditions is real-time, technology is a crucial enabler.
It is important to highlight that we are talking about automating hedging transactions using financial derivatives. (The possibility with physical transactions is much more limited, especially for non-centralized non-electronic markets).
From an IT standpoint, automated hedge execution is already possible in FX and commodities. A company needs quality forecast production data, up-to-date knowledge of its existing hedges and current exposures, and coordination with its hedging policies. The system should automatically integrate with exchange platforms, FX platforms or Bloomberg.
Monitor and control
What is the best measure of risk for a corporate treasurer? Is it value at risk (VaR), cash flow at risk (CFaR), or hedge ratios?
Before you start thinking about using VaR or CFaR, identify all of the corporation’s risks forensically. Successful hedging policies based on VaR or CFaR models are not implemented overnight. If you have a VaR model up and running, it is an excellent starting point. Adding a CFaR will enable you to attach probabilities to future cash flows.
Most companies have simpler ways of measuring risk and use hedge ratios to control their risk. A hedge ratio shows how much of your forecast consumption has already been protected with hedges. A hedging team must then hit these ratios at different points in the production life cycle (the ratios are likely to rise as you get closer to delivery).
Some of our more advanced clients have started creating multi-dimensional VaR reports that include FX and commodities; these reports are then broken out by different sections of risk. For example, VaR limits can be applied by location, operating unit, or trader, with each limit set according to perceived risk. VaR models have the obvious benefit of historical market behaviors, making it easier for risk managers to control their exposure based on the recent past. You can go far with VaR!
Business benefits
What are the tangible benefits of implementing a technological solution to managing commodity price risk?
At Redbridge we see clients embark on two types of journeys:
- Companies that do not yet have a formal company-wide hedging policy come to us to define their strategy in terms of identifying, quantifying and managing their hedging system.
- For companies that are already hedging, we help with operational transformation and change management. For example, we helped a large Geneva-based metals company instigate structural change to their systems following divestiture by their parent company. We helped them create their target operating model for their treasury management system (TMS) and FX platforms and integrate these with the rest of their systems. And we managed the negotiation with the vendors – all in a short time frame.
FIS has helped many companies with simple challenges, such as removing the manual creation and maintenance of reports in Excel; this frees up staff time to analyze and act on the data. Removing manual processes and speeding up month-end reporting improves companies’ control of risk, allowing them to get a better view of their core business. Strong hedging policies that have been well implemented allow you to remove noise caused by market price fluctuations.
Risk management strategy
With increasing market volatility and the chance that commodity prices could fall again if we face a second wave of COVID-19, is there a place for options in a corporate hedging strategy?
From a purely theoretical point of view, options sizably enhance the number of payoffs available to investors and, in this way, they “complete the market.” From an operational point of view, a static option strategy can allow you to efficiently enter or exit strategies at pre-selected price levels. It is simple to put this in place and can be attractive to commodity end-users. Companies are worried about making an upfront payment at the start of a contract (the premium), but for many companies it makes a lot of sense to use a simple option strategy.
Businesses that trade commodities (rather than transforming them into an end product) generally require a more advanced strategy to monitor the price and volatility of the underlying commodities and related securities in real-time. It is a specialized job that requires subject matter experts and proper IT systems.
Trends in supply chain finance
What trends do you see in trade finance and supply chain finance related to the current crisis? Have credit lines been affected? Is credit insurance in higher demand?
For Redbridge, trade finance is a discipline, and supply chain finance is a sub-section of that. Cheap bank or capital markets finance is not always easy to access, so debt financing backed by receivables and inventories can be attractive to corporates and banks that provide funding, especially if they are covered by credit insurance. Structuring the finance agreement plays the most important role — the better the structure, the lower the pricing. However, there will be upward pressure on pricing (with potential higher regulatory capital requirements) and more stringent structuring requirements.
With recent defaults in the Asian energy space, structuring has become even more critical, and some banks have closed for new business, concentrating on managing their existing exposure. Companies that seek supply chain finance solutions need to recognize the risk perception that banks have of the credit and structure risks: in the case of suppliers, financing secured by the company; in the case of receivables, financing secured by the creditworthiness of the company’s clients. Credit allocation for supply chain finance is often an internal political decision, with many banks preferring a direct relationship with the borrower.
Banks may require credit insurance to mitigate perceived risk. Companies must try and understand the risk appetites of their banks. Those that do trade finance or supply chain finance may not be at the head of the queue! Corporate treasurers should assess the right mix of risky unsecured lending vs. risk-mitigated lending based on working capital finance using payables, receivables and inventories. Lower commodity prices also mean less revenue for the same physical volumes; hence banks will return hungrier than before. However, they will come back.
Supply chain finance (i.e., supplier finance and receivables finance) will continue to develop with alternative lenders, dedicated providers, and advances in technology. An effective TMS will show you which facilities can be allocated against specific trades or groups of trades, helping you manage your financing.
Measuring the commodity content of items in the supply chain
How can commodity-intensive companies measure their commodity exposure if their commodities are tied up in stock keeping units (SKUs) in a factory enterprise resource planning (ERP) system?
Getting production forecast data into your CTRM system is vital. We have seen a trend for consumer packaging group (CPG) companies merging their commodity procurement analysis into their CTRM system. The combination provides a detailed analysis of their procurement performance and links the data directly to treasury’s commodity forecasting.
Including a breakdown of every SKU facilitates the automation of commodity forecasts. This means that the forecasts are updated when a change in demand is seen by procurement at the factory, rather than on a monthly or quarterly basis.
Watch the webinar
Speakers:
- Alessandro Mauro, MKS
- Mihai Andreoiu, Redbridge
- Roger Frost, FIS
- Alex Hofmann, FIS
Password: Redbridge1