Price Risk in Commodities - A Lens on the largest Losses

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Price volatility in raw materials is a serious business risk for manufacturers. Unmitigated, price risk can lead to large swings in company P&Ls, threatening jobs and overall continuity. At ChAI, we are passionate about helping manufacturers reduce the negative impact of price volatility in their input materials. Therefore, we are obsessed with commodity prices and what can be learned from some of the largest losses suffered to date. In this blog, we share our takeaways.

Commodity prices - like riding an unbroken horse

In capital markets, commodities like steel, oil and soy beans are known to be the most volatile of asset classes. For example, the quarterly volatility in crude oil has ranged from 13% to more than 90% since the early 1980s. Similarly, both sugar and silver have experienced quarterly price swings from 10% to 100% in the same period. Why? Commodity price risk is normally explained by 4 key factors:

  • Liquidity: Compared to equities, bonds or currencies, commodities offer much less trading volume on a daily basis, making them more sensitive to the impact of events.

  • Nature: Weather and natural catastrophes impact many commodities. For example, an earthquake in Chile can cause copper prices to soar, just like drought in the US may drive the price of corn sky-high.

  • Geopolitics: Because most commodities are produced or extracted in specific areas of the globe, political issues like wars, violence or tariffs in those regions affect their prices. Remember how Iraq’s invasion of Kuwait in 1990 made the price of crude oil double?

  • Supply and demand: Like any other asset. However, whereas the production/extraction of commodities in highly earth-bound, demand in ubiquitous - increasing the likelihood of a mismatch and thus, price volatility.

Not only are commodity prices volatile - they have become more volatile over time. Supply and demand shocks (like the 2008 financial crisis) explain much of this development, but speculation in commodity markets is also believed to play a role. A self-fulfilling prophecy perhaps, as price volatility is fundamentally what attracts speculators to any market.

In parallel however, data on commodity supply and demand has become more available - combined with advances in interpreting such large data sets. Has the management of commodity price risk therefore become easier in recent years? Eye-watering headlines of recent trading losses suffered by the likes of Trafigura, Societe Generale, Unipec and China Aviation Oil point to the contrary - and they are supposed to be at the top of their game. Can we understand more about the challenge that raw material price risk poses by taking a closer look at some of the largest losses ever experienced in the commodities space? Let’s find out.

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#1 Amaranth Advisors

In 2006, natural gas hedge fund Amaranth Advisors lost 65% of its assets - $6.6 billion - in the biggest ever energy-related trading disaster. Weakened by an aggressive leverage stance, Amaranth had wrongly anticipated a replay of Hurricane Katrina (which caused huge price spikes across the energy complex in 2005) in the hurricane season of 2006.

#2 Sumitomo Corp

Even people who are believed to be risk management experts can go wrong. In 1996, key employee at Sumitomo Corp, Yasuo Hamanaka, lost his employer $3.4 billion when trying to secure the price of copper. Sumitomo owned large amounts of physical copper that was warehoused and stored in factories, as well as numerous futures contracts. Literally, Hamanaka controlled 5% of the world’s copper supply and was taking huge positions in copper commodity futures on the London Metal Exchange. He tried to use the firm’s large cash reserves to keep the price of copper artificially high for the entire decade leading up to 1995, garnishing premium profits on the sale of Sumitomo’s physical assets.

In 1995, due to resurgence of mining in China, the price of copper started to revive which further inflated prices. As the market finally dropped, losses accelerated for Sumitomo and the deception was uncovered.

#3 Metallgesellscahft

In 1993, MG's US oil subsidiary, MG Refining & Marketing (MGRM), designed an innovative program aimed at rapid expansion in a mature but evolving business - the marketing of petroleum products. MGRM combined over-the-counter (OTC) and futures instruments for oil that featured a speculation on the relationship between near and distant prices.

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When MGRM's speculation moved against it for a period of time, although short-lived, it suffered large "mark-to-market" losses and large margin payment calls. The mark-to-market losses initially remained paper losses, but the margin calls were a drain on MG's cash flow that were larger than what management was willing to tolerate. MGRM's parent company terminated the strategy abruptly, leading to $2.2 billion of losses (critics mention that a more patient unwinding would have led to smaller losses).

#4 Bank of Montreal

Despite a reputation as the safest bank in Canada, Bank of Montreal came under serious pressure in 2007 when it was revealed that it had made a $680 million loss due to mismanagement of its commodity portfolio - in particular related to natural gas options. David Lee, the bank’s senior trader had been overstating the value of some of his positions. With highly volatile natural gas prices in 2006 (remember Amaranth Advisors), the subsequent sharp decline created suspicions within the bank’s risk team, who hired an independent consultant to look into the potential mismatch. By the end of 2007, the Bank of Montreal had discovered the true extent of the fraud and announced the full loss to its shareholders.

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#5 Gordon Brown (yes, the former UK prime minister)

Although somewhat hypothetical, this one is too good to leave out… As Chancellor of the Exchequer in 1997, Gordon Brown sold 395 tonnes of gold, about 58% of the government’s total reserves of 715 tonnes, over a 3-year period. The official reason was to reduce the portfolio risk of the UK's reserves by diversifying away from gold.

This gold was sold at an average price of $277 per ounce over 3 years, compared with the average price of $376 per ounce over the subsequent 3 years - equating to a hypothetical loss of $1.3 billion at the time. However, as gold steadily recovered over the next 10-year period to trade at a peak price of $1,780 per ounce by September 2011, the loss to the exchequer was valued at an even larger hypothetical $19 billion. Participating gold dealers at the time referred to gold prices as being at the “Brown Bottom“.

Our takeaways from the debacle

Fraudulent activity seems to catch up with you at some point - therefore, stay away from trying to cheat the market. Beyond the obvious, there are a couple of things to take away from this embarrassing list of situations:

  • Broaden your horizons on what data to use for price predictions: A better understanding of weather patterns and geopolitical developments might have saved both Amaranth and Sumitomo from bleeding money. Taking alternative data such as satellite imagery, freight movements and political events into consideration may be a route to better predictions.

  • Understand the confidence you have in price predictions: Making a prediction is easy - the hard part is knowing how likely a particular price point is to actually materialise. Make sure to attach a probability to each prediction made.

ChAI exists to remove the pain of commodity price volatility for its clients, de-risking their supply chains. We help mitigate the negative business impact of commodity price volatility for buyers (e g manufacturers) and sellers (e g mines) by forecasting their prices on a daily basis. We do so by leveraging the latest in AI techniques on established and alternative data, generating transparent predictions with a full probability distribution attached. This enables our clients to understand what the probability of a certain price movement will be at any given point in time from one day up to one year into the future.








Marcus Dixon