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Using Monte Carlo Simulation to Hedge Against Volatile Oil Prices

Jan 2, 2023

The escalation of Russian’s invasion of Ukraine and the ongoing response to the COVID-19 pandemic contributed to crude oil price volatility and a surge to over $130 a barrel in the spring of 2022. These high price levels, which have not been seen since 2008, occurred just two years after hitting an all-time low early in the pandemic and going into negative territory in April 2020. Yet now, as 2022 winds down, crude futures have given up all the year’s gains due to factors such as reduced demand, the surging value of the U.S. dollar, and China’s strict response to COVID-19.

2022 is not completely unique. Such wild fluctuations in oil markets have happened before, such as when oil crashed to $30 per barrel in response to the global financial crisis of 2008. It’s important to account for variability and uncertainty as part of the planning process heading into next year, whether oil represents a raw materials cost or is the product you sell.

A common tactic for mitigating unpredictable oil prices is to hedge against future movements in the price of crude via instruments known as swaps. A swap in an agreement whereby the market price is exchanged for a fixed price over a specified period of time. The name derives from the fact that buyers and sellers of swaps are “swapping” cash flows.

Energy consumers, for whom oil is an input expenditure, utilize swaps to lock in costs, while energy producers use swaps to fix their revenues. In an oil swap, an oil producer operator enters into an agreement with a second party that is beneficial (reduces risk) for each. Specifically, an oil swap has three parameters: the swap cap, the swap floor, and the swap volume. If the price of oil is above the swap cap, the operator loses some revenue; the revenue is the swap cap times the swap volume, plus the actual price times the remaining volume. If the price of oil is below the swap floor, the operator gains some revenue; the revenue is the swap floor times the swap volume, plus the actual price times the remaining volume. If the actual price is between the swap floor and the swap cap, the revenue isn’t affected by the swaps.

In order to determine the best hedging strategy, a sophisticated understanding of likely future movements of oil prices is needed. Monte Carlo simulation fits this bill very well, providing a view into hundreds or thousands of possible future outcomes, with or without a hedging strategy in place.  Furthermore, simulation of time-series variables – or factors that move over time, such as commodity prices – is critical. In addition, Monte Carlo can be performed on multiple hedging strategies, enabling decision-makers to identify the optimal strategy to meet their goals within their specific risk tolerances.

The model linked below provides a simple example of how swaps can significantly improve the likelihood of better outcomes (expressed as frequency and amount of negative working capital) for an oil producer. The model uses @RISK Industrial to run the analysis, which includes advanced functions for modeling time-series variables.

Download the example model — Hedging with Oil Swaps

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