Energy Risk Management: Protecting Against Price Swings
Book: Commodities: Markets, Performance, and Strategies
Editors: H. Kent Baker, Greg Filbeck, Jeffrey H. Harris
Publisher: Oxford University Press, 2018
ISBN: 9780190656010
Why Energy Companies Need Risk Management
Chapter 24, written by Thomas Barkley, Mingwei Liang, and Andrew Spieler, is a practical guide to energy risk management. It covers the instruments, the metrics, and the various types of risk that energy companies face.
Here is the starting point: global energy consumption is growing. According to the International Energy Outlook 2016, total world energy consumption was expected to expand from 549 quadrillion British thermal units (Btu) in 2012 to 815 quadrillion Btu in 2040. That is a 48 percent increase. Non-OECD countries in Asia, including China and India, account for more than half of that growth.
With all that demand comes price uncertainty. And energy companies, from oil producers to airlines to power plants, need ways to manage that uncertainty. That is where derivatives come in.
The Toolkit: Energy Derivatives
The chapter walks through the main financial instruments used in energy risk management. Think of it as a toolbox with increasingly specialized tools.
Futures contracts are the simplest. They are standardized, exchange-traded, and cover crude oil, natural gas, gasoline, heating oil, jet fuel, coal, electricity, and even biofuels. The two biggest exchanges are NYMEX (now part of CME Group) and the Intercontinental Exchange (ICE). Because trades clear through a clearinghouse, credit risk is minimal.
Forward contracts trade over the counter (OTC). They are customized to meet the specific needs of the buyer and seller regarding price, quantity, delivery time, and location. The downside is higher credit risk since one party might default.
Commodity swaps work like interest rate swaps but are based on commodity prices. Imagine an energy wholesaler locked into a long-term deal to deliver natural gas at a fixed price. If they worry about rising gas prices, they can enter a swap where they pay a fixed price and receive the floating market price each month. As long as the swap fixed price is below the delivery price, they profit.
The chapter gets more interesting with the exotic instruments:
Differential swaps (or “diff” swaps) are based on the price gap between two related products. An airline might hedge jet fuel exposure using gasoline swaps, but if the price relationship between jet fuel and gasoline shifts, they need a diff swap to lock in that specific spread.
Swaptions give the buyer the right, but not the obligation, to enter a swap at a future date. A call swaption lets you buy at a fixed price. A put swaption lets you sell at a fixed price.
Double-up swaps let you get a better fixed price than the market rate in exchange for giving your counterparty the option to double the notional size of the swap. A coal mining company might get $54 per ton instead of $50 by selling a call swaption alongside the swap.
Crack spreads represent the profit margin from “cracking” crude oil into refined products like gasoline. Oil refineries use crack spread options to hedge their margins. Similarly, power generators use spark spreads to hedge the gap between electricity prices and natural gas costs.
Asian options have payoffs based on the average price of the underlying commodity over the option’s life, rather than a single price at expiration. A shipping company buying diesel fuel monthly might use a 12-month Asian call option to smooth out fuel cost volatility.
Measuring Market Risk: Value at Risk
The chapter devotes considerable space to Value at Risk (VaR), which became popular after JP Morgan published its RiskMetrics document in the 1990s. VaR answers one question: what is the worst loss we can expect with a certain probability over a specific time period?
For example, a VaR of $390,000 at the 5 percent confidence level over one day means there is a 5 percent chance the portfolio will lose more than $390,000 in a single day.
Three methods exist for computing VaR:
Historical simulation takes actual past returns, ranks them, and picks the loss at your chosen confidence level. No assumptions about the distribution of returns are needed. The downside is that it assumes history will repeat and weights all past observations equally.
Variance-covariance (analytical) method assumes returns follow a normal distribution and uses the portfolio’s standard deviation to compute VaR. It is the simplest method but breaks down when returns have fat tails or nonlinear payoffs.
Monte Carlo simulation generates thousands of hypothetical price scenarios using a stochastic model and then computes VaR from the resulting distribution. It is the most flexible method but requires serious computing power.
Here is the thing about VaR: it only tells you the loss you will not exceed with a given probability. It says nothing about how bad things get in the tail. That is why Conditional VaR (CVaR) exists. CVaR measures the expected loss given that you are already past the VaR threshold. Two portfolios can have the same VaR but very different CVaR values if one has fatter tails.
Credit Risk: When Counterparties Fail
Energy companies used to be considered very creditworthy because they held valuable assets and generated stable cash flows. Then Enron collapsed in 2001. Then Lehman Brothers went bankrupt in 2008. Credit risk became a much bigger deal.
The chapter outlines several tools for managing credit risk:
Margining agreements are the most common. They set out terms for collateral exchanges between trading parties, typically governed by ISDA master agreements.
Credit limits restrict how much exposure you take with any single counterparty. Bigger companies with better ratings get higher limits.
Portfolio compression replaces many trades with large notional amounts with fewer trades that have the same risk profile but smaller notional values. This reduces both credit and operational risk.
Credit sleeves involve a broker who provides collateral on behalf of one company to another, reducing the credit risk between the two actual trading parties.
Netting allows counterparties with multiple positions to offset them, reducing overall exposure.
Other Risks You Cannot Ignore
Beyond market and credit risk, the chapter covers three more categories:
Liquidity risk comes in two flavors. Market liquidity risk means you cannot sell a position quickly enough. Funding liquidity risk means you cannot meet your margin obligations. The classic example is Metallgesellschaft, a German company that used futures to hedge OTC forward sales. When spot prices fell, massive margin calls created a funding crisis that forced them to close contracts at a huge loss.
Operational risk is the risk of losses from inadequate processes, people, or systems. China Aviation Oil Corporation lost $550 million in oil trading in 2005 because of weak corporate governance, poor market monitoring, and inadequate risk management knowledge.
Legal risk is growing as regulations like the Dodd-Frank Act add layers of compliance requirements. The CFTC, SEC, FERC, and DOJ all have some jurisdiction over energy trading. Companies also face environmental regulations from the EPA and state-level fracking rules.
My Take
This chapter is essentially a textbook within a textbook. It is thorough and well-organized, but it reads like a reference manual rather than a narrative. That said, the practical examples are genuinely useful. The diesel fuel Asian option example and the crack spread explanation make abstract concepts concrete.
The most important takeaway for me is how many different types of risk energy companies face simultaneously. Market risk gets the most attention, but the Enron and Metallgesellschaft stories show that credit risk and liquidity risk can be just as deadly. You can have the perfect hedge and still go bankrupt if you cannot meet your margin calls.
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