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We know that energy prices can fluctuate wildly, but we don’t know when those fluctuations will occur. As of April 2020, oil in Cushing, Oklahoma traded at minus $ 38 a barrel, and in February 2021, electricity prices in ERCOT traded at $ 9,000 per kilowatt hour (kwh). During these stressful times, both within a 12-month window, some organizations thrived while others stumbled. So how can a business best prepare to thrive?
Timely Risk Management Series
In the summer of 2019, Opportune launched a series explaining our take on the essential capabilities organizations need to effectively manage commodity price risk. Our previous articles have described how a business might approach risk management and our view of critical capabilities to effective management of raw material risks. One of these capabilities is to assess economic performance under unknown conditions, i.e. unknown price conditions.
READ MORE: Align the essential elements of raw material risk management
This article presents a case for a company to develop a robust economic forecasting model that simulates future cash flows, allowing the performance evaluation of a specific energy asset or portfolio of assets.
Model an asset
Energy assets come in many forms. Consider those that offer the owner choices as to how they operate the asset in physical markets. Storage tanks provide flexibility as to when a product is bought or sold. The capacity of the pipeline provides flexibility as to where the product is bought and sold. Railcars and ships provide flexibility in where and when product is bought and sold. A merchant gas-fired power plant creates flexibility in when natural gas is purchased to generate electricity.
READ MORE: What could possibly go wrong? Building resilience in the face of chaos
Energy prices are volatile, But they are detectable in the spot and futures markets. Futures markets form the basis of defensive trading strategies that protect financial results. But these markets also offer opportunities for optimization strategies that can add significant value beyond the purely logistical purpose for which an asset may have been originally intended. In addition, investment decisions that increase operational flexibility, such as adding battery storage to a wind farm or insulating a gas turbine enclosure, should also be considered in a business model that uses statistically determined forward prices to produce probable future cash flows supporting the valuation of the assets.
Typically, a physical market participant will realize the modeled value of its asset position in or near spot markets, capturing market movements as part of the logistical exercise built into company operations. Monetizing the full potential value of an asset requires knowledge of the futures markets and the ability to prudently engage in transactions in the futures markets. Such activity can add a lot of value, but it also introduces new requirements for risk management, analysis and liquidity management.
There are many advantages to having an objective, statistic-based business model, including:
Normally, these models reside in an Excel workbook with individual sheets representing different elements and user interfaces of the tool. Standard user interfaces include historical market settlement data, futures market data (fixed price and volatility), parameter inputs, and simulated model results.
It is important to distinguish between a normal financial model that can be used occasionally to predict future results, perhaps to guide investors, and a risk management evaluation model described here. The valuation model should include several variables to facilitate future decisions made in response to constantly changing market conditions. Such a problem often fits well in a Monte-Carlo frame which uses historical price data, futures market prices and volatilities to predict future prices and, subsequently, how the asset might perform and its expected future cash flows.
READ MORE: Navigating Energy Volatility: Understanding the Principles of Commodity Risk Management
Ideally, companies will assign responsibility for risk management to an independent risk manager and provide tools like the model described above: to understand likely outcomes and help envision what could go wrong. A statistic-based economic forecasting and valuation model must be integrated into the risk manager’s value-at-risk and stressed exposure process in order to understand the risks to the business.
LISTEN TO PODCAST: Enterprise at risk: effective management of commodity risks
In previous writings, we have suggested the benefits for an organization of employing rigor and learning regarding risk management. Powerful synergies occur across the organization when senior leadership, sales staff and risk managers share their modeling expertise for investment decisions, portfolio valuation and risk management.
summary
Negative oil prices and polar vortices remind us that energy prices are volatile and can go where no one else has envisioned. Companies that thrive in these unfamiliar and extreme conditions rely on tools such as an asset valuation model and related processes to understand the risk, as well as the optionality built into their operations.
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