We know energy prices can swing wildly, but we don’t know when these swings will occur. In April 2020, oil at Cushing, Oklahoma traded at negative $38 a barrel, and in February 2021, power prices in ERCOT traded $9,000 per kilowatt-hour (kwh). During these stressful times, both occurring inside a 12-month window, certain organizations prospered while others stumbled. So, how can an enterprise best prepare itself to prosper?

Opportune Risk Management Series

In the summer of 2019, Opportune began a series explaining our views around essential capabilities organizations need to effectively manage commodity price risk. Our prior articles outlined how an enterprise might approach risk management and our view of essential capabilities for effective commodity risk management. One such capability is to evaluate economic performance during unknown conditions–namely unknown price conditions.

READ MORE: Aligning The Essential Elements Of Commodity Risk Management

This article presents a case for an enterprise to develop a robust economic forecasting model that simulates future cash flows, allowing for performance evaluation of a specific energy asset or portfolio of assets.

Modeling An Asset

Energy assets come in many forms. Consider those which offer the owner choices in how they operate the asset in physical markets. Storage tanks provide flexibility as to when a product is purchased or sold. Pipeline capacity provides flexibility as to where the product is purchased and sold. Rail cars and marine vessels create flexibility as to where and when product is purchased and sold. A gas-fired merchant power plant creates flexibility around when natural gas is purchased to make electricity.

READ MORE: What Could Go Wrong? Building Resiliency In The Face Of Chaos

Energy prices are volatile, but they’re discoverable in both spot and forward markets. Forward markets provide the basis for defensive commercial strategies that protect financial outcomes. But such markets also provide opportunities for optimization strategies that can add significant value beyond the pure logistical purpose for which an asset may have been originally intended. Furthermore, investment decisions that expand operational flexibility such as adding battery storage to a wind farm or insulating a gas turbine enclosure should also be contemplated within an economic model that uses statistically driven forward prices to produce probable forward cash flows supporting asset valuation.

Typically, a physical market participant will realize the modeled value of their asset position in spot or near-spot markets, capturing market moves as part of the logistical exercise embedded in the company operations. Monetizing an asset’s potential full value requires awareness of forward markets and the capability to prudently engage in transactions in the forward markets. Such activity can add a lot of value, but it also introduces new requirements for risk management, analytics, and liquidity management.

There are numerous benefits in having an objective, statistically driven economic 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, forward market data (fixed price and volatility), parameter inputs, and the simulated model results.

It’s important to distinguish between a normal financial model that may be run occasionally to predict future results, perhaps for investor guidance, versus a risk management valuation model outlined here. The valuation model should include multiple variables to facilitate future decisions made in response to continuously changing market conditions. Such a problem often fits nicely into a Monte Carlo framework that utilizes historical price data, forward market prices, and volatilities in forecasting future prices and, subsequently, how the asset might operate and its expected future cash flows.

READ MORE: Navigating Energy Volatility: Understanding The Principles Of Commodity Risk Management

Ideally, enterprises will assign risk management accountability to an independent risk manager and provide tools like the model described above: to understand likely outcomes and assist with contemplating what might go wrong. A statistically driven economic forecasting and valuation model should be incorporated within the risk manager’s value-at-risk and stressed exposure process to understand risks to the enterprise.

LISTEN TO THE PODCAST: Risky Business: Effective Commodity Risk Management

In prior writings, we’ve suggested the benefits to an organization of employing rigor and learning concerning risk management. Powerful synergies occur throughout the organization when senior management, commercial personnel, and risk managers share modeling expertise for investment decisions, portfolio valuation, and risk management.

Summary

Negative oil prices and Polar Vortexes remind us that energy prices are volatile and can go places no one contemplated. Companies that prosper in these unknown and extreme conditions rely on tools such as an asset valuation model and related processes to understand risk, as well as embedded optionality in their operations.