Private Equity Firm Secures Cash Flow Certainty Among Oil and Gas Producing Portfolio Companies

Market: Oil and Gas | Commodity: Crude Oil | Customer: Private Equity

 

Situation

A private equity-backed oil and gas developer, operating under an investment mandate that required a specified percentage cash return, engaged us to evaluate:

  • How to increase return certainty through optimized financial hedge selection and sizing 
  • The probability of underperformance, specifically the likelihood of falling short of pro forma return targets
 oil and gas producer success story

 

Solution

A common hedging strategy when targeting a specific level of revenue is to use simple swaps, where floating prices are converted into fixed prices. However, if a company aims for 100% certainty by hedging all revenue exposures, the result is often over-hedging. Why? Because this approach assumes that unhedged volumes would generate zero revenue, which is an overly conservative assumption.

The Challenge: Find the precise volume to hedge that makes achieving the target sufficiently likely, rather than certain.

Many companies default to a credit-driven hedging strategy—often dictated by their lenders—where hedge volumes are sized to minimize the risk of defaulting on interest payments. But that approach didn’t align with our customer’s performance mandate. Others rely on “rule of thumb,” often mislabeled as best practices. But each company’s assets and risk profile are unique. Peer benchmarks can be misleading—either too liberal to be safe or too conservative to be cost-effective. Our customer was seeking a more tailored, optimized solution. They used AEGIS risk systems to estimate monthly cash flow at risk throughout the forecast period, helping identify the optimal hedge volume.

With this approach, the customer could define two distinct goals:

  • A baseline revenue target that met the minimum return required by investors 
  • A stretch goal aligned with upside price scenarios - ambitious, yet achievable

Outcome

Establishing two mathematically grounded revenue targets delivered several key benefits. First, the hedging team gained confidence that, given the current price environment, they had hedged the appropriate amount to meet investor expectations. Second, as market prices evolved, they had a repeatable methodology for adapting.

  • If prices declined, the model reassessed whether the must-have goal remained protected.
  • If prices rose, the team could test incremental hedges to improve the likelihood of achieving their stretch goal.

This dynamic, data-driven approach replaced guesswork with precision, empowering smarter, more responsive hedging decisions.

 

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This case study is not required to be and has not been filed with the Commodity Futures Trading Commission ("CFTC"). The CFTC does not pass upon the adequacy or accuracy of this commodity trading advisor disclosure. Consequently, the CFTC has not reviewed or approved this case study. See further disclaimer below.
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