“A stable historical correlation is a structural law.”
Not quite.The linkage comes through changing dispatch economics and can weaken or reverse across regimes.
Power & Commodities
Power–Gas–CO₂ Correlation Structure
Power, gas, and CO₂ prices move together through the merit order: correlation structure drives hedging and risk.
Mental model
Fuel, carbon, and power are linked by generation economics, so their joint movement matters for hedging.
Thermal power prices depend on fuel and CO₂ costs, so price moves are often correlated across these commodities.
Portfolio risk depends not only on individual volatilities but on cross-commodity correlation and common factors.
Explore the mechanism
How do fuel efficiency, carbon cost, and generator position connect gas and CO2 moves to power prices?
This chart shows normalised indices for power, gas, and CO₂ driven by a shared factor. Use the controls to change factor strength and horizon and see how cross-commodity correlation emerges.
Corr(P,G) ≈ 0.63 · Corr(P,CO₂) ≈ 0.77 · Corr(G,CO₂) ≈ 0.77
Numbers
Corr(Power, Gas) ≈ 0.63
Corr(Power, CO₂) ≈ 0.77
Corr(Gas, CO₂) ≈ 0.77
Interpretation
A common factor (merit-order logic) drives power, gas, and CO₂ together. Betas control how strongly each curve reacts, while idiosyncratic noise breaks the link.
For hedging a generation or supply book, the question is not only “what is power vol?” but “how much of that vol is shared with gas and CO₂?” Correlation structure tells you how far a fuel or CO₂ hedge will follow power through the cycle.
UNDERSTAND
What to notice
Hedging generation or supply portfolios requires modelling these joint dynamics rather than each curve in isolation.
Where it appears
- hedging thermal generation margins
- building cross-commodity scenarios
- estimating portfolio covariance
Where the intuition breaks
What this view leaves out
- Correlations are state-dependent and can change when a different technology becomes marginal or physical constraints dominate.
Common mistakes
Where this concept sits
Follow the strongest prerequisites, applications, extensions, and contrasts.
Connections for Power–Gas–CO₂ Correlation Structure
Power–Gas–CO₂ Correlation StructureYOU ARE HERE
UsesCovariance Estimation and CorrelationCross-commodity dependence is quantified with covariance and correlation estimates.
SupportsSpark and Dark SpreadsThe joint behaviour of power, fuel and carbon prices drives the distribution of generation spreads.
Used byScenario and Stress EngineCross-commodity scenarios should preserve or deliberately stress dependence between power, gas and carbon.
Uses · Covariance Estimation and Correlation
Cross-commodity dependence is quantified with covariance and correlation estimates.
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