Alberta's electricity futures market: An empirical analysis of price formation
Bibliographic record
Abstract
Alberta operates an energy-only electricity market, which allows the unilateral exercise of market power to create investment incentives and resolve the ‘missing money’ problem. This stands in contrast to other jurisdictions that have implemented administratively complex capacity markets to ensure adequate supply. A key feature of Alberta's market is its futures market, where contracts that settle against realized spot prices are exchanged. The futures market enables market participants to hedge against price volatility. It informs investment decision, enhances price transparency and aids in competitive price formation by diminishing the incentive to exercise market power in the spot market. Our empirical work explores Alberta's electricity futures market in two main areas: the relationship between futures and realized spot prices, and the evolution of futures prices influenced by expected spot market conditions. We find that (i) electricity futures prices do not provide an unbiased forecast of spot prices, (ii) a portion of the realized futures premium can be explained by information that is obtained after the futures price has been set, and (iii) futures prices appear to efficiently reflect changes in available information. • Alberta operates an energy-only electricity market, with a futures market. • Electricity futures hedge volatility, enhance transparency, and reduce market power. • Electricity futures prices are weak and biased predictors of spot prices. • Natural gas futures are significantly better predictors of spot prices. • Futures prices appear to efficiently reflect changes in available information.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".