Why do Energy Markets in Europe Rely on One Instrument?
Bibliographic record
Abstract
A common feature to electricity markets with derivative contracts is that the traded contracts are almost only futures and that they are traded multiple times the overall electricity demand. To understand why this occurs in practice, we study the efficiency of risk aligning in the competitive energy industry through derivative contract trading. We derive conditions that need to hold for a contract to provide the most efficient risk aligning, among all possible derivative contracts imaginable. We develop a game‐theoretical model that includes multiple competing producers and retailers whose decision‐makers are heterogeneous in their risk preferences that are captured using mean‐variance utilities. We derive closed‐form equilibrium quantities for the exchanged contracts and contract prices. We show using a simplified setup that, under an inelastic spot price, the optimal quantity and the price of the traded futures contract are driven by the proportion of the electricity spot price that retailers pass down to the consumers. In particular, this proportion impacts who are the buyers and the sellers of the contracts, what quantities of contracts are traded, and at what price the contracts are traded. We apply the model to the German electricity market and show that the conditions for guaranteeing a futures contract to be the most efficient risk aligning contract hold approximately. Our result can explain the dominance of futures contract trading in the energy industry and also the counter‐intuitively large quantity of traded futures, which exceeds multiple times the supplied quantity of electricity. Our results can aid decision‐makers in deciding on the types, amounts, and prices of contracts to trade. For market regulators, our results are helpful in providing understanding regarding the contract trading behavior on the market.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".