Essays on the Interaction Between Risk and Market Structure in Electricity Markets
Bibliographic record
Abstract
This thesis proposes a new framework to jointly analyze electricity spot market and hedging decisions in an oligopolistic setup. Firstly, we find that, when exogenous, both quantity of electricity hedged by contract and vertical integration decrease the equilibrium spot price. Secondly, we use a hybrid approach and show that market structure can affect a generator’s decision to vertically integrate under uncertain demand. Thirdly, we consider uncertainty in costs and demand and show that concentration in the spot market, for a given hedge quantum, can increase forward prices and affect the slope of the forward curve. Our empirical results indicate that the model fits the New Zealand electricity market well. This evidence that market structure and hedging decisions are closely connected is further explored in a three period equilibrium model for the spot and forward markets, where hedging occurs prior to the submission of supply curves. Taking into account demand-side and supply-side uncertainties, we find that when hedging is endogenous, hedging quantities are affected by spot market parameters, but market power is itself mitigated in the conscious hedging choice of generators. We also show that forward markets can coexist with highly vertically integrated markets. The importance of our results is general. Our models can be used by policy makers to analyze investment and forward price implications of changes in the spot market structure. Our results also indicate that electricity generators, in equilibrium, face a trade-off between market power and hedging. Given that it is socially beneficial to manage risk, the equilibrium impact of their choices on welfare should not be considered in isolation by competition authorities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".