On the valuation of legacy power production in liberalized markets via option-pricing
Bibliographic record
Abstract
Legacy assets can constitute entry barriers in liberalized power markets. Regulations pertaining to such assets have many objectives, the most important of which are to transfer the benefits of an economical production technology to consumers and foster competition. To that end, countries have adopted various regulations but there is no consensus today on identifying the first best solution. Inspired by the French regulation of historical nuclear production and considering the market risk that now prevails in the sector, we propose an option-based approach to regulating legacy assets that reflects production costs and encompasses optionality at the same time. To achieve that aim, we study a competitive, but financially incomplete market where the incumbent and several competitors exchange legacy production via a regulated call option. Agents do not face the same risk exposure and their attitudes toward risk, which we model by coherent risk measures, might differ. The result is a stochastic equilibrium model of regulated option-pricing in incomplete markets that we calibrate numerically and solve for the French market. We quantify the option value and assess its impact on the system for various regimes of the spot market, including the one of very high and volatile prices of the recent energy crisis. We also analyze the impacts of risk aversion and the option’s maturity. Based on our analysis, we provide recommendations for enhancing the current French regulation of historical nuclear production. • Legacy assets constitute entry barriers in power markets, requiring proper regulation. • The French regulation of nuclear production (ARENH) fails to capture optionality. • We propose a transition to a call option-based regulation of legacy assets. • We accommodates risk aversion in incomplete markets and extrinsic option values. • We do so via stochastic equilibria, which we successfully apply to the French case.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".