Impact of Price–Quantity Uncertainties and Risk Aversion on Energy Retailer’s Pricing and Hedging Behaviors
Bibliographic record
Abstract
The joint uncertainties of wholesale price and end-user demand quantity often poses huge pricing challenges to energy retailers. However, the literature lacks analysis of such uncertainties’ impacts on retailer pricing behaviors and possible hedging behaviors. To study these impacts, this paper proposes four models: a risk-averse or a risk-neutral retailer deciding retail price with or without forward contract. We present closed-form solutions for these four models on optimal retail price, as well as optimal forward position (if allowed). We propose a novel approach of volatility decomposition to describe the relationship between behaviors and different volatility sources. Comparative statics gives detailed analysis of the pricing and hedging behaviors in both uncertainties, as well as their correlation. We obtain profit distributions using Monte Carlo simulations in the context of the California Electricity Market. Results show that the price and quantity uncertainties and their correlation create significant differences in the retailer’s behaviors, and the determinants of these differences are different. In addition, forward contract increases expected profit and decreases profit volatility for risk-averse retailers simultaneously. These results could serve as a benchmark for analyses of deregulated, imperfect energy markets coupled with contingent financial markets under both price and quantity uncertainties.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".