Optimal Intraday Power Trading with a Gaussian Additive Process
Bibliographic record
Abstract
Trading activity in intraday (ID) electricity markets has increased significantly over the last few years. We study the problem of a financial agent wishing to maximize a constant relative risk-aversion expected utility of their terminal wealth while operating in an ID market. Assuming that the price of traded hours follows an additive Ornstein– Uhlenbeck process, we derive the optimal strategy via the Hamilton–Jacobi–Bellman equation. The optimal portfolio in the log case is totally myopic with respect to time to maturity, while in the power case it becomes more and more risky as final maturity approaches. In order to implement our strategy, it is necessary to estimate the model parameters. One cannot resort to known results, as it is typical for time series to be unevenly time spaced, with more and more transactions as maturity approaches. Thus, we present an estimation procedure for unevenly spaced observations, based on maximum likelihood estimation and a bootstrap bias correction, in order to compensate for having few observations at the beginning of the observation frame. Finally, we backtest our method and conclude.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".