Explaining time-varying risk of electricity forwards: trading activity and news announcements
Bibliographic record
Abstract
We elaborate economic explanations for the time-varying risk of month, quarter and year base load electricity forward contracts traded on the Nord Pool Energy Exchange from January 2006 to March 2010. Daily risk quantities are generated by decomposing realized volatility in its continuous and discontinuous jump component. First, we analyze the relation between volatility and trading activity. Coherent with existing studies we find that the driving factor of the relation between continuous variation and trading activity is the number of trades. New insights are obtained by considering the relation between jump factor and trading activity. Our results indicate that the number of trades and absolute order imbalance, which can be explicitly measured in our dataset, are positively related to the jump factor, a result in line with theoretical models. Second, we study unscheduled news announcements causing high volatilities. For this, a unique dataset of urgent market messages (UMMs), published by the Nord Pool Energy Exchange, is created. We extract relevant unscheduled UMMs, here failures, from both transmission system operators (TSOs) and market participants (MPs), and measure their impact over varying event windows. We find that certain unscheduled TSO/MP-UMMs have a significant impact on continuous variation, especially when they are published close to maturity, their content refers to a rare and extreme event or the contract is a month forward. The analysis also provides economic evidence for the occurrence of price jumps.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".