A Count Model Analysis of the Traded Contracts in JEPX Forward Market
Bibliographic record
Abstract
The number of forward contracts traded in Japan Electric Power Exchange (JEPX) is desired to increase. However, few studies have clarified what factors have contributed to impacting the number of forward contracts traded. In this study, the authors analyzed the number of forward contracts using four kinds of count regression models. As a result, negative binomial regression model and zero-inflated models were able to better express the expected counts, by incorporating the overdispersion and excess zeros present in the observed data. Among others, the spot market can carry positive influences on the expected counts, by about 12% for 1 yen/kWh increase in price, and by about 27% for 0.1%-point increase in volumes. The zero-inflated models revealed that as many as three fourth of the entire forward products have high probability of zero counts, while the rest one fourth may see an increased number of counts as the spot market price and/or the spot volume become higher.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".