A Count Model Analysis of the Traded Contracts in JEPX Forward Market
Why this work is in the frame
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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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it