On the price volatility of steel futures and its influencing factors in China
Bibliographic record
Abstract
Steel futures have the function of price discovery and hedging. Steel related enterprises can judge the hedging strategy through the direction of steel futures price volatility, and reasonably avoid the risk brought by price volatility. Therefore, it is particularly important to study steel futures price volatility and its influencing factors. Because steel futures in China have characteristics of peak and rear tail aggregation, the paper constructs Model of GARCH (1,1) to make positive analysis of futures price volatility and its influencing factors of deformed steel bars and hot rolled coils, and the following conclusions have been drawn: (1) The volume and open interest of deformed steel bars have very significant explanatory ability to futures price volatility of deformed steel bars; (2) The volume and open interest of hot rolled coils also have very significant explanatory ability to futures price volatility of hot rolled coils; (3) The sustainable capacity of the price volatility of deformed steel bars and hot rolled coils is relatively small; (4) Iron ore price have no obvious explanatory ability to futures price volatility. Finally, some managerial implications and suggestions are derived from the analysis of the proposed model.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".