The effect of commodity index trading in agricultural futures markets: a Factor-Augmented Vector Autoregressive (FAVAR) approach
Bibliographic record
Abstract
Abstract Commodity index trading in futures markets is a relatively new investment strategy whose consequences are not fully understood. This paper tests the hypothesis that long-only, passive index trading in agricultural futures markets influences futures prices. Vector Autoregressive (VAR) models are a common empirical research approach for analyzing index trading. Factor-Augmented Vector Autoregression (FAVAR) models are a new approach to analyzing index trading. FAVAR models can incorporate a large data set into the traditional VAR framework. Using a FAVAR model improves the analysis by including additional market factors relevant to futures price formation. Models were estimated for 13 agricultural commodities (corn, soybean, soybean oil, soybean meal, soft red winter wheat, hard red winter wheat, cotton, cocoa, sugar, coffee, live cattle, feeder cattle, and lean hog) from January 2006 to December 2022. The results demonstrate the added value of FAVAR models in explaining the dynamics between prices and index trading. The conclusions are similar to other findings that prices lead index positions; however, adding demand-related data through a FAVAR model allows for a better understanding of market dynamics.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".