Determinants of Corn and Soybean Futures Prices Traded on the Brazilian Stock Exchange: An ARDL Approach
Bibliographic record
Abstract
This work aims to understand the determinants of the prices of corn and soybean futures traded on the Brazilian Stock Exchange (B3) based on the influence of international commodity prices on domestic prices. Using a theoretical model developed by Mundlack and Larson (1993) that considers the one-price law hypothesis, we estimate the Autoregressive Distributed Lag (ARDL) bounds test for cointegration (Pesaran et al., 2001), who tested the existence of a long-term relationship between the variables, as well as short-term influences. The database comprises the period from February 2011 to December 2019 and corresponds to the prices of corn and soybean futures contracts traded on the Brazilian Stock Exchange; and corn, soybeans and oil traded on the Chicago Mercantile Exchange, in addition to incorporating in the analysis the Brazilian macroeconomic variables exchange rate, inflation and GDP. The main results showed a long-term relationship between domestic prices, the exchange rate, and international prices negotiated in the United States for both commodities. Soybean prices are mostly affected by international prices in comparison to corn prices. In the short term, we found that soybean prices are affected by trading prices of the same commodity in the United States.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".