Price Discovery Mechanism and Volatility Spillover between National Agriculture Market and National Commodity and Derivatives Exchange: The Study of the Indian Agricultural Commodity Market
Bibliographic record
Abstract
Agricultural commodity markets are critical to the global economy. This study investigates the price discovery mechanism, lead-lag relationship, and volatility spillover between spot prices on the National Agriculture Market (E-NAM) and futures and spot prices on the National Commodity and Derivative Exchange (NCDEX) in the Indian agricultural commodity market. The Johansen Cointegration, Vector Error Correction (VEC), Granger causality tests, and bivariate GARCH models were applied to daily data from April 2016 to December 2020 for twelve agricultural commodities traded on the E-NAM and NCDEX. We discovered the long-run relationship using the Johansen Cointegration test and concluded that the NCDEX spot and futures market is dominant in the price discovery mechanism, and the NCDEX futures and spot markets lead the E-NAM spot prices having a unidirectional or bidirectional relationship. Furthermore, the bivariate GARCH model suggested a volatility spillover from E-NAM spot prices to NCDEX futures and spot markets for most commodities, except for bajra, barley, and jeera, which have no volatility spillover. The study’s findings have important implications for various stakeholders, including policymakers, farmers, investors, traders, and others who want to reduce price risks by using information from the E-NAM market’s spot prices.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".