Information Transmission Performance of the GIFT Nifty Futures: Evidence from High-Frequency Data
Bibliographic record
Abstract
This paper investigates the information transmission performance of GIFT Nifty futures using high-frequency data, a novel area of study given their recent introduction. We employ Johansen cointegration tests, Granger causality tests, GARCH models, Hasbrouck’s Information Share (IS) model, and Gonzalo–Granger’s Component Share (CS) model to assess market integration, volatility, and price discovery dynamics. Our findings reveal significant bidirectional Granger causality and cointegration between the GIFT Nifty futures price and the Nifty index price, indicating a stable long-term equilibrium. Additionally, the GARCH model captures substantial volatility, reflecting the market’s responsiveness to new information. The IS and CS models confirm that the GIFT Nifty futures play a crucial role in the price discovery process, leading the Nifty index. This research is timely, within eight months of the first anniversary of GIFT Nifty futures trading since its launch. The findings highlight the information transmission performance and importance of the GIFT Nifty futures in enhancing market stability and transparency, offering valuable insights into market behaviour, integration, and forecasting abilities.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".