An Empirical Model for the Indian Foreign Investment and Stock Market Volatility: Evidence From ARDL Bounds Testing Analysis
Bibliographic record
Abstract
Foreign investment is a major factor to determine volatility in the stock market. To discover the influence on Stock Market volatility of foreign investment we have considered FE, FD, and FDI as proxy variables of foreign investment and Indian stock market volatility is represented by Indian vix. The period for this study is 2009 to 2017 (monthly data). To address this issue of volatility in the long/short-run we have applied the ARDL. The preference given to the ARDL model over Johansen co-integration is to the difference in the order of integration among the variables. ARDL model allows us to combine the I(0) and I(1) series whereas I(1) required in the case of Johansen approach. Results of unit root confirm the I(0)/I(1) order of integration, which allows us to apply the ADRL bound test. F-statistics is higher than the upper bound critical value at 10%, 5% and providing the evidence of co-integration among variables at a 5% level of significance. Hence, there is a long-run relationship amid the variables. Long-run form results show the negative sign of the coefficient and it is significant. The ECM value is (-0.9671) and it confirms that nearly 96.71 % of the inaccuracy rose in each period and automatically corrected in specified time period.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".