Impact of FII Investments on Stock Market Volatility and Foreign Exchange Reserves: The Indian Experience
Bibliographic record
Abstract
Volatility refers to the amount of uncertainty or risk about the size of changes in a security's value. The increased interest of foreign institutional investors (FIIs) in Indian equity market has been correlated frequently with the volatility in stock markets in India. This paper investigates the nature of the causal relationship between Net FII flows, the Stock Price Movements, and Foreign Exchange Reserves (FERs). The unit root test was applied to ascertain stationarity of the time series data and then by applying the Granger Causality Test, the causal relationships using monthly data for the 20 years period was tested. The results show that there is bi-directional Granger Causality between BSE (Bombay Stock Exchange) Sensex and FII Flows. Thus FII Flows are Granger Caused by BSE Sensex and BSE Sensex is Granger Caused by FII Flows. The FERs do Granger Cause BSE Sensex but BSE Sensex does not Granger Cause FERs. There is bi-directional Granger Causality between FERs and FII Flows. Thus FII flows Granger cause FERs and similarly FERs Granger Cause FII Flows.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".