Investment Behavior of Foreign Institutional Investors and Implied Volatility Dynamics: An Empirical Study on the Indian Equity Derivatives Market
Bibliographic record
Abstract
The aim of this study is to examine the association between the capital flows of foreign institutional investors (FIIs) in the equity derivatives market in India and the implied volatility of options. Previous studies on FIIs and realized volatility in the equity market provide the basis for this study. Covering a period of ten years (2012–2021), this study established the importance of FII capital flows in explaining the implied volatility of options. The Granger causality test confirms the unidirectional flow of causality between FII and implied volatility (VIX) in the Indian stock market. The vector autoregression model developed in the study confirms the dynamic relationship between implied volatility and the investment behavior of foreign institutional investors (FIIs). The outcome of this study will help options traders to understand the mispricing of options because of FII’s buying pressure on implied volatility. The results will also help policymakers understand how institutional investors influence option pricing so that appropriate decisions can be made.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".