Ideal Investment Protection in Optimistic Perceptions: Evidence From the Indian Equity Options Market
Bibliographic record
Abstract
The study is an experiential assessment on the ability of the Indian equity options market to resist the adverse impacts that arise from unexpected changes in the underlying equity market, focusing on two distinct investor perceptions within optimistic dimension in the market, viz. the recovery phase and the growth phase, which were evident in the Indian market scenario post the period of financial upheavals due to global economic crisis during the latter half of 2000s. The risk mitigation capability of the options is examined in terms of long run integration and short run re-equilibrating relationship shown by near month calls and puts with varied stages of exercisability with their underlying equity segment in the National Stock Exchange of India. Further, the ideal hedge sizes of the options and the hedge gains resulting from affecting them in the investment profile are identified under minimum variance framework, using Diagonal BEKK GARCH. The results are indicative that all different options segments express to have the expected resistance ability during both bullish perceptions under consideration, and prove that optimal use of options with equity portfolio provides assured hedge gains in terms of reduction in un-anticipatable variances.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".