Impact of Global Financial Crisis on Stock Market Volatility: Evidence from India
Bibliographic record
Abstract
This paper studies the global financial crisis and the effect of the crisis on stock market volatility by employing the GJR GARCH model. Daily closing price of indices in the National Stock Exchange (NSE) and the Mumbai Stock Exchange (BSE) from March 1st, 2005 to December 30th 2012 were considered for the analysis. The study covers two periods: pre-crisis (from March 01, 2005 to January, 30 2008) and post-crisis (from February 01, 2008 to December 30, 2012). To demonstrate the influence of crisis on stock returns volatility, a dummy variable was introduced in the GJR GARCH model. It is found that the volatility of mean returns had increased during the post crisis period as compared to the pre-crisis period. The findings also suggest that the recent financial crisis had an adverse impact on mean returns and the volatility in the Indian stock market.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".