Global Stock Market Volatility and Its Spillover on the Indian Stock Market: A Study Before and During the COVID-19 Period
Why this work is in the frame
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Bibliographic record
Abstract
This study investigates the impact of global stock market volatility on the Indian stock markets before and during the COVID-19 pandemic period. The study focuses on 11 stock markets, including Brazil, Canada, China, France, Hong Kong, India, Japan, Russia, Turkey, the UK, and the US, and applies the threshold generalized autoregressive conditional heteroskedasticity (TGARCH) model to capture the current asymmetry in returns influenced by past negative/positive shocks, and the diagonal Baba Engle Kraft Kroner (BEKK) model to examine the cohesion of the Indian equity market with global markets. The importance of the Indian stock market lies in its ability to provide capital to companies, attract foreign investment, and provide investment opportunities for both domestic and international investors. Data for the study was sourced from https://www.investing.com for the period September 2019 to September 2021 and Stata software was used for data analysis. The study finds that Brazil, Canada, France, Russia, UK, and the USA are the primary sources of financial weight on India’s stock returns. The results suggest that Indian investors can diversify their funds into other asset classes while restricting investments in these markets, particularly during downturns. Investors can make informed decisions to diversify their portfolio and minimize risk. The results can also benefit society by promoting a more stable and resilient financial system. The study can also be expanded to include other financial and economic indicators such as inflation and interest rates to provide a more comprehensive analysis of the impact of global market volatility on Indian equity markets.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it