Analysing Market Volatility and Economic Policy Uncertainty of South Africa with BRIC and the USA During COVID-19
Bibliographic record
Abstract
The contagious COVID-19 disease not only brought about a global health crisis but also a disruption in the global economy. The uncertainty levels regarding the impact of the disease increased volatility. This study analyses stock market volatility and Economic Policy Uncertainty (EPU) of South Africa (SA) with that of the United States of America (USA) and Brazil, Russia, India, and China (BRIC) during the COVID-19 pandemic. The study aims to analyse volatility spillovers from a developed market (USA) to emerging markets (BRIC countries) and also to examine the causality between EPU and stock returns during the COVID-19 pandemic. By employing the GARCH-in-Mean model from a sample of daily returns of national equity market indices from 1 January 2020 to 31 March 2022, SA and China are shown to be the most volatile during the pandemic. By using the diagonal Baba, Engle, Kraft, and Kroner (BEKK) model to analyse spillover effects, evidence of spillover effects from the US to the emerging countries is small but statistically significant, with SA showing the strongest impact from US market shocks. From the Granger causality test, Brazil’s and India’s equity markets are shown to be highly sensitive to changes in EPU relative to the other countries.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".