Shocks that shook the world:how emerging & developed economies react in moments of crisis:a cross examination of the BRICS & G7 nations
Bibliographic record
Abstract
This paper examines 19 major events which include (7) financial crises, (7) terror attacks, and (5) natural disasters over the past three decades and the impact on the financial markets of the BRICS and G7 bloc of nations.The BRICS, emerging nations, comprise of Brazil, Russia, India, China, and South Africa, whilst the G7 developed nations comprise of the US, UK, Canada, France, Germany, Italy, and Japan.The analysis on how 19 catastrophic events affect the BRICS and G7 economies was conducted using an Event Study Methodology implementing the Market Adjusted Model.The methodology popularized by Fama, examines the Abnormal Returns (ARs), Average Abnormal Returns (AARs), Cumulative Abnormal Return (CARs), and Cumulative Average Abnormal Returns (CAARs) to determine if there are statistical significances using the t-statistical significance tests over an event window of (-5, +5).Returns of the market indices are obtained from Yahoo Finance for Brazil's returns and Thomas Reuters DataStream for the remaining 11 for the period between 1989 to 2018.The indices are benchmarked against the MSCI All Country World Index (ACWI) Index, which captures large and mid-caps across 23 developed and 26 emerging markets, covering approximately 85% of equities markets globally.We find that emerging economies (BRICS) react stronger to financial crises and terrorist attacks and for a prolonged period in comparison to those of the developed nations (G7).Furthermore, it was found that there was no statistical significance in neither the BRICS nor G7 nations during natural disaster events, apart from The Great Thoku (Fukushima) Earthquake and Tsunami in Japan.Moreover, this study shows a contamination effect between nations that share a geographical, and more importantly, trade partnership, but this spill-over effect has reduced over time as market participants have become more acquainted and resilient to these events occurring, especially with terror attacks.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".