Testing Financial Contagion in Emerging Countries: Evidence From BRICS Countries
Bibliographic record
Abstract
This study uses a Multivariate Generalised Autoregressive Conditional Heteroskedasticity (MGARCH) model to examine the pure form of financial contagion in BRICS countries (Brazil, Russia, India, China, and South Africa) in the wake of two major international financial crises namely the U.S. sub-prime and Eurozone sovereign debt crises (EZDC). The pure form of contagion refers to the spread of shocks that are unrelated to macroeconomic fundamentals and are simply the product of irrational phenomena like panics, herd behaviour, loss of confidence, and risk aversion. To investigate contagion the present study analyses the pairwise dynamic cross-correlation between the US and Eurozone equity markets as ‘source’ (ground zero) markets and individual BRICS stock markets as ‘target’ markets.For the each of the two crises that are examined the sets of data used, were divided into two sub-periods (1) the crisis period and (2) the stable period. For the Sub-prime crisis, the findings of the present study indicate the presence of cross-conditional volatility between the US and BRICS stock markets. The results also showed that the cross-conditional volatility coefficient is high in magnitude during periods of financial upheaval compared to a tranquil period, hence the conclusion that there was financial contagion during in BRICs stock markets (except in Chinese market) following the U.S. sub-prime crisis. As for the EZDC, equity markets in Brazil, India and China seemed to react equally (in both the ‘crisis’ and ‘post-crisis’ periods) from shocks emanating from European equity market. Hence the conclusion that there was no contagion in Brazil, India and China following the Eurozone sovereign debt crisis.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".