An Empirical Investigation into Contagion and The Effects of The 2007 Global Financial Crisis On the Cross-Country Linkages Amongst Developed and Emerging Economies, and The Resulting Impact On the Diversification Opportunities for a Global Investor
Bibliographic record
Abstract
Using data from 12 stock markets the conditional and unconditional correlations around the 2007 global financial crisis are examined across the 2000-2015 period, testing for the effects of contagion spanning across a range of sample periods. A statistical comparison of the Pearson correlation coefficients, Forbes and Rigobon adjusted coefficients, and Engle’s dynamic conditional correlation coefficients, has been conducted in order to determine which method provides the most robust results. It is demonstrated that the DCC model provides the most intuitive and robust estimates for the correlation coefficients as well as assessing the time-varying dynamic nature of these linkages, to display intricate patterns among each market and the corresponding fluctuations in correlations over time. Due to this time-varying nature, it is also found that sample size specification can result in differing interpretations based on the length of time that the sample periods tested around the crisis period are supporting the findings of Dungey and Zhumabekova (2001). Finally, from a novel three-sample testing framework, the nature of linkages can consistently be observed through understanding how the patterns change between markets. Whereby for USA, Canada, Australia, South Africa, Mexico, and Estonia, the correlation patterns suggest a long-term effect of contagion from the UK market upon these markets. Whereas observing the patterns for Australia, India, Turkey, and Argentina these countries display a short-term effect of contagion from the UK in that these markets display signs of recovery, following on from the crisis.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".