Bibliographic record
Abstract
We study the interconnectedness between the United States and thirty three international stock markets during the period of January 2003 to December 2012, with an emphasis on the global financial crisis of autumn 2008. By applying the DCC-GARCH model, our results show evidence of the increase in correlation during the period of crisis. The largest increase was reported for Argentina and India. The average increase was 0.164. Within the sample period, the US stock market was found to be the most correlated with markets of Brazil, Canada, France, Germany, Euro Area and Mexico and the least correlated with markets of China, Malaysia and New Zealand. In the second part of the thesis we study the relationship between the four selected markets (China, Euro Area, Japan and United States) and macroeconomic variables (exchange rate, total trade, industrial production and interest rates). The markets show positive relationship with the exchange rate, trade and the industrial production. The interest rate does not reveal any specific, negative nor positive, relationship. We conclude that more indices respond to a shock in one index in a very similar way. Powered by TCPDF (www.tcpdf.org)
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".