An attempt to measure the impact of financial crises on the interconnectedness and integration of emerging and developed financial markets
Bibliographic record
Abstract
This study aims to examine the impact of financial crises on the integration/disintegration of financial markets and how the global financial crisis spreads to other countries' markets. It relies on a sample of ten developed and emerging markets, divided between Europe, America, and Asia, namely: France, Italy, Hong Kong, Japan, Canada, the United States, Indonesia, Malaysia, Brazil, and Mexico. The study data consists of the closing prices of the main market indices, extracted from the Morgan Stanley Capital International (MSCI) database. The study period covers the period from September 3, 1989, to December 31, 2014, with a monthly frequency of 303 observations. This period was chosen to obtain a sufficient number of observations to conduct the necessary tests for studying integration across sub-periods. This period also witnessed several financial crises. To address the research problem, we used a set of statistical models: cointegration tests and the Autoregressive Distributed Lag (ARDL) model. The study concluded that financial crises directly affect the degree of interconnection between markets, often leading to increased volatility and instability. Financial crises also stimulate market integration; that is, financial markets become more integrated during and after crises due to the increased correlation between these markets during periods of turmoil. Furthermore, we found that the US market readily transmits financial crises to the largest global financial markets, whether developed or emerging, regardless of their economic strength. Because the US market has a strong relationship with developed markets, these markets are highly susceptible to its effects, unlike emerging markets, which are characterized by a degree of stability and are therefore less affected.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".