Global Financial Crisis and Stock Market Integration between Northeast Asia and Europe
Bibliographic record
Abstract
This study examines the effect of financial crisis on the level of stock market integration. In particular, we investigated the dynamic movements of two regional stock markets, Northeast Asia and Europe during the period between January 1st, 2000 and December 31st, 2012, with particular attention placed on the global financial crisis (GFC). For this purpose, the paper employs various approaches including DCC-MGARCH, Risk Decomposition, GVAR, and CCOR models to ensure the robustness of empirical findings. The findings of this study are as follows. First, the Northeast Asian stock market remains independent from the European and global stock market movements during the sample period. Second, the European stock market shows an increasing trend of joint integration with Northeast Asian stock market. However, the level of integration is not economically significant. Third, the level of market integration between European and global stock markets had temporally increased during the GFC. However, the level returned to its pre-crisis level in the post-crisis era. The overall empirical evidence suggests that, for either European or global stock market portfolio, constructing a portfolio with Northeast Asian stock market would result in a more efficient portfolio. The results in this paper do not support the view of previous empirical studies which suggested the increased level of integration since the GFC. An increased integration is found to be only unique to the crisis period. In sum, the market integration is a dynamic process, and the financial crisis did not uniformly affect the level of stock market integration.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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".