Integration Of Financial Markets In Post Global Financial Crises And Implications For Britain: Analysis Based On A Panel Var Model
Why this work is in the frame
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Bibliographic record
Abstract
This study analysed the dynamics of integration among global financial markets in context of Global Financial Crisis (2008) by employing a Panel Vector Auto-regression (VAR) model on the data of nine countries and three markets from Jan 2003 to Oct 2015. It was found that there has been a shift in the association among the global financial markets since Global Financial Crisis. Moreover, the National Financial Structure in Post Global Financial crisis world clearly showed change in the association. Particularly, the emerging markets including China, Brazil and India showed a comparatively more significant impact on the UK financial sector implying the increased importance of the latter in the recent past. The Canadian, German and USA financial sector also showed a change in its impact in the post financial crisis world. It showed that Canada, Germany and USA financial sectors have become competitive to the UK financial Sector as the surge in them lead to relative response from the UK financial sector which could be associated with the portfolio adjustment. The disaggregated analysis in post GFC association between UK and under analysis global stock, Bonds and Forex markets showed a change. Important factor to note report was that after the GFC the response became mostly positive across most of the markets including the developing markets like Brazil and India. Except China and India the yield on British sovereign bonds showed a positive response to the positive shock. It implied that the sovereign risks which increase the yield on the bonds and contingent in under analysis sovereign bonds markets, perhaps there could be some exceptions and room for the portfolio adjustments in case of developing markets like China and India. The forex market analyse showed that although the heterogeneity in response to different markets existed pre and post GFC yet the response itself became more pronounced after the GFC.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 it