Volatility spillovers between foreing-exchange and stock markets
Bibliographic record
Abstract
This paper empirically analyses the evidence of intra-spillovers and inter-spillovers between foreign exchange and stock markets in the seven economies which concentrate the majority of foreign exchange transactions (i.e. United Kingdom, Euro area, Australia, Swiss, Canada, United Kingdom and Japan), using daily data, during the period 1990 to 2015 and during the pre-global and post-global financial crisis periods. To that end, we employ two econometric methodologies: the C-GARCH methodology by Engle and Lee (1999) and the SVAR framework (Sohel Azad et al., 2015). Results suggest that: (i) permanent and transitory components of the conditional variance exhibit several well-known peaks in volatilities; (ii) the long-run volatility relationships are stronger than the short-run linkages volatility with a reinforcement during the post-global financial crisis period; (iii) the presence of intra-spillovers and inter-spillovers increases substantially during the post-global financial crisis period and (iv) in all samples, the stock markets play a dominant role in the transmission of long-run and short-run volatility, except for in the period after the Global Financial Crisis, where the foreign-exchange markets are the main long-run volatility triggers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".