Revisiting Quantile Granger Causality Between the Stock Price Indices and Exchange Rates for G7 Countries
Bibliographic record
Abstract
The daily data of the stock price index and the foreign exchange rate in G7 were utilized for the period between January 4, 1999 and June 30 2015. From the empirical study of Granger causality test in quantiles, there are three main findings. Firstly, there is no long-run significant relationship between the stock price index and exchange rate in G7. Secondly, different types of short-run relationships exist between the two variables among G7 countries. In Canada, Italy, and U.S.A., the relationship is bidirectional, and the asymmetric effect is at different quantiles. In France and Japan, the relationship is unidirectional, from the stock price index to the exchange rate, and the relationship is at different quantiles for the two countries. In Germany and U.K., the relationship is unidirectional in the opposite direction and is also at different quantiles. Lastly, it shows that international trading effects at different quantiles exist in Canada (at high quantile), Italy (at median quantile), and U.K. (at low quantile). On the other hand, portfolio balance effects at different quantiles exist in Germany (at low and median quantiles) and U.S.A. (at high quantile). The study shows neither effect in France and Japan. The empirical findings in this paper have important implications for academicians, international institutional investors, and policy-makers on the G7 markets.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".