An Empirical Study on the Asymmetric Effects of Trading Volume Information in int‘l Currency Futures Markets: Advanced vs Emerging Markets
Bibliographic record
Abstract
This paper tested the conditional mean spillover effects between trading volume and price changes in international currency futures markets. We use 8 kinds of currency futures markets such as Japanese yen, British pound, Australian and Canadian dollar as the advanced market and Korean Won/Dollar, Brazilian Real, Russian rubul and South African's land futures as the emerging markets. The sample period is covered from May 19, 2006 to March 15, 2009. For this purpose we employed dynamic time series model such as Nelson (1991)'s Exponential GARCH (1, 1)-M. The major empirical results are as follows; First, according to the empirical results of 4 advanced currency futures markets, we find that the open interests have a strong impact on the price changes at a statistically significant level. In case of the British pound and Canadian dollar futures, the price changes also have influence on the open interests. Second, according to the empirical results of 4 emerging currency futures markets, only Russian currency futures' open interest has an impact on the price change but the price changes of the remain 3 countries have an impact on open interests respectively at a significant level. Third, we also find that there is a asymmetric volatility spillover effects between open interests and price changes in all the advanced and emerging currency futures markets. Fourth, according to Granger causality test the influence of Japanese yen, Australian and Canadian dollar and Brazilian Real futures on the other currency futures markets are dominant. From these empirical results we infer that most of currency futures markets have a much better price discovery function than currency cash market and are inefficient to the information.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".