Bibliographic record
Abstract
In this paper, I study on intradaily basis six currency ETFs' tracking error volatility around the Great Recession - 12/30/2002 to 04/05/2013. I study the Australian Dollar ETF (FXA), the British Pound ETF (FXB), the Canadian Dollar ETF (FXC), the Euro ETF (FXE), the Swiss Franc ETF (FXF) and the Japanese Yen ETF (FXY). I find that the FXA, FXB and FXE tracking errors are non-stationary, whereas FXC, FXF and FXY tracking errors are stationary. The FXC, FXF and FXY ETFs tracking errors which are stationary and do not exhibit clustering of volatility are best described by a simple AR(1) model specification. The FXA, FXB and FXE ETFs tracking errors which are non-stationary and exhibit ARCH effects are best described by a AR(1)-GARCH(1,1) model. An arbitrageur can use these models to forecast these six currency ETFs tracking errors and identify a signal for an arbitrage opportunity.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".