Asymmetric interdependence between currency markets' volatilities across frequencies and time scales
Bibliographic record
Abstract
Abstract We investigate the dynamics of interdependence between realized variances and realized semivariances of six major currencies across frequencies and time scales. The empirical results are derived, first, through constructing daily measures of realized variance and semivariance from a high frequency 5‐min interval data, and second, by fitting wavelet squared coherence and wavelet cohesion measure with time‐varying weights. The realized volatilities of the currencies and their cross‐currency influences are found to increase during the global financial crisis. The realized volatilities of the Euro, Swiss Franc, and British Pound are closely synchronized over the short‐term horizon. However, over the long‐run, the Euro, Swiss Franc, and Japanese Yen lead the realized volatilities of the British Pound, Australian Dollar, and Canadian Dollar. The synchronization structure of positive and negative realized volatilities indicates asymmetric dependence among the currencies across time horizons. We further observe strong positive (negative) cohesion among the realized volatilities over the medium‐ and long‐term horizons. Finally, significant counter cyclical comovements among the currencies are observed over the medium‐ and long‐term horizons. These findings have important implications for foreign exchange portfolio managers.
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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 | 0.008 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".