Exchange rate volatility and volatility asymmetries: an application to finding a natural dollar currency
Bibliographic record
Abstract
Based on six daily spot nominal exchange rate returns denominated in the US dollar, viz-à-viz UK Pound, Japanese Yen, Swiss Franc, Canadian dollar, Australian dollar and Singapore dollar, this paper tries to find a natural Dollar currency by comparing the linear/nonlinear dynamics in the conditional variance of these bilateral exchange rate returns (time varying volatility vs. asymmetries). The characteristics of the unconditional distribution of the FX returns justified the use of the GARCH class of models of conditional volatility. Strong time varying symmetric effects are apparent in all the series examined, especially in the Australian dollar. Further asymmetric effects in unexpected appreciations and depreciations of currencies are examined based on the GJR model, the ST GARCH model and the ANST-GARCH model (which encompasses several asymmetric models). The estimates of asymmetric models show weak evidence of asymmetries in most of the currencies, except in the Japanese Yen and UK Pound. Further findings show that the Japanese Yen is a non-natural Dollar country. However, there may possibly exist some mild deterministic asymmetric effect in the UK Pound. Based on the symmetric GARCH model, a trader/investor may consider Australian dollar as the relatively most ‘likable’ currency, i.e. relatively the least volatile currency and relatively the most synchronized with the US dollar.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".