Bibliographic record
Abstract
This study tests whether gold can effectively hedge exchange rate risks. We take into account the asymmetric characteristic of exchange rate fluctuations and use the dynamic panel threshold model in order to select gold prices in major gold-related currencies in the world: the Australian dollar, the Canadian dollar, the euro, the Indian rupee, the Japanese yen, the South African rand, and the British pound. Using monthly data from January 1999 to January 2010, with lagged one-period exchange rate returns (US dollar depreciation rate) as the threshold variable, the estimation results suggest that there are two thresholds at –7.5% and –3.7%. These can be divided into regime 1 (exchange rate returns ≤ –7.5%), regime 2 (–7.5% < exchange rate returns ≤ –3.7%), and regime 3 (exchange rate returns > –3.7%). Regarding the effectiveness of gold hedging, regime 2 is higher than is regime 3. The risk hedging effect of regime 1 is not significant because it might be caused by the excessive devaluation of the US dollar in the short-term and the overshooting of the exchange rate adjustment, making gold unable to hedge the devaluation risks of 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 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".