Bibliographic record
Abstract
Purpose - The purpose of this study was to examine gold and bitcoin hedging against 10 exchange returns. Design/Methodology/Approach - This study collected financial data on exchange, gold, and bitcoin from FRB, St. Louis. A multiple Vector-BEKK regression analysis was used to analyze the data. Findings - First, strong negative effects from exchange markets onto gold were found to exist in the EU, Switzerland, Australia, Brazil, Canada, Japan, and Korea, while there were weak effects in the UK. Bitcoin shows the weak hedging against all markets. Second, the paper also revealed that in EU, the cross-shock term significantly decreased gold volatility, but not bitcoin volatility, while in Japan it decreased bitcoin volatility. The significantly negative asymmetries in gold, but insignificant asymmetries in bitcoin, were found in most exchange markets. Exchange market volatility increases gold volatility in Japan while it decreased in the Indian and Korean markets. Cross-terms among three variables with bi-directional causality are valuable. Research Implications or Originality - The study of the hedging of gold and bitcoin against various exchanges together shows that bi-variate models are useful to reconfirm the strong hedging of gold. Bitcoin, if well prepared to be immune to its deficiencies, might be very carefully used, but not at a magnitude equal to gold as a hedge against exchange. The results may enhance strategic risk management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".