On the Connectedness Between Bitcoin, Gold, Gold-Backed Cryptocurrencies and the G7 Banking Sector Stock Indices During Crises: Evidence from Quantile Vector Autoregression and Temporal Frequency Connectivity approach
Bibliographic record
Abstract
This study investigates the dynamic connectedness among conventional cryptocurrencies (Bitcoin), gold-backed currencies (PAXG and DGX), gold, and G7 banking sector indices (USA, Germany, Canada, France, UK, Italy, and Japan). Employing Quantile Vector Autoregression and Temporal-Frequency Connectivity methodologies, our analysis reveals nuanced relationships among these market blocks. France and Germany’s banking indices exhibit the highest connectedness, emphasizing their central roles. Peaks in the Total Connectedness Index coincide with global events, underscoring the market’s sensitivity to external shocks. G7 banking sectors emerge as stable information transmitters, while Bitcoin, PAXG, DGX, and gold act as net receivers of shocks, reflecting their effectiveness as hedges during economic uncertainties. Our time-quantile space approach unveils a symmetrical pattern in dynamic connectivity, emphasizing robust interconnections between positively and negatively shifted assets. The time-frequency connectedness analysis highlights the market’s short-term sensitivity, emphasizing the need for adaptive risk management. Decomposing net directional connectivity into short and long-term dynamics provides valuable insights for investors and risk managers. Ultimately, our findings contribute to a deeper understanding of dynamic connectedness in the cryptocurrency market, offering insights for effective risk management and decisionmaking in this evolving financial landscape.
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".