Dependence of Stock Markets with Gold and Bonds under Bullish and Bearish Market States
Bibliographic record
Abstract
This paper examines the dependence of gold and benchmark bonds with ten stock markets including five larger developed markets (e.g. USA, UK, Japan, Canada and Germany) and five Eurozone peripheral GIPSI countries (Greece, Ireland, Portuguese, Spain and Ireland) stock markets. We use a novel quantile-on-quantile (QQ) approach to construct the dependence estimates of the quantiles of gold and bond with the quantiles of stock markets. The QQ approach, recently developed by Sim and Zhou (2015), captures the dependence between the entire distributions of financial assets and uncovers some nuance features of the relationship. The empirical findings primarily show that gold is strong hedge and diversifier for the stock portfolio except when both the markets are under stress. Further, the flight to safety phenomenon is short-lived because national benchmark bonds exhibit a positive dependence with their respective countries stock indices at various quantiles. Unlike the existing literature, the QQ approach suggest that bonds act as safe havens for the stock portfolio but gold does not. Our findings also suggest that dependence between stock-gold and stock-bond pairs is not uniform and this relationship is market state (e.g. bearish, mild bearish, optimistic or bullish) and country specific.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".