Financial market risks and the hedging powers of unconventional assets under different conditions
Bibliographic record
Abstract
The global financial ecosystem has become increasingly precarious for investors in the face of diverse risks such as macroeconomic, policy uncertainty, geopolitical, and systemic risks. This study examines hedging these risks with alternative classes of unconventional assets; clean stocks, precious metals, Shariah-compliant stocks, and REITs, as contribution to the literature that contains fragmented analysis of individual assets or specific risks. The study employs a generalized least squares estimator that carefully eliminates salient econometric problems alongside quantile analysis using daily data spanning 5/17/2010 to 12/16/2024. The striking findings therefrom are: (i) precious metals, especially gold, are the best hedging candidates except against geopolitical risk where clean stocks come in to provide cover; (ii) analyses of quantiles provide fresh insights that indicate that most of the hedging powers of the assets are found during bearish market condition. The study accentuates the use of gold for portfolio diversification and for keeping foreign reserves.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".