Optimal Portfolio Allocation between Global Stock Indexes and Safe Haven Assets: Gold versus the Swiss Franc (1999–2021)
Bibliographic record
Abstract
This paper contributes to the literature on safe haven assets, analyzing gold and the Swiss Franc’s defensive properties inside various global stocks portfolios. The analysis relies on monthly data extending over the last two decades. Drawing on Multivariate Garch DCC models, the hedging effectiveness of bivariate Swiss Franc-hedged portfolios is found to be notably higher than that of gold-hedged portfolios. Value-at-Risk simulations, assuming equal or “optimal” portfolio weights, confirm these results inside a multivariate asset framework, while a regression approach with quantile dummies provides further support in this regard. Since the better hedge and safe haven properties of the Swiss Franc are likely to persist in the future, the main policy implication of the paper concerns asset allocation strategies giving relatively more weight to the Swiss currency in global stock portfolios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".