Assessing the performance of safe haven assets during major crises
Bibliographic record
Abstract
This paper investigates the safe-haven characteristics of three assets, namely gold, crude oil and Bitcoin, and their ability to reduce downside risk of different portfolios during two severe financial crises: the 2008 global financial crisis (GFC) and the 2019 Coronavirus pandemic (COVID-2019). We examine the left-tail behaviour of portfolios consisting of 60/40 equity returns and bond yield from six G20 member nations by applying EVT, BMM in the context of portfolio optimisation and examine which selection of safe-haven assets between gold, crude oil and Bitcoin can be amalgamated to the stock/bond mix for an optimal portfolio during crises. The portfolios are from three developed countries: Canada, United States of America (USA) and United Kingdom (UK), while the three emerging countries are Russia, Brazil and South Korea. The sample data is from 2007 to 2009 for the GFC and 2019 to 2023 for COVID-19. The findings of the paper show that during the GFC, the addition of gold and crude oil and the combination of the two allowed the heavy Fréchet-type tails to transform into thin Weibull-type tails. This implies that the two assets acted as safe-haven assets during the crisis and gold being the best safe-haven option for all countries. Contrarily, COVID-19 yielded mixed results, all the assets including the digital cryptocurrency acted as a safe haven for only two emerging countries, namely Russia and Brazil, improving both tail behaviours to Weibull-type tails, with gold and Bitcoin serving safe-haven characteristics for both countries.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".