Alternative investments during turbulent times comparison of dynamic relationship
Bibliographic record
Abstract
The coronavirus pandemic, like the Russian aggression on Ukraine, had a significant impact on many financial markets and asset prices. The latter additionally led to large fluctuations on financial markets. In this paper, we try to compare the performance of safe haven assets during turbulent times, such as the recent global financial crises, eurozone debt crises, the COVID-19 pandemic and the Russian aggression on Ukraine. We investigate the dynamic relationship between indices from the European countries like the Czech Republic, France, Germany, Great Britain, Poland, Slovakia, Spain, and popular instruments such as gold, silver, Brent Crude Oil, Crude Oil WTI, US Dollar, Swiss Franc, and Bitcoin. The study estimated the parameters of either DCC or CCC models, to compare the dynamic relation between the above-mentioned stock markets and financial instruments. The results showed that in most cases, the US Dollar and Swiss Franc were able to protect investors from stock market losses during turbulent times. In those periods, gold was the closest to being a safe haven instrument for investors from France, Poland, the Czech Republic and Slovakia. Our findings are in line with other literature which points out that safe haven instruments can change over time and across countries. In that literature, we can find research performed for the USA, China, Canada, and Great Britain, but there is no such research for Poland, Italy, the Czech Republic or Slovakia. The purpose of this paper is therefore to try to fill this research gap.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".