CRYPTOCURRENCIES AND G7 CAPITAL MARKETS INTEGRATE IN PERIODS OF EXTREME VOLATILITY?
Bibliographic record
Abstract
The purpose of this study is to examine the synchronism between the US capital markets (DJ, S&P 500), the United Kingdom (FTSE 100), Canada (S&P/TSX), Germany (DAX 30), France (CAC 40), Japan (Nikkei 225), Italy (Italy Ds Market and major cryptocurrencies such as Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), and the Crypto 10 index, from February 2018 to November 2021. Based on the findings, we found that BTC and ETH cryptocurrencies drastically reduced their level of integration with their peers over the 2020 worldwide pandemic era, whereas LTC maintained. We also discovered that the Dow Jones, S&P 500, and DAX 30 stock indexes lowered their level of integration when compared to the pre-covid subperiod. For the UK capital market (FTSE 100), Canada (S&P/TSX), Japan (Nikkei 225), France (CAC 40), and Italy (Italy Ds Market) the level of integration increased significantly. These findings support, in part, our research question, that during periods of stress and uncertainty in the global economy capital markets tend towards integration, thus calling into question the hypothesis of efficient portfolio diversification.
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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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".