Do Crypto and Equity Go Hand in Hand? An Empirical Study of G-7 Economies Using VECM, Granger Causality and Panel Data Analysis
Bibliographic record
Abstract
Cryptocurrencies have emerged as attractive investment options, offering the potential for portfolio diversification and serving as hedging instruments. The primary aim of the study is to explore investment opportunities and strategies of causal linkages between crypto and equity markets, focusing on the world’s largest developed markets within a group of seven countries (G-7), namely Canada, France, Germany, Italy, Japan, the United Kingdom and the United States of America. The causality analysis of G-7 equity markets with the crypto index (CCI30) spread over thirty digital currencies is done using the vector error correction model (VECM), Granger Causality tests, and panel data approaches. The results of VECM have shown a long-term equilibrium between the equity index and crypto index only in France. However, causal linkages have also been found in four other countries, that is, Germany, Japan, the USA and the UK, with varying levels of significance. The panel data analysis has shown that, as a group, the G-7 equity indices have a significant impact on the cryptocurrency index, suggesting promising opportunities for portfolio diversification across these economies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".