Causality between stock indices and cryptocurrencies during the Russia-Ukraine war
Bibliographic record
Abstract
Abstract This article examines the causal relationship between stock indices and cryptocurrencies during the current war between Russia and Ukraine. The econometric investigation runs from February 24, 2022 to April 12, 2023, focusing on seven stock market indices (S&P500, DAX, CAC40, Nikkei, TSX, MOEX and PFTS) and seven cryptocurrencies (Bitcoin, Ethereum, Litcoin, Dash, Ripple, DigiByte and XEM). In this article, we try to understand how investors react to fluctuations in financial assets to seek safe havens in crypto currencies. We used dynamic causality in the Granger (1969) sense to detect a possible causal relationship in the short term, and seven models to estimate the long-term relationship between cryptocurrencies and financial assets. The causal relationship between financial market indexes and cryptocurrency coins in the short run indicate that three famous cryptocurrencies (BITCOIN, ETHEREUM, RIPPLE) and the two digital asset with minor popularity (XEM, Digibyte) are impacted by the German, Russian and Ukrainian stock markets. In the long-run we found a positive and significate effect of the American, Canadian, French and Ukrainian stock market indexes on Bitcoin. Thus, the stability of the traditional financial markets during the current war period can be explained on the one hand by investors’ fears of an unstable business climate, and on the other hand, by speculators’ sentiment towards new electronic products which are perceived as hedging instruments and a safe haven in the face of the conflict between Ukraine and Russia. JEL Classifcation: C5 · C22 · G1
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".