Analysing the Nexus: Stock Indices and Cryptocurrencies During the Conflict Between Russia and Ukraine
Bibliographic record
Abstract
Background: The global economy faced increased instability due to the simultaneous occurrence of two major events, the global pandemic in 2020 and the Russian-Ukrainian conflict in 2022, which impacted the financial markets. Purpose: This paper aimed to evaluate the comovements between the USA (S&P 500), Germany (DAX 30), France (CAC40), Japan (Nikkei 225), Canada (TSX), Russia (MOEX) and Ukraine (PFTS) stock markets and the cryptocurrencies Bitcoin (BTC), Ethereum (ETH), Litcoin (LTC) Dash (DASH/USD), Ripple (XRP) DigiByte (DGB) and Nem (XEM), from February 24, 2022, to April 12, 2023 Methods: The approach to our research question will involve using the causality econometric model, Granger SVAR (Vector Autoregressive). Results: The results showed that stock indices and digital currencies show sharp structural breaks, and not all markets influence cryptocurrencies. The MOEX stock market affects the price formation of BTC, ETH, DGB, XEM, and XRP, while the DAX 30 stock index impacts ETH, LTC, DASH, DGB, and XEM. The Ukraine market (PFTS) influences ETH, but the other stock markets do not influence any of the cryptocurrencies analysed. Conclusion: Investors, policymakers, and other participants operating in the digital currency markets can find valuable information in the study's conclusions when seeking to rebalance their portfolios.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".