Investigating the Impact of Geopolitical Risks and Uncertainty Factors on Bitcoin
Bibliographic record
Abstract
With the rise of cryptocurrencies and their appeal as alternative investment assets, this study, using daily and weekly data from early 2015 to late 2023, aims to analyze the influence of economic and geopolitical uncertainty factors on cryptocurrencies, particularly Bitcoin, and forecast their volatility using GARCH, EGARCH, and GJR-GARCH models. Our findings reveal that the Geopolitical Acts Index (GPAs), the U.S. Economic Policy Uncertainty Index (EPU), and the Volume of Bitcoin transactions exhibit a positive significant impact on its returns, whereas the Cryptocurrency Uncertainty Index (UCRY), S&P 500, and Volatility Index (VIX) demonstrate a negative one. Furthermore, by decomposing geopolitical turbulence into Geopolitical Risks (GPRs) and Threats (GPTs), these variables were found to be less significant compared to Geopolitical Acts. Finally, the asymmetry analysis (leverage effects) reflects on how negative shocks exhibit a greater influence than positive ones on Bitcoin returns, indicating that adverse news in the media tends to impact the cryptocurrency returns more profoundly. Our conclusions contribute to the existing literature by exploring the role that Bitcoin, and cryptocurrencies in general, play as investment assets, when taking into consideration the volatility they entail, especially following negative shocks in an economy.
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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 | 0.000 | 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 | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 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".