An Empirical Investigation of Bitcoin Hedging Capabilities against Inflation using VECM: The Case of United States, Eurozone, Philippines, Ukraine, Canada, India, and Nigeria
Bibliographic record
Abstract
This study examines Bitcoin's potential as an inflation hedge in different countries, including the United States, the Eurozone, the Philippines, Ukraine, Canada, India, and Nigeria. The study reveals varying results across countries using the Vector Error Correlation Model (VECM) with secondary monthly data from January 2012 to June 2023 for Bitcoin prices and inflation rates. Bitcoin exhibits an insignificant short-term relationship in the United States but a significant long-term negative correlation, suggesting it may not be a reliable inflation hedge. Similarly, no significant relationship was found in the Eurozone, the Philippines, Ukraine and Nigeria, indicating Bitcoin's limited effectiveness as an inflation hedge. Contrastingly, the study identifies a significant positive relationship between Bitcoin and inflation in Canada and India, indicating potential hedging against inflation within these economies. Therefore, investors, portfolio managers, and policymakers should consider these country-specific findings when evaluating Bitcoin's role as an inflation hedge. Furthermore, this study contributes valuable insights into cryptocurrencies and their potential in financial risk management.
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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 | 0.000 |
| 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".