Bitcoin as a Global Currency: Exploring the Wild West of Cryptocurrency
Bibliographic record
Abstract
Bitcoin, and its contemporary substitute cryptocurrencies, are an exciting new evolution in our concept of money. However, there are currently factors holding back Bitcoin, the largest player in the cryptocurrency market, from a wider mainstream acceptance and adoption. The greatest force working against cryptocurrency’s ability to be an accepted method of exchange is its extreme price volatility which cannot be completely attributed to insufficient liquidity (Dyhrberg 2018). This research reexamines several GARCH models using a larger window with more observations than previous researchers, and determine that a GARCH(1,1) with an AR(6) term in the mean equation provide the best fit. After identifying the proper tool, a basket of explanatory macroeconomic variables was tested and further improved the fit. Notably, a strong relationship exists between currencies, commodities, and Bitcoin price variance furthering the common interpretation that Bitcoin exists somewhere in the ether of the two classes. Bitcoin also exhibited significant volatility responses to geopolitical events that imply a use by nefarious state actors. The objective of this project is to gain an understanding of the nature of cryptocurrency and its utilization in the macroeconomy.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".