Volatility Scenario of Bitcoin: The restraining role as a store of value and a unit of account
Bibliographic record
Abstract
Bitcoin and other cryptocurrencies are subject to unusual price fluctuations that increase the concern of the people and institutions to transact with it and to invest in it. The daily price volatility in the case of Bitcoin scales even up to 50 per cent in some days. Studies on market efficiency, volatility, demand drivers and so on of Bitcoin are done on considerable scale to bring out pertinent information about its different behavioural dimensions. However, its acceptance and use are limited primarily on account of the volatility and the political risks associated to it. This paper pioneers in the assessment of the temporal sequence and magnitude of volatility of Bitcoin by analysing 2018 daily price data. The study found that the coin shows unusually high daily price changes of 10 per cent or more only on 70 days (3.47%) out of the 2017 daily returns computed from the price data. Noticeably, the number of days with positive returns in the 70 days is 31 as against the 39 days with losses. These unusual daily price changes of 3 to 4 times out of 100 found in the study are the cause of volatility concern spreading around Bitcoin. There are six instances of more than 100 days gap between the two unusual price changes in the 70 cases. No significant correlation is found between the unusual daily returns and the corresponding volume of trade on these days. The unusual daily volatility of Bitcoin occurring once in a while may dampen its role as a store of value and a unit of account.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".