Yield and Volatility of Cryptocurrency under Long-term Uncertain Situation: Evidence from Covid-19 Pandemic
Bibliographic record
Abstract
The birth of Bitcoin, the development of cryptocurrency, the establishment of digital platforms, and the investment of international capital. As the meta-cosmic era approaches, how does the blockchain platform occupy a place in the financial market, and how does it affect the world economy? What changes have taken place in the international financial sector? How has the economic downturn caused by the epidemic positively affected Bitcoin? As a market participant, how to reach a consensus on bitcoin, and how to invest, is a rational decision. This article is intended to use the Augmented Dickey-Fuller (ADF) Unit Root Test, Vector Autoregression (VAR) Model, ARMA-GARCH Model, Impulse Response, and the application of models such as ARMA-GARCH Estimation Results and Variance Equation analyzes the causes of bitcoin price fluctuations in the epidemic and how the epidemic has positively affected cryptocurrencies. Based on data, assisted by ICONS, the text analyzes the connection between the epidemic and cryptocurrencies.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".