An Examination of Cryptocurrency Volatility: Insights from Skewed Error Innovation Distributions Within GARCH Model Frameworks
Bibliographic record
Abstract
With escalating public interest in the cryptocurrency market, largely driven by its perceived potential for rapid wealth accumulation and various advantages over traditional currencies, there is an imperative to understand its inherent volatility.This study addresses the dynamic behaviour of cryptocurrencies by utilizing skewed error innovation distributions to model the volatility of five key cryptocurrencies.Data was sourced from Yahoo Finance, encompassing daily closing prices from September 11, 2017, to April 8, 2022.The significance of the skewness parameter in all optimal volatility models (p<.05) substantiates the application of skewed error innovation distributions.Notably, the observed influence of past negative events on volatility was consistently greater than that of positive events across most examined cryptocurrencies.While Value at Risk (VaR) models are frequently used for risk measurement in this domain, this study's findings suggest that their reliability is not universal across all cryptocurrency cases.Consequently, caution is advised when employing VaR models for risk assessment associated with 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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".