Collapsing bubbles in the prices of cryptocurrencies
Bibliographic record
Abstract
This paper investigates the existence of bubbles in the daily prices of the most popular cryptocurrencies, Bitcoin (BTC), Ether (ETH), and Ripple (XRP), employing the recursive methods of Phillips et al. (2015) and Phillips et al. (2011) for testing and date-stamping episodes of exuberant behaviour over a period spanning seven years (2018–2024), including the COVID-19 pandemic crisis (2020–2021). The critical values of the tests are computed through the composite wild bootstrap technique by Phillips and Shi (2020) to make them robust to time-varying unconditional heteroscedasticity and the multiplicity issue in recursive tests. Results indicate that the prices of the most popular cryptocurrencies traded on decentralized ledgers, BTC and ETH, exhibited multiple episodes of exuberant behaviour, unambiguously for BTC and depending on the tests for ETH. Bubbles detected in the prices of BTC were due to the halving of the crypto, to market exuberance and to the pandemic crisis; bubbles detected on ETH prices were due to the launch of NFTs on the Ethereum blockchain, and to the change in investors’ expectations (from exuberant to pessimistic); the change in the stance of monetary policy burst the bubbles of BTC and ETH prices in 2024. No test supports the exuberance of XRP that is traded on a centralized ledger; weekly data confirm the absence of multiple bubbles. By looking at the presence of bubbles in these different digital ecosystems, we also consider how the technological differences can impact, possibly asymmetrically, bubbles' formation.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".