Detecting Structural Changes in Bitcoin, Altcoins, and the S&P 500 Using the GSADF Test: A Comparative Analysis of 2024 Trends
Bibliographic record
Abstract
Understanding structural regime shifts in crypto asset markets is vital for early detection of systemic risk. This study applies the Generalized Sup Augmented Dickey–Fuller (GSADF) test to daily high-frequency price data of five major crypto assets—BTC, ETH, SOL, AAVE, and BCH—from 2023 to 2025. The results reveal asset-specific structural breaks: BTC and BCH aligned with macroeconomic shocks, while DeFi tokens (e.g., AAVE, SOL) exhibited fragmented, project-driven shifts. The S&P 500 index, in contrast, showed no persistent regime shifts, indicating greater structural stability. To examine inter-asset linkages, we construct co-occurrence matrices based on GSADF breakpoints. These reveal strong co-explosivity between BTC and other assets, and unexpectedly weak synchronization between ETH and AAVE, underscoring the sectoral idiosyncrasies of DeFi tokens. While the GSADF test remains central to our analysis, we also employ a Markov Switching Model (MSM) as a secondary tool to capture short-term volatility clustering. Together, these methods provide a layered view of long- and short-term market dynamics. This study highlights crypto markets’ structural heterogeneity and proposes scalable computational frameworks for real-time monitoring of explosive behavior.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| 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) | 0.002 | 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".