Exploring the Asymmetric Multifractal Dynamics of DeFi Markets
Bibliographic record
Abstract
The rapid growth of decentralized finance (DeFi) has revolutionized the global financial landscape, providing decentralized alternatives to traditional financial services. This study investigates the asymmetric multifractal behavior of nine DeFi markets—AAVE, Pancake Swap (CAKE), Compound (COMP), Curve Finance (CRV), Maker DAO (MKR), Synthetix (SNX), Sushi Swap (SUSHI), UniSwap (UNis), and Yearn Finance (YFI)—using Asymmetrical Multifractal Detrended Fluctuation Analysis (A-MFDA). The use of generalized Hurst exponents, Rényi exponents, and singularity spectrum functions revealed that DeFi markets exhibit multifractal behaviors. The analysis uncovered clear differences between uptrend and downtrend fluctuation functions, highlighting asymmetric multifractal behavior. The asymmetry intensity was analyzed through excess differences in uptrend and downtrend generalized Hurst exponents. AAVE, COMP, SNX, UNis, SUSHI, and MKR exhibit negative asymmetry, with stronger correlations during negative trends. CAKE shifts from positive to negative asymmetry, showing sensitivity to both trends. CRV is more volatile in negative trends, while YFI consistently displays positive asymmetry across market fluctuations. The results also reveal that long-term correlations and heavy-tailed distributions contribute to the multifractality of DeFi assets. This study highlights the need for dynamic risk management in DeFi markets, urging investors to adopt adaptive strategies for volatile assets and prepare for sudden price fluctuations to safeguard investments.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".