Analysis of Self-Similarity in Short and Long Movements of Crude Oil Prices by Combination of Stationary Wavelet Transform and Range-Scale Analysis: Effects of the COVID-19 Pandemic and Russia-Ukraine War
Bibliographic record
Abstract
This paper examines the self-similarity (long memory) in prices of crude oil markets, namely Brent and West Texas Instruments (WTI), by means of fractals. Specifically, price series are decomposed by stationary wavelet transform (SWT) to obtain their short and long oscillations. Then, the Hurst exponent is estimated from each resulting oscillation by rescaled analysis (R/S) to represent hidden fractals in the original price series. The analysis is performed during three periods: the calm period (before the COVID-19 pandemic), the COVID-19 pandemic, and the Russia-Ukraine war. In summary, prices of Brent and WTI exhibited significant increases in persistence in long movements during the COVID-19 pandemic and the Russia-Ukraine war. In addition, they showed a significant increase in anti-persistence in short movements during the pandemic and a significant decrease in anti-persistence during the Russia-Ukraine war. It is concluded that both COVID-19 and the Russia-Ukraine war significantly affected long memory in the short and long movements of Brent and WTI prices.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".