Wavelet Entropy for Efficiency Assessment of Price, Return, and Volatility of Brent and WTI During Extreme Events
Bibliographic record
Abstract
This study analyzes the market efficiency of crude oil markets, namely Brent and West Texas Intermediate (WTI), during three different periods: pre-COVID-19, during the COVID-19 pandemic, and during the ongoing Russia–Ukraine military conflict. To evaluate the efficiency of crude oil markets, wavelet entropy is computed from price, return, and volatility series. Our empirical results show that WTI prices are predictable during the Russia–Ukraine military conflict, but Brent prices are difficult to predict during the same period. The prices of Brent and WTI were difficult to predict during the COVID-19 pandemic. Returns in Brent and WTI are more difficult to predict during the military conflict than they were during the pandemic. Finally, volatility in Brent and WTI carried more information during the pandemic compared to the military conflict. Also, volatility series for Brent and WTI are difficult to predict during the military conflict. These findings offer insightful information for investors, traders, and policy makers in relation to crude oil energy under various extreme market conditions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".