Price disorder and information content in energy and gold markets: The effect of the COVID-19 pandemic
Bibliographic record
Abstract
• Estimate the correlation dimension, Lyapunov exponent, and approximate entropy in gold and energy markets. • Examine the effect of the COVID-19 pandemic. • The pandemic altered price disorder and information content. • Gold market not attractive during the pandemic. • Heating oil and gasoline markets offer interesting investment opportunities during the pandemic. In this paper, we examine market efficiency in fossil energy and gold markets. Specifically, we study price disorder and information content in various energy markets and in gold market before and during the COVID-19 pandemic. The set of energy markets include West Texas Intermediate (WTI), Brent, natural gas, heating oil, and gasoline. For each market, we estimated the correlation dimension, Lyapunov exponent, and approximate entropy for periods before and during the pandemic. In this regard, we contribute to the literature by using different nonlinear features to provide a rich description of the nonlinear dynamics in price evolution before and during the pandemic, considering five various energy and gold markets, and examining a longer and recent period spanning from November 2017 to November 2022. The empirical results show that, during the pandemic, complexity increased in gold and natural gas markets, stability strongly decreased in WTI and natural gas markets, and irregularity obviously increased in gold market but decreased in all energy markets. Besides, heating oil and gasoline markets appear to be unaffected by the COVID-19 pandemic especially in terms of complexity and stability compared to WTI, Brent, and gas markets. We conclude that the gold market maybe not attractive compared to energy markets for investors and traders during the pandemic. Besides, heating oil and gasoline markets offer interesting investment opportunities during the world economic downturn caused the outbreak of the pandemic.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".