ASSESSING FOR TIME VARIATION IN OIL RISK PREMIA: AN ADCC-GARCH-CAPM INVESTIGATION
Bibliographic record
Abstract
This paper focuses on oil market dynamics through the investigation of oil systematic risk and oil risk premium dynamics over the period 1997-2012, which includes several different economic episodes, enabling us to capture a considerable number of statistical properties for oil prices. Interestingly, unlike previous studies, the authors retained data for several developed and emerging oil markets and used different oil prices in order to provide a comprehensive and wide-ranging vision of oil price dynamics. To this end and in order to take eventual time variation and asymmetry in oil price dynamics into account, the authors applied recent econometrics tests associated with the ADCC-GARCH class of model. This modelling enabled us to appropriately specify the dynamics of oil conditional variance and time-varying oil risk premium. Accordingly, this study offers three interesting findings. First, the hypotheses of asymmetry and time variation in oil risk premia are not rejected. Second, the recent global financial crisis has increased systematic oil risk and oil risk premia in different regions. Finally, oil risk premia in emerging countries are significantly higher than those in developed countries, suggesting the inclusion of additional premium induced by political instability and geopolitical changes in emerging economies.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".