Crude oil, daily returns, market volatility, extreme value analysis
Bibliographic record
Abstract
Crude oil markets are highly volatile and risky. Extreme value theory (EVT), an approach to modelling and measuring risks under rare events, has seen a more prominent role in risk management in recent years. This paper presents an application of EVT to the daily returns of crude oil prices in the Canadian spot market between 1998 and 2006. We focus on the peak over threshold method by analyzing the generalized Paretodistributed exceedances over some high threshold. This method provides an effective means for estimating tail risk measures such as Value-at-Risk and Expected Shortfall. The estimates of risk measures computed under different high quantile levels exhibit strong stability across a range of the selected thresholds. At the 99 th quantile, the estimates of VaR are approximately 6.3 % and 6.8 % for daily positive and negative returns, respectively. Keywords: JEL Classifications:
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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 teacher head, 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".