A lognormal/normal regime-switching commodity pricing model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Inspired by the negative price of WTI crude oil observed during the COVID-19 pandemic, we develop a new model for commodity pricing which allows structural change between price normality and lognormality under a Markov regime-switching (RS) framework. We augment the Extended Kalman Filter to calibrate the structural changing model. The model performance in calibration is compared to that of the common RS model with historical WTI spots, various futures and hypothetical scenarios. We conclude that our model is superior in capturing price dynamics especially in the oil market downturns. Encouragingly, the regime probabilities estimated with the new model indicate that during severe events including the 2008–2010 financial crisis, 2014–2016 oil crash and the outbreak of COVID-19 in 2020, WTI spot itself follows normal rather than the widely assumed lognormal process. This finding is consistent with our empirical studies. In addition, we assess the probability density of spot prices with the new model. Finally, we present the PDE finite difference and Monte Carlo approaches to price commodity options under the new model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it