Notice bibliographique
Résumé
Abstract The biggest uncertainty in oil and gas economics is the commodity price. To determine this information, many experts produce very detailed price forecasts. These forecasts tend to follow very smooth trends. Unfortunately, both recent and past events have shown that hydrocarbon prices can change very rapidly. As well, several competing trends can be found in oil and gas prices. Long-term historical data indicate that hydrocarbon prices tend to revert back to historical averages. However, short-term price fluctuations are unpredictable. To address this need, a price fluctuation model has been developed for the Canadian oil industry. A random walk model with mean-reversion was developed and tuned to fit Canadian hydrocarbon prices. Starting with the current spot price, the model will generate a random but equiprobable prediction of future prices. The model can be used as input into a Monte-Carlo simulation. Alternately, the model can be run multiple times in order to generate "high," "low," and "expected" price predictions. Introduction In recent years, there have been dramatic swings in the price of crude oil and natural gas commodities. Between January 1, 1999 and January 1, 2004, the nominal monthly average price of light oil delivered to refineries in Edmonton, Alberta has varied between CDN$17 and CDN$54. Though these price extremes are not unprecedented, the speed with which prices dropped, then rebounded, have surprised many analysts. Even ignoring the recent swings in prices, Plourde and Watkins(1) found that oil prices are among the most volatile of all commodities. These price swings make it very difficult for an analyst to determine the economic viability and risk of a proposed investment. This is because the price of oil and gas over the first five years or so of a project often determines the project's overall economic success. However, quantitative predictions of spot oil and gas prices are quite unreliable past three months into the future. Options and hedging strategies can offload some of the uncertainty, but they must be valued. And, to evaluate an option, one must understand the price behaviour of the underlying asset. Analysts have devoted much time and effort to better understand oil and gas price fluctuations. In general, this effort has been directed towards several benchmark prices. The most widely studied of these benchmark prices is the West Texas Intermediate (WTI). This is a light, sweet oil, normally priced for delivery to Cushing, Oklahoma. Another commonly studied benchmark is the Brent Blend, which is priced for delivery at the Sullom Voe Terminal in Scotland. Often, other energy commodities are priced in relation to these benchmarks. For example, the price of light oil to be delivered to refineries in Edmonton is often assumed to be about CDN$1/bbl less than the WTI price. Due to the maturity of the markets trading these two commodities, both spot and futures price data have been available for a large number of years. Using this data, a tremendous number of structural and statistical models have been developed for WTI and Brent prices.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».