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Record W11902808 · doi:10.1211/0022357021778484

Operating Options and Commodity Price Processes

2004· article· en· W11902808 on OpenAlexaff
Manle Lei, Glenn Fox

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsEconomicsVolatility (finance)Spot contractEconometricsCommodityContangoFlexibility (engineering)Mid pricePrice levelMicroeconomicsMonetary economicsFinancial economicsFutures contractFinance

Abstract

fetched live from OpenAlex

Abstract This paper discusses the short-run dynamics of commodity prices. It deals with the interrelationships between price, inventory and price volatility as well as the effects of inventory and the producers’ operating flexibility on the dynamics of price in the short-run. It also illustrates how to model and estimate the stochastic process of commodity prices. We conclude that, in the short-run, producers’ operating flexibility reduces price volatility when the spot price is higher than the threshold price causing expansion in the scale of operations. However, we also conclude that operating flexibility can increase price volatility when the spot price is lower than the threshold price resulting in a contraction of operations. We demonstrate the failure of currently used parametric models in describing the stochastic process of commodity prices and suggest using non-parametric methods. We also recommend including the time trend in such a model. Key Words: Price, inventory, price volatility, operating options

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.053
Threshold uncertainty score0.176

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.003
Scholarly communication0.0090.009
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0530.003

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.

Opus teacher head0.030
GPT teacher head0.229
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2004
Admission routes1
Has abstractyes

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