Volatility Spillovers and Nonlinear Dynamics between Jet Fuel Prices and Air Carrier Revenue Passenger Miles in the US
Bibliographic record
Abstract
This paper investigates the nonlinearities in the behavior of jet fuel prices and air carrier yields as measured by revenue passenger miles(RPMs), where one RPM is defined as one passenger flown one mile in revenue traffic. It indicates that previous research might have overlooked the possibilities of nonlinear dynamics between these two series. Drawing on existing tests of nonlinearities and chaos, this paper first investigates the existence of chaotic behavior as the source of nonlinearities in the monthly prices of jet fuel and RPMs. The findings show strong evidence that the two series exhibit nonlinear dependencies. Evidence is found, however, that this behavior may be inconsistent with chaotic structure. We propose and estimate bivariate GARCH(1, 1) and bivariate EGARCH(1, 1) models to ascertain the flow of information between jet fuel prices and revenue passenger miles. Estimation results of the bivariate GARCH models offer evidence that the shock transmission between the two series is mainly asymmetric, that is that positive and negative shocks impart degree of volatility differently. It is shown that the positive shocks to jet fuel prices show a substantially higher reaction from the revenue passenger miles. The conclusion is that, RPMs are quite responsive to upward volatility in prices of jet fuel, while falling jet fuel prices may not translate into efficiency gains.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".