Multi-period binding freight contract using swing options
Bibliographic record
Abstract
• Swing options are introduced as multi-period binding freight contracts. • The swing option can be used to solve the load rejection problem and hedge against spot rate volatility. • Proposing swing option is priced based on the Hull-White tree model and dynamic programming. • Several tendering strategies are compared under different market conditions. • A short-term call swing option benefits the shipper for their short to medium-term shipping needs. The truckload freight market is a critical component of the US transportation industry, yet freight contracts used in this market are considered non-binding. Most shippers are challenged with freight rejection problems and forced to source from the spot market. The swing option is an exotic option that allows the holder to purchase or sell a defined quantity of underlying assets. This study proposes the use of swing options as multi-period binding freight contracts between shippers and carriers and builds a fair valuation framework using a mean-reverting process for the freight rates, the Hull-White tree model, and dynamic programming. Numerical examples are given to explain the multi-period price behaviours of the swing option freight contract. Sensitivity analyses are conducted on several key model parameters to understand the impacts of model parameters on the price of the swing option freight contract. Moreover, the performance of six tendering strategies is compared under different market conditions, which helps shippers make informed decisions on purchasing or entering into swing option contracts. Our results show that the shipper in an existing freight contract with a high freight rate benefits the most by tendering to the spot market and purchasing the short-term call swing option freight contract at a low strike price.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 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".