Pricing KTB Futures: An Application of Black-Karasinski Model
Bibliographic record
Abstract
Traditionally, people values KTB futures contracts using the model based on the cost-of-carry argument. However, the underlying commodity for the KTB futures is non-tradable, and so the cost of carry argument cannot be applied to the KTB futures. This paper regards KTB futures contracts as interest-rate derivatives, and prices them using the Black-Karasinski (B-K) term structure model. This paper documents that (1) the market prices of KTB futures are more close to B-K model price than the price by the cost-of-carry argument, though the KTB futures are generally underpriced in the market even under the B-K model; (2) The extent of underpricing is a decreasing function of the remaining maturity of the futures, and becomes smaller recently; (3) The cost of carry argument relatively overprices the KTB futures, and the degree of overpricing is a decreasing function of interest rates and the remaining maturity of the futures; (4) The daily resettlement in the futures contracts affects the futures price very little; (5) The trading strategies based on the theoretical pricing models produce very high trading profit.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".