Bibliographic record
Abstract
This paper constitutes the first formal empirical investigation of a bond option valuation model based on exchange-traded bond options. Using the transactions-level dataset maintained by the Montreal Exchange, I conduct tests of the Schaefer and Schwartz (1987) time-dependent variance model aiming to establish the model's performance in pricing and in hedging long-term Government of Canada bond options for the May 1986 - December 1988 period. The option contracts covered by this investigation are similar to bond options contracts traded over-the-counter in that their underlying asset is a specific long term bond (with an approximate term to maturity of ten years) rather than a notional bond with a specific maturity date. My findings reveal that, in spite of its apparent simplicity, the Schaefer and Schwartz (1987) model generates pricing errors that compare favourably with the typical price deviations documented in the equity options literature. This paper is the first one documenting the existence of a volatility 'smile' in the bond options market, which is a phenomenon that is well documented in the equity options literature. Additional sources of bias relating to the term to maturity of the options and to the term to maturity (duration) of the underlying bond are also documented. The model's tendency to underprice very short-dated options (less than 15 days to maturity) lends support to the notion that the 'true' interest rate yield process may be better described by a jump-diffusion specification, and its tendency to underprice options on longer-term bonds in relation to shorter-term bonds is consistent with the proposition that bond yields exhibit mean-reversion. Finally, simulated trading strategies attempting to capture the model's mis-pricings yield, at times, substantial profits but, given that the most profitable 'arbitrage' trades observed in my sample are concentrated towards a relatively small subset of the sample that includes the most illiquid and infrequently-traded contracts, I conclude that the Shaefer & Schwatz model offers a useful tool for the valuation of exchange-traded bond options.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".