Determinants of Violations in the SET50 Index Options Pricing Relationships: Put-Call-Futures Parity and Box Spread Tests
Bibliographic record
Abstract
Although SET50 Index Options, the only option product on Thailand Futures Exchange, has been traded since October 29, 2007, it has faced the liquidity problem. The SET50 Index Options market must offer a risk premium to compensate investors for liquidity risk. It may cause violations in options pricing relationships. This research therefore uses daily data from October 29, 2007 to December 30, 2016 to compare the violations in SET50 Index Options pricing relationships before and after change in contract specification on October 29, 2012 and investigate determinants of these violations using Tobit model. Two tests of SET50 Index Options pricing relationships, Put-Call-Futures Parity and Box Spread, are employed. The test results of Put-Call-Futures Parity show that the percentage and baht amount of violations in many cases are greater in the period before the modification of SET50 Index Options. Without transaction costs, we also see more Box Spread violations before contract adjustment. However, after taking transaction costs into account, there are more percentage and baht amount of Box Spread violations in the later time period. The estimation of Tobit model shows that the violation sizes of both Put-Call-Futures Parity and Box Spread, excluding transaction costs, depend on the liquidity of SET50 Index Options market measured by option moneyness and open interest. The SET50 Index Options contract specification, especially exercise price, also significantly affects the size of violations, though the direction of a relationship is not cleared.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".