Trading Volume Dynamics and Macroeconomic Influences in Thailand’s Equity Derivatives Market: A VAR Approach
Bibliographic record
Abstract
The study focuses on the SET50 Index, a benchmark of the fifty largest companies listed on the Stock Exchange of Thailand (SET). Thailand, despite being part of the options-dominated Asia-Pacific region, has a unique market structure where SET50 Index Futures dominate derivatives trading, while SET50 Index Options remain comparatively underused. Given this divergence from common regional trends, this study aims to examine the factors influencing trading volume dynamics in Thailand’s equity derivatives market using a Vector Autoregressive (VAR) model with three lags. The empirical results, based on the sample period from May 2014 to December 2024, show the existence of a bidirectional relationship between the trading volumes of SET50 Index Futures and SET50 Index Options. The impulse response function results are consistent with the VAR(3) model estimate, showing that SET50 Index Options trading volume has a positive impact on SET50 Index Futures trading volume, but not vice versa. In addition, underlying market liquidity is positively related to the trading volumes of SET50 Index Futures and Options, while underlying market volatility positively affects only SET50 Index Futures trading volume. Except for the exchange rate, other macroeconomic factors are related to the trading volumes of SET50 Index Futures and SET50 Index Options. The growth rate of private investment positively impacts on the trading volumes of SET50 Index Futures and SET50 Index Options. Inflation and interest rates are related to the trading volume of SET50 Index Futures, while the leading economic index is related to the trading volume of SET50 Index 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".