SET50 Index Futures contract is the first and most actively traded equity index futures contract on the Thailand Futures Exchange. However, futures trading is risky and requires that investors understand factors affecting risk. Therefore, this study uses monthly data from May 2014 to December 2024 for identifying the determinants of SET50 Index Futures contract’s risk, quantified by Value at Risk (VaR). To calculate VaR of SET50 Index Futures, this study employs parametric method using EGARCH model for estimating volatility. Factors, including macroeconomic factors, futures trading activity, and underlying SET50 Index factors, are included as independent variables in a regression model. Using ordinary least squares approach, the results show that inflation rate and rate of change in exchange rate are the only macroeconomic factors affecting SET50 Index Futures contract’s VaR. In addition, volume and open interest of SET50 Index Futures as well as liquidity and past volatility of its underlying asset affect VaR. Therefore, investors and related agencies should track inflation rate and exchange rate since they are crucial factors affecting risk for SET50 Index Futures. Understanding the determinants of risk for SET50 Index Futures contract allows for better risk assessment, informed trading strategies, and more successful participation in the futures market.

This study compares the performance of protective put and covered call strategies and analyzes their return determinants. The analysis uses SET50 Index Options contracts with trading volume, covering maturities from January 2021 to December 2025. The empirical model investigates three groups of explanatory variables: market expectation variables (implied volatility and basis), option market condition variables (open interest and trading volume), and option-specific characteristics (time to maturity and moneyness). The model also incorporates fixed effects for different maturity years (with 2025 as the base year) and quarterly maturity dummies. Standard errors are clustered by monthly expiration groups, and statistical significance is further validated using the wild cluster bootstrap method to improve the reliability of p-values. Overall, the findings indicate that the covered call strategy outperforms the protective put strategy over the sample period, except in 2025. Option strategy performance is primarily driven by market expectation variables rather than contract-specific characteristics. Implied volatility and the basis are the most important determinants of returns for both protective put and covered call strategies, while option market condition variables are relevant mainly for covered call strategies. These results highlight the importance of market conditions in shaping hedging strategy outcomes in the Thai options market.

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.

Prior to September 2021, USD Futures could only be traded during the regular hours in Thailand Futures Exchange (TFEX). An additional trading session at night, while trading in both London and New York exchanges is active, enables investors to better handle their foreign exchange exposure or speculative needs of the moment. However, understanding volatility behavior is crucial for achieving successful trading. Therefore, this study investigates the impact of night trading sessions on the USD Futures volatility using GARCH family models. The USD Futures market volatility is examined through comparative analysis before and after the introduction of the night trading session. Both TARCH and EGARCH models have revealed no existence of leverage effect over the sample period - from January 2, 2020 to December 30, 2022. The GARCH model has proved to be the most accurate model for describing USD Futures volatility. Following the launch of nighttime trading, USD Futures market has experienced higher and more persistent volatility. In response to an increase in the volatility of USD Futures, TFEX should increase its margin requirement and monitor the speculative movements in futures market for their possible destabilizing effect. Investors should also adjust their hedge ratio to manage risk more appropriately and incorporate an extended period of increased uncertainty into their trading strategies.

This paper investigates how the introduction of foreign exchange futures has an impact on spot volatility and considers the contemporaneous and dynamic relationship between spot volatility and foreign exchange futures trading activity, including trading volume and open interest in the Thailand Futures Exchange context, with the examples of the EUR/USD futures and USD/JPY futures. The results of the EGARCH (1,1) model show that the introduction of foreign exchange futures decreases spot volatility. It also increases the rate at which new information is impounded into spot prices but decreases the persistency of volatility shocks. A positive effect of unexpected trading volume and a negative effect of unexpected open interest on contemporaneous spot volatility are in line with the VAR(1) model results of the dynamic relationship between spot volatility and foreign exchange futures trading activity. With the impact on spot volatility caused by unexpected open interest rate being stronger than by unexpected trading volume, foreign exchange futures trading stabilizes spot volatility.

Following the introduction of EUR/USD futures and USD/JPY futures on 31 October 2022, Thailand Futures Exchange first entered the top 11 list of derivatives exchanges based on foreign exchange derivative volumes in 2022. This paper investigates the dynamics of foreign exchange futures trading volumes in Thailand through the VAR(2) model. Trading volumes of EUR/USD futures, USD/JPY futures, and USD/THB futures are considered over the sample period from 31 October 2022 to 12 January 2024. The empirical results provide no evidence that the trading volume of EUR/USD futures is dependent on the past trading volumes of USD/JPY futures and USD/THB futures. The Granger causality test results show the existence of bidirectional causality between the trading volumes of USD/JPY futures and USD/THB futures. The results of the impulse response function are consistent with the sign results of the VAR(2) model, showing that the USD/JPY futures trading volume has a negative impact on the USD/THB futures trading volume, and vice versa. The analysis of variance decomposition shows that the variability of the USD/JPY futures trading volume and USD/THB futures trading volume, apart from its own shock, is explained by other FX futures trading volume shocks. Therefore, traders should pay more attention to new FX futures trading activity due to its negative impact on the USD/THB futures trading volume and its contribution to the variance in the USD/THB futures trading volume. Understanding the futures trading volume relationship also helps Thailand Futures Exchange develop new products and services that can foster market liquidity and stability.

The Stock Exchange of Thailand (SET) first launched derivative warrants on SET50 index (SET50 DWs) on April 17, 2014. They are currently the most active DWs on the SET. This research uses the GARCH family models augmented with dummy variable to analyze the effect of SET50 DWs on stock market volatility. The sample data consist of daily returns of SET50 index from the period October 30, 2012 to December 30, 2019. The empirical results indicate that the coming into market of SET50 DWs reduces stock market volatility. The GARCH (1,1) TARCH (1,1), and EGARCH (1,1) models are not radically different from each other in their output. However, the asymmetric TARCH (1,1) model is found to provide the best fit in modelling volatility. The SET50 index shows the existence of leverage effect, where negative shocks have a greater impact on the volatility than positive shocks. Introducing SET50 DWs lowers the price volatility of SET50 index so investor having a portfolio investment with a correlation to the performance of SET50 index should adjust hedge ratio appropriately to manage investment risk. There is also a suggestion for policy makers to support the launch of DWs to lower the volatility in underlying spot market resulting in improved efficiency.

The Thailand Futures Exchange launched USD Futures as the first currency futures contract on 5 June 2012. However, it has been available for night trading since 27 September 2021. This research aims to analyze the effect of adding a night trading session on USD Futures market liquidity and to make a liquidity comparison between day and night session trading. By adding a dummy variable into the vector autoregression model of order 5 to capture the effect of a night session introduction on market liquidity, the results show that market depth and breadth are even stronger after a longer trading session. In addition, the t-test results show the presence of lower tightness but stronger depth and breadth in day session trading than in night session trading, because of the availability of a large number of orders and the ability of the market to have smoother trading in day as opposed to night. Due to the positive effect of extended trading hours on market depth and breadth, TFEX should consider a longer night session in line with other global futures markets. Night traders should also be aware of liquidity risk due to low night session trading volume.

This research shows a positive impact of one day lagged volume of 50 Baht gold futures on current volumes of gold futures. There is an evidence of bi-directional causality between 50 Baht and 10 Baht gold futures trading volumes. The 50 Baht and 10 Baht gold futures trading volumes are useful in forecasting gold online futures trading volume. The response of gold futures trading volume to its own shock and 50 Baht gold futures trading volume shock vanishes within 5 days. The variability of gold futures volume is caused not only by its own innovations but also by 50 Baht gold futures trading volume shock. Therefore, it is significant to promote trading activity in 50 Baht gold futures market. Moreover, special care should be taken when there is a negative exogeneous shock to 50 Baht gold futures trading volume due to its contribution to the variability in trading volume of each gold derivatives contract.

Thailand’s Single Stock Futures market has grown recently over the last ten years, evidenced by its 8th place in top 10 exchanges in the world by number of single stock futures traded in 2021. Since the main goal of any futures exchange is to list a successful contact, it is important to demonstrate the determinants of the success of Single Stock Futures. This study uses the sample consisting of 89 companies, on which stocks are underlying for Single Stock Futures in the period between January 2017 and December 2021, and finds that the best fitting method in modelling determinants of the success of Single Stock Futures is the fixed effects model. As expected, the results confirm the existence of a positive relationship between characteristics of underlying stock, including size, volatility, and liquidity, and the successful futures contract. Furthermore, the findings show the negative effects of the first year of contract trading and the tightened daily price limit of Single Stock Futures in response to the COVID-19 pandemic situation on contract success. AcknowledgmentThe author is grateful to the Department of Economics, Faculty of Economics, Kasetsart University for financial support to conduct this research.

Value at Risk (VaR) is the most widely used measure of risk. This study uses SET50 daily returns from the period from July 3, 2015 to December 27, 2019 to estimate VaR for the assessment of risk exposure at the Stock Exchange of Thailand and Thailand Futures Exchange using the three following methods: non-parametric method with the historical simulation approach, parametric method with GARCH family models, and semi-parametric method with volatility-weight historical simulation of the GARCH family models. Accuracy of the estimated models is also assessed by performing the VaR backtests of unconditional coverage, independence, and conditional coverage. In forecasting VaR with the confidence level of 95%, historical simulation and asymmetric GARCH models (TARCH and EGARCH models) give solid results and outrank volatility weight historical simulation. Moreover, a comparison of stock investments with a correlation to the performance of SET50 Index and SET50 Index Futures investment indicates that SET50 Index Futures investment carries higher risk. Therefore, investment decisions on SET50 Index Futures should be taken more carefully since this market is more volatile than the underlying spot market.

This paper studies the effect of new gold derivatives products, including Gold-D and Gold Online Futures, on the futures price volatility of existing gold futures with two contract sizes, 50 baht-weight and 10 baht-weight, using symmetric and asymmetric GARCH family models, namely: GARCH (1,1), TARCH (1,1), and EGARCH (1,1) models. The results reveal the existence of leverage effect in TARCH (1,1) and EGARCH (1,1) models. Moreover, TARCH (1,1) is found as the best fitting model in modelling gold futures price volatility. The results confirm that the coming into market of Gold-D significantly reduces the price volatility of existing gold futures. There is not a significant negative relationship between the introduction of Gold Online Futures and the existing gold futures price volatility. Therefore, the results suggest regulatory authority to lower the level of margin requirements for the related futures contracts, along with the issuance of new derivatives products.

This study provides a new empirical evidence to demonstrate the factors influencing investors’ behavior at SET50 index futures and options markets via two types of simultaneous equation estimation, Two Stage Least Squares (2SLS) and Three Stage Least Squares (3SLS). Both 2SLS and 3SLS estimation results show a bilateral causality between stock market volatility and derivatives market activities. Between futures trading and options trading, there is a bilateral causality in 3SLS model, but there is a unidirectional causality from futures market activity to options market activity in 2SLS model. Turnover ratio has a positive impact on stock market volatility, but its relationship with speculative behavior relative to hedging behavior in SET50 index futures is negative in both methods. Only 3SLS model shows a negative impact of turnover ratio on the relative importance of speculative activity at SET50 index options market. Moreover, the proportion of foreign investors shows a positive relationship with speculative investor behavior at SET50 index futures market. There is an increase in SET50 index options trading by foreign and institutional investors for speculation, relative to hedging. Speculative trading is mostly related to put options, while hedging trading is mostly related to call options.

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.

Abstract—This paper tests the efficiency of SET50 Index Options market and investigates the impact of contract adjustment on market efficiency. The options data set I employ to conduct call & put butterfly spreads test of market efficiency covers the period from October 29, 2007 to December 30, 2016. When I ignore transaction costs, the results report frequent and substantial violations of pricing relationships. For an option maturing within 90 days, size of violations tends to be higher for options farther from the money or further away from expiration. Almost no violations remain after considering the bid-ask spread as transaction costs. Therefore, our results support the efficiency of SET50 Index Options market before and after the modification of contract specification. Comparing the results before and after contract adjustment, I do not observe any improvement of market efficiency after the modification of contract.

This study is aimed to test the internal efficiency of SET50 Index Options market and focus on the difference between SET50 Index Call Options pricing relationship and SET50 Index Put Options pricing relationship using the conditions of Call & Put Spreads and Call & Put Butterfly Spreads. Over the sample period from October 29, 2012 through March 30, 2016, although the mispricing and arbitrage opportunities for SET50 Index Options are observed under the case of no transaction costs, there are not much opportunities, less than 11 percent. Even with modest transaction costs, including exchange fees, brokerage commissions, and opportunity cost of initial margin deposit, the frequency of arbitrage opportunities drops to less than 5 percent. Using bid and ask prices rather than closing prices, the arbitrageurs can earn riskless profit with SET50 Index Put Options trading only. With the existence of arbitrage opportunities, Put Spread generates more profit than Call Spread, but the mean sizes of violations for Call & Put Butterfly Spreads are not statistically different. Taking all transaction costs (exchange fees, brokerage commissions, interest on initial margin deposit, and bid-ask spread) into account, arbitrage opportunities in every case are almost eliminated. The SET50 Index Options market is therefore efficient. The result would boost the investors' confidence to invest in options market.

This paper provides the box spread test of the SET50 index options market efficiency using daily data from October 29, 2012, through March 30, 2016. The results show that the market frictions imposed by the bid-ask spread, along with brokerage commissions, exchange fees, and interest on initial margin deposit, appear to have a significant effect on arbitrageurs’ abilities to take advantage of the mispricing of the box spreads. When using bid-ask prices rather than closing prices, the box spread arbitrage opportunities drop to

This research studies determinants of silver futures price volatility in Thailand Futures Exchange using generalized autoregressive conditional heteroskedasticity model. The sample data consist of daily closing price, volume, and open interest of silver futures from the period June 21, 2011 to December 26, 2012 for the nearby month contract with 376 sample data points. I construct data sample by switching or rolling over to the next maturing contract one day before the expiration date. The empirical results reveal there is no significant relationship between volatility and time to expiration. There are a negative role for trading volume and a positive role for open interest in determining silver futures price volatility. The analysis of silver futures price volatility insists the Clearing House that margin requirements for silver futures should not be affected as the time to maturity of the contract decreases. The findings are also helpful to risk managers dealing with silver futures and predicting silver futures price volatility.

This research studies determinants of the gold futures price volatility in Thailand Futures Exchange using Linear Regression Model and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) Model. The data sample consists of daily settlement price, volume, and open interest of gold futures from the first trading day in Thailand Futures Exchange to June 26, 2013. We examine the nearby futures contract, which is switched over to the next maturing contract one day before the expiration date. The results of both models confirm that price returns volatility of gold futures increases when (1) the futures contract approaches expiration (2) trading volume increases and (3) open interest decreases. We also present an analysis of the role of covariance between changes in spot prices and carry costs in explaining the maturity effect in futures. Our results provide very strong evidence in favor of this negative covariance hypothesis. Therefore, the results of this study would be of interest to investors due to their importance to forecast future prices of gold. Hedgers should adjust their optimal hedging position according to volatility changes. The analysis of gold futures volatility also insists the clearing house in setting different margin requirements as each contract approaches maturity differently. Margin requirements should be raised for nearby gold futures contract as its volatility increases. Either an increase in trading volume or a decrease in open interest should also increase margin requirements for gold futures.

Modern retailers use return policy to create customer satisfaction. This paper analyzes the return behavior of consumers in Bangkok metropolis which includes surrounding urbanized provinces. The ordered logit model and questionnaire survey data from 400 samples were used. The analysis reveals that most of the respondents had incorrect understanding of the return policy conditions. Almost three-fourths of the consumers received defective products or were dissatisfied with product quality; these were mostly found in electrical appliances from hypermarkets. The consumers decided to exchange or return the products within 7 days after purchase. The exchange/return procedure usually takes 30 minutes or less but most consumers found the exchange/return process difficult. Most people chose to exchange for new items without making additional purchase. The analysis shows that the following factors had a positive effect on the probability of product exchange/return: 1) travel frequency to modern retailers, 2) knowledge of the return policy, 3) perceived information on the return policy prior to the purchase, 4) frequency of experiencing defective or unsatisfactory products, and 5) travel expenses incurred in exchanging/returning products. The government should regulate retailers to provide correct and clear return policy information. The retailers should procure from reliable suppliers to reduce the incidence of defective products. The retailers should develop a better logistics and improve on the product return database.