This study examined Factors Affecting Price’s Fluctuation of the SET50 Index Futures in Thailand Derivative Market during the Year 2011 – 2015. The objective was to study all of the factors which was affecting SET50 Index Futures’s fluctuation. According to daily closing price data of SET50 Index Future used for computing continuous return the period before the maturity and the volume of SET50 Index Futures were calculated the fluctuation by using GARCH (11). The results show that the data and fluctuation in the past affected the data and fluctuation in the present by the same direction considerated by the coefficient in ARCH and GARCH term of GARCH Model (11). The period before the maturity affected the return rate by the opposite direction which means the return rates had much more fluctuation when the period of time closed to the contract maturity. It showed that the coefficient of the period before the maturity was quite less than the one of ARCH and GARCH. Therefore the fluctuation of return rate in the present was possibly depended on data and the period of fluctuation in the past more than the period before the maturity. The remainder of the study can give information to the investors that they should be concerned not only about data and fluctuation in the past but the period before the maturity of SET50 index futures which may affect the forecast of SET50 index futures price for Hedge management. Thailand Future Exchange should be considered to adjust proper risk margin inaccordance with fluctuation rate in a period of time.

This research studied behavior of price clustering in Electronic Trading Transaction and Block Trading Transaction in Thailand Futures Exchange market from SET50 Index Futures using daily closing price data of SET50 index Futures from April 28 2006 to June 302012 Descriptive statistics and quantitative analysis included Chi-square to test Uniform Distribution; Hirshmann Herfindal Index(HHI) to measure price density standardized Range and Excess Clustering to measure price clustering; and Multiple Regression to test behavior of price Clustering. Results showed that the behavior of price clustering in SET50 Index Futures was characterized in the same way. That is the most frequent last two digits were 00 (32.31%) and 50 (13.82%) while other last two digits had the frequency of not greater than 8%. There was more price clustering along with the trading volume. It also found that the opening price the bid-ask spread. And the volatility had positive impacts on price clustering while the closing price had negative impact. For the total retum no impact was found on price clustering at the signicant level of 0.05