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.

The objective of this research is to select a proper cumulative function for trend in an analysis of simultaneous multiple time series data with autocorrelation trend and outliers using a Bayesian method. The proposed model is applied to fluctuate stock price data in the stock exchange of Thailand. The data consist of the closing prices of 5 stocks. The algorithms are written in OpenBUGS and the performance of the model is evaluated via a simulation study in R. The cumulative distribution functions considered here are a Weibull cumulative distribution function and an exponential cumulative distribution. They are compared using RMSE MAPE and MAE as criteria. The research found that an exponential cumulative function for trends gives smaller values of the three criteria in both model fitting and model validation for the4 stocks EA NWR and KTB. However a Weibull cumulative distribution function gives smaller values of three criteria for the PTT in both model fitting and model validation.