Volatility in Thailand Stock Market Using High-Frequency Data

The objective of this research is twofold: First, we aim to investigate the performance of conventional GARCH and GARCH-jump models when the data has high frequency. Second, the obtained conditional volatility from the best fit model is used to forecast and matched with the macroeconomic news announcement. We use GARCH and GARCH-jump models with high-frequency dataset of log return of Thailand stock market index (SET) from January, 2008 to December, 2015. We find that the volatility estimations by these two models have the same pattern but volatility estimation by GARCH-jump is higher than conventional GARCH model. However, the GARCH (1,1) and GARCH (1,1)-jump performances are non-stationary to estimate the volatility for 5 min interval return of SET but are stationary to estimate for 15 min, 30 min, 1 h, and 2 h returns of SET. Our results also show the matching jump point with macroeconomic news announcement. The empirical results support our assumption that macroeconomic news announcement may lead to volatility change in SET.