Volatility Segmentation of SET100 Indices Using GARCH Models
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Abstract
This study aims to examine the relationship between volatility and stock returns in the SET100 Index using daily closing price data. The GARCH (p, q) model is employed to forecast volatility, while the ARIMA model is used to forecast stock returns. The forecasted results are then used to classify stocks into four groups based on the median values of both forecasts. The empirical results indicate that volatility persistence, measured by the sum of the GARCH parameters (), is high and close to unity, reflecting the persistence of volatility in the Thai stock market. Over the 14-day forecasting horizon following the data endpoint, most stocks are classified into the high volatility–low return and low volatility–high return groups. Stocks identified as having investment potential due to high returns and low risk include TTB, TOP, WHA, RATCH, CENTEL, BCP, BCH, and BAM. The findings suggest that the proposed approach can be applied to support investment strategy planning.
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