Abstract
The stock market is continuously changing with uncertainties that can create risks. Prompt information dissemination and rapid capital flow will cause stock price fluctuations, causing volatility in stock prices. This research examines the behavior of volatility patterns in the infrastructure, utility, and transportation sectors using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. This study uses monthly data from January 2014 to December 2019. The results show that the volatility of all stocks in the study is influenced by the previous month's error and volatility return. Investors and securities analysis can use these results in making decisions to invest in the infrastructure, utilities, and transportation sectors.
Highlights
Research related to volatility has been conducted several times by various researchers in the world, including a study conducted by Wang, Ma, Liu, and Yang (2020), which forecasts stock price volatility using the Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling (GARCH-MIDAS) technique
The results show that the GARCH-MIDAS model has a significant influence in predicting long-term stock market volatility
The results of this study indicate that the capital market has a volatility symptom where the GARCH models found are GARCH (1,1), Threshold ARCH (TARCH) (1,1), and Exponential GARCH (EGARCH) (1,1)
Summary
Investment is an investment in the long term with the hope of getting benefits in the future as compensation for delayed consumption, the impact of inflation, and the risks borne. Research related to volatility has been conducted several times by various researchers in the world, including a study conducted by Wang, Ma, Liu, and Yang (2020), which forecasts stock price volatility using the Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling (GARCH-MIDAS) technique. Fang et al (2020) researched by testing the stock market's long-term volatility using the Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling (GARCH-MIDAS) model. Ismail et al (2016) researched by testing volatility forecasting on the stock market in Africa using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and Maximal Overlap Discrete Wavelet Transform- Generalized Autoregressive Conditional Heteroskedasticity (MODWT-GARCH). Vipul (2016) tested volatility forecasting on the stock market using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) models. The Exponentially Weighted Moving Average (EWMA) model is more comfortable to implement than the Realized Generalized Autoregressive Conditional Heteroskedasticity (RGARCH) model
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