Volatility Modeling of LQ45 Energy Stocks Using GARCH: Evidence from 2022–2025
DOI:
https://doi.org/10.54099/aijb.v4i2.1421Keywords:
Energy Stocks, , LQ45 , GARCH , Forecasting Model Indonesia Stock ExchangeAbstract
The energy sector plays a vital role in Indonesia’s economy as it supports industrial operations and daily activities. The volatility of energy stock prices on the Indonesia Stock Exchange reflects complex dynamics influenced by both global commodity prices and increasing investor interest in renewable energy. These conditions necessitate a comprehensive understanding of stock volatility to support optimal investment risk management.
This study aims to model the volatility of energy sector stocks listed in the LQ45 Index during the period from January 2022 to March 2025. The research focuses on seven energy stocks and utilizes the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) approach. The LQ45 Index is selected due to its representation of highly liquid and large-cap stocks, providing a broader view of the market. The objective is to identify the best-fitting models and assess the long-term risk level of each stock.
A quantitative approach was used by processing daily closing prices into stock return data as the primary variable. The analysis consisted of descriptive statistics, stationarity tests, ARCH effect tests, and GARCH model estimation. The models were then applied to forecast stock volatility for April 2025 as a form of predictive evaluation.
The results show that each stock exhibits distinct volatility characteristics. The ARMA(1,1)-GARCH(1,1) model is most suitable for ADMR, while AR(1)-GARCH(1,1) fits ADRO, and MA(2)-GARCH(1,1) suits AKRA. ITMG and PTBA also display valid and accurate estimations. In contrast, stocks such as MEDC and PGAS did not exhibit heteroskedasticity, rendering GARCH modeling inapplicable. Long-term volatility estimates generally indicate manageable risk despite price fluctuations.
These findings serve as a basis for constructing adaptive investment strategies. Investors are advised to consider volatility persistence and forecast accuracy when planning asset allocation and risk mitigation. This research also provides a foundation for developing more advanced models to enhance understanding of energy stock market behavior.
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