Pertamina Geothermal Energy Stock Price Prediction and Risk Analysis: ARIMA-GARCH and VaR with Cornish-Fisher Expansion - M. Fariz Fadillah Mardianto; Doni Muhammad Fauzi; Idrus Syahzaqi; Elly Pusporani (Universitas Muhammadiyah Mataram, 2026)
The geothermal energy sector makes a strategic contribution to supporting long-term domestic energy sustainability and attracts investor attention due to high market volatility. Therefore, analysis that can accurately describe stock price dynamics and risks is needed. This study aims to model and predict the share price of PT Pertamina Geothermal Energy (PGEO) and estimate the associated investment risk. This study uses a quantitative time series approach with ARIMA–GARCH modeling and the Value at Risk method using Cornish–Fisher Expansion. This study uses weekly closing price data for PGEO stocks from February 2023 to September 2025. The methods used include ARIMA-GARCH modeling for stock price prediction and Cornish–Fisher Expansion based Value at Risk to estimate investment risk. The results indicate that the ARIMA(2,2,0)–GARCH(2,0) model provides the most adequate representation of PGEO stock price dynamics and volatility, achieving an RMSE value of 258.33 and a MAPE of 16.21% as measures of forecasting performance. Meanwhile, risk measurement using the Cornish–Fisher Expansion Value at Risk method produced a VaR value that increased along with the holding period and confidence level, with a risk range of 8.21% to 19.95%. The novelty of this research lies in the integration of ARIMA–GARCH volatility modeling and the Value at Risk method using Cornish–Fisher Expansion, thereby providing a more comprehensive analytical framework for price prediction and investment risk estimation in renewable energy stocks. The findings of this study are expected to serve as an empirical reference for investors and policymakers in assessing potential risks and supporting more informed investment decisions within the renewable energy sector.