RAGAM: Journal of Statistics and Its Application
Vol 5, No 2 (2026): RAGAM: Journal of Statistics & Its Application

VOLATILITY AND VALUE AT RISK ANALYSIS OF BANK RAKYAT INDONESIA STOCK USING THE GJR-GARCH MODEL

M. Bagus Hernawan (Universitas Negeri Yogyakarta)
Indira Ihnu Brilliant (Universitas Negeri Yogyakarta)



Article Info

Publish Date
30 Sep 2026

Abstract

Shares of PT Bank Rakyat Indonesia (Persero) Tbk (BBRI) are among the leading stocks in the Indonesian capital market, with price movements that tend to be volatile and exhibit heteroskedastic characteristics. This study aims to examine volatility behavior and estimate investment risk of BBRI shares using the GJR-GARCHmodel, as well as to measure risk through the Value at Risk (VaR) approach based on the Cornish-Fisher expansion. The data used consist of daily closing prices of BBRI shares from May 1, 2022 to May 1, 2025, obtained from Yahoo Finance and analyzed using the R programming language. The analytical procedures include return calculation, data characteristic testing, ARIMA modeling, ARCH effect testing, and volatility estimation using the GJR-GARCH model. Forecasting performance is evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results indicate that the GJR-GARCH(1,1) model effectively captures asymmetric volatility effects. VaR estimation using the Cornish-Fisher approach yields risk values of 5.96% at the 99% confidence level, risk values 3.86% at the 95% confidence level, and risk values 2.88% at the 90% confidence level. The 90% confidence level is recommended as it presents the lowest potential risk of loss.

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Journal Info

Abbrev

ragam

Publisher

Subject

Humanities Computer Science & IT Economics, Econometrics & Finance Mathematics Public Health

Description

RAGAM Journal publishes scientific articles in the field of statistics and its applications, including: * Biostatistics * Parametric and nonparametric statistics * Quality control * Econometrics and business * Industrial statistics * Time series analysis * Spatial statistics * Data mining * ...