Analisis Pengaruh BI Rate dan Kurs Terhadap Volatilitas Saham BCA Dengan GARCH-X
DOI:
https://doi.org/10.47065/bulletincsr.v6i1.898Keywords:
Stock Volatility; Exchange Rate; BI Reference Rate; sGARCH Model; BBCAAbstract
The Indonesian banking sector exhibits high sensitivity to macroeconomic dynamics, where market uncertainty is frequently triggered by external shocks and domestic policy adjustments. The main issue addressed in this study is the extent to which macroeconomic variables influence stock return volatility under fluctuating economic conditions. This research specifically examines the impact of the Bank Indonesia (BI) reference interest rate and the USD/IDR exchange rate on the return volatility of PT Bank Central Asia Tbk (BBCA) over the period from January 1, 2018, to October 31, 2025. The analysis employs a time-series econometric approach focusing on conditional volatility modeling using the Generalized Autoregressive Conditional Heteroskedasticity model with exogenous variables (GARCH-X), in which the BI Rate and exchange rate are incorporated into the variance equation. The estimation procedure is conducted using the R programming language and includes stationarity testing, detection of heteroskedasticity effects, and comparative evaluation of the sGARCH-X, eGARCH-X, and GJR-GARCH-X models. Model selection is based on the maximum log-likelihood value as well as the minimum Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The empirical results indicate that the eGARCH-X model provides the best fit, suggesting the presence of asymmetric behavior in BBCA stock volatility. Within this model, the BI Rate exhibits a negative and statistically significant effect on volatility, implying that monetary policy plays a stabilizing role in mitigating market risk. In contrast, the exchange rate tends to exert a positive influence on stock volatility, although its effect is marginal and not consistently significant at the 5% significance level. These findings highlight the importance of asymmetric GARCH modeling in capturing the response of banking stock volatility to monetary policy and exchange rate dynamics, and they provide relevant insights for investors in formulating effective risk management strategies.
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