Application of VAR-GARCH for Modeling the Causal Relationship of Stock Prices in the Mining Sub-sector
Abstract
Accurate modeling is expected to minimize risk and maximize profit in investment portfolios, one of
which is in stock price modeling. This research aims to model the causal relationship between stock
prices using the Vector Autoregressive - Generalized Autoregressive Conditional Heteroskedasticity
(VAR-GARCH) model. The VAR-GARCH model is used to overcome heteroscedasticity and model
dynamic volatility. The data used for the modeling consists of daily stock prices from July 2023 to
May 2024 for mining sub-sector companies listed on the Jakarta Islamic Index (JII), including ADMR,
ADRO, and ANTM. The results showed that the VAR(1) model is stable, but this model indicates the
presence of heteroskedasticity or ARCH effects. Therefore, the VAR(1) model was combined with the
GARCH model, and the results showed that the best model is VAR(1)-GARCH(1,1). The VAR(1)-
GARCH(1,1) model is appropriate and meets the homoskedasticity assumptions for modeling the stock
prices of the mining sub-sector in the Jakarta Islamic Index (JII). This indicates that the VAR-GARCH
model could successfully handle the volatility of stock price data. In general, this research is in line
with previous research, i.e., the VAR-GARCH model showed a better model for capturing the volatility
patterns in the data.
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