<![CDATA[Stock market volatility forecasting plays a crucial role in supporting investment decision-making and risk management under uncertain market conditions. This study proposes a hybrid LSTM with GARCH-to modelling IDX Composite volatility. The GARCH-MIDAS-X model is first employed to decompose stock return volatility into short-run and long-run components while incorporating multiple low-frequency exogenous variables, including market news sentiment, crude oil prices, and exchange rates. The residual generated by the GARCH-MIDAS-X model is subsequently used as input for the LSTM network to capture complex nonlinear patterns and temporal dependencies that may not be fully explained by the econometric model. Model performance is evaluated through both in-sample and out-of-sample forecasting using several accuracy measures, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The empirical results indicate that the hybrid model produces forecasting performance comparable to that of the GARCH-MIDAS-X model, with only marginal differences in prediction accuracy. These findings suggest that the GARCH-MIDAS-X model is capable of capturing most of the relevant volatility dynamics, while the addition of the LSTM component provides limited incremental forecasting benefits for the observed period. Therefore, the hybrid approach may serve as an alternative forecasting framework, although its superiority over the standalone econometric model is not evident in this study.]]>
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