Predicting the Volatility of Jakarta Composite Index Using GARCH and LSTM with Volume-Up Strategy Approach - Dharmaningrat, I Made Adhi; Margaretha, Helena; Saputra, Kie Van Ivanky (Universitas Airlangga, 2025)
Background: Stock market volatility forecasting is essential for financial decision-making, although the complexity presented significant challenges. This prompted previous studies to identify correlations between the volatilities of international stock indices and Jakarta Composite Index (JKSE), describing the potential of hybrid econometric and deep learning models in the prediction process. Objective: This study aims to develop an optimized hybrid model for forecasting the volatility of JKSE by integrating Generalized Autoregressive Conditional Heteroskedasticity (GARCH), Long Short-Term Memory (LSTM), and Volume-Up (VU) strategy, in the context of an emerging market recovering from economic disruptions. Methods: Historical daily data from five major stock indices, namely JKSE, DJI, SPX, N225, and HSI, covering the period from January 1, 2000, to December 31, 2023, were used to formulate eleven datasets. Furthermore, a hybrid model was developed and evaluated by combining GARCH, LSTM, and VU strategy for conditional volatility estimation, sequential prediction, and data transformation, respectively. Hyperparameter tuning was performed to determine the best activation functions, batch sizes, and timesteps. Based on this perspective, Mean Squared Error (MSE) was used to assess predictive accuracy. Results: GARCH-LSTM exhibited superior performance over a standalone LSTM model, improving RMSE by 11.43%. The incorporation of VU strategy further enhanced accuracy, with an optimal setting (α = 0.5) leading to a total RMSE improvement of 17.35%. The best hyperparameters included SELU + tanh activation function and a batch size of 128 or 256. Meanwhile, a timestep of 1 provided the best predictive performance, depicting the importance of recent market movements in forecasting. Conclusion: In conclusion, this study proved the effectiveness of hybrid models in predicting stock market volatility in emerging markets. The results outlined the advantage of integrating econometric and deep learning approaches, with VU strategy playing a significant role in refining predictions. Keywords: GARCH, LSTM, Volatility Prediction, Volume-Up Strategy, Emerging Markets, Economic Recovery