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Using an LSTM Neural Network to Improve Symmetric and Asymmetric GARCH Volatility Forecast

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Analysis reveals Extended LSTM hybrid model outperforms GARCH frameworks in volatility forecasting, suggesting improved accuracy.

The Extended LSTM hybrid model enhances volatility forecasting accuracy, outperforming GARCH-type models.
Results indicate that integrating LSTM with GARCH frameworks captures complex volatility patterns effectively.
The model architecture, including ReLU and GRU layers, addresses over-smoothing and adapts to market fluctuations.
This approach suggests valuable insights for investors and risk managers in financial risk management.

Rahmawanto et al. (2025) studied this question.

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