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To develop a hybrid model for accurately forecasting volatility in energy markets using HAR, LSTM, and GARCH techniques.

A Hybrid HAR-LSTM-GARCH Model for Forecasting Volatility in Energy Markets

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To develop a hybrid model for accurately forecasting volatility in energy markets using HAR, LSTM, and GARCH techniques.
Developed a hybrid HAR-LSTM-GARCH model.
Utilized high-frequency data on Brent Crude futures for analysis.
Compared performance against traditional models (HAR, GARCH(1,1), LSTM).
The HAR-LSTM-GARCH model outperformed benchmark models in statistical accuracy.
Significant improvement in economic significance of volatility forecasts.
Demonstrated effective handling of non-linear patterns and long-memory dynamics.

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This analysis reveals a hybrid approach that improves volatility forecasting in energy markets, indicating better risk management strategies.

Romdhane et al. (2026) studied this question.

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This analysis reveals a hybrid approach that improves volatility forecasting in energy markets, indicating better risk management strategies.