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A Hybrid VAR-LSTM-GARCH Model for Multivariate Volatility Forecasting

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This research develops a hybrid model combining VAR, LSTM, and GARCH, enhancing volatility forecasts in financial markets.

The VAR-LSTM-GARCH model significantly reduces prediction errors in multivariate volatility forecasting.
With improved forecasting accuracy, the model integrates linear, non-linear, and volatility aspects for better performance.
By using five years of historical data from VIX, US Dollar Index, and S&P 500 E-mini futures, the model demonstrates superior results over standalone models.
The progressive three-stage modeling approach shows each component's contribution to forecasting performance, emphasizing the effectiveness of the GARCH model.

Chege et al. (2025) studied this question.

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