#3159. Forecasting volatility of crude oil futures using a GARCH–RNN hybrid approach

October 2026publication date
Proposal available till 11-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Finance;
Business, Management and Accounting (all);
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Abstract:
Forecasting volatility is a critical task in financial forecasting. The research proposes a framework of hybrid models for forecasting volatility of crude oil. The scholars create three new forecasting models named GARCH–LSTM, GJR-LSTM, and GARCH-GJRGARCH LSTM to forecast crude oil volatility using the parameters of generalized autoregressive conditional heteroscedasticity (GARCH) and Glosten–Jagannathan–Runkle (GJR)-GARCH with long short-term memory (LSTM). GG–LSTM is a strong contender for forecasting crude oil volatility under different forecasting regimes and rolling-window schemes. The research enhances the forecasting ability of crude oil futures volatility, which is essential for trading, hedging, and purposes of arbitrage, and that the proposed model dwells upon existing literature by fusing a neural network model with multiple econometric models.
Keywords:
GARCH; hybrid models; LSTM; neural networks; volatility forecasting

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