GBK-Net: A Hybrid GARCH--BiLSTM--KAN Model for Crude Oil Price Forecasting

Crude oil prices exhibit complex dynamics---such as volatility clustering, asymmetric temporal dependencies, and nonlinear responses to geopolitical and economic shocks---that pose challenges to accurate forecasting. To address this, we propose a hybrid model, GBK-Net, which integrates three complementary components: GARCH for modeling time-varying volatility, BiLSTM for capturing bidirectional temporal dependencies, and Kolmogorov-Arnold Network (KAN) for refining nonlinear relationships. Empirical validation is conducted on 39 years of daily WTI crude oil prices (1986--2025), covering major events such as the 2008 financial crisis and the COVID--19 pandemic. Comparative experiments against benchmark models---including GARCH, EGARCH, LSTM, and CNN--LSTM--KAN---demonstrate that GBK-Net achieves the lowest prediction error (RMSE = 2.49; R2 = 0.981), with statistical tests confirming its superiority. We also benchmark nested specifications using approximately unbiased tests [1]. This work contributes a robust forecasting tool for energy economics, with implications for strategic planning, risk hedging, and financial decision-making in volatile energy markets. Unless otherwise noted, we model and evaluate on shifted log-returns to accommodate the 2020 negative-price episode; results using arithmetic returns are reported as robustness checks in the Appendix.