Hybrid Cryptocurrency Price Prediction: Integrating EGARCH and LSTM with Explainable AI

The cryptocurrency market, characterized by extreme volatility and complex dynamics, presents significant challenges for accurate price prediction. This study introduces a novel hybrid predictive model that integrates Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) with Long Short-Term Memory (LSTM) networks, augmented by Explainable AI (XAI) techniques such as SHAP (SHapley Additive exPlanations). The EGARCH model captures statistical properties like volatility clustering and leverage effects, while the LSTM component effectively models nonlinear dependencies and long-term temporal patterns. SHAP enhances interpretability by quantifying feature contributions, offering actionable insights into the decision-making process. Using historical data from major cryptocurrencies such as Bitcoin and Ethereum, the hybrid model outperforms standalone EGARCH and LSTM models across diverse market conditions, achieving up to 95% accuracy. Visual analyses, including performance comparisons and SHAP-based feature importance graphs, provide clarity on prediction outcomes and error patterns. By addressing limitations in both accuracy and transparency, this research advances hybrid financial modeling methodologies and offers practical tools for traders, investors, and policymakers. These findings enable data-driven decision-making and effective risk management in highly volatile markets.