Forecasting Non-Fungible Token (NFT) Market Volatility Through Hybrid GARCH–LSTM Models

This study forecasts price volatility in the non-fungible token (NFT) digital asset market. We develop a hybrid forecasting framework that integrates long short-term memory (LSTM) networks with a set of generalized autoregressive conditional heteroskedasticity (GARCH)-type models. Using daily data, we conduct a comparative evaluation of nine competing forecasting models across five representative NFT submarkets (i.e., OpenSea, Atomic, CryptoKitties, Gods Unchained, and Decentraland). Employing standard error metrics (RMSE, MSE, MAE, and RAE), a robust loss QLIKE function, and the Diebold–Mariano (DM) statistical test, our empirical results show that the hybrid GARCH-TGARCH-LSTM model achieves the lowest error metrics. Compared with the baseline LSTM model, it achieves RMSE reductions of approximately 14% for Atomic, 49% for CryptoKitties, 13% for Decentraland, 8% for Gods Unchained, and 29% for OpenSea. Furthermore, the EGARCH conditional variance specification underperforms the TGARCH counterpart in the most NFT submarkets. This finding suggests that combining the volatility clustering characteristics captured by standard GARCH models with the asymmetric volatility features captured by TGARCH models improves forecasting performance. This study contributes to research on hybrid deep learning approaches to volatility forecasting in emerging digital asset markets.