Regime-dependent bitcoin volatility forecasting under alternative information sets: evidence from econometric, deep learning, and hybrid models

Abstract Bitcoin volatility is challenging to forecast because accuracy depends on model choice, information sets, and market regimes. Existing studies often compare models under inconsistent volatility targets, predictors, samples, or evaluation designs, leaving unclear whether forecast gains arise from model architecture, information content, or regime shifts. This study addresses this gap by proposing a unified rolling out-of-sample framework for daily Bitcoin volatility forecasting that compares GARCH-type models, standalone LSTMs, and state-augmented GARCH–LSTM hybrids under the same volatility target, nested information sets, one-step-ahead forecast horizon, and evaluation design, using balanced daily data from 1 February 2018 to 19 July 2025. Predictors are organized into technical (T), technical plus sentiment (TS), and technical, sentiment, and blockchain (TSB) groups to measure the marginal value of added information. Forecast accuracy is assessed mainly with QLIKE, plus RMSE, MAE, and Diebold–Mariano tests across normal and crisis regimes. Predictability is strongly regime-dependent. In normal markets, LSTM-T achieves the lowest loss, with TGARCH and Hybrid_GARCH_T statistically indistinguishable from it. In crises, hybrids minimize QLIKE, but the best specification differs: Hybrid_TGARCH_TS during COVID-19, Hybrid_EGARCH_TSB during the China mining ban, and Hybrid_APARCH_TS during the crypto winter. Hence, hybrid gains are regime-specific and depend on the interaction of volatility dynamics, information sets, and regime characteristics. For daily one-step-ahead Bitcoin forecasts, the evidence supports regime-sensitive modeling and cautions against broad claims of model superiority based on pooled samples.