Bitcoin Volatility Forecasting Through Market Sentiment, Blockchain Fundamentals, and Endogenous Market Uncertainty

The study develops and empirically evaluates a forecasting-orientated structural model in which future Bitcoin historical volatility is modelled as being associated with market sentiment and blockchain fundamentals through market uncertainty. Market Sentiment (MS) is specified as a behavioural construct, Blockchain Fundamentals (BF) as network conditions, and Market Uncertainty (MU) as an endogenous regime construct that consolidates signals shaping historical volatility at t+1. Using 262 weekly observations from January 2021 to January 2026, the analysis applies partial least squares structural equation modelling (PLS-SEM) with formative constructs and a forward-dated volatility target to preserve temporal ordering. Paths are evaluated with bootstrapping, effect sizes, and mediation analysis, while predictive performance is assessed using PLSpredict, the cross-validated predictive ability test (CVPAT), benchmark-based comparison, and Diebold-Mariano (DM) tests. MU emerges as the dominant predictor of Future Historical Volatility, denoted as HV(t+1) in the structural model (β = 0.864, p-value < 0.001; f2 = 2.036). The effect of BF is largely indirect, with 91.02% of the total effect transmitted via uncertainty, indicating indirect-only mediation. The model explains substantial variation in HV(t+1) (R2 = 0.791) and shows predictive relevance (Q2 predict = 0.287), while the benchmark-based results indicate mixed but competitive forecasting performance relative to persistence-based and econometric alternatives. These findings are consistent with a regime-based interpretation of Bitcoin volatility and highlight the explanatory and predictive relevance of an integrated behavioural-network-uncertainty architecture.