When Bitcoin moves, who follows? State-dependent predictability across gold and green assets

Abstract

This study examines whether Bitcoin contains economically meaningful predictive information for gold, green bonds, and renewable-energy assets and whether such relationships vary across market conditions. Using daily data from February 2018 to February 2023, we combine multi-horizon predictive regressions, volatility-state analysis, out-of-sample forecasting, local projections, forecast-error variance decomposition, and explainable machine learning. Bitcoin returns show no significant unconditional predictive power across the 1-, 5-, and 22-day horizons. State-dependent effects emerge selectively, with the strongest evidence for bio-clean fuel under high market uncertainty, but do not generalize across assets or horizons. Out-of-sample forecasting gains are modest and rarely statistically significant. Bitcoin shocks generate limited persistent responses and explain less than 1% of forecast-error variance across all assets. SHAP evidence further shows that Bitcoin features contribute relatively little to nonlinear forecasts. The findings characterize Bitcoin–green asset linkages as selectively state-dependent rather than persistently predictive.