Cryptocurrencies trade continuously while major traditional markets close over the weekend. We examine whether weekend cryptocurrency volatility predicts Monday variance in equity, commodity and fixed-income markets. Using daily range-based variance measures for Bitcoin, Ether, XRP, the S&P 500, gold and a US Treasury ETF over 2017-2026, we augment heterogeneous autoregressive models with a common weekend cryptocurrency factor. Predictive content is weak before 2024 but significant thereafter. In the post-2024 sample, a one-standard-deviation increase in the factor is associated with 27-54% higher fitted Monday variance. Adding the factor reduces out-of-sample QLIKE losses by 11-22%, although statistical evidence varies across markets. Friday cryptocurrency volatility does not reproduce the result, and coin-specific variation adds no significant predictive information beyond the common factor. An endogenous procedure estimates a break in late 2023, during growing anticipation of US spot-Bitcoin ETF approval. However, ETF-flow interactions provide no evidence of amplification through greater measured Monday flow activity, leaving the mechanism unresolved. The findings suggest that calendar-specific volatility measures reveal predictive information that aggregate connectedness measures may obscure.
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