Weekend Crypto Volatility and Monday Market Risk

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.

Authors

Publication Details

Journal
arXiv (Cornell University)
Published
2026-10-26
DOI
https://doi.org/10.48550/arxiv.2610.35702
Primary Topic
Financial Risk and Volatility Modeling
Type
preprint
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preprint

Weekend Crypto Volatility and Monday Market Risk

Nneka Umeorah, Pedram Ebadollahyvahed
arXiv (Cornell University)
Financial Risk and Volatility Modeling
preprint

Weekend Crypto Volatility and Monday Market Risk

Nneka Umeorah, Pedram Ebadollahyvahed
preprint en

Abstract

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.

arXiv (Cornell University)
Financial Risk and Volatility Modeling
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