Paper: arXiv 2610.35702

Authors: Pedram Ebadollahyvahed, Nneka Umeorah

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.

Complexity vs Empirical Score

  • Math Complexity: 6.0/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced econometric models (HAR, structural break analysis) and robust statistical tests (QLIKE, Diebold-Mariano) with a clear empirical focus. The novelty lies in exploiting the calendar mismatch for predictive insights, particularly the emergence of the relationship post-2023.

Research Flowchart

  flowchart TD
    A[Research Goal: Do Weekend Crypto Volatility Predict Monday Market Risk?] --> B{Methodology: Augment HAR Models with Common Weekend Crypto Factor};
    B --> C[Data Inputs: Daily Range-Based Variance (2017-2026) for BTC, ETH, XRP, S&P 500, Gold, US Treasury ETF];
    C --> D{Computational Process: Model Estimation & Predictive Power Assessment (In-sample R2, Out-of-sample QLIKE, Endogenous Break Analysis)};
    D --> E[Finding 1: Weak Predictive Content Pre-2024, Significant Post-2024 (27-54% higher fitted Monday variance)];
    E --> F[Finding 2: Out-of-sample QLIKE losses reduced by 11-22%; Friday crypto volatility & coin-specific variation are not predictive];
    F --> G[Finding 3: Endogenous Break in Late 2023 (US spot-Bitcoin ETF anticipation), but no evidence of amplification via ETF flows];
    G --> H[Outcome: Calendar-specific volatility reveals predictive information that aggregate connectedness measures may obscure].