This study aims to examine the influence of day-of-the-week patterns on cryptocurrency market volatility and return dynamics. Specifically, it investigates whether investor behavior differs between weekdays and weekends in major digital assets and whether these behavioral shifts create asymmetric volatility structures.
The study employs the Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) framework to analyze daily return volatility across 12 leading cryptocurrencies from January 2020 to December 2025. Dummy variables representing weekdays and weekends are incorporated into the conditional variance equation to capture calendar anomalies and asymmetric investor responses.
The empirical findings reveal statistically significant day-of-the-week effects in cryptocurrency volatility. Weekend trading periods are associated with lower trading intensity and reduced volatility persistence, while Mondays and Tuesdays exhibit heightened volatility and stronger information absorption. Negative shocks produce larger volatility responses than positive shocks, indicating asymmetric market reactions. The results further suggest that cryptocurrency traders display behavioral biases linked to investor sentiment, cognitive heuristics and information-processing patterns throughout the week.
This study focuses on highly capitalized cryptocurrencies, which may limit the generalizability of the results to smaller or illiquid digital assets. Future studies may extend the analysis by incorporating decentralized finance (DeFi) tokens, NFTs or cross-market spillover effects. Additional research could also examine macroeconomic announcements and regulatory interventions as moderating variables.
The findings provide important implications for portfolio management, algorithmic trading strategies, volatility forecasting and risk management. Understanding weekly volatility structures may assist investors and regulators in improving market timing decisions and assessing systemic risk in cryptocurrency ecosystems.
Although calendar anomalies have been extensively investigated in traditional financial markets, limited evidence exists regarding asymmetric volatility patterns in cryptocurrencies using recent post-pandemic data. This study contributes to the literature by combining calendar anomaly theory with volatility asymmetry analysis in digital asset markets using an updated dataset and robust econometric methodology.
