With the rapid development of the cryptocurrency market, price volatility and liquidity have become key indicators for understanding market behavior. However, their joint dynamics are often shaped by higher-order, multi-attribute interactions that are not well captured by conventional low-order analyses. Existing approaches largely emphasize pairwise relationships and thus struggle to provide a system-level and time-varying characterization of whether multiple market attributes convey redundant overlap or synergistic complementarity, leaving an important gap in understanding multivariate market organization. This paper proposes a multi-scale network framework based on information dynamics, built on O-information theory and quantified through three parallel measures: global O-information rate (global OIR), local OIR, and the OIR gradient, to characterize redundancy–synergy structures in multi-attribute cryptocurrency systems. Using Binance Vision spot-market data for twelve major cryptocurrencies from August 1, 2020 to November 30, 2025 across four sampling intervals (2 h, 6 h, 12 h, and 1d), we conduct rolling-window estimation and systematic cross-scale comparisons. The results reveal pronounced cross-asset heterogeneity and support a distinction between leading and non-leading assets, with BTC, ETH, XRP, and BNB identified as leading assets while also exhibiting within-group differences. The findings provide a holistic and dynamic perspective on higher-order information organization in digital-asset markets and suggest that HOI-based indicators offer information that complements conventional connectedness measures in financial research.
Xiao et al. (Thu,) studied this question.