A Time-Varying Network for Cryptocurrencies
构建加密货币的时变网络，利用收益交叉可预测性和技术相似性揭示风险传播与市场分割，提出动态聚类方法估计社区结构，发现跨社区投资可分散风险，跨加密货币动量策略日收益达1.08%。
Cryptocurrencies return cross-predictability and technological similarity yield information on risk propagation and market segmentation. To investigate these effects, we build a time-varying network for cryptocurrencies, based on the evolution of return cross-predictability and technological similarities. We develop a dynamic covariate-assisted spectral clustering method to consistently estimate the latent community structure of cryptocurrencies network that accounts for both sets of information. We demonstrate that investors can achieve better risk diversification by investing in cryptocurrencies from different communities. A cross-sectional portfolio that implements an inter-crypto momentum trading strategy earns a 1.08% daily return. By dissecting the portfolio returns on behavioral factors, we confirm that our results are not driven by behavioral mechanisms.