A Time-Varying Network for Cryptocurrencies

Cryptocurrencies return cross-predictability and technological similarity yield information on risk propagation market segmentation. To investigate these effects, we build a time-varying network for cryptocurrencies, based the evolution of similarities. We develop dynamic covariate-assisted spectral clustering method to consistently estimate latent community structure cryptocurrencies that accounts both sets information. demonstrate investors can achieve better diversification by investing in from different communities. A cross-sectional portfolio implements an inter-crypto momentum trading strategy earns 1.08% daily return. By dissecting returns behavioral factors, confirm our results are not driven mechanisms.