Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2021 016

A Time-Varying Network for Cryptocurrencies

Li Guo
Wolfgang Karl Härdle
Yubo Tao

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 con rm that our results are not
driven by behavioral mechanisms.

Community detection, Dynamic stochastic blockmodel, Covariates, Co-clustering,
Network risk, Momentum

JEL Classification: