Here.
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.
From:
Li Guo
Wolfgang Karl H ̈ardle
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 confirm that our results
are not driven by behavioral mechanisms.
From:
Li Guo
Wolfgang Karl H ̈ardle
Yubo Tao
.