Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2019 009

Dynamic Network Perspective of Cryptocurrencies

Li Guo
Yubo Tao
Wolfgang K. Härdle

Cryptocurrencies are becoming an attractive asset class and are the focus of
recent quantitative research. The joint dynamics of the cryptocurrency market
yields information on network risk. Utilizing the adaptive LASSO approach, we
build a dynamic network of cryptocurrencies and model the latent communities
with a dynamic stochastic blockmodel. We develop a dynamic covariate-assisted
spectral clustering method to uniformly estimate the latent group membership of
cryptocurrencies consistently. We show that return inter-predictability and
crypto characteristics, including hashing algorithms and proof types, jointly
determine the crypto market segmentation. Based on this classification result,
it is natural to employ eigenvector centrality to identify a cryptocurrency’s
idiosyncratic risk. An asset pricing analysis finds that a cross-sectional
portfolio with a higher centrality earns a higher risk premium. Further tests
confirm that centrality serves as a risk factor well and delivers valuable
information content on cryptocurrency markets.

Community Detection, Dynamic Stochastic Blockmodel, Spectral Clustering, Node
Covariate, Return Predictability, Portfolio Management

JEL Classification: