Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2018 032

Understanding Latent Group Structure of Cryptocurrencies Market: A Dynamic Network Perspective

Li Guo
Yubo Tao
Wolfgang Karl Hardle

In this paper, we study the latent group structure in cryptocurrencies market
by forming a dynamic return inferred network with coin attributions. We develop
a dynamic covariate-assisted spectral clustering method to detect the communities
in dynamic network framework and prove its uniform consistency along the horizons.
Applying our new method, we show the return inferred network structure and
coin attributions, including algorithm and proof types, jointly determine the market
segmentation. Based on the network model, we propose a novel \hard-to-value"
measure using the centrality scores. Further analysis reveals that the group with a
lower centrality score exhibits stronger short-term return reversals. Cross-sectional
return predictability further conrms the economic meanings of our grouping results
and reveal important portfolio management implications.

Community Detection, Dynamic Network, Return Predictability, Behavioural
Bias, Market Segmentation, Bitcoin