2018/02/11 by Li Guo, Wolfgang Karl Härdle, Guo, Li +3
Economics, Econometrics and Finance · #62F12 (Primary) #62H30 #91D30 (Secondary) #Applications (stat.AP) #Complex Systems and Time Series Analysis #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Financial Markets and Investment Strategies #Market Dynamics and Volatility #Methodology (stat.ME) #Portfolio Management (q-fin.PM) #Risk Management (q-fin.RM)
paper · pdf · doi:10.48550/arxiv.1802.03708
openalex publication_date 2018/02/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.