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- W3139011280 abstract "Cryptocurrencies are becoming an attractive asset class and are the focus ofrecent quantitative research. The joint dynamics of the cryptocurrency marketyields information on network risk. Utilizing the adaptive LASSO approach, webuild a dynamic network of cryptocurrencies and model the latent communitieswith a dynamic stochastic blockmodel. We develop a dynamic covariate-assistedspectral clustering method to uniformly estimate the latent group membership ofcryptocurrencies consistently. We show that return inter-predictability andcrypto characteristics, including hashing algorithms and proof types, jointlydetermine the crypto market segmentation. Based on this classification result,it is natural to employ eigenvector centrality to identify a cryptocurrency'sidiosyncratic risk. An asset pricing analysis finds that a cross-sectionalportfolio with a higher centrality earns a higher risk premium. Further testsconfirm that centrality serves as a risk factor well and delivers valuableinformation content on cryptocurrency markets." @default.
- W3139011280 created "2021-03-29" @default.
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- W3139011280 date "2018-02-11" @default.
- W3139011280 modified "2023-09-24" @default.
- W3139011280 title "A Dynamic Network Perspective on Cryptocurrencies" @default.
- W3139011280 hasPublicationYear "2018" @default.
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