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A time-varying network for cryptocurrencies

Li Guo, Wolfgang Härdle () and Yubo Tao

No 2021-016, IRTG 1792 Discussion Papers from Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"

Abstract: Cryptocurrencies return cross-predictability and technological similarity yield information on risk propagation and market segmentation. To investigate these effects, we build a timevarying 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.

Keywords: Community detection; Dynamic stochastic blockmodel; Covariates; Co-clustering; Network risk; Momentum (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-ban, nep-cwa, nep-isf, nep-net, nep-ore and nep-pay
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https://www.econstor.eu/bitstream/10419/241273/1/1769656677.pdf (application/pdf)

Related works:
Journal Article: A Time-Varying Network for Cryptocurrencies (2024) Downloads
Working Paper: A Time-Varying Network for Cryptocurrencies (2022) Downloads
Working Paper: A Time-Varying Network for Cryptocurrencies (2021) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:irtgdp:2021016

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