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
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/241273/1/1769656677.pdf (application/pdf)

Related works:
Journal Article: A Time-Varying Network for Cryptocurrencies (2024)
Working Paper: A Time-Varying Network for Cryptocurrencies (2022)
Working Paper: A Time-Varying Network for Cryptocurrencies (2021)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zbw:irtgdp:2021016

Access Statistics for this paper

More papers in IRTG 1792 Discussion Papers from Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series" Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().