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A time-varying network for cryptocurrencies

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  • Guo, Li
  • Härdle, Wolfgang
  • Tao, Yubo

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.

Suggested Citation

  • Guo, Li & Härdle, Wolfgang & Tao, Yubo, 2021. "A time-varying network for cryptocurrencies," IRTG 1792 Discussion Papers 2021-016, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
  • Handle: RePEc:zbw:irtgdp:2021016
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    Cited by:

    1. Paola Stolfi & Mauro Bernardi & Davide Vergni, 2022. "Robust estimation of time-dependent precision matrix with application to the cryptocurrency market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-25, December.

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    Keywords

    Community detection; Dynamic stochastic blockmodel; Covariates; Co-clustering; Network risk; Momentum;
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