Arbeitspapier
A time-varying network for cryptocurrencies
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.
- Language
-
Englisch
- Bibliographic citation
-
Series: IRTG 1792 Discussion Paper ; No. 2021-016
- Classification
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Wirtschaft
- Subject
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Community detection
Dynamic stochastic blockmodel
Covariates
Co-clustering
Network risk
Momentum
- Event
-
Geistige Schöpfung
- (who)
-
Guo, Li
Härdle, Wolfgang
Tao, Yubo
- Event
-
Veröffentlichung
- (who)
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Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
- (where)
-
Berlin
- (when)
-
2021
- Handle
- Last update
-
10.03.2025, 11:42 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Guo, Li
- Härdle, Wolfgang
- Tao, Yubo
- Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
Time of origin
- 2021
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