Artikel
Volatility spillovers among cryptocurrencies
The cryptocurrency market has experienced stunning growth, with market value exceeding USD 1.5 trillion. We use a DCC-MGARCH model to examine the return and volatility spillovers across three distinct classes of cryptocurrencies: coins, tokens, and stablecoins. Our results demonstrate that conditional correlations are time-varying, peaking during the COVID-19 pandemic sell-off of March 2020, and that both ARCH and GARCH effects play an important role in determining conditional volatility among cryptocurrencies. We find a bi-directional relationship for returns and long-term (GARCH) spillovers between BTC and ETH, but only a unidirectional short-term (ARCH) spillover effect from BTC to ETH. We also find spillovers from BTC and ETH to USDT, but no influence running in the other direction. Our results suggest that USDT does not currently play an important role in volatility transmission across cryptocurrency markets. We also demonstrate applications of our results to hedging and optimal portfolio construction.
- Language
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Englisch
- Bibliographic citation
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Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 14 ; Year: 2021 ; Issue: 10 ; Pages: 1-12 ; Basel: MDPI
- Classification
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Wirtschaft
General Financial Markets: General (includes Measurement and Data)
International Financial Markets
Pension Funds; Non-bank Financial Institutions; Financial Instruments; Institutional Investors
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
- Subject
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cryptocurrency
volatility spillovers
vector autoregression (VAR)
bitcoin
multivariate GARCH (MGARCH)
- Event
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Geistige Schöpfung
- (who)
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Smales, Lee A.
- Event
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Veröffentlichung
- (who)
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MDPI
- (where)
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Basel
- (when)
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2021
- DOI
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doi:10.3390/jrfm14100493
- Handle
- Last update
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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
- Artikel
Associated
- Smales, Lee A.
- MDPI
Time of origin
- 2021
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