Does volatility in cryptocurrencies drive the interconnectedness between the cryptocurrencies market? Insights from wavelets
We present a multi-scale and time-frequency analysis of the degree of integration and the lead-lag relationship between six cryptocurrencies (i.e., Bitcoin, Bitcoincash, Ethereum, Litecoin, Ripple, and Tether) and the cryptocurrency-implied volatility index (VCRIX). As a result, the wavelet techniqu...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
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| Series: | Cogent Economics & Finance |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/23322039.2022.2061682 |
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| Summary: | We present a multi-scale and time-frequency analysis of the degree of integration and the lead-lag relationship between six cryptocurrencies (i.e., Bitcoin, Bitcoincash, Ethereum, Litecoin, Ripple, and Tether) and the cryptocurrency-implied volatility index (VCRIX). As a result, the wavelet techniques—bi-wavelet, partial wavelet, bivariate contemporary correlations (BCC), wavelet multiple correlations (WMC) and wavelet multiple cross-correlations (WMCC) are applied. Findings from the study provide that the interdependencies between the cryptocurrencies and VCRIX are high and mostly positive across investment horizons. Furthermore, the comovements between the cryptocurrencies designate long memory dynamics. The high comovements between cryptocurrencies are highly influenced by idiosyncratic shocks they possess rather than the VCRIX. In addition, the BCC and the WMC indicate that there is a high integration among all the cryptocurrencies. Categorically, the VCRIX could not lead or lag the interdependencies among the cryptocurrencies in the WMCC analysis. Findings from the study, therefore, divulge that investing in a single or few cryptocurrencies is highly risky due to the adverse impact of the VCRIX on individual cryptocurrencies. In general, investors should effectively hedge against volatilities in the cryptocurrency markets due to the significant predictive ability of VCRIX as an effective proxy. |
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| ISSN: | 2332-2039 |