Analyzing volatilities, correlations, and lead–lag relationships across financial assets is important for portfolio and risk management. As cryptocurrencies gain traction, research in this area is growing. Reference [1] studies the causal relationships, volatility, and correlations among major cryptocurrencies and the Crypto Volatility Index (CVI).
A distinctive aspect of this work is that, unlike prior time-series studies, it uses time–frequency domain methods, specifically wavelet analysis, to examine the relationships. The paper analyzes volatility as a driver of interlinkages between cryptocurrencies and the CVI across different investment horizons. It employs daily spot prices of Bitcoin, Ethereum, Tether, USD-Coin, BNB, and the CVI from April 2019 to August 2022.
The authors pointed out,
It can be inferred from their estimates that the CVI exhibits significant short-term, medium-term and long-term interlinkages with Bitcoin, Ethereum, Tether, USD-Coin and BNB. The time-frequency analysis using partial wavelet coherence depicts that CVI does not drive the perseverance of high correlations between cryptocurrencies. Specifically, there is no change in the degree of interlinkages between Bitcoin and other cryptocurrencies, Ethereum and other cryptocurrencies, Tether and other cryptocurrencies, USD-Coin and other cryptocurrencies, or BNB and other cryptocurrencies when CVI is included as a covariate. These results depict the prevalence of idiosyncratic shocks over the common interlinkages within individual cryptocurrency markets. As a result, CVI may not generally serve as a hedging proxy for these cryptocurrencies when included in the same portfolio. The estimates of wavelet bivariate correlations highlight that Bitcoin and Ethereum exhibit the highest degrees of co-movements across various time scales, followed by Bitcoin and BNB, and Ethereum and BNB. The co-movements in these pairs are positive from intraweek scales to quarterly scales, representing short-term and medium-term real economic transactions. These positive movements make diversification impracticable between these pairs, as they tend to exhibit behavior associated with speculative bubbles. The estimates from multiple wavelet correlations confirm that the degree of interlinkages is relatively high in the long term, with undulations or surges over the time horizon. This accentuates a higher convergence of cryptocurrencies’ returns in the long term, even in the presence of CVI. The wavelet multiple cross-correlations coefficients depict Ethereum as the most influential variable in the short term, Bitcoin as the most influential variable in the short to medium term, CVI as the most influential variable in the monthly to quarterly scale (i.e., in the medium term). The estimates also confirm that Ethereum leads or lags at the intraweek and weekly scales, Bitcoin leads at the fortnightly and monthly scales, and CVI lags at the monthly to quarterly scale.
In short, the study finds that CVI shows short-, medium-, and long-term interlinkages with major cryptocurrencies such as Bitcoin, Ethereum, Tether, USD-Coin, and BNB, but it does not drive high correlations across them. Wavelet correlation estimates reveal strong co-movements, especially between Bitcoin–Ethereum, Bitcoin–BNB, and Ethereum–BNB, which remain positive across short- to medium-term horizons. Long-term interlinkages remain high, with Ethereum most influential in the short term, Bitcoin in the short to medium term, and CVI in the medium term, confirming varying lead–lag roles across time scales.
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References
[1] Vandana Dangi, Cryptocurrency Implied Volatility as a Driver of the Interlinkages Across Cryptocurrencies’ Returns: A Wavelet Analysis, VIKALPA The Journal for Decision Makers 1 –29, 2025
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