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Abstract
This paper examines the high frequency multiscale relationships and nonlinear multiscale causality between Bitcoin, Ethereum, Monero, Dash, Ripple, and Litecoin. We apply nonlinear Granger causality and rolling window wavelet correlation (RWCC) to 15 min—data. Empirical RWCC results indicate mostly positive co-movements and long-term memory between the cryptocurrencies, especially between Bitcoin, Ethereum, and Monero. The nonlinear Granger causality tests reveal dual causation between most of the cryptocurrency pairs. We advance evidence to improve portfolio risk assessment, and hedging strategies.
Highlights
Analysis of co-movements and Granger causality across frequencies attracts a special attention in much of the contemporary theoretical and empirical research in finance with regards to analysis on contagion, volatility spillovers, predictability, bubbles, and crashes (e.g., Wang et al 2017; Saâdaoui et al 2017; Rehman and Apergis 2019; Bouri et al 2019).1 In recent times, the finance literature has increasingly borrowed estimation techniques from physics—i.e. wavelet transformation of data to different time-scales— to analyze the multiscale relationship and directional Granger causality between assets and/or markets2 (Mensi et al 2019)
This may be due to the upside trend of Bitcoin and Ethereum prices and an uptick in their volatility—observations that are shared by Corbet et al (2018a, 2019)—which are observed in Figs. 1 and 2
The pattern of time-variant correlation coefficients is quite similar for the cryptocurrency pair BTC-LTC
Summary
Analysis of co-movements and Granger causality across frequencies attracts a special attention in much of the contemporary theoretical and empirical research in finance with regards to analysis on contagion, volatility spillovers, predictability, bubbles, and crashes (e.g., Wang et al 2017; Saâdaoui et al 2017; Rehman and Apergis 2019; Bouri et al 2019). In recent times, the finance literature has increasingly borrowed estimation techniques from physics—i.e. wavelet transformation of data to different time-scales— to analyze the multiscale relationship and directional Granger causality between assets and/or markets (Mensi et al 2019). The finance literature has increasingly borrowed estimation techniques from physics—i.e. wavelet transformation of data to different time-scales— to analyze the multiscale relationship and directional Granger causality between assets and/or markets (Mensi et al 2019). These analyses have important implications on diversification benefits, hedging strategies, and portfolio risk assessment. Cryptocurrencies have exhibited spectacular growth since their inception in 2008, with the range of different currencies recently surpassing 3000 This digital money (Financial technology) reduces the transaction costs, provides higher quality services, Mensi et al Financ Innov (2021) 7:75 and increases customer satisfaction (Kou et al 2021a). A better understanding of the multiscale interactions among major cryptocurrencies may provide new opportunities for investors
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