Abstract
Cryptocurrencies have, over the years, gained an unprecedented prominence in financial discourse, with the market fielding over 5,300 digital currencies and reaching over $2 trillion in market capitalisation in 2022. The surge in market values of digital currencies and their popularity in the world of e-commerce have remained unabated and equally received special attention from researchers focusing on identifying the underlying factors that drive changes in their market values. Thus, this study models the dynamics of the prices of cryptocurrencies alongside their interconnectedness, focusing on Bitcoin, Ethereum, and Litecoin along the time and frequency dimensions of monthly data from 1 March 2016 to 05/31/2022. Based on the ARDL model, results show that the volume of transactions of Bitcoin, Ethereum, and Litecoin, oil prices, and gold prices exert a more significant positive influence on their prices in the longrun than in the shortrun. However, the publicity of the selected cryptocurrencies (google search rates) does not significantly influence their prices. Interestingly, results from the Wavelet Granger causality tests show no causality between the raw series of Bitcoin, Ethereum, and Litecoin prices. However, a bi-directional causality exists between Bitcoin and Ethereum prices during the longrun in their low frequencies, a unidirectional causality running from Bitcoin to Litecoin prices during the longrun in their low frequencies, and a unidirectional causality running from Litecoin to Ethereum prices during the shortrun, medium run and longrun in their high, medium, and low frequencies. These findings have profound implications for the global financial market and investor decisions.
Abbreviations
AIC | = | Akaike Information Criterion |
ARDL | = | Autoregressive Distributed Lag |
CBOE | = | Chicago Board Options Exchange |
EMH | = | Efficiency Market Hypothesis |
GARCH | = | Generalised Autoregressive Conditional Heteroskedasticity |
GFT | = | Greater Fool Theory |
LA | = | Least Asymmetric |
MODWT | = | Maximum Overlap Discrete Wavelet Transform |
MRA | = | Multiresolution Analysis |
VAR | = | Vector Autoregression |
VIX | = | Volatility Index |
Availability of data and materials
The authors have provided the link to the data sources in the manuscript and the dataset analysed is also available on request.
Acknowledgments
The authors are grateful to all the anonymous reviewers and the editor for their valuable and helpful comments. The authors take responsibility for any further errors.
Disclosure statement
No potential conflict of interest was reported by the authors.
Additional information
Funding
Notes on contributors
Chekwube V. Madichie
Chekwube V. Madichie Lagos Business School, Pan-Atlantic University, Lagos, Nigeria.