A network analysis of electricity demand and the cryptocurrency markets

Abstract

This article examines the connectedness and information spillover in the Electricity‐Crypto Network (ECN) system. The Bitcoin and Ethereum markets are studied due to the level of electricity demand for active trading and mining in the three leading crypto mining economies (United States, China, and Japan). Among other findings, the leading net transmitter of information is the return of the Bitcoin market while the demand for electricity in the U.S. and Japan are the leading net information receivers in the ECN system. In a nutshell, the return and trading volumes of the cryptocurrency markets are net information transmitters while the markets' volatility and the demand for electricity in the U.S., China, and Japan are net information receivers in the system. As a policy relevance, given the favourable developments in these crypto markets, greener sources of electrical energy are expedient to mitigate emissions while mining these coins. This will reduce the impact of human activities on the climate.

Interconnection between cryptocurrencies and energy markets: an analysis of volatility spillover

“…Similarly, considering the literature of Okorie (2021) on electricity demand for cryptocurrencies mining using network connectedness, it was identified that electricity markets are largely influenced by the information spillover. And trading volume for these currencies acts as a transmitter of information for increased electricity demand in the US electricity markets.…”

The environmental consequences of blockchain technology: A Bayesian quantile cointegration analysis for Bitcoin

“…The existing literature on the possible spillover effects of the use of BTC to environmental degradation is still in its infancy. Many related studies focus solely on the energymining nexus, whereas scant attention has been paid to the environmental consequences of cryptocurrency mining (see for example Das & Dutta, 2020;De Vries, 2018;Elsayed et al, 2020;Hanly, Morales, & Cassells, 2018;Krause & Tolaymat, 2018;Li, Li, Peng, Cui, & Wu, 2019;O'Dwyer & Malone, 2014;Okorie, 2020). Das and Dutta (2020), which is the most related study to our work, investigate the relationship between Bitcoin's energy consumption measured by electricity load for mining BTC and its revenue level.…”

Analyzing the frequency dynamics of volatility spillovers across precious and industrial metal markets

“…On the basis of the variance decompositions of the VAR model, Diebold and Yilmaz (2009, 2012) provided a new method to study the volatility spillovers between different financial markets, namely spillover index. The advantage of the DY framework is that it can describe the direction of volatility spillovers, and can analyze the dynamics use a simple rolling window method (see Ji et al, 2018; Okorie, 2021; Z. Yang & Zhou, 2017; Yarovaya et al, 2016). Particularly, according to the Fourier transforms of the impulse response functions (IRFs), Baruník and Křehlík (2018) proposed an extension of the DY framework in the frequency domain (see Balli et al, 2019; Liang et al, 2020; Lovcha & Perezlaborda, 2020; Maghyereh et al, 2019; Tiwari et al, 2018; Uddin et al, 2019; X. Wang & Wang, 2019; Y. Wang et al, 2020; Xia et al, 2020).…”