Bitcoin’s multifractal influence: deciphering the relationship with conventional and renewable energy markets

The annual electricity consumption of cryptocurrency mining has witnessed significant growth in recent years, fueled by an increase in market participation and the escalating complexity of the mining process. This has led to carbon emissions that exceed those generated by several developed nations. The growing impact of global warming and rising environmental concerns has brought increased scrutiny to Bitcoin's energy consumption, particularly its potential to influence prices in unforeseen ways. This study investigates multifractal behavior in the cross-correlation of the Cambridge Bitcoin Electricity Consumption Index (CBECI) with both conventional and renewable energy prices using the Multifractal Detrended Cross-Correlation Analysis (MFDCCA) method. For renewable energy, we considered WilderHill Clean Energy, S&P Global Eco, S&P Global Clean Energy, OMX Solar Energy, and OMX Renewable Energy Index. For conventional energy, we considered the daily prices of WTI crude oil, Brent oil, heating oil, Newcastle coal, and natural gas. The daily price data range from 2 April 2013, to 29 August 2023, encompassing 1709 observations. Additionally, we employed a rolling window analysis to uncover the time-varying dynamics in the cross-correlations and persistence levels between Bitcoin electricity consumption and energy prices. The findings reveal the existence of a cross-correlation between the CBECI and energy markets. Overall, the CBECI exhibits a persistent cross-correlation with both energy markets; however, it is more persistent in the fossil fuel market, specifically in the coal market. These findings suggest the incorporation of dynamic changes in the CBECI in portfolio management for effective risk management strategies. The results of this study, which analyses multifractal cross-correlation of Bitcoin Electricity Consumption Index (CBECI) with both conventional and renewable energy prices, reveal the existence of a cross-correlation between variables under analysis. Results are relevant, suggesting the possibility to use CBECI in portfolio management, but also gives information for policymakers relevant, for example, to issues like global and environmental concerns.