The Dynamics of Fossil Fuels, Cryptocurrencies, and Clean Energy: Dose the Energy market's price volatility create an incentive for cryptocurrency mining?

Roudari, Soheil and Omidi, Vahid and Ahmadian-Yazdi, Fazaneh (2024): The Dynamics of Fossil Fuels, Cryptocurrencies, and Clean Energy: Dose the Energy market's price volatility create an incentive for cryptocurrency mining?

Abstract

Countries with significant fossil fuel deposits may start to think about mining coins if the price of fossil fuels drops. This suggests that countries that export energy may decide to use the power produced from their fossil fuel stockpiles as a substitute method of cryptocurrency mining. To determine the extent of this trend, this research employs the TVP-VAR-EJC model to analyze the vulnerability and impact of the renewable energy market, cryptocurrencies, and fossil fuel energy between 18/01/2018 and 17/02/2023. The results reveal that the cryptocurrency market transmitted net shocks throughout the majority of the period. While the intensity of this relationship decreased in recent months, there is not enough evidence to validate the claim that energy-rich countries typically employ fossil fuels as a cryptocurrency mining input.

• Item Type: MPRA Paper
• Language: English
• Keywords: Oil, Natural Gas, Coal, Bitcoin, Ethereum, TVP-VAR, Graph Theory
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C58 - Financial Econometrics; G - Financial Economics > G1 - General Financial Markets > G11 - Portfolio Choice ; Investment Decisions; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q41 - Demand and Supply ; Prices

References

Aguiar-Conraria, L., Azevedo, N., & Soares, M.J. (2008). Using Wavelets to Decompose the Time-Frequency effects of Monetary Policy. Physica A: Statistical Mechanics and its Applications, 387, 2863–2878.

Akyildirim, E., Corbet, S., & Lucey, B. M. (2021). China, Coal, Calamities and Cryptos. Available at SSRN 3851253.

Attarzadeh, A., & Balcilar, M. (2022). On the dynamic return and volatility connectedness of cryptocurrency, crude oil, clean energy, and stock markets: a time-varying analysis. Environmental Science and Pollution Research, 29(43), 65185-65196.

Balcilar, M., Gabauer, D., & Umar, Z. (2021). Crude Oil futures contracts and commodity markets: New evidence from a TVP-VAR extended joint connectedness approach. Resources Policy, 73, 102219.

Bariviera, A. F., Basgall, M. J., Hasperué, W., & Naiouf, M. (2018). Some stylized facts of the Bitcoin market. Physica A: Statistical Mechanics and its Applications, 492, 1027-1035.

Będowska-Sójka, B., & Kliber, A. (2022). Can cryptocurrencies hedge oil price fluctuations? A pandemic perspective. Energy Economics, 115, 106360.

Bondy, J. A., & Murty, U. S. R. (2008). Graph Theory, 6 Springer. Grad. Texts in Math, 244.

Bouri, E., Molnár, P., Azzi, G., Roubaud, D., & Hagfors, L. I. (2017). On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier? Finance Research Letters, 20, 192-198.

Cafaro, C., Ceci, P., & Fardelli, A. (2022). The Italian Pathway for Energy Transition: From the Coal Phase Out to the Problems Related to Natural Gas. Atmosphere, 13(11), 1872.

Chitkasame, T., Rakpho, P., & Khiewngamdee, C. (2022). Analyzing structural change and causality between energy consumption and Bitcoin’s activity. Energy Reports, 8, 736-743.

Corbet, S., Lucey, B., & Yarovaya, L. (2021). Bitcoin-energy markets interrelationships-New evidence. Resources Policy, 70, 101916.

Dogan, E., Majeed, M. T., & Luni, T. (2022). Are clean energy and carbon emission allowances caused by bitcoin? A novel time-varying method. Journal of Cleaner Production, 347, 131089.

Ghazani, M. M., & Jafari, M. A. (2021). Cryptocurrencies, gold, and WTI crude oil market efficiency: a dynamic analysis based on the adaptive market hypothesis. Financial Innovation, 7(1), 1-26.

Ha, L. T., & Nham, N. T. H. (2022). An application of a TVP-VAR extended joint connected approach to explore connectedness between WTI crude oil, gold, stock and cryptocurrencies during the COVID-19 health crisis. Technological Forecasting and Social Change, 183, 121909-121909.

Jareño, F., González, M. D. L. O., López, R., & Ramos, A. R. (2021). Cryptocurrencies and oil price shocks: A NARDL analysis in the COVID-19 pandemic. Resources Policy, 74, 102281.

Jiang, S., Li, Y., Lu, Q., Wang, S., & Wei, Y. (2022). Volatility communicator or receiver? Investigating volatility spillover mechanisms among Bitcoin and other financial markets. Research in International Business and Finance, 59, 101543.

Katsiampa, P. (2017). Volatility estimation for Bitcoin: A comparison of GARCH models. Economics Letters, 177, 44-47.

Le, T. H. (2023). Quantile time-frequency connectedness between cryptocurrency volatility and renewable energy volatility during the COVID-19 pandemic and Ukraine-Russia conflicts. Renewable Energy, 202, 613-625.

Loh, L. (2013). Co-movement of Asia–Pacific with European and US Stock Market Returns: a Cross-Time-Frequency Analysis. Research in International Business and Finance, 29, 1–13.

Lu, X., Huang, N., Ye, Z., Lai, K. K., & Cui, H. (2022). The spillovers among cryptocurrency, clean energy and oil. Procedia Computer Science, 214, 649-655.

Malfuzi, A., Mehr, A. S., Rosen, M. A., Alharthi, M., & Kurilova, A. A. (2020). Economic viability of bitcoin mining using a renewable-based SOFC power system to supply the electrical power demand. Energy, 203, 117843.

Meiryani, M., Tandyopranoto, C. D., Emanuel, J., Lindawati, A. S. L., Fahlevi, M., Aljuaid, M., & Hasan, F. (2022). The effect of global price movements on the energy sector commodity on bitcoin price movement during the COVID-19 pandemic. Heliyon, 8(10), e10820.

Okorie, D. I., & Lin, B. (2020). Crude oil price and cryptocurrencies: Evidence of volatility connectedness and hedging strategy. Energy economics, 87, 104703.

Omura, A., Cheung, A., & Su, J. J. (2023). Does natural gas volatility affect Bitcoin volatility? Evidence from the HAR-RV model. Applied Economics, 1-12.

Pham, S. D., Nguyen, T. T. T., & Do, H. X. (2022). Dynamic volatility connectedness between thermal coal futures and major cryptocurrencies: evidence from China. Energy Economics, 112, 106114.

Rehman, M. U., & Kang, S. H. (2021). A time–frequency comovement and causality relationship between Bitcoin hashrate and energy commodity markets. Global Finance Journal, 49, 100576.

Ren, B., & Lucey, B. (2022, a). A clean, green haven? —Examining the relationship between clean energy, clean and dirty cryptocurrencies. Energy Economics, 109, 105951.

Ren, B., & Lucey, B. (2022, b). Do clean and dirty cryptocurrency markets herd differently?. Finance Research Letters, 47, 102795.

Rösch, A., & Schmidbauer, H. (2016). WaveletComp 1.1: A guided tour through the R package. URL: http://www. hsstat. com/projects/WaveletComp/WaveletComp_guided_tour. pdf.

Roueff, F., & Sachs, R. (2011). Locally Stationary Long Memory Estimation. Stochastic Processes and their Applications, 121(4), 813–844.

Salisu, A. A., Ndako, U. B., & Vo, X. V. (2023). Oil price and the Bitcoin market. Resources Policy, 82, 103437.

Sharif, A., Brahim, M., Dogan, E., & Tzeremes, P. (2023). Analysis of the spillover effects between green economy, clean and dirty cryptocurrencies. Energy Economics, 106594.

Shah, A. A., & Dar, A. B. (2021). Exploring diversification opportunities across commodities and financial markets: Evidence from time-frequency based spillovers. Resources Policy, 74, 102317.

Torrence, C. & Compo, G. (1998). A Practical Guide to Wavelet Analysis. Bulletin of the American Meteorological Society, 79, 61–78.

Torrence, C. & Webster P. J. (1998). The Annual Cycle of Persistence in the El Niño–Southern Oscillation. Quarterly Journal of the Royal Meteorological Society, 124, 1985–2004.

Wen, Y. (2005). Understanding the Inventory Cycle. Journal of Monetary Economics, 52(8), 1533-1555.

Yin, L., Nie, J., & Han, L. (2021). Understanding cryptocurrency volatility: The role of oil market shocks. International Review of Economics & Finance, 72, 233-253.