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A network analysis of electricity demand and the cryptocurrency markets

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  • David I. Okorie

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.

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  • David I. Okorie, 2021. "A network analysis of electricity demand and the cryptocurrency markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 3093-3108, April.
    • Handle: RePEc:wly:ijfiec:v:26:y:2021:i:2:p:3093-3108
    DOI: 10.1002/ijfe.1952
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    as
    1. Santos Silva, J. M. C. & Cardoso, F. N., 2001. "The Chow-Lin method using dynamic models," Economic Modelling, Elsevier, vol. 18(2), pages 269-280, April.
    2. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(1), pages 122-150, February.
    3. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    4. Mert Demirer & Francis X. Diebold & Laura Liu & Kamil Yilmaz, 2018. "Estimating global bank network connectedness," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(1), pages 1-15, January.
    5. Pérez-García, Julián & Moral-Carcedo, Julián, 2016. "Analysis and long term forecasting of electricity demand trough a decomposition model: A case study for Spain," Energy, Elsevier, vol. 97(C), pages 127-143.
    6. Angelopoulos, Dimitrios & Siskos, Yannis & Psarras, John, 2019. "Disaggregating time series on multiple criteria for robust forecasting: The case of long-term electricity demand in Greece," European Journal of Operational Research, Elsevier, vol. 275(1), pages 252-265.
    7. He, Feng & Wang, Ziwei & Yin, Libo, 2020. "Asymmetric volatility spillovers between international economic policy uncertainty and the U.S. stock market," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    8. Csereklyei, Zsuzsanna, 2020. "Price and income elasticities of residential and industrial electricity demand in the European Union," Energy Policy, Elsevier, vol. 137(C).
    9. Koutmos, Dimitrios, 2018. "Return and volatility spillovers among cryptocurrencies," Economics Letters, Elsevier, vol. 173(C), pages 122-127.
    10. Jelica, D. & Taljegard, M. & Thorson, L. & Johnsson, F., 2018. "Hourly electricity demand from an electric road system – A Swedish case study," Applied Energy, Elsevier, vol. 228(C), pages 141-148.
    11. Paul J. Burke and Ashani Abayasekara, 2018. "The Price Elasticity of Electricity Demand in the United States: A Three-Dimensional Analysis," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    12. Zhiguang (Gerald) Wang, 2009. "Volatility Risk," Issue Briefs 2009513, South Dakota State University, Department of Economics.
    13. Fasanya, Ismail & Akinbowale, Seun, 2019. "Modelling the return and volatility spillovers of crude oil and food prices in Nigeria," Energy, Elsevier, vol. 169(C), pages 186-205.
    14. John Cotter & Mark Hallam & Kamil Yilmaz, 2017. "Mixed-frequency macro-financial spillovers," Working Papers 201704, Geary Institute, University College Dublin.
    15. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    16. Koop, Gary & Pesaran, M. Hashem & Potter, Simon M., 1996. "Impulse response analysis in nonlinear multivariate models," Journal of Econometrics, Elsevier, vol. 74(1), pages 119-147, September.
    17. Dahl, Roy Endré & Oglend, Atle & Yahya, Muhammad, 2020. "Dynamics of volatility spillover in commodity markets: Linking crude oil to agriculture," Journal of Commodity Markets, Elsevier, vol. 20(C).
    18. Francis X. Diebold & Kamil Yilmaz, 2009. "Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets," Economic Journal, Royal Economic Society, vol. 119(534), pages 158-171, January.
    19. Guang, Fengtao & He, Yongxiu & Wen, Le, 2019. "Impacts of hybrid time-varying tariffs on residential electricity demand: The case of Zhejiang Province," Utilities Policy, Elsevier, vol. 61(C).
    20. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    21. Kang, Sang Hoon & Lee, Jang Woo, 2019. "The network connectedness of volatility spillovers across global futures markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    22. Adeoye, Omotola & Spataru, Catalina, 2019. "Modelling and forecasting hourly electricity demand in West African countries," Applied Energy, Elsevier, vol. 242(C), pages 311-333.
    23. Chow, Gregory C & Lin, An-loh, 1971. "Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series," The Review of Economics and Statistics, MIT Press, vol. 53(4), pages 372-375, November.
    24. Bostanci, Gorkem & Yilmaz, Kamil, 2020. "How connected is the global sovereign credit risk network?," Journal of Banking & Finance, Elsevier, vol. 113(C).
    25. Fan, Jing-Li & Hu, Jia-Wei & Zhang, Xian, 2019. "Impacts of climate change on electricity demand in China: An empirical estimation based on panel data," Energy, Elsevier, vol. 170(C), pages 880-888.
    26. Nick MacMackin, & Miller, Lindsay & Carriveau, Rupp, 2019. "Modeling and disaggregating hourly effects of weather on sectoral electricity demand," Energy, Elsevier, vol. 188(C).
    27. Adam Hayes, 2015. "A Cost of Production Model for Bitcoin," Working Papers 1505, New School for Social Research, Department of Economics.
    28. Obringer, R. & Kumar, R. & Nateghi, R., 2019. "Analyzing the climate sensitivity of the coupled water-electricity demand nexus in the Midwestern United States," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    29. Iliopoulos, Nikolaos & Esteban, Miguel & Kudo, Shogo, 2020. "Assessing the willingness of residential electricity consumers to adopt demand side management and distributed energy resources: A case study on the Japanese market," Energy Policy, Elsevier, vol. 137(C).
    30. Garman, Mark B & Klass, Michael J, 1980. "On the Estimation of Security Price Volatilities from Historical Data," The Journal of Business, University of Chicago Press, vol. 53(1), pages 67-78, January.
    31. Diebold, Francis X. & Yilmaz, Kamil, 2015. "Financial and Macroeconomic Connectedness: A Network Approach to Measurement and Monitoring," OUP Catalogue, Oxford University Press, number 9780199338306, Decembrie.
    32. Das, Debojyoti & Dutta, Anupam, 2020. "Bitcoin’s energy consumption: Is it the Achilles heel to miner’s revenue?," Economics Letters, Elsevier, vol. 186(C).
    33. David Iheke Okorie & Dak-Adzaklo Cephas Simon-Peter, 2017. "Risks, Bad News, and Good News of Exchange Rate Volatility and National Elections in Nigeria: (Empirical Evidence via Threshold GARCH and GARCH-inmean Methodologies)," International Journal of Academic Research in Business and Social Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Business and Social Sciences, vol. 7(3), pages 298-309, March.
    34. Zhang, Weiping & Zhuang, Xintian & Lu, Yang & Wang, Jian, 2020. "Spatial linkage of volatility spillovers and its explanation across G20 stock markets: A network framework," International Review of Financial Analysis, Elsevier, vol. 71(C).
    35. Cialani, Catia & Mortazavi, Reza, 2018. "Household and industrial electricity demand in Europe," Energy Policy, Elsevier, vol. 122(C), pages 592-600.
    36. Sax, Christoph & Steiner, Peter, 2013. "Temporal Disaggregation of Time Series," MPRA Paper 53389, University Library of Munich, Germany.
    37. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    38. Burke, Paul J. & Csereklyei, Zsuzsanna, 2016. "Understanding the energy-GDP elasticity: A sectoral approach," Energy Economics, Elsevier, vol. 58(C), pages 199-210.
    39. Roberts, Mike B. & Haghdadi, Navid & Bruce, Anna & MacGill, Iain, 2019. "Characterisation of Australian apartment electricity demand and its implications for low-carbon cities," Energy, Elsevier, vol. 180(C), pages 242-257.
    40. Zeng, Sheng & Liu, Xinchun & Li, Xiafei & Wei, Qi & Shang, Yue, 2019. "Information dominance among hedging assets: Evidence from return and volatility directional spillovers in time and frequency domains," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    41. Okorie, David Iheke & Lin, Boqiang, 2021. "Stock markets and the COVID-19 fractal contagion effects," Finance Research Letters, Elsevier, vol. 38(C).
    42. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    43. Miller, Mark & Alberini, Anna, 2016. "Sensitivity of price elasticity of demand to aggregation, unobserved heterogeneity, price trends, and price endogeneity: Evidence from U.S. Data," Energy Policy, Elsevier, vol. 97(C), pages 235-249.
    44. Okorie, David Iheke & Lin, Boqiang, 2020. "Crude oil price and cryptocurrencies: Evidence of volatility connectedness and hedging strategy," Energy Economics, Elsevier, vol. 87(C).
    45. BenSaïda, Ahmed, 2019. "Good and bad volatility spillovers: An asymmetric connectedness," Journal of Financial Markets, Elsevier, vol. 43(C), pages 78-95.
    46. Campbell, Alrick, 2018. "Price and income elasticities of electricity demand: Evidence from Jamaica," Energy Economics, Elsevier, vol. 69(C), pages 19-32.
    47. Alberini, Anna & Prettico, Giuseppe & Shen, Chang & Torriti, Jacopo, 2019. "Hot weather and residential hourly electricity demand in Italy," Energy, Elsevier, vol. 177(C), pages 44-56.
    48. David Iheke Okorie, 2020. "Could stock hedge Bitcoin risk(s) and vice versa?," Digital Finance, Springer, vol. 2(1), pages 117-136, September.
    49. An, Pengli & Li, Huajiao & Zhou, Jinsheng & Li, Yang & Sun, Bowen & Guo, Sui & Qi, Yajie, 2020. "Volatility spillover of energy stocks in different periods and clusters based on structural break recognition and network method," Energy, Elsevier, vol. 191(C).
    50. Ji, Qiang & Bouri, Elie & Lau, Chi Keung Marco & Roubaud, David, 2019. "Dynamic connectedness and integration in cryptocurrency markets," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 257-272.
    51. N. Melisa Bilgin & Kamil Yilmaz, 2018. "Producer Price Inflation Connectedness and Input-Output Networks," Koç University-TUSIAD Economic Research Forum Working Papers 1813, Koc University-TUSIAD Economic Research Forum.
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