IEDA Dept Seminar - Bitcoin Mining and Electricity Consumption

11:00am - 12:00pm
Room 5562 (lift 27-28)

We propose a dynamic industry equilibrium model for Bitcoin electricity consumption in a general framework, including Bitcoin miners’ optimal entry and exit with technology innovation. Using average operating costs to approximate the true operating costs, we overcome the difficulty of strong path dependency incurred by the interaction among entry, exit, and technology innovation. The model can capture the upside and downside co-movements of miners’ computing power, electricity consumption, and mining revenue. Our model predicts that the Bitcoin electricity consumption will not grow indefinitely and the ratio of Bitcoin electricity consumption to the miners’ revenue fluctuates within a certain range.

 

Joint Work with Steven Kou, Shuaijie Qian, Ling Qin

 

Keywords: Industry Equilibrium, Bitcoin Mining, Electricity Consumption.

講者/ 表演者:
Prof. Min DAI
Chair Professor of Applied Statistics and Financial Mathematics, The Hong Kong Polytechnic University

Min Dai is Chair Professor in Applied Statistics and Financial Mathematics, Department of Applied Mathematics, The Hong Kong Polytechnic University (HKPolyU). Prior to joining HKPolyU in 2021, he taught at National University of Singapore and Peking University after receiving his PhD degree from Fudan University in 2000. His research focuses on financial derivative pricing, portfolio selection with market imperfections, corporate finance, and financial technology. He published in peer-reviewed journals of different disciplines, such as Journal of Econometrics, Journal of Economic Theory, Management Science, Mathematical Finance, Review of Financial Studies, and SIAM Journals.  Currently he is a Co-editor of Digital Finance and serves in editorial boards of many academic journals, including Finance and Stochastics, Journal of Economic Dynamics and Control, SIAM Journal on Financial Mathematics, and Mathematics and Financial Economics.

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Department of Industrial Engineering & Decision Analytics
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