Quantifying the asymmetric information flow between Bitcoin prices and electricity consumption

被引：12

作者：

Maiti, Moinak

Vukovic, Darko B. ^{[1
,2
]}

Frommel, Michael ^{[3
]}

机构：

[1] St Petersburg State Univ, Grad Sch Management, Volkhovskiy Pereulok 3, St Petersburg 199004, Russia

[2] Geog Inst Jovan Cvij SASA, Djure Jaks 9, Belgrade 11000, Serbia

[3] Univ Ghent, Dept Econ, Sint Pietersplein 5, B-9000 Ghent, Belgium

来源：

FINANCE RESEARCH LETTERS | 2023年 / 57卷

关键词：

Bitcoin; Electricity consumption; Shannon; Re & PRIME; nyi; Transfer entropy; Energy mix; TIME-SERIES;

D O I：

10.1016/j.frl.2023.104163

中图分类号：

F8 [财政、金融];

学科分类号：

0202 ;

摘要：

The present study uses transfer entropy and effective transfer entropy to quantify the asymmetric information flow between monthly Bitcoin prices (Price) and total Bitcoin electricity consumption (Electricity). Both the Shannon and Re & PRIME;nyi estimates confirm a statistically significant information flow from Price to Electricity. However, Shannon and Re & PRIME;nyi methods yield mixed results in quantifying the information flow for Electricity to Price. That indicates a possible non homoge-neous and chaotic impact of total Bitcoin electricity consumption on Bitcoin prices. However, the Re & PRIME;nyi transfer entropy value converges with Shannon's as the value of q approaches to 1. The study findings are highly useful for managing the energy mix and carbon emissions associated with Bitcoin & other cryptocurrency mining.

引用

页数：5

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