Abstract
This paper investigates the relationship between the bitcoin price and the hashrate by disentangling the effects of the energy efficiency of the bitcoin mining equipment, bitcoin halving, and of structural breaks on the price dynamics. For this purpose, we propose a methodology based on exponential smoothing to model the dynamics of the Bitcoin network energy efficiency. We consider either directly the hashrate or the bitcoin cost-of-production model (CPM) as a proxy for the hashrate, to take any nonlinearity into account. In the first examined subsample (01/08/2016–04/12/2017), the hashrate and the CPMs were never significant, while a significant cointegration relationship was found in the second subsample (11/12/2017–24/02/2020). The empirical evidence shows that it is better to consider the hashrate directly rather than its proxy represented by the CPM when modeling its relationship with the bitcoin price. Moreover, the causality is always unidirectional going from the bitcoin price to the hashrate (or its proxies), with lags ranging from one week up to six weeks later. These findings are consistent with a large literature in energy economics, which showed that oil and gas returns affect the purchase of the drilling rigs with a delay of up to three months, whereas the impact of changes in the rig count on oil and gas returns is limited or not significant.
Highlights
There is a growing interest in bitcoin price dynamics both among the general public and in academia, see Burniske and Tatar (2018); Brummer (2019); Fantazzini (2019); Schar and Berentsen (2020)
The lagged effects of bitcoin prices on the hashrate were generally longer than the same effects on the CPMs, and these longer lags are more realistic given that it takes time to update the mining equipment. This initial bivariate evidence seems to highlight that it is better to consider the hashrate directly rather than its proxy represented by the bitcoin cost-of-production model when modeling its relationship with the bitcoin price
No particular difference was found when using the CPM1 or the CPM2. This multivariate evidence confirms the previous bivariate analysis, showing that it is better to consider directly the hashrate rather than its proxy represented by the bitcoin cost-of-production model when modeling its relationship with the bitcoin price
Summary
There is a growing interest in bitcoin price dynamics both among the general public and in academia, see Burniske and Tatar (2018); Brummer (2019); Fantazzini (2019); Schar and Berentsen (2020). Some works in the financial literature went further and theorized that the movements of the hashrate are useful in predicting the bitcoin price (Hayes 2017; Hayes 2019; Aoyagi and Hattori 2019) At first glance, such a notion might seem wrong because producers are price-takers in competitive markets, and the amount of effort they put into the production of a good or service have no impact over the market price. If we focus only on the influence of the hashrate on the bitcoin price dynamics, we can resort either to econometric models or to a general equilibrium model that omits the inner workings of miners’ decision-making but directly models the relationship between the hashrate and the price Such an approach was first proposed by Hayes (2017), who put forward a methodology able to predict the bitcoin price using the total hashrate and the miners’ energy efficiency as inputs. Several works explained the dynamics of the bitcoin price using econometric models and various sets of explanatory variables, and they mostly found that the hashrate is not statistically significant and it does not help in predicting the bitcoin price, see Kjærland et al (2018) and references therein
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