No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Exploring the relationship between Bitcoin price and network’s hashrate within endogenous system
Abstract
Bitcoin pricing mechanism is a complex system of interactions between factors that are not standard for traditional financial assets. Its understanding is essential for assessing specific topics, most prominently the interaction between Bitcoin price and network’s hashrate as it directly translates into its power demand and consumption and thus also environmental implications. We examine an intertwined system of equations, controlling for various statistical caveats connected to such system, providing a coherent picture of the system dynamics and thus delivering the most rigorous and complex approach in explaining the pricing dynamics of the Bitcoin system up to date. We shown that the whole system is very well structured and delivers economically and logically sound results, pointing at the network security narrative in the Bitcoin price-hashrate nexus.
... Such limitations do not spare conventional financial markets, but much lower data availability makes this less evident. For cryptoassets, specifically Bitcoin, Kubal and Kristoufek (2022) and Kristoufek (2023) provided an outline of how to treat endogenous crypto-systems, although multi-assets treatment poses additional challenges. However, these are possible issues for interpreting the economics behind it, but not for forecasting and portfolio studies that do not consider the classification. ...
The driving forces behind cryptoassets’ price dynamics are often perceived as being dominated by speculative factors and inherent bubble-bust episodes. Fundamental components are believed to have a weak, if any, role in the price-formation process. This study examines five cryptoassets with different backgrounds, namely Bitcoin, Ethereum, Litecoin, XRP, and Dogecoin between 2016 and 2022. It utilizes the cusp catastrophe model to connect the fundamental and speculative drivers with possible price bifurcation characteristics of market collapse events. The findings show that the price and return dynamics of all the studied assets, except for Dogecoin, emerge from complex interactions between fundamental and speculative components, including episodes of price bifurcations. Bitcoin shows the strongest fundamentals, with on-chain activity and economic factors driving the fundamental part of the dynamics. Investor attention and off-chain activity drive the speculative component for all studied assets. Among the fundamental drivers, the analyzed cryptoassets present their coin-specific factors, which can be tracked to their protocol specifics and are economically sound.
... Such limitations do not spare conventional financial markets, but much lower data availability makes this less evident. For cryptoassets, specifically Bitcoin, Kubal and Kristoufek (2022) and Kristoufek (2023) provided an outline of how to treat endogenous crypto-systems, although multi-assets treatment poses additional challenges. However, these are possible issues for interpreting the economics behind it, but not for forecasting and portfolio studies that do not consider the classification. ...
The driving forces behind cryptoassets' price dynamics are often perceived as being dominated by speculative factors and inherent bubble-bust episodes. Fundamental components are believed to have a weak, if any, role in the price-formation process. This study examines five cryptoassets with different backgrounds, namely Bitcoin, Ethereum, Litecoin, XRP, and Dogecoin between 2016 and 2022. It utilizes the cusp catastrophe model to connect the fundamental and speculative drivers with possible price bifurcation characteristics of market collapse events. The findings show that the price and return dynamics of all the studied assets, except for Dogecoin, emerge from complex interactions between fundamental and speculative components, including episodes of price bifurcations. Bitcoin shows the strongest fundamentals, with on-chain activity and economic factors driving the fundamental part of the dynamics. Investor attention and off-chain activity drive the speculative component for all studied assets. Among the fundamental drivers, the analyzed cryptoassets present their coin-specific factors, which can be tracked to their protocol specifics and are economically sound.
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.
We develop a strong diagnostic for bubbles and crashes in Bitcoin, by analysing the coincidence (and its absence) of fundamental and technical indicators. Using a generalized Metcalfe's Law based on network properties, a fundamental value is quantified and shown to be heavily exceeded, on at least four occasions, by bubbles that grow and burst. In these bubbles, we detect a universal super-exponential unsustainable growth. We model this universal pattern with the Log-Periodic Power Law Singularity (LPPLS) model, which parsimoniously captures diverse positive feedback phenomena, such as herding and imitation. The LPPLS model is shown to provide an ex ante warning of market instabilities, quantifying a high crash hazard and probabilistic bracket of the crash time consistent with the actual corrections; although, as always, the precise time and trigger (which straw breaks the camel's back) is exogenous and unpredictable. Looking forward, our analysis identifies a substantial but not unprecedented overvaluation in the price of Bitcoin, suggesting many months of volatile sideways Bitcoin prices ahead (from the time of writing, March 2018).
This paper aims to enhance the understanding of which factors affect the price development of Bitcoin in order for investors to make sound investment decisions. Previous literature has covered only a small extent of the highly volatile period during the last months of 2017 and the beginning of 2018. To examine the potential price drivers, we use the Autoregressive Distributed Lag and Generalized Autoregressive Conditional Heteroscedasticity approach. Our study identifies the technological factor Hashrate as irrelevant for modeling Bitcoin price dynamics. This irrelevance is due to the underlying code that makes the supply of Bitcoins deterministic, and it stands in contrast to previous literature that has included Hashrate as a crucial independent variable. Moreover, the empirical findings indicate that the price of Bitcoin is affected by returns on the S&P 500 and Google searches, showing consistency with results from previous literature. In contrast to previous literature, we find the CBOE volatility index (VIX), oil, gold, and Bitcoin transaction volume to be insignificant.
This study back-tests a marginal cost of production model proposed to value the digital currency Bitcoin. Results from both conventional regression and vector autoregression (VAR) models show that the marginal cost of production plays an important role in explaining Bitcoin prices, challenging recent allegations that Bitcoins are essentially worthless. Even with markets pricing Bitcoin in the thousands of dollars each, the valuation model seems robust. The data show that a price bubble that began in the Fall of 2017 resolved itself in early 2018, converging with the marginal cost model. This suggests that while bubbles may appear in the Bitcoin market, prices will tend to this bound and not collapse to zero.
Bitcoin has emerged as a fascinating phenomenon of the financial markets.
Without any central authority issuing the currency, it has been associated with
controversy ever since its popularity and public interest reached high levels.
Here, we contribute to the discussion by examining potential drivers of Bitcoin
prices ranging from fundamental to speculative and technical sources as well as
a potential influence of the Chinese market. The evolution of the relationships
is examined in both time and frequency domains utilizing the continuous
wavelets framework so that we comment on development of the interconnections in
time but we can also distinguish between short-term and long-term connections.
Digital currencies have emerged as a new fascinating phenomenon in the financial markets. Recent events on the most popular of the digital currencies - BitCoin - have risen crucial questions about behavior of its exchange rates and they offer a field to study dynamics of the market which consists practically only of speculative traders with no fundamentalists as there is no fundamental value to the currency. In the paper, we connect two phenomena of the latest years - digital currencies, namely BitCoin, and search queries on Google Trends and Wikipedia - and study their relationship. We show that not only are the search queries and the prices connected but there also exists a pronounced asymmetry between the effect of an increased interest in the currency while being above or below its trend value.
This paper proposes new tests for detecting the presence of a unit root in quite general time series models. Our approach
is nonparametric with respect to nuisance parameters and thereby allows for a very wide class of weakly dependent and possibly
heterogeneously distributed data. The tests accommodate models with a fitted drift and a time trend so that they may be used
to discriminate between unit root nonstationarity and stationarity about a deterministic trend. The limiting distributions
of the statistics are obtained under both the unit root null and a sequence of local alternatives. The latter noncentral distribution
theory yields local asymptotic power functions for the tests and facilitates comparisons with alternative procedures due to
Dickey & Fuller. Simulations are reported on the performance of the new tests in finite samples.
As the resource intensity of running Bitcoin has increased over recent years, it has become a serious concern for its potential impact on health and climate. Within this context, there exists a growing need for accurate information. Various organizations need this for multiple purposes like properly assessing the urgency of the problem, implementing the right policy response in the right locations and for setting up mitigation programs.
We propose a market dynamics approach to evaluate the current methods for obtaining information on Bitcoin's energy demand. This allows us to establish that, while historically the Bitcoin mining industry has been growing most of the time, this growth allows market participants to pursue strategies that don't necessarily involve the best devices, device settings, or locations. The bigger the profitability of mining, the more it allows market participants to make decisions that result in suboptimal power efficiency of the Bitcoin network. Specifically, while the profitability of mining peaked during 2019, we find that market participants primarily used older generations of devices with better availability and lower acquisition costs. Common estimation approaches don't only fail to capture this behavior, but also fail to properly capture the market circumstances, like seasonal and geographic variation in electricity prices, that help enable participants to do so in the first place. This combination leaves common approaches prone to providing optimistic estimates during growth cycles. We conservatively estimate the Bitcoin network to consume 87.1 TWh of electrical energy annually per September 30, 2019 (equaling a country like Belgium).
Bitcoin as a major cryptocurrency has come up as a shooting star of the 2017 and 2018 headlines. After exploding its price twenty times just in the twelve months of 2017, the tone has changed dramatically in 2018 after major price corrections and increasing concerns about its mining power consumption and overall sustainability. The dynamics and interaction between Bitcoin price and its mining costs have become of major interest. Here we show that these two quantities are tightly interconnected and they tend to a common long-term equilibrium. Mining costs adjust to the cryptocurrency price with the adjustment time of several months up to a year. Current developments suggest that we have arrived at a new era of Bitcoin mining where marginal (electricity) costs and mining efficiency play the prime role. Presented results open new avenues towards interpreting past and predicting future developments of the Bitcoin mining framework and their main possible directions are outlined and discussed here as well.
Participation in the Bitcoin blockchain validation process requires specialized hardware and vast amounts of electricity, which translates into a significant carbon footprint. Here, we demonstrate a methodology for estimating the power consumption associated with Bitcoin’s blockchain based on IPO filings of major hardware manufacturers, insights on mining facility operations, and mining pool
compositions. We then translate our power consumption estimate into carbon emissions, using the localization of IP addresses. We determine the annual electricity consumption of Bitcoin, as of November 2018, to be 45.8 TWh and estimate that annual carbon emissions range from 22.0 to 22.9 MtCO2. This means that the emissions produced by Bitcoin sit between the levels produced by the
nations of Jordan and Sri Lanka, which is comparable to the level of Kansas City. With this article, we aim to gauge the external costs of Bitcoin and inform the broader debate on the costs and benefits of cryptocurrencies.
Dynamics of the Bitcoin price as the most prominent cryptocurrency is studied in the light of two fundamental economic laws — the Law of One Price and the Equation of Exchange. Utilizing the unprecedented data availability of Bitcoin statistics, we are able to construct a theoretical exchange rate base of the cryptocurrency with respect to the US dollar. Through the concept of statistical equilibrium verified by the cointegration relationship, we show several interesting points. First, the Bitcoin price dynamics is very well captured by the underlying transaction data which leads to a strong fundamental basis of the cryptocurrency. Second, several historical bubbles hit the confidence intervals around the fundamental price but then start their correction back to the equilibrium. Third, the fundamental price implied by the transaction data is not ever-increasing but reflects the ups and downs of Bitcoin utility in transactions. And fourth, our theory-implied fundamental price suggests that the current (December 2018) Bitcoin price is very close to its fundamental price estimated at around $3,500.
We study which variables can explain and predict the return, volatility and trading volume of Bitcoin. The considered variables are return, volatility, trading volume, transaction volume, change in the number of unique Bitcoin addresses, the VIX index and Google searches for “Bitcoin”. We use realized volatility calculated from high-frequency data and find that the heterogeneous autoregressive model is suitable for Bitcoin volatility. Trading volume further improves this volatility model. The trading volume of Bitcoin can be predicted from Google searches for “Bitcoin”. However, none of the considered variables can predict Bitcoin returns.
This paper aims to identify the likely determinants for cryptocurrency value formation, including for that of bitcoin. Due to Bitcoin’s growing popular appeal and merchant acceptance, it has become increasingly important to try to understand the factors that influence its value formation. Presently, the value of all Bitcoins in existence represent approximately $7 billion, and more than $60 million of notional value changes hands each day. Having grown rapidly over the past few years, there is now a developing but vibrant marketplace for bitcoin, and a recognition of digital currencies as an emerging asset class. Not only is there a listed and over-the-counter market for bitcoin and other digital currencies, but also an emergent derivatives market. As such, the ability to value bitcoin and related cryptocurrencies is becoming critical to its establishment as a legitimate financial asset.
Bitcoin is a "crypto currency", a decentralized electronic payment scheme based on cryptography. It implements a particular type of peer-to-peer payment system. Bitcoin depends on well-known cryptographic standards such as SHA-256. In this paper we revisit the cryptographic process which allows one to make money by producing new bitcoins. We reformulate this problem as a specific sort of Constrained Input Small Output (CISO) hashing problem and reduce the problem to a pure block cipher problem, cf. Fig. 1. We estimate the speed of this process and we show that the amortized cost of this process is less than it seems and it depends on a certain cryptographic constant which is estimated to be at most 1.89. These optimizations enable bitcoin miners to save countless millions of dollars per year in electricity bills.
IN SOME APPLICATIONS of the general linear model, the usual assumptions of homoscedastic disturbances and fixed coefficients may be questioned. When these requirements are not met, the loss in efficiency in using ordinary least squares (OLS) may be substantial and, more importantly, the biases in estimated standard errors may lead to invalid inferences. This has caused a number of writers to propose models which relax these conditions and to devise estimators for their more general specifications, e.g., Goldfeld and Quandt (8) for heteroscedasticity and Hildreth and Houck (11) for random coefficients. However, because the effect of introducing random coefficient variation is to give the dependent variable a different variance at each observation, models with this feature can be considered as particular heteroscedastic formulations for the purpose of detecting departure from the standard linear model. A test for heteroscedasticity with the same asymptotic properties as the likelihood ratio test in standard situations, but which can be computed by two least squares regressions, thereby avoiding the iterative calculations necessary to obtain maximum likelihood estimates of the parameters in the full model, is considered in this paper. The approach is based on the Lagrangian multiplier
This is the first article that studies BitCoin price formation by considering both the traditional determinants of currency price, e.g., market forces of supply and demand, and digital currencies specific factors, e.g., BitCoin attractiveness for investors and users. The conceptual framework is based on the Barro (1979) model, from which we derive testable hypotheses. Using daily data for five years (2009–2015) and applying time-series analytical mechanisms, we find that market forces and BitCoin attractiveness for investors and users have a significant impact on BitCoin price but with variation over time. Our estimates do not support previous findings that macro-financial developments are driving BitCoin price in the long run.
which the relationships are not exact, so that a set of ideal economic variables is assumed to be generated by a set of dynamic stochastic relationships, as in Koopmans [12], and the actual economic time series are assumed to differ from the ideal economic variables because of random disturbances or measurement errors. The asymptotic error variance matrix for the coefficients of one of the relationships is obtained in the case in which these relationships are estimated using instrumental variables. With this variance matrix we are able to discuss the problem of choice that arises when there are more instrumental variables available than the minimum number required to enable the method to be used. A method of estimation is derived which involves a characteristic equation already considered by Hotelling in defining the canonical correlation [10]. This method was previously suggested by Durbin [7]. The same estimates would be obtained by the maximum-likelihood limited
We propose a test of the null hypothesis that an observable series is stationary around a deterministic trend. The series is expressed as the sum of deterministic trend, random walk, and stationary error, and the test is the LM test of the hypothesis that the random walk has zero variance. The asymptotic distribution of the statistic is derived under the null and under the alternative that the series is difference-stationary. Finite sample size and power are considered in a Monte Carlo experiment. The test is applied to the Nelson-Plosser data, and for many of these series the hypothesis of trend stationarity cannot be rejected.
Improved estimators of security price volatility are formulated. These estimators employ data of the type commonly found in the financial pages of a newspaper, namely the high, low, opening and closing prices, and the transaction volume. The new estimators have much higher relative efficiency than the standard estimators.
The paper considers a number of problems arising from the test of serial correlation based on the d statistic proposed earlier by the authors (Durbin & Watson, 1950, 1951). Methods of computing the exact distribution of d are investigated and the exact distribution is compared with six approximations to it for four sets of published data. It is found that approximations suggested by Theil and Nagar and by Hannan are too inaccurate for practical use but that the beta approximation proposed in the 1950 and 1951 papers and a new approximation, called by us the a + bdU approximation and based, like the beta approximation, on the exact first two moments of d, both perform well. The power of the d test is compared with that of certain exact tests proposed by Theil, Durbin, Koerts and Abrahamse from the standpoint of invariance theory. It is shown that the d test is locally most powerful invariant but that the other tests are not. There are three appendices. The first gives an account of the exact distribution of d. The second derives the mean and variance to a second order of approximation of a modified maximum likelihood statistic closely related to d. The third sets out details of the computations required for the a + bdU approximation.
Weak instruments arise when the instruments in linear instrumental variables (IV) regression are weakly correlated with the included endogenous variables. In generalized method of moments (GMM), more generally, weak instruments correspond to weak identification of some or all of the unknown parameters. Weak identification leads to GMM statistics with nonnormal distributions, even in large samples, so that conventional IV or GMM inferences are misleading. Fortunately, various procedures are now available for detecting and handling weak instruments in the linear IV model and, to a lesser degree, in nonlinear GMM.
This paper studies estimators that make sample analogues of population orthogonality conditions close to zero. Strong consistency and asymptotic normality of such estimators is established under the assumption that the observable variables are stationary and ergodic. Since many linear and nonlinear econometric estimators reside within the class of estimators studied in this paper, a convenient summary of the large sample properties of these estimators, including some whose large sample properties have not heretofore been discussed, is provided.
This paper investigates the impact of international migration on technical efficiency, resource allocation and income from agricultural production of family farming in Albania. The results suggest that migration is used by rural households as a pathway out of agriculture: migration is negatively associated with both labour and non-labour input allocation in agriculture, while no significant differences can be detected in terms of farm technical efficiency or agricultural income. Whether the rapid demographic changes in rural areas triggered by massive migration, possibly combined with propitious land and rural development policies, will ultimately produce the conditions for a more viable, high-return agriculture attracting larger investments remains to be seen.
This paper estimates the risk preferences of cotton farmers in Southern Peru, using the results from a multiple-price-list lottery game. Assuming that preferences conform to two of the leading models of decision under risk--Expected Utility Theory (EUT) and Cumulative Prospect Theory (CPT)--we find strong evidence of moderate risk aversion. Once we include individual characteristics in the estimation of risk parameters, we observe that farmers use subjective nonlinear probability weighting, a behavior consistent with CPT. Interestingly, when we allow for preference heterogeneity via the estimation of mixture models--where the proportion of subjects who behave according to EUT or to CPT is endogenously determined--we find that the majority of farmers' choices are best explained by CPT. We further hypothesize that the multiple switching behavior observed in our sample can be explained by nonlinear probability weighting made in a context of large random calculation mistakes; the evidence found on this regard is mixed. Finally, we find that attaining higher education is the single most important individual characteristic correlated with risk preferences, a result that suggests a connection between cognitive abilities and behavior towards risk.
The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy
- Garcia
Bitcoin and ethereum mining hardware
- Zade