Due to data limitations on bitcoin-related emissions, assessing the environmental impacts of bitcoin appear difficult. This data in brief article presents constructed daily frequency dataset on ...bitcoin annualised carbon footprint spanning July 7, 2010 to December 4, 2021 with 4,158 observations. The 12 data variables capture floor, ceiling, and optimal annualised carbon footprint from coal, oil, gas, and the average from the 3 sources. The constructed bitcoin carbon footprint data are measured in kgCO2 using emission factors for electricity generation from IEA World Energy Outlook. The data will benefit multidisciplinary research on cryptocurrency from environmental, energy, and economics disciplines.
•The paper proposes a new model that explains the dynamics of bitcoin prices, based on a correlation network VAR process that models the interconnections between different crypto and classic asset ...prices.•The main methodological contribution of the paper consists in the introduction of partial correlations and correlation networks into VAR models. This allows to describe the correlation patterns between bitcoin prices and to disentangle the autoregressive component of prices from its contemporaneous part, explained by the comovement with other market prices. The introduction of VAR correlation networks also allows to build a bitcoin price predictive model, that leverages the information contained in the correlation patterns.•The empirical findings of the paper show that bitcoin prices from different exchanges are highly interrelated, as in an efficiently integrated market, with prices from larger and/or more connected trading exchanges driving the others. Our results also confirm that bitcoin prices are typically unrelated with classical market prices, thus bringing further support to the “diversification benefit” property of crypto assets.•Another important advantage of the proposed model is that it can be used to predict bitoin prices. The empirical findings show that the proposed model is able to predict bitcoin prices with an error that can be approximated to about 11% of the average price. This error, however, varies considerably among different exchange markets: prices from central bitcoin exchange markets are easier to predict. For almost all markets, the inclusion of a contemporaneous component in the predictive model leads to a predictive accuracy higher than that obtained with a simpler pure autoregressive model.
We aim to understand the dynamics of cryptocurrency prices and, specifically, how price information is transmitted between different crypto market exchanges, and between crypto markets and traditional ones.
To this aim, we propose an extended Vector Autoregressive model, aimed at explaining the evolution of bitcoin prices. The extension is based on network models, which improve over pure autoregressive models, as they introduce a contemporaneous contagion component, that describes contagion effects between prices.
Our empirical findings show that the proposed model is able to well describe the correlation structure between bitcoin prices in different exchange markets, which appear rather strong, whereas the correlation of bitcoin prices with traditional assets is low. The model is also able to improve bitcoin price predictions, with respect to a simpler autoregressive model.
Bitcoin trading offers security, ownership verifications, easy transactions, and traceability in almost every sector and industry. The base of Bitcoin is blockchain technology, which uses a mining ...process to secure and validate Bitcoin transactions. With promising positive returns, Bitcoin mining demands large energy and material resources and contributes to environmental degradation. The study uses behavioral reasoning theory to understand intentions to practice Bitcoin mining. We present a relative view of personal, technological, psychological, and environmental factors that attract or repel miners to perform Bitcoin mining, where the institutional environment moderates the relationship between attitude and intention by analyzing the cross-sectional data from 308 Bitcoin miners. The study indicates that attracting factors positively and significantly explain miners' attitudes, which leads to positive intentions toward Bitcoin mining. The regulatory environment weakens the positive relationship between attitude and intentions. The study provides a comparative view of facilitating and inhibiting factors with significant implications for academicians and policymakers.
A vast digital ecosystem of entrepreneurship and exchange has sprung up with Bitcoin’s digital infrastructure at its core. We explore the worldwide spread of infrastructure necessary to maintain and ...grow Bitcoin as a system (Bitcoin nodes) and infrastructure enabling the use of bitcoins for everyday economic transactions (Bitcoin merchants). Specifically, we investigate the role of legal, criminal, financial, and social determinants of the adoption of Bitcoin infrastructure. We offer some support for the view that the adoption of cryptocurrency infrastructure is driven by perceived failings of traditional financial systems, in that the spread of Bitcoin infrastructure is associated with low trust in banks and the financial system among inhabitants of a region, and with the occurrence of country-level inflation crises. On the other hand, our findings also suggest that active support for Bitcoin is higher in locations with well-developed banking services. Finally, we find support for the view that bitcoin adoption is also partly driven by cryptocurrencies’ usefulness in engaging in illicit trade.
•Investigate the safe haven properties of Bitcoin during the Covid-19 bear market.•Bitcoin is not found to be a safe haven.•Bitcoin decreases in price in lockstep with the S&P 500 during the bear ...market.•A small allocation to Bitcoin substantially increases portfolio downside risk.•Results cast doubt on the ability of Bitcoin to shelter investors from market turbulence.
The Covid-19 bear market presents the first acute market losses since active trading of Bitcoin began. This market downturn provides a timely test of the frequently expounded safe haven properties of Bitcoin. In this paper, we show that Bitcoin does not act as a safe haven, instead decreasing in price in lockstep with the S&P 500 as the crisis develops. When held alongside the S&P 500, even a small allocation to Bitcoin substantially increases portfolio downside risk. Our empirical findings cast doubt on the ability of Bitcoin to provide shelter from turbulence in traditional markets.
In the past decade, Bitcoin as an emerging asset class has gained widespread public attention because of their extraordinary returns in phases of extreme price growth and their unpredictable massive ...crashes. We apply the log-periodic power law singularity (LPPLS) confidence indicator as a diagnostic tool for identifying bubbles using the daily data on Bitcoin price in the past two years. We find that the LPPLS confidence indicator based on the daily Bitcoin price data fails to provide effective warnings for detecting the bubbles when the Bitcoin price suffers from a large fluctuation in a short time, especially for positive bubbles. In order to diagnose the existence of bubbles and accurately predict the bubble crashes in the cryptocurrency market, this study proposes an adaptive multilevel time series detection methodology based on the LPPLS model and a finer (than daily) timescale for the Bitcoin price data. We adopt two levels of time series, 1 h and 30 min, to demonstrate the adaptive multilevel time series detection methodology. The results show that the LPPLS confidence indicator based on this new method is an outstanding instrument to effectively detect the bubbles and accurately forecast the bubble crashes, even if a bubble exists in a short time. In addition, we discover that the short-term LPPLS confidence indicator being highly sensitive to the extreme fluctuations of Bitcoin price can provide some useful insights into the bubble status on a shorter time scale – on a day to week scale, while the long-term LPPLS confidence indicator has a stable performance in terms of effectively monitoring the bubble status on a longer time scale – on a week to month scale. The adaptive multilevel time series detection methodology can provide real-time detection of bubbles and advanced forecast of crashes to warn of the imminent risk in not only the cryptocurrency market but also other financial markets.
•The short timescale crash number increases as Bitcoin long timescale bubble grows.•We propose an adaptive multilevel time series detection method to detect bubbles.•Our method provides effective real-time detection of bubbles and forecast of crashes.•Our method is applicable to not only Bitcoin but also general financial markets.
•Bitcoin reactions to COVID19•Bitcoin comovement with COVID19•Bitcoin as a safe haven•Wavelet coherence s of Bitcoin and COVID19
We apply wavelet methods to daily data of COVID-19 world deaths and ...daily Bitcoin prices from 31th December 2019 to 29th April 2020. We find, especially for the period post April 5, that levels of COVID-19 caused a rise in Bitcoin prices. We contribute to the fast-growing body of work on the financial impacts of COVID-19, as well as to ongoing consideration of whether Bitcoin is a safe haven investment. Our results should be of great interest to both scholars and policy makers, as well as investment professionals interested in the financial implications of both COVID-19 and cryptocurrencies.
The novel Coronavirus disease (COVID-19) has quickly evolved from a provincial health scare to a global meltdown. While it has brought nearly half the world to a standstill it has affected the ...financial markets in unseen ways by eroding a quarter of wealth in nearly a month. This paper investigates the reaction of financial markets globally in terms of their decline and volatility as Coronavirus epicentre moved from China to Europe and then to the US. Findings suggest that the earlier epicentre China has stabilized while the global markets have gone into a freefall especially in the later phase of the spread. Even the relatively safer commodities have suffered as the pandemic moves into the US.
Our study investigates the hedging ability of Gold and Bitcoin to hedge against financial market crashes. We also examined the ability of the VIX fear gouge to improve the ability of those financial ...assets to hedge financial risks. We found a positive dependency between the current daily prices of Gold and Bitcoin with a stronger impact of Gold on Bitcoin than vice versa. We also find that in recent years (2021-2023), Gold price changes are negatively correlated to yesterday's price change of the S&P500 a day before and positively correlated to yesterday's NASDAQ price change.
This paper examines the correlation between three prospective investing options: the Bitcoin cryptocurrency price, gold, and the Dow Jones stock index. The main research question is whether there is ...a causal effect of gold and the DWJ on Bitcoin and how this effect varies on time. The study begins with a background analysis that explains the definitions and operation of cryptocurrencies, followed by a brief overview of gold and its derivatives. In addition, a historical review of stock markets is provided, with a focus on the Dow Jones index. Then, a literature review follows. Daily data from three separate periods are used, each spanning four years. The first period, running from October 2014 to September 2018, provides an overview of the introduction of official cryptocurrency price data. The second period, running from Oct 2018 to Sept 2022, captures more recent trends preceding COVID-19. The third period, from January 2020 to December 2023, is the whole COVID-19 period with the initiation, embedded, and terminal phases. Classical inductive statistical methods (descriptive, correlations, multiple linear regression) as well as time series analysis methods (autocorrelation, cross-correlation, Granger causality tests, and ARIMA modeling) are used to analyze the data. Rigorous testing for autocorrelation, multicollinearity, and homoskedasticity is performed on the estimated models. The results show a correlation of Bitcoin with gold and the DWJ. This correlation varies over time, as in the first period the correlation mainly concerns the DWJ and in the second it mainly concerns gold. By using ARIMA models, it was possible to make a forecast in a time horizon of a few days. In addition, the structure of the forecasting mechanism of gold and DWJ on Bitcoin seems to have changed during the COVID-19 crisis. The findings suggest that future research should encompass a broader dataset, facilitating comprehensive comparisons and enhancing the reliability of the conclusions drawn.
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