Oonagh McDonald examines the challenges, opportunities and threats that cryptocurrencies pose to cash and to existing fiat currencies and their potential to change how global finance operates.
There are different studies that point out that the price of electricity is a fundamental factor that will influence the mining decision, due to the cost it represents. There is also an ongoing ...debate about the pollution generated by cryptocurrency mining, and whether or not the use of renewable energies will solve the problem of its sustainability. In our study, starting from the Environmental Performance Index (EPI), we have considered several determinants of cryptocurrency mining: energy price, how that energy is generated, temperature, legal constraints, human capital, and R&D&I. From this, via linear regression, we recalculated this EPI by including the above factors that affect cryptocurrency mining in a sustainable way. The study determines, once the EPI has been readjusted, that the most sustainable countries to perform cryptocurrency mining are Denmark and Germany. In fact, of the top ten countries eight of them are European (Denmark, Germany, Sweden, Switzerland, Finland, Austria, and the United Kingdom); and the remaining two are Asian (South Korea and Japan).
Alternative assets such as fine art, wine, or diamonds have become popular investment vehicles in the aftermath of the global financial crisis. Correlation with classical financial markets is ...typically low, such that diversification benefits arise for portfolio allocation and risk management. Cryptocurrencies share many alternative asset features, but are hampered by high volatility, sluggish commercial acceptance, and regulatory uncertainties. This collection of papers addresses alternative assets and cryptocurrencies from economic, financial, statistical, and technical points of view. It gives an overview of their current state and explores their properties and prospects using innovative approaches and methodologies.
•We assess herding in conventional cryptocurrencies, NFT and DeFi digital assets.•No evidence of static herding.•Time-varying herding is identified in conventional cryptocurrencies and DeFi ...assets.•DeFi markets exhibit herding only during low volatility days.•Cryptocurrency bubble of 2021 has not amplified herding.
We examine the static and time-varying herding behavior in three cryptocurrency classes: ‘conventional’ cryptocurrencies, non-fungible tokens, and DeFi assets during the most recent cryptocurrency bubble of 2021. While static herding analysis failed to demonstrate any evidence of herding, the time-varying herding has been identified in conventional cryptocurrencies and DeFi assets for the short investment horizons. The herding asymmetry analysis reveals that herding is not evident in conventional cryptocurrencies and NFT during up/down market, high/low volatility days, and high/low trading days. We only find herding in DeFi assets during the low volatility days.
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.
In this study, we examine the asymmetric efficiency of cryptocurrencies using 1-hour data of Bitcoin, Ethereum, Litecoin, and Ripple. In doing so, we utilize the asymmetric multifractal detrended ...fluctuation analysis (MF-DFA). We find significant asymmetric multifractality in the price of cryptocurrencies and that upward trends exhibit stronger multifractality than downward trends. Using the time-varying deficiency measure, we show that the COVID-19 outbreak adversely affected the efficiency of the four cryptocurrencies, given a substantial increase in the levels of inefficiency during the COVID-19 period. Bitcoin and Ethereum are the hardest hit, and at the same time, these two largest cryptocurrencies recovered faster at the end of March 2020 from their sharp dip towards inefficiency. The findings confirm previous evidence that market efficiency is time varying; also, unprecedented catastrophic events, such as the COVID-19 outbreak, have adverse effects of on the efficiency of leading cryptocurrencies.
•Study asymmetric efficiency of cryptocurrencies using asymmetric MF-DFA.•Find significant asymmetric multifractality in the price of cryptocurrencies.•COVID-19 outbreak adversely affected the efficiency of cryptocurrencies.•The efficiency of Bitcoin and Ethereum is the hardest hit.•The efficiency of these large cryptocurrencies recovered faster around March 2020.
We examine the existence and dates of pricing bubbles in Bitcoin and Ethereum, two popular cryptocurrencies using the (Phillips et al., 2011) methodology. In contrast to previous papers, we examine ...the fundamental drivers of the price. Having derived ratios that are economically and computationally sensible, we use these variables to detect and datestamp bubbles. Our conclusion is that there are periods of clear bubble behaviour, with Bitcoin now almost certainly in a bubble phase.
Ponzi schemes are financial frauds which lure users under the promise of high profits. Actually, users are repaid only with the investments of new users joining the scheme: consequently, a Ponzi ...scheme implodes soon after users stop joining it. Originated in the offline world 150 years ago, Ponzi schemes have since then migrated to the digital world, approaching first the Web, and more recently hanging over cryptocurrencies like Bitcoin. Smart contract platforms like Ethereum have provided a new opportunity for scammers, who have now the possibility of creating “trustworthy” frauds that still make users lose money, but at least are guaranteed to execute “correctly”. We present a comprehensive survey of Ponzi schemes on Ethereum, analysing their behaviour and their impact from various viewpoints.
•We collect Ponzi schemes from the Ethereum blockchain.•We inspect their source code, identifying security vulnerabilities.•We measure the gains and losses of users, and their gain ratio.•We analyse their temporal behaviour under various viewpoints.•We measure the inequality of payments to and from the schemes.
PDF
