Purpose – This study attempts to establish if the markets for the two most popular cryptocurrencies in the world, Bitcoin and Ethereum, follow weak-form market efficiency across various landmarks in time. Design/Methodology/Approach – Traditional testing for establishing weak-form market efficiency rests on whether the price series exhibits a random walk process, which implies that future prices cannot be predicted. However, not all random walk series automatically imply weak-form market efficiency, since some asset price behaviors may exhibit non-constant variance. In such cases, the GARCH model can be used to test for the presence of market efficiency. Since structural breaks in the prices of both cryptocurrencies are common, tests for market efficiency were carried out using sub-temporal price windows. In both price series, the last time window coincided with the 2020 COVID-19 pandemic period. Findings – Results of the GARCH analyses showed that the volatility and persistence parameters (α and β, respectively) in the Bitcoin and Ethereum models were all statistically significant, implying that prices in their sub-temporal markets were generally weak-form inefficient. The observed market inefficiency in both cryptocurrencies can be attributed to various factors like the price manipulation of crypto whales, security issues, and increased media attention, which led to inflows of information that helped big investors beat and gain from the market by successfully predicting the trend in future prices. During the 2020 COVID-19 pandemic period, both cryptocurrencies’ prices were observed to rise significantly, similar to the case of the 2017 Bitcoin price bubble. A cointegrating regression between Bitcoin and Ethereum prices during this period, however, showed a spurious relationship. Despite the absence of a long run relationship between these two price series, the current price bubbles in the cryptocurrency markets are speculated to be tied together. Research Implications – Players in the cryptocurrency market must always be cautious in making investment decisions regarding this type of asset since the markets are generally price inefficient and risky; any idiosyncratic decision that may be triggered by a price bubble burst in one cryptocurrency market may or may not serve as a signal that the other market will do the same. Since the Bitcoin and Ethereum prices were shown to exhibit volatility spillover and persistence, investors can use this information to make informed decisions as to whether to invest in these cryptocurrencies despite the huge risks that are magnified during the COVID-19 pandemic.
I. S. Ivanchenko, Marina V. Charaeva, Alla A. Lysochenko, Ilya A. Nozhenkov
Since 2009, cryptocurrencies being a modern form of electronic means of payment have become widespread in the global financial market. In this regard, a study aimed to find an answer to the question: “Are cryptocurrencies a modern form of money?” was conducted. An analysis of the scientific works of leading economic schools has led to the conclusion that cryptocurrencies are a modern form of private money that performs the main monetary function being a means of payment, which corresponds to the idea of the Austrian economic school of full-fledged means of payment. The study attempts to predict the market rate of the three most popular cryptocurrencies at present being Bitcoin, Ethereum and Ripple due to the fact that modern cryptocurrencies demonstrate a high level of volatility in their market value, and reliable funds must maintain their purchasing power. The analysis of the cryptocurrency market with regard to the information efficiency has led to the conclusion that cryptocurrencies have been demonstrating instability of qualitative properties over the past five years. The authors proposed to improve the predictive characteristics of the HAR-RV model by additionally calculating the Shannon information entropy of the initial time series to level their insensitivity to unexpected information shocks in the cryptocurrency market being the main drawback of regression models. The study has proved that cryptocurrencies are a promising modern form of electronic money, their market rate is quite predictable, and the popularity of cryptocurrencies and their use in payment transactions will further increase.
Purpose This article examines the susceptibility of cryptocurrencies to coronavirus disease 2019 (COVID-19) induced panic in comparison with major stock indices. Design/methodology/approach The author employs the Bayesian structural vector autoregression to examine the phenomenon in Bitcoin, Ethereum and Litecoin from 2nd January 2020 to 30th June 2021. A similar analysis is conducted for major stock indices, namely S&P 500, FTSE 100 and SSE Composite for comparison purposes. Findings The results suggest that cryptocurrencies returns suffered immensely in the early days of the COVID-19 outbreak following declarations of the disease as a global health emergency and eventually a pandemic in March 2020. However, the returns for all three cryptocurrencies recovered by April 2020 and remained resistant to further COVID-19 panic shocks. The results are dissimilar to those of S&P 500, FTSE 100 and SSE Composite values which were vulnerable to COVID-19 panic throughout the timeframe to June 2021. The results further reveal strong predictive power of Bitcoin on prices of other cryptocurrencies. Research limitations/implications The article provides evidence to support the cryptocurrency as a safe haven during COVID-19 school of thought given their resistance to subsequent shocks during COVID-19. Thus, the author stresses the need for diversification of investment portfolios by including cryptocurrencies given their uniqueness and resistance to shocks during crises. Originality/value The author makes use of the novel corona virus panic index to examine the magnitude of shocks in prices of cryptocurrencies during COVID-19.
In times of exogenous systemic shocks, such as the COVID-19 pandemic, it is important to identify hedge or safe haven assets. Therefore, this paper analyzes changes in the idiosyncratic risk of Bitcoin in a portfolio of commodities and global stocks. For this purpose, the M-GARCH model employed considers the interdependence among all the portfolio assets by using a time-varying asset pricing framework. This framework measures the impact of commodities and global stock prices as sources of systemic risk for Bitcoin returns before and after the COVID-19 pandemic. The evidence suggests that during the COVID-19 pandemic, the effects of changes in commodities and global prices on the idiosyncratic risk of Bitcoin were statistically significant. The idiosyncratic risk of Bitcoin measured as a percentage of total variance not accounted for by the proposed model rose from 86.06% to 95.05% during the pandemic. These results are in line with previous studies regarding the properties of Bitcoin as a hedge or safe haven asset for a portfolio composed of commodities and global stocks.
The three largest cryptocurrencies by market value are examined for overreaction to positive and negative outlier return events. Bitcoin and Ethereum show significant reversals in value following outlier negative events suggesting overreaction. For positive events, significant cumulative gains (not reversals) followed outlier positive events for Ethereum and Tether showed a significant reversal in value after the positive events. Evidence is, therefore, mixed among the three main cryptocurrencies when it comes to how revaluations occur after outlier positive events.
We study the information dynamics between the largest Bitcoin exchange markets during the bubble in 2017-2018. By analysing high-frequency market-microstructure observables with different information theoretic measures for dynamical systems, we find temporal changes in information sharing across markets. In particular, we study the time-varying components of predictability, memory, and synchronous coupling, measured by transfer entropy, active information storage, and multi-information. By comparing these empirical findings with several models we argue that some results could relate to intra-market and inter-market regime shifts, and changes in direction of information flow between different market observables.
The purpose of this paper is to investigate the viability as compared with other financial assets of cryptocurrencies as a currency or as an asset investment. This paper also aims to see which macro variable relates more to the price of cryptocurrencies, especially Bitcoin. Since the whole concept of cryptocurrencies is quite novel, an attempt has been made to briefly explain the underlying blockchain technology that forms the bedrock of cryptocurrencies. In this study, we use secondary data, i.e., the price history of Bitcoin from September 2014 to September 2021 for the last seven years, captured from trading exchanges. We predicted monthly returns of Bitcoin with that of Standard & Poor’s 500 Index (S&P 500), gold, and Treasury Bonds. Our findings show that Bitcoin has very high volatility compared to S&P 500, Gold and Treasury Bonds. Also, our findings show that there is a positive correlation between Bitcoin’s price volatility and the other three financial assets before and during COVID-19. Hence, Bitcoin is acting more as a speculative asset rather than a steady store of value. This can be drawn from the comparison with the debt market i.e., a Treasury Bond that invests in long-dated (30 years) US treasuries with which Bitcoin shows no relationship. The findings of this study could help with understanding the future of Bitcoin. This has important implications for Bitcoin investors. The current study contributes to the extant literature by providing empirical evidence on long-term social sustainability vis-à-vis supply chain traceability.
The results of empirical analyses confirm that analysed unsystematic factors, the Stock-to-Flow index (S2F), and information on the Bitcoin (BTC) are directly correlated with BTC values. These results are expected and in line with the economic theory; however, this research paper aimed to investigate the impact of unsystematic factors on the value of decentralised virtual cryptocurrency BTC. Its aim was also to analyse the reasons for significant oscillations of market values in relation to the S2F and S2FX model and thus confirm the reliability of these models in the estimation of BTC value. The research further confirms the strong influence of non-technical information directly linked with the BTC. The limitations of this paper are the lack of possibilities for examining the impact of non-technical information affecting the Bitcoin price deviation regarding the S2F model. In addition to all mentioned limitations, the research results indicate the relevance of the S2F and S2FX models and show a strong impact of (half) the information on the value of cryptocurrencies.
Ulrich Gallersdörfer, Lena Klaaßen, Christian Stoll
The energy consumption and related carbon emissions of cryptocurrencies such\nas Bitcoin are subject to extensive discussion in public, academia, and\nindustry. As cryptocurrencies continue their journey into mainstream finance,\nincentives to participate in the networks and consume energy to do so remain\nsignificant. First guidance on how to allocate the carbon footprint of the\nBitcoin network to single investors exist, however a holistic framework\ncapturing a wider range of cryptocurrencies and tokens remains absent. This\nwhite paper explores different approaches of how to allocate emissions caused\nby cryptocurrencies and tokens. Based on our analysis of the strengths and\nlimitations of potential approaches, we propose a framework that combines key\ndrivers of emissions in Proof of Work and Proof of Stake networks.\n
Using the asymmetric stochastic volatility model, this study investigates the day-of-the-week and holiday effects on the returns and volatility of Bitcoin from January 1, 2013 to August 31, 2019; in this context, we also discuss the characteristics of Bitcoin as a financial asset. The results of the estimation are threefold. First, the finding shows a small day-of-the week effect in volatility on Saturday and Sunday than in the rest of the week. Second, although the holiday effects are examined in active trading countries, namely Japan, China, Germany, and the United States, the positive post-holiday effect on the returns and weak positive pre-holiday effect on the volatility are only observed in the United States. Finally, the asymmetry effect is not observed. A comparison of Bitcoin to several assets such as stock, currency, and gold shows Bitcoin's positioning between stock, currency, and gold in relation to the week and holiday effects, its reaction to federal funds and medium of exchange characteristics, and the lack of asymmetry effect.
In this paper, I examine how social media affects cryptocurrencies and more traditional stocks. I use data on Twitter posts in combination with daily stock prices to estimate the causal effect of a tweet on stock and coin prices. To do this, I use a difference-indifference regression with index funds as my control group, which allows me to capture general market trends that coins and stocks would follow if not for intervention. I find that tweets have a significant impact on cryptocurrencies that last up to three days after the post. The increase in coin prices is driven by tweets from Tyler Winklevoss and tweets about Tezos and Ethereum specifically. Meanwhile, Twitter posts have no impact on more traditional stocks. These results suggest that social media can provide the public with valuable information in real time for fast moving and volatile crypto assets, while their effects on more stable and institutionalized traditional stocks are more muted.