The virtual nature of digital money is fueling the conflict between usability, functionality and trust in the digital form. Institutional trust drivers should move forward in understanding the nature of confidence in digital money. Do central banks digital money (CBDC – central bank digital currency) and private cryptocurrencies demonstrate the same or different trust patterns? The paper used the general regression method to discover the relationship between trust in different forms of digital money and selected variables that may generate this trust. Simple empirical tests were sufficient to find the fundamental importance of age as a confidence driver relevant to CBDC and cryptocurrencies. It is found that traditional factors associated with the inflation history and quality of monetary order (central banks independence and rule of law) do not play a role in the case of CBDC, but are important in the case of cryptocurrencies. Structural features (like FinTech development or social trust) that should support trust in digital money are not found to be important. Societies with larger fraction of younger generations demonstrate higher confidence in centralized and decentralized forms of digital money. This challenges the traditional approach to money and calls into question the future role of monetary stability institutions in the digital age. Digitalization is perceived as an improvement in welfare only when fiat money institutions become fragile. The efficiency and credibility of central banks are not a bonus to confidence in CBDC. This is a challenge for the institutional design of the future digital-based monetary order.
Human behavior as they engaged in financial activities is intimately connected to the observed market dynamics. Despite many existing theories and studies on the fundamental motivations of the behavior of humans in financial systems, there is still limited empirical deduction of the behavioral compositions of the financial agents from a detailed market analysis. Blockchain technology has provided an avenue for the latter investigation with its voluminous data and its transparency of financial transactions. It has enabled us to perform empirical inference on the behavioral patterns of users in the market, which we explore in the bitcoin and ethereum cryptocurrency markets. In our study, we first determine various properties of the bitcoin and ethereum users by a temporal complex network analysis. After which, we develop methodology by combining k -means clustering and Support Vector Machines to derive behavioral types of users in the two cryptocurrency markets. Interestingly, we found four distinct strategies that are common in both markets: optimists, pessimists, positive traders and negative traders. The composition of user behavior is remarkably different between the bitcoin and ethereum market during periods of local price fluctuations and large systemic events. We observe that bitcoin (ethereum) users tend to take a short-term (long-term) view of the market during the local events. For the large systemic events, ethereum (bitcoin) users are found to consistently display a greater sense of pessimism (optimism) towards the future of the market.
Sérgio Adriani David, Claudio Marcio Cassela Inacio, Rafael Amorim Belo Nunes, J. A. Tenreiro Machado
Introduction: Cryptocurrencies have been attracting the attention from media, investors, regulators and academia during the last years. In spite of some scepticism in the financial area, cryptocurrencies are a relevant subject of academic research. Objectives: In this paper, several tools are adopted as an instrument that can help market agents and investors to more clearly assess the cryptocurrencies price dynamics and, thus, guide investment decisions more assertively while mitigating risks. Methods: We consider three methods, namely the Auto-Regressive Integrated Moving Average (ARIMA), Auto-Regressive Fractionally Integrated Moving Average (ARFIMA) and Detrended Fluctuation Analysis, and three indices given by the Hurst and Lyapunov exponents or the Fractal Dimension. This information allows assessing the behaviour of the time series, such as their persistence, randomness, predictability and chaoticity. Results: The results suggest that, except for the Bitcoin, the other cryptocurrencies exhibit the characteristic of mean reverting, showing a lower predictability when compared to the Bitcoin. The results for the Bitcoin also indicate a persistent behavior that is related to the long memory effect. Conclusions: The ARFIMA reveals better predictive performance than the ARIMA for all cryptocurrencies. Indeed, the obtained residual values for the ARFIMA are smaller for the auto and partial auto correlations functions, as well as for confidence intervals.
Alexander Brauneis, Roland Mestel, Ryan Riordan, Erik Theissen
This paper investigates the efficacy of low-frequency transactions-based liquidity measures to describe actual (high-frequency) liquidity. We show that the Corwin and Schultz (2012) and Abdi and Ranaldo (2017) estimators outperform other measures in describing time-series variations, irrespective of the observation frequency, trading venue, high-frequency liquidity benchmark, and cryptocurrency. Both measures perform well during high and low return, volatility and volume periods. The Kyle and Obizhaeva (2016) estimator and the Amihud (2002) illiquidity ratio outperform when estimating liquidity levels. These two estimators also reliably identify liquidity differences between trading venues. Overall, the results suggest that there is not yet a universally bestmeasure but there are reasonably good low-frequency measures.
The aim of this study is to examine the daily return spillover among 18 cryptocurrencies under low and high volatility regimes, while considering three pricing factors and the effect of the COVID-19 outbreak. To do so, we apply a Markov regime-switching (MS) vector autoregressive with exogenous variables (VARX) model to a daily dataset from 25-July-2016 to 1-April-2020. The results indicate various patterns of spillover in high and low volatility regimes, especially during the COVID-19 outbreak. The total spillover index varies with time and abruptly intensifies following the outbreak of COVID-19, especially in the high volatility regime. Notably, the network analysis reveals further evidence of much higher spillovers in the high volatility regime during the COVID-19 outbreak, which is consistent with the notion of contagion during stress periods.
Bitcoin futures were introduced in December 2017 and this was seen by some as a sign of the most popular cryptocurrency finally being accepted by the financial community. In this paper, we examine the impact of the introduction of Bitcoin futures on the Bitcoin spot market in terms of five characteristics – returns, volatility, skewness, kurtosis and liquidity, using a Bayesian diffusion-regression (state-space) structural time-series model. Our results indicate that the introduction of bitcoin futures potentially exerted a downward impact on the USD bitcoin spot market return and skewness and an upward one on volatility, kurtosis and liquidity, which became higher after futures were introduced. Therefore, our paper offers important insights for investors and regulators, while providing some guidance as to the potential impact of futures markets on other cryptocurrencies.
This paper uses new and recently introduced methodologies to study the similarity in the dynamics and behaviours of cryptocurrencies and equities surrounding the COVID-19 pandemic. We study two collections; 45 cryptocurrencies and 72 equities, both independently and in conjunction. First, we examine the evolution of cryptocurrency and equity market dynamics, with a particular focus on their change during the COVID-19 pandemic. We demonstrate markedly more similar dynamics during times of crisis. Next, we apply recently introduced methods to contrast trajectories, erratic behaviours, and extreme values among the two multivariate time series. Finally, we introduce a new framework for determining the persistence of market anomalies over time. Surprisingly, we find that although cryptocurrencies exhibit stronger collective dynamics and correlation in all market conditions, equities behave more similarly in their trajectories, extremes, and show greater persistence in anomalies over time.
Cryptocurrencies are attracting more investors and are reaching higher prices than ever, hence it becomes important to analyse whether the new asset class is a bubble or not. Previous literature on examination of cryptocurrency bubbles has primarily focused on Bitcoin, but the newer cryptocurrencies such as Ethereum are innovating the space with smart contracts, upstaging Bitcoin on some aspects, hence it becomes important to analyse the newer cryptocurrencies apart from Bitcoin as well for rational bubbles. The methods of recursive unit root tests suggested by Phillips, Shi and Yu (2015) has been used in this study to check for the presence of bubbles and to date stamp the periods of exuberance in three major cryptocurrencies: Bitcoin, Ethereum and Ripple from 2016 to 2021. Similarity in exuberance periods of Bitcoin and Ethereum is also detected in this research.
The blockchain technology and cryptocurrency are now in the centre of the financial market. The raise of the cryptocurrencies represented by Bitcoin have attracted a large group of scholars to analyze the underlying dynamics of their price fluctuations. Intensive debate emerged on the intrinsic features of Bitcoin. In theoretical analysis, we developed the principle of monetary convention to define the concept of monetary consensus, capturing the nature of monetary system, and categorize it into three types: traditional, algorithm and hybrid. Based on the Wavelet Coherence Analysis, we try to analyze Bitcoin price dynamics in both time and frequency domains, comparing Bitcoin with financial assets, economic and financial indexes, and other cryptocurrencies.
The paper is deeply motivated by the need to explore the impressive Bitcoin price dynamics by addressing Bitcoin as money in its essential attribute as a medium of exchange. We differ from previous research on Bitcoin in investigating the Bitcoin price dynamics on the theoretical ground. First, we show that the impressive Bitcoin price development observed since its inception can be interpreted as a hyperdeflation. Second, we resort to a representative agent modelling strategy within a money-in-the-utility function framework capturing the role of Bitcoin as a medium of exchange. We show that the specific monetary features of Bitcoin, its constant nominal stock and divisibility down to eight decimal places, account for a strong possibility of speculative hyperdeflationary paths. Alternative scenarios of the dynamics of real Bitcoin balances are also open for discussion.
This paper studies cryptocurrency. Firstly, this paper discusses the currency attribute of cryptocurrency. Secondly, this paper analyzes the advantages and disadvantages of cryptocurrency. Thirdly, this paper discusses the impact of cryptocurrency on the currency structure. Finally, this paper constructs the returns according to the daily price and statistically analyzes the yield difference of Bitcoin, Ethereum and Dogecoin. The ARIMA model is used to predict the return of cryptocurrency. This paper also gives the corresponding investment suggestions.