Abstract In recent years, the use of cryptocurrencies has increased. As these currencies continue to play a larger role, they eventually will be an important component of banking system activity. Moreover, in addition to the standard role of financial intermediaries to facilitate lending, intermediaries can be valuable firms that help provide safekeeping of tokens. The objective of this paper is to demonstrate these important functions in a microfounded model of monetary exchange. Furthermore, we also consider the possibility that central banks issue their own digital currencies that may affect the level of intermediation in the private banking system.
Abstract Cryptocurrency returns diverge excessively from normality, with the interrelationship of Skewness and Kurtosis being accordant with a parabolic form, yet this connection is scantly documented. We begin by demonstrating diagrammatically the attributes of the SâK plane for cryptocurrencies. Moreover, by taking advantage of the panel structure of the data, we estimate a quadratic model for the SâK plane. Then we investigate whether the type and the infrastructure of the cryptocurrency, as well as the period under examination, alter the architecture of the plane. We find that the squared Skewness of tokens substantially lowers the slope of Kurtosis, while the same applies to the earlier era of the market.
Cryptocurrencies are a digital way of money in which all transactions are held electronically. It is a soft currency which doesnât exist in the form of hard notes physically. Here, we are emphasizing the difference of fiat currency which is decentralized that without any third-party intervention all virtual currency users can get the services. However, getting services of these cryptocurrencies impacts on international relations and trade, due to its high price volatility. There are several virtual currencies such as bit-coin, ripple, ethereum, ethereum classic, lite coin, etc. In our study, we especially focused on a popular cryptocurrency, i.e., bitcoin. From many types of virtual currencies, bitcoin has a great acceptance by different bodies such as investors, researchers, traders, and policy-makers. To the best of our knowledge, our target is to implement the efficient deep learning-based prediction models. Specifically long short-term memory (LSTM) and gated recurrent unit (GRU) to handle the price volatility of bitcoin and to obtain high accuracy. Our study involves comparing these two time series deep learning techniques and proved the efficacy in forecasting the price of bitcoin.
Previous research has shown volatility jumps and co-jumping behaviours in cryptocurrency markets. Motivated by these findings, we employ the herding effect and financial contagion channel to outline a theoretical framework of volatility-state-dependent correlations in cryptocurrency markets. We show that digital currency markets are more strongly correlated when experiencing an identical volatility regime, which echoes co-jumping behaviours addressed by the literature. Moreover, the strong correlation that occurs when the paired cryptocurrencies simultaneously experience a high volatility regime results in the least effectiveness of diversification in terms of a minimum portfolio risk reduction. Last but not least, the proposed state-dependent approach in this study proves effective at the task of risk forecasting and risk reduction for cryptocurrency portfolios, beyond the bivariate GARCH-based models, which are a pure and simple time-dependent approach.
Zaheer Anwer, Saqib Farid, Ashraf Khan, Noureddine Benlagha
In the wake of proliferation of cryptocurrencies and growing concerns regarding their environmental impact, we investigate the dynamic co-movement of digital assets and environmentally sustainable assets. We use daily data of five global indices from 01 March, 2017 to 15 May, 2022. The results suggest that environmentally sustainable indices and cryptocurrency indices demonstrate co-movements during pandemic. However, in the normal times, they mostly remain detached from each other. Therefore, it can be argued that both the asset classes can serve as hedge against each other. The findings carry important implications for the investment industry and regulators.
This study investigates the relationship between expected returns on cryptocurrencies and macroeconomic fundamentals. Investors employ a lot of macroeconomic indicators for their investment decision, and hence adopting a few macroeconomic indicators is not sufficient in capturing a change in economic states. Moreover, due to aggregation, macroeconomic indicators are not measured precisely. To overcome these problems, we employ a dynamic factor model and extract common factors from a large number of macroeconomic indicators. We find that the common factors are strongly linked to the cryptocurrency expected returns at a quarterly frequency, while we do not observe this relationship using macroeconomic indicators such as inflation and money supply. This suggests that macroeconomic information matters in a longer term, which contrasts with the previous literature that explores a short-term relationship. The cryptocurrency prices are not determined by macroeconomic fundamentals in a short-term period since speculators impact the prices. However, in a long-term period, the prices are more linked to macroeconomic fundamentals.
Marek Zatwarnicki, Krzysztof Zatwarnicki, Piotr Stolarski
In 2020 and 2021, the cryptocurrency market attracted millions of new traders and investors. Lack of regulation, high liquidity, and modern exchanges significantly lowered the entry threshold for new market participants. In 2021, over 5 million Americans were regularly involved in cryptocurrency trading. At that time, the interest in market indicators and trading strategies remained low, leading to the conclusion that most investors did not use decision-support indicators. The correct and backtested use of technical analysis signals can give the trader a significant advantage over most market participants. This work introduces an algorithmic approach to examining the effectiveness of the signals generated by one of the most popular market indicators, the Relative Strength Index (RSI). A model corresponding to an actual cryptocurrency exchange was used to backtest the strategies. The results show that the RSI as a momentum indicator in the cryptocurrency market involves high risk. Using alternative RSI applications can allow traders to gain an advantage in the cryptocurrency market. Comparing the results with the traditional buy and hold strategy shows the credible potential of the indicated method and the usage of signals generated by the technical analysis indicators.
By now, cryptocurrencies have almost become a part of the modern financial asset space, but the cryptocurrency market itself is not homogeneous, and individual cryptocurrencies can differ significantly in their properties and functions. For example, the cryptocurrency Ether is second in capitalization after Bitcoin, but the Ethereum and Bitcoin blockchains differ significantly in their properties and functions. In particular, Ethereum is the most popular digital platform for creating decentralized applications (dApps). The purpose of this work is to try to answer the question "Does the market take into account the features of the Ethereum blockchain in the price dynamics of the Ether cryptocurrency?" This question is also directly related to the search for potential fundamental factors that can explain the price dynamics of Ether. The main econometric method used in the study is generalized autoregressive conditional heteroskedasticity (GARCH) models. Having evaluated about 15 thousand different specifications of GARCH models, where various Ethereum blockchain usage metrics were used as explanatory variables, we obtained the results that Ethereum network usage metrics do not significantly correlate with Ether cryptocurrency returns. Moreover, these metrics are also unable to explain the relative strengthening/weakening of Ether relative to Bitcoin. Thus, we conclude that despite the presence of a number of special functional properties of the Ethereum blockchain, the price dynamics of the Ether cryptocurrency does not reflect them.
Yang Wan, Yuncheng Song, Xinqian Zhang, Zhichao Yin
We explore the dynamics and determinants of volatility connectedness between cryptocurrencies and energy. We employed a block dynamic equicorrelation model and a group volatility connectedness measurement to measure the cross-equicorrelation and volatility connectedness between cryptocurrencies and energy. We also adopted dynamic model averaging to identify the time-varying drivers. The results suggest that changes in cross-equicorrelation between the two groups were affected by influential global events and increased after the COVID-19 pandemic. Volatilities were transmitted in both directions between cryptocurrencies and energy, but the transmission from energy to cryptocurrencies is by far the strongest. The driver identification implies that the factors related to cryptocurrencies and global financial markets had important roles in explaining the volatility connectedness from cryptocurrencies to energy in some periods after the COVID-19 pandemic, but the effects were marginal. In contrast, factors such as electricity consumption, cryptocurrency turnovers, and VIX were important in affecting the volatility connectedness from energy to cryptocurrencies, and the effects depended on factors and changed over time.
This paper introduces a unique perspective towards Bitcoin safe haven and hedge properties through the Bitcoin halving cycle. The Bitcoin halving cycle suggests that Bitcoin price movement follows specific sequences, and Bitcoin price movement is independent of other assets. This has significant implications for Bitcoin properties, encompassing its risk profile, volatility dynamics, safe haven properties, and hedge properties. Bitcoinâs institutional and industrial adoption gained traction in 2021, while recent studies suggest that gold lost its safe haven properties against the S&P500 in 2021 amid signs of funds flowing out of gold into Bitcoin. Amid multiple forces at play (COVID-19, halving cycle, institutional adoption), the potential existence of regime changes should be considered when examining volatility dynamics. Therefore, the objective of this study is twofold. The first objective is to examine gold and Bitcoin safe haven and hedge properties against three US stock indices before and after the stock market selloff in March 2020. The second objective is to examine the potential regime changes and the symmetric properties of the Bitcoin volatility profile during the halving cycle. The Markov Switching GARCH model was used in this study to elucidate regime changes in the GARCH volatility dynamics of Bitcoin and its halving cycle. Results show that gold did not exhibit safe haven and hedge properties against three US stock indices after the COVID-19 outbreak, while Bitcoin did not exhibit safe haven or hedge properties against the US stock market indices before or after the COVID-19 pandemic market crash. Furthermore, this study also found that the regime changes are associated with low and high volatility periods rather than specific stages of a Bitcoin halving cycle and are asymmetric. Bitcoin may yet exhibit safe haven and hedge properties as, at the time of writing, these properties may manifest through sustained adoption growth.
Juan PiĂąeiro Chousa, Aleksandar Ĺ eviÄ, Isaac GonzĂĄlez-LĂłpez
In our study, we have evaluated the impact of tweets, social indicators, uncertainty, and attention indices on the selected variables calculated from a pool of 51 decentralised finance entities. In so doing, we have identified some evidence that returns are impacted by tweets, but not by social indicators that appear to be more relevant for volatility. We have further confirmed that the S&P500 Index negatively influences cryptocurrency returns, which means that these two asset classes are substitutes. Uncertainty and attention indices are relevant in determining returns and the alternative measurement of volatility. However, they remain insignificant for illiquidity and our initial volatility choice.
We considered the daily price dynamics of the US Bitcoin market in the period from 2015 to 2022. In the first step, we used a singular value decomposition (SVD) entropy method for assessing time-varying informational efficiency over different time scales, from weeks to quarters. It was shown that the US Bitcoin market has been informationally efficient most of the time, except for some isolated periods where the returns exhibited deviations from the random behavior. The COVID-19 pandemic has not impacted the informational efficiency. This suggests that the Bitcoin market is unpredictable, and no reliable predictions can be obtained. A further analysis was carried out by considering the recurrence intervals for different positive and negative returns. We found that the distribution of recurrence intervals for positive and negative returns is asymmetric, with mean values higher for negative returns. We found that the distribution of recurrence intervals can be described by a stretching exponential distribution, such that the empirical and analytical hazard probabilities as functions of the elapsed time show good agreement.
We investigate the multifractal properties of daily price returns and trading volume variations in 35 cryptocurrencies by using the method of wavelet leaders prior and during the COVID-19 pandemic. The obtained results from the analysis of scaling exponent functions and multifractal spectrums show that, in general, price returns and trading volume variations exhibit multifractal properties prior to the COVID-19 pandemic and that they tend to exhibit monofractal behavior during the pandemic. As a result, the level of multifractality diminished during the COVID-19 for both price returns and trading volume variations. Since complexity in price returns and trading volume variations decreased during the pandemic, cryptocurrencies may offer an interesting investment during times of serious world economic downturns.
This study estimates the effects of the dual long memory property and structural breaks on the persistence level of six major cryptocurrency markets. We apply the Bai and Perron structural break test, InclĂĄn and Tiaoâs iterated cumulative sum of squares (ICSS) algorithm, and the fractionally integrated generalized autoregressive conditional heteroscedasticity (FIGARCH) model, with different distributions. The results show that long memory and structural breaks characterize the conditional volatility of cryptocurrency markets, confirming our hypothesis that ignoring structural breaks leads to an underestimation of the persistence of volatility modeling. The ARFIMA-FIGARCH model, with structural breaks and a skewed Student-t distribution, fits the cryptocurrency marketâs price dynamics well.
In this paper we develop a linear expectile hidden Markov model for the analysis of cryptocurrency time series in a risk management framework. The methodology proposed allows to focus on extreme returns and describe their temporal evolution by introducing in the model time-dependent coefficients evolving according to a latent discrete homogeneous Markov chain. As it is often used in the expectile literature, estimation of the model parameters is based on the asymmetric normal distribution. Maximum likelihood estimates are obtained via an Expectation-Maximization algorithm using efficient M-step update formulas for all parameters. We evaluate the introduced method with both artificial data under several experimental settings and real data investigating the relationship between daily Bitcoin returns and major world market indices.
Among the most recent and quickly growing industries worldwide is metaverse cryptocurrency. Despite the fact that the first cryptocurrency namely Bitcoin was only developed 13 years ago, the use and value of digital currencies have increased dramatically. Their marketplaces are among the most intricate and unpredictable markets over the globe. Despite being a decentralized system that does not have a single point of failure, it is commonly known that many individuals who invest in it lose their money. In this article, we attempt to use Monte Carlo simulation to forecast the price of a selected few well-known cryptocurrencies for the next 1000 days namely Doge coin (DOGE), Ethereum (ETH), Litecoin (LTC) and HEX.The mean return on a cryptocurrency and the standard deviation of past returns are the two key factors that affect how the simulations end out. Additionally, the study evaluates the projected returns for cryptocurrencies as well as the correlation between all the different currencies. It also looks into the statistics of cryptocurrency, average true range, volatility and cumulative returns. By calculating the growth rate and sharpe ratio, the entire research would also assist investors in deciding if they should purchase a specific cryptocurrency or not.
Pavlos I. Zitis, Shinji Kakinaka, Ken Umeno, M. P. Hanias ¡ 6 authors
This article investigates the dynamical complexity and fractal characteristics changes of the Bitcoin/US dollar (BTC/USD) and Euro/US dollar (EUR/USD) returns in the period before and after the outbreak of the COVID-19 pandemic. More specifically, we applied the asymmetric multifractal detrended fluctuation analysis (A-MF-DFA) method to investigate the temporal evolution of the asymmetric multifractal spectrum parameters. In addition, we examined the temporal evolution of Fuzzy entropy, non-extensive Tsallis entropy, Shannon entropy, and Fisher information. Our research was motivated to contribute to the comprehension of the pandemic's impact and the possible changes it caused in two currencies that play a key role in the modern financial system. Our results revealed that for the overall trend both before and after the outbreak of the pandemic, the BTC/USD returns exhibited persistent behavior while the EUR/USD returns exhibited anti-persistent behavior. Additionally, after the outbreak of COVID-19, there was an increase in the degree of multifractality, a dominance of large fluctuations, as well as a sharp decrease of the complexity (i.e., increase of the order and information content and decrease of randomness) of both BTC/USD and EUR/USD returns. The World Health Organization (WHO) announcement, in which COVID-19 was declared a global pandemic, appears to have had a significant impact on the sudden change in complexity. Our findings can help both investors and risk managers, as well as policymakers, to formulate a comprehensive response to the occurrence of such external events.