Major Cryptocurrencies Research Articles

Cryptocurrency Forecasting: More Evidence of the Meese-Rogoff Puzzle

This paper tests the random walk hypothesis in the cryptocurrency market. Based on the well-known Meese–Rogoff puzzle, we evaluate whether cryptocurrency returns are predictable or not. For this purpose, we conduct in-sample and out-of-sample analyses to examine the forecasting power of our model built with autoregressive components and lagged returns of BITCOIN, compared with the random walk benchmark. To this end, we considered the 13 major cryptocurrencies between 2018 and 2022. Our results indicate that our models significantly outperform the random walk benchmark. In particular, cryptocurrencies tend to be far more persistent than regular exchange rates, and BITCOIN (BTC) seems to improve the predictive accuracy of our models for some cryptocurrencies. Furthermore, while the predictive performance is time varying, we find predictive ability in different regimes before and during the pandemic crisis. We think that these results are helpful to policymakers and investors because they open a new perspective on cryptocurrency investing strategies and regulations to improve financial stability.

SoK: Assumptions Underlying Cryptocurrency Deanonymizations

In recent years, cryptocurrencies have increasingly been used in cybercrime and have become the key means of payment in darknet marketplaces, partly due to their alleged anonymity. Furthermore, the research attacking the anonymity of even those cryptocurrencies that claim to offer anonymity by design is growing and is being applied by law enforcement agencies in the fight against cybercrime. Their investigative measures require a certain degree of suspicion and it is unclear whether findings resulting from attacks on cryptocurrencies’ anonymity can indeed establish that required degree of suspicion. The reason for this is that these attacks are partly based upon uncertain assumptions which are often not properly addressed in the corresponding papers. To close this gap, we extract the assumptions in papers that are attacking Bitcoin, Monero and Zcash, major cryptocurrencies used in darknet markets which have also received the most attention from researchers. We develop a taxonomy to capture the different nature of those assumptions in order to help investigators to better assess whether the required degree of suspicion for specific investigative measures could be established. We found that assumptions based on user behaviour are in general the most unreliable and thus any findings of attacks based on them might not allow for intense investigative measures such as pre-trial detention. We hope to raise awareness of the problem so that in the future there will be fewer unlawful investigations based upon uncertain assumptions and thus fewer human rights violations.

The Relationships among Cryptocurrencies: A Granger Causality Analysis

The topic of Cryptocurrencies is emerging rapidly. Many investors and public want to invest in digital currencies. So, the nature of cryptocurrency and its theoretical understanding is essential currently. The aim of this research is to study major Cryptocurrencies having capitalization above $1bn and construct directed networks. Eleven major Cryptocurrencies are Bitcoin (BTC), Ethereum (ETH), Cardano (ADA), Binance Coin (BNB), Tether (USDT), Ripple (XRP), Doge Coin (DOGE), USD Coin (USDC), Bitcoin Cash (BCH), Chainlink (Link) and Litecoin (LTC). By using returns Granger Causality test was conducted. This test help in concluding that whether change in one price of cryptocurrency cause a change in other Cryptocurrencies or not. After analysis of results, it was concluded that there is a variation in the Cryptocurrencies relation. Some shows a direct cause and effect relation, while some have a unidirectional relation. However, some of them also showed no meaningful relationship. The relationship among these Cryptocurrencies was then showed using directed networks.

МОЖЛИВОСТІ ІНВЕСТУВАННЯ У КРИПТОВАЛЮТИ В УМОВАХ АКТИВНОЇ ФАЗИ ПАНДЕМІЇ COVID-19 У 2020–2021 РР. ТА КОНВЕНЦІЙНОЇ РОСІЙСЬКО-УКРАЇНСЬКОЇ ВІЙНИ 2022 Р.

Since the rise of the first cryptocurrency in 2008 until 2022, we can observe significant price volatility of cryptocurrency coins, periodic decline in prices for cryptocurrency coins and their growth, changes in the volume of cryptocurrency coin trading. Investors perceive cryptocurrency as an alternative (hedging) investment, a means of circulation and a measure of value. They increasingly use it in global socio-economic and military-political crises. Studying the price dynamics of cryptocurrencies during the active phase of the Covid-19 pandemic in 2020-2021 (the previous pandemic was a hundred years ago – the so-called "Spanish flu") and the conventional Russian-Ukrainian war of 2022 (and a similar scale of conflict after World War II 77 years), we will try to conduct an economic analysis to examine how these crisis factors affected the dynamics of prices for major cryptocurrencies – Ether and Bitcoin. Highlighting previously unresolved parts of the overall problem. The influence of various global factors on the price of cryptocurrencies has been insufficiently studied due to the short historical period of cryptocurrencies existence. We will try to identify the dependence of cryptocurrencies' values dynamics on the presence of global positive or negative factors (pandemics, wars, etc.). The purpose and objectives of the study. The main objectives of the article are to prove that the presence of global positive or negative factors (pandemics, wars, etc.) has a significant impact on the structure of investment, in general, and the price dynamics of cryptocurrency units in particular. Methods. In the research process, in particular, the following methods were used: analysis – to reveal the object and subject of research; abstract-logical (theoretical generalizations and formulations of conclusions concerning the impact of the Covid-19 pandemic in 2020-2021 and the conventional Russian-Ukrainian war in 2022 on the value of the cryptocurrency Bitcoin). Results. The significant impact of the Covid-19 pandemic in 2020-2021 and the conventional Russian-Ukrainian war in 2022 on various components of the financial market has been revealed. It was found that extreme socio-economic events provoke a fall in the stock market, do not provoke significant changes in the gold market and stimulate the major cryptocurrencies value growth. Bitcoin and Ether price dynamics have been shown to be highly correlated and are more suitable as short-term safe investment havens for pandemic or conventional war events than gold for economic operators who want to save and increase their savings. Conclusions. Due to the significant increase in the price of Bitcoin and Ether cryptocurrencies during the peak periods of the Covid-19 pandemic in 2020-2021 and the conventional Russian-Ukrainian war in 2022, cryptocurrencies are an effective and appropriate tool for investing and saving financial resources in the short term.

Connectedness between cryptocurrencies, gold and stock markets in the presence of the COVID-19 pandemic

PurposeThis paper analyzes the connectedness with network among the major cryptocurrencies, the G7 stock indexes and the gold price over the coronavirus disease 2019 (COVID-19) pandemic period, in 2020.Design/methodology/approachThis study used a multivariate approach proposed by Diebold and Yilmaz (2009, 2012 and 2014).FindingsFor a stock index portfolio, the results of static connectedness showed a higher independence between the stock markets during the COVID-19 crisis. It is worth noting that in general, cryptocurrencies are diversifiers for a stock index portfolio, which enable to reduce volatility especially in the crisis period. Dynamic connectedness results do not significantly differ from those of the static connectedness, the authors just mention that the Bitcoin Gold becomes a net receiver. The scope of connectedness was maintained after the shock for most of the cryptocurrencies, except for the Dash and the Bitcoin Gold, which joined a previous level. In fact, the Bitcoin has always been the biggest net transmitter of volatility connectedness or spillovers during the crisis period. Maker is the biggest net-receiver of volatility from the global system. As for gold, the authors notice that it has remained a net receiver with a significant increase in the network reception during the crisis period, which confirms its safe haven.Originality/valueOverall, the authors conclude that connectedness is shown to be conditional on the extent of economic and financial uncertainties marked by the propagation of the coronavirus while the Bitcoin Gold and Litecoin are the least receivers, leading to the conclusion that they can be diversifiers.

Dynamic volatility connectedness between thermal coal futures and major cryptocurrencies: Evidence from China

Despite the crucial role of thermal coal in generating the electricity used for cryptocurrency mining, the volatility linkage between the cryptocurrency and thermal coal markets is yet to be studied. We investigate the time-varying volatility connectedness between the two markets using their realized variances and semi-variances. Employing a multivariate Heterogeneous Autoregressive model, which accounts for both long memory and structural breaks in realized volatility time series, we find that China's thermal coal futures market is significantly dependent on the cryptocurrency market's volatility while the impact of the energy market on the cryptocurrency market is inconsequential. Moreover, the connectedness is asymmetrical in the sense that the bad volatility connectedness is greater than the good volatility connectedness. Finally, the determinants of the dynamic connectedness highlight the role of the production channel in fuelling the volatility transmission between these two markets.

The Complexity of Cryptocurrencies Algorithmic Trading

In this research, we provided an answer to a very important trading question, what is the optimal number of technical tools in order to achieve the best trading results for both swing trade that uses daily bars and intraday trade that uses minutes bars? We designed Machine Learning (ML) systems that can trade four major cryptocurrencies: Bitcoin, Ethereum, BNB, and Solana. We found that more indicators do not necessarily mean better trading performance. Swing traders that use daily bars should trade Bitcoin and Solana using Ichimoku Cloud (IC) plus Moving Average Convergence Divergence (MACD), Ethereum with IC plus Chaikin Money Flow (CMF), and BNB with IC alone. With regard to intraday trading, we documented that different cryptocurrencies should be trading using different time frames. These results emphasize that the optimal number of indicators that are used to trade daily bars is one or, at maximum, two. The Multi-Layer (MUL) system that consists of all three examined technical indicators failed to improve the trading results for both days (swing) and intraday trades. The main implication of this study for traders is that more indicators does not necessarily improve trades performances.

Investor behavior and cryptocurrency market bubbles during the COVID-19 pandemic

PurposeCryptocurrencies lack fundamental values and are often subject to behavioral bias leading to market bubbles. This study aims to investigate the contribution of the coronavirus pandemic to the creation of market bubbles.Design/methodology/approachThis study identifies four major cryptocurrency market bubbles by using the Phillips et al. (2016) (hereafter PSY) test. Subsequently, the co-movements of the coronavirus proxies with PSY measurement using the wavelet approach were studied.FindingsShort-lived bubbles are detected at the beginning of the studied period, and more extended bubble periods are identified at the end. Besides, the empirical results show evidence of significant negative co-movement between each pandemic proxy and each cryptocurrency bubble measurement.Research limitations/implicationsGiven the complex financial dynamics of the cryptocurrency markets due to some behavioral biases in some circumstances, investors can benefit from the date stamping of the bubbles bursting to make the best trading positions. In the same way, governments could support the healthy development of cryptocurrencies by preventing bubbles during such pandemics.Originality/valueThe financial bubble is commonly attributed to a change in investor behavior. Because traders and investors think they can resell the asset at a higher price in the future. This study explored the contribution of the COVID-19 pandemic in the creation of these bubbles by date stamping their occurrence and explosive periods. To the best of the authors’ knowledge, this study is the first attempt that explores the contribution of the COVID-19 pandemic to the creation of bubbles caused by a change in the investors’ behavior.

Volatility Co-Movement between Bitcoin and Stablecoins: BEKK–GARCH and Copula–DCC–GARCH Approaches

This paper aims to investigate and measure Bitcoin and the five largest stablecoin market volatilities by incorporating various range-based volatility estimators to the BEKK- GARCH and Copula-DCC-GARCH models. Specifically, we further measure Bitcoins’ volatility related to five major stablecoins and examine the connectedness between Bitcoin and the stablecoins. Our empirical findings document that the connectedness between Bitcoin and stablecoin market volatility behaviors exhibits the presence of stable interconnection. This study is of particular importance since it is crucial for market participation in the ongoing crypto assets to be informed about both the volatility patterns of major cryptocurrencies and the relative volatility of Bitcoin against the stablecoin markets. Eventually, we find that there is no systematic evidence for the various parity deviations of the stablecoins that are profoundly impacted by Bitcoin volatility. Thus, Bitcoin and the largest stablecoin Tether could stabilize together. However, Bitcoin shall not be generalized to other stablecoins in terms of stability results.

Impact of COVID-19 effective reproductive rate on cryptocurrency

The importance of cryptocurrency to the global economy is increasing steadily, which is evidenced by a total market capitalization of over $2.18T as of December 17, 2021, according to coinmarketcap.com (Coin, 2021). Cryptocurrencies are too confusing for laymen and require more investigation. In this study, we analyze the impact that the effective reproductive rate, an epidemiological indicator of the spread of COVID-19, has on both the price and trading volume of eight of the largest digital currencies—Bitcoin, Ethereum, Tether, Ripple, Litecoin, Bitcoin Cash, Cardano, and Binance. We hypothesize that as the rate of spread decreases, the trading price of the digital currency increases. Using Generalized Autoregressive Conditional Heteroskedasticity models, we find that the impact of the spread of COVID-19 on the price and trading volume of cryptocurrencies varies by currency and region. These findings offer novel insight into the cryptocurrency market and the impact that the viral spread of COVID-19 has on the value of the major cryptocurrencies.

Information transmission among energy markets, cryptocurrencies, and stablecoins under pandemic conditions

This paper investigates the directional causal relationship and information transmission among the returns of West Texas Intermediate (WTI), Brent, major cryptocurrencies, and stablecoins by drawing on daily data from July 2019 to July 2020. Applying effective transfer entropy, a non-parametric statistic, the results show that the direction of the causal relationship and the nature of information spillovers changed after the COVID-19 pandemic. More precisely, our findings reveal that WTI and Brent are leading the prices of Bitcoin and Bitcoin Cash. Conversely, Bitcoin futures and stablecoins (TrueUSD and USD Coin) are leading WTI and Brent prices. In addition, the stablecoin Tether became a leader against Brent prices after the pandemic, although it is still following WTI prices. Moreover, Ethereum and USD coin preserved their position as leaders against Brent prices. Interestingly, our results also reveal that Ethereum, Litecoin, and Ripple preserved their position as leaders of WTI prices. The change in the nature of directional causality and the spillover effect after the COVID-19 crisis provide valuable information for practitioners, investors, and policymakers on how the ongoing pandemic influences the connection and network correlation among the energy, cryptocurrency, and stablecoin markets.

A New Hybrid Model of Fuzzy Time Series and Genetic Algorithm Based Machine Learning Algorithm: A Case Study of Forecasting Prices of Nine Types of Major Cryptocurrencies

This study pertains to the usage and effectiveness of the fuzzy time series (FTS) models and machine learning methods in forecasting movements of financial data. The datasets used in this study are the actual closing prices and transaction volumes of 9 different cryptocurrencies, from the earliest time obtainable in Yahoo! Finance, all the way to 31 Oct 2021. Firstly, this paper presents a study of the severe drawbacks of all existing literature on FTS. In particular, this article outlines severe shortcomings of all existing FTS based algorithms that caused inaccuracies among all existing FTS-based algorithms in yielding meaningful prediction. Then, a novel structure of our improvised FTS, denoted as QFTS, is presented in this paper, which aims to rectify all flaws exist in all conventional FTS based models in literature. A further hybrid of QFTS with ANN is also presented. Later, a comparative analysis of all the aforementioned FTS models is presented in terms of overall forecasting accuracy and forecasting accuracy under specific conditions. The results are being compared in terms of MAPE. The newly invented QFTS model and the QFTS-ANN hybrid is found to profoundly outperform all the existing FTS models in literature, which includes Singh's FTS model. Such innovation profoundly rectifies severe shortcomings in financial forecasting that have persisted for many years in the past literature.

Multivariate cryptocurrency prediction: comparative analysis of three recurrent neural networks approaches

As a new type of currency introduced in the new millennium, cryptocurrency has established its ecosystems and attracts many people to use and invest in it. However, cryptocurrencies are highly dynamic and volatile, making it challenging to predict their future values. In this research, we use a multivariate prediction approach and three different recurrent neural networks (RNNs), namely the long short-term memory (LSTM), the bidirectional LSTM (Bi-LSTM), and the gated recurrent unit (GRU). We also propose simple three layers deep networks architecture for the regression task in this study. From the experimental results on five major cryptocurrencies, i.e., Bitcoin (BTC), Ethereum (ETH), Cardano (ADA), Tether (USDT), and Binance Coin (BNB), we find that both Bi-LSTM and GRU have similar performance results in terms of accuracy. However, in terms of the execution time, both LSTM and GRU have similar results, where GRU is slightly better and has lower variation results on average.

Real-Time Big Data Architecture for Processing Cryptocurrency and Social Media Data: A Clustering Approach Based on k-Means

Cryptocurrencies have recently emerged as financial assets that allow their users to execute transactions in a decentralized manner. Their popularity has led to the generation of huge amounts of data, specifically on social media networks such as Twitter. In this study, we propose an iterative kappa architecture that collects, processes, and temporarily stores data regarding transactions and tweets of two of the major cryptocurrencies according to their market capitalization: Bitcoin (BTC) and Ethereum (ETH). We applied a k-means clustering approach to group data according to their principal characteristics. Data are categorized into three groups: BTC typical data, ETH typical data, BTC and ETH atypical data. Findings show that activity on Twitter correlates to activity regarding the transactions of cryptocurrencies. It was also found that around 14% of data relate to extraordinary behaviors regarding cryptocurrencies. These data contain higher transaction volumes of both cryptocurrencies, and about 9.5% more social media publications in comparison with the rest of the data. The main advantages of the proposed architecture are its flexibility and its ability to relate data from various datasets.

Dynamic asymmetric dependence and portfolio management in cryptocurrency markets

As a new form of digital assets based on blockchain technology, the cryptocurrency has received increasing attention from researchers and practitioners. However, less attention has been paid to their joint dynamics from the perspective of portfolio management. This paper investigates the dependence dynamics across four major cryptocurrencies and their economic importance in portfolio management using the data from January 2014 to June 2020. Our empirical analysis shows that significant economic gains can be obtained from modelling dynamic asymmetric dependence among cryptocurrencies. We show that our results are robust to the period of the recent market fluctuations caused by COVID-19.

Risk Management for Crypto Assets: Towards Volume-Adjusted Metrics

Cryptocurrencies (or coins) have attracted a significant interest from amateur and professional investors alike. Those currencies are traded on specialized exchanges and are characterized by extreme price dynamics and pockets of significant volatility with liquidity risk being a major concern. The article studies 20 cryptocurrencies over the period Q4.2013-Q2.2021 to glean key stylized facts about their dynamics. We demonstrate that traditional risk metrics may be insufficient to fully evaluate their risk profiles and so propose to leverage a set of novel volume-adjusted metrics. Adjustments to the Sharpe ratio, the Value at Risk (VaR) and the Expected Tail Loss (ETL) measures are outlined so they better reflect the specifics of cryptocurrencies. This enhances the classical two-dimensional mean-variance optimization with a third dimension – volume traded, thus engendering a new three-dimensional asset map that can be used for improved risk management. This new framework is illustrated over the 20 major cryptocurrencies and corresponding adjusted metrics are calculated and interpreted.

Examining interconnectedness between media attention and cryptocurrency markets: A transfer entropy story

This note aims at using transfer entropy as a broader concept of causality than Granger’s approach to measure the direction and the intensity of spillovers between five major cryptocurrencies. For this purpose, we use two channels of transmission: price movements and Google-based investor attention. Our results shed light on the dominance of only a few cryptocurrencies, in their influences on price movements as well as on media attention in the other cryptocurrencies.

The Interaction of Major Crypto-assets, Clean Energy, and Technology Indices in Diversified Portfolios

Cryptocurrencies have gained high interest from media, regulatory authorities, and both retailer and institutional investors especially in the COVID-19 pandemic followed by a tremendous academic interest. Classification of cryptos as an asset is one important issue while another crucial topic is the significantly high return and volatility fluctuations which does not make life easier for portfolio investors. High energy consumption to produce cryptos and carbon emission issues are also not developing the favor of cryptocurrencies. Moreover, in financial literature recent studies show that prices of renewable energy stocks have long-term cointegrating relationship with technology companies. In this context first we employ an asymmetric VAR-GARCH model to study spillover effects between major crypto-currencies, clean energy, and technology indices. Using daily data of the two major cryptocurrencies for the period of 1/1/2016 and 30/09/2021, we relate risk and return of different mean-variance portfolio strategies to Bitcoin (BTC), Etherium (ETH), S&P Global Clean Energy Index (SPGCE) and MSCI World Information Technology Index (MSCIWIT). Secondly, we apply the Markowitz mean-variance framework to assess risk-return benefits of cryptocurrency-portfolios. Our main goal is to offer optimal portfolio allocation approaches including cyptocurrencies with traditional financial assets. We will combine cryptocurrencies, clean energy, and technology indices to maximize return and Sharpe ratio. Furthermore, we will use our asymmetric VAR-GARCH models results to understand and cross check the Markowitz portfolio allocation results in details.

On technical trading and social media indicators for cryptocurrency price classification through deep learning

Predicting the prices of cryptocurrencies is a notoriously challenging task due to high volatility and new mechanisms characterising the crypto markets. In this work, we focus on the two major cryptocurrencies for market capitalisation at the time of the study, Ethereum and Bitcoin, for the period 2017–2020. We present a comprehensive analysis of the predictability of price movements comparing four different deep learning algorithms (Multi Layers Perceptron (MLP), Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) neural network and Attention Long Short Term Memory (ALSTM)). We use three classes of features, considering a combination of technical (e.g. opening and closing prices), trading (e.g. moving averages) and social (e.g. users’ sentiment) indicators as input to our classification algorithm. We compare a restricted model composed of technical indicators only, and an unrestricted model including technical, trading and social media indicators. We found an increase in accuracy for the daily classification task from a range of 51%–55% for the restricted model to 67%–84% for the unrestricted one. This study demonstrates that including both trading and social media indicators yields a significant improvement in the prediction and accuracy consistently across all algorithms.

Time Varying Dependence in the Cryptocurrency Market and COVID 19 Panic Index: An Empirical Investigation

The aim of this paper is to study the interdependence between the Coronavirus Panic Index (PI) and major cryptocurrencies throughout the period of the Covid-19 pandemic.We investigate the evolution of cryptocurrency’s value following changes in the Covid panic index levels; then, we use the DCC-MIDAS specification to extract the long- and short-term volatility components of cryptocurrencies and study the responses of the cryptocurrencies to the pandemic panic index. The results show that the panic level metrics could be considered as a potential driver of cryptocurrencies volatility and has a significantly positive influence on the long-term cryptocurrencies correlation during stress levels. Our findings contribute to the existing literature in several ways. First ,we provide specific evidence on the driving effect of Covid panic index on cryptocurrencies’correlation during a crisis. Second, our paper adds to the current literature on cryptocurrencies and fills in the existing gap related to the lack of academic research on the pandemic’s impact on the cryptocurrency market. Third, the results offer interesting insights for future research and have important implications for investors, especially in understanding the cryptocurrencies’behavior and exploring whether cryptocurrencies can serve as a hedge against the COVID-19 crisis.