Στόχος της παρούσας μελέτης ήταν η διερεύνηση της συμπεριφοράς των τιμών πέντε κρυπτονομισμάτων BTC, LTC, ETH, XMR και XRP, των διακυμάνσεων, των πιθανών μέγιστων τιμών, των ελάχιστων τιμών και εάν υπάρχει σύνδεση, συνεργασία στη συμπεριφορά των κρυπτονομισμάτων. Για τον λόγο αυτό, οι ημερήσιες τιμές των πέντε κρυπτονομισμάτων από το 2013 έως το 2020 ανακτήθηκαν από την ιστοσελίδα www.coinmarketcap.com. Αρχικά, πραγματοποιήθηκε ανάλυση συσχέτισης με τη χρήση κυλιόμενου παραθύρου 100 ημερών κάθε ζεύγους κρυπτονομισμάτων, BTC - LTC, BTC - ETH, BTC - XMR, BTC - XRP, LTC - ETH, LTC - XMR, LTC - XRP, ETH - XMR, ETH - XRP και XMR – XRP. Επίσης, πραγματοποιήθηκε μια ανάλυση συνολοκλήρωσης με τη χρήση της δοκιμής Johansen. Η ανάλυση κυλιόμενης συσχέτισης κατέληξε στο συμπέρασμα ότι και τα πέντε κρυπτονομίσματα πριν από το έτος 2017 παρουσίασαν ένα ασταθές μοτίβο. Εν αντιθέσει, μετά το 2017, το επίπεδο συσχέτισης ήταν υψηλότερο από 0,6 και για τα πέντε κρυπτονομίσματα το οποίο αποτελεί ένδειξη σταθερού και παρόμοιου μοτίβου μεταξύ των κρυπτονομισμάτων. Τέλος, η ανάλυση δοκιμής Johansen/συνολοκλήρωσης κατέληξε στο συμπέρασμα ότι υπήρξε μια εξίσωση συνολοκλήρωσης για την περίοδο 2017 έως το 2020. Αυτό το αποτέλεσμα ήταν σύμφωνο με το αποτέλεσμα της ανάλυσης κυλιόμενου παραθύρου.
Based on high-frequency data, we study the difference in cryptocurrency market before and during the COVID-19. We analyze the multifractality of three major cryptocurrencies via the multifractal detrended fluctuation analysis (MFDFA). To investigate the source of multifractality, we construct shuffled, surrogated and truncate data. The results show that market efficiency of cryptocurrency has decreased during COVID-19. The cryptocurrency multifractal characteristics mainly come from non-Gaussian distribution. Additionally, the components of multifractal nature have changed during the pandemic. The results provide evidence for the impact of COVID-19 on cryptocurrency market.
A reputation of high volatility accompanies the emergence of Bitcoin as a financial asset. This paper intends to nuance this reputation and clarify our understanding of Bitcoin's volatility. Using daily, weekly, and monthly closing prices and log-returns data going from September 2014 to January 2021, we find that Bitcoin is a prime example of an asset for which the two conceptions of volatility diverge. We show that, historically, Bitcoin allies both high volatility (high Standard Deviation) and high predictability (low Approximate Entropy), relative to Gold and S&P 500. Moreover, using tools from Extreme Value Theory, we analyze the convergence of moments, and the mean excess functions of both the closing prices and the log-returns of the three assets. We find that the closing price of Bitcoin is consistent with a generalized Pareto distribution, when the closing prices of the two other assets (Gold and S&P 500) present thin-tailed distributions. However, returns for all three assets are heavy tailed and second moments (variance, standard deviation) non-convergent. In the case of Bitcoin, lower sampling frequencies (monthly vs weekly, weekly vs daily) drastically reduce the Kurtosis of log-returns and increase the convergence of empirical moments to their true value. The opposite effect is observed for Gold and S&P 500. These properties suggest that Bitcoin's volatility is essentially an intra-day and intra-week phenomenon that is strongly attenuated on a weekly time-scale, and make it an attractive store of value to investors and speculators, but its high standard deviation excludes its use a currency.
This article investigates the excess volatility in Bitcoin prices using an unbiased extreme value volatility estimator. We capture the time-varying nature of the excess volatility using bootstrap, multi-horizon, sub-sampling and rolling-window approaches. We observe that Bitcoin price changes are almost efficient. Although Bitcoin prices exhibit high volatility and show signs of excess volatility for a few periods, it is decreasing over time. After controlling for the outliers, we also notice that the Bitcoin market shows signs of increasing maturity. Overall, Bitcoin prices show a sign of increasing efficiency with decreasing volatility. Our findings have implications for investors making investment decisions and for regulators making policy choices.
Purpose The purpose of this paper is threefold. First, it models and forecasts the risk of the five leading cryptocurrencies, stock market indices (developed and BRICS) and gold returns. Second, it conducts different backtesting procedures forecasts. Third, it focuses on the hedging potential of cryptocurrencies and gold. Design/methodology/approach The authors used the generalized autoregressive score (GAS) models to model and forecast the risk of cryptocurrencies, stock market indices and gold returns. They conduct different backtesting procedures of the 1% and 5%-value-at-risk (VaR) forecasts. They also use the generalized orthogonal generalized autoregressive conditional heteroskedasticity (GO-GARCH) model to explore the hedging potential of cryptocurrencies by estimating the dynamic conditional correlation between cryptocurrencies and gold, on the one hand, and stock markets on the other hand. Findings When conducting different backtesting procedures of VaR, our finding suggests that Bitcoin has the highest VaR among cryptocurrencies and Gold and the BRICS indices returns have lower VaR compared to the developed countries. Finally, we provide evidence that the risks among developed stock markets can be hedged by Bitcoin and Gold. Bitcoin can be considered as the new Gold for these economies. Unlike Bitcoin, Gold can be considered as a hedge for Chinese and Indian investors. However, Gold and Bitcoin can be considered as diversifier assets for the other BRICS economies while Dash and Monero are diversifier assets for developed stock markets. Originality/value The first paper's empirical contribution lies in analyzing optimal forecast models for cryptocurrencies (other than Bitcoin) returns and risk. The second contribution consists of studying the hedging potential of five leading cryptocurrencies. To the best of our knowledge, no previous studies have investigated the role of cryptocurrencies for BRICS investors.
Bitcoin is a peer-to-peer electronic payment system that has rapidly grown in popularity in recent years. Usually, the complete history of Bitcoin blockchain data must be queried to acquire variables with economic meaning. This task has recently become increasingly difficult, as there are over 1.6 billion historical transactions on the Bitcoin blockchain. It is thus important to query Bitcoin transaction data in a way that is more efficient and provides economic insights. We apply cohort analysis that interprets Bitcoin blockchain data using methods developed for population data in the social sciences. Specifically, we query and process the Bitcoin transaction input and output data within each daily cohort. This enables us to create datasets and visualizations for some key Bitcoin transaction indicators, including the daily lifespan distributions of spent transaction output (STXO) and the daily age distributions of the cumulative unspent transaction output (UTXO). We provide a computationally feasible approach for characterizing Bitcoin transactions that paves the way for future economic studies of Bitcoin.
Beata Szetela, Grzegorz Mentel, Yuriy Bilan, Urszula Mentel
Abstract The aim of the paper is to verify the existence of short- and long-term relationships between the strength of a trend and the volume in bullish and bearish cryptocurrency markets. We applied the vector error correction model to bitcoin daily data from 14.01.2015 to 22.12.2019. Based on the prices and following Wilder’s algorithm, the average directional movement index was calculated, and upward and downward trend periods were determined. No long-term relationship was found to exist between the strength of a trend and the volume in both bearish and bullish markets. Hence, trends do not react to volume changes. However, a long-term relationship exists between volume and trend—but only for the downward trend—with an adjustment speed of 88%. In the short-term, a statistically significant but very weak dependency is revealed; hence, the conclusion that trend strength is insensitive to volume changes can be reached.
This paper investigates the long memory property of four cryptocurrencies (Bitcoin, Dash, Ethereum, and Litecoin) using the Rescaled Range Hurst analysis. The presence of long memory test for the validity of efficient market hypothesis in the cryptocurrency markets. First, we use traditional long memory tests (Hurst-Mandelbrot R/S, GSP and GPH) to investigate the long memory property in the returns and volatilities of cryptocurrency markets. We find that the volatility shows strong long memory property. Second, we employs the rolling sample approach and calculate time-varying long memory propertty in the returns and volatilities of cryptocurrency markets. Emprical results show that both the volatility and returns of cryptocurrency markets possess the time-varying long memory property. The average Hurst exponents are well above 0.5, indicating the presence of long memory. The long memory property of volatility is stronger than that of returns. The time-varying Hurst exponent values for BTC are significant higher than those of other cryptocurrencies (DASH, ETH, and LTC). This finding indicates that BTC is less efficient than other cryptocurrency markets. Therefore, the presence of long memory is important to predict future cryptocurrency prices, for asset allocation, and for portfolio assessment.
Cryptocurrencies have several features that set them aside from traditional currencies. In terms of market capitalization, the top five cryptocurrencies are considered for the analysis to strengthen the research's validation. The most successful crypto asset, being Bitcoin, possesses several characteristics that pose advantages and disadvantages in the financial markets. Digital convenience ensures the safety and ease of use for Bitcoin users, while decentralization also poses a primary benefit. Extreme volatility and the impact of negative externalities on the value of Bitcoin contribute to the assessment of Bitcoin trends in the market. The recent outbreak of the coronavirus (COVID-19) has shown evidence of influencing Bitcoin prices as the virus is spread across continents, leaving the global financial environment in turmoil. The classification of Bitcoin as a hedge is dependent on various factors, including global economic uncertainty. The extent to which the coronavirus impacts cryptocurrencies' hedging capabilities, especially that of Bitcoin’s, is of particular interest during the 2020 pandemic. Analyzing the literature on the influence of crisis on Bitcoin movement will explain why COVID-19 has had such a significant impact on the global financial markets, especially that of cryptocurrencies. The performance of Bitcoin, Ethereum, XRP, Tether, and Bitcoin Cash is compared to that of seven factors including commodities and indices: gold, USD, S&P 500 index, SSE index, world and emerging markets MSCI indices, and Economic Uncertainty, to better understand the hedging capabilities throughout the time of the crisis. This is done using four different multivariate GARCH specifications that account for the nature of the interaction between the cryptocurrencies and the financial variables. Although previous research finds that Bitcoin should act as a hedge during times of economic turmoil, the performance observed during COVID-19 suggests otherwise.
David Vidal-Tomás, Ana M. Ibáñez, José Emilio Farinós Viñas
We analyze the economic efficiency of the cryptocurrency market after the launch of Bitcoin futures by means of the Data Envelopment Analysis and Malmquist Indexes. Our results show that the introduction of Bitcoin futures did not affect the economic efficiency of the cryptocurrency market. However, we observe that Bitcoin obtained the highest risk-return trade-off due to its liquidity compared to the rest of cryptocurrencies. Therefore, our paper underlines the support of investors on Bitcoin to the detriment of the rest of cryptocurrencies.
Ana Fernández Vilas, Rebeca P. Dı́az Redondo, Daniel Couto Cancela, Alejandro Torrado Pazos
Cryptocurrencies are a type of digital money meant to provide security and anonymity while using cryptography techniques. Although cryptocurrencies represent a breakthrough and provide some important benefits, their usage poses some risks that are a result of the lack of supervising institutions and transparency. Because disinformation and volatility is discouraging for personal investors, cryptocurrencies emerged hand-in-hand with the proliferation of online users’ communities and forums as places to share information that can alleviate users’ mistrust. This research focuses on the study of the interplay between these cryptocurrency forums and fluctuations in cryptocurrency values. In particular, the most popular cryptocurrency Bitcoin (BTC) and a related active discussion community, Bitcointalk, are analyzed. This study shows that the activity of Bitcointalk forum keeps a direct relationship with the trend in the values of BTC, therefore analysis of this interaction would be a perfect base to support personal investments in a non-regulated market and, to confirm whether cryptocurrency forums show evidences to detect abnormal behaviors in BTC values as well as to predict or estimate these values. The experiment highlights that forum data can explain specific events in the financial field. It also underlines the relevance of quotes (regular mechanism to response a post) at periods: (1) when there is a high concentration of posts around certain topics; (2) when peaks in the BTC price are observed; and, (3) when the BTC price gradually shifts downwards and users intend to sell.
Purpose This study broadly attempts to explore adaptive or dynamics patterns of calendar effects existed in the cryptocurrency market as per the adaptive market hypothesis (AMH) framework. Another agendum of this study is to investigate the quantum of extra returns which may result from the presence of calendar effects. Design/methodology/approach The present study considers both parametric and non-parametric approaches to verify calendar effects empirically. Specifically, this study has implemented Generalised Autoregressive Conditional Heteroscedasticity (1, 1) and Kruskal–Wallis tests in the rolling window approach to reveal adaptive patterns of calendar effects. Additionally, the present study has used the implied trading strategy to evaluate the volume of excess returns resulted from calendar effects than buy-and-hold (BH) strategy. Findings The overall results of the current study exhibit that calendar effect in the cryptocurrency market is dynamic rather than static which indicates the calendar effect is a time-varying phenomenon. Moreover, this study also confirmed that ITS is not suitable to obtain extra returns despite the existence of calendar effects. Research limitations/implications The present study has covered some broad aspects of calendar anomalies in the cryptocurrency market, keeping aside certain other limitations which need to be addressed in the following dimensions. Future studies may aim at addressing issues like, Turn-of-the-Year effect, Halloween effect, weather effect, and Month-of-the-Year effects, and try to explore the reasons of presence of dynamic patterns of calendar effects. Practical implications The significant implication of this study is that it alerts investors about market return predictability due to calendar patterns or effects in different periods. It also suggests the period in which the ITS can perform better than the BH strategy. Originality/value It is the first study in the cryptocurrency literature which has adopted the AMH framework to verify adaptive calendar effects or anomalies. Furthermore, this study, instead of a mere examination of the presence of calendar effects, has evaluated the potential of calendar effects to produce extra returns through trading strategies.
Abstract In this study, we characterized the dynamics and analyzed the degree of synchronization of the time series of daily closing prices and volumes in US$ of three cryptocurrencies, Bitcoin, Ethereum, and Litecoin, over the period September 1,2015–March 31, 2020. Time series were first mapped into a complex network by the horizontal visibility algorithm in order to revel the structure of their temporal characters and dynamics. Then, the synchrony of the time series was investigated to determine the possibility that the cryptocurrencies under study co-bubble simultaneously. Findings reveal similar complex structures for the three virtual currencies in terms of number and internal composition of communities. To the aim of our analysis, such result proves that price and volume dynamics of the cryptocurrencies were characterized by cyclical patterns of similar wavelength and amplitude over the time period considered. Yet, the value of the slope parameter associated with the exponential distributions fitted to the data suggests a higher stability and predictability for Bitcoin and Litecoin than for Ethereum. The study of synchrony between the time series investigated displayed a different degree of synchronization between the three cryptocurrencies before and after a collapse event. These results could be of interest for investors who might prefer to switch from one cryptocurrency to another to exploit the potential opportunities of profit generated by the dynamics of price and volumes in the market of virtual currencies.
This paper aims to study the impacts of long memory in conditional volatility and conditional non-normality on market risks in Bitcoin and some other cryptocurrencies using an Autoregressive Fractionally Integrated GARCH model with non-normal innovations. Two tail-based risk metrics, namely Value at Risk (VaR) and Expected Shortfall (ES), are adopted to study the tail behaviour of market risks in Bitcoin and some other cryptocurrencies. Empirical investigations for the tail behaviour based on real exchange rate data of cryptocurrencies are conducted. An extreme-value-theory-based approach is used to study potential improvements in the estimation for the risk metrics under GARCH-type models. The possibility of explosive regimes in cryptocurrencies’ volatilities is examined using Markov-switching GARCH models.
Purpose This paper aims to identify and quantify directional predictability between returns and volume in major cryptocurrencies markets. Design/methodology/approach The empirical analysis relies on the cross-quantilogram approach that allows one to assess the temporal (lag-lead) association between two stationary time series at different parts of their joint distribution. The data are daily prices and trading volumes from four markets (Bitcoin, Ethereum, Ripple and Litecoin). Findings Extreme returns either positive or negative tend to lead high volume levels. Low levels of trading activity have in general no information content about future returns; high levels, however, tend to precede extreme positive returns. Originality/value This is the first work that uses the cross-quantilogram approach to assess the temporal association between returns and volume in cryptocurrencies markets. The findings provide new insights about the informational efficiency of these markets and the traders’ strategies.
Marco Ortu, Nicola Uras, Claudio Conversano, Giuseppe Destefanis · 5 authors
This work aims to analyse the predictability of price movements of\ncryptocurrencies on both hourly and daily data observed from January 2017 to\nJanuary 2021, using deep learning algorithms. For our experiments, we used\nthree sets of features: technical, trading and social media indicators,\nconsidering a restricted model of only technical indicators and an unrestricted\nmodel with technical, trading and social media indicators. We verified whether\nthe consideration of trading and social media indicators, along with the\nclassic technical variables (such as price's returns), leads to a significative\nimprovement in the prediction of cryptocurrencies price's changes. We conducted\nthe study on the two highest cryptocurrencies in volume and value (at the time\nof the study): Bitcoin and Ethereum. We implemented four different machine\nlearning algorithms typically used in time-series classification problems:\nMulti Layers Perceptron (MLP), Convolutional Neural Network (CNN), Long Short\nTerm Memory (LSTM) neural network and Attention Long Short Term Memory (ALSTM).\nWe devised the experiments using the advanced bootstrap technique to consider\nthe variance problem on test samples, which allowed us to evaluate a more\nreliable estimate of the model's performance. Furthermore, the Grid Search\ntechnique was used to find the best hyperparameters values for each implemented\nalgorithm. The study shows that, based on the hourly frequency results, the\nunrestricted model outperforms the restricted one. The addition of the trading\nindicators to the classic technical indicators improves the accuracy of Bitcoin\nand Ethereum price's changes prediction, with an increase of accuracy from a\nrange of 51-55% for the restricted model, to 67-84% for the unrestricted model.\n